From 87fa04dfe4f7a097f01033c362f2bd6e5e7b46bd Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Mon, 10 Jul 2023 10:36:32 -0400 Subject: [PATCH 01/75] Updated the HDA example exercise and solution --- ...flowsheet_with_distillation_exercise.ipynb | 70 ++++++++++++++++++- ...flowsheet_with_distillation_solution.ipynb | 70 ++++++++++++++++++- 2 files changed, 134 insertions(+), 6 deletions(-) diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb index a8a353b7..601b0144 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb @@ -26,6 +26,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -58,7 +59,7 @@ "example, toluene will be reacted with hydrogen gas at high temperatures\n", " to form benzene via the following reaction:\n", "\n", - "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "**C6H5CH3 + H2 → C6H6 + CH4**\n", "\n", "\n", "This reaction is often accompanied by an equilibrium side reaction\n", @@ -82,6 +83,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -93,6 +95,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -131,6 +134,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -154,6 +158,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -200,6 +205,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -224,6 +230,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -247,6 +254,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -269,6 +277,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -296,6 +305,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -321,6 +331,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -350,6 +361,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -381,6 +393,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -390,6 +403,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -409,6 +423,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -445,6 +460,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -452,6 +468,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -504,6 +521,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -527,6 +545,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -545,6 +564,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -572,6 +592,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -593,6 +614,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -609,6 +631,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -642,6 +665,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -660,6 +684,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -685,6 +710,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -717,6 +743,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -736,6 +763,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -768,6 +796,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -788,6 +817,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -808,6 +838,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -828,6 +859,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -856,6 +888,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -881,6 +914,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -909,6 +943,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -926,6 +961,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -943,6 +979,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -977,6 +1014,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -990,10 +1028,14 @@ "outputs": [], "source": [ "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " if hasattr(unit,\"fix_initialization_state\"):\n", + " unit.fix_initialization_state(outlvl=idaeslog.INFO)\n", + " else:\n", + " unit.initialize(outlvl=idaeslog.INFO)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1012,6 +1054,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1035,6 +1078,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1095,6 +1139,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1132,6 +1177,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1148,6 +1194,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1195,6 +1242,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1211,6 +1259,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1227,6 +1276,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1254,6 +1304,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1266,6 +1317,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1290,6 +1342,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1306,6 +1359,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1327,6 +1381,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1353,6 +1408,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1398,6 +1454,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1426,6 +1483,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1446,6 +1504,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1471,6 +1530,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1489,6 +1549,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1508,6 +1569,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1555,6 +1617,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1583,6 +1646,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1626,4 +1690,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb index 247e5621..dfbdfd74 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb @@ -26,6 +26,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -58,7 +59,7 @@ "example, toluene will be reacted with hydrogen gas at high temperatures\n", " to form benzene via the following reaction:\n", "\n", - "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "**C6H5CH3 + H2 → C6H6 + CH4**\n", "\n", "\n", "This reaction is often accompanied by an equilibrium side reaction\n", @@ -82,6 +83,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -93,6 +95,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -131,6 +134,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -168,6 +172,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -214,6 +219,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -238,6 +244,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -261,6 +268,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -283,6 +291,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -310,6 +319,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -335,6 +345,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -383,6 +394,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -414,6 +426,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -423,6 +436,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -442,6 +456,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, 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"metadata": {}, "source": [ @@ -766,6 +791,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -798,6 +824,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -817,6 +844,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -866,6 +894,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -886,6 +915,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -906,6 +936,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -926,6 +957,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -954,6 +986,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -993,6 +1026,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1021,6 +1055,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1038,6 +1073,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1055,6 +1091,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1089,6 +1126,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1102,10 +1140,14 @@ "outputs": [], "source": [ "def function(unit):\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " if hasattr(unit,\"fix_initialization_state\"):\n", + " unit.fix_initialization_state(outlvl=idaeslog.INFO)\n", + " else:\n", + " unit.initialize(outlvl=idaeslog.INFO)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1124,6 +1166,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1147,6 +1190,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1207,6 +1251,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1244,6 +1289,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1260,6 +1306,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1307,6 +1354,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1323,6 +1371,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1339,6 +1388,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1366,6 +1416,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1378,6 +1429,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1402,6 +1454,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1418,6 +1471,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1439,6 +1493,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1479,6 +1534,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1524,6 +1580,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1571,6 +1628,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1591,6 +1649,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1630,6 +1689,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1648,6 +1708,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1667,6 +1728,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1714,6 +1776,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1742,6 +1805,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1785,4 +1849,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} From f22d48e89f92fb46c072acab1f43481400a3f243 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Tue, 8 Aug 2023 11:23:38 -0400 Subject: [PATCH 02/75] Adding supercritical CO2 example --- .../hda_flowsheet_with_distillation.ipynb | 1360 +++++++++++++++- ...flowsheet_with_distillation_exercise.ipynb | 116 +- ...flowsheet_with_distillation_solution.ipynb | 1420 ++++++++++++++++- .../SCO2_example/ALAMO/500_Points_DataSet.csv | 501 ++++++ .../ALAMO/SCO2_alamo_surrogate.ipynb | 555 +++++++ .../ALAMO/SCO2_flowsheet_alamo.py | 241 +++ .../SCO2_flowsheet_alamo_surrogate.ipynb | 645 ++++++++ .../ALAMO/SCO2_properties_alamo_surrogate.py | 314 ++++ ...properties_alamo_surrogate_embedding.ipynb | 461 ++++++ .../SCO2_example/ALAMO/alamo_run.trc | 82 + .../SCO2_example/ALAMO/alamo_surrogate.json | 1 + .../SCO2_example/ALAMO/alamo_train_parity.pdf | Bin 0 -> 29828 bytes .../ALAMO/alamo_train_residual.pdf | Bin 0 -> 47095 bytes .../ALAMO/alamo_train_scatter2D.pdf | Bin 0 -> 67174 bytes .../SCO2_example/ALAMO/alamo_val_parity.pdf | Bin 0 -> 22863 bytes .../SCO2_example/ALAMO/alamo_val_residual.pdf | Bin 0 -> 27075 bytes .../ALAMO/alamo_val_scatter2D.pdf | Bin 0 -> 33248 bytes .../surrogates/SCO2_example/OMLT/.mdl_co2.h5 | Bin 0 -> 66976 bytes .../SCO2_example/OMLT/500_Points_DataSet.csv | 501 ++++++ .../SCO2_example/OMLT/SCO2_flowsheet_keras.py | 242 +++ .../OMLT/SCO2_flowsheet_keras_surrogate.ipynb | 643 ++++++++ .../OMLT/SCO2_keras_surrogate.ipynb | 1058 ++++++++++++ .../OMLT/SCO2_properties_keras_surrogate.py | 311 ++++ ...properties_keras_surrogate_embedding.ipynb | 456 ++++++ .../SCO2_example/PySMO/500_Points_DataSet.csv | 501 ++++++ .../PySMO/SCO2_flowsheet_pysmo.py | 241 +++ .../SCO2_flowsheet_pysmo_surrogate.ipynb | 1404 ++++++++++++++++ .../PySMO/SCO2_properties_pysmo_surrogate.py | 313 ++++ ...properties_pysmo_surrogate_embedding.ipynb | 460 ++++++ .../PySMO/SCO2_pysmo_surrogate.ipynb | 605 +++++++ 30 files changed, 12296 insertions(+), 135 deletions(-) create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/500_Points_DataSet.csv create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo.py create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate.py create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_surrogate.json create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_train_parity.pdf create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_train_residual.pdf create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_train_scatter2D.pdf create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_val_parity.pdf create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_val_residual.pdf create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_val_scatter2D.pdf create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/.mdl_co2.h5 create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/500_Points_DataSet.csv create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras.py create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate.py create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/500_Points_DataSet.csv create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo.py create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate.py create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb index b1250e16..c0a0128c 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb @@ -735,7 +735,15 @@ "cell_type": "code", "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -950,7 +958,42 @@ "cell_type": "code", "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" + ] + } + ], "source": [ "# Set scaling factors for heat duty, reaction extent and volume\n", "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", @@ -1000,7 +1043,15 @@ "solution" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "# Todo: Check the degrees of freedom\n", "print(degrees_of_freedom(m))" @@ -1059,7 +1110,15 @@ "cell_type": "code", "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.s03\n" + ] + } + ], "source": [ "for o in heuristic_tear_set:\n", " print(o.name)" @@ -1076,7 +1135,20 @@ "cell_type": "code", "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.H101\n", + "fs.R101\n", + "fs.F101\n", + "fs.S101\n", + "fs.C101\n", + "fs.M101\n" + ] + } + ], "source": [ "for o in order:\n", " print(o[0].name)" @@ -1146,7 +1218,124 @@ "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-07-10 13:46:06 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:07 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:07 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:07 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:08 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:08 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:08 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-10 13:46:08 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-10 13:46:08 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:08 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:08 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:09 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:09 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:09 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:09 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", + "2023-07-10 13:46:09 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:09 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:10 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:10 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:10 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:10 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:11 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:11 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:11 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:11 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:11 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:11 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:12 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:12 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:12 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", + "2023-07-10 13:46:12 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", + "2023-07-10 13:46:12 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", + "2023-07-10 13:46:12 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:13 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-10 13:46:13 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:13 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:14 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:14 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:14 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:14 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:14 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:14 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-10 13:46:14 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-10 13:46:15 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:15 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:15 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:15 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-10 13:46:16 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:16 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:16 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:16 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:16 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:17 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:17 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-10 13:46:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-10 13:46:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:17 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:17 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:18 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-10 13:46:18 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:18 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:18 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:19 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:19 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:19 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:19 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:19 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-10 13:46:19 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-10 13:46:19 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:19 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:20 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:20 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-10 13:46:20 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:20 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:21 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:21 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:21 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:21 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:21 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:21 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-10 13:46:21 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-10 13:46:21 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:22 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-10 13:46:22 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:22 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-10 13:46:22 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2023-07-10 13:46:22 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:22 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:23 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:23 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:23 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:23 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", + "2023-07-10 13:46:23 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:23 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:23 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:23 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:23 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:23 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", + "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", + "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", + "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" + ] + } + ], "source": [ "seq.run(m, function)" ] @@ -1165,7 +1354,117 @@ "cell_type": "code", "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix\n", + "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", + "that contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 1097\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 877\n", + "\n", + "Total number of variables............................: 363\n", + " variables with only lower bounds: 8\n", + " variables with lower and upper bounds: 155\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 363\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", + " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", + " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.43e+03 - 1.00e+00 9.82e-01h 1\n", + " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 6.42e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 2.97e-05 2.81e+03 -3.8 2.13e-07 - 1.00e+00 1.00e+00H 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.7855284385533683e+04 1.7855284385533683e+04\n", + "Constraint violation....: 2.4734281289795490e-10 2.9668448405573148e-05\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.4734281289795490e-10 1.7855284385533683e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 12\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 12\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.026\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "# Create the solver object\n", "solver = get_solver()\n", @@ -1213,7 +1512,617 @@ "cell_type": "code", "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for 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[WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2023-07-10 13:46:27 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2023-07-10 13:46:27 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2023-07-10 13:46:27 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:27 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:27 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:27 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2023-07-10 13:46:51 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:51 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:51 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2023-07-10 13:46:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2023-07-10 13:46:52 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:52 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2023-07-10 13:46:52 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:52 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2023-07-10 13:46:53 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2023-07-10 13:46:53 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" + ] + } + ], "source": [ "# Add distillation column to the flowsheet\n", "m.fs.D101 = TrayColumn(\n", @@ -1312,7 +2221,131 @@ "cell_type": "code", "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix\n", + "'fs.D101.condenser.control_volume.properties_out[0.0].scaling_factor' that\n", + "contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", + "that contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4042\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2376\n", + "\n", + "Total number of variables............................: 1169\n", + " variables with only lower bounds: 112\n", + " variables with lower and upper bounds: 365\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", + " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", + " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", + " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 7.45e-09 5.93e-02 -3.8 3.58e-08 - 1.00e+00 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.5042542854672720e+04 1.5042542854672720e+04\n", + "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.8207660913467407e-11 1.5042542854672720e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.129\n", + "Total CPU secs in NLP function evaluations = 0.024\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.27869677543640137}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "solver.solve(m, tee=True)" ] @@ -1346,7 +2379,26 @@ "cell_type": "code", "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252124\n", + "operating cost = $ 427596.7305680538\n", + "capital cost = $ 14704.740184467468\n", + "\n", + "Distillate flowrate = 0.16196898920633476 mol/s\n", + "Benzene purity = 89.49161665800843 %\n", + "Residue flowrate = 0.10515007120697811 mol/s\n", + "Toluene purity = 43.32260291055274 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603166 %\n" + ] + } + ], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -1388,7 +2440,16 @@ "testing" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "427596.7305680538\n", + "14704.740184467468\n" + ] + } + ], "source": [ "import pytest\n", "\n", @@ -1409,7 +2470,40 @@ "cell_type": "code", "execution_count": 57, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 0.0000 : watt : True : (None, None)\n", + " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", + " temperature kelvin 600.00 771.85\n", + " pressure pascal 3.5000e+05 3.5000e+05\n", + "====================================================================================\n" + ] + } + ], "source": [ "m.fs.R101.report()" ] @@ -1425,7 +2519,40 @@ "cell_type": "code", "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.F101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -70343. : watt : False : (None, None)\n", + " Pressure Change : 0.0000 : pascal : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Vapor Outlet Liquid Outlet\n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", + " temperature kelvin 771.85 325.00 325.00 \n", + " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", + "====================================================================================\n" + ] + } + ], "source": [ "m.fs.F101.report()" ] @@ -1446,7 +2573,25 @@ "cell_type": "code", "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Reactor Light Gases\n", + "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", + "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", + "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", + "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", + "temperature kelvin 771.85 325.00 \n", + "pressure pascal 3.5000e+05 3.5000e+05 \n" + ] + } + ], "source": [ "from idaes.core.util.tables import (\n", " create_stream_table_dataframe,\n", @@ -1753,7 +2898,140 @@ "cell_type": "code", "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", + "that contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4073\n", + "Number of nonzeros in inequality constraint Jacobian.: 6\n", + "Number of nonzeros in Lagrangian Hessian.............: 2391\n", + "\n", + "Total number of variables............................: 1176\n", + " variables with only lower bounds: 113\n", + " variables with lower and upper bounds: 372\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 3\n", + " inequality constraints with only lower bounds: 2\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 1\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", + " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", + " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", + " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", + " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", + " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", + " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", + " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", + " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", + " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", + " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", + " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", + " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", + " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", + " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", + " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", + " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", + " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", + " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", + " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", + " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", + " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", + " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", + " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", + " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", + " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", + " 29 4.3884157e+05 6.56e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", + " 31 4.3883992e+05 3.05e-07 1.25e-05 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.46e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 1.49e-08 1.07e-07 -8.0 5.40e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842627057e+02 4.3883989842627058e+05\n", + "Dual infeasibility......: 1.0693122464843572e-07 1.0693122464843573e-04\n", + "Constraint violation....: 5.8207660913467407e-11 1.4901161193847656e-08\n", + "Complementarity.........: 9.0909948039747601e-09 9.0909948039747593e-06\n", + "Overall NLP error.......: 9.0909948039747601e-09 1.0693122464843573e-04\n", + "\n", + "\n", + "Number of objective function evaluations = 35\n", + "Number of objective gradient evaluations = 34\n", + "Number of equality constraint evaluations = 35\n", + "Number of inequality constraint evaluations = 35\n", + "Number of equality constraint Jacobian evaluations = 34\n", + "Number of inequality constraint Jacobian evaluations = 34\n", + "Number of Lagrangian Hessian evaluations = 33\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.347\n", + "Total CPU secs in NLP function evaluations = 0.047\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] @@ -1787,7 +3065,26 @@ "cell_type": "code", "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.8984262706\n", + "operating cost = $ 408883.53148307273\n", + "capital cost = $ 29956.366943197827\n", + "\n", + "Distillate flowrate = 0.17999999002639896 mol/s\n", + "Benzene purity = 98.99999900049087 %\n", + "Residue flowrate = 0.10851616424263705 mol/s\n", + "Toluene purity = 15.67617808620809 %\n", + "\n", + "Conversion = 93.38705916369607 %\n", + "\n", + "Overhead benzene loss in F101 = 17.340617931156185 %\n" + ] + } + ], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -1829,7 +3126,16 @@ "testing" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "408883.53148307273\n", + "29956.366943197827\n" + ] + } + ], "source": [ "import pytest\n", "\n", @@ -1851,7 +3157,23 @@ "cell_type": "code", "execution_count": 75, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.9232042951996 K\n", + "\n", + "R101 outlet temperature = 790.3655425698917 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.74143399528367 K\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -1907,7 +3229,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb index 601b0144..95cb3a76 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "tags": [ "header", @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "tags": [ "exercise" @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -265,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -286,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "tags": [ "exercise" @@ -370,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -412,7 +412,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -442,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -480,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "tags": [ "exercise" @@ -493,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -533,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "tags": [ "exercise" @@ -556,7 +556,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -580,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "tags": [ "exercise" @@ -601,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -623,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -647,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -676,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -693,7 +693,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -726,7 +726,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -754,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -781,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": { "tags": [ "exercise" @@ -805,7 +805,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -826,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -847,7 +847,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -868,7 +868,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -902,7 +902,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": { "tags": [ "exercise" @@ -927,7 +927,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -952,7 +952,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -970,7 +970,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -990,7 +990,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1044,7 +1044,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": { "scrolled": false }, @@ -1066,7 +1066,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1099,7 +1099,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1150,7 +1150,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1186,7 +1186,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1205,7 +1205,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1251,7 +1251,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1268,7 +1268,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1290,7 +1290,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1351,7 +1351,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1368,7 +1368,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1396,7 +1396,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": { "tags": [ "exercise" @@ -1425,7 +1425,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1468,7 +1468,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": { "tags": [ "exercise" @@ -1492,7 +1492,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1518,7 +1518,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "metadata": { "tags": [ "exercise" @@ -1539,7 +1539,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1561,7 +1561,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1580,7 +1580,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1626,7 +1626,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1685,7 +1685,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb index dfbdfd74..5dcbdc8a 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb @@ -759,7 +759,15 @@ "cell_type": "code", "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -774,7 +782,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -807,7 +815,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -835,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -862,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "tags": [ "exercise" @@ -878,7 +886,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "tags": [ "solution" @@ -903,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -924,7 +932,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -945,7 +953,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -966,9 +974,44 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" + ] + } + ], "source": [ "# Set scaling factors for heat duty, reaction extent and volume\n", "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", @@ -1000,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": { "tags": [ "exercise" @@ -1013,13 +1056,21 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": { "tags": [ "solution" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "# Todo: Check the degrees of freedom\n", "print(degrees_of_freedom(m))" @@ -1039,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1064,9 +1115,17 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.s03\n" + ] + } + ], "source": [ "for o in heuristic_tear_set:\n", " print(o.name)" @@ -1082,9 +1141,22 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.H101\n", + "fs.R101\n", + "fs.F101\n", + "fs.S101\n", + "fs.C101\n", + "fs.M101\n" + ] + } + ], "source": [ "for o in order:\n", " print(o[0].name)" @@ -1102,7 +1174,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1135,7 +1207,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1156,11 +1228,128 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 45, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-07-10 13:48:18 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:19 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:19 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:19 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:19 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:20 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:20 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-10 13:48:20 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-10 13:48:20 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", + "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:21 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:21 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:21 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:21 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:21 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:21 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:21 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:21 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", + "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", + "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", + "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:22 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-10 13:48:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:23 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:23 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:23 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:23 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:24 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:24 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:24 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-10 13:48:24 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-10 13:48:24 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:24 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:24 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:24 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-10 13:48:25 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:25 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:25 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:25 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:25 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:25 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:26 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:26 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-10 13:48:26 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-10 13:48:26 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:26 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:26 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-10 13:48:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:27 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:27 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:27 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:27 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:27 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:27 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-10 13:48:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-10 13:48:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:28 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:28 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-10 13:48:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:28 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:29 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:29 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:29 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:29 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:29 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:29 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-10 13:48:29 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-10 13:48:29 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:29 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-10 13:48:30 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:30 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-10 13:48:30 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2023-07-10 13:48:30 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:30 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:30 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:31 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:31 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:31 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", + "2023-07-10 13:48:31 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:31 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", + "2023-07-10 13:48:32 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", + "2023-07-10 13:48:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", + "2023-07-10 13:48:32 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" + ] + } + ], "source": [ "seq.run(m, function)" ] @@ -1178,9 +1367,119 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix\n", + "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", + "that contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 1097\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 877\n", + "\n", + "Total number of variables............................: 363\n", + " variables with only lower bounds: 8\n", + " variables with lower and upper bounds: 155\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 363\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", + " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", + " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.43e+03 - 1.00e+00 9.82e-01h 1\n", + " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 6.42e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 2.97e-05 2.81e+03 -3.8 2.13e-07 - 1.00e+00 1.00e+00H 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.7855284385533683e+04 1.7855284385533683e+04\n", + "Constraint violation....: 2.4734281289795490e-10 2.9668448405573148e-05\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.4734281289795490e-10 1.7855284385533683e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 12\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 12\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.038\n", + "Total CPU secs in NLP function evaluations = 0.003\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "# Create the solver object\n", "solver = get_solver()\n", @@ -1211,9 +1510,619 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2023-07-10 13:48:34 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2023-07-10 13:48:34 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2023-07-10 13:48:34 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2023-07-10 13:48:58 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2023-07-10 13:48:58 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2023-07-10 13:48:58 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" + ] + } + ], "source": [ "# Add distillation column to the flowsheet\n", "m.fs.D101 = TrayColumn(\n", @@ -1262,7 +2171,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1298,9 +2207,133 @@ }, { "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix\n", + "'fs.D101.condenser.control_volume.properties_out[0.0].scaling_factor' that\n", + "contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", + "that contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4042\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2376\n", + "\n", + "Total number of variables............................: 1169\n", + " variables with only lower bounds: 112\n", + " variables with lower and upper bounds: 365\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", + " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", + " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", + " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 7.45e-09 5.93e-02 -3.8 3.58e-08 - 1.00e+00 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.5042542854672720e+04 1.5042542854672720e+04\n", + "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.8207660913467407e-11 1.5042542854672720e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.072\n", + "Total CPU secs in NLP function evaluations = 0.013\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.1654343605041504}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "solver.solve(m, tee=True)" ] @@ -1317,9 +2350,28 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252124\n", + "operating cost = $ 427596.7305680538\n", + "capital cost = $ 14704.740184467468\n", + "\n", + "Distillate flowrate = 0.16196898920633476 mol/s\n", + "Benzene purity = 89.49161665800843 %\n", + "Residue flowrate = 0.10515007120697811 mol/s\n", + "Toluene purity = 43.32260291055274 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603166 %\n" + ] + } + ], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -1363,9 +2415,42 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 0.0000 : watt : True : (None, None)\n", + " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", + " temperature kelvin 600.00 771.85\n", + " pressure pascal 3.5000e+05 3.5000e+05\n", + "====================================================================================\n" + ] + } + ], "source": [ "m.fs.R101.report()" ] @@ -1380,9 +2465,42 @@ }, { "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.F101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -70343. : watt : False : (None, None)\n", + " Pressure Change : 0.0000 : pascal : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Vapor Outlet Liquid Outlet\n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", + " temperature kelvin 771.85 325.00 325.00 \n", + " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", + "====================================================================================\n" + ] + } + ], "source": [ "m.fs.F101.report()" ] @@ -1402,9 +2520,27 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Reactor Light Gases\n", + "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", + "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", + "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", + "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", + "temperature kelvin 771.85 325.00 \n", + "pressure pascal 3.5000e+05 3.5000e+05 \n" + ] + } + ], "source": [ "from idaes.core.util.tables import (\n", " create_stream_table_dataframe,\n", @@ -1463,7 +2599,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -1480,7 +2616,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -1508,7 +2644,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 56, "metadata": { "tags": [ "exercise" @@ -1521,7 +2657,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 57, "metadata": { "tags": [ "solution" @@ -1551,7 +2687,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -1594,7 +2730,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 59, "metadata": { "tags": [ "exercise" @@ -1610,7 +2746,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 60, "metadata": { "tags": [ "solution" @@ -1637,7 +2773,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -1663,7 +2799,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 62, "metadata": { "tags": [ "exercise" @@ -1676,7 +2812,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 63, "metadata": { "tags": [ "solution" @@ -1698,7 +2834,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -1720,9 +2856,142 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 65, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", + "that contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4073\n", + "Number of nonzeros in inequality constraint Jacobian.: 6\n", + "Number of nonzeros in Lagrangian Hessian.............: 2391\n", + "\n", + "Total number of variables............................: 1176\n", + " variables with only lower bounds: 113\n", + " variables with lower and upper bounds: 372\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 3\n", + " inequality constraints with only lower bounds: 2\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 1\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", + " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", + " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", + " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", + " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", + " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", + " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", + " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", + " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", + " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", + " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", + " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", + " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", + " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", + " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", + " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", + " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", + " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", + " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", + " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", + " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", + " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", + " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", + " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", + " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", + " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", + " 29 4.3884157e+05 6.56e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", + " 31 4.3883992e+05 3.05e-07 1.25e-05 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.46e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 1.49e-08 1.07e-07 -8.0 5.40e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842627057e+02 4.3883989842627058e+05\n", + "Dual infeasibility......: 1.0693122464843572e-07 1.0693122464843573e-04\n", + "Constraint violation....: 5.8207660913467407e-11 1.4901161193847656e-08\n", + "Complementarity.........: 9.0909948039747601e-09 9.0909948039747593e-06\n", + "Overall NLP error.......: 9.0909948039747601e-09 1.0693122464843573e-04\n", + "\n", + "\n", + "Number of objective function evaluations = 35\n", + "Number of objective gradient evaluations = 34\n", + "Number of equality constraint evaluations = 35\n", + "Number of inequality constraint evaluations = 35\n", + "Number of equality constraint Jacobian evaluations = 34\n", + "Number of inequality constraint Jacobian evaluations = 34\n", + "Number of Lagrangian Hessian evaluations = 33\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.440\n", + "Total CPU secs in NLP function evaluations = 0.054\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] @@ -1739,9 +3008,28 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.8984262706\n", + "operating cost = $ 408883.53148307273\n", + "capital cost = $ 29956.366943197827\n", + "\n", + "Distillate flowrate = 0.17999999002639896 mol/s\n", + "Benzene purity = 98.99999900049087 %\n", + "Residue flowrate = 0.10851616424263705 mol/s\n", + "Toluene purity = 15.67617808620809 %\n", + "\n", + "Conversion = 93.38705916369607 %\n", + "\n", + "Overhead benzene loss in F101 = 17.340617931156185 %\n" + ] + } + ], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -1785,9 +3073,25 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 67, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.9232042951996 K\n", + "\n", + "R101 outlet temperature = 790.3655425698917 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.74143399528367 K\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -1844,7 +3148,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/500_Points_DataSet.csv b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/500_Points_DataSet.csv new file mode 100644 index 00000000..d963f97b --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/500_Points_DataSet.csv @@ -0,0 +1,501 @@ +CO2SM.Pressure,CO2SM.Temperature,CO2SM.CO2_Enthalpy,CO2SM.CO2_Entropy,CO2SM.Enthalpy,CO2SM.Entropy,CO2SM.status,graph.error +13.44352,853.1312,-368176.883626,8.552332,-87996.3871,2.044056,0,0 +31.863708,909.520515,-365486.064526,4.127018,-87353.2659,0.986381,0,0 +21.132666,713.351479,-376015.521854,-5.327858,-89869.8666,-1.273389,0,0 +21.981615,809.477728,-370818.552442,1.169161,-88627.7611,0.279436,0,0 +18.081246,960.589169,-362401.453621,12.388151,-86616.0262,2.960839,0,0 +16.411093,622.409936,-380610.848384,-10.110284,-90968.176,-2.416416,0,0 +25.2327,988.23463,-360964.852117,10.959979,-86272.6702,2.619498,0,0 +27.432593,881.338355,-366986.624327,3.776188,-87711.9083,0.902531,0,0 +34.305683,679.214229,-378468.856697,-12.986071,-90456.2277,-3.103745,0,0 +28.446215,625.267485,-381232.605846,-15.637125,-91116.7796,-3.737363,0,0 +30.619529,852.199657,-368678.674512,0.855035,-88116.318,0.204358,0,0 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part -1)\n", + "\n", + "## 1. Introduction\n", + "This notebook demonstrates leveraging of the ALAMO surrogate trainer and IDAES Python wrapper to produce an surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "### 1.1 Need for ML Surrogate\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebric, we will use the surrogates and relate enthalpy and entropy with the pressure and temperature as an algebric equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "![image.png](attachment:image.png)\n", + "\n", + "The above flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train a model using AlamoTrainer for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.alamopy import AlamoTrainer, AlamoSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset to have cover different ranges of pressure and temperature. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", we change \".\" to \"_\" as ALAMO accepts alphanumerical characters or underscores as the labels for input/output. Further, the input variables are ***CO2SM_Pressure***, ***CO2SM_Temperature***, while the output variables are ***CO2SM_CO2_Enthalpy***, ***CO2SM_CO2_Entropy***, hence we slice them and create the input and output data. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=7, suppress=True)\n", + "\n", + "csv_data = pd.read_csv('./500_Points_DataSet.csv') \n", + "\n", + "### ALAMO only accepts alphanumerical characters (A-Z, a-z, 0-9) or underscores as input/output labels\n", + "cols=csv_data.columns\n", + "cols=[item.replace(\".\", \"_\") for item in cols]\n", + "csv_data.columns=cols\n", + "\n", + "data = csv_data.sample(n=500,random_state=0) \n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogate with ALAMO\n", + "\n", + "IDAES provides a Python wrapper for the ALAMO machine learning tool via an imported AlamoTrainer class. Regression settings can be directly set as config attributes, as shown below. In this example, allowed basis term forms include constant and linear functions, monomial power order 2 and 3, variable product power order 1 and 2, and variable ratio power order 1 and 2. ALAMO naturally seeks to minimize the number of basis terms; here, we restrict each surrogate expression to a maximum of 10 basis terms.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ***************************************************************************\n", + " ALAMO version 2023.2.13. Built: WIN-64 Mon Feb 13 21:30:56 EST 2023\n", + "\n", + " If you use this software, please cite:\n", + " Cozad, A., N. V. Sahinidis and D. C. Miller,\n", + " Automatic Learning of Algebraic Models for Optimization,\n", + " AIChE Journal, 60, 2211-2227, 2014.\n", + "\n", + " ALAMO is powered by the BARON software from http://www.minlp.com/\n", + " ***************************************************************************\n", + " Licensee: Javal Vyas at Carnegie Mellon University, jvyas@andrew.cmu.edu.\n", + " ***************************************************************************\n", + " Reading input data\n", + " Checking input consistency and initializing data structures\n", + " \n", + " Step 0: Initializing data set\n", + " User provided an initial data set of 400 data points\n", + " We will sample no more data points at this stage\n", + " ***************************************************************************\n", + " Iteration 1 (Approx. elapsed time 0.47E-01 s)\n", + " \n", + " Step 1: Model building using BIC\n", + " \n", + " Model building for variable CO2SM_CO2_Enthalpy\n", + " ----\n", + " BIC = 0.750E+04 with CO2SM_CO2_Enthalpy = - 0.38E+06\n", + " ----\n", + " BIC = 0.569E+04 with CO2SM_CO2_Enthalpy = 58. * CO2SM_Temperature - 0.42E+06\n", + " ----\n", + " BIC = 0.542E+04 with CO2SM_CO2_Enthalpy = 55. * CO2SM_Temperature - 0.61E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", + " ----\n", + " BIC = 0.516E+04 with CO2SM_CO2_Enthalpy = 49. * CO2SM_Temperature + 4.0 * CO2SM_Pressure^2 - 0.15E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", + " ----\n", + " BIC = 0.502E+04 with CO2SM_CO2_Enthalpy = 0.16E+03 * CO2SM_Temperature - 0.16 * CO2SM_Temperature^2 + 0.76E-04 * CO2SM_Temperature^3 - 0.56E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.44E+06\n", + " ----\n", + " BIC = 0.484E+04 with CO2SM_CO2_Enthalpy = 0.14E+03 * CO2SM_Temperature + 2.5 * CO2SM_Pressure^2 - 0.14 * CO2SM_Temperature^2 + 0.66E-04 * CO2SM_Temperature^3 - 0.11E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.43E+06\n", + " \n", + " Model building for variable CO2SM_CO2_Entropy\n", + " ----\n", + " BIC = 0.219E+04 with CO2SM_CO2_Entropy = - 0.48E+03 * CO2SM_Pressure/CO2SM_Temperature\n", + " ----\n", + " BIC = 0.147E+04 with CO2SM_CO2_Entropy = 1.9 * CO2SM_Pressure - 0.15E+04 * CO2SM_Pressure/CO2SM_Temperature\n", + " ----\n", + " BIC = 0.115E+04 with CO2SM_CO2_Entropy = 0.77E-01 * CO2SM_Temperature - 0.38E+03 * CO2SM_Pressure/CO2SM_Temperature - 50.\n", + " ----\n", + " BIC = 713. with CO2SM_CO2_Entropy = 0.20 * CO2SM_Temperature - 0.94E-04 * CO2SM_Temperature^2 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 89.\n", + " ----\n", + " BIC = 443. with CO2SM_CO2_Entropy = 0.52 * CO2SM_Temperature - 0.60E-03 * CO2SM_Temperature^2 + 0.26E-06 * CO2SM_Temperature^3 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", + " ----\n", + " BIC = 317. with CO2SM_CO2_Entropy = 0.54 * CO2SM_Temperature - 0.63E-03 * CO2SM_Temperature^2 + 0.27E-06 * CO2SM_Temperature^3 - 0.26E+03 * CO2SM_Pressure/CO2SM_Temperature + 0.79E-01 * CO2SM_Temperature/CO2SM_Pressure - 0.16E+03\n", + " ----\n", + " BIC = 259. with CO2SM_CO2_Entropy = 0.47 * CO2SM_Temperature + 0.15E-01 * CO2SM_Pressure^2 - 0.53E-03 * CO2SM_Temperature^2 + 0.23E-06 * CO2SM_Temperature^3 - 0.70E-03 * CO2SM_Pressure*CO2SM_Temperature - 0.46E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", + " ----\n", + " BIC = 240. with CO2SM_CO2_Entropy = - 2.1 * CO2SM_Pressure + 0.55 * CO2SM_Temperature + 0.76E-01 * CO2SM_Pressure^2 - 0.63E-03 * CO2SM_Temperature^2 - 0.94E-03 * CO2SM_Pressure^3 + 0.27E-06 * CO2SM_Temperature^3 - 0.23E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", + " ----\n", + " BIC = 224. with CO2SM_CO2_Entropy = - 1.9 * CO2SM_Pressure + 0.49 * CO2SM_Temperature + 0.83E-01 * CO2SM_Pressure^2 - 0.57E-03 * CO2SM_Temperature^2 - 0.10E-02 * CO2SM_Pressure^3 + 0.25E-06 * CO2SM_Temperature^3 - 0.73E-08 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", + " ----\n", + " BIC = 193. with CO2SM_CO2_Entropy = - 3.9 * CO2SM_Pressure + 0.52 * CO2SM_Temperature + 0.17 * CO2SM_Pressure^2 - 0.56E-03 * CO2SM_Temperature^2 - 0.21E-02 * CO2SM_Pressure^3 + 0.24E-06 * CO2SM_Temperature^3 - 0.10E-02 * CO2SM_Pressure*CO2SM_Temperature - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.20 * CO2SM_Temperature/CO2SM_Pressure - 0.12E+03\n", + " \n", + " Calculating quality metrics on observed data set.\n", + " \n", + " Quality metrics for output CO2SM_CO2_Enthalpy\n", + " ---------------------------------------------\n", + " SSE OLR: 0.515E+08\n", + " SSE: 0.659E+08\n", + " RMSE: 406.\n", + " R2: 0.999\n", + " R2 adjusted: 0.999\n", + " Model size: 6\n", + " BIC: 0.484E+04\n", + " Cp: 0.659E+08\n", + " AICc: 0.482E+04\n", + " HQC: 0.483E+04\n", + " MSE: 0.168E+06\n", + " SSEp: 0.659E+08\n", + " RIC: 0.659E+08\n", + " MADp: 0.594\n", + " \n", + " Quality metrics for output CO2SM_CO2_Entropy\n", + " --------------------------------------------\n", + " SSE OLR: 541.\n", + " SSE: 558.\n", + " RMSE: 1.18\n", + " R2: 0.997\n", + " R2 adjusted: 0.997\n", + " Model size: 10\n", + " BIC: 193.\n", + " Cp: 178.\n", + " AICc: 154.\n", + " HQC: 169.\n", + " MSE: 1.43\n", + " SSEp: 558.\n", + " RIC: 606.\n", + " MADp: 0.130E+04\n", + " \n", + " Total execution time 0.20 s\n", + " Times breakdown\n", + " OLR time: 0.11 s in 3863 ordinary linear regression problem(s)\n", + " MINLP time: 0.0 s in 0 optimization problem(s)\n", + " Simulation time: 0.0 s to simulate 0 point(s)\n", + " All other time: 0.94E-01 s in 1 iteration(s)\n", + " \n", + " Normal termination\n", + " ***************************************************************************\n" + ] + } + ], + "source": [ + "# Create ALAMO trainer object\n", + "trainer = AlamoTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + ")\n", + "\n", + "# Set ALAMO options\n", + "trainer.config.constant = True\n", + "trainer.config.linfcns = True\n", + "trainer.config.multi2power = [1, 2]\n", + "trainer.config.monomialpower = [2, 3]\n", + "trainer.config.ratiopower = [1]\n", + "trainer.config.maxterms = [10] * len(output_labels) # max terms for each surrogate\n", + "trainer.config.filename = os.path.join(os.getcwd(), \"alamo_run.alm\")\n", + "trainer.config.overwrite_files = True\n", + "\n", + "# Train surrogate (calls ALAMO through IDAES ALAMOPy wrapper)\n", + "has_alamo = True\n", + "try:\n", + " success, alm_surr, msg = trainer.train_surrogate()\n", + "except FileNotFoundError as err:\n", + " if \"Could not find ALAMO\" in str(err):\n", + " print(\"ALAMO not found. You must install ALAMO to use this notebook\")\n", + " has_alamo = False\n", + " else:\n", + " raise\n", + "\n", + "if has_alamo:\n", + " # save model to JSON\n", + " model = alm_surr.save_to_file(\"alamo_surrogate.json\", overwrite=True)\n", + "\n", + " # create callable surrogate object\n", + " surrogate_expressions = trainer._results[\"Model\"]\n", + " input_labels = trainer._input_labels\n", + " output_labels = trainer._output_labels\n", + " xmin, xmax = [7,306], [40,1000]\n", + " input_bounds = {\n", + " input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))\n", + " }\n", + "\n", + " alm_surr = AlamoSurrogate(\n", + " surrogate_expressions, input_labels, output_labels, input_bounds\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing Surrogates\n", + "\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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PD8cBFf9//LHaJRJPfn4+N3PmTG7MmAWcTlfGARz/T6cr48aMWcDNnDmTy8/PV7uohIzk5+dzubm5XG5uLvfLL3ncli353C+/5PGf0fPXHqZnOn/+Nc7Hp/yffqqcmz//Gj1TAZw/z3F791b8T7iG0PFbM5Ywc65evYoNGzagbdu28Pf3BwB88803qFu3LrZv346EhATEx8djyJAhuHr1Kv93mZmZSEpKsrhWcnIyMjMzAQAlJSU4fPiwxTk+Pj5ISkrizzl8+DBKS0stzmncuDFq167Nn5OZmYkWLVqgZs2aFt9TVFSE//3vf3bv686dOygqKrL4xxoFBQU4fPgShg3jUF5e8Vl5OfDKKxwOH76kKctRSUkJAODq1QhYvwoc54OrV8MtziM8j4KCAixbtgwffvghhg8/hIcfjsLzz0fg4YejMHz4IXz44YdYtmyZptq1t2N6pu+99xkmTAhFebkOAFBersOECaF4773P6Jk6YNUqoE4d/OMVqPidkA9NibBJkyYhODgYEREROHfuHL7++mv+2P/93//h7Nmz2LJlCz755BOsXbsWhw8ftnBZ5uXlWQgjAKhZsyaKiopw69Yt5Ofno6yszOY5eXl5/DUCAgIQFhbm8Bxb1zAds8ecOXOg1+v5f3FxcQJrRhlMndv77+/kOzYTZWU6vP/+d5rs3MLDC6DTlVt8ptOVIzz8qp2/YBuTi/XixYs4fPgSvviiQjibPtPa85ETk8A2GkPwzTfdeDHOcT745ptuMBpDLM4j2EdrkytW3KVamGCzUldSoqoIe+ONN6DT6Rz+O3nyJH/+hAkT8Ouvv2LXrl3w9fXFyy+/DI7jAADl5eW4c+cOPvnkEzz22GPo0KEDVq1ahX379uHUqVNq3aIoJk+eDKPRyP87f/682kWywNRpORMtrHRuQtHrr6N79+38Pel05ejefTv0+usql0w8ZNlxDWcDNqE9WJ5csRaLqoUJtqda6PzU/PJx48ZhwIABDs+pW7cu/3NkZCQiIyPRsGFDNGnSBHFxcTh48CAMBgNiYmLg5+eHhg0b8uc3adIEQMVKxUaNGiE6OrrSKsZLly4hNDQUVatWha+vL3x9fW2eEx0dDQCIjo5GSUkJCgsLLaxh1udYr6g0XdN0ji0CAwMRGBjosD5YwCRaTJYDpUTLhQvAX38BDRoAtWpJe+2WLX9FvXqncfVqOMLDr2pSgAHOLTv16p2GXn9dc0JZbkwDtrkQY2XAJlxDrX7KGSbBYzSGYPHi0eA4S3fp33+vhl5/HWlpaYiIiFCkTNYTbHvvgVr9xoULwLBhsLLQAcnJ0o8FSqOqCIuKikJUVJRLf1v+z9O4c+cOAKBdu3a4e/cusrOzUa9ePQDAn3/+CQCoU6cOAMBgMODbb7+1uE5GRgYMBgMAICAgAK1atcKePXvQs2dP/nv27NmDtLQ0AECrVq3g7++PPXv2oFevXgCAU6dO4dy5c/x1DAYD3nnnHVy+fBk1atTgvyc0NBRNmzZ16X5ZQynRUlBQgJKSEmzcWBUTJ+pRXq6Djw+HefOM6Nv3FgICAiTrqPT666p20KZ7BYDcXB/k5PghIeEuYmMr2rqYe3Vk2VF7EJIb83q0ha16ZHXA9iZceW7OYHFyJcRdqtZEicX3oKCgAAcPAuXlls++rAzIyipA1apQTKzKgaoiTChZWVk4dOgQHn30UVSvXh3Z2dmYNm0a6tWrxwufpKQktGzZEoMGDcLixYtRXl6OkSNH4sknn+StY8OHD8eyZcswceJEDBo0CHv37sXmzZuxY8cO/rvGjh2L/v37o3Xr1njkkUewePFi3Lx5EwMHDgQA6PV6DB48GGPHjkV4eDhCQ0MxatQoGAwGtGnTBgDQuXNnNG3aFC+99BLmzZuHvLw8TJ06FSNHjtSEpUsocosWoTPGfv368cJbq5juFQCOHHmoUifYsuWvACB4duytlh3zegQqLIJXr0YgPLzAoq3aqkcWB2xvwZ3n5gy1J1f2YPUdZek9MB8DdLrRlepq//51OH5cWauh1GhChAUFBSE9PR0zZszAzZs3ERMTgy5dumDq1Km8qPHx8cE333yDUaNGoX379ggODsZTTz2FBQsW8NdJSEjAjh07MGbMGCxZsgS1atXCxx9/jOTkZP6c1NRUXLlyBdOnT0deXh4efPBB7Ny50yLQftGiRfDx8UGvXr1w584dJCcn44MPPuCP+/r6Yvv27RgxYgQMBgOCg4PRv39/zJ49W4Ha8hyczRizshLRufNufPrpp5p8Cc1n/vn5+QCkcyOyOKNVAvP6cSRm7dUjqwO2p+Puc9MiLL+jrLwHpuftrK603C40IcJatGiBvXv3Oj0vNjYWW7dudXhOhw4d8Ouvvzo8Jy0tjXc/2qJKlSpYvnw5li9fbvecOnXqVHJ9Eq5ha8YIAAcOGJCYmOWy6T4gIEDS88Rge+Yfj5s3gyRzI7I0o1UaZ2KWYBNveG6FhYX8z/XqnUavXlsBcIiLu+Ax9ygHntqfaUKEEd6NXn8dBkMmDhxoZ3XEvRiniIgIpKWlSR6HIgR7M3+gHAAH4N4KJaEuioKCAt6iZoKVGa3SCImJKygogNFoFHQ9OYQ4URkpYhnVnFw5o6CgAJs3bwbg2OKndeRaROWJ/RmJMEIwanZuiYlZOHDAAPOsKlLET6jtwrSe+VfcXzl0Ok6Ui0JoTI234Czexmg0YtOmTU6v07t3b9SoUUP1duItSBEnpebkyhlCVy9rEbkXUXlqn0YijBCMmp2bXn8dPXqwGT/hDrZm/oAPevXajODgYsFmd6ExNSY83bLjLIaktLTU4nx7HXxYWJiig7UcKwS1hFRxUqzXEWurl92dYMuddsOTrYYkwghRqNm5eWJMgL2Zv734kPz8fIcDsbMZdkpKCmJjY5kfpKRAaHthpYO3tmbaQ41FKEqKQ098z60RYvFTcqLk7gRbrrQbhYWFHmk1NIdEGKEpPC0mwNnM39pCk56eDsD+QOysE4yMjPQKAWbCWXthqYO3HqDsWeeUXgkmtzj0xlhGZ+99amqq4u+pFN8nZdoNU/zc1avxTFkNpYZEGMEsrLjM5LYC2Jv5u7JUn9XcQ3JjekZCA+39/Cq6PtbcQiZYss7l5uZafCalOBQby8hKnyAFjix+er1exZK5jpRpN4Rm8Qe03S5IhBHMEhERgX79+uHTTz91eq5cL6GcSSTNsZ75u2qhYTn3kFwItdT07t0bYWFhCAgIENXBKw0r1jlb9Sq1OBQay+ipbnRPtPhJ7U5m0WooJSTCCKapV6+eqiud5Eoi6Uw0umOh8YaYGnOE1n1YWBhiYmIAABcvXgTApmhlxTpnXa8XLsTIJg6dCU9vc6NrHanFpSdaDU2QCCNkQ6q9EFnofKW2TtgLhM3Pz0d6errbFhpPnGELRexSdtZEKyvWOXPX7pEjD2Hbtm4wTxEDSCcOWRGecsNyDjPW8dQ+jUQYIQtS74WoNnIMEo7uW6yFhjr3CoS4y2wlabXXwatRXyxY5woKCvg8aqYJiLUAA6QTh6wIT7lhOYcZoQ4kwghZEJqUUCt7fqkxSIix0FDnLsxaKSZ+TM0krWpb58zbke1cdpBUHLIgPJXCE99BmgS6DokwQlY8xc2g1iAhxgTviZ27GIS0NTHxY0rXp/UAxYp1Ljc3BtZbaQHlGDz4Y9SqdVGy71FbeGoJ1pL60iTQdUiEEbLiSW4GJQYJoQNscXExH1xu7zre1uG50tZY2gqFxYHMaAzB7t1JsBRgHJ58cnclASaFOPTUuB8pUWrFtlik/i5vsa6RCCNkxdPcDHIPEkIG4uLiYkFpO7QSbycVYtsaK7m4zGHtedl2ReoQG3svd5g76SO0NNCyYn2Sa8W2O0i1CMscFiclckAijJAdLbsZlBgkxHbu1hYwVjKrq4V53Ttqa+b5wVjJxcU6QqyL7uTv0spAy+KWUqy0YTkXYan93JWARBghCdZCwlO2IZF7kHC3c2fRmqM0Yp6RScB6Sqyi3DizLvbu3dvtgVILG6SzuKUUK23Y0xZhKQ2JMMJthAoJrSLnICG0Y7J1HiszYRYQ+4w8KVZRbhxZF8PCwtQrmEiksmaxMvFhrQ2zIgq1RuV1xwQhEluzxJyceBiNIU7/loVYD5YQU3eOOj3CMSYLj05XEbOi9VhFqbG1UjMh4Wyl+tHC+1tQUICLFy/a3APT1rvmaGJkb+Ij5H2VGtbasEkUmkMTG+eQJYyQFGf7v0VGRvLnshDrwRJiZ9iszYRdRcmAZzHxY3LCSpC3PbQSq+UMe9YvV61ZrFl7WIi3LSwsBOB5i7CUgkQYIRlC9n8z7d1HWOKKa9ETOj2lA55ZEBcsBnnbgnWBJQRX3Pjm8azWbYHFiY+a8bYFBQXYvHkz/3u9eqfRq9dWABzi4i5oqi9SCxJhhGS4M0tk3TIgN2LqzjTzBNiYCdtC6PN0JybOVdRuR2rcM1GB0RiC//2vmcN3LT093eJYWloa/zMLEx+W0noITZdB2IdEGCEZrs4StWIZkBOhdWc98wTYyaxuQszzJNhKGOvJmIsE6x0AHPVT1mJY7YkPC9Zca2iRkOuQCCMkw9VZIovLv5VGaN0JrYPU1FTVBKu7Kz69SZCQ9UAZrEVChQCrEGKmegeAnJx4m22PtS2lWJuMCrHka2ERhxqQCCMkxd1ZorcNSu4GitsTLXq9Xr5Ci0SosPK2Z0/WA+Wwl/k/OXknmjb9A9nZ9bF48Wi7bY9F6xNLOLPkqzUp1EKYC4kwwm2kmiV646DkTueuBdEitIze+OxZW2nnydgTCU2b/gEAgtqe2oM1yziz5KsxKWR1j01rSIQRbiPVLNFbByVXOgAtiBYxZVTj2as9S2ZxpZ2n4kgk5OTEe2W/IzVqx8pZw+Iem7YgEUZIghSDFQ1KwtGCYBVTRqWfPQuLQVhYaScHaotb6+8yYU8kUL9jiTvPj8Xt6VifsJIII5jBUwclOdDCwCGkjKZB0tmzlzqoV800EawkjJUDFsStOY6s9Pn5+UhPT6d+xwyxz4+ldBn2YH3CSiKMYArWTNqswsLAYW/GbEp2KaSM1oPk9OlXcOaMH+Lj7yI29mEAD/MduGnzbVuwEGArFDmDvNW2Qqklbl25b08Ww64idqW6VG1ZznbL+oSVRBghGLleFNaWf7MMKwOH0BmzkDKat5mYGKBVK9e+yx3ritKpMeQQQqxZoZTC1fumFY+OcRRHZb6rAOBePcndblmYsDqCRBghCDlfFOoM7WNL+Pbu3Rt3794FAPj7+1daeeRuhyhlpnvr/UJdKaPc1hV3VpmqbXkyh8VM/EqIW3fu2xv7FCE4i6Oy3lUAcF0kKZEnkmUPC4kwQhByvyjUGVZGacuGHJnuWd8v1J2gXW+1PAlFrRQq3pbwVw5ciaOSQtzL2WZYXDQAkAgjXEAL+ak8ATksG44sN9YuBjHfp9WBz52gXRYtT+ao+UzUWpFGfZM0OIqjkqtdSd1mtLBoACARRoiE9eW+LOOu68rdzk+o5UYsSg98Ug4CrAftuooSz8RWezYJeTVWpFHfJB324qiys+vL1q6kbjNaCXMhEUaIwtmLkp+fz0TDZg13XVdSDKpiLTJCxI7SA5+QYGEh7U+O1BisWAOVeCbO2rMa4pb1VARawzqOCgC/tRPgXrsyF/Cm91aONqOFcYhEGCEKZy+KKWDTW+Ng7OHuptZKz/CFij6hA58UAexigoWdtT+hqTGEtmGW3GBKiBFn7VnpvG+A51o1lcTRSnWpdhawJ+BZX8UoFyTCCFFYvyhAOQyGzErnqb0VhBqIibcSYzWRa1C1VwYxok/IwCdVALuYehDS/pylxhAKa24wVsRIy5a/Ii2tAYqKargsbsWg1iDO0gpZd7HlwjMltZWqXTla5MXyKka5IBFGiMb0omRlJSIz04ADB9ohM9Pg1UGwYuKtxFpN5BhUHZVBiNgR485zN4Dd9F1qiAshwpoVN5hauw84onnzMMTEyCtA1Myd54krZB1tSSS1yLXXD3lTnkgSYYQgbL0AmZkGZmb/aiNUaLhiNZG683NWBiFiR4w7z1GmeyGYf5den4tZs2L5ekhK2o2rVysGDanbndABlhXLk9QuVjGoGQ+nZgA26ytkpUAukeusHzLPMagla6JYSIQRgjB1dLm5uUhPT2dm9q81xNSbXJ2fszIItaS46s5zZcA2fdewYRdhNC7G1avhyM2Nxe7dSbLFYQkdOFmyPEnlYhUDC/FwnjpAs4BcItdZP8R6jkGpIBFGCMY8Uzors38lkSLmS0y9ydX5OStDSkoKhg2LlMWSIsWAbarPTz55mQlLrJz1xTqsxcOpjRoWQSVi0uRow944htiCRBjhEs5m/2LSBWgBd2K+kpJ2Izb2It8xi3EtSll3QmOGYmNjERERIbklRcoBmyVLrGnGrpTliSVYeg6uIpWIUcMiqOWYNG9dDWkNiTDCZRy5yMSkC9AC7sR8ZWQ8CUBn0TGrsfG2EjFDSiXwVGMWzUoeMBYQuliC9WBqqUSMWhZBrcekeeNqSGtIhBGicJRHxh6sdgByYEtoADoAlTvmgQOftNjcWgmroZwxQ0ok8FRrBSALcU8sYS7o77uvCJMm6VFWpoOvL4e5c4vQt+8LmrCCSyViWLEIsjJRcGRdNBqNFr/bG0NYF/BSQSKMEIV555uTk4OMjAz+mL0OoLCw0OMCLMXEfJnjyYGnSiTwVGMFIMU92cZUx+PGAampwOnTQP36OtSqFQYgTM2iKQ4L8U1KThSciaxNmzY5vUbv3r0RFhZm85gWBLxUkAgjRGN6OcyD0R11AHfv3lWlnNYoEftROZktB5MlDPDOwFNzpEjgqfQKQFasHCxTq1bFP60j1pLESm42JScKUu1BGxYW5lGTUFchEUa4jdEYgm3busOe240FlIr9SElJQUoKUK+e/TQKrNSJWiiRwFMKPCXuSSo8KTO8LVyxJKmZm80cJScKcuxB682QCCPc5vz5OJhbe4CKDuD8+VrQ60/I/v1CBgelYj8iIyMREBDAxzkkJJxF8+bHFQ/CZwGtd7724p58fDhMn56LlJQn4efnh5KSEj4hrRpCRAlxpOVVeEJwx5KkRm42a9R0hzp6zymW0jkkwghNI3RwSE1NtfjdVYHgSjZ5W2jdauAMT+l8zeOeOne+hgULvv7nWV+H2QJgC5QUIkqJI62vwnOG1l3OaqV7cPSeUyylMEiEEW4TF3ceQDkA806sHHFxF2T/bqGdfmlpKf+zKwJBbOyHJwssZ3hq5xsZeRsJCWednqekEPF0cSQXJuuhKa6VhcB6V1Bz30xn77nWha1SkAgjXMbf3x9AhTDp0WM7tm3rhgohVo4ePe4JE9N5SuDMwuWqQJAq9sPT42oA7VsVCOdo2dVsy3qodmC9q6hpdXf2nmtV2CoNiTDCZfR6Pf+zo1mY+XlCcUWsCLFwuSMQ3I398PS4Ggpk9w607mq2169Y92EVefzY33pKrbI5e8+ldJF68uSVRBjhMkITt4oddMWIFRNCLVxqzs7kch2x0kHJkcCTlXuzBYvWILnL5KmuZhPmfZjYPH4st1U5ECKypHCRevrklUQY4TJymcKtr2dvYDE/T6iFi6X9yuzdl/Vm4CZs1SVrHZSUCTxZuzdzWLQGKVEmcjXbhqW2KrcYFBqHJlUyVk+PeyQRRriF1PsMAsKTwJrjzMLl53evqau1d6M5ju4r3d6yO1TuxFnuoNxN4MnqvbFoDVKqTJ4Y5yOF9VBoG8zNza10rpQWMiXEoNqrv1m0QLsDiTBCFYR0FmIGFmcWrrCwMGbSRrgzYGp1tudJsGgNkrtMrGSGlxqlLZr2JlhSWciUmrio5fZj0QLtLiTCCFUQ4nIUMrCIWaLNSryAnAOmp80SWYLFhQdKlYmVzPBSIqf1UOx7SJMr57BogZYCEmGE6tib3XhqYlQh9+WKmPLEWaI5agtMqRceSBG7I8diCEffZUKtzPBSInQyJOQ5mcPSe6j2OyMlLFqgpYBEGKEqzmY3zoLotbgiydl9udKJe+os0YSjOhGzkMFdpFp4IGXsjpSLIbwBMdZDoc+pd+/eAJy/h0qKIpbEoBR4YjwiQCKMUBlnsxtHLkaj0YhNmzY5/Q5Wli4LcZ26KqY8dZYIOK8TMQsZpMSdhQdyxe64uxjCGxBjPTTtCeqMu3fvAnD8HmZn11dMFKk5KZNrYszSynYpIRFGqIqQ2Y29/GMcx1n8LiSVhZo4cp3m5+cjPT3dqZgyt/qYizpPnSUC7glMVp69FGjR6ssqclkP7b2H/v4liooipSZl1m1SjomxmlszKQGJMEJVnM1uOnbsiOrVq/Pn+/v7Q6/Xo7i4GMXFxfznWjG9O+t4nIkpa6tPv379ALC9as1V8SDUbeQNsJSHytMQYz20N9Ez38LN1ntYWhqoqKVaiXdG6Ap3dyfGWoz7FQOJMEJ1HM1u9u3bV+n8fv364dNPP+V/94R4KKEpAKwJCgpyumqtuLgYJSUldl0rcnZg7ogHR26jadNyAWjj2QrBWayQ3KkHyMrmHEcTPb1eb/M9DA29jAMHfoXRGKLIRELJVCLO2pqUE2NPbnskwgjJEdKhC93yyBbmFjDAM+KhrGd7aWmnsGFDFi9KHQ3SjlatFRQU4MMPP3T6/XJZUNwVD/bcRr6+vjC/LS2vAlPbiktWNucImejZeg8LCoADB5SzVKuZSsT8HQSg+YmxUmhGhPXo0QNHjx7F5cuXUb16dSQlJWHu3LmIjY0FAMycOROzZs2q9HdBQUG4efMm//uWLVswbdo0nDlzBg0aNMDcuXPx9NNP88c5jsOMGTPw0UcfobCwEO3atcOKFSvQoEED/pyrV69i1KhR+Oabb+Dj44NevXphyZIlqFatGn/OsWPHMHLkSBw6dAhRUVEYNWoUJk6cKEfVMIWYDt3axGyKizIhdGD197/jEe4q644xIeEsAPcGaVazzruCudvI3KintohxB1etuFKKTk9qI3Lh6kRPDVGkRioR63fQYMjU/MRYKTQjwjp27Ig333wTMTEx+PvvvzF+/Hg899xzOHDgAABg/PjxGD58uMXfPPHEE3j44Yf53w8cOIAXXngBc+bMQbdu3bBx40b07NkTR44cQfPmzQEA8+bNw9KlS7Fu3TokJCRg2rRpSE5Oxh9//IEqVaoAAF588UVcvHgRGRkZKC0txcCBAzFs2DBs3LgRAFBUVITOnTsjKSkJK1euxO+//45BgwYhLCwMw4YNU6K6VEPMvo+ONscVOrCanwdwAHQOXXhac7so5WpVMs2DlGjdFe3K4K5l0alV3Imx8rT8atbYegcPHDB4xMRYCTQjwsaMGcP/XKdOHbzxxhvo2bMnSktL4e/vj2rVqllYon777Tf88ccfWLlyJf/ZkiVL0KVLF0yYMAEA8NZbbyEjIwPLli3DypUrwXEcFi9ejKlTp+KZZ54BAHzyySeoWbMmvvrqK/Tp0wcnTpzAzp07cejQIbRu3RoA8P777+Ppp5/G/PnzERsbiw0bNqCkpASrV69GQEAAmjVrhqNHj2LhwoUeL8LMEZvbyfSZ0IHV+jxAB6Acgwd/jFq1Ksc/adHtIrWr1Z4oVirNg9RuQ626ol3NdO/s3cjPz3dbOGvZtSs1nrpdk5TYegcBHxgM+5GZafCodBJyoBkRZs7Vq1exYcMGtG3bll+VYs3HH3+Mhg0b4rHHHuM/y8zMxNixYy3OS05OxldffQUAyMnJQV5eHpKSkvjjer0eiYmJyMzMRJ8+fZCZmYmwsDBegAFAUlISfHx8kJWVhWeffRaZmZlo3769xYuZnJyMuXPn4tq1axar/cy5c+cO7ty5w/9eVFQkvFIYw53cTkIHVnsvf2mpZYdoeg5adLtIucrJVQuKVPUh9PvFxBSytIWQGOwtOvDx4TB9ei5SUp6Ev78/v6DCNEFx9m6Y3itXhTNZ2SzxxO2apMbeO5iYmIXExCybC66Ie2hKhE2aNAnLli1DcXEx2rRpg+3bt9s87/bt29iwYQPeeOMNi8/z8vJQs2ZNi89q1qyJvLw8/rjpM0fn1KhRw+K4n58fwsPDLc5JSEiodA3TMXsibM6cOTbj2rSIUCFla9YtVHg4Oy8lJQWxsbF2O0gtzPilSlCotttO6Pe7ElMo93Y9cmG+6KBz52tYsODrf9ruddibowh9N1wRzmq3EVbxdHeiuzjro2y1HVYnR2qgqgh74403MHfuXIfnnDhxAo0bNwYATJgwAYMHD8bZs2cxa9YsvPzyy9i+fTt0Op3F33z55Ze4fv06+vfvL1vZ5WDy5MkWlrqioiLExcWpWCLXcZS0MCcnHuHhBXYzSAsVHs7Oi4yMtDsIsz7jlzpBoRBRLKcoFSrKxVgrTTGFnrBdT2TkbX4hhiPkzBquVdcuoQ5C+yhrUlNTmZ4cKY2qImzcuHEYMGCAw3Pq1q3L/xwZGYnIyEg0bNgQTZo0QVxcHA4ePAiDwWDxNx9//DG6detWyaIVHR2NS5cuWXx26dIlREdH88dNn5kHjV+6dAkPPvggf87ly5ctrnH37l1cvXrV4jq2vsf8O2wRGBiIwMBAu8e1hK3B4v77j2HVqiH/dPTlqIjhqhDQ1rNuRy91UFAQ/7MrAkULM36pExQ6s6DIJUqVcht6y3Y9HTt2BLCPb/P+/iUoLQ2E0RjidtulpLiEGKz7KKGr2/V6veJlZRlVRVhUVBSioqJc+tvy8nIAsIihAiriuvbt24dt27ZV+huDwYA9e/Zg9OjR/GcZGRm8iEtISEB0dDT27NnDi66ioiJkZWVhxIgR/DUKCwtx+PBhtPrHLr13716Ul5cjMTGRP2fKlCn8ogHT9zRq1MiuK9ITMRdI/v4lZgIMAKxjuSxn3SkpKYiMjKx0jkl4uCNQtDLjl2K2KCSwWE5RKmafPltowWWsJKb+Q6+/LtlehO4Gn2ttxTEhHVr1NLCEJmLCsrKycOjQITz66KOoXr06srOzMW3aNNSrV6+SFWz16tWIiYnBU089Vek6r7/+Oh5//HEsWLAAXbt2xeeff45ffvmFT2ap0+kwevRovP3222jQoAGfoiI2NhY9e/YEADRp0gRdunTB0KFDsXLlSpSWliItLQ19+vThc5b17dsXs2bNwuDBgzFp0iQcP34cS5YswaJFi+StKAawl4Q1JyfeRhC9Jeaz7sjISIcpLNzp1L1pxu8osDggIAHp6b/KLkpd3aePlY5cDZHhTHxKKZzdCT7X4opjQl604GlgCU2IsKCgIKSnp2PGjBm4efMmYmJi0KVLF0ydOtXCfVdeXo61a9diwIAB8PX1rXSdtm3bYuPGjZg6dSrefPNNNGjQAF999RWfIwwAJk6ciJs3b2LYsGEoLCzEo48+ip07d/I5wgBgw4YNSEtLwxNPPMEna126dCl/XK/XY9euXRg5ciRatWqFyMhITJ8+XRPpKcwHnNxcH+Tk+CEh4S5iYyssj84GHHsmalvCByiHTgfFlzDLGVfDIvYCiy9erHimSopSoW5DVjpyNUSGEPEptXB2NfhciyuOTZAFTx604mlgBU2IsBYtWmDv3r1Oz/Px8cH58+cdnvP888/j+eeft3tcp9Nh9uzZmD17tt1zwsPD+cSs9rj//vvx008/OS4wY5gPOI4GAmcDjq1j9oSP0GBOKZA62F3rsJwDiZWOXGmRIVR8ihHOJDYqQxY8+WDR08DyO6AJEUYog6mROhsIXB1w7AkfpZYwSx3srnVYzoHEYkeuBM7EpynGVKhwVlpsiInhU3Ng1LIFj3VY8zSwLrhJhBGVkNIKIXSj7t69eyMsLIz/G7leBm8RWEJhLQcSy9Y5ORG6ijQqKkqUcFZSbIiJ4WN9YCTEw6qnQcxWempAIoyohJRWCLI+EWJwZJ0LDm6I0tIE+Pn58ZnkzVGiHcm1WtPVVaQsCGdAfAwfa5Yoe8+1sLDQ4d9R33UPJft6V62orCz2MYdEGFEJqc3J1EkRYrBlnauwnGxy+rdyWk7k7sBdXUUqBrlEJCsxfK7g6Llu3rzZ6d8raa1jObYJUKavd9WKyspiH2tIhBE2EZMB2RNhvbPzNlyxnEj5DJXuwOVIPiuniHRmPTftfWmvztXKByfFc1XKWkcu3ApcdS+yOlEgEUbYxV78lqdDnZ32kfoZytmBKyH45RKRQmP4zDOpp6WlWVxDTReRmOeqduJg1ly4riB1WxfTdlhd7EMijJAN6xeusLAQd+/e5X/39/e32MKCFesS64GchPMBUaoBS+5tl5QS/M7EhslSZY2YvID5+fmoV2+xU+u5eZ2r7SISOjCzGEuktigUi9RtXWzbYW3VpgkSYQSP0IFEyHnWL5x1h2GvA2HNusRi5+vtKPlM3N12yRlKWTeciQ1zS5U1YvICirWeq+UiEmrBA9QXiraQ+x2Qwzor9eTWlbbDYpgNiTCCR8rVLebXsO4w7r//GI4du99mB8KSdYnFztfbcfWZuGM1UCJgXi7EiA17uPpOCqlztVxEQnLkGY1GbNq0iblYIrn7JSWss1KISKFtR2iaJLXS3pAIIyyQYw886w7jt98eAKDjf2dV2LDW+RKuPRMprQZyBMybI7WLScjeoXLgrM5ZyAcnNEee0kLRXhjHtWvXAMjfL8kdjiGViBQ6sWA9TZJLImzfvn3o2LGj1GUhGMDdvSOtsdVhmASYCbmFjaumdVYDOb0Zsc9ES9ZMuVxMzvYONSGVABRS5yzv1mCNkrFEQqxQSvZLcrRJKUWkUPciC+3IHi6JsC5duqBWrVoYOHAg+vfvj7i4OKnLRaiAVHtHmmN7424O5kLMvAMxGo2IiYmR5H4A57FpJmzdE6uBnN6Iq5YTrVgz1RaLUg62Quuctd0arFEjA7wQ65JS/ZJcbdJdEcm6e1EsLomwv//+G+vXr8e6deswa9YsdOrUCYMHD0bPnj01c+NEZeTYO9JWh2EeEwaUIylpN/8Sbdq0SdLgfEexaUJi0VgM5PRGXLWcaMWaqaZYlHqw1UqdO4MFN5a9BU316p3G6NG2V6IKHYPteQhMK2XlapPuikgWnouUuCTCIiMjMWbMGIwZMwZHjhzBmjVr8Oqrr+LVV19F3759MXjwYDzwwANSl5VQCKlfPltCpkaNy9i9Owkc54Pdu5NQteptWYPzxQw0njbT8hTEWE5YiDkSg5rCRar3XYo6Zy1JspoDuZgFTSkpKYiMjBRcP2q7Pd2d3GpFYAnB7cD8li1bIjo6GhEREXj33XexevVqfPDBBzAYDFi5ciWaNWsmRTkJBZHj5TMXMkZjCC/AAGVcL2IGGk+baXkjWok5kkMsihUyUr3v7ta50FV5vXv3RlhYmM1jLDxTKRC7oCkyMlJUGIeQ4Hup3Z40ubWNyyKstLQUX3/9NVavXo2MjAy0bt0ay5YtwwsvvIArV65g6tSpeP755/HHH39IWV5CAaR4+Ry9SGq4XsQONJ7QkXs7rMccAdKLRTHpBeQQgO7UuVALuLP9HFnLNWgPW2LZkStQrgVNjsI0pAzHoMmtbVwSYaNGjcJnn30GjuPw0ksvYd68eWjevDl/PDg4GPPnz0dsbKxkBSWURQpzsfULl5OTg4yMDFVcLxRkT7CKlGJRTPLXmJgYpq2Frq7WZCnXoD2ciWWxC5pcRUiYhpQWK28TWEJwSYT98ccfeP/995GSkoLAwECb50RGRmLfvn1uFY5QF3f3jrT3wrkjiNyJGWExyJ61GBjCs3AmZFi1FgpZram1bXvMcSYUnS1okmoS6cwrYYo1s4b6JelwSYTt2bPH+YX9/PD444+7cnnCC3BFELmTbsKEu8JSSmijcLbRukDW6pZbQqwzWr03e9jqy2z1kZ067ZU0TYYzr4TYWDNCPC7HhJ06dQrvv/8+Tpw4AQBo0qQJRo0ahUaNGklWOEJZpNw7UghiBZEr6SaUvicx0EbhbFJQUIDLly87jT0C2BXIaucdcwdn1hln92a+GTnrQhlw3JcNHPgk/Pz8cPfuXf58f39/6PV6/nd37lGMV0LrkxJWcUmEbd26FX369EHr1q1hMBgAAAcPHkTz5s3x+eefo1evXpIWklAGuQMnpRJEQjph82z/qampKC0thZ+fn81VVSx0Hp42s9cqQq2TJlgVyFpJUmsLZ9YZZ/dmvRk5q0IZcN6XibFEuSqShHgl3LXak4Czj0sibOLEiZg8eTJmz55t8fmMGTMwceJEEmEaRs4XQSqR56wTnjo1B99808xM0GS5lO1fKbRstfA07LVNrcUfaTlhqjPrjNh7Y1UoA9KJZbEiSWy6CHes9lKEkXgyLomwixcv4uWXX670eb9+/fDee++5XSjCc5HiJXPUCUuZ7V8ptGy18HSMxhBkZSXiwAEDAPtWStZm+lpcDSx0myB79wYAOTnxmhHKgHRiWaxIcmdCLNZq7+6uJZ6OSyKsQ4cO+Omnn1C/fn2Lz3/++Wc89thjkhSMIOzhaIDJyYnXnKDRstXCkzEfMEzYslKytMBCjf0OpcKZMDAajdi0aROAyveWnV0fixePZtadby3STXFrcohloSLJlbbojtWeLP62cUmE9ejRA5MmTcLhw4fRpk0bABUxYVu2bMGsWbOwbds2i3MJQmrsDTBaFDRatFp4OtYDhjnWol5Mbi650XpCTEflss5rlp+fj/T0dGYHd5PwKiwsdLjIQ0qxLHdduGO1J4u/bVwSYa+++ioA4IMPPsAHH3xg8xgA6HQ6lJWVuVE8grCPrTgGtXKQuQuLOcyEIFedqe3es52xvAJnol7t+DEt1rdQbJWBxcFd7AIPvf46Bg580iInlyt1LndduDPJ1eIEWQlcEmHl5eVSl4MgnCJ0VqhWDjKxaH0vNbnccHJe15nQMGE7YzkAOBb1WlzlypI71RVYHNwdxWgBsNm3SJGTS+66cGeSSxZ/27i9gTdBKIUjV4vJNWFCiRxk7qJ115Fcbjg5ritmc2jA9oBhMGQiMTGrkrvIVA5W3WLO0Hq+OtYHd8vYQu6ff/KIdCXqwh2rvVYt/nIiWIQtXbpU8EVfe+01lwpDEM6QW5AoPZCyKrBcQS43nBTXFSo0dLp7e/M5GjBSU1MRFRWFiIgIXLx4EQCbbjGxaMmSp4VFCJVjC3Uw7f8oV98ih9CR0mrP0q4lLCBYhC1atEjQeTqdjkQYoThSJYL1hIFUDRwN3uYZzK1xZukTel0xFkNH19Tr9S5bJ1l0i4lBSBJkliyzWrAkO4otBKTrW+QObXCnrlnetYQFBIuwnJwcOctBMMyFC8BffwENGgC1aqldGttI1SFrfSBVA2eDt3UGc2vsxRqJva6QmCUhlk4ltoBhEaGZ6FmKDWOlHPawH1tYgVR9ixKC1NW/1YJYVhOKCSNsYgpi3rixKiZO1KO8XAcfHw7z5hnRt+8tJl8aKcqj9YFUDYRaD8XGGtm77vnztXD16i2XYpbksHRqwS0mBKETEFZjw1jEVn/CcYB5TJhUfQtr/bE5LJdNbVwWYRcuXMC2bdtw7ty5Si/lwoUL3S4YoR6mIGajMeSfBIgVMQzl5TpMmBCKv/9eDb3+OlMzYimh4FFxCBm8XYk1sm1FKMfWrc+5HLPkrKyuuNw8ZaZvLRiAisUI1hQWFrq9io8FlErLYd2fANCkSFcKraRLkQqXRNiePXvQo0cP1K1bFydPnkTz5s1x5swZcByHli1bSl1GQmFML4Azq4Enz4i9PXhUTDoHZ9ZDVxc72BIFgI6fFLgS2OysrK663JydW1BQwAfw24KVgcUkGLKyEpGZacCBA+2QmWmwELubN2/W/ARM7rQczmK0TD+npqZCr9cz8/zVxhv3mXRJhE2ePBnjx4/HrFmzEBISgq1bt6JGjRp48cUX0aVLF6nLSKiEN8VHeXPwqLXgMt8epuJ32x1hv379+J8dWQ/FugDtufdu3gzGF188L/g69hBi6ZRygsF6Hi5bbToz0+BQNGt9AiZ3Wg5PsY4qjTfuM+mSCDtx4gQ+++yzigv4+eHWrVuoVq0aZs+ejWeeeQYjRoyQtJCEOrAcH+XOYgFz0ZGb64OcHD8kJNxF7969cffuXfj7+0Ov11f6O0/sNO3PPEOg11932BEGBQU5HGhMudvEinnrAcx8expXJwX2LBNGY4jsmz6ztK2RLUz1nZubi/T0dLuiOSsrEZ0771aljHIiV1oOsX2Ft7nhHKHVnHuu4JIICw4O5htLTEwMsrOz0axZMwCOl6MT2oOl+CgpFguYi47KnW8WWrb8FUZjCO6/vxdatKiC2NiK3SE8tQN0NPNMStqN3buT3F5J6IqYt3VddyYF1kLD1v0qlRNL7W2NbBEREcG3BXsr+g4cMFRKVqt1WBnsWbeWKo27C2jsTbRZ7M9dEmFt2rTBzz//jCZNmuDpp5/GuHHj8PvvvyM9PZ3f0JtgF7FWJBbio6RaLOAsu/mtW1V44WE9MHtyB2irPjIykgBIs5JQKjHvznXMhYZag68WkqHq9ddhMGTiwIF2Vkc8L18eK3kBhbpHc3NzbVrLWBIVUuBOKIzjiTZ7/blLImzhwoW4ceMGAGDWrFm4ceMGNm3ahAYNGtDKSMZZtQoYNgwoLwd8fIAPPwQGD1a7VM6RerGAves4svyYru2JbgPbSSV93IoJlCqBpByJKNUYfFmxutjDvP4SE7Nw4IAB5iLcE+NBWYx7dSQcTG55Tw9Wd8fqLXSixUpcmUsirG7duvzPwcHBWLlypWQFIuShoKAAZ87cxbBhNVBebrIiAa+8wuHBBy8jPt5PEy+wVJ2mvfQHzgZmT129Y69ezV2SYmMCpQpOliPIWYnB1yTWTSEarFhd7BEREYHU1FRs2rQJev119OjBZjyolLAW9+pMOHhLsDrgvvWc9ffNhFvJWktKSnD58mWUl5dbfF67dm23CkVIi0k45OTEo7y8v8WxsjId3n33RwQH38K4cT3QokV1plcKStVp2rqOdQwUUHlg9tTVO/bqtWXLX9G8+XGXO0KphKjUglbOwbegoACXL1/G5s2bLT5n0epijfmCFJbiQeWEpft0JBwAMG1JlQN3QmG08L4BLoqwP//8E4MHD8aBAwcsPuc4DjqdDmVlZZIUjpAGxwG395Jfrl/P/eOeZHt5tZzxRVWr3hY0MLPuWnIFe/XKQkygVMid3d5egLXJYuqOZVEJ5N6DkBVYvU9HwkEty46S4RdSGgBYs3LawyURNnDgQPj5+WH79u2IiYmBTqeTulyEDDhLfllersMrrwDJyUCtWrZfKvOgfjWRShhYX0eowNOKqVssYupVjgFK7g5f7vxNtgKss7IS+fgqk8U1NjaXyYzp3pLfitX7dCYclLbsKB1+IfVzYcnKaQ+XRNjRo0dx+PBhNG7cWOryEDLjLPllWRmQlVWAqlUru4C0GtQvFiFCRCumbmcIHfh79+6NsLAwi7+TeoBSqsNXamA1d1ebMC3+GD16MfT660hJSUFkZCRTwoaVcsgNq/fpyCKttGVHjfALOUIPWBRfJlwSYU2bNqV8YBrGPFGlLSGxf/86HD9+L92DpwT1A9JZG8R0iCyvpmTJIuBJ8XbW7mpzzC2mkZGRHrEPI+EeQt2jall2PDH8ghUEi7CioiL+57lz52LixIn417/+hRYtWsDf39/i3NDQUOlKSMiGMyFRUlLiNKj//fe/Q0LCWdlXA0oVK2BLdFhv0+Po2uZ/J6RD1MJqStYEtCd0+LZTflSgRYspIS+OJkOmHSNMqGHZ0VL4BcsLy2whWISFhYVZxH5xHIcnnnjC4hwKzNcezoSEo6B+88FEbuuElBYb63NiYmIEX9t6E2ZnHaInWXeUQksdvj3sZZ0HXHchsWxRlQpvuEd72LsvFkSFlsIvWLLuC0GwCNu3b5+c5SBUROjMymDI5Df2VWOliZwvjdBru9oheoJ1Rym01OHbw5aV2WDIdHnbH2/Y1kYLVmOhCBWTQs9TW1RoZaWhCdbbhzmCRdjjjz/O/3zu3DnExcVVWhXJcRzOnz8vXekISXB3hmQZYFyOtm33e9weckJxtUP0BOuOUmitw7eHEHe10HdT6LY2WraoeorVWKhg7tevHz799FP+d9ZFpxZWGmoRlwLzExIScPHiRdSoUcPi86tXryIhIYHckYxhLRysYwzsYTQacf48ZxVg7IPMzIqNfL0VVzpET7DuSIm1BaCwsBBXrlzhf9dqhy80wDo1NRVRUVEutSUt7D/pDlq3GgsVzMXFxfzPWhGdrK801CIuiTBT7Jc1N27cQJUqVdwuFCE9rnT2mzZtQk5OPDiuicXnallwxG48zhKeYt1xhhD3CgAHbifL3xMSzspeZimR23WkdYEiBE+yGgsRzEKfqRrxcizEo3k6okTY2LFjAQA6nQ7Tpk1DUFAQf6ysrAxZWVl48MEHJS0goS5qW3BMHc/GjVUxcaIe5eU6+PhwmDfPiL59bzEVYOkMrVp3hCI0pqd37978z9aD1P33H8OxY/c7HLRY7/DlbI+eJFDsoXafIxVCxZWQZ6pWvBwL8WiejigR9uuvFZ0hx3H4/fffLTrDgIAAPPDAAxg/fry0JSQkR8wgpqYFx9TxGI0hWLx4tEVm/wkTQvH336uh119nJmZCCNbmfFNnevx4If+ZVjs1oTE9d+/eBWB7kPrttwcA6PjfzQetlJQUxMbGarJupMJTBIojPMVqLFQwC3mmnhIvJxfmVsLcXB/k5PghIeEuYmMr9rVmuU8VJcJMKyQHDhyIJUuWUD4wjSJkdlNYWMhvQCxlgLEYTOVz1pnZug9WXJeO6sW8M/3kE8vOVEvC0hp3LAAmAWbCOrGpVutEKpwJFHtJtFkehGzhCVZjoYJZjOhU2h2thRWr5mV0JFBZ7VNdiglbs2aN1OUgFEZsY7QXkKmEdULs7J+l7ZXsLYpw1plqeUbrjgUA4GAuxDzNyiMFjgSKqW3ZcwPLvfWUlGg9CFyMuGJ1z1otWOBM363VPtUlEXbz5k28++672LNnDy5fvozy8nKL4//3f/8nSeEI9lHCOiG0M1NjeyUhFjdb3+nJsT3uWABsxYRpvT6kQOiqS3sDpdEYgrlz/6uYBcOVIHJPDAIXY9Fjec9aLSwI0Wqf6pIIGzJkCH788Ue89NJLiImJsblSkiCkxFlnpuT2So4WC3TocAfnz1dB69Z6h25QT47tcdcC0KnTXk27oeRAyLY29gbKW7eqYPfuJMUsGK4mlvWUIHChgtl8YZuz65nqRK14OS0IHK32qS6JsO+++w47duxAu3btpC4PQdjF0UxRqe2VHC0WGD8+FDpdRefkbAWnpwQf28MdC4DW3VBy4Ux82BsoTQLM9LvcFgx3EstKLbDUSOsgRky6sl2aGvFyWhA4Wu1TXRJh1atXR3h4uNRlIRhCq64BuV9ER4sFAB9wXMVPQlZwekLwsSPsiSl/f3+XrsdaW2MN2zF2lfevVNKCoWZiWTW3ehJ6PVe/V+mJilYEjhb7VJdE2FtvvYXp06dj3bp1gk2qhLbQsmtAiRfR/gbN93C2ghPwLKuPUJEUFRVVqW0VFhbyqSuACqGm1+strs1iW2MJWwNlUtJuC0sYoJwFQ+04Ik/a6omFSbHYflWttBFa61NdEmELFixAdnY2atasifj4+Eoz2yNHjkhSOEJd1B70hHQoRmMIsrKCEBd32+JzuV9E6wEPKEfFqj7vXdnnjnCPiYmRs2heg62BsmrV25JZMITuiACwFUek9a2eWJkUC+1XtZ42QklcEmE9e/aUuBgEURlnHc/GjVUxe7Yeixbp4OMTim7dHlK0Y7Ue8LKz6zsd7FiY0cqJt3eoUuFOLJP1QCmVZVhozqjU1FQA7MQRqW2Rkwo13i1X+ysl00ZovU91SYTNmDFD6nIQhE1sdTymVBSmlYlARQyWGh2r+YAnZLBjZUZLsIvYBJlCBhcpLMNCc0aVlpby38lCHBFLFjmt4W5/JXXd23Nx9u7dG3fv3q0UxiCkjGojSoT997//RatWreDr62vz+J07d/D1119b7A1HEFLiKBUFCx2rkMGO1c6AYAOxCTJtDZRGoxGbNm1y+l2uWAfEWJZYCJRmxSInN3LFYLnTX0lZ945dnFn8e5GamoqbN6tb3H9JSQkKCgqY7HvtRxXbwGAwoKCggP89NDTUIjFrYWEhXnjhBelKZ0aPHj1Qu3ZtVKlSBTExMXjppZeQm5trcc7333+PNm3aICQkBFFRUejVqxfOnDljcc4PP/yAli1bIjAwEPXr18fatWsrfdfy5csRHx+PKlWqIDExEf/9738tjt++fRsjR45EREQEqlWrhl69euHSpUsW55w7dw5du3ZFUFAQatSogQkTJlgEHhOuYZ2KwhwhLzerJmmCsMae2DEaQyqdGxERgZiYGP5f48aNkZaWhmHDhtn952o8jiPrBgD4+VnO7fX660hIOOuya95dTBY5U39ha6unixcvWoxtWsMkUD788EMMH34IDz8cheefj8DDD0dh+PBD+PDDD7Fs2TLF79FZ3YvBmYvT9F5MmvQnM/cvBFGWMM60/t7O7/Y+k4KOHTvizTffRExMDP7++2+MHz8ezz33HA4cOAAAyMnJwTPPPIOxY8diw4YNMBqNGDNmDFJSUviFAjk5OejatSuGDx+ODRs2YM+ePRgyZAhiYmKQnJwMANi0aRPGjh2LlStXIjExEYsXL0ZycjJOnTqFGjVqAADGjBmDHTt2YMuWLdDr9UhLS0NKSgr2798PACgrK0PXrl0RHR2NAwcO4OLFi3j55Zfh7++Pf/3rX7LUj7fhzNWRkpKCyMhIi7+RwiRNIk5e1MjrxCruunLkqidn1o2wsDDmXO7WFjkAyMmJR3h4AdLT0/nztBoozvLWPVJbQ51NAli7f2e4FBPmCLmy548ZM4b/uU6dOnjjjTfQs2dPlJaWwt/fH4cPH0ZZWRnefvtt+PhUPIDx48fjmWee4c9ZuXIlEhISsGDBAgBAkyZN8PPPP2PRokW8CFu4cCGGDh2KgQMHAgBWrlyJHTt2YPXq1XjjjTdgNBqxatUqbNy4EZ06dQJQsZdmkyZNcPDgQbRp0wa7du3CH3/8gd27d6NmzZp48MEH8dZbb2HSpEmYOXOmRw/kSm6c7ejljoyMlGXFnbXrR063j7ehhc2C5aagoIDfhJtVN5qQWC8Wno+9zPWu7oGoVsoFMbAa/yblanVH7wWr9+8IyUWYEly9ehUbNmxA27Zt+fQYrVq1go+PD9asWYMBAwbgxo0bWL9+PZKSkvhzMjMzkZSUZHGt5ORkjB49GkDFC3j48GFMnjyZP+7j44OkpCRkZmYCAA4fPozS0lKL6zRu3Bi1a9dGZmYm2rRpg8zMTLRo0QI1a9a0+J4RI0bgf//7Hx566CGb93Xnzh3cuXOH/72oqMiNWlIeNTbOViMnjHlHGxMTU2nWb91Bs9A5awG1NgtmxfpmK7mowZCJAwcMANhKkMlCrJczzCdMzrZ2cragRyspF1gV7lLibBKgtfsXLcL++OMP5OXlAahwPZ48eRI3btwAAH4GJxeTJk3CsmXLUFxcjDZt2mD79u38sYSEBOzatQu9e/fGK6+8grKyMhgMBnz77bf8OXl5eRbCCABq1qyJoqIi3Lp1C9euXUNZWZnNc06ePMlfIyAgAGFhYZXOMdWLve8xHbPHnDlzMGvWLIG1wQ5qbJzNEhEREQ73k+zb95ZqQaFyzN7ltggomVKAJeubIxFqMOxHYmIWU2JHC0kxrZ+Zq5YSlt195ri6IlXqd1rutBH2JgGsrMgVg2gR9sQTT1jEfXXr1g1AhRuS4zhR7sg33ngDc+fOdXjOiRMn0LhxYwDAhAkTMHjwYJw9exazZs3Cyy+/jO3bt0On0yEvLw9Dhw5F//798cILL+D69euYPn06nnvuOWRkZGhik/HJkydj7Nix/O9FRUWIi4tTsUTOUXLjbFZxtJ+ks62LlCgXIN3sXQmLgJIuBbWsb46wNdBnZhqQmJilWBlsofV8TID7liItuLtcyWwv9TutRCoee5MALVhpzRElwnJyciT98nHjxmHAgAEOz6lbty7/c2RkJCIjI9GwYUM0adIEcXFxOHjwIAwGA5YvXw69Xo958+bx53/66aeIi4tDVlYW2rRpg+jo6EqrGC9duoTQ0FBUrVoVvr6+8PX1tXlOdHQ0ACA6OholJSUoLCy0sIZZn2O9otJ0TdM5tggMDERgYKDD+mANpTbOZhlH+0kK2bpI7nJJOXtXwiKghkuFpYSeQgd6pcWOJ+S4c9dSohV3nxgrpVzvtBLtwJ7lWgtWWhOiRFidOnVEXfzVV1/F7NmzK61SMxEVFYWoqChR1zRRXl5hJjXFUBUXF/MB+SZM+cxM51q7JwEgIyMDBoMBQEUH0qpVK+zZs4ffFaC8vBx79uxBWloagIrYM39/f+zZswe9evUCAJw6dQrnzp3jr2MwGPDOO+/g8uXL/IrKjIwMhIaGomnTpi7dL+soZQZmeTbOagctx+xdTouAu23JFdcKSxYOZ+0oJSUFsbGxqogdlgWWUNyxlGjR3SUUlt4BW1j36VrfisqErIH5n376KcaPH29XhAklKysLhw4dwqOPPorq1asjOzsb06ZNQ7169Xjh07VrVyxatAizZ8/m3ZFvvvkm6tSpwwfCDx8+HMuWLcPEiRMxaNAg7N27F5s3b8aOHTv47xo7diz69++P1q1b45FHHsHixYtx8+ZNfrWkXq/H4MGDMXbsWISHhyM0NBSjRo2CwWBAmzZtAACdO3dG06ZN8dJLL2HevHnIy8vD1KlTMXLkSM1ZusSghBnY3dm4nKs3We2g5RCHcgtOV9uSI9eKwZDJx1WlpqZaTABZEtDO2lFkZKRHiCE1ccdS4qht2ouLlttCKMXklKV3wBYRERHo3bs3Nm/e7LLlmkVXuawiTKqcYUFBQUhPT8eMGTNw8+ZNxMTEoEuXLpg6dSovajp16oSNGzdi3rx5mDdvHoKCgmAwGLBz505UrVoVQEXw/o4dOzBmzBgsWbIEtWrVwscff8ynpwAqsu1euXIF06dPR15eHh588EHs3LnTItB+0aJF8PHxQa9evXDnzh0kJyfjgw8+4I/7+vpi+/btGDFiBAwGA4KDg9G/f3/Mnj1bkvpgGSXMwK52Zkqs3mQxHkEOcaiE4HSlLTlyrRw40A4HDhjQo8d2ABVpRUy7e7AmoFlsR1pGagu6vbZpWoGp9OIOKVzFrL0DtjCFADmz2smVJ1IONJGiokWLFti7d6/T8/r06YM+ffo4PKdDhw749VfHJsu0tDTe/WiLKlWqYPny5Vi+fLndc+rUqVPJ9Umog9KrN5WKRxBj1RMzqDty55nP9KUWClIOlLY66QosZ8zmu1iwJny0FNfCOkrFs6m5uEOKPoy1d8Aezqx2cuWJlANNiDCCcBVPW73pLBWGo4FEyKAudKWUmGsKRcqB0lYnbcJRnIuawkfJeEd3cqOxkldNLO6USUids7S4wx2kfAfkSmejBaudUEiEER6NJ63eVCIVhtCVUnIh1eBt3UmbY/7cTYmcnaFELIlS1hp3cqOxlFdNSRw9G1MiWNYD25VG7nQ2WrHaOYNEGOEWLK9WNEfOmZNSdSA2FYY75XJ3QFH7eQP3OumsrES7WedNe7+yYtlR4nvcyY3GYl41pXD2bFgPbLeHXP2XEulsPMFlL6sI69evH0JDQ+X8CkJltJQ7SK6Zk9g6cHeFptDO3p1nIyRNgr1Vz6w8b6Cik+7ceTcSE7PsPndWyqo07lg7PcX1JiVadZHJ3YeThdAxLomw8vLySjm5TJ9fuHABtWvXBgCsWLHCvdIRmkBLg5hcMydndVBQUICLFy+6FMtljZjO3tVnIyRNglYCXwHPmDFLjTuDIw2sttGqi0zOPlxKC6FWPC9iECXCioqKMGTIEHzzzTcIDQ3FK6+8ghkzZvBJUa9cuYKEhASUlZXJUliC0CJyxHIp0dlrcUBhsfNlNZDdncFRq643JSDBb4mUFkIteV6EIkqETZs2Db/99hvWr1+PwsJCvP322zhy5AjS09P5zk+q3GAE4SnIta2REp291gYUUyd9+fJlbN682en5cos2lgPZ3Rkctep6k4Pi4mL+Z3vPF1BngsDKBEDKCZ3Q8sqZmFtKRImwr776CuvWrUOHDh0AAD179kTXrl3RvXt3bNu2DQA0sVE2QagBWQ+UISIigpkZM+uB7O4Mjlq0lEpNQUEBPv30UwCOn2+/fv0UF9msTQCUmNC5k8JHLUSJsCtXrljsHxkZGYndu3cjOTkZTz/9ND7++GPJC0gQ7sBSDIEWrAcs1Ze7sNTZshzI7s7gqDVLqdQIXQEYFBSkWtkANicAUqNECh85ECXCateujRMnTiAhIYH/LCQkBLt27ULnzp3x7LPPSl5AgnAHViwiJtyxHsgpkMzdFu3b98X//Z8vate+g+joiozy/v7+0Ov1/PVZ6sS0AEuB7O60I08S6VLC0vO1Rq0JgNJtRa6wD7kRJcI6d+6MNWvW4Omnn7b4vFq1avj+++/x5JNPSlo4gpAC1gSDq9YDuQSl3EkVCbZc0e60I7UmNazENtmDpedrjVoC0dW2olQKH1YQJcJmzZqF3Nxcm8dCQkKQkZGBI0eOSFIwgiAqI8dAo0RSRW+HNVe0O+3I22ObbMHa8zVHTVEi9HlIGcvF8rOwhSgRVr16dVSvXt3u8ZCQEDz++ONuF4ogCOVh2aViD7n2ppMDCmR3Da3ENrH6fFkXJVpN4SMVopO13r17F4sWLcJnn32GP//8EwDQsGFD9O3bF6+//rrgvdgIwlvQShyN1sz4WnSjensguzsoEdvkrqhn9fmyLEq0nMJHCkSJsFu3buHJJ59EZmYmkpKS0L59ewDAiRMnMGnSJGzbtg27du1ClSpVZCksQWgR1hYH2KPyptflSErazXdkRqORqSz5rLlRbcUtFRYWCvpbtQW4FpDbUqtFUS8G1kWJ1iaBUiFKhL377rs4f/48fv31V9x///0Wx3777Tf06NED7777LmbOnCllGQlC82il027Z8lfculUFu3cngeN8sHt3EqpWvY2WLX/Fpk2bmBqATALH2eBcWFgou3h0HLd077zU1FR+lakJFgS4FpB7kHZF1LNs5RbynUZjCHbtKkXz5pdUd+Er4TY1GkPw7be38OCD6t+vCVEi7PPPP8fChQsrCTAAeOCBBzB//nxMmTKFRBhBaBSjMYQXYADbwfl375rSZ9wBwAEwTxTNwd+/xOI8OREat6TX65myJmoJpWKbxFjcWLZy2ypbYWEhv5MEi9Y+OVP4sHi/gEgRdvbsWTzyyCN2j7dp0wbnzp1zu1AEoSVY2R5DiiB1qV0+SgTOl5YGwlKAAYAOpaXKWx9YTsrqCSgR2yTW4sayFdNe2Vhx4dtCyhQ++fn5SE9PZ/p+RYmw0NBQXL58GXFxcTaP5+XlISQkRJKCEQTLsLY9hjvxLOYzSCldPkrF2LAUS6LFFaZSoVQuL7ljm1hfTSgFntpO7bUvlu9XlAjr2LEj/vWvf2Hr1q02j7/77rvo2LGjJAUjvBtWrEu2YHF7DHeC1CMiItC7d29s3rxZ0gFIqcB5lgZNlgShksiZy0uNuCuWVxOa46rw1VI7NbWl48cL+c/ECnqW71eUCJsxYwYSExPRpk0bjB07Fo0bNwbHcThx4gQWLVqEP/74AwcPHpSrrISHw5p1yR4sb4/h6owvLCyM/1nqAUiJWSgrgyZLglBJ5MzlpVbcFeurCd0Rviy1U0fi2bwtffKJ6xZ0lu7XGlEirGnTpsjIyMDgwYPRp08f6HQVFgCO49C4cWPs2rULzZo1k6WghGfDonXJGSzOrqQqk5QDkFz1ZJ2T0F6Zlc5dyIogVAO5YuJYeedZwl3hy0o7tRbZcsVxsXK/1ohO1tqmTRv873//w9GjRy2StT744INSl43wItS0LrkaPM7i7MqbymSd6sHd86SEdSuKXLAce+MMltNNOMKZWDl+vBD79wcgIeEuAgLyLf6WlXZqq3+Voy2xcr/miBZhRUVFqFatGh588EEL4VVeXo4bN24gNDRUyvIRXobS1iV3g8dZnF25Uia5ByA56omlQZOlsqiJs/c3P/+eCGAlvMCEWLcnK5uKOxIr2dn1MWtWI7M+LQstW7r3fUptFcaip0EORImwL7/8EpMmTcLRo0cRFBRkcezWrVt4+OGHMX/+fHTv3l3SQhLeg9KWHCmCx1mcXYktkxJxN1LXk3WZjUYjSktLLc7x8/NDSUkJLl68KOugyHK+KCWx9/4CQE5OPNasyWBm021biNlwmpVNxe2JFX//Epddw/YmC0ruKiDFWKCFyZEoEbZixQpMnDixkgADgODgYEyaNAnLli0jEUa4hRrWJTlM3+azfhMsD8SslssR5laJTZs28Z+rMShaX9faUmISgyZYbgvuYP3+ZmfX/yfOk71Nt12FpU3F7YmV0tJAh31aSkoKIiMjK13PUbuUY8Wz9Xti3m+6OxZoYXIkSoQdP34cH3zwgd3j7du3x9SpU90uFEEItZpIZRp3Zvp2FFdhjzVrMhQTAizO+JQsE0uDIlDZUmIP1ixBUmF6f+VMXsuCO5CV5Ly2xIrRGOKwT4uMjHR59wapJq32LYoh/HXctaCz/n6JEmHXrl1zuAVIaWkprl275nahCEIIUprGHZm+jxx5SHRchdJCgMUZnxplYmVQtL5ne5Y5rVqCrLEnpOUK1GdF5LK0EMFarMgZ2iFVvJbQyZMnI0qExcfH45dffkHjxo1tHv/ll19Qp04dSQpGEM6Q2jRubzbp6NrmJn2hS6ulggVLgDOU/n6WBkUT3jC42EszIFdwNSsiV83gcSEWZLlCO6QWeK70mZ6yyEWUCEtJScGUKVPw5JNPombNmhbH8vLyMHXqVPTr10/SAhLegTuuKykHXuvZpLNr2zLpKyEEhAYGp6am2k3RwIJIkxrWVlSxYplTArXSuKgpctVMCWPP0mwSwOZllKM8rgg86/CRo0dv8X2XmPg1T+q7RImwN954A19//TUaNGiAfv36oVGjRgCAkydPYsOGDYiLi8Mbb7whS0EJz8Yd15WcA68r1xayTN/dTkSoGd8UrK7m6i0lYS1PGouWOaWRc6ENCyJXyvsTa91W+90VI/Bsh4/UhE43GklJuyWNX1MqjYYUiBJhISEh2L9/PyZPnoxNmzbx8V9hYWHo168f3nnnHdrAm3AZV18KdwZeZxY4V65t/TdAOQyGTP64aZYqhQASMgixEKiuJCzlbmPNMqcWclljWBG5UtyfVGkvhHoViouLXS+sCzgKH9m9OwlJSbuxe3eS25MnJdNoSIHoZK16vR4ffPABli9fjvz8fHAch6ioKH4LI3P279+P1q1bIzAwUJLCEoQ9XB14hZj0Xbm26W+yshKRmWnAgQPtkJlpkFwAORuEWLAUqAErudvcmSBoIebPHkqtjFVL5Mpxf1Kt8I2IiEC/fv3w6aef8p/ZEnSffvqpaCEixX3b67NiY3MxevRitydPcqTRkBPRIsyETqdDVFSUw3OeeuopHD16FHXr1nX1awhCMK4OvEI6IVevnZlpkFUAORuEWLEUyA2LKTpMuBo7w8LqP1dRamWsWu5nOe9PiomTeS5PKS3hUty3oz5LysmTVvo+l0WYEDiOk/PyBMEU1gO8Ep2As0HIW9xhrKXosG4L9gYXe6KQldV/7qBUXavlfpbr/qTsN+SwhKshnF2ZPGml75NVhBGEqwhxxchp/XDl2iYhkJubK+vyfGtMg9D587UA6BAXd54/xlqgupywZBGSUhR6Q4oLsbgrcm3BivtXyn6DVWuQI+Es1UpIrfR9JMII5hAToCq19cO8I05NTbW5F2FYWJjda0dERPB/r2QnkJ1d3+5A7ajD09rWSlpCijr01pg+Z0ht+WRpL0gp+w2WrUH2hLM7mfytcdb3sbBykkQYwRxiAlSlelkB6eJwCgsL+Z/lcpWYz/CFDNT2Ojwlt1ZSG3ctHWpYSli1ZLCAlHXN2rZXUvUb7go6VqyD7mCv75s6NQfffNNM9ZWTsoowWysmCXZg/QVT2gogRRxOQUEBNm/e7PQa7hIREYHU1FRs2rTJ5YGahcFGKdwV2GoFyrNsyfBEWLI8ShWk7qqgk9o6yNLiGZZWTlJgvpfCkvndHmpaAVyNwxGzF5q7nY0pE76zgVrNrZVYwV2BrVagvFbiWjwFNfscOUWKK4JOauugUotnhNQPSxZmWUXY9evUUbAKa+Z3W6hlBZBCoDi7RmpqKiIiIiSxRjobqNXaWolV3A10VzpQnqXks56OmpZHKUWKlIJOygmbEhN6R/Uo956mriBKhHXq1EnQeXv37nWpMITysGwRcSYuzIPKpXSdSiFQnF1Dr9dL6uISO1Cz1AkpibvtXan3RerVf6yHHrCC2pZHqZ6BlIJOixM2dyeuSiJKhP3www+oU6cOunbtCn9/f7nKRCgI6y+YLXFhcgWtWZMhi+tUCoEi5BpSu7jEuBxY6oSUxN32rtT7IuUgqvXEr0rjKZZHqZ6lp07YWHnOokTY3LlzsWbNGmzZsgUvvvgiBg0ahObNm8tVNkIBtPCCmYsLJVynUggUsddwxcXlrsuBlU5ISdxt70q+L1INokLfC09bjOEOrGx7xQKePGFj4TmLEmETJkzAhAkTkJmZidWrV6Ndu3Zo1KgRBg0ahL59+yI0NFSuchIyweILZk80KOk6lUKgCL2Gq/clhbWEhU5ISdxt7yy+L2KRa8WulmFp5R6LeOOETSlcCsw3GAwwGAxYsmQJtmzZguXLl2P8+PHIzc0lIaZBWHvBrMWFKZhSbleQHFm4hYgcd+5LrLWEBhv327s7f692bBZl37cNa9tesYgnTNhY7P/cWh155MgR/Pjjjzhx4gSaN29OcWIahrUXzFZnJ7crSIqOWOjLW1xcjOLiYgDKu7i8cbBxV2BLIdDVjs1iaRGO2mLUFp7W5t2FRcHiLiz2f6JFWG5uLtauXYu1a9eiqKgI/fr1Q1ZWFpo2bSpH+QiZ0OILpoXsz0Je8uLiYnz66af870q7uLxxsHG385Wi81Z7U25WFuGoLUYJYbAmWKTqv1lrU6JE2NNPP419+/ahc+fOeO+999C1a1f4+dHOR1qEtRdMKM5cQfZe1MLCQotM9vaQouN39vcXL16s9BlrLmFPRO7nKgY13IKsLMKhhQLagZX+XwvJxV1FlILauXMnYmJicO7cOcyaNQuzZs2yed6RI0ckKRwhL1prrCbsuYKECi1HqNHxm3coCQlnKx1nyRpJuI9abkFWFxXQQgHvRah1SwvJxV1FlAibMWOGXOUgCLsIFSFi9iplpeN31KGkpKQgNjZWs2KZsI3SbkHz98eRxVUNsU8LBbwXMdYt83NYiWuUChJhBPMIdZ0KjblhpeN31qFERkaSAPNAlHYLshp64IkDKiEcodat3Nxc/jxW4hqlRJKArh9//BE3b96EwWBA9erVpbgkQVggZIAwj7Wy91Kz1PF7YodCOEcNt6D5+2PLBVRSUsK/P0oJMmr/BGBfjNeokYfS0kCLnVFYiWuUEtEZ82/cuIG33noLAMBxHJ566ins2rULAFCjRg3s2bMHzZo1k76kBCEQR0KLpY7fEzsUQhhqLcRgaWUitX8CsC/GP/54CADLSTSrcY3u4OP8lHts2rTJYpuiL774Av/5z3/w008/IT8/H61bt7YbrE8QSuFIaJk6fnPU6vhNHYqpPJ7QoRD2sZVrLCHhbKXnLWdsli2XfU5OPIzGEIfnyQG1fwKAzT4Z4GCSJ6ZJtKmNtmz5K0aPXoz+/ddi9OjFmo8hFGUJy8nJwf3338///u233+K5555Du3btAABTp07F888/L20JCY9H6vxdjmbYLMykWA6U9kRYSQzKWmyWWrGR1P4Jc2z1yc68FawlF3cHUSLs7t27CAwM5H/PzMzE6NGj+d9jY2ORn58vWeEIz0cO94gzoaV2x8/aYCwVSoodod/FkvsNYCctjJqxkZ7a/gnXMe+T/f1LsGrVEJfc1FoU7qJEWL169fCf//wHdevWxblz5/Dnn3+iffv2/PELFy7Qi0OIQsrEjUJn2KmpqdDr9Tb/Xqn262nviZJiR8x3UWJQ26gdG+lp7Z+wj70Jk7XBxty65WgSnZKSgsjIyErX06pwFyXCRo4cibS0NPz00084ePAgDAaDxXZFe/fuxUMPPSR5IQlCCDTDVg8lxQ4JK/ehoHhCCYROmKxxNImOjIxETEyMlMVUFVEibOjQofD19cU333yD9u3bV8oblpubi0GDBklaQMK7cDeJKgks27ASF6U2rCTpVRsWYiO1iNLvkdbfW6G5G1NSUgAA6enp/GdC4r60Xj+AC3nCBg0aZFdoffDBB24XiFAWlhoxK0lUPQ014qKUFDtCv4valyW0X6k4lH6PWItndBdH719kZKTgeC7TeZ6ynyTtvu3FsPSSs5RE1dNQ2n0nldgREksi9LuofVVgK02GrfvXYoCz3Cj9Hgm1ImnB7S7k/RMbTuIp+0mKEmGlpaWYMmUK0tPTER4ejuHDh1tYxS5duoTY2FiUlZVJXlBCeoQ2ztzcXLvnSmUpUztQmJAGqcSOkAmCmO+i9lUBxU1Kh5LWXikmNmp6PYS+f658v9YnWKJE2DvvvINPPvkE48ePR2FhIcaOHYusrCz8+9//5s/hOE7yQhLqYu6nt4UUljJPDRRmyd1rQs7BQyqxI2SCIOa7PLV9uQIJLPdR0rUthchQ2+sh5/un9QmWqIz5GzZswMcff4zx48fj7bffxi+//IK9e/di4MCBvPjS6XSyFLRHjx6oXbs2qlSpgpiYGLz00ksWG3sCwObNm/Hggw8iKCgIderUwXvvvVfpOj/88ANatmyJwMBA1K9fH2vXrq10zvLlyxEfH48qVaogMTER//3vfy2O3759GyNHjkRERASqVauGXr164dKlSxbnnDt3Dl27dkVQUBBq1KiBCRMm4O7du+5XBIO4Y+41uT2cZc/WonvE1PF9+OGHdv8tW7YMBQUFipXpyJGHsHjxaKxb1x+LF4/GkSPSrmZWckcCId/lye2LUAd7osh61wGpcCQyhHL58mVB58nlupNzdwSWdkFxBVGWsL///tti26L69evjhx9+QKdOnfDSSy9h3rx5khfQRMeOHfHmm28iJiYGf//9N8aPH4/nnnsOBw4cAAB89913ePHFF/H++++jc+fOOHHiBIYOHYqqVasiLS0NQEXG/65du2L48OHYsGED9uzZgyFDhiAmJgbJyckAKrZmGjt2LFauXInExEQsXrwYycnJOHXqFGrUqAEAGDNmDHbs2IEtW7ZAr9cjLS0NKSkp2L9/PwCgrKwMXbt2RXR0NA4cOICLFy/i5Zdfhr+/P/71r3/JVkfuosbKMWv3yPTpV3DmjB/i4+8iNvZhAA9r1j3CWioFOc321mLH3qo7KcWOkO/y5PZFqIPSlhd3rUgFBQXYvHmzxWdq9PVyLQTR+kpfUSIsOjoa2dnZiI+P5z+77777sG/fPnTs2BEDBgyQuHj3GDNmDP9znTp18MYbb6Bnz54oLS2Fv78/1q9fj549e2L48OEAgLp162Ly5MmYO3cuRo4cCZ1Oh5UrVyIhIQELFiwAADRp0gQ///wzFi1axIuwhQsXYujQoRg4cCAAYOXKldixYwdWr16NN954A0ajEatWrcLGjRvRqVMnAMCaNWvQpEkTHDx4EG3atMGuXbvwxx9/YPfu3ahZsyYefPBBvPXWW5g0aRJmzpzJ5KxbzZVj5gNgTAzQqpUiX+t1yDl4yC127A0ab7+d4PS7WGtfLLqovQUp6l5p17a7IsP6fvfvN2D37iRF+nqlFoJoeaWvKBHWqVMnbNy4EU888YTF57Gxsdi7dy86dOggZdnscvXqVWzYsAFt27aFv78/AODOnTsICgqyOK9q1aq4cOECzp49i/j4eGRmZiIpKcninOTkZH7rpZKSEhw+fBiTJ0/mj/v4+CApKQmZmZkAgMOHD6O0tNTiOo0bN0bt2rWRmZmJNm3aIDMzEy1atEDNmjUtvmfEiBH43//+Zzeh7Z07d3Dnzh3+96KiIhdqRzxaD2wkhOHO4CF28JJS7Dhb2h4TU1N1YSUUtWNzvBmp6l4Ny4tUImP/fgMyMp4EUBE2JHdfr+RCEK3uJylKhE2bNg0nT560eey+++7Djz/+iIyMDEkKZotJkyZh2bJlKC4uRps2bbB9+3b+WHJyMsaMGYMBAwagY8eOOH36NG/xunjxIuLj45GXl2chjACgZs2aKCoqwq1bt3Dt2jWUlZXZPMd033l5eQgICEBYWFilc/Ly8vhzbF3DdMwec+bMwaxZs0TUiDQItZDYs0bk5+fT7J1h3HUVqikcPG2CwJqL2ptwt+6FbotmNBol6Q+ltiIZjSHIyEiCSYCZkDuIXa5xQWxeMVYRJcLq1KmDOnXq2D0eGxuL/v37C77eG2+8gblz5zo858SJE2jcuDEAYMKECRg8eDDOnj2LWbNm4eWXX8b27duh0+kwdOhQZGdno1u3bigtLUVoaChef/11zJw5Ez4+otYfqMbkyZMxduxY/veioiLExcXJ9n2mxinEQuLIGmFaPUmzd8eola3dXVehmsJB6yufCM/B9B5dvnwZmzdvtiuKNm3aBMD9/lBqK9LVqxGwtRZPS0Hs5nhKuhWXkrVu2bIFn332Gf78808AQMOGDdG3b18899xzoq4zbtw4p3FkdevW5X+OjIxEZGQkGjZsiCZNmiAuLo7fw1Kn02Hu3Ln417/+hby8PERFRWHPnj0W14iOjq60ivHSpUsIDQ1F1apV4evrC19fX5vnREdH89coKSlBYWGhhTXM+hzrFZWma5rOsUVgYCACAwMd1oeUmDfi++4rwqRJepSV6eDry2Hu3CJ07vwk0tPTBVsjaPZuH7WztbMWF+UMoRME1me5LEIxaRW4MimKiIhQdFIi5XOw9S4BHJKSdmt2QuMJ7VSUCCsvL8cLL7yALVu2oGHDhryF6n//+x9SU1Px/PPP47PPPhOcpiIqKgpRUVHiS/1PWQBYxFABgK+vL+677z4AwGeffQaDwcB/h8FgwLfffmtxfkZGBgwGA4CKzqdVq1bYs2cPevbsyX/Pnj17+BWWrVq1gr+/P/bs2YNevXoBAE6dOoVz587x1zEYDHjnnXdw+fJlfkVlRkYGQkNDLTY8ZwFTIx43DkhNBU6fBurX16FWrTAUFFQk3SVrhHt4mktNCZxNEPr2fcEjxILS1lE1XMssij61J0XWWNeR0WhEaWkp/7ufn5/FpN+VOrMORwDK8eSTu9GuXabFdQllESXClixZgt27d2Pbtm3o1q2bxbFt27Zh4MCBWLJkCR/oLhVZWVk4dOgQHn30UVSvXh3Z2dmYNm0a6tWrxwuf/Px8fPHFF+jQoQNu376NNWvWYMuWLfjxxx/56wwfPhzLli3DxIkTMWjQIOzduxebN2/Gjh07+HPGjh2L/v37o3Xr1njkkUewePFi3Lx5k18tqdfrMXjwYIwdOxbh4eEIDQ3FqFGjYDAY0KZNGwBA586d0bRpUz5tR15eHqZOnYqRI0cqaukSS61aFf9MmAbCI0cu45NPKNGlWEwdmjMRq7WOTynh4GiCAITJ9r1CkEJYqCEElHYts7gQQepJkbvvg9A6ssaVOnMUy5aamqr5SY0WESXC1qxZg/fee6+SAAMqkqnOmzdPFhEWFBSE9PR0zJgxAzdv3kRMTAy6dOmCqVOnWoiadevWYfz48eA4DgaDAT/88AMeeeQR/nhCQgJ27NiBMWPGYMmSJahVqxY+/vhjPj0FUNEQr1y5gunTpyMvLw8PPvggdu7caRFov2jRIvj4+KBXr164c+cOkpOTLTYv9/X1xfbt2zFixAgYDAYEBwejf//+mD17tqT1ogQRERFo3rxEk3lY1J6Bm0TsmTN3sX49h/LyexZiX18Oo0Y9hfh4P011fGpZEKwnCGoihbDwFusoiwsRpLTsS/E+uHrvQv9OaIC/q14pwj1EibC//vqrUooHc5KSkni3nZS0aNECe/fudXhOZGQkn0bCER06dMCvvzp+SdLS0hzeR5UqVbB8+XIsX77c7jl16tSp5PrUMlrLw8LKDDwiIgIREcCHHwKvvAKUlQG+vsC//61Dq1Y1nV/ADKlFpdjreYtwcIYUwoIVF79ai0XURKo8X3K9D1I/E08JYBeL2pNwoYgSYVWrVkVhYSFq165t83hRURGqVKkiScEI9tBSHhbWZuCDBwPJySaXmnOrjnUHUlhYWCnrtS2EikoxItUEK8JBy7C04EANq6Zaoq+goACFhYUApNvVQY73wdYzqZj8uldnLIgNqXEksoxGI79K1REsrOgXJcIMBgNWrFiBFStW2Dy+fPlyPkaL8Fy8cfYsBUJdaq7GiADSi8+SkhKmhAOLiHkfWFlwoIZVUy1Xtq33yZZlPzU1FVFRUYLrXurM+baeybZt3aDTgZkFBKzgTh9pDgsr+kWJsClTpqBDhw4oKCjA+PHj0bhxY3AchxMnTmDBggX4+uuvsW/fPrnKSkiEWDOt+eDqqCP11kFYaljoGMxhRTiwiCvCgoUFB0pbNdV0Zdt7n6wt+3q9XlAblmufVFvPBPABx1X85K3uf1uI7SNZNhyIEmFt27bFpk2bMGzYMGzdutXiWPXq1fHZZ5+hXbt2khaQkBZXYqXMA8xnz64Bjru35cWOHd0xfXqi5gLMCXGwIBxYw5mwyM/Pr/Q31mJVrQUHSu9/6EmubLn2SbWdx8sSrdaZ3DgSWaylI7FGdLLWZ599FsnJyfj+++/x119/AahI1tq5c+dKezcS7OGquyoiIgLHjgH/pGfjKSvT4fr1mmBJfxUUFNgcAAlpYGmlopo4ExamnSSsUTMORS4rjjOUFn2OkMIqIkfyY1t5vCq2GLq3qppSA1XGkcjSwmIiUSJs7969SEtLw8GDB/Hss89aHDMajWjWrBlWrlyJxx57TNJCEmzQoAHg42MpxHx9KwLN3UHKVSxSxQqwjtTmdZbN9aziqrBQ090slxXHHmqJPnvIbRVxpS9ztCdldnZ91euMZZyJLC1YYEWJsMWLF2Po0KEIDQ2tdEyv1+OVV17BwoULSYR5KLVq2Uq14J5VROpUEvZXy3iOyJB6IGHdXM8aQoUFq0hlxREqOJQUfY6Q2yrial9mK4WEecb8tLRTyM0NQvPmVRSvM9ZxJrJYssDaQ5QI++233xxuuN25c2fMnz/f7UIR7OIo1YIrs0AlUklofTGBuYAEIHm2b9bN9axhT1iEhl7GgQPeIV5dFRxy71tqqw8yhSbIbRVxpy+z7hdjYmL4n1u0cK9cnowzkaWFiZIoEXbp0iX4+/vbv5ifH65cueJ2oQi2sRUTxEpyVGvsiYy0tAZo2bIG87NJawFpMGRKsgWSJ26ppGRyRlvC4uLFEhw4cO8cT7K+WsNaHj7AeR+kBauIGmglqakthIgsR4nGWejfRImw++67D8ePH0d9O0FAx44ds1DwhPcgtLPNzc21OFfuAHp7IqOoiF0BZuoYbAnIzEwDKgJ2792TK1sgedqWSqxNAsjFqzzO+iCl49K0IMJZe2+E4iiOzryue/fubbHxufU1WLgnUSLs6aefxrRp09ClS5dKmfFv3bqFGTNm2NxXkiBM2FsxJhf2Zr/x8XcVLYcYTAJp3z5g0aLKAnL48Bv46KPgf3J1ubYFkul7pNpSSW1Yssx4o4tXC4IDqBiw09IaoKiohqxxaVoR4Sy9N2LwpK2YRImwqVOnIj09HQ0bNkRaWhoaNWoEADh58iSWL1+OsrIyTJkyRZaCEtIg1l2ldezNfis6X3aJiIhAmza2V6NOmVINU6YI3wLJGWK3VCIco4UVWVLCquCwJwybNw9DTIx8g7M3inA10ILAEoIoEVazZk0cOHAAI0aMwOTJk8H9k8pXp9MhOTkZy5cvR82a2ppBexueNIMQim1zNdsiDHC+GlVKsUS5v9zHG7d3YlVwqCkMtSzCtWLR9CREJ2utU6cOvv32W1y7dg2nT58Gx3Fo0KABqlevLkf5CBkwF1gXLgB//VWRA8wTBmHrAc68U0lIOGv3PFZhwUqlxcBdNQYTb9zeiUXBobYw1OoCAFYtmp6OaBFmonr16nj4YfatCYR9Vq0Chg2rcHf5+FRYXQYPVrtUthEqmswHwo0bq2L2bD3Ky3Xw8eEwb54Rffve0txAqKaVSouBu2oOJt62vROLgkMtYchaYloxqC1cvRmXRRihbS5cuCfAgIr/X3mlwuqi1oCfkpKCkpIayMnxQ0LCXcTGVhROrGiKiIjAhQvAxInm96fDpElhSE0NY2qLJdbRWuAuS4OJJ7t4WRYcaglDlhLTioVFi6a3QCLMS/nrL1v7QFbM3F0ZOKTobHftisPEiWGSWOakvj+10aJLUA1oMFEGFgUHC8JQycS0UsKiRdNbIBHmpUi9D6SjgP/8/HynqSmMxpB/XIcVv7trmZNrn0s10KJLUGm8MShebWHOmuBgURiyjljhqnab80RIhHkpcuwDaS/gPzbW+cB39WqERcJQwD3LlRz3pxZacwmqgbcFxZMwtw1rwpB1xAhXanPyQCLMi5Fr5V3lgH/naTHy86tg/XppLVcsrCz0NFhewu5NQfEkzAmpECpcqc3JA4kwL0fq4GH7Af8R/PfYSosREyOP5cqTg6OVRktL2Om5EwShBUiEES5jKz7g4MEAlJdbmqLN3YqO0mKQ5YpdWFp1SFTGnoXSfG9WT3LHEoSnQCKMcAl78QFGYwh0utEWgdEmt6KQtBhkwWALU0Cus1WHnhTwrjUcWSitF8SkpqZCr9fbvA6JNGFQcHoFLIcmaAkSYYRL2OuErFfZ+Ppy+Pe/dahVC9i3z7PSRngDpsDdM2fuYv16zmLxhK8vh1GjnkJ8vJ9XDDosItZCuWnTJofXYyWomlWhQ8HpFWgpNIF1SIQRkmO+V+OoUU+hVauK/UQ9KW2Emig9A42IiEBEhK2YPR3/bAl1kDovGgtB1SwLHQpOp9AEqSERRsiCXn/9HzfVZVy8WKG6fH2BhQtDMG5cNc2njVASc1efoxmo3C5BitljD09MsklCh20oIbK0kAgjZMVWktZff30NBQXVaSAXiLlLcPbsGuC4Cpcgx/lgx47umD49UTGXIMXssYGzJJsAkJMTb9daSvE8hFi8MSGyEpAIIxQnMvI2WrRQuxTaIiIiAseO2Yqp0+H69Zq0H6aXYZ1kMy3tFDZsyEJ4+FVkZ9fH4sWj7cbrCInnYSkmi2XByHLZpMbbEiIrBYkwghCBmoOT1DF1LA20hHisn01Cwlmn8TpC4nlYisliOQCc5bLJhTclRFYKEmEEIRC1Bycpt2JS+14IeXAWryMknoeVmCwlA8DFTkg8ITjd3UkYhSZIA4kwwiW80e/PwuAkVXC8u/dCVjS2EBqvo6VAfqUCwMVMSJQum1zQJIwdSIQRLmEdkwJUZOe2FYhvDxrIXUPtGSh14OzhLF7n6aefwubNm+0G8puEQ0BAQKV3Uq24J6UEo5gJiacEp7MwoSQqIBFGuIw7AywN5NrDJJrNt8JxBHXgyuIsXsd80jR9+hWcOeOH+Pi7iI19GMDD/KTn4sWL/DXViHtytvLTXDAqDQWnE1JDIoxQBU+eiZlEiid1xkJFsxKQBdU5tqyl5nUSEwO0auX4GmrFPVlb2R0JRjlwZvmj4HRCSkiEEZJw4QLwyy9BMBpDnHbQtlwenoS5S1YrljxnAw8rz4ssqMqhZtyTWMEoFWItf2qHBkiFN6XaYA0SYYTbrFpl2phbDx+fMZg3z4i+fW/ZPNeWy8OTYUW8OMJdl5OSHbgnW1BZg+UgfjmsoZ6w4tEVvDHVBkuQCCPc4sIFkwCr+L28XIdJk8KQmhrmETNER3jC7NHdgYc6cM+D5ZgsQD5rqNZXPLqCtwpPliARRrjFX3/ZyuJeESfhaSJM6B6OWsB0L84GHnsDrdEYgvPn41zqwKW0YniCEGYNtWOynCGXNZRly59ceKPwZA0SYYRbOMrifuFChUhr0MAzBJmjPRy1Nns0v5f16zmUl+v4Y76+HEaNesrufpTmAtQaZx24lFYMrQthllErJssV3BXirFv+5MBTUm14AiTCCLewl8X9++/vuSl9fCrOGTxY7dK6j709HLU4e4yIiEBEhK3np0OrVjVt/o21+8IaZx24UOtEbm4uX0Yh5dCiECbcRwohzrrlTw4o1QY7kAgj3MY6izsA1KljHidWMcgnJ9+ziAmdYbE4E6uw/llaj3S6cty8GSxodShriMnCb8t9YUKnK8eMGbkYNkx4B27PimFaYWrPIkZuFO/AluvalAJGSiGuJcufVFCqDTYgEUZIgvlS7X37nMeJ2cq4bw2rM7FatYB584yYMCGUn4FzHPDFF89r1i3mbKm9I/cFUI7nnvsCcXEXkJbWX/AzE2LFsNc+vDF+x9tw5romIS4dWki14an5AUmEEZLjKE7MHPMXRmvxYwMGlOHvvxfj/Pla+OKL5wDYno2zaMlzBefui8dEB9TbsmLUqJGHWrXspy/xxvgdb8WZ65qEuPfgyfkBSYQRkmMvTsyeuLqXZ0w78WMRERGYMqU/9u0Dvvii8my8Xbv+6NDBva2dWENK94U9K8bHHw9Bjx72LYneGL9D2MaZECc8B0/OD0gijJAFoXFGlfOMVY4fY5WIiAi0aWPb6peYWBH07qm4676w7dYEAOdxPd4Yv0PYjh9s2fJX1Kt3GlevhiM8/KpFmyFrKKEFSIQRsiFkoNZ6njGxVj+iAmsrhjneFNfjqXEuUuMoflCvv46BA59EZGQkfz7VG6EVSIQRqiI0foxlxKwu9HbMrRMtW/6KGjXy8PHHQ2CKqQO8J67HOs7F3kpRLca5SImt+MFt2yytpZGRkYiJiVGzmAThErbXmhOEQpgsSb6+Fb9r1ZJUqxbQoYP2yq00ppiulJQUAECtWhfRo8d26HQVKtyb4nrMLWBHjjyExYtHY926/li8eDSOHHnI5nneiO20KD7IykpUpTyE+hiNIcjJiYfRGKJ2UdyGLGGE6pAlybuwtup4e1wPJZ51THh4AYByWNsMMjMNSEzMojryMjxtpwwSYQQTaCFPDXEP81im3Fwf5OT4ISHhLmJjKyxazmJytJwnTmoo35VtzNORtG2biQMH2lkcpzryPjxxwkIijCAIUZjHMjmalTqLZfIGgeWIwsJCAJTvyh4RERHo3bs3Nm/ejMTELGRmGqiOvBSTIHc2YdGi5ZxiwgiCcMiFCxW7IFy4UPG7yXplb1ZqitPw9lgmRxQUFGDz5s0AgOzs+uC4e8e8KS7OGWFhYQDurab1xthB4p7lfNSoLvDx4SyO+fpyGDXqKc0uYCFLGEF4KO7sQmByN27cWBUTJ+pRXq6Djw+HefOM6Ny5Yu8+cqO5jrWQNZ8PcxxQr95plUrGFtarab05dlDruJuOJSKiIvdi5ZRAOrRqVVOOIisCiTCC8EDc2YXA5G40GkOwePFocFzFRuXl5TpMmBCKv//OgF5v343m71+CnJx45Ob6gLIGOMbeyj8SshVQ7KBnIOW2Q562kItEGEF4EAUFBThz5i6GDauB8nKTeAJeeYXDgw9eRny8n9NOzjTgObN02do25v77j2HVqiHgOB+sX89pYgsqNRESD+btVh4SWNpH6m2HPGkhF4kwgvAQTLPNnJx4lJf3tzhWVqbD++9/h4SEs4JjJ4QIBHMXkb9/CS/AgArLmRjx54042/8wNTWV6o0gPBgSYQRhB3diqtTANIt0Jp6EzjaFbpBssorl5MRXspy5Iv68DUexTnq9XsWSEQQhNyTCCMIG7sRUqY1Q8SQERwLBGqnEnzdiErIEQXgXJMIIwooLF+4JMMAUU1URDKoFixggTjw5Q6hAkFL8EQRBuIK7qzCVhkQYQVjx11+WG4oDFcuhT5/WjggD5LeupKSkIDIyEvn5+UhPTwcgrfjzZIQG23t6UD6LAyaLZfI07G1W7y5SrsJUChJhBGFFgwYVLkhzIebrW7EcmrhHZGQkYmzkoCDXmnO0lHpBLlHC4oDJYpk8DTn3fpR6FaYSkAgjCDMKCgrg61uCefOqYtIkPcrKdPD15TB3rhG+vrdQUMDGwCgGsbNOstIogxbakbUosdeWXBEl1gOhvWsrOWBqcRDXAqa+wtnej97Yp5AII4h/sB5wXnsthHer3bhxHR9+WPG5q7NgNVZb2pp1OsOelcZ8o25KOeEdmLcBRxYMd0WJnNYRQn1Mfcq+fcCiRZVzD7Zr1x8dOmhjYiI1tHckQfyD9UCi119HQsLZStYjVwacVauAOnWATp0q/l+1yq2i2sR6Fmlv1pmfX8XptSIiIhATE8P/+/bbGDzySE08/3wEHnmkJr76yvs6S2/G2T6hrF7bXYzGEOTkxDNRFq0TERGBNm0i4GOlOnx9gcTECK8UYIAGRdidO3fw4IMPQqfT4ejRoxbHjh07hsceewxVqlRBXFwc5s2bV+nvt2zZgsaNG6NKlSpo0aIFvv32W4vjHMdh+vTpiImJQdWqVZGUlIS//vrL4pyrV6/ixRdfRGhoKMLCwjB48GDcuHFDdFkI78DeakvThthSYZptDhs2DMOGDUPbtv1tZrwvKKgu6rrOyk/uS2Ww3khdSRztnsDytd3hyJGHsHjxaKxb1x+LF4/GkSMPqVoeT6BWrYp0P76+Fb9X7P2orQVPUqM5d+TEiRMRGxuL3377zeLzoqIidO7cGUlJSVi5ciV+//13DBo0CGFhYRg2bBgA4MCBA3jhhRcwZ84cdOvWDRs3bkTPnj1x5MgRNG/eHAAwb948LF26FOvWrUNCQgKmTZuG5ORk/PHHH6hSpcKC8OKLL+LixYvIyMhAaWkpBg4ciGHDhmHjxo2Cy0J4D0qutjSfTbZpI80CA2fl11KQudZwtJF63763FKtXIbsnsHhtV3EWu0S4jqft/egumhJh3333HXbt2oWtW7fiu+++szi2YcMGlJSUYPXq1QgICECzZs1w9OhRLFy4kBc+S5YsQZcuXTBhwgQAwFtvvYWMjAwsW7YMK1euBMdxWLx4MaZOnYpnnnkGAPDJJ5+gZs2a+Oqrr9CnTx+cOHECO3fuxKFDh9C6dWsAwPvvv4+nn34a8+fPR2xsrKCySEV5eTkFiUpEaWkpgoODK31eXFwMjuNcvq5aqy1Ns85XXqkQTa7OOoWUnwSWOITEBzrfSH019PrriqzUkzMHHIv55Zztm0q4h1J7P8qVCkNKNCPCLl26hKFDh+Krr75CUFBQpeOZmZlo3769hcsjOTkZc+fOxbVr11C9enVkZmZi7NixFn+XnJyMr776CgCQk5ODvLw8JCUl8cf1ej0SExORmZmJPn36IDMzE2FhYbwAA4CkpCT4+PggKysLzz77rKCy2OLOnTu4c+cO/3tRUZHDOikpKUFOTg7Krc0UhEuUlZWhXbt2lT6/desWDh06hNu3b7t0XanEkCtIMeuUq/xa2xZKKoTuxiB0I3VXJmGu1L2cOeBYyy/HonWOcI75mOtosQdLoRGaEGEcx2HAgAEYPnw4WrdujTNnzlQ6Jy8vDwkJCRaf1axZkz9WvXp15OXl8Z+Zn5OXl8efZ/539s6pUaOGxXE/Pz+Eh4dbnOOsLLaYM2cOZs2aZbsSrOA4DhcvXoSvry/i4uLgYx3tSIimpKQEhYWFFp9xHIdr166hUaNGlVzgYlDTBC/FrFPq8mt5Wyh3cGU3BqkEgRSuTSlzwFkPhPaureSAafouZ9Y5lgZx4h6m0IgzZ+5i9uwavPWY43ywY0d3TJ+eyNzKblVF2BtvvIG5c+c6POfEiRPYtWsXrl+/jsmTJytUMnWYPHmyhaWuqKgIcXFxNs+9e/cuiouLERsba9MySIjHx8cHfn6VXwm9Xo+oqCgEBAS45fpVygQvF1KV3xO2hXKFgoICHDwIlJdbDgBlZUBWVgGqVrXt1pXCXeeqa1PORRcsxhJal2n69Cs4c8YP8fF3ERv7MICHKb6RcSIiInDsmK04Vh2uX68J1h6dqiJs3LhxGDBggMNz6tati7179yIzMxOBgYEWx1q3bo0XX3wR69atQ3R0NC5dumRx3PR7dHQ0/7+tc8yPmz4zzwR+6dIlPPjgg/w5ly9ftrjG3bt3cfXqVaffY/4dtggMDKx0j/YoKysDQDMyKbFnTfTx8YGPjw/8/f1RUlKiiTqXKsu5HNnSPWVbKDGYiyCdbnQlq9b+/etw/Lj9+C533XWuujblFkosihnzMsXEAK1aqVgYwiW0tOuJqiIsKioKUVFRTs9bunQp3n77bf733NxcJCcnY9OmTUhMTAQAGAwGTJkyBaWlpfD39wcAZGRkoFGjRrz7z2AwYM+ePRg9ejR/rYyMDBgMBgBAQkICoqOjsWfPHl50FRUVISsrCyNGjOCvUVhYiMOHD6PVP2/n3r17UV5eLqosUqHT6SS9njfj5+eHGjVqVIqxu337Nq5fv47evXsjODiYyYHDHKm2XpFrCxctdZBSYRIxzqxajsSOFK5AV1ybrLd3grBGzThcsWgiJqx27doWv1erVg0AUK9ePdT6p1b79u2LWbNmYfDgwZg0aRKOHz+OJUuWYNGiRfzfvf7663j88cexYMECdO3aFZ9//jl++eUXfPhPKnSdTofRo0fj7bffRoMGDfgUFbGxsejZsycAoEmTJujSpQuGDh2KlStXorS0FGlpaejTpw9iY2MFl4VgE1vuyPLycvj6+qJGjRp8mhIlcDVwXaqtV+TawkVLHaQcqBmEzuJKRIKQA62kwtCECBOCXq/Hrl27MHLkSLRq1QqRkZGYPn26RUqItm3bYuPGjZg6dSrefPNNNGjQAF999RWfIwyoyEN28+ZNDBs2DIWFhXj00Uexc+dOi8F3w4YNSEtLwxNPPAEfHx/06tULS5cuFVUWb2XAgAFYt24dgHsLGu6//3688MILGDBggOAFBmvXrsXo0aMrBdJ7Cp4euM5yB6nEqk01NzlnbSUiQciFFuJwNSnC4uPjbeZtuv/++/HTTz85/Nvnn38ezz//vN3jOp0Os2fPxuzZs+2eEx4ezidmtYeQsngrXbp0wZo1a1BWVoZLly5h586deP311/HFF19g27ZtNq1RWsWVmCpvCVxnsYNkSfzKGRSvpggkCOIenjPaEaKQI+BaKIGBgfwChfvuuw8tW7ZEmzZt8MQTT2Dt2rUYMmQIFi5ciDVr1uD//u//EB4eju7du2PevHmoVq0afvjhBwwcOBDAvZi4GTNmYObMmVi/fj2WLFmCU6dOITg4GJ06dcLixYsrpRVRAldjqpQIXDcaQ7B/fwDatGFPCKkFa+KXxdWDBEFIC4kwL0SugGt36NSpEx544AGkp6djyJAh8PHxwdKlS5GQkID/+7//w6uvvoqJEyfigw8+QNu2bbF48WJMnz4dp06dAnAvTrC0tBRvvfUWGjVqhMuXL2Ps2LEYMGBApT1ClcDVmCq5A9dNSQwXLfJR3Nqjpvh3BourNklgEYRnQyLMC5Er4NpdGjdujGPHjgGAxQrW+Ph4vP322xg+fDg++OADBAQEQK/XQ6fTVUr5MWjQIP7nunXrYunSpXj44Ydx48YNXqixjpyB69Z74ilp7WFR/Jvjyas2WdhknWUBThBqQSKMYAaO43j34u7duzFnzhycPHkSRUVFuHv3Lm7fvo3i4mKHyWkPHz6MmTNn4rfffsO1a9f4dBPnzp1D06ZNFbkPKZArcN1WniilrD2sin8TcopftUWQ2q5NawFub08/tQQ4QagFiTCCGU6cOIGEhAScOXMG3bp1w4gRI/DOO+8gPDwcP//8MwYPHoySkhK7IuzmzZtITk5GcnIyNmzYgKioKJw7dw7Jycma3OTc1cB1RwO5rTxR9qw9agsHJTFZaZ5+GsjK8jHLkl6OixfdFyhqiyBTGdTC/L4d7emnxfeUINyBRBjBBHv37sXvv/+OMWPG4PDhwygvL8eCBQv4lBWbN2+2OD8gIIDfNcDEyZMnUVBQgHfffZff7umXX35R5gYYwtmAf999RZg0SY+yMp1Daw8LwkEJ7LlJjx+3/N1dK43S9cTiBunW7nCO88E333RDvXqnabUm4ZWQCCMU586dO8jLy7NIUTFnzhx069YNL7/8Mo4fP47S0lK8//776N69O/bv34+VK1daXCM+Ph43btzAnj178MADDyAoKAi1a9dGQEAA3n//fQwfPhzHjx/HW2+9pdJdqoujAX/cOCA1VZirU+sCSwisu0ldgaVUG+Y42zaJILwNYZkxCUJCdu7ciZiYGMTHx6NLly7Yt28fli5diq+//hq+vr544IEHsHDhQsydOxfNmzfHhg0bMGfOHItrtG3bFsOHD0dqaiqioqIwb948REVFYe3atdiyZQuaNm2Kd999F/Pnz1fpLtmmVi2gQwd2LCQmTKkzLlxQuyTSceECsG8fFLmngoICHD58CcOGcVapNjgcPnwJBQUF8hfCASZ3uDnOtk0iCE+GLGGEoqxduxZr1651et6YMWMwZswYi89eeukli99XrFiBFStWWHz2wgsv4IUXXrD4zFZiXyXwppgqKVAzdYZcKGmRMrlVc3LiUV7e3+JYWZkO77//HRISzqoa/O5s26TCwkLExMSoUjaCUAMSYV4IiQNl8JaYKilQM3WGXCid/NXUzpxt0q22W9W0bdL587UA6BAXd54/tnnzZlohSXgVJMK8EBIHykF1eA9Hol7N1BlyoVbyV6U36RaS/8ua7Oz6tEKSIEAizGshcUAojSPxn5vrg/XrOZSX6/jPtJwotaCgAKGhd+HjU8PqnjiEhFxGQYGfrO+gUpt0C83/1a9fP4tzaIUkQVRAIowgCMWwJzxiYuRLlKo05sKkWzfLnFhdu27H9u0VFh+53W5KbNItNP9XUFAQUlNTsWnTJlohKRDaYcA7IBFGEAzhzR2vXLsEOEPqGEnz5+fIIuVJbjch1i29Xg/Aecwawf4WX4R0kAgjCEaQu+PVgsBzdZcAd5A7RlIJi5TaiLFuKR2zpkU8MXcdYRsSYQTBCHJ2vDSzdow33rOUiLVuKRWz5unk5+fb/JyFCRUhDBJhBOEF0Mza81Ez9Ywr1i25LYRasPy6S3p6ut1j3jqh0hokwgiCUBVvGCyVQO3UM86sW0qKRLL80oRKK5AIIzyKH374AR07dsS1a9cQFhYm6G/i4+MxevRojB49WtayEZWhwVJa1K4jR9YtJUUiWX4JrUB7RxKKMmDAAOh0OgwfPrzSsZEjR0Kn02HAgAHKF4xQBRostY1Y61ZERARiYmLs/lNbRBKE0pAljFCcuLg4fP7551i0aBGqVq0KALh9+zY2btyI2rVrq1w6gnAPb9oWTG0XKEFoHRJhhOK0bNkS2dnZSE9Px4svvgigIsC0du3aSEhI4M+7c+cOJkyYgM8//xxFRUVo3bo1Fi1ahIcffpg/59tvv8Xo0aNx/vx5tGnTBv3796/0fT///DMmT56MX375BZGRkXj22WcxZ84cBAcHy3+zhNfhbcJEC/dhL5M/q1gLdK2VnxAOiTBCFQYNGoQ1a9bwImz16tUYOHAgfvjhB/6ciRMnYuvWrVi3bh3q1KmDefPmITk5GadPn0Z4eDjOnz+PlJQUjBw5EsOGDcMvv/yCcePGWXxPdnY2unTpgrfffhurV6/GlStXkJaWhrS0NKxZs0bJW3aKN1lQPB0tCBNvwVEmf1YxF/IbN1bF7Nl6lJfr4OPDYfr0vwGsUruIhESQCCNw4ULFZsMNGiiXKLNfv36YPHkyzp49CwDYv38/Pv/8c16E3bx5EytWrMDatWvx1FNPAQA++ugjZGRkYNWqVZgwYQJWrFiBevXqYcGCBQCARo0a4ffff8fcuXP575kzZw5efPFFPui+QYMGWLp0KR5//HGsWLECVapUUeaGBSCnBYUEHuGNaHmfyoiICFy4AEyceG8j+PJyHd566z689loI8+UnhEEizMtZtQoYNqziJffxqdi/b/Bg+b83KioKXbt2xdq1a8FxHLp27YrIyEj+eHZ2NkpLS9GuXTv+M39/fzzyyCM4ceIEAODEiRNITEy0uK7BYLD4/bfffsOxY8ewYcMG/jOO41BeXo6cnBw0adJEjttzGbksKN7mIiMIQFwmfxb56697AsxEWZlOUPlpQqUNSIR5MRcu3BNgQMX/r7xSsX+fEhaxQYMGIS0tDQCwfPlyWb7jxo0beOWVV/Daa69VOuZtiwBIYBHegkmAOMvkz7pQadCgYnJsLsR8fYFx455BZORtAEBurg9ycvyQkHAXsbEVJ9KESjuQCPNibM+yKjZQVkKEdenSBSUlJdDpdEhOTrY4Vq9ePQQEBGD//v2oU6cOAKC0tBSHDh3iXYtNmjTBtm3bLP7u4MGDFr+3bNkSf/zxB+rXry/fjRAuQ25SQg7MLb/33VeESZP0KCvTwdeXw9y5Rejb9wVNCJVatSq8E6+8UtE3+/oC//430KJFdQDqeTII6SAR5sXYm2UppVd8fX1516Kvr6/FseDgYIwYMQITJkxAeHg4ateujXnz5qG4uBiD/+llhg8fjgULFmDChAkYMmQIDh8+jLVr11pcZ9KkSWjTpg3S0tIwZMgQBAcH448//kBGRoagJKGEvJCblJALU5sZNw5ITa2YXNavr0OtWmEAwtQsmigGD67wTlSU/94EWW1PBiENJMK8GHuzLCVf4NDQULvH3n33XZSXl+Oll17C9evX0bp1a3z//feoXr1iFli7dm1s3boVY8aMwfvvv49HHnkE//rXvzBo0CD+Gvfffz9+/PFHTJkyBY899hg4jkO9evWQmpoq+70RwiCBRchNrVraFia2yq+2J4OQBh3HcZzahSBsU1RUBL1eD6PRWEms3L59Gzk5OUhISHB7hd+FC5VnWcQ9pKxrgiAIKbhwAahTp7In48wZ6sdZwNH4bQ5tW0SgVi2gQwd6cQmCILSCyZNhiuRQw5NBuA+5IwmCIAhCg9iLFyO0A4kwgiAIgtAoWo9383bIHUkQBEEQBKECJMIIgiAIgiBUgESYxqHFrfJDdUwQBEHIAYkwjWJKbuooySUhDaY6tk4oSxAEQRDuQIH5GsXPzw9BQUG4cuUK/P394eNDeloOysvLceXKFQQFBcHPj14XgiAIQjpoVNEoOp0OMTExyMnJwdmzZ9Uujkfj4+OD2rVrQ6fTqV0UgiAIwoMgEaZhAgIC0KBBA3JJykxAQABZGgmCIAjJIRGmcXx8fGgrHYIgCILQIDS9JwiCIAiCUAESYQRBEARBECpAIowgCIIgCEIFKCaMYUxJQouKilQuCUEQBEEQQjGN286SfZMIY5jr168DAOLi4lQuCUEQBEEQYrl+/Tr0er3d4zqO9mRhlvLycuTm5iIkJMQrc1QVFRUhLi4O58+fR2hoqNrF0SxUj9JA9eg+VIfSQPUoDXLWI8dxuH79OmJjYx2mOCJLGMP4+PigVq1aahdDdUJDQ6mjkQCqR2mgenQfqkNpoHqUBrnq0ZEFzAQF5hMEQRAEQagAiTCCIAiCIAgVIBFGMEtgYCBmzJiBwMBAtYuiaagepYHq0X2oDqWB6lEaWKhHCswnCIIgCIJQAbKEEQRBEARBqACJMIIgCIIgCBUgEUYQBEEQBKECJMIIgiAIgiBUgEQYoTr/+c9/0L17d8TGxkKn0+Grr76yOM5xHKZPn46YmBhUrVoVSUlJ+Ouvv9QpLKM4q8MBAwZAp9NZ/OvSpYs6hWWYOXPm4OGHH0ZISAhq1KiBnj174tSpUxbn3L59GyNHjkRERASqVauGXr164dKlSyqVmE2E1GOHDh0qtcnhw4erVGI2WbFiBe6//34+majBYMB3333HH6e2KAxn9ahmWyQRRqjOzZs38cADD2D58uU2j8+bNw9Lly7FypUrkZWVheDgYCQnJ+P27dsKl5RdnNUhAHTp0gUXL17k/3322WcKllAb/Pjjjxg5ciQOHjyIjIwMlJaWonPnzrh58yZ/zpgxY/DNN99gy5Yt+PHHH5Gbm4uUlBQVS80eQuoRAIYOHWrRJufNm6dSidmkVq1aePfdd3H48GH88ssv6NSpE5555hn873//A0BtUSjO6hFQsS1yBMEQALgvv/yS/728vJyLjo7m3nvvPf6zwsJCLjAwkPvss89UKCH7WNchx3Fc//79uWeeeUaV8miZy5cvcwC4H3/8keO4irbn7+/PbdmyhT/nxIkTHAAuMzNTrWIyj3U9chzHPf7449zrr7+uXqE0SvXq1bmPP/6Y2qKbmOqR49Rti2QJI5gmJycHeXl5SEpK4j/T6/VITExEZmamiiXTHj/88ANq1KiBRo0aYcSIESgoKFC7SMxjNBoBAOHh4QCAw4cPo7S01KI9Nm7cGLVr16b26ADrejSxYcMGREZGonnz5pg8eTKKi4vVKJ4mKCsrw+eff46bN2/CYDBQW3QR63o0oVZbpA28CabJy8sDANSsWdPi85o1a/LHCOd06dIFKSkpSEhIQHZ2Nt5880089dRTyMzMhK+vr9rFY5Ly8nKMHj0a7dq1Q/PmzQFUtMeAgACEhYVZnEvt0T626hEA+vbtizp16iA2NhbHjh3DpEmTcOrUKaSnp6tYWvb4/fffYTAYcPv2bVSrVg1ffvklmjZtiqNHj1JbFIG9egTUbYskwgjCC+jTpw//c4sWLXD//fejXr16+OGHH/DEE0+oWDJ2GTlyJI4fP46ff/5Z7aJoGnv1OGzYMP7nFi1aICYmBk888QSys7NRr149pYvJLI0aNcLRo0dhNBrxxRdfoH///vjxxx/VLpbmsFePTZs2VbUtkjuSYJro6GgAqLTi59KlS/wxQjx169ZFZGQkTp8+rXZRmCQtLQ3bt2/Hvn37UKtWLf7z6OholJSUoLCw0OJ8ao+2sVePtkhMTAQAapNWBAQEoH79+mjVqhXmzJmDBx54AEuWLKG2KBJ79WgLJdsiiTCCaRISEhAdHY09e/bwnxUVFSErK8vCn0+I48KFCygoKEBMTIzaRWEKjuOQlpaGL7/8Env37kVCQoLF8VatWsHf39+iPZ46dQrnzp2j9miGs3q0xdGjRwGA2qQTysvLcefOHWqLbmKqR1so2RbJHUmozo0bNyxmHDk5OTh69CjCw8NRu3ZtjB49Gm+//TYaNGiAhIQETJs2DbGxsejZs6d6hWYMR3UYHh6OWbNmoVevXoiOjkZ2djYmTpyI+vXrIzk5WcVSs8fIkSOxceNGfP311wgJCeFja/R6PapWrQq9Xo/Bgwdj7NixCA8PR2hoKEaNGgWDwYA2bdqoXHp2cFaP2dnZ2LhxI55++mlERETg2LFjGDNmDNq3b4/7779f5dKzw+TJk/HUU0+hdu3auH79OjZu3IgffvgB33//PbVFETiqR9XboiprMgnCjH379nEAKv3r378/x3EVaSqmTZvG1axZkwsMDOSeeOIJ7tSpU+oWmjEc1WFxcTHXuXNnLioqivP39+fq1KnDDR06lMvLy1O72Mxhqw4BcGvWrOHPuXXrFvfqq69y1atX54KCgrhnn32Wu3jxonqFZhBn9Xju3Dmuffv2XHh4OBcYGMjVr1+fmzBhAmc0GtUtOGMMGjSIq1OnDhcQEMBFRUVxTzzxBLdr1y7+OLVFYTiqR7Xboo7jOE5+qUcQBEEQBEGYQzFhBEEQBEEQKkAijCAIgiAIQgVIhBEEQRAEQagAiTCCIAiCIAgVIBFGEARBEAShAiTCCIIgCIIgVIBEGEEQBEEQhAqQCCMIgiAIglABEmEEQTBLXl4eRo0ahbp16yIwMBBxcXHo3r27xX55Bw4cwNNPP43q1aujSpUqaNGiBRYuXIiysjL+nDNnzmDw4MFISEhA1apVUa9ePcyYMQMlJSUW3/fRRx/hgQceQLVq1RAWFoaHHnoIc+bM4Y/PnDkTOp0OXbp0qVTW9957DzqdDh06dBB0b6Zr6XQ6+Pn5IT4+HmPGjMGNGzdE1hJBEFqF9o4kCIJJzpw5g3bt2iEsLAzvvfceWrRogdLSUnz//fcYOXIkTp48iS+//BK9e/fGwIEDsW/fPoSFhWH37t2YOHEiMjMzsXnzZuh0Opw8eRLl5eX497//jfr16+P48eMYOnQobt68ifnz5wMAVq9ejdGjR2Pp0qV4/PHHcefOHRw7dgzHjx+3KFdMTAz27duHCxcuoFatWvznq1evRu3atUXdY7NmzbB7927cvXsX+/fvx6BBg1BcXIx///vflc4tKSlBQECACzUpHyyWiSA0hSKbIxEEQYjkqaee4u677z7uxo0blY5du3aNu3HjBhcREcGlpKRUOr5t2zYOAPf555/bvf68efO4hIQE/vdnnnmGGzBggMMyzZgxg3vggQe4bt26cW+//Tb/+f79+7nIyEhuxIgR3OOPPy7g7u5dy5yhQ4dy0dHRFsc/+ugjLj4+ntPpdBzHVdz74MGDucjISC4kJITr2LEjd/ToUf4aR48e5Tp06MBVq1aNCwkJ4Vq2bMkdOnSI4ziOO3PmDNetWzcuLCyMCwoK4po2bcrt2LGD4ziOW7NmDafX6y3K8+WXX3Lmw4SrZSIIwjbkjiQIgjmuXr2KnTt3YuTIkQgODq50PCwsDLt27UJBQQHGjx9f6Xj37t3RsGFDfPbZZ3a/w2g0Ijw8nP89OjoaBw8exNmzZ52Wb9CgQVi7di3/++rVq/Hiiy+6bRWqWrWqhYv09OnT2Lp1K9LT03H06FEAwPPPP4/Lly/ju+++w+HDh9GyZUs88cQTuHr1KgDgxRdfRK1atXDo0CEcPnwYb7zxBvz9/QEAI0eOxJ07d/Cf//wHv//+O+bOnYtq1aqJKqMrZSIIwjbkjiQIgjlOnz4NjuPQuHFju+f8+eefAIAmTZrYPN64cWP+HFvXf//993lXJADMmDEDKSkpiI+PR8OGDWEwGPD000/jueeeg4+P5Xy1W7duGD58OP7zn/+gVatW2Lx5M37++WesXr1a7K3yHD58GBs3bkSnTp34z0pKSvDJJ58gKioKAPDzzz/jv//9Ly5fvozAwEAAwPz58/HVV1/hiy++wLBhw3Du3DlMmDCBr7sGDRrw1zt37hx69eqFFi1aAADq1q0rupyulIkgCNuQCCMIgjk4jpPlXAD4+++/0aVLFzz//PMYOnQo/3lMTAwyMzNx/Phx/Oc//8GBAwfQv39/fPzxx9i5c6eFEPP390e/fv2wZs0a/N///R8aNmyI+++/X1Q5AOD3339HtWrVUFZWhpKSEnTt2hXLli3jj9epU4cXOwDw22+/4caNG4iIiLC4zq1bt5CdnQ0AGDt2LIYMGYL169cjKSkJzz//POrVqwcAeO211zBixAjs2rULSUlJ6NWrl+hyu1ImgiBsQyKMIAjmaNCgAR9Qb4+GDRsCAE6cOIG2bdtWOn7ixAk0bdrU4rPc3Fx07NgRbdu2xYcffmjzus2bN0fz5s3x6quvYvjw4Xjsscfw448/omPHjhbnDRo0CImJiTh+/DgGDRok9hYBAI0aNcK2bdvg5+eH2NjYSu5Ma1fsjRs3EBMTgx9++KHStcLCwgBUrLrs27cvduzYge+++w4zZszA559/jmeffRZDhgxBcnIyduzYgV27dmHOnDlYsGABRo0aBR8fn0qCtrS0tNL3uFImgiBsQzFhBEEwR3h4OJKTk7F8+XLcvHmz0vHCwkJ07twZ4eHhWLBgQaXj27Ztw19//YUXXniB/+zvv/9Ghw4d0KpVK6xZs6aSi9EWJhFnqwzNmjVDs2bNcPz4cfTt21fM7fEEBASgfv36iI+PFxRP1rJlS+Tl5cHPzw/169e3+BcZGcmf17BhQ4wZMwa7du1CSkoK1qxZwx+Li4vD8OHDkZ6ejnHjxuGjjz4CAERFReH69esW92qK+ZKiTARBVIZEGEEQTLJ8+XKUlZXhkUcewdatW/HXX3/hxIkTWLp0KQwGA4KDg/Hvf/8bX3/9NYYNG4Zjx47hzJkzWLVqFQYMGIDnnnsOvXv3BnBPgNWuXRvz58/HlStXkJeXh7y8PP77RowYgbfeegv79+/H2bNncfDgQbz88suIioqCwWCwWca9e/fi4sWLill8kpKSYDAY0LNnT+zatQtnzpzBgQMHMGXKFPzyyy+4desW0tLS8MMPP+Ds2bPYv38/Dh06xMfNjR49Gt9//z1ycnJw5MgR7Nu3jz+WmJiIoKAgvPnmm8jOzsbGjRstFh+4WiaCIOxD7kiCIJikbt26OHLkCN555x2MGzcOFy9eRFRUFFq1aoUVK1YAAJ577jns27cP77zzDh577DHcvn0bDRo0wJQpUzB69GjodDoAQEZGBk6fPo3Tp09b5PYC7sWUJSUlYfXq1VixYgUKCgoQGRkJg8GAPXv2VIp3MmFr5aac6HQ6fPvtt5gyZQoGDhyIK1euIDo6Gu3bt0fNmjXh6+uLgoICvPzyy7h06RIiIyORkpKCWbNmAQDKysowcuRIXLhwAaGhoejSpQsWLVoEoML6+Omnn2LChAn46KOP8MQTT2DmzJlOA+udlYkgCPvoOLFRrQRBEARBEITbkDuSIAiCIAhCBUiEEQRByEC1atXs/vvpp5/ULh5BEAxA7kiCIAgZOH36tN1j9913H6pWrapgaQiCYBESYQRBEARBECpA7kiCIAiCIAgVIBFGEARBEAShAiTCCIIgCIIgVIBEGEEQBEEQhAqQCCMIgiAIglABEmEEQRAEQRAqQCKMIAiCIAhCBUiEEQRBEARBqMD/A73T4mqlkO6gAAAAAElFTkSuQmCC", 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", 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i11pvtFW/T58+uOyyy7Bu3ToAQEREBJ599ln89NNPWLFiBT7//HNMnToVANCjRw8sXrwY8fHxKCsrQ1lZGR588EEA7pTzxx9/HD/88AM++OADFBcXY9SoUYYeixCWET5ffvklJkyYgB07dmDz5s2oqalB3759cfbsWc82999/Pz788EO8++67+PLLL1FaWoohQ4aYOGptSEx0wGZz+bxns7mQmHjapBFpR0kJsGWL+/8EQRAEG7Rv3x7FxcUAgEmTJuHaa69FZmYm+vTpgyeeeAJr1qwB4HbF2e122Gw2NGvWDM2aNUPjxo0BAKNHj8YNN9yA1q1bo3v37nj22WexceNGnDlzxqzDAmAhV9cnn3zi83r58uVITU3Frl270KtXLzidTixZsgSrVq1Cnz59AADLli3DJZdcgh07dqB79+5mDFsT7PZKDBq0oV6Mj95urpIS4JdfgLZtgYwM7fe/ZAkwbhzgcgEREcCrrwIFBdp/D0EQBKEMjuM8rqvCwkLMmTMHBw4cQEVFBWpra/Hnn3+iqqoqYOHGXbt24dFHH8UPP/yAP/74Ay6X+wH+t99+Q4cOHQw5DiEsY/Hxx+l0AgASE93unl27dqGmpgZ5eXmebdq3b4+WLVti+/btovs5f/48KioqfP5jkS5dvsekSYsxcuRyTJq02BPYrBdLlgCtWgF9+rj/P2WKtlaZkpILogdw/3/8eLL8EARBsMD+/fuRlZWF4uJiDBw4EJ07d8batWuxa9cuvPDCCwACu+DOnj2Lfv36IT4+Hm+99Ra+/fZbvP/++5J/ZwSWFD4ulwuTJk1Cz5490alTJwDuJnHR0dFISEjw2bZp06Y4duyY6L7mzJkDu93u+a9FixZ6Dj0o7PZKZGUd0dXS43A4sGvXcYwbx/mIkgULgJYtOTzzjDYmyl9+uSB6eOrqgEOHNNk9QRAEoZLPP/8cP/74I4YOHYpdu3bB5XLh6aefRvfu3XHxxRejtLTUZ/vo6GjU1dX5vHfgwAE4HA489dRTuPrqq9G+fXtPg1GzsaTwmTBhAvbt24d33nkn6H1Nnz4dTqfT89/Ro0c1GKE2REdHa7qdFHyGwXPPfQKXq35ZcI6z4f77G+KLLw7B4XAE9V1t27rdW95ERgJt2gS1W4IgCEIB58+fx7Fjx/D7779j9+7d+Pe//42///3vGDhwIO644w60adMGNTU1eO655/Drr79i5cqVePnll332kZmZiTNnzuCzzz7DqVOnUFVVhZYtWyI6Otrzd+vXr8fjjz9u0lH6YpkYH56JEydiw4YN+Oqrr5DhFXjSrFkzVFdXo7y83Mfqc/z4cU+qnhAxMTGIiYnRc8iqSUpKwsSJEw2r8cB/Dx9M7Z9JBrizyZYv/wZZWUdUp1g6HA5ERlZj3rxYTJtmR12dDZGRHObOdSIy8hwcDqpGTRAEYQSffPIJ0tLS0KBBAzRp0gSXXXYZnn32WYwcORIRERG47LLLsHDhQsydOxfTp09Hr169MGfOHNxxxx2effTo0QN33XUX8vPz4XA4POnsy5cvx//93//h2WefRZcuXbBgwQLcdNNNJh6tGxvHcZzZg5ADx3G455578P777+OLL75A27ZtfT53Op1ISUnB22+/jaFDhwIADh48iPbt22P79u2yg5srKipgt9vhdDoRHx+v+XHoTTDFsMrKyvDqq68C8C2Y6I3N5sKkSYtht1di3LhxSEtLUzy++tWoE5GYeJqqURMEYTn+/PNPFBUVISsrCxdddJGiv2Whjo/VCHS+5a7flrH4TJgwAatWrcJ//vMfxMXFeeJ27HY7YmNjYbfbUVBQgMmTJyMxMRHx8fG45557kJuba+mMLiVoeRN16fI9srMPYefObti2LReANtlk/qLMbq8U3J/ZwW8EQRB6Y7RVn3BjGeHz0ksvAQB69+7t8/6yZcs8BZEWLVqEiIgIDB06FOfPn0e/fv3w4osvGjxS8/C/ecR6e8kVFXZ7Jfr2LUS3bjsFrTIEQRBEcJCoMR7LCB85HrmLLroIL7zwgifVLtTxd2udOnXK8+9Avb2UImaVIQiCIAirYRnhQ/gSyK1Fvb3UoVXBRr0LPxIEQRDqsWQ6OxHYXUW9veTh3S7Dv2DjkiXq9qnVfgiCIAh9IOETgqjt7aVVPSAr4C1QWrYExo4Nvoo0VaMmCIJgHxI+IYLTGYeiokw4nXGe3l68+JGbjcVnGAwbNkzWdxollLRsZCpUmZrj3P95U1cH7NzpUFSokapREwRBsA/F+IQAYoHM2dmHFGdjJSUl6ZpiKVcsVVVVoaysDKtWxWLqVDtcLhsiIjjMm+fE8OHnVH0/HxdVVJQJl2tkwG1tNhe2bl2BffsqZaX/OxwOxMfXIiIi1afqdWQkh7i4E3A4GlD2BkEQBAOQ8GEcsYKEfAaXVCCzkODhxUcwxQ7VIkdUVVVV4c0334TTGYfFiyeB49xCwuWyYcqUePz++1LY7fIEiTeBK1O7YLPBRzzy504q/d870HzgQF8ROmDABmzY4M6moyJkBEEQ5kPCh2HkFCQMFMhst1diyJAhSE5O9nzGixkzK4ZK7a+srAyA9LGpLXLIuwL9rWS8hSwqqho1NTEet6EU/Diczjg0afIHCgpeR01NdD1LGxVlJAginPjiiy9w7bXX4o8//qjXQFyMzMxMTJo0CZMmTdJtXCR8GEbOQilkvfAOZE5OThZsKyF3ETZzsZY6tmAQcwUePtymniA6deqUqPXL4XDg1KlTgu7GrKwjQY+TIAhCL0aNGoUVK1Zg/Pjx9RqPTpgwAS+++CJGjhyJ5cuXmzNAnaDgZosjFcgsN6bGOziaFdQGaSvZf1bWEc/+xNyGy5ZtxvPPP18v0Jm3mi1btlnw71g6lyyjZfA6QRDKaNGiBd555x2cO3fO896ff/6JVatWoWXLliaOTD/I4hMC8NaLyy+/GR07xiA9vSuArrJjdLSs8qw1aoO01aDUtca/lvo7QpwlSy6UAIiIAF59FSgoMHtUBBE+dOnSBYcPH8a6detw++23AwDWrVuHli1bIisry7Pd+fPnMWXKFLzzzjuoqKjAlVdeiUWLFqFr166ebT7++GNMmjQJR48eRffu3TFyZP0kkm+++QbTp0/Hd999h+TkZPzjH//AnDlz0KhRI/0P9i/I4hMi2O2VuP76KOTkNEVaWhrS0tJkiR4xKwdL1gp/y4xeqK1/pPbvwh2qe0QQvphl/Rw9ejSWLVvmeb106VLceeedPttMnToVa9euxYoVK7B79260adMG/fr1w+nT7nnu6NGjGDJkCAYNGoQ9e/ZgzJgxeOihh3z2cfjwYfTv3x9Dhw7F3r17sXr1anzzzTeYOHGi/gfpBQkfC6GHO4qqPF9ArWtNb5ec1ZA7eVPdI4K4gJlV30eMGIFvvvkGR44cwZEjR7B161aMGDHC8/nZs2fx0ksvYf78+bjhhhvQoUMHvPbaa4iNjcWSvwb60ksvITs7G08//TTatWuH22+/3dNAnGfOnDm4/fbbMWnSJLRt2xY9evTAs88+izfeeAN//vmnYcdLri6LoJc7Ss8AYtaQE++k1rVmpEuOZeS6rqjuEUFcQMz62a+fMf3+UlJSMGDAACxfvhwcx2HAgAE+2cCHDx9GTU0Nevbs6XkvKioKV111Ffbv3w8A2L9/P7p16+az39zcXJ/XP/zwA/bu3Yu33nrL8x7HcXC5XCgqKsIll1yix+HVg4SPBZDbdPSXX35BeXk5UlNTZS8aYqndZi7ccgOylVaO5msInTx5EqtXr/a873TG4fTpJCQmOjy1j4SOv7y83Oc1X0uJR6qLfSi3BHE4HCgursW4cReEjHvy5nD55SeQmXlByFDdI4LwJZD106hGx6NHj/a4nF544QVdvuPMmTMYP3487r333nqfGRlITcKHYfiFUswddfRoBuz2/Z73tmzZ4vm31KLhvQgHslaYsVjrWTk6KSnJZ79KLGlr1qzx/PuCWJKu9TNkyBCkp6eH7CIeqCJ2XZ0Nzz23EVlZRzzXpPf5D3TtUd0jIlxo29ZtIfUWP5GRQJs2xo2hf//+qK6uhs1mQ79+/Xw+y87ORnR0NLZu3YpWrVoBAGpqavDtt9966u1ccsklWL9+vc/f7dixw+d1ly5d8L///Q9tjDwwAUj4MAwvAHbvPoE33vCvNAysXXszqquFF2qpRUNPcaEFRnyvXEuaP0rdjsnJySEreoDAFbG93abe15qQlY0gwpWMDLdbePx4t6UnMhJ45RXjrD0AEBkZ6XFbRUZG+nzWqFEj3H333ZgyZQoSExPRsmVLzJs3D1VVVSj4y5d911134emnn8aUKVMwZswY7Nq1q179n2nTpqF79+6YOHEixowZg0aNGuF///sfNm/eLKugrlaQ8GGcpKQkdOpUjUGDNmD9+oHwjkeXu1AH2nc4oyYNXY1YCmUXlzdSblOn0wkAePXVur9akbBXPoEgzKKgwB3Tc+iQ29JjpOjhiY+PF/3sqaeegsvlwj//+U9UVlbiyiuvxKeffoomTZoAcLuq1q5di/vvvx/PPfccrrrqKvz73//G6NGjPfvo3LkzvvzyS8yYMQNXX301OI5DdnY28vPzdT82b0j4WIDo6Gh06fI9oqPP4733bvH5jOrFqEfKQuHd7uPo0aPYuHGj6hYhoYZ3nzfvWKdArqvVq1d79V9TZmUjrEVJiTtupW1bcxZwq5KRYez5kqrI/MEHH3j+fdFFF+HZZ5/Fs88+K7r9wIEDMXDgQJ/3/NPiu3btik2bNonuo7i4OOCYtICEjwXwdnmtXRseGVhGIGWh4Nt9OBwObNy4EYD6FiGhhH+fN7fbKlMyOBygYo/hABWlJFiHhI9F8HZ5XXC1uNClyy4cPdoCwFFaOFQgJw3dOzbFbq9EXl4hCgvzmMmCMxq1weFAeJVPCEfMTssmCDlQAUOL0aXL95g0aTF69NgKANi1qyvee+8WLFp0P3bvvsLk0VkTJZWhd+++wiN6ABfy8grDNj5FTdVvlos9hnvPMC2On4pSElaALD4WZdu2XPjqVhvFSgjgHYvC41+PRwx+Oz6GxX+hByJQWJiHTp32heU5V+u2klvs0YigcP76WLUqFlOn2uFy2RARwWHePCeGDz8XsjFa/mjlnmIhLZsgpCDhYyG86/oIGeu8Fx29Fw0hQeGNUQtGoHE4nU6fQoVi5Ofnw2631/sb77o9gLyF3swMLiW/iRa/n1q3lX8quzd8cLgR1w8fq3Qh4JovvGjDlCnx+P33pbDbK0O+kKJW7imHw4HIyGrMmxeLadPsqKuzITKSw9y5TkRGnoPDEdoikuM4s4cQFmhxnkn4WAg+yPnHH//AG29wnomaJyKCw8iRPdG58yDBCUZqsauqqkLDhg1FP+cXI//gVjH4KqDBLLBaCBsp7Ha7rIBkqYU+Pz/ftIld6W8id9tAxyMVHM6LmFOnTmHdunUApGOCjAwO568rKUEbyoUUHQ4HduwAXC7f37muDti504HYWHllL/yvv3vvjfNY9M6cqcSrr7rfZ0VEapl1FhUVBcA9f8bGxmowOiIQVVVVAC6cdzWQ8LEYSUlJ6N07Ca+9BowdC/Di122etuHaa9sK/p3chVEKqaKH3pw4caKe1URsn2JCzciiVlJILfS81cgM5P4mShZxOdsGclv5ixi1BSP1htWAa2/RX1oagaKiBsjKqkV6uts0E6xVzNviZbNNqnf8W7euwL598ixe/teKWGYfCyJS66yzyMhIJCQk4MSJEwCAhg0bwmazSfwVoRSO41BVVYUTJ04gISGhXpFFJZDwsSh8savt292vc3MDP7loNeEo2U9tbW1Q+9R6kgzkYpH7N2qakWplaVOCkmNVc168kVt5mVVXIWv96hwOh6efnNMZh507u2H79lxBK1kwFhT+mpQ6fhbEilbolXXWrFkzAPCIH0I/EhISPOdbLSR8LExGBnDLLdLb6U2wC6dW3xPoczXd7cX+RmyhF1q0tbS08W5GoYXIu4igkmNVc17UNpFl2VWoRtDqgff1snv3FZLV2rUQJU5nHJo0+QMFBa+jpiba1OPXE63cekLYbDakpaUhNTUVNTU1qvZx7Bhw5AjQqhUQ5LoeskRFRQVl6eEh4RMiSFkV+HYBF16rFxHeKFk4gxFIUt/j/3leXiHS08uQmOgAAMUuFim3jNwKzVpa2uSIKCXuJLWuJ7V93lh2FfLjM3vBP3nyJAD3b+Mveni0KPjocDhw6tQpwfsqK+uI6v2yipZuvUBERkaqWpip6KOxkPAJAZRaFZSKCDExo2ThVGNZkPs9Qp9v3nw9ABtsNhdyc7crTruWcsuYUaFZjohSkmIeTBVltYuDmZYV/4cDbysZCzgcDk+w/s6d3SBWZi3Y+CNvEfDhh+HRPoT/3Q8fbgPvpCCz3XoOhwPFxbUYNy4VLhefVQiMH8/h8stPIDOzARPB4KEGCR8GCDa1WMnNqkZE+E+GfH0buQtnsEGtUt8j9Dlg82y3bVuu4uBVswJeg3UbKhm31Lb+VkJ/5MYe+bu8xCwr5eXluqWxsxYoLwR/Hzudcdi+PVdwGy3ij+Rms4US5eXlnnnI13UIZGebU12RvyaLijLhco30+ayuzobnntuIrKwjzGTChRIkfExGSRqyFhe/GhHh/XlxcbGnwZzcRTbYCVbqe4Q+9yUCublb6wWICgXU8v+WcsuoDcLVMg5JaF9S4/a2ckhtK6dUgJzrkneN8QG7YvAZgHpM9ME8yRsdcC0s5IGOHX9E376bNRMlrGazaY3D4cCaNWtw+nSmwHk1T+jx16TU7xBKweWsQMLHZPRIQw60uKoREd6fe3fVlVo4+ToLwU6w0rEhvp8DHHiLD/9d3brtRLduO9Gq1XXo0CEazZplAciqVwvCP35l5syTKC5ugMzMWqSndwXQVbVVIpCwUWoVC7SvQO4kvp4OT7CuJ7nXZVJSki7XulrE7hG5sVt6Ina/aCl6AG2z2dQGvBuBXIFhFqxlFYYDJHxCDCmrgZybLDd3u6clhtRNmJ19CEOHrgXAoUWLEs92+fn5SElJkfWdYpOh9/uBFuhhw4YhP9+G7OzFOH06EaWl6aJNRMvLP8C2bfW/y9vK4L3QpaUBOTliZ1s+UsJGiVVMTuA1UF/kCOG/0POUl5fLqsHEW5Cs1Noh0D1iRuyWP0YuhHKErxyxojbg3UhYFhisZBXysFKZXy9I+IQQYgtiauox1NTEeJ5uxcSK/4KQm7sV3brthN1eiWuvvRZbtmzx+b5AC4jdbg/aeqJ0Mp0xI8Wr4Jv7u+LjT2DbNukgar2tDFLCRsnTqJzAa7nIXejFLCTe4kqpi0pNPFOwEzKrRRT9MbKfmVjM1ZAhQ5Ceni77N7XCQsiawPBGTVahHgLFP/xC7D61cuwRCR/GCCa4VWxBfP31MeCtN50778XevZ3riRWhBWH79lx067YTANCkSROf/ZaUpMlaQIK1nii5sYS+q6ysWtDC449Yhk+wTzb8wiQlbORYxeSa7LXOVpIbe6REPKrJ8tMiHs5KAb2BFsJhw4YhNTVV14UnOTlZk/2rXZz1sjqwULZAC/QSKN7nPNB9auXYIxI+DBFMyjcgFuTLgc9i4LgI/PDDZfDOeOLFipIFQaiwWqDtWTKbyrFc+BPMk4231ap58wq/5o0VGD78Np/KzYGsYg6HuyaRlEgSOha1gloPC4nafWoRI6RHnIfR1/ewYcNwySWXaLY/PeDPiVyXqf89ZpbVwcwmw0rRS6DwWbtWsY6qgYQPIwRzkQXKRBJL8+bhxYrcBUEoJTTQ9kZnrQVCrbAUmjiULHb8/x94AMjPBw4dAtq0sSEjIwFAgs/fBbKKCbkOf/jhLPbt+0CzIpP+6GEhMdPqonWch5bXt9xFNzU1VdZ2wX6PWhEQ6JyICRj/e8kMq4NU5XCWHuC80VKg8BlwgLWso0oh4cMIwRaT814QJ048iLfe2omoqGosWTJGwALkm/HE+7rlLAhiqbZi27OSyaP15KB2scvICK4nkL87Lz29DEePio8/2OMWE8RnzzaC0xmnS60hsfpBwbjw5AbKK13stby+gwkQVirE9QxE9t8vL3ZKS9PqJR3wAsb7t/X+DYy0OgSqHK6HBUorAaqlQPH+7VjNgtMCEj4mU1VVBUD6IuO3E8P/ZuPLzvuLGaEYH/7mCLQgBEpNB1woKHgdGRllnnfEblaj+nr5I2dykDs2VsScHNROioGtiMB7792i2Hokt0aSnPpBgLJryQpZR4C6AGE1Qtyo4/S21ng/dPkLGH/37LBhwwBos6hrITD0sEBpdU3qJVDs9krk5RWKZshaGRI+JsPHdkgtBoG6d3vjcDg8PlpAWMz06fO5aFp4QkJCvX16Twhi4+RFT6Cgy2BjmIJBanIINDaxp1EW8RcDUsctdjzek/KpU6eQnb0YR49m4L33boZ3zJiSp285WX5yY0LUXEtGLPZmCHtWhbi/tUbIzX70aAZOnz5X73zV1tYC0GZR11L0am2BCuaa5Od5vdL0d+++wiN6AHf/Q6Pma70h4cMQXbp8j9TUY/jtt5Zo2fI3HwuKHMSe/PyzGPxfy80QCSY13axAOTlWBqmxiT2Neh+bGVYsf8TEwKBBG7BhwyC4XL5B1VKTvfdn7lpD5yA3oF3OPtXWSGI16NJMYe+NUdejmHuNf1AQc4vz2Gwuj5AWO19aLepaiV5W4l68Y3EA7dP064vWCBQW5qFTp31k8SG0JdiJU8y3zk+AYhNiQkKCrIkhmEXLrAkjkJUhOjoL69Z9r3hs/NMowNZiJyYGunT5HjNndkNlZVPRoOpAyE3JN8oaxsri441RYkxIbHhbJI26HuW418SzTG0el6kc6yFLtXdYiXsREpxapunLucdYt34HgoQPI2g9cfpPgGL1e4zCzAlDTLCVlbmCGpvUb6ZXbSAhpCaq9HQX5BYkFlpchw0bhtraWtjtpXjsseaKrUdqERLrrAgwb4wQY1Jiw0hLmBy3mZC1Ji+vEOnppTh7thHee+8Wn+0DnS9Wau+wUv3ZO5wBCGzlU3M/SN1jUhlwrEPChxG0nDiFJkCx+j1G3bCsTBjeSLnBAKCoKFPUZSD1m+lVG0joGLQSA3Ke5O+7Lw7XXFOAnBy7YuuREsSsF3Z7JaZMOYQFC9rC5bIhIoLDvHn6CjApjBD2UmLDTEuY2MIrZq1xOuMCni//HnpimCFyzbZA+bu5Aln51AoUqfk6UAacFSDhwwhaTpzCvnXh+j1uF5hTt/5EeqURa4FYGYDExNM4fLgNFi+eFNBCFsxvplWgqZwCiUrEgNwn+auuqkJamn6Tn5T1omHDt3HffXGea2nUqJFISjK+x5bcTDUjrm+zrKpy+gP6iwM5CyvLWXhmWqC8z4nUfaJUoLA8X2sJCR9G0NIiEsi3foELE2JNTU1QYw8E62nEQmUApCYT/mk0mN8sUE0apedDSYFEqyDHeuG9+JhVOiDYfnTB4G9lMcOqKqdhrnfvuFOnTnksoVKWE5ZcKUYUfVQzR2pt5WN9vtYKEj4mo4fC9p8AARf8LT42m+Cf6oLcm4SVyqhSk4n/06hQwLQUUp3T1brC+AKJDocDZWX6nUsh4abF7yPXdWc2YtdqeroL6enVul+rYlYWo5/S5TTM9bYm+49BzHLCmkVBT0EQTEFUPe4Tq4saOZDwMRm9bijvCVBpIKEWKBUxLLW2kDOZSAVM86hNLQ7GgqHluVTa2yzY38ffdTd1qh0ul42JmDAeI69V7/uIF5tSVpY777zex8oC6PfQoHTh1WK+M+sBSa95J5g6TIcPt/krO84NC/cJKw+wgSDhwwBaXQRiT1NSgYQNGmh7GahZGKRS8Xn0dGloHa9hVqq7VgXt1Ixfi9/H23XXu/dxPPfcRo/1goWaSUYVDBS7j5RaWfREjXstmPmOpQcko+GFr78A9q6txXFAdvYhz+tAc5UeAsUqvw8JnxDC/2mK96dLTU5C1ZqDIVgRY5Zg0DJeg9Uie3JhZfzp6S5P+xVWaiYZhdj9wYIbMFgXvdpFl9Uq1Voi18oqnMRyQQAHyujSS6BY5fch4RNiiF2kwaRgek9SpaURKCpqgKysWqSnu906gcSA0sXK7AVXi8rCgPygQxYsGEKwViTQ7OuCJVjIIAu2oaoVrAJmoGS+lBLAgTK6rCJQ9IKETxihJgXTe5IKdFMKTVJqFivWFlylKAnONcqCIUdc8eKW74zOglXBG62uCz3M+2aIV+8Hmdtv74ZOnfTNIBNCq+7twWzH6oODGpTOl0oFsFC8WLhCwochgrGsCKFlV2Kpm1JoklKzWLG24CpFqq5O377XY926dYZZMOSIK6EncKlJ1agFR8sCjXpYGox0vwmlr9vtlejUqZ9hMT16oPZaMtP1qYeADjRf8p/7n6MnnsiS5ZKXe+2HCyR8GCEYy4oYWmaMSYmYU6dOecSa3V4OQJ2IYbHCs1IC1dUpKzsHwBjLllxxJXZ9iLlHjVxwtCzQqMbSEKg3lpHut1CNb1J7XGa6PvVy1YnNl6Wl6XjjjTsEz5E7kL2ppEs+VF1WaiHhwwjBWFYCoZXZW0rEPPxwET78sONfN2cTDBp0haczuFIRY3ZJeC3h6+rwGNnwUwtx5e8eNWPBMatAo9QCZ5RbNlTim3gRqYVwNNMlrnV8TKBs0ry8QhQW5un+24eSy1AOJHx0RI3ritUYl0CWGKnO4HJEjFULmylF6xYTQugprqSuT71/H38hqSdSC5dRzVJZnROUICQigzkuq7vEvRHKJv3hh7PYt+8DQ357Pa2JrAoqEj46odZ1xfINLSZipCsdS4uYcCmVDuhvwQhWXIlNVsOGDUNVVSJWruTgcl0o/R0ZyeGee25AZmYDS/0+wU7K/MPAhg2DVHWrl4oTqaqqAsBmN3qlCB2nmrmOpd5oPFos7v7ZpOnpZTh61L2vQOfIO0i5qqoKDRs2FNy/WDCz1tZE7/MeaN0z+5ol4aMTUq6r1NRjqKmJQXFxLbznR9ZjXIREjNQE5t+vBxAWMVZaNLVCLwuGWnEVaLKqrXVbK+fNc/qIqVdesSEnp6n2B6EjgY6TXyTKy8sl99Oly/eYOLEtqqrSFXWrlxsnMmLECDRs2FA366BZ8GLB25Ujt/ih9wPS/ff/il9/jUCLFufRrFkWgCw0aNAA1dXVKCsrM611SLDIFXhSrW/E4M//2bMNNbXg8r9PcXEtHnssFRxn8+zzo48GYebMbkw8IJHw0Rkxa8iSJWPAcRFYuZLDvHnl6Nv3giK3WoyL1M1pZCVZKaxQTl1r5Iorqac/70n23nsvdEYfPHgkAG3PmZ6/k5Lj9P87oSf7bdvWAAD+9reJkHse5MZ/NGzYEGlpaSHVgNZfLOTlFSI9vVR28UP+d3c4HPjyy1cBAEePin/fsGHD6hVp1eI+1zP2yl/gTZx4EG+9tVOT9cD//Lt7OV5Yo4K14CYlJWHvXsDl270HdXU2VFY2BQvTKwkfnRHrlM6/drlsmDIlHr//vhne9abU1NzRA7mKX61Y0zqFX+q7qHCaOEriCbyvT60zRvT+ndTETch5stc7c8bI+Ca9EBILhYV5mDRpMez2C93cq6qqPFYbIaKjo2Wf7zVr1gi+H+x9rnf8jf/Y+ArmwSB0/t3rk1v8aGXBbdsWiIjwFT+RkUCbNkHtVjNI+OiMvzXEX10D0jeL95Om0QSKveFbYvAoFWt6pPAHgoV+YCyiVUd0raw0elWVVXucoZJVxQJy+oxFR0fj1VdfldzXsGHDfF4rjbUJ9j6Xuo6842pYsSSLnf+bb16DRo2q8MADf8ellzYJ+nsyMoBXXwXGjwfq6tyi55VX2BHuJHwMwNsaEhVV7XFz8QSadP0FQatWVbjvPqNG7oa/YUtKgF9+cat5LS5gvVL45RCqdVHUIBYMHRHBYeBAefFlVrCmeR+n3V6K2bPTZcWWGFVzicXsF62RIzrl3u+1tbWefxt5P6uNv9Hy2ld7vdx++1WCyQkPPXTNX66t4EUPT0EB0K8f755lR/QAJHwMw9saEuhm4U29TqcTu3dHYvbsNj4BYg880Ah/+9txwwPEliwBxo1zmy4jItxqfvDg4CtDA8an69ITfH2EgqHj4k5gw4YLi0egydYqvX/44xw3rgxO52KPaxYAiooyBY9NbLGOitLmWMJJhOuRvCF1P2stKtXG32h17cu5XsSOuVOnBLz6qs3PEiPs2tLCgsuqe5aEjwkEiofhTb3Tpv2M9esHArD5/G1dnQ3PPbcRWVlHDHl6djgcKC6uxbhxqZ6nBJcLGD+ew+WX13qyTsSQc3MYncIfCnVR9ISfrMrKLjjozVyc9bKG8A8jgY4tPz8fNTU1OHzY213NeRIUxM6D1KLBZ4ypFeFWC9JX0s1drkuaJ9D9fPhwG12uWz3ib+Qg53qRulflWGKsYMENhpAUPi+88ALmz5+PY8eO4bLLLsNzzz2Hq666ytAxSFk5AsXDFBfX4sMPB8I/FgjwFQR6Pz3zF39RUSZcrpE+nwUjwHiXWXy8+/iMTuFnuVYSi5hpIdNbcEkdG9/hukuXdUhNPYbXXx8D/r4UOw9K+iKpEeFWXJSU1OnyDmiW8/sHssiZcd3q6baUul7k3qtSlhirWHDVEnLCZ/Xq1Zg8eTJefvlldOvWDYsXL0a/fv1w8OBBpKamGjYO/xvdPxBYDKfTiT17osFx9U2PRtf04ccuJRTkXPz8E+qqVbGYOtUOl8uGiIhUDBzobm1hZAo/67WSWEPN4qzF5K+n4OK70Cs5tpqaGMhJTFCyGKgpTmjVRUlN13s5v7/Y/VxTE2O4ZVfvuj5S14vZldWtQsgJn4ULF2Ls2LG48847AQAvv/wyPvroIyxduhQPPfSQoWNR87S1evVqOJ1xsNkm+V3ALhQUvI6MDOH0Tj0JVijwT6hOZxwWL57kiVlyuWyeiQwQ7j6sF1arlWQGSlpfeC+yWk3+erkkHQ4HVq9eDUCZ8NC6gnJ+fj7sdnvIFSfUCqnfPyoqyvO+0P3snkeNs+waVdcnMfEYZs1K++vhkcO0aUWIiXHvP9A1OmLEiLC+nrwJKeFTXV2NXbt2Yfr06Z73IiIikJeXh+3btwv+zfnz53H+/HnP64qKCl3GpmRSFBMaZogenmCEAr8oik1kO3d2w/btubrHj4RLPzCtUNL6gndPaDH5693I1VukBRL1w4YN8ywUerQAsdvtmhQnVGJds1JskNTvb7fbBd1nTqcTq1evNrythRF1fRwOB+rqnsd9910oIMqLHiDw9RwoFjPcCCnhc+rUKdTV1aFpU183UdOmTXHgwAHBv5kzZw5mz56t+9jk+LjLy8s9xbZYtEgEW1RRuJijyyN6AOGFUqsJKpz6gWmF0tYXWkz+RjRy9UbsXvOu9qtHCxDvOi+RkUC7du5/Oxzyj02JdY2F2CA5wktJPy6hcaalpdVr+llc3ACZmbVIT+8KoKsu97kRsYP8MQWai1lcO1gjpISPGqZPn47Jkyd7XldUVKBFixa6fJfSG42V6s1aITSR5eZux7ZtPX22814ovZ+6eYKpJ0SiRj2BAiK1ttLo3cjVG6XxSHq0APFHjviQ2j8vrPhF3uzYICXCK1jh4t/0Mycn6OGLwmLjVK3XjlCrMxVSwic5ORmRkZE4fvy4z/vHjx9Hs2bNBP8mJiYGMTExRgzPkmh9wfs/jQDwsfgAvgsl/9QtHBzNYd48J4YPPxdWlhoW3RVaWWmEjk2tNUQOemaNBWP9kiM+pPbvLawmTpxY7++NXsyUCC/v3n56C5dg8bckG2VhAoR/Q61/11CsMxVSwic6Oho5OTn47LPPMHjwYACAy+XCZ599JnjjWx29nyBWrYr9KxhZ2wve/2lEKnA6UHC0u8/ZUtjtlUyl7+oFC+4KMYKx0jgcDpw4cUK0r5I3Wh2b3mn6ers+lOzfX3SwsJiJLdDeLkB/WH3AMdLCxCP0GwLQ5Hfl1xape8Sq8ZAhJXwAYPLkyRg5ciSuvPJKXHXVVVi8eDHOnj3ryfIKBYYMGYL09HRdJ4CSEmDqVDu8q0aLLQrBXvxSPmmp4Gj+CZe19F090NNdoaUlSUnFViV1bwDtXDFaBqN6nzu+OKGcbEg1T+eBXCt5eYU4fTrJ87kQLFQuDyS8pMp+hMMDjhRCv+H69QNhs0GT35W3Ym3ZAixaVP8e6dlzJHr3tm7oQMgJn/z8fJw8eRIzZ87EsWPHcPnll+OTTz6pF/BsZZKTk3W94BwOB3bsAFwu3+/guAh06jQYPXpUewq7afUEJscnTYUH66PVU7OZliSzBKtW11Ogc+ct6keN+huSkrKwbp17gVdrdfF2rZw6dQrZ2e7WG6Wl6SgszJPcn9mVy+UKL2oiLI7QbwhEgON83wnmd01KSkL37sJd1rt1S4JFNQ+AEBQ+wIXgOKsh13Kip3nR263kX0vIZnNh374PcPSoMreSVuMNpcKDQtYVp9OJmpoaAECDBg18soqA+udRy6dmswNfhZCyhqi1UGkdjCp1TnhR365dPwCNPMcWjNXF+7j47d944w5Z+zP7AUKO8GLBFcfDYkydWIast8UHCL78A+td1tUSksLHqrCQbu2dLhloURAao1i2ldBxya1k7U8opGoqde14M2zYMADBL5xSi7UcS5LaYGVvhCZjpzMOO3d2w7ZtuQCEF75gLFR6B6PKcV9pbXVRsj+pe9vfWqj1nCMlvFhwxfGwFlMnJdrPnbuontXPbq9Efn6+6vGx3GVdLSR8GIMln6kSkSHUvb2g4MLn/selxLrlv3DKcYt5L7ilpREoKmqArKxapKe7PPs161zLacIotnjW1tYCkL/QqYkhUWJJCjThy100eDHn/93ex+W/8AVrodIrGFWupUJrq4uc/cltFCr0QDJs2LB6Fkjv/Sq5l6SEl9muOG/kNkw1yhIaqDP84cNtPKIHcMd78dceH5qgFla7rKuFhA8REDkio6TkgugB+O7t7qcEsZtFbdNCOXgvuIEWIhaCJNVmZshZ6NS4C5Q+bQf6/eQuBryY8/9ub8xa+JQg59zpVfNFjhtYjuVVbGGXyraTcy/JFV5muOLELJPe1i9W3G9CneHr3zsRKCzMQ6dO+0y7Z1h0EfKQ8AkBginoFyxigdB1dcDOnQ7ExopbsfS66PmbTWohMjtIMpjMDKmFTq27QOxp++jRDJw+fS7opqOBrE/CAZtupBY+FgqsybFUaO1mkysm+O0C7VfOwh6MxUPqYYcXYUbH8smxTBrhfgtGKBhhJVMyPtZchP6Q8LE4Ui4mPXA647B1azTatfsD69aJB0Jv3boC+/YFX19HbdA3SyZzIdRkZkg1Zgy0bznHLhY0uXbtzUE96cpZVIW/2/39/tYQPZqiBotcS4WWbjat4gLlLOxanGe584CRsXxy3Fl6zyVqhYLciul8iQUh5MbqKRkfi8kS3pDwsTBqXEzBwk9+ixZFICKCw8CBV6BLl+8VB0IrQe3kbnb2ihRqMjPEGjPy8E/Nao/d/2kbcAGwQU49JzGkFtUzZ84Ifjff0qRbt52eAM2UlBTNm6JqhRJLhZZuAC2emKUWdrntMZSO3RsWmggLibvs7EM4e7Yh3PeCPnOJWqEgVTF9wIAbsXr16qBdlawLGaWQ8LEwv/ziW18BcLuYDh3SR/j4T34ul80z+en9hOZ9U8p17bGe/i42PqB+jI9/rIbafYsdu5jL5OzZRnjvvVt8tlX6pCu1qG7atAmAO4B23LgEP/dPawCtRRdTFqx6St1NLLoBpISykvYYgLqxG5HVKiQ4edEmJu7cFtgIABx48aNmLgkkdv0z6ZS4bgNVTC8rOydrbFYRLFpBwseiOBwOxMfXIiIiFS6XzfN+ZCSHuLgTcDgaaPLU5Y3U5CcnEDpYlLr2hBYiflIpLY2AV0sgUxBbKNWKSKWLMI//osNbjtxuzOCsZmKLalSU72SbkJCAtLQ0Re4fFqx6Shds1jKFAGmhHOg3LCrK1Gzsehdm9RacvuddfH67gA02G4ehQ9egRYsSxaJHbgmLYFyKoZZ9pRckfCyI9000cKDvTTJgwAZs2KA+a0lsEnc6nTh6lMPKlZyP0DJqkXE4HCgursW4cReEntu1xyEt7RSqq6Nx5ZV2wZveW5B5TyorV3KYN6/c8Canckz6as38wTw1C72nhdWsvvuMA8dFYMmSMUH3EpIO8nYG3IdWv7na/bASnwQEFspC57lz571YsmQME2OXg1RcWHb2IZEYswtwXAQaNaqqd/1L3Zdyxa7erlutkgBYSCYIBhI+FsT7Jgo0WWn11OVwOLB69WoA9YWWEa4jXugVFWXC5Rrp81ldnQ0DB7qf1CIiOLz6qg033ii8HyFXnRlNTgOJS6nKzXLGp/QYxKpI8yixHPmP13sfqanH8PrrY8DHSQQzqUtlR5WXt8WaNWs8160YZpY0kBs3o6colxtXA/heB1FR1R7RIzR2lhE775MmLa4n7i64udx4P+gNGTIEycnJin+fQGJXT9etViJbbZkMloSSKuFz9uxZNGrUSOuxECrR28UkV2jp/f3CwcCcj5AZP57D5s1nBPfDUpNToYkyzQS/m1wT/NixNygWYrw4KS0txbp161BTEwPvRQQIvpcQj5R7jAV3kj9i1+NPP3VAx47/84mb0UugSVkInU6nj3jk55qiokzTY6vUEmge8J/fDh9uI/qgx4ue6upq0Vpj/veIlNjVy3WrlSVJzX5YsmryqBI+TZs2xbBhwzB69Gj87W9/03pMBOMYEcsj9r31M458J7C6OhtWrNiKrCx3w1q73R50plMoI9cEz8ffKMU7tdWs88/ixAuIC/lNm/pj8+a+PuM8ceKEblafQPtNS0sTjP1i9V7ytl6Wl5d7imMCwB9//AFA+jr0nt8CPej5i0Kxe8e7b6ScOEk92oloZUmSux/ekigllPTM0AuEKuHz5ptvYvny5ejTpw8yMzMxevRo3HHHHUhPT9d6fEQIoOXFHcjcDtRP/fZerFnP8jIbvQWCnuefX/D4xY5f5FhKd/dHKPYJEC4bsGbNGtPccnrFfmlNoOBlAH/9O05y7Ndffz02b97s2Y/Ygx7nVXAr0L3j/XAhRzAqbSci57oIVqjKrRfkXShz4sSJ2LIFWLSovlDq2XMkevc2r0WTKuEzePBgDB48GCdPnsTKlSuxfPlyPPLII+jXrx9Gjx6Nm266CQ0aUPhQuMH7vL3RIz7BeyJSOvmGQpNTb+TWg5EqyW+UQNDj/Ady17GQ7h4I/nz89FMHbNrU3+czjovAzp3d0LdvIQD2Uo5Zu5fEgpfdgpKDf8NbsbFnZWXJShCQWyHeGynR5T2Hym0nIjRO7wcBOd+rJGlCqF7Q8OG31Zvrk5KS0L27O/vWu+xKZCTQrVsSzOwWFJQ6SUlJweTJkzF58mQ899xzmDJlCj7++GMkJyfjrrvuwkMPPYSGDRtqNVaCcZKTkw2PU1Ez+ZrlqtOawOm5F45vxIgRePPNNwNup6dA0LswnZi7LirqPM6ebcicS0bofHTs+D9s3ty33m+wbVuup4AjC7BQZFCK+n2rbBCzpImdVzkPa3xcj9J7J9CcJTaHKrHGij0I+H/vuHE3wm6X3yIlUL0gIEHwbzIy3CVHxo9315iLjAReecX8lPughM/x48exYsUKLF++HEeOHMHNN9+MgoIClJSUYO7cudixY4enOBlB6EWoCBmlyG3bUFVVJbldVNR56FWZ1ojCdDz1n/RtcFfDdjHjkvE+H979qXJzt2Pbtp5+W7NjnQKC/y29LY+lpREoKmqArKxapKe7JP9WLoF6vgHSgl6paJPjRgpGMAbbONj/QYf/G/9QACUoqRdUUODuJuAWSuaLHkCl8Fm3bh2WLVuGTz/9FB06dMC//vUvjBgxwifro0ePHrjkkku0GifhhdreVWahdxNV1lIljUbuxCi23blzF6GwMA8XqtPaNBcIRvjyhZ/0AXf/Mxduvll54Tm9EDof3brtxLZtudCrLYJWqP0tvS0RgYR6sLFM4j3f3AilpPOoEV5y4p2CEYzBWGNZCexnrbCiKuFz55134tZbb8XWrVvRtWtXwW3S09MxY8aMoAZHCGPkEzS/L7XbadFENdD3y7mxrSYUlSJ3YhTbbvNmXvQAvOgpKHgdGRnCKbqsEvhJX13hOSOx2ytx001sBQxridyYmGBjmYSECF+PRyglXQv3vByXu9r5WG1gMsuB/WajSviUlZVJxu7ExsZi1qxZqgZFSGNkNLxaoaVVE9VA7RTk3NhGC0WjkTsxim0nJIZqanwFAUsCQYxAT/paFJ7TA//zylrAsB4YEXDufx7d36vvOdXL5a42g471wH4zUSV8GjZsiLq6Orz//vvYv38/AOCSSy7B4MGDKZsrRFFTDXjHDsDl8v27ujpg504HYmOV7dN7Wz5mRerG9o5tMXuB0xO5E6PQdnl5hSgszBMVTUOGDEF6erolzp9YengwT/ladlEXIikpCfn5+YJFAkMVo2oA+Z9HqXOq1CVvpCVZjSBmtdYSC6hSKT/99BMGDRqE48ePo127dgCAuXPnIiUlBR9++CE6deqk6SAJa8H78t0NLifVu/G2bl2BffvUt4jgrY1SN3Y4ZRTKnRiFtouN/TNgdVoriB4e/zpPNTXRqp/y5WbNBRuTkpKSIms7K1jd5KBXDSA156eqqgplZWVYtSoWU6fa4XLZEBHBYd48p2QPP70tycFm0LFYa4kVVAmfMWPGoFOnTti1axeaNGkCwF0Vc9SoURg3bhy2bdum6SAJa8FPBFI3nh6+/HC+seVaCvy3s7p7RUm/KaHtxZCbNRfsdRzqrlgh9Ljm/M+jf+VmAIiKioLdbgfgFj1vvvkmnM44LF48CRzHNz/27eHXq9dwOByJgtlnev4mWlwXVr+39UKV8NmzZw++++47j+gBgCZNmuDJJ58UDXYmwhO9b7xwvrHlLuByLF9Wdq94LxBCix1wYcFTIyCMCBJlRdTonYHpjR7XnG//tsDuTDl1eA4fboPZs7N1yT6Tg5r9q7EU6e3SZQ1Vwufiiy/G8ePH0bFjR5/3T5w4gTZt2mgyMCJ00HtRtfKiHQxKngj9tysvL8eaNWskv8Mq7hV+UtajgGaoB4nyi54ad08oIOYyj4qq1jX7TC+UWorkNio2q2WKHqgSPnPmzMG9996LRx99FN27dwcA7NixA4899hjmzp2LiooKz7bx8fHajJQgLIIRRdp45O7Hfzv/5pNChPqCJxepWDL/xpE8Vjh/3vF4gdw9Wix6rJaVEHOZ19TEWFbwKvmt5DYqZlXoqUGV8Bk4cCAAYNiwYbDZ+FLg7oZtgwYN8ry22Wyoq6vTYpwEYQmMKtKmBWZ/v1WQiiXjSysILRbDhg3zKezKmhjiFzMpq5YWix7LsUxCLnN3ckZ4ZUUFU/DQSu4yVcJny5YtWo+DIEICvYq06WVFstJkZSaBYsnEFgunMw5z5/5X8ywwPTAq9dmo45YbqyTWzgEwN3nCSKsxTzCxbFZzl6kSPtdcc43W4yAI2agxmQcTtKlmEtIyLkQvK5JR6dqhglAsmVQbED2ywPQglDIk5VaLX7Uq9i/3nrh1w4zkCbOsxsHMWXKvaVaufdXVBsvLy7FkyRJPAcOOHTti9OjRnlRBInzR25cv12QOQHWNDh61k5CWT9B6WZGMSte2KnKuT7HFwrsopBmtAtQIfatnSDocDhQX12LcuFS4XHysEjB+PIfLLz+BzMwGSEpK8mw3dWqqJ6Yp0G9kdPKEUa09/NFyzmK9f6Iq4fPdd9+hX79+iI2NxVVXXQUAWLhwIZ588kls2rQJXbp00XSQhLUwwpcvV7AEG7SpdhLS4wlar+wi6unjRsjtl5+fj5qaGgBAgwYNPPE6fNsU4TYZwm1A9A6K1SI7y6oZkvz9XlSUCZdrpM9ndXU2PPfcRmRlHcGIESPw5ptvCm4X6DcyYyE3OptQqzmLlcaogVAlfO6//37cdNNNeO211zwtKmprazFmzBhMmjQJX331laaDJIxByxoeZrtGtA7aVDMJyX2ClnKllZeXA9AvDiPU07XloNbtp6YNiJ7jNyI7i0X4+0fqHuHb2Ci5l8QWcr2zz8xoORGs1c8qD1GqLT7eogdwPw1NnToVV155pWaDI4xDiy7qLKLV5KF2P1JP0HJdafy+9IjDMHqCZTGgOhi3n9I2IEqQG19mZHaWEowsiAgE17fOe7v8/HxwHIeSEuCxx9r7uMQ2bBiE++/vwMyxBEuwrTG8scpDlCrhEx8fj99++w3t27f3ef/o0aOIi4vTZGCEcWjVRZ1FtJo89JqE5LrSePSIw9Di2OSKGdYDqtU+serRBkRJfBmPUhGrZzyeWQ9TwfSt82bNmjV/ucQu8Xnf5bJh+fJvkJV1RPfr1Ii4Ky1DE6zSGFWV8MnPz0dBQQEWLFiAHj16AAC2bt2KKVOm4LbbbtN0gIT+/PLLBdHDU1cHHDpkfeEDaDd5yNmP2oVEyZOSHnEYwZwjuWKGf4rmYSWg2uFweIoQHj3aQtbvIOd3DvZ3UhNfplTE6hGPJzfIWE/U9q3j4eO6pBZy465Tm6571+r3sEp2oCrhs2DBAthsNtxxxx2evjhRUVG4++678dRTT2k6QEJfHA4H4uNrERFxYZICgMhIDnFxJ+Bw6D9JGYFWYkFqP2oXEhaelNSeo0Buotzc7ejWbSfs9kqsXr3asx0rsQBCVhV/hH4Hod/Z6XT6HKMYSq0nSt0HSkWslve33CBjq8Qamb2QWyFQGPC9pgNdf6y0wFEsfOrq6rBjxw48+uijmDNnDg4fPgwAyM7OltUMkWAH70l/4EDfG2zAgA3YsIGdKsNWQs250mKCVTqpaO3mEBIz27b1xLZtubjpJt8Jm5VYADGrygXEfwej2oCoEcVmZWfJDTK2UokEo9P8+QBsqYcDfjsWYLkqtxCKhU9kZCT69u2L/fv3IysrC5deeqke4yIMwPsiDXRzW2mS0hoj+wsF+g3y8/MD1shSM6loPVkJiRk39a05LFi4vBEb+803v4dOnfbL3o8eE7vZVgc1WHHMgTBSSPIGBKmHA7MNDSwmKchFlaurU6dO+PXXX5GVlaX1eAgTsWoNDyG0EixGP8mI/QZ2u12XzuNaTkzCNW3c+FtzWFsYxYRYixYlpozHHysWF/QfMwAUFWUiMdGh+XfJvd/ligXvjGWzYO3hwBvWkxSkUPXrPvHEE3jwwQfx+OOPIycnB40aNfL5nDqyE3ojlSarpWDR68b1DqplvdKpHPzFjDdCEzZLi7lcIWZmjEKgBxOWO5/b7ZX1YlWaN6/AAw8o21ege17J/S5nOxas3Kw9HHhj9arvqoTPjTfeCAC46aabPN3ZAerITqhDbq0PpZVpWXzS4FFSvwdgJyhQCl7M7NzZDdu25QIIPGGzZGUMJMSGDBmC9PR0Zq8plmMshGJVpk61o3fv47IzvOSkxss9NjnblZWVydqXN3rULArm4cCIGkqsJCkohbqzE0ER7M0lt9ZHqFWmlZuqzPqCK4TdXom+fQvRrdtO1dYcs4SemBBLTk42/DdQasVh9RoRilVxueRleJmVGq/03OtZs0jJw4EWbUuUwEqSglJUCZ+srCy0aNHCx9oDuC0+R48e1WRgBPsEc7MrndBYrUwbLFLHY8aCqxSxRUJswtYjUDsUYdmKowS1GV5mpsYraYS8a9dxU2sW8ZjxcMhyHFIgVAufsrIypKam+rx/+vRpZGVlkatLJkaXc9eKYJ/CgpnQrHqjiREKx8MvEidOnMCaNWskt09JSWFmsTYrNkbuvc/KeZKL0HlSG6tidmq83EbIrNQsMuPhkOU4pECoEj58LI8/Z86cwUUXXRT0oMIBFnpjqZn0tbjZg5nQWLvR5PZSEoM/nvXrBwKIQKC6MSyTlJRkSQuFkWPWyg3B8gOT9/nkO9gDwcWqsHbP82gpzLx/09jY4MS40Q9TLCUpyEWR8Jk8eTIAwGaz4ZFHHvFJDayrq8POnTtx+eWXazrAUIOFcu48aiZ9LW92qQmttDQCBw64J4PIyAt/x8qNpqSXkv9v6nQ6fV7bbADHuf/vv50eaex6wYqoUSJIjRhzsG4Io2M3gkFsHMEEskvd82aKwWCFWf2H4ODEuNZCUehestvL632nFQQPjyLh8/337omc4zj8+OOPPoozOjoal112GR588EFtRxhCsGYaBdRP+lrdXGIT2u7dV+Cxx1L/mgw4zJzp6z5l4UZT00uJh+8FJPW3/HYswXrhsmAEqV4E44YItcB+tYjd824xaK71XO3DmHiD6KSgBJxWD4fi91ITDBp0hU/2qVBJDpaqS3ujSPjw2Vx33nknnnnmGarXoxCzfdZao9XN5T+hXRADFyb42bPTMWlSnOR3eFuJjHryCyazwWpZEf6Fy8QwcxEORpDqjRo3RKgG9muB0xmHxx6z+wkH463ngPKHMYfDgR07AJfLd4x1dcDOnQ7ExgZnjdTi4VDuvST2gPHmm28yKciF6stLsmzZMhI9QcBbS2w2993Kis9aDXZ7JbKyjmg69kATfCB2774CV12Vij59gFatODz9dDnKysrgcGhfKdYbfjHzRmox4yvDSv2tmgqyDocDZWVlov8Fcz5OnDjh89rpjENRUSaczjif91lYhNVeR3oSzL2v5joLdU6fTvJprgxcsJ4///zzut/7auEfILZtWyH4m27duoKp8Qe6l8REET8nsDAX+KMquPns2bN46qmn8Nlnn+HEiRNwuXx/uF9//VWTwYUyrMSpiGGmz1zNU7GQlcgoN4Aat19CQoKsv+W3k4ueFhmHw+GTtbV1ay4KC/OY7RzNasac2nuf1SBfMYzImLOq9Zwfl9Rvysr4A51nq1mtAZXCZ8yYMfjyyy/xz3/+E2lpaYIZXoQ0LMSpeCMngNIIpCaDIUOGIDk5GQA8mSNmuwGCEbJaimD/4xRrhaHmfHj/zdatudi8+XoA7nufxYqtLAsFtfc+6w9M3gSbMSdnvmH5N5aLVr+pnkKzfjsaF/LyCj1jZfEBIxCqhM/GjRvx0UcfoWfPnlqPhzAJuQGU+fn5QX+XnBsv0GSQnJxcL9OJhaf7YISsHiJYTisMNTidcdi8OQ+86OFh8SnPSkJBLqw9MAUiGCtrIOHknSqfnX0IQ4euBcChRYsSw86NlkJDi99U79IMXbp8j3PnLvJYeQsL8xAb+ye6dPnecuJTlfBp0qQJEhPN85MT2iM3gJLjOFn7C3Szi92g3pMZoGwykHrys1paeLDo2UPn9OkkCIUHGi00xbLL+MavPFYSCoQvUou0XuJe7thYq1vl/11ahiw4nXEe0QP4zilWe8BQJXwef/xxzJw5EytWrPCp5UNYHynLSUJCgiY3ux6TAX/zHT2aAcCGFi0utE9ZvXq15nE+wTzx6R3/oKffXegaATgf07fe+Mcysd7dntXu6XJhsWgiCw0yWctW8kbrIrlSc4qVHjBUCZ+nn34ahw8fRtOmTZGZmYmoqCifz3fv3q3J4EINK0x+cnzmLN/shw+3EX0C1CLOx9/KkJ+f71Nrp0GDBj4ByWIiUO+nRT1df0L+/uuvL0TPntuD3rdcvM+b2qd+I++zYH5vs+cNFqrM+1NeXm6JoFozBKOWRXK9rykWwgm0QpXwGTx4sMbDCA9YNI0KYZbZMtgJXu8nQK0zpvT8nfUO+pS6RowSFXK62/OB8P7jM/o+U/t9Zs0bLFWZ9x/XmjVrkJgYx/RCbIZg1LpIblJSEoYNG4Y1a9aonlNYtGKqEj6zZs3Sehxhg9miRi5CRQVPn07Cvn3lnve0mGy1sqAA+hcDlJsxVVpaKrpIGbng6i1gxUzb+fn5hh2jnO72oRDbZfS8wWKVeR65qeCAeYuueEVmt+VHLyueHkVyveffQHOK0EMGCw/yQigSPv/973+Rk5ODSO/GSV6cP38e//nPfzBs2DBNBkewgbcr4Y03tCv7LzdOI9B3mGWKDeRe8Q7QFkLPxcJ/ohQTJ2oWBLl/k5KSonjfagkl8ztLmFVlXqlrKNBCbKQA90ZuRWY9rXh6WnzF5hQrPWQoEj65ubkoKytDamoqACA+Ph579uxB69atAbj9rrfddhsJnxBCz7L/cuM0An2HFqZYpch1qWlZQ0cuek6oQvt2Op31LHTV1dUoKysL6rvkEgp1XFjGyPOr1jUkthDb7XbNxyiFd1kQm21SPcG4desK7NtnTF81q2VaGYki4eOfyiyU2iw33ZlgC7GneSMCCIONzZFritUKOefE7DRbI/btcDiwevVqyb/Re5KnCV5f9D6/rMYSqYG1isxWyrQyElUxPoGgKs7WxP9pnq+pY4QrIVhxVV5eLvBu/euwvLxcE1Os1DlhIc3WCPSsEq2UcJ3gvWPkSksjUFTUAFlZtUhPdweXaGVx0+v8shxLFCyhIsiVxCOxWPZACM2FD2FdhCYWI0zdwYorb7EdyNKilSiXOidWSLPVGqMtXGaneLOAd4xcoPPPsmgwK5bIKKwgyKXEipT7vLy8HDabDcuXR2LqVE6w1RFr159i4fO///0Px44dA+B2ax04cABnzpwBUL9iKhEaGJEdFIy44n35UpYWLX3+gYolhlvArRkWLrHJuLy8HLW1tQCAqKgon3gjgN0sEzXwx65nHJ5RhFOsltMZh61bo9G9u/lWEblxVWL3DF9aQKrVEWviW7Hwue6663zieAYOHAjA/TTNcRy5ukIUvZ9ctBBXci0tYq0OeOQWkhMrlig1iQs9IFh5QTbLwuV/vvy7x4vB2iQcLKFiYZQ7B1jZ2sdb5hYtitClto/cY66qqsKuXceDjquS2+qINfGtSPgUFRXpNY6AFBcX4/HHH8fnn3+OY8eOIT09HSNGjMCMGTN8fui9e/diwoQJ+Pbbb5GSkoJ77rkHU6dONWXMhHKCFVdyLC3BFCFMSkpCfn4+Vq9eLfmUHWgSF0t3t+qCzIqFi6WYIyMRO/9RUdUoKspEaWkElIa2mSUu5MwBVikE64//nOFf20cL5JybqqoqvPnmm5rGVbEyB8hFkfBp1aqVop3/61//wmOPPSZYOVUJBw4cgMvlwiuvvII2bdpg3759GDt2LM6ePYsFCxYAACoqKtC3b1/k5eXh5Zdfxo8//ojRo0cjISEB48aNC+r7wxEjJj6tv0OOuVzuoie2He8uk/OUrVTIWXVBZtFNYWZWndEInf/OnfdiyZIx4LgIrFzJYd68ckXxFqyLC1ZEjZJgXqE5w7+2jxZI7Yd3/WoZV8XiHBAIXYOb33zzTTz44INBC5/+/fujf//+ntetW7fGwYMH8dJLL3mEz1tvvYXq6mosXboU0dHR6NixI/bs2YOFCxeS8FGBkokvkOuID3wTi68ZMWJEwEa3SidXpS4ztc0tpSYN7yqm/l3nWW+oqQaWMljCJavOG+/zHxVV7RE9gPp4C1bEBasIxccMHiz+kCY2ZxhZ28cbrcUKS3OAFLoKHz1r+jidTiQmJnpeb9++Hb169fKxDvTr1w9z587FH3/8gSZNmug2llBFzk2oRZdsrW94uZaWYKwCUpOGWBXTULJE6FklOhhCJeZFKfz5LyrKtFS8hVrMSp0OXHeoFiNGjEB5eWNPaYHo6BNYt24dM7V9vNFarFghiw2waDr7oUOH8Nxzz3msPQBw7NgxZGVl+WzXtGlTz2diwuf8+fM4f/6853VFRYUOIw5dlHTJ1ivmQonLTG4mjJzvCjRpCI0p1CwRrLpDrBZvoDVWOn617m6zOsbLqTv0xx+J2LBh0F9p3cC8eTGebVi0iugtVpzOOHz88TlcfvlxzetLqcVU4fPQQw9h7ty5AbfZv38/2rdv73n9+++/o3///rjlllswduzYoMcwZ84czJ49O+j9KEFtVhHLSC3qelo6lCzAvH9brVUgmMU+FC0RLF6nwZjwrXRviokGo+IttLC4KL2fzK7yLFV3KCqq+q/zfmFs06bZce+9capj/8xAq5R7VutLmSp8HnjgAYwaNSrgNnwfMMDd9fraa69Fjx498Oqrr/ps16xZMxw/ftznPf51s2bNRPc/ffp0TJ482fO6oqICLVq0kHsIigkmq0jrcWg5wQda1AHobumQGqvD4UBZWZknlTyYp2K1v4uVnsS1wiwhoebJmpV7Uy5i1dYB/SwL/O+5alUspk61a1KsTu72LFV5FhOXNTUxAgHMNk0fbvR28fmm3Af+bQNZ7FiuL2Wq8ElJSZHdyfn333/Htddei5ycHCxbtgwREb4XV25uLmbMmIGamhpERUUBADZv3ox27doFjO+JiYlBTEyM6OdaE2xWkRboMcEHWtTNtnQIHa8ZWQhWy3wIFqOFRLAxR1ZMhw903rS2LHg34DSjWB1rVZ6FxKW7Oal+Dzd6u/jqp9wH/m2FLHa8ADd73g+ErsJnxIgRiI+PD3o/v//+O3r37o1WrVphwYIFOHnypOcz3pozfPhwzJ49GwUFBZg2bRr27duHZ555BosWLQr6+/XEjAwfPSZ4qUXdTEuH2HEojdFRSzBxQVZGC5GvxGKkZcxRKAWhawUrxepYeoDwF5d6jU1LF1+geUbNbyv2vSxbuFUJH5fLVc/iwr9fUlKCli1bAgBeeuml4Eb3F5s3b8ahQ4dw6NAhZPjZ9vjMMbvdjk2bNmHChAnIyclBcnIyZs6cyXQqe6DJNVD7Dy3dA1pO8GKLOksTlT/eExefgq61+4XVIGDWUWMx0uIchloQutawsKCxGCTMo/XYtHbxBbLSaPnbsjzvKxI+FRUVGDNmDD788EPEx8dj/PjxmDVrFiIjIwEAJ0+eRFZWFurq6jQd5KhRoyRjgQCgc+fO+PrrrzX9br2QmlzFqvvyaGFO1mOCFzOvszRRiVm4xFLQtYBEjXLrplluYZZN9IEwqtoyKwuaHkHCWsXPqBmblAtWSxef93xUUgIcPBgNpzMubGr7KBI+jzzyCH744QesXLkS5eXleOKJJ7B7926sW7fO86PpWbsnlAh2ctVistdiglcyibJQ54VcGOagxXk3yi3MgkVDDUZaFlld0ILBiBR578Km3sj5XfQQnBeOOQk22yTPfRnqtX0UCZ8PPvgAK1asQO/evQEAgwcPxoABAzBo0CCsX78eAKhJqUxYmFy1GIPUZCtVudlIFw+5MMxBi/NupGBlxaKhBqMr/1rhnEihJH5GyUOakFAP1qqslSgROmb/+zIUflsxFAmfkydP+vTrSk5ORmFhIfr164cbb7wRr7/+uuYDDFXkTq56PuVqNcEHmmz1ch2pwaouDKsT7Hk3Q7CGokWDqI/S+BmxBz3/tjR6CvVgRElJCfDdd0589dUKnD6dVO+YtZgPzWpwqwRFwqdly5bYv3+/T4XkuLg4bNq0CX379sU//vEPzQcYasjN8AGMecoNpwmeBStbOBLseTdKsLLagiNckIqv0WNBVRM/I2VVCyTUy8vLDX8YrF97yQ6bbRLy8gp1mQ+tkMyhSPj07dsXy5Ytw4033ujzfuPGjfHpp5/i+uuv13RwoYjURcE/Oej5lBtuEzx/HFIWrlA5XlbQ6rwbJVitMGGbiR7CQ0lBRD1/n2Ct397HHEior1mzxtACmGK1lzguAoWFecjLK0RhYZ7sY5b727J+jygSPrNnz0ZpaangZ3Fxcdi8eTN2796tycBCGTkXhZ5PueE2wfsf78yZJ1Fc3ACZmbVIT+8KoGtIHS8raHXeg12UlNYC0gortb+Qg9bzhpqCiHqer2Cs30lJScjPz8fq1auZKbDo/V1i60l6eikmTVoseMz+gdhWu14DoUj4NGnSJGAV5Li4OFxzzTVBD4qQfso9depUUBdiqFzAcvE+3rQ0ICfHxMGEEcGcdy0KP5rVhsJq7S/kouVYWSmI6E0w8TN8AofWwfFaWNoCrSdix6w0ENtb6JeWRni607PSmNQbxQUMa2trsWjRIrz99tv4+eefAQAXX3wxhg8fjvvuu8/TLoJQh1z3AB9IZ7WJkyDkooWFwaxaQFZsf2EWoRh7p2XspBb3gd6Zit5Cn9XGpN4oEj7nzp3D9ddfj+3btyMvLw+9evUC4O6gPm3aNKxfvx6bNm3CRRddpMtgwwH+Ine7FNdJ3jw0cRKhDAuTZLBQ7ajAWLl8QCC0SAm/EPCdFHRDUj0TWfh1iOXGpN4oEj5PPfUUjh49iu+//x6dO3f2+eyHH37ATTfdhKeeegqPPvqolmMMO5KSkjwXSKjXUyCIUIZqR8kjnLJL5aJHQUW91xOrlAyp33ArAO+88w4WLlxYT/QAwGWXXYYFCxZg1apVmg2OIAhCS5zOOBQVZcLpjDPk+wItBIQvdnslsrKOGLpAslhzxuFwYNeu4xg3joPLHR7jKai4a9dxOBwOQ8ah5ph5t6U3LLotFVl8jhw5gquuukr08+7du+O3334LelAEEQoYkdUTaplDemKGyykU41dCCdYyXLVuSCoXrTK4pNyWYs23jZ6nFAmf+Ph4nDhxAi1atBD8/NixY4iLM+ZJKtwwqk9RKMCCGDAiqydUM4f0wCyXE0vxKyzcFyyi1TFrYT3SuiGp3DGlp6drdh4CuS0DNd82cp5SJHyuvfZa/Pvf/8batWsFP3/qqadw7bXXajIw4gIUHCkfVsSAEdlERmcsWXnhNDP2gIX4FVbuC29YdDMFg5bWIy3bCZlh0QoUS8RChqMi4TNr1ix069YN3bt3x+TJk9G+fXtwHIf9+/dj0aJF+N///ocdO3boNdawhIIjlWFW+nKoo3ThZEUk8Yum1BO01osra9XRWUyvZ83NpAVajlUrwczS+WPlIV6R8OnQoQM2b96MgoIC3HrrrZ5O7BzHoX379ti0aRM6duyoy0CtjtKFgJ8QpZ5UrfI0RFgbJYKSJeuC9+LavHkFpk2zo67OhshIDnPnVmD48Nt0WVz9F/Xy8nLU1tb6bBMVFYXq6mqUlZUZusCzsvgAbC3KLGKVrF456xBLD/GKCxh2794dP/30E/bs2eNTwPDyyy/Xemwhg5qFgJ84i4trsXIlB5fL5tkuMpLDPffcgMzMBjRxEMzBmtWNv0ceeADIzwcOHQLatLEhIyMBQILu3+twOLBmzRrJ7Y0QgiwtPkToEMh6x/efZCnVXbHwqaioQOPGjXH55Zf7iB2Xy4UzZ84gPj5ey/GFBGoXgqSkJCQlues3jB8P1NUBkZHAK6/YkJPTVI+hEkRIk5Eh3PlbT1gSgiwtPkRoISXaWcpwVFTH5/3338eVV16JP//8s95n586dQ9euXfHhhx9qNjjCTUEBUFwMbNni/n+wRayI0ESLGjUOhwNlZWX1/hNLQyWshVXqrBChBx+wzV9/ZmY4KrL4vPTSS5g6dSoaNmxY77NGjRph2rRpeP755zFo0CDNBki4MeNJVQ2sBLWyht7lCLSI25DrkiWCw8zSFCyl1xP1CbVMN39YyHAEFAqfffv24cUXXxT9vFevXnj44YeDHhRhTVgKauUxa5HxnpgCiZJgJjD+b6XiNuR+h16uFqpBdQEWAotZWXyI+oRiphtrGY6AQuHzxx9/1MtM8KampgZ//PFH0IMirAkLsQxGCA45eAenP/ZYKjiOz4CMwEcfDcLMmd2CDk7nv2PLFmDRovpxGz17jkTv3vpkzsgVMyws9KxgZmAxi4sPIQxLokYLCz6LYk6R8MnMzMR3332H9u3bC37+3XffoVWrVpoMjCDUYITgUDKWvXvh6bfDU1dnQ2VlU0gNQe6k0727u4mh9/dERgLduiVJfocapASl3E7N4YaZgcUsLj6EOcidV/wt+GIPO3Is+KxdV4qEz5AhQzBjxgxcf/31aNrUN6vo2LFjePjhhzFixAhNB0gQSglWcGhJ27bCoqRNm8B/p8RtmJGRJJD5p09MmNMZhw0bBgUUlHwTRapB5YvZWS2sLT6E8SgRM97iKNDDjhWLwSoSPg899BD+85//oG3bthgxYgTatWsHADhw4ADeeusttGjRAg899JAuA7Uy/hO82MUWbguBnqgVHFqTkSFUjkBalCh1GxYUAP368TVq9AuEP306yaemFFBfUFINKl/4+1oqsJjuf0Jv5IqZ0tJSz3ahaLlVJHzi4uKwdetWTJ8+HatXr/bE8yQkJGDEiBF48sknqUmpAN5m5lWrYvHYY3a4XDZERHCYN8+J4cPPqTYzq/XBhnr2lVrBoQdGiRIjMv8SEx2IiPAXM/UFJdWguoC/m2nmzJMoLm6AzMxapKd3BdDV8vcbYS3ExExq6jHU1MRg2bLNHlETirWfFBcwtNvtePHFF/HCCy/g1KlT4DgOKSkpnvYV3mzduhVXXnklYmJiNBmslUlKSkJJCTB16gUrhMtlw7RpCcjPT1DlflGbRcVi9pUeGCU45GCVcgRS2O2VmDfPiWnTEmQJSpZ+AzPxvo/S0oCcHBMHQ4Q9YmLm9dfHAPC1AJntotUDxcKHx2azISUlJeA2N9xwA/bs2YPWrVur/ZqQ4pdfhOJO3IuCmgVBbRaVHtlXDoeDySJ3wQqOULeMeSPXJTtqVJ1X6wfp8xsqoo8gQgUhMQNw4Gsa+7uzQq32k2rhIweO4/TcveVgJe5Ea8QsSFaLZfIXOeXl5cz0WDICpS5Zq4iZcBKvoQj9ftojJGYCubNCrfaTrsKH8MWsuBN/S4zWlhmhSUkocO6JJ7KQnp7O5CQVTNViuZYxK0zgerhkzSRc3LqhipYp1XK/j/V7VCu8xUxUVDWWLBkT0J1llU7xciDhYzBmxDysW7dO/y/xQixwbubMk8xOGnqnZFppAdbaJWsmLBTVJNRjZEq10SJLT8QEnP9Dr7eYUevOYtWCHwgSPiYQ6jEPYoFzxcUNwjaoU+kCbGbPnlB1yRLWxYiU6lCpWyNXwPkTyJ01ZMgQJCcn1/sbq1rAdBU+QpleROgjlgWQmSne7oTwxT8FurQ0AkVFDZCVVYv0dLci0WvSYakUAEEAxqZUW71ujVwBN2TIEAC+HgExd1ZycjLS0tJ0HrlxUHAzoTliWQDumiWEXHhRs2QJMG6c2wITEeEWJQUF+n43a2no4RR7QdTHyJTqUKlbIyXgkpOTVVmWQ+Fe1FX4VFZa5yKxIlq5OfTIvhI2m5LwUUpJyQXRA7j/P368W5ToLUZYcclaKT5KjFBYLNSixbEbmVKtpcgy83eXI+CU9nALhXsRUCh8+vTpI2u7zz//XNVgCGUEcodER5+QGdRcgGeeaa5ZJWlvQikLwCxCKdBYLVYPUA6VxUINWh67USnVWomsw4cP48033/S8NjpYWq6AU/Ld/veY2DGxei/yKBI+X3zxBVq1aoUBAwYgKipKrzERChBzh8ybJ10t2+mM84geQH3acrj3ItPzuCjQWFvkBnpqidWFWzBofexGPUwFK7IcDoeP6Nm6NReFhXmGBkvrbSULFD/EOoqEz9y5c7Fs2TK8++67uP322zF69Gh06tRJr7ERMhFyh0ybZse998YFvMiFG04qtybo2YvMKOSIF6czDpdddjM6dYrRPcCYhwKNg8P7dw00UYeaKGfVtaZUeMr9XZxOp+bHFIzI8j73W7fmYvPm6wG451ojg6X1spJZPQBckfCZMmUKpkyZgu3bt2Pp0qXo2bMn2rVrh9GjR2P48OGIj4/Xa5xEAITdITbJYDy5DSflYPXCd1JZVL6CTnmAcTDp6awFGlsJ707xjz2WCo67sPh89NEgzJzZLeQ6xbPqWlNjIeB/vxMnTgSsor569WoAwR2THiUknM44bN6cB1708BgZLK2HlczqAeCqgptzc3ORm5uLZ555Bu+++y5eeOEFPPjggygtLSXxYwJi7pAHHvg7kpP/BCCeDt2xo00za4LV41HE3IZz5wLTpgUXYBxsejorgcZWJCkpCXv3Cj8cVFY2ZV6UK4VF11owFoKkpCRDjklpoK8cTp9OAt//yhs9m3waUQPM6o1Lg8rq2r17N7788kvs378fnTp1orgfkxBzh1x6aRMAgdOhtbQmhEI8irDbUBtBZ2Z6erjD0rVpRpyR2WhtIdDrHGptARNrBpqXV6jbb6+HgPPH6o1LFQuf0tJSLF++HMuXL0dFRQVGjBiBnTt3okOHDnqMj5CJmICRkw6tlTUhFOJRhKxWLhdgswHeZanULppmpqeHOmI96KKjo5GRkcTEtWnlgNBg0NJCoNU5NCIOyl8gAC5cf30hevbcHtR+pTDChWnlxqWKhM+NN96ILVu2oG/fvpg/fz4GDBiABg2o6wUrCAkYo91PVo9HEbMMzJkDTJ8e/KJpdXegGcjNGgxUvmHixIkoKEgy9do0KyCUBQuTVhYCrc6h3DioYcOGISEhAYB6IWRlgeCP/70oFj/EerKAItXyySefIC0tDb/99htmz56N2bNnC263e/duTQZHBI8ZJn4rx6OIWa0KCoDbbgt+0WTJ5WIVAmUNzpz5O4Alkvvgn+zNuDb5RUDK3aPHYmG2hcn7mAIJALnHrpXLTG4skH9Atdrg6UABxqyLBG+McKMZgSLhM2vWLL3GQchEqXk2FNxPRiNmtdJi0Qy138OotGmxrMHHH28uWbbBbLwzy1au9M+i5HDPPTfokllmdsoxf20MGzYMtbX1+/RFRUXBbrcrukb0CqqVaxWTK5jkipkRI0YwLxL8sdp4hSDhYyHUpqla3f1kBnpaBkLl9zA6bVpt2QYWSEpKQlKSkOi1ISenqS7faWbKsV7Xhh5BtUJWMbdlSr17MFQsI6GKJgE6X375Jc6ePYvc3Fw0adJEi10SAgST0mll91MoIvV7sFqAzhuj06aF3YScZVJoAWNEL29tkLKO6Oli0fra0NplxiNkFVu/fuBfyQzBuQfNvj8JcRRXbj5z5gwef/xxAO7u6zfccAM2bdoEAEhNTcVnn32Gjh07aj9SgggTWC1AZzZCbsK5c504c4Zta48/ej+EeFsbmjevwLRpdtTV2RAZyWHu3AoMH34bE8JZCXpZUISsYkCEJ4PTahWJCXkoEj6rV6/GtGnTPK/fe+89fPXVV/j6669xySWX4I477sDs2bMDVtgkCCtgpsWFxQJ0rOBvMYmMPIdXXzV7VMYh97rkr80HHgDy8/nzZUNGRgKABEPG6o0WmWVGNfL0x0oViQl5KBI+RUVF6Ny5s+f1xx9/jJtvvhk9e/YEADz88MO45ZZbtB0hQRgMWVzYxttiUlZm7liMRO11abab2+zMskAI1dlxt5e4EIBupYrEZmMFFz2gUPjU1tYiJuZC1+/t27dj0qRJntfp6emiRcQI4/D+DVi50KwEWVysg9waP1ZKGRbDitelEZllahbbQDFDhw+3sWxFYr0JdK6dTqenZ1ogWHhgVCR8srOz8dVXX6F169b47bff8PPPP6NXr16ez0tKSkw/IKJ+ITcWLjSC0INANX7mzXNi+PBzJP51Rmgx5B++9M4sU2sF848Z8l60tQyeDiXknmspWBDmioTPhAkTMHHiRHz99dfYsWMHcnNzfVpVfP7557jiiis0HyQRHCxcaAShF2I1fqZNS0B+fkLINSFlCanFUO9mlsFYwbyFUFpaGqWfSxBK64gi4TN27FhERkbiww8/RK9everV9SktLcXo0aM1HSBxgXB+2iDYw4gu0HKhViDmILUYStXdYWlOC2dRE24oruMzevRoUXHz4osvBj0gQhx/8+ypU6cC9iciCD1hqUhbOLYCYaEHlxy6dPkeEye2RUVFKjIza5Ge3hVAV12uDauck3CA5d+COoxaDO+JorQ0AkVFmUxeWIR6WLKkSOF9PZaUuC0vbdsab2UJtVYgUrCcKSVEp04JSEvTVwBb7ZxYnUDChvXfQpHwqampwYwZM7Bu3TokJibirrvu8rH+HD9+HOnp6airq9N8oIQvS5YA48alwuUayeSFRaiHJUuKXNzXo9viEhHhFiEFBcaOIVRagUhhdg8uqbGZ8ZTP8jkJRQIJGyv8FoqEz5NPPok33ngDDz74IMrLyzF58mTs3LkTr7zyimcbji95SehGSQm/yLhrTbB4YVkZFiwuLIkaKS5cj+7XLpfb8tKvnzmWn1AVPGZ2eZeDmU/5ZvYlU4pVat2IISVsrPBbKBI+b731Fl5//XUMHDgQADBq1CjccMMNuPPOO7F06VIAgM1mC7QLQgOEAjlZu7CsDGsWFzNdSHIIp8BiMxcts7q8y8Hsp3y9s8e0IhSKo0oJGzN7xMlFkfD5/fff0alTJ8/rNm3a4IsvvkCfPn3wz3/+E/PmzdN8gER9hAI5A93kLFxoVoOVSYcFF5IU4RJYzMKiZUaXdzmY9ZTPz21WyR6zYhFKHrnNb8eOvYH5HnGKhE+zZs1w+PBhZGZmet5r3rw5tmzZgmuvvRajRo3SeHiEEPUDOS9cWP6wcqFZGbMsLiy5kAIRLoHFLC1arMQzmd0J3t86O3PmSRQXN9A9eywcUdL89pJL2OgRJ4Yi4dOnTx+sWrUK1113nc/76enp+Pzzz9G7d28tx0YEwHfic19YJSUJkgu0Ff3LZo7ZTIuLlVxIrCzE4QQL8UwsdIL3LUQI5OTo9lWaw3LKtxBKmt+ycH2KoUj4PPLIIzhw4IDgZ82bN8eXX36JzZs3azIwQhrvC0vOAs2CqV4pZo7ZbIuL1VxILE90hH6w1gneKrCe8i2Fle/3COlNLtCqVSv069dP9PP09HSMHDky6EERyhBboEtKfLdjyVQvFzPHHMjiYgS8Cyky0v06VF1IROiQkQH07k3XqBRiweBOZ5zJIwsPFAkfnnfffRdDhgxBp06d0KlTJwwZMgTvvfee1mMT5Pz587j88sths9mwZ88en8/27t2Lq6++GhdddBFatGgRNsHWZi/QoQpvcfHGaItLQQFQXAxs2eL+P2uBzQRBKCdQMDihP4pcXS6XC7fddhveffddXHzxxWjfvj0A4KeffkJ+fj5uueUWvP3227qmtE+dOhXp6en44YcffN6vqKhA3759kZeXh5dffhk//vgjRo8ejYSEBIwbN0638bCA1VwiVoGVoF0rm5TDEb4zeVVVFRo2bCi6HYvxdIQxWCX9PlRRJHyeeeYZFBYWYv369Z5aPjzr16/HnXfeiWeeeQaTJk3ScoweNm7ciE2bNmHt2rXYuHGjz2dvvfUWqqursXTpUkRHR6Njx47Ys2cPFi5cGPLCh5UFOhTROmjXisHlhDKU9M9jKZ6O0B+rpd+HKoqEz7JlyzB//vx6ogcAbrrpJsybN0834XP8+HGMHTsWH3zwgeBT1Pbt29GrVy+fC6Zfv36YO3cu/vjjDzRp0kRwv+fPn8f58+c9rysqKjQfuxFQVo1+aGVxsWJwOXEB/8VIi4wcluLpCP1Rk35PD0vao0j4/PLLL8jLyxP9PC8vDxMnTgx6UP5wHIdRo0bhrrvuwpVXXoni4uJ62xw7dgxZWVk+7zVt2tTzmZjwmTNnDmbPnq35mM0gnF0ivHuB5Ukg2EBtmgDNxXvRevXVOixenG7ZjBzCPJSk39PDkj4oEj6xsbEoLy9Hy5YtBT+vqKjARRddJHt/Dz30EObOnRtwm/3792PTpk2orKzE9OnTlQxXFtOnT8fkyZM9rysqKtCiRQvNv4dFrFZDIhDe7oVQnARoAmSDpKQklJQAjz3GgeNCq1ceCWv2sGImrhVQJHxyc3Px0ksv4aWXXhL8/IUXXkBubq7s/T3wwAOS1Z5bt26Nzz//HNu3b0dMTIzPZ1deeSVuv/12rFixAs2aNcPx48d9PudfN2vWTHT/MTEx9fYbqnib6gPVkGDJv6xmLKE4CdAEyA7uLErfBA6r98ojYU2EE4qEz4wZM9C7d284HA48+OCDaN++PTiOw/79+/H000/jP//5D7Zs2SJ7fykpKUhJSZHc7tlnn8UTTzzheV1aWop+/fph9erV6NatGwC3KJsxYwZqamoQFRUFANi8eTPatWsn6uYKN7ybHD72WKrPE+tHHw3CzJndTGtyKIa3e+HUqVNYtmxzyFipCGvizqL0bRJq9YwcloU1WaIIrVEkfHr06IHVq1dj3LhxWLt2rc9nTZo0wdtvv42ePXtqOkAA9VxrjRs3BgBkZ2cj46+gluHDh2P27NkoKCjAtGnTsG/fPjzzzDNYtGiR5uOxMklJSdi7V6jujw2VlU3B4vzBT2qrVsVi8eJJYRVXwU/6fAwTYT4ZGcC8eU5MmRIvmJFDaAdZonwJpfAEM1EkfADgH//4B/r164dPP/0Uv/zyCwDg4osvRt++fQPWrNAbu92OTZs2YcKECcjJyUFycjJmzpwZ8qnsarBi3Z+SEmDqVHvIxVUEQu6kTxjP8OHn8PvvS3H6dKLH0lNUlCm6IMlZsMiyUR+WLVFGY/UWFyyhSPh8/vnnmDhxInbs2IF//OMfPp85nU507NgRL7/8Mq6++mpNB+lPZmYmOI6r937nzp3x9ddf6/rdoYAV6/6EYlyFFOEwmVuV6Oho2O2VsNsrJRckOfF0ZNkgAiHW4iKUH/z0RJHwWbx4McaOHYv4+Ph6n9ntdowfPx4LFy7UXfgQwWO1uj+hEFchN1CbpeBygCwRQsiJl0tIOIPy8say4unIskEEIlCLCxI+ylEkfH744YeA6ed9+/bFggULgh4UYQxWqvsTCnEV/sXLSksjUFTUAFlZtUhPd/sdWRMRZIkQRypeLienKbZssVY8HQ+rsSSsjksv+IcgqRYXrD0ssY4i4XP8+HFPxpTgzho0wMmTJ4MeFEEIMWpUHX7/fbFkXAXLkwAvDpYsAcaNcy+KERFu16MWDUi1PnayRARGKl7OivF0rMaSsDouPfF+WGrevALTptlRV2dDZCSHuXMrMHz4bcw9LFkBRcKnefPm2LdvH9qI3LV79+5FWlqaJgMjCH+SkpIwY8ZIVFdXY9WqWEydaofLZUNEBId585wYPvycJSaBkpILogdw/3/8eLfrUY0FbsiQIUhOTrbEsYcaUvFyVomn4wWzVCyJlsJajguVJ5xjXPh7+oEHgPx8PjzBhoyMBAAJZg7NsigSPjfeeCMeeeQR9O/fv16F5nPnzmHWrFmCfbwIQiv4yrlTp3oLBxumTUtAfn4C0+4DHnegtu97dXXuCU3uguht8k9OTqYHDhORipezQjwdb1nYsgVYtKh+LEnPniPRuzc0E9ZyXaj5+fkAKMaFx0rhCSyjSPg8/PDDWLduHS6++GJMnDgR7dq1AwAcOHAAL7zwAurq6jBjxgxdBkoQPFoIBzMJ1v3hb/Jv3rwCDzwQ+G8oQFlfpBYkpQuWGbEsSUlJ6N5d+Nrs1i1J04cKua5RPnuXYlwILVEkfJo2bYpt27bh7rvvxvTp0z0Xpc1mQ79+/fDCCy94GoMShF5YMW7CG7nuD6Fu4EePtqhn8p82zY78fPGFlQKUrYWZsSysueYSEhJCKsaFHkDYQHEBw1atWuHjjz/GH3/8gUOHDoHjOLRt25baQhCGwdrkrAY57g/vwMZVq2Lx2GP2erWMAHeWUCBrl9yn69LSUs/3EsZiRoyNGKy55kIlxoUeQNhBsfDhadKkCbp27arlWAhCNqxNzmqQ4/4QimnyRytrF9/hniZe4zE6xkYKo2NJ5Lr2rBzjQhmS7KBa+BCE2Vh5ElSCUEwTjx7WLu+JV8jdJrRAUYxF8BgZY6MUPV004ZimTpgLCR+CYByhmKaICOCdd4DcXH3Fn5i7zWolBKwCi25cPV004ZymTphHhPQmBEGYCb8YRka6X0dGul/fcosxC2JSUhLq6tIwdWqCJ8aILyFQV5dGokdjCgqA4mJgyxb3/7UobBkMerpoAqWpE4RekMWHICyA2TFNVi8hYDVC3Y1LrRjCr/0GS5DwIQiLoNViqGbCtXoJAYItwr0VA8U1mQsJH4IIcbyfmtVOuCzGnhDWJlTS1JVCcU3mQ8KHIEIc/ul69+4TmD27Xb0JNzX1GDIyyiT3Y7a7jWADPVw0oe7aAy48gEi13whl9x4rkPAhiDAgKSkJZ882EJxwX399DG666YLlJ9DEGw4LVLjjn7p+6tQpz7/JRaMe/gGkuLgWK1dyPsVIIyM53HPPDcjMbBCy7j2WIOFDEGHClVfaERHBCVR/jsBHHw3CzJndaOINcwKlrpOLJniSktz1mOq7jW3IyaF2T0ZBwocgwgR3nI4N48YJZWjZUFnZ1BLd7YOF+iWJE+i8kItGO8htbC4kfAgijCgoADp3Brp1A/7qMQwgfDK0qF+SeoRSz8lFox5yG5sHFTAkiDCja1fgtdd8CyKGS4YW9UtSj91eiUGDNsBmc5sLvV00JHoIK0EWH4IpSkrcxfLatg2PhVguWrtnyNROCOF0Ov1e+2ZwdenyPbKzD+Hyy29Gnz4t6bohLAkJH4IZliyBJ/4kIsIdAGh2uX4W0Ms9Q6Z2wp+amhrPv8UyuOz2SlxxhZOuHcKykPAhmKCkBD5Bty6XO+uhX7/wWZzFrDre6cSAeB0Vcs8QWkEZXAQQuokAJHwIJgiVXlBqXXX+Vh0xcUN1VLSF+iUJI5XBRYQ+oZwIQMKHYIJQ6AUVjKvO+6lKTNzQU3jwlJeXe/5NIrI+DRq4lwSp5qH8dkToEsqJAJTVRTAB3wvKiplGDocDu3Ydx7hxnJ+rjsOuXcfhcDhk70tM3PCWCbGncEIah8OBNWvWAAh8nsOZhIQEAO4Mrs6d9wLgax5w6Nx5r0dg89sRhBUh4UMwQ0EBUFwMbNni/r8VApt5c/Bzz31SryJyXZ0Nzz23Ec8//7xs8RNI3PBP4d54P4UTgfF+MpUSkeFejM/pjMPevZ0B8Ne0DXv3dg57YUiEBiR8CKbIyAB697aGpQe4sJhKiRK55uBA+/Gvo8K7Z+z2Sjidcdi6NRolJcEeUXgQ6Dzn5+dbLmZBa8i6SIQy5KglCA3gRYl/zIjS2Bup/fB1VNwWILcY4mNVFi2KQEQEh3nznBg+/JxlMy6MwG6vRF5eIQoL8+qdZ7vdbvbwTIO3dEnF+IS7RYywNiR8CEIjhESJHvtxL87u9/xjVVwuG6ZMicfvvy+F3V5pyYwLI9i9+wqP6AFcyMsrDPvAZuBCB/Hq6mo0b16BadPsqKuzITKSw9y5FRg+/DYS1ITlIeFDEBriLUr02M+QIUOQnJyMU6dOYd26dZJpx1bMuNAbf7EIRKCwMA+dOu2j7DjAI2oeeADIz+ere9uQkZEAIMHMoRGEJpDwIQgGkOs6SE9P93nalnJJEPWhGjXyoere4Yv/nCRW88qKbk8SPkTIYqW+X94uBjGEXAxaxRaFEyQWCUIa7zlp1apYPPaYHS6XLSTiCEn4EKajR1l0K/b9UjuBaBVbFOrwT6ZSYtGKT7AEoQdJSUkoKQGmTvVuJ2TDtGkJyM9PgAU1DwASPoTJ6FEW3ci+X3IXSb0XU61ii0IZf6vazJknUVzcAJmZtUhP7wqgq2WfYAlCL0KlnZA3JHwIU9GjLLqRN6paFxVhDt6/Q1oakJNj4mAIwgKEQjshf0j4ECGH0TeqkKjxji8izUMQhBloEUbAtxMaP979AGmldkJikPAhQg6zb1Qj4otYcbERBMEmWoYRFBS4QwXcpQ2sLXoAEj5EiGLWjWpUfBG52AiCCITWYQShVNqAhA8Rsphxo2odXxQoJZ9EDUEQhHJI+BCEhmgZX2TFlHyCffQoH0EQVoKED0FoiFbxRUam5BPsoZc40aN8BEFYDRI+hKmEYpCuFvFFoVg7g5CHnuJEj/IRWkBWKMJISPgQphKqQbrBxheFYu0MQh7+94JYjyQtxInYvo2ErFDGwMJvzQokfAjTocmsPman5BNssHv3FfVaa3Tp8j3z+1YCq1aoUELv39pqFjsSPgTBKKFWO4NQhtMZ51msAHcH+Q8/HIjs7ENBP7Hrue9gIcuENvDhAVK/dbBhBFa02JHwIQiGCaXaGVYkUDkBvTl9OsmngzzgXrROn04MWhDoue9gYMUKFQrwYQRbtgCLFtX/rXv2HInevYO3uFvRYhchvQlBEET44HA4UFZWhqefLkerVhz69AFateLw9NPlKCsrg8PhMGQciYkO2Gy+Ee42mwuJiaeZ3rdaxCwTTmecaWOyOklJSejePQkRfit9ZCTQrVsSMxYYoyHhQxBhREkJsGWL+/9EfXiz/fz5b2PKlHi4XDYAgMtlw5Qp8Zg//208//zzhogfu70SgwZt8AgU3gKihUVGz32rJZAVilAPHy8YGel+TfGC5OoiCB+CcW1oFeCnV6AgFUSUhj/vUq4go8z2Xbp8j+zsQzh9OhGJiaeDFibe8RyB9m1G+QjeCuV93s22QoUKFC/oCwkfgviLYISBVgF+egUKUkFEZaLWzEXYX3TY7ZWCgkeNOGG5fARvhfKP8aEAZ22geMELkPAhCAQvDLQK8NMrUDDcCyIqFbVmLsJ6ixPW4jpYtkIRoQkJH4JA6AuDcC2I6HA4UFxci3HjUr3idYDx4zlcfvkJZGY2EBUCWriZvN2WpaURKCpqgKysWqSnu38IMQHDmjjRE5atUERoQsKHIBD6wiAcCyLybsOioky4XCN9Pqurs+G55zYiK+tIQLehmJtJyfcDgdO0WapvYhbhfvxWxopth0j4ECGF2sDgcBAG4RbgyF8HUvE6egUq8/uVKiDHUn0TglCKFS12JHyIkCHYwOBwEAbhGOBodtAsq8UCCUIrvOdTM4t+yoWEDxEyaBEYrKcwcDrjsHVrNLp3Z3dCCFXkxuvoYbanNG0iXLBKyQwSPgRhAHyMx6JFEUxPCKGMnHgdPcz2ZlucCMIIrFQyg4QPQWhAIAuAf4xHoAnBioGCoYYesQhaFyIkCNawUmYsCR+C0IBAloKtW6PrNQkUmxD0sDjoVQlaK6wQE6AFwWSIEQTrWCkzloQPQWiEmHjo3l3ZhKClCNGrErRWWCUmgCCIwFgpM5aalBKEzpjZJFCvStBaIBYToFUDVbPdhmZ/P0EYTUEBUFzsboRcXMzuQwxZfAjCAMIhVV4pescEmF1fxOzvJwgzsELJDEsJn48++giPPfYY9u7di4suugjXXHMNPvjgA8/nv/32G+6++25s2bIFjRs3xsiRIzFnzhw0aGCpwyRUwvoTthUmBCMxIibAbFFh9vcTBFEfyyiCtWvXYuzYsfj3v/+NPn36oLa2Fvv27fN8XldXhwEDBqBZs2bYtm0bysrKcMcddyAqKgr//ve/TRw5YRT0hG0trBQTQBBE6GDjOI4zexBS1NbWIjMzE7Nnz0aBiNNw48aNGDhwIEpLS9G0aVMAwMsvv4xp06bh5MmTsp/yKyoqYLfb4XQ6ER8fr9kxEIQZlJWV4dVXX5Xcbty4cUhLSzNgRPUpKSEXoB6wns1HEFojd/22hMVn9+7d+P333xEREYErrrgCx44dw+WXX4758+ejU6dOAIDt27fj0ksv9YgeAOjXrx/uvvtu/PTTT7jiiisE933+/HmcP3/e87qiokLfgyEIwmdRjowE2rVzv19W5v4/LcrB4Z/N53TG4fTpJCQmOnxS6qlBKhGOWEL4/PrrrwCARx99FAsXLkRmZiaefvpp9O7dGz///DMSExNx7NgxH9EDwPP62LFjovueM2cOZs+erd/gCYLwgfUU+1DA29ITqDM8NUglwhFT09kfeugh2Gy2gP8dOHAArr+iH2fMmIGhQ4ciJycHy5Ytg81mw7vvvhvUGKZPnw6n0+n57+jRo1ocGkEwAYsB3yyn2IcaYp3hnc44k0dGEOZhqsXngQcewKhRowJu07p1a5T9Zf/u0KGD5/2YmBi0bt0av/32GwCgWbNm+O9//+vzt8ePH/d8JkZMTAxiYmLUDJ8gmIcCvo2FtSrU1BmeIOpjqvBJSUlBSkqK5HY5OTmIiYnBwYMH8be//Q0AUFNTg+LiYrRq1QoAkJubiyeffBInTpxAamoqAGDz5s2Ij4/3EUwEEW4EEjUlJcDevews1FaGxSrU1BmeIOpjicrN8fHxuOuuuzBr1ixs2rQJBw8exN133w0AuOWWWwAAffv2RYcOHfDPf/4TP/zwAz799FM8/PDDmDBhAll0CEKAJUuAVq2APn3c/1+yxOwRaUdJibt6rFZVoOV8n55VqNXCd4a32dwDo87wBGGR4GYAmD9/Pho0aIB//vOfOHfuHLp164bPP/8cTZo0AQBERkZiw4YNuPvuu5Gbm4tGjRph5MiReOyxx0weOUGwh9hCLdQx3moYbXlxOBzYsQNwuXwta3V1wM6dDsTGmlvIkDrDE4QvlhE+UVFRWLBgARYsWCC6TatWrfDxxx8bOCqCsCZ6t4swC6MFHZ+h5nTGwWabVM+ltHXrCuzbV2l6hhp1hieIC1jC1UUQhLbw7SK80bpdhBkEEnR6wAeNS7mUjM5QYzGbjyBYwTIWH4IgtMPMdhF6LspG9P8SgyWXknc2X3l5OWprawEAx441wG+/xaBly/No0cKG6upqOBwOyuojwgoSPkTYE66l/c3qGK9nir3Z/b9YciklJSXB4XBgzZo1AAIXMjTbFUcQRkLChwhrwr2KsFkd4/U8l2YJOjOQqhvEi0uxQobZ2Ydgt1dSsUgirCDhQ4Q1elcRDldrkhmES/8v/jhXrYrF1Kl2uFw2RERwmDfPieHDzwkeJxUyJIgLkPAhCJ0Id2uSkYTLufbOIlu8eBI4zgYAcLlsmDIlHr//vhR2e/0sMipkSBAXoKwugtAJ6kllHOFyrvnxB7LgeG/HQ4UMCeICZPEhCIJQiVlp42osOCxlnRGEmZDwIQiCUIlZTWB5C45/lpaUmGEp64wgzIKED0EQRBCYFTNEFhyCUAcJH4IgCIsiZcGhCs4EUR8SPkRYQwsDEcqY5YqzKlR+Ijwg4UOENeG4MNDkHl7QbymPcCmJQJDwIQjdJjEWrUmhOrmzeK71IFyO0wzCpSQCQcKHIHSDRWtSqE7uLJ5rPQiX4yQIPSHhQxA6QguQcYTLuWb5OMPBjXrq1CnB90Ph2MIFEj4EQRBE0ISqG9WfdevWiX5m9WMLF6hlBUEQBBE0oeZGdTrjUFSUCaczTvbfWOXYwh2y+BAEQRCa43TG4fTpJCQmOixXXHH37ivqVcXu0uV7s4dFaAQJH4IgCEJTrCwcnM44z9gBd/PXDz8ciOzsQ5YTcIQw5OoiCIIgNENMOChxGZkBXwLg9OnAne8J60PChyDCCKoDQ+iNVYUDXyrgnnv6IyKC8/ksIoJDYuJpk0ZGaA25uggijKA6MITeJCY6YLO5fMSPzeayhHBISkpCUhLw6qvA+PFAXR0QGQnMnevEmTPk5goVSPgQRJhBoobQE7u9EoMGbagX42Ol+JiCAqBfP+DQIaBNGyAy8hxeffXC51YO3CZI+BAEQRAa4O0e7dLle2RnH8Lp04lITDztIw6s4kbNyHD/BwAOx4UxBwrctsqxhTs2juM46c3Ch4qKCtjtdjidTsTHx5s9HIIgCMsQypWbHQ4HiotrcdVVqXC5bJ73IyM57Nx5ApmZDSx7bKGC3PWbLD4EQRCEJoTywp+UlIS9ewGXy/f9ujobKiubIoQPPeSgrC6CIAiCkEHbtkCE36oZGemOAyKsAwkfgiAIgpBBRoY74ysy0v06MhJ45ZULsUCENSBXF0EQBEHIxD/ji0SP9SDhQxAEQRAK8M74IqwHuboIgiAIgggbSPgQBEEQBBE2kPAhCIIgCCJsIOFDEARBEETYQMKHIAiCIIiwgYQPQRAEQRBhAwkfgiAIgiDCBhI+BEEQBEGEDSR8CIIgCIIIG0j4EARBEAQRNpDwIQiCIAgibKBeXX5wHAcAqKioMHkkBEEQBEHIhV+3+XVcDBI+flRWVgIAWrRoYfJICIIgCIJQSmVlJex2u+jnNk5KGoUZLpcLpaWliIuLg81mM3s4hlNRUYEWLVrg6NGjiI+PN3s4loXOY/DQOdQGOo/aQOdRG/Q8jxzHobKyEunp6YiIEI/kIYuPHxEREcjIyDB7GKYTHx9PN7cG0HkMHjqH2kDnURvoPGqDXucxkKWHh4KbCYIgCIIIG0j4EARBEAQRNpDwIXyIiYnBrFmzEBMTY/ZQLA2dx+Chc6gNdB61gc6jNrBwHim4mSAIgiCIsIEsPgRBEARBhA0kfAiCIAiCCBtI+BAEQRAEETaQ8CEIgiAIImwg4ROGfPXVVxg0aBDS09Nhs9nwwQcf+HzOcRxmzpyJtLQ0xMbGIi8vD7/88os5g2UYqfM4atQo2Gw2n//69+9vzmAZZs6cOejatSvi4uKQmpqKwYMH4+DBgz7b/Pnnn5gwYQKSkpLQuHFjDB06FMePHzdpxGwi5zz27t273jV51113mTRiNnnppZfQuXNnT4G93NxcbNy40fM5XYvSSJ1Ds69DEj5hyNmzZ3HZZZfhhRdeEPx83rx5ePbZZ/Hyyy9j586daNSoEfr164c///zT4JGyjdR5BID+/fujrKzM89/bb79t4AitwZdffokJEyZgx44d2Lx5M2pqatC3b1+cPXvWs83999+PDz/8EO+++y6+/PJLlJaWYsiQISaOmj3knEcAGDt2rM81OW/ePJNGzCYZGRl46qmnsGvXLnz33Xfo06cP/v73v+Onn34CQNeiHKTOIWDydcgRYQ0A7v333/e8drlcXLNmzbj58+d73isvL+diYmK4t99+24QRWgP/88hxHDdy5Eju73//uynjsTInTpzgAHBffvklx3Hu6y8qKop79913Pdvs37+fA8Bt377drGEyj/955DiOu+aaa7j77rvPvEFZlCZNmnCvv/46XYtBwJ9DjjP/OiSLD+FDUVERjh07hry8PM97drsd3bp1w/bt200cmTX54osvkJqainbt2uHuu++Gw+Ewe0jM43Q6AQCJiYkAgF27dqGmpsbnmmzfvj1atmxJ12QA/M8jz1tvvYXk5GR06tQJ06dPR1VVlRnDswR1dXV45513cPbsWeTm5tK1qAL/c8hj5nVITUoJH44dOwYAaNq0qc/7TZs29XxGyKN///4YMmQIsrKycPjwYfzf//0fbrjhBmzfvh2RkZFmD49JXC4XJk2ahJ49e6JTp04A3NdkdHQ0EhISfLala1IcofMIAMOHD0erVq2Qnp6OvXv3Ytq0aTh48CDWrVtn4mjZ48cff0Rubi7+/PNPNG7cGO+//z46dOiAPXv20LUoE7FzCJh/HZLwIQiduPXWWz3/vvTSS9G5c2dkZ2fjiy++wHXXXWfiyNhlwoQJ2LdvH7755huzh2JpxM7juHHjPP++9NJLkZaWhuuuuw6HDx9Gdna20cNklnbt2mHPnj1wOp147733MHLkSHz55ZdmD8tSiJ3DDh06mH4dkquL8KFZs2YAUC9L4fjx457PCHW0bt0aycnJOHTokNlDYZKJEydiw4YN2LJlCzIyMjzvN2vWDNXV1SgvL/fZnq5JYcTOoxDdunUDALom/YiOjkabNm2Qk5ODOXPm4LLLLsMzzzxD16ICxM6hEEZfhyR8CB+ysrLQrFkzfPbZZ573KioqsHPnTh//LKGckpISOBwOpKWlmT0UpuA4DhMnTsT777+Pzz//HFlZWT6f5+TkICoqyueaPHjwIH777Te6Jr2QOo9C7NmzBwDompTA5XLh/PnzdC0GAX8OhTD6OiRXVxhy5swZH2VdVFSEPXv2IDExES1btsSkSZPwxBNPoG3btsjKysIjjzyC9PR0DB482LxBM0ig85iYmIjZs2dj6NChaNasGQ4fPoypU6eiTZs26Nevn4mjZo8JEyZg1apV+M9//oO4uDhPrITdbkdsbCzsdjsKCgowefJkJCYmIj4+Hvfccw9yc3PRvXt3k0fPDlLn8fDhw1i1ahVuvPFGJCUlYe/evbj//vvRq1cvdO7c2eTRs8P06dNxww03oGXLlqisrMSqVavwxRdf4NNPP6VrUSaBziET16Fp+WSEaWzZsoUDUO+/kSNHchznTml/5JFHuKZNm3IxMTHcddddxx08eNDcQTNIoPNYVVXF9e3bl0tJSeGioqK4Vq1acWPHjuWOHTtm9rCZQ+gcAuCWLVvm2ebcuXPcv/71L65JkyZcw4YNuX/84x9cWVmZeYNmEKnz+Ntvv3G9evXiEhMTuZiYGK5NmzbclClTOKfTae7AGWP06NFcq1atuOjoaC4lJYW77rrruE2bNnk+p2tRmkDnkIXr0MZxHGeMxCIIgiAIgjAXivEhCIIgCCJsIOFDEARBEETYQMKHIAiCIIiwgYQPQRAEQRBhAwkfgiAIgiDCBhI+BEEQBEGEDSR8CIIgCIIIG0j4EARBEAQRNpDwIQjCh2PHjuGee+5B69atERMTgxYtWmDQoEE+/Ym2bduGG2+8EU2aNMFFF12ESy+9FAsXLkRdXZ1nm+LiYhQUFCArKwuxsbHIzs7GrFmzUF1d7fN9r732Gi677DI0btwYCQkJuOKKKzBnzhzP548++ihsNhv69+9fb6zz58+HzWZD7969ZR0bvy+bzYYGDRogMzMT999/P86cOaPwLBEEYVWoVxdBEB6Ki4vRs2dPJCQkYP78+bj00ktRU1ODTz/9FBMmTMCBAwfw/vvvY9iwYbjzzjuxZcsWJCQkoLCwEFOnTsX27duxZs0a2Gw2HDhwAC6XC6+88gratGmDffv2YezYsTh79iwWLFgAAFi6dCkmTZqEZ599Ftdccw3Onz+PvXv3Yt++fT7jSktLw5YtW1BSUuLTcXzp0qVo2bKlomPs2LEjCgsLUVtbi61bt2L06NGoqqrCK6+8Um/b6upqREdHqziT+sHimAjCUhjWHIMgCOa54YYbuObNm3Nnzpyp99kff/zBnTlzhktKSuKGDBlS7/P169dzALh33nlHdP/z5s3jsrKyPK///ve/c6NGjQo4plmzZnGXXXYZN3DgQO6JJ57wvL9161YuOTmZu/vuu7lrrrlGxtFd2Jc3Y8eO5Zo1a+bz+WuvvcZlZmZyNpuN4zj3sRcUFHDJyclcXFwcd+2113J79uzx7GPPnj1c7969ucaNG3NxcXFcly5duG+//ZbjOI4rLi7mBg4cyCUkJHANGzbkOnTowH300Uccx3HcsmXLOLvd7jOe999/n/OemtWOiSAIYcjVRRAEAOD06dP45JNPMGHCBDRq1Kje5wkJCdi0aRMcDgcefPDBep8PGjQIF198Md5++23R73A6nUhMTPS8btasGXbs2IEjR45Ijm/06NFYvny55/XSpUtx++23B239iI2N9XG/HTp0CGvXrsW6deuwZ88eAMAtt9yCEydOYOPGjdi1axe6dOmC6667DqdPnwYA3H777cjIyMC3336LXbt24aGHHkJUVBQAd9f08+fP46uvvsKPP/6IuXPnonHjxorGqGZMBEEIQ64ugiAAuBdXjuPQvn170W1+/vlnAMAll1wi+Hn79u092wjt/7nnnvO4uQBg1qxZGDJkCDIzM3HxxRcjNzcXN954I26++WZERPg+lw0cOBB33XUXvvrqK+Tk5GDNmjX45ptvsHTpUqWH6mHXrl1YtWoV+vTp43mvuroab7zxBlJSUgAA33zzDf773//ixIkTiImJAQAsWLAAH3zwAd577z2MGzcOv/32G6ZMmeI5d23btvXs77fffsPQoUNx6aWXAgBat26teJxqxkQQhDAkfAiCAABwHKfLtgDw+++/o3///rjlllswduxYz/tpaWnYvn079u3bh6+++grbtm3DyJEj8frrr+OTTz7xET9RUVEYMWIEli1bhl9//RUXX3wxOnfurGgcAPDjjz+icePGqKurQ3V1NQYMGIDnn3/e83mrVq08AgMAfvjhB5w5cwZJSUk++zl37hwOHz4MAJg8eTLGjBmDlStXIi8vD7fccguys7MBAPfeey/uvvtubNq0CXl5eRg6dKjicasZE0EQwpDwIQgCgNtKwQcli3HxxRcDAPbv348ePXrU+3z//v3o0KGDz3ulpaW49tpr0aNHD7z66quC++3UqRM6deqEf/3rX7jrrrtw9dVX48svv8S1117rs93o0aPRrVs37Nu3D6NHj1Z6iACAdu3aYf369WjQoAHS09Prucr83XxnzpxBWloavvjii3r7SkhIAODOFhs+fDg++ugjbNy4EbNmzcI777yDf/zjHxgzZgz69euHjz76CJs2bcKcOXPw9NNP45577kFEREQ9EVlTU1Pve9SMiSAIYSjGhyAIAEBiYiL69euHF154AWfPnq33eXl5Ofr27YvExEQ8/fTT9T5fv349fvnlF9x2222e937//Xf07t0bOTk5WLZsWT33lRC8cBIaQ8eOHdGxY0fs27cPw4cPV3J4HqKjo9GmTRtkZmbKig/q0qULjh07hgYNGqBNmzY+/yUnJ3u2u/jii3H//fdj06ZNGDJkCJYtW+b5rEWLFrjrrruwbt06PPDAA3jttdcAACkpKaisrPQ5Vj6GR4sxEQRRHxI+BEF4eOGFF1BXV4errroKa9euxS+//IL9+/fj2WefRW5uLho1aoRXXnkF//nPfzBu3Djs3bsXxcXFWLJkCUaNGoWbb74Zw4YNA3BB9LRs2RILFizAyZMncezYMRw7dszzfXfffTcef/xxbN26FUeOHMGOHTtwxx13ICUlBbm5uYJj/Pzzz1FWVmaYZSMvLw+5ubkYPHgwNm3ahOLiYmzbtg0zZszAd999h3PnzmHixIn44osvcOTIEWzduhXffvutJw5q0qRJ+PTTT1FUVITdu3djy5Ytns+6deuGhg0b4v/+7/9w+PBhrFq1yieAW+2YCIIQh1xdBEF4aN26NXbv3o0nn3wSDzzwAMrKypCSkoKcnBy89NJLAICbb74ZW7ZswZNPPomrr74af/75J9q2bYsZM2Zg0qRJsNlsAIDNmzfj0KFDOHTokE/tHeBCjFBeXh6WLl2Kl156CQ6HA8nJycjNzcVnn31WL36FRyjjTE9sNhs+/vhjzJgxA3feeSdOnjyJZs2aoVevXmjatCkiIyPhcDhwxx134Pjx40hOTsaQIUMwe/ZsAEBdXR0mTJiAkpISxMfHo3///li0aBEAt5XtzTffxJQpU/Daa6/huuuuw6OPPioZnCw1JoIgxLFxSqMUCYIgCIIgLAq5ugiCIAiCCBtI+BAEETI0btxY9L+vv/7a7OERBMEA5OoiCCJkOHTokOhnzZs3R2xsrIGjIQiCRUj4EARBEAQRNpCriyAIgiCIsIGED0EQBEEQYQMJH4IgCIIgwgYSPgRBEARBhA0kfAiCIAiCCBtI+BAEQRAEETaQ8CEIgiAIImwg4UMQBEEQRNjw/4yQWz4vK+05AAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if has_alamo:\n", + " # visualize with IDAES surrogate plotting tools\n", + " surrogate_scatter2D(alm_surr, data_training, filename=\"alamo_train_scatter2D.pdf\")\n", + " surrogate_parity(alm_surr, data_training, filename=\"alamo_train_parity.pdf\")\n", + " surrogate_residual(alm_surr, data_training, filename=\"alamo_train_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if has_alamo:\n", + " # visualize with IDAES surrogate plotting tools\n", + " surrogate_scatter2D(alm_surr, data_validation, filename=\"alamo_val_scatter2D.pdf\")\n", + " surrogate_parity(alm_surr, data_validation, filename=\"alamo_val_parity.pdf\")\n", + " surrogate_residual(alm_surr, data_validation, filename=\"alamo_val_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the \"SCO2_properties_alamo_surrogate_embedding.ipynb\" file." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo.py new file mode 100644 index 00000000..ac295be0 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo.py @@ -0,0 +1,241 @@ +""" + +SCO2 baseline cycle from the NETL baseline report + +Case Baseline620 - Turbine inlet temperature 893.15 K (620 C). +Case Basleine760 - Turbine inlet temperature 1033.15 K (760 C). + +""" +from pyomo.environ import (ConcreteModel, + Block, + Var, + Param, + Constraint, + SolverFactory, + TransformationFactory, TerminationCondition, + value, Expression, minimize, units) +from pyomo.network import Arc, SequentialDecomposition + +# Import IDAES libraries +from idaes.core import FlowsheetBlock, UnitModelBlockData +from idaes.models.unit_models import (Mixer, MomentumMixingType, + PressureChanger, Heater, + Separator, HeatExchanger) +from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption +from idaes.core.util.model_statistics import degrees_of_freedom +from idaes.core.util.initialization import propagate_state +from SCO2_properties_alamo_surrogate import SCO2ParameterBlock +import idaes.logger as idaeslog + +def main(): + # Setup solver and options + solver = SolverFactory('ipopt') + outlvl = 0 + tee = True + + # Set up concrete model + m = ConcreteModel() + + # Create a flowsheet block + m.fs = FlowsheetBlock(dynamic=False) + + # Create the properties param block + m.fs.properties = SCO2ParameterBlock() + + # Add unit models to the flowsheet + m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True) + + m.fs.turbine = PressureChanger(dynamic=False, + property_package= m.fs.properties, + compressor=False, + thermodynamic_assumption=ThermodynamicAssumption.isentropic) + + m.fs.HTR_pseudo_shell = Heater(dynamic= False, + property_package= m.fs.properties, + has_pressure_change= True) + + m.fs.HTR_pseudo_tube = Heater(dynamic=False, + property_package= m.fs.properties, + has_pressure_change= True) + + m.fs.LTR_pseudo_shell = Heater(dynamic= False, + property_package= m.fs.properties, + has_pressure_change=True) + + m.fs.LTR_pseudo_tube = Heater(dynamic= False, + property_package= m.fs.properties, + has_pressure_change=True) + + m.fs.splitter_1 = Separator(property_package= m.fs.properties, + outlet_list= ["bypass", "to_cooler"]) + + m.fs.co2_cooler = Heater(dynamic= False, + property_package=m.fs.properties, + has_pressure_change= True) + + m.fs.main_compressor = PressureChanger(dynamic= False, + property_package= m.fs.properties, + compressor= True, + thermodynamic_assumption= ThermodynamicAssumption.isentropic) + + m.fs.bypass_compressor = PressureChanger(dynamic= False, + property_package= m.fs.properties, + compressor= True, + thermodynamic_assumption= ThermodynamicAssumption.isentropic) + + m.fs.splitter_2 = Separator(property_package= m.fs.properties, + ideal_separation= False, + outlet_list= ["to_FG_cooler", + "to_LTR"]) + + m.fs.FG_cooler = Heater(dynamic= False, + property_package= m.fs.properties, + has_pressure_change= True) + + m.fs.mixer = Mixer(property_package= m.fs.properties, + inlet_list=["FG_out", "LTR_out", "bypass"]) + + # # Connect the flowsheet + m.fs.s01 = Arc(source=m.fs.boiler.outlet, + destination=m.fs.turbine.inlet) + m.fs.s02 = Arc(source=m.fs.turbine.outlet, + destination=m.fs.HTR_pseudo_shell.inlet) + m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet, + destination=m.fs.LTR_pseudo_shell.inlet) + m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet, + destination=m.fs.splitter_1.inlet) + m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, + destination=m.fs.co2_cooler.inlet) + m.fs.s06 = Arc(source=m.fs.splitter_1.bypass, + destination=m.fs.bypass_compressor.inlet) + m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet, + destination=m.fs.main_compressor.inlet) + m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, + destination=m.fs.mixer.bypass) + m.fs.s09 = Arc(source=m.fs.main_compressor.outlet, + destination=m.fs.splitter_2.inlet) + m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler, + destination=m.fs.FG_cooler.inlet) + m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR, + destination=m.fs.LTR_pseudo_tube.inlet) + m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, + destination=m.fs.mixer.LTR_out) + m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, + destination=m.fs.mixer.FG_out) + m.fs.s14 = Arc(source=m.fs.mixer.outlet, + destination=m.fs.HTR_pseudo_tube.inlet) + + # NETL Baseline + m.fs.boiler.inlet.flow_mol.fix(121.1) + m.fs.boiler.inlet.temperature.fix(685.15) + m.fs.boiler.inlet.pressure.fix(34.51) + + m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C + m.fs.boiler.deltaP.fix(-0.21) + + m.fs.boiler.initialize(outlvl=outlvl) + + propagate_state(m.fs.s01) + + m.fs.turbine.ratioP.fix(1/3.68) + m.fs.turbine.efficiency_isentropic.fix(0.927) + m.fs.turbine.initialize(outlvl=outlvl) + + propagate_state(m.fs.s02) + m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15) + m.fs.HTR_pseudo_shell.deltaP.fix(-0.07) + + m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl) + + + propagate_state(m.fs.s03) + + m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15) + m.fs.LTR_pseudo_shell.deltaP.fix(-0.07) + m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl) + + + propagate_state(m.fs.s04) + m.fs.splitter_1.split_fraction[0, "bypass"].fix(0.25) + + m.fs.splitter_1.initialize(outlvl=outlvl) + + propagate_state(m.fs.s05) + m.fs.co2_cooler.outlet.temperature.fix(308.15) + m.fs.co2_cooler.deltaP.fix(-0.07) + m.fs.co2_cooler.initialize(outlvl=outlvl) + + + propagate_state(m.fs.s06) + m.fs.bypass_compressor.efficiency_isentropic.fix(0.85) + m.fs.bypass_compressor.ratioP.fix(3.8) + m.fs.bypass_compressor.initialize(outlvl=outlvl) + + propagate_state(m.fs.s07) + m.fs.main_compressor.efficiency_isentropic.fix(0.85) + m.fs.main_compressor.ratioP.fix(3.8) + m.fs.main_compressor.initialize(outlvl=outlvl) + + propagate_state(m.fs.s09) + + m.fs.splitter_2.split_fraction[0, "to_FG_cooler"].fix(0.046) + m.fs.splitter_2.initialize(outlvl=outlvl) + + propagate_state(m.fs.s10) + m.fs.FG_cooler.outlet.temperature.fix(483.15) + m.fs.FG_cooler.deltaP.fix(-0.06) + m.fs.FG_cooler.initialize(outlvl=outlvl) + + + propagate_state(m.fs.s11) + + m.fs.LTR_pseudo_tube.deltaP.fix(0) + m.fs.LTR_pseudo_tube.heat_duty[0].\ + fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0])) + m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl) + + # Add constraint heats of the LTR_pseudo shell and tube + m.fs.LTR_pseudo_tube.heat_duty[0].unfix() + m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] == + -m.fs.LTR_pseudo_tube.heat_duty[0]) + + propagate_state(m.fs.s08) + propagate_state(m.fs.s12) + propagate_state(m.fs.s13) + + m.fs.mixer.initialize(outlvl=outlvl) + + propagate_state(m.fs.s14) + + m.fs.HTR_pseudo_tube.heat_duty[0].\ + fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0])) + m.fs.HTR_pseudo_tube.deltaP.fix(-0.07) + m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl) + + m.fs.HTR_pseudo_tube.heat_duty[0].unfix() + m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] == + -m.fs.HTR_pseudo_tube.heat_duty[0]) + + TransformationFactory("network.expand_arcs").apply_to(m.fs) + + print("--------------------------------------------------------------------") + print("The degrees of freedom for the flowsheet is ", degrees_of_freedom(m)) + print("--------------------------------------------------------------------") + + solver.solve(m, tee=tee) + + # + from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity + # Print reports + for i in m.fs.component_objects(Block): + if isinstance(i, UnitModelBlockData): + i.report() + + # Converting units for readability + print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\ + -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\ + -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW) + return m + +if __name__ == "__main__": + m = main() diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb new file mode 100644 index 00000000..68ed5a5e --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb @@ -0,0 +1,645 @@ +{ + "cells": [ + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - SCO2_flowsheet_optimization_alamo_surrogate (Part 3)\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. \n", + "\n", + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory, TerminationCondition,\n", + " value, Expression, minimize, units)\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (Mixer, MomentumMixingType,\n", + " PressureChanger, Heater,\n", + " Separator, HeatExchanger)\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from SCO2_properties_alamo_surrogate import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to get the total work done. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-08 10:29:36 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-08 10:29:36 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:29:37 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:29:37 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-08 10:29:37 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:29:37 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-08 10:29:37 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:29:37 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:29:37 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-08 10:29:37 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:29:37 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:29:38 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:29:38 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:29:38 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-08 10:29:38 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:29:38 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-08 10:29:38 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:29:38 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:29:38 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-08 10:29:38 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 452\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 118\n", + "\n", + "Total number of variables............................: 178\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 59\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 178\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 9.79e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.43e-01 1.25e-02 -1.0 2.50e+01 - 9.88e-01 1.00e+00h 1\n", + " 2 0.0000000e+00 8.54e-06 1.06e-06 -1.0 2.50e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 7.45e-09 2.83e-08 -2.5 1.79e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.3897e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 34.510 34.300\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -1.1759e+06 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 692.18\n", + " pressure pascal 34.300 9.3207\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.2825e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 692.18 489.15\n", + " pressure pascal 9.3207 9.2507\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.2825e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 560.75 747.89\n", + " pressure pascal 34.560 34.490\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.1004e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507 9.1807\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.1004e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet\n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 416.53 598.89\n", + " pressure pascal 34.620 34.620\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825. \n", + " temperature kelvin 354.15 354.15 354.15 \n", + " pressure pascal 9.1807 9.1807 9.1807 \n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -3.4109e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet\n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807 9.1107\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 3.7116e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet\n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 416.53\n", + " pressure pascal 9.1107 34.620\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.4569e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet\n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 473.64\n", + " pressure pascal 9.1807 34.886\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR\n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 416.53 416.53 416.53\n", + " pressure pascal 34.620 34.620 34.620\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 21707. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet\n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 416.53 483.15\n", + " pressure pascal 34.620 34.560\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 598.89 473.64 560.75\n", + " pressure pascal 34.560 34.620 34.886 34.560\n", + "====================================================================================\n", + "659.042605510511 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory('ipopt')\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True)\n", + "\n", + " m.fs.turbine = PressureChanger(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.splitter_1 = Separator(property_package= m.fs.properties,\n", + " outlet_list= [\"bypass\", \"to_cooler\"])\n", + "\n", + " m.fs.co2_cooler = Heater(dynamic= False,\n", + " property_package=m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.main_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.splitter_2 = Separator(property_package= m.fs.properties,\n", + " ideal_separation= False,\n", + " outlet_list= [\"to_FG_cooler\",\n", + " \"to_LTR\"])\n", + "\n", + " m.fs.FG_cooler = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.mixer = Mixer(property_package= m.fs.properties,\n", + " inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"])\n", + "\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet,\n", + " destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet,\n", + " destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet,\n", + " destination=m.fs.LTR_pseudo_shell.inlet)\n", + " m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet,\n", + " destination=m.fs.splitter_1.inlet)\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler,\n", + " destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(source=m.fs.splitter_1.bypass,\n", + " destination=m.fs.bypass_compressor.inlet)\n", + " m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet,\n", + " destination=m.fs.main_compressor.inlet)\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet,\n", + " destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(source=m.fs.main_compressor.outlet,\n", + " destination=m.fs.splitter_2.inlet)\n", + " m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler,\n", + " destination=m.fs.FG_cooler.inlet)\n", + " m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR,\n", + " destination=m.fs.LTR_pseudo_tube.inlet)\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet,\n", + " destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", + " destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline \n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1/3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0) \n", + " m.fs.LTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.LTR_pseudo_tube.heat_duty[0])\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.HTR_pseudo_tube.heat_duty[0])\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW)\n", + " return m\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate.py new file mode 100644 index 00000000..19d12d08 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate.py @@ -0,0 +1,314 @@ +############################################################################## +# Institute for the Design of Advanced Energy Systems Process Systems +# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the +# software owners: The Regents of the University of California, through +# Lawrence Berkeley National Laboratory, National Technology & Engineering +# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia +# University Research Corporation, et al. All rights reserved. +# +# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and +# license information, respectively. Both files are also available online +# at the URL "https://github.com/IDAES/idaes-pse". +############################################################################## +""" +Surrogate property package for SCO2 cycle. + +Valid Pressure Range = 7.49 MPa to 35 MPa +Valid Temperature Range = 306.25 K to 1000 K + +""" + +# Changes the divide behavior to not do integer division +from __future__ import division + +# Import Python libraries +import logging + +# Import Pyomo libraries +from pyomo.environ import Constraint, Param, \ + Reals, Set, value, Var, NonNegativeReals, units +from pyomo.opt import SolverFactory, TerminationCondition + +# Import IDAES cores +from idaes.core import (declare_process_block_class, + PhysicalParameterBlock, + StateBlockData, + StateBlock, + MaterialBalanceType, + EnergyBalanceType, + LiquidPhase, + Component) +from idaes.core.util.initialization import solve_indexed_blocks +from idaes.core.util.model_statistics import degrees_of_freedom +from idaes.core.util.misc import extract_data +from idaes.core.solvers import get_solver +from pyomo.util.check_units import assert_units_consistent +from idaes.core.surrogate.surrogate_block import SurrogateBlock +from idaes.core.surrogate.alamopy import AlamoSurrogate + +from pyomo.util.model_size import build_model_size_report + +# Some more information about this module +__author__ = "Javal Vyas" + + +# Set up logger +_log = logging.getLogger(__name__) + + +@declare_process_block_class("SCO2ParameterBlock") +class PhysicalParameterData(PhysicalParameterBlock): + """ + Property Parameter Block Class + + Contains parameters and indexing sets associated with properties for + supercritical CO2. + + """ + def build(self): + ''' + Callable method for Block construction. + ''' + super(PhysicalParameterData, self).build() + + self._state_block_class = SCO2StateBlock + + # List of valid phases in property package + self.Liq = LiquidPhase() + + # Component list - a list of component identifiers + self.CO2 = Component() + + @classmethod + def define_metadata(cls, obj): + obj.add_properties({ + 'flow_mol': {'method': None, 'units': 'kmol/s'}, + 'pressure': {'method': None, 'units': 'MPa'}, + 'temperature': {'method': None, 'units': 'K'}, + 'enth_mol': {'method': None, 'units': 'kJ/kmol'}, + 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}}) + + obj.add_default_units({'time': units.s, + 'length': units.m, + 'mass': units.kg, + 'amount': units.mol, + 'temperature': units.K}) + +class _StateBlock(StateBlock): + """ + This Class contains methods which should be applied to Property Blocks as a + whole, rather than individual elements of indexed Property Blocks. + """ + def initialize(blk, state_args=None, hold_state=False, outlvl=1, + state_vars_fixed=False, solver='ipopt', + optarg={'tol': 1e-8}): + + ''' + Initialisation routine for property package. + + Keyword Arguments: + flow_mol : value at which to initialize component flows + (default=None) + pressure : value at which to initialize pressure (default=None) + temperature : value at which to initialize temperature + (default=None) + outlvl : sets output level of initialisation routine + + * 0 = no output (default) + * 1 = return solver state for each step in routine + * 2 = include solver output infomation (tee=True) + state_vars_fixed: Flag to denote if state vars have already been + fixed. + - True - states have already been fixed by the + control volume 1D. Control volume 0D + does not fix the state vars, so will + be False if this state block is used + with 0D blocks. + - False - states have not been fixed. The state + block will deal with fixing/unfixing. + optarg : solver options dictionary object (default=None) + solver : str indicating whcih solver to use during + initialization (default = 'ipopt') + hold_state : flag indicating whether the initialization routine + should unfix any state variables fixed during + initialization (default=False). + - True - states varaibles are not unfixed, and + a dict of returned containing flags for + which states were fixed during + initialization. + - False - state variables are unfixed after + initialization by calling the + relase_state method + + Returns: + If hold_states is True, returns a dict containing flags for + which states were fixed during initialization. + ''' + if state_vars_fixed is False: + # Fix state variables if not already fixed + Fcflag = {} + Pflag = {} + Tflag = {} + + for k in blk.keys(): + if blk[k].flow_mol.fixed is True: + Fcflag[k] = True + else: + Fcflag[k] = False + if state_args is None: + blk[k].flow_mol.fix() + else: + blk[k].flow_mol.fix(state_args["flow_mol"]) + + if blk[k].pressure.fixed is True: + Pflag[k] = True + else: + Pflag[k] = False + if state_args is None: + blk[k].pressure.fix() + else: + blk[k].pressure.fix(state_args["pressure"]) + + if blk[k].temperature.fixed is True: + Tflag[k] = True + else: + Tflag[k] = False + if state_args is None: + blk[k].temperature.fix() + else: + blk[k].temperature.fix(state_args["temperature"]) + + # If input block, return flags, else release state + flags = {"Fcflag": Fcflag, "Pflag": Pflag, + "Tflag": Tflag} + + else: + # Check when the state vars are fixed already result in dof 0 + for k in blk.keys(): + if degrees_of_freedom(blk[k]) != 0: + raise Exception("State vars fixed but degrees of freedom " + "for state block is not zero during " + "initialization.") + + if state_vars_fixed is False: + if hold_state is True: + return flags + else: + blk.release_state(flags) + + def release_state(blk, flags, outlvl=0): + ''' + Method to relase state variables fixed during initialisation. + + Keyword Arguments: + flags : dict containing information of which state variables + were fixed during initialization, and should now be + unfixed. This dict is returned by initialize if + hold_state=True. + outlvl : sets output level of of logging + ''' + if flags is None: + return + + # Unfix state variables + for k in blk.keys(): + if flags['Fcflag'][k] is False: + blk[k].flow_mol.unfix() + if flags['Pflag'][k] is False: + blk[k].pressure.unfix() + if flags['Tflag'][k] is False: + blk[k].temperature.unfix() + + if outlvl > 0: + if outlvl > 0: + _log.info('{} State Released.'.format(blk.name)) + + +@declare_process_block_class("SCO2StateBlock", + block_class=_StateBlock) +class SCO2StateBlockData(StateBlockData): + """ + An example property package for ideal gas properties with Gibbs energy + """ + + def build(self): + """ + Callable method for Block construction + """ + super(SCO2StateBlockData, self).build() + self._make_state_vars() + + def _make_state_vars(self): + # Create state variables + + self.flow_mol = Var(domain=NonNegativeReals, + initialize=1.0, + units=units.kmol/units.s, + doc='Total molar flowrate [kmol/s]') + + self.pressure = Var(domain=NonNegativeReals, + initialize=8, + bounds=(7.38, 40), + units=units.MPa, + doc='State pressure [MPa]') + + self.temperature = Var(domain=NonNegativeReals, + initialize=350, + bounds=(304.2, 760+273.15), + units=units.K, + doc='State temperature [K]') + + self.entr_mol = Var(domain=Reals, + initialize=10, + units=units.kJ/units.kmol/units.K, + doc='Entropy [kJ/ kmol / K]') + + self.enth_mol = Var(domain=Reals, + initialize=1, + units=units.kJ/units.kmol, + doc='Enthalpy [kJ/ kmol]') + + inputs=[self.pressure,self.temperature] + outputs=[self.enth_mol,self.entr_mol] + self.alamo_surrogate = AlamoSurrogate.load_from_file("alamo_surrogate.json") + self.surrogate_enth = SurrogateBlock() + self.surrogate_enth.build_model( + self.alamo_surrogate, + input_vars=inputs, + output_vars=outputs, + ) + + def get_material_flow_terms(self, p, j): + return self.flow_mol + + def get_enthalpy_flow_terms(self, p): + return self.flow_mol*self.enth_mol + + def default_material_balance_type(self): + return MaterialBalanceType.componentTotal + + def default_energy_balance_type(self): + return EnergyBalanceType.enthalpyTotal + + def define_state_vars(self): + return {"flow_mol": self.flow_mol, + "temperature": self.temperature, + "pressure": self.pressure} + + def model_check(blk): + """ + Model checks for property block + """ + # Check temperature bounds + if value(blk.temperature) < blk.temperature.lb: + _log.error('{} Temperature set below lower bound.' + .format(blk.name)) + if value(blk.temperature) > blk.temperature.ub: + _log.error('{} Temperature set above upper bound.' + .format(blk.name)) + + # Check pressure bounds + if value(blk.pressure) < blk.pressure.lb: + _log.error('{} Pressure set below lower bound.'.format(blk.name)) + if value(blk.pressure) > blk.pressure.ub: + _log.error('{} Pressure set above upper bound.'.format(blk.name)) diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb new file mode 100644 index 00000000..422fcdca --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_alamo_surrogate.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Changes the divide behavior to not do integer division\n", + "from __future__ import division\n", + "\n", + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, Param, \\\n", + " Reals, Set, value, Var, NonNegativeReals, units\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component)\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occuring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + " def build(self):\n", + " '''\n", + " Callable method for Block construction.\n", + " '''\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties({\n", + " 'flow_mol': {'method': None, 'units': 'kmol/s'},\n", + " 'pressure': {'method': None, 'units': 'MPa'},\n", + " 'temperature': {'method': None, 'units': 'K'},\n", + " 'enth_mol': {'method': None, 'units': 'kJ/kmol'},\n", + " 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}})\n", + "\n", + " obj.add_default_units({'time': units.s,\n", + " 'length': units.m,\n", + " 'mass': units.kg,\n", + " 'amount': units.mol,\n", + " 'temperature': units.K})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the SCO2_alamo_surrogate.ipynb file) using the Alamopy Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\",\n", + " block_class=_StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " # Create state variables\n", + "\n", + " self.flow_mol = Var(domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol/units.s,\n", + " doc='Total molar flowrate [kmol/s]')\n", + " \n", + " self.pressure = Var(domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc='State pressure [MPa]')\n", + "\n", + " self.temperature = Var(domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760+273.15),\n", + " units=units.K,\n", + " doc='State temperature [K]')\n", + "\n", + " self.entr_mol = Var(domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ/units.kmol/units.K,\n", + " doc='Entropy [kJ/ kmol / K]')\n", + " \n", + " self.enth_mol = Var(domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ/units.kmol,\n", + " doc='Enthalpy [kJ/ kmol]')\n", + " \n", + " inputs=[self.pressure,self.temperature]\n", + " outputs=[self.enth_mol,self.entr_mol]\n", + " self.alamo_surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.alamo_surrogate,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol*self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure}\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error('{} Temperature set below lower bound.'\n", + " .format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error('{} Temperature set above upper bound.'\n", + " .format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error('{} Pressure set below lower bound.'.format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error('{} Pressure set above upper bound.'.format(blk.name))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would interm fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the varible with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + " def initialize(blk, state_args=None, hold_state=False, outlvl=1,\n", + " state_vars_fixed=False, solver='ipopt',\n", + " optarg={'tol': 1e-8}):\n", + "\n", + " '''\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output infomation (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating whcih solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states varaibles are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " relase_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " '''\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag,\n", + " \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\")\n", + "\n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " '''\n", + " Method to relase state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " '''\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags['Fcflag'][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags['Pflag'][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags['Tflag'][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info('{} State Released.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, SCO2_flowsheet_alamo_surrogate.ipynb. To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc new file mode 100644 index 00000000..2b751874 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc @@ -0,0 +1,82 @@ +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.261E+07, 0.529E+07, 257., 0.999, 10, 932., 0.529E+07, 0.529E+07, 911., 918., 0.767E+05, 0.529E+07, 0.331, 0.31250000E-01, 6895, 66, 0, 0, 0, 27526, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 16, 19, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.21875000, 0.0000000, 0.46875000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 1041.4065790833753908373 * CO2SM_Pressure + 68.156763379511218658990 * CO2SM_Temperature + 0.52180735374859055220043 * CO2SM_Pressure^2 - 0.57352529763859468048270E-001 * CO2SM_Temperature^2 - 0.79796955407835379325832E-001 * CO2SM_Pressure^3 + 0.37080412164940595781334E-004 * CO2SM_Temperature^3 - 0.46482864120965861065571 * CO2SM_Pressure*CO2SM_Temperature - 589493.34674830385483801 * CO2SM_Pressure/CO2SM_Temperature + 2017895.8839375132229179 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 407610.55757002189056948 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 26.6, 56.7, 0.842, 0.998, 10, 16.3, 112., -3.28, -4.33, 2.03, 0.822, 56.7, 287., 0.31250000E-01, 6899, 66, 0, 0, 0, 27526, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 16, 19, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.21875000, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = 1.5387004739707315081887 * CO2SM_Pressure + 0.31258429218462185916749 * CO2SM_Temperature + 0.40415537794532684079396E-001 * CO2SM_Pressure^2 - 0.37004662122688402204479E-003 * CO2SM_Temperature^2 - 0.68466935828583822681859E-003 * CO2SM_Pressure^3 + 0.17256750967855762367928E-006 * CO2SM_Temperature^3 - 0.13932547245790831685203E-002 * CO2SM_Pressure*CO2SM_Temperature - 1598.1594111635131412186 * CO2SM_Pressure/CO2SM_Temperature + 5863.2140424644012455246 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 83.288209873480312239735 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 3, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.149E+06, 0.302E+06, 61.5, 0.999, 10, 703., 0.302E+06, 0.302E+06, 682., 689., 0.438E+04, 0.302E+06, 0.331, 0.31250000E-01, 6895, 66, 0, 0, 0, 27526, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 16, 19, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.21875000, 0.0000000, 0.46875000E-01, 0, 2023.2.13, CO2SM_Enthalpy = 248.90214597288871800629 * CO2SM_Pressure + 16.289857408341408273600 * CO2SM_Temperature + 0.12471495179240883743876 * CO2SM_Pressure^2 - 0.13707583599714506100709E-001 * CO2SM_Temperature^2 - 0.19071930078724062818107E-001 * CO2SM_Pressure^3 + 0.88624312072332341949990E-005 * CO2SM_Temperature^3 - 0.11109671154533536097109 * CO2SM_Pressure*CO2SM_Temperature - 140892.29125890653813258 * CO2SM_Pressure/CO2SM_Temperature + 482288.69105920498259366 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 97421.261370659878593870 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 4, 0, 3, 1, 1, 1, T, 0, T, 0, F, 1.52, 3.24, 0.201, 0.998, 10, -213., 58.7, -56.8, -233., -227., 0.470E-01, 3.24, 287., 0.62500000E-01, 6899, 66, 0, 0, 0, 27526, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 16, 19, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.21875000, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_Entropy = 0.36775824705096504807500 * CO2SM_Pressure + 0.74709452990136288041967E-001 * CO2SM_Temperature + 0.96595448507430844292676E-002 * CO2SM_Pressure^2 - 0.88443290953274744875881E-004 * CO2SM_Temperature^2 - 0.16363991571365991127031E-003 * CO2SM_Pressure^3 + 0.41244638359645497788774E-007 * CO2SM_Temperature^3 - 0.33299582290955543175184E-003 * CO2SM_Pressure*CO2SM_Temperature - 381.96928146862740049983 * CO2SM_Pressure/CO2SM_Temperature + 1401.3419607660844121710 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 19.906362088807021848424 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 5, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -80.0, -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 66, 0, 0, 0, 27526, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 16, 19, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.21875000, 0.0000000, 0.0000000, 0, 2023.2.13, CO2SM_status = 0.00 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 6, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -80.0, -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 66, 0, 0, 0, 27526, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 16, 19, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.21875000, 0.0000000, 0.0000000, 0, 2023.2.13, graph_error = 0.00 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.292E+07, 0.337E+07, 205., 1.00, 8, 887., 0.337E+07, 0.337E+07, 870., 875., 0.474E+05, 0.337E+07, 0.278, 0.62500000E-01, 7814, 54, 0, 0, 0, 31206, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.78125000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 2976.0837176096429175232 * CO2SM_Pressure - 16.123726355989308700600 * CO2SM_Pressure^2 + 0.34666694774448086890928E-001 * CO2SM_Temperature^2 - 2.0531284581077700046592 * CO2SM_Pressure*CO2SM_Temperature + 0.15764993815560679007960E-004 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 1082093.1181237096898258 * CO2SM_Pressure/CO2SM_Temperature + 3615513.7676737615838647 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 392988.42838402802590281 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 30.5, 35.8, 0.669, 0.999, 8, -29.3, 76.8, -28.2, -46.3, -40.7, 0.504, 35.8, 265., 0.31250000E-01, 7814, 54, 0, 0, 0, 31206, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = 10.144444851288497488895 * CO2SM_Pressure - 0.51918117347813000361789E-001 * CO2SM_Pressure^2 + 0.51709399940546584592375E-004 * CO2SM_Temperature^2 - 0.79639125454940163512108E-002 * CO2SM_Pressure*CO2SM_Temperature + 0.64227352851863486524723E-007 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 3748.2832012864482749137 * CO2SM_Pressure/CO2SM_Temperature + 13134.108666753896613955 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 17.444307740033309528371 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 3, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.167E+06, 0.192E+06, 49.0, 1.00, 8, 658., 0.192E+06, 0.192E+06, 641., 646., 0.271E+04, 0.192E+06, 0.278, 0.78125000E-01, 7814, 54, 0, 0, 0, 31206, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.78125000E-01, 0, 2023.2.13, CO2SM_Enthalpy = 711.30107977521538487053 * CO2SM_Pressure - 3.8536630872177859430394 * CO2SM_Pressure^2 + 0.82855389043072144583668E-002 * CO2SM_Temperature^2 - 0.49070947856399155240226 * CO2SM_Pressure*CO2SM_Temperature + 0.37679239526745838378128E-005 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 258626.46227382461074740 * CO2SM_Pressure/CO2SM_Temperature + 864128.52956905798055232 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 93926.488619610885507427 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 4, 0, 3, 1, 1, 1, T, 0, T, 0, F, 1.74, 2.05, 0.160, 0.999, 8, -258., 43.1, -62.0, -275., -270., 0.288E-01, 2.05, 265., 0.46875000E-01, 7814, 54, 0, 0, 0, 31206, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_Entropy = 2.4245806694344911313976 * CO2SM_Pressure - 0.12408729789168567586577E-001 * CO2SM_Pressure^2 + 0.12358843747180170395011E-004 * CO2SM_Temperature^2 - 0.19034209480960735064864E-002 * CO2SM_Pressure*CO2SM_Temperature + 0.15350708450221131999899E-007 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 895.86120641232685102295 * CO2SM_Pressure/CO2SM_Temperature + 3139.1273837636931602901 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 4.1692899403054735074647 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 5, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -80.0, -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 54, 0, 0, 0, 31206, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.0000000, 0, 2023.2.13, CO2SM_status = 0.00 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 6, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -80.0, -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 54, 0, 0, 0, 31206, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.0000000, 0, 2023.2.13, graph_error = 0.00 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.130E+07, 0.142E+07, 133., 1.00, 10, 826., 0.142E+07, 0.142E+07, 806., 812., 0.206E+05, 0.142E+07, 0.205, 0.78125000E-01, 8100, 54, 0, 0, 0, 31778, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.46875000E-01, 1, 0, 0.0000000, 0.0000000, 0.34375000, 0.0000000, 0.93750000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 2587.3012838721542721032 * CO2SM_Pressure - 13.999632516355060118940 * CO2SM_Pressure^2 + 0.33684985769771247365867E-001 * CO2SM_Temperature^2 - 1.6772077843304822319936 * CO2SM_Pressure*CO2SM_Temperature + 0.11982500188075953750878E-004 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 858387.69547329889610410 * CO2SM_Pressure/CO2SM_Temperature + 81.187960165435598014483 * CO2SM_Temperature/CO2SM_Pressure + 2367457.9840258257463574 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 0.39361311566155982033521 * (CO2SM_Temperature/CO2SM_Pressure)^2 - 397926.35644059121841565 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 14.8, 21.5, 0.518, 0.999, 8, -70.1, 62.5, -42.5, -87.1, -81.5, 0.303, 21.5, 47.1, 0.78125000E-01, 7814, 54, 0, 0, 0, 31778, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.46875000E-01, 1, 0, 0.0000000, 0.0000000, 0.34375000, 0.0000000, 0.78125000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = 8.6854143677138022638928 * CO2SM_Pressure + 0.59660135484465702407440E-004 * CO2SM_Temperature^2 - 0.67401043835774263491417E-003 * CO2SM_Pressure^3 - 0.79297105009381325252393E-002 * CO2SM_Pressure*CO2SM_Temperature + 0.60514874414795146898080E-007 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 3289.9038728593459381955 * CO2SM_Pressure/CO2SM_Temperature + 9383.1333489137232390931 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 17.569774263608586295504 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 3, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.740E+05, 0.810E+05, 31.8, 1.00, 10, 597., 0.811E+05, 0.810E+05, 577., 583., 0.117E+04, 0.810E+05, 0.205, 0.93750000E-01, 8100, 54, 0, 0, 0, 31778, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.46875000E-01, 1, 0, 0.0000000, 0.0000000, 0.34375000, 0.0000000, 0.10937500, 0, 2023.2.13, CO2SM_Enthalpy = 618.37984788611970543570 * CO2SM_Pressure - 3.3459924742438693634483 * CO2SM_Pressure^2 + 0.80509048208559867015888E-002 * CO2SM_Temperature^2 - 0.40086228110170796234968 * CO2SM_Pressure*CO2SM_Temperature + 0.28638862773471474521480E-005 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 205159.58304791187401861 * CO2SM_Pressure/CO2SM_Temperature + 19.404388169156909782487 * CO2SM_Temperature/CO2SM_Pressure + 565836.03826390730682760 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 0.94075792386407910972146E-001 * (CO2SM_Temperature/CO2SM_Pressure)^2 - 95106.681748685528873466 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 4, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.844, 1.23, 0.124, 0.999, 8, -299., 42.3, -62.8, -316., -311., 0.173E-01, 1.23, 47.1, 0.62500000E-01, 7814, 54, 0, 0, 0, 31778, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.46875000E-01, 1, 0, 0.0000000, 0.0000000, 0.34375000, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_Entropy = 2.0758637316472947631496 * CO2SM_Pressure + 0.14259114520775341490209E-004 * CO2SM_Temperature^2 - 0.16109233197681875138577E-003 * CO2SM_Pressure^3 - 0.18952461955303000207262E-002 * CO2SM_Pressure*CO2SM_Temperature + 0.14463400913552109215941E-007 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 786.30585278140347327280 * CO2SM_Pressure/CO2SM_Temperature + 2242.6224471657051253715 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 4.1992768629498247179299 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 5, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -80.0, -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 54, 0, 0, 0, 31778, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.46875000E-01, 1, 0, 0.0000000, 0.0000000, 0.34375000, 0.0000000, 0.0000000, 0, 2023.2.13, CO2SM_status = 0.00 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 6, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -80.0, -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 54, 0, 0, 0, 31778, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.46875000E-01, 1, 0, 0.0000000, 0.0000000, 0.34375000, 0.0000000, 0.0000000, 0, 2023.2.13, graph_error = 0.00 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.133E+08, 0.154E+08, 439., 0.999, 6, 1000., 0.154E+08, 0.154E+08, 987., 991., 0.211E+06, 0.154E+08, 0.555, 0.46875000E-01, 5812, 54, 0, 0, 0, 23198, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.18750000, 0.0000000, 0.46875000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 176.55059769150571469254 * CO2SM_Temperature + 1.3198067554064116801982 * CO2SM_Pressure^2 - 0.18224852391170778820317 * CO2SM_Temperature^2 + 0.85541428308563406896284E-004 * CO2SM_Temperature^3 - 73901.888114777990267612 * CO2SM_Pressure/CO2SM_Temperature - 438640.43116398435086012 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 150., 176., 1.48, 0.995, 6, 89.2, 206., 108., 76.1, 80.6, 2.41, 176., 202., 0.31250000E-01, 5812, 54, 0, 0, 0, 23198, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.18750000, 0.0000000, 0.46875000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = 0.62506866399844585036050 * CO2SM_Temperature - 0.75213429572650069340062E-003 * CO2SM_Temperature^2 + 0.32507143114294706824857E-006 * CO2SM_Temperature^3 - 557.63176358158557377465 * CO2SM_Pressure/CO2SM_Temperature + 3055.8068419703354265948 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 171.97232386939020898353 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 3, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.760E+06, 0.880E+06, 105., 0.999, 6, 771., 0.880E+06, 0.880E+06, 758., 762., 0.121E+05, 0.880E+06, 0.555, 0.31250000E-01, 5812, 54, 0, 0, 0, 23198, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.18750000, 0.0000000, 0.46875000E-01, 0, 2023.2.13, CO2SM_Enthalpy = 42.196605565648880542540 * CO2SM_Temperature + 0.31544138526528625598999 * CO2SM_Pressure^2 - 0.43558442616133292990543E-001 * CO2SM_Temperature^2 + 0.20444892042552407200205E-004 * CO2SM_Temperature^3 - 17662.975173862170777284 * CO2SM_Pressure/CO2SM_Temperature - 104837.57914973552396987 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 4, 0, 3, 1, 1, 1, T, 0, T, 0, F, 8.54, 10.0, 0.354, 0.995, 6, -140., 40.8, -58.0, -153., -148., 0.137, 10.0, 202., 0.46875000E-01, 5812, 54, 0, 0, 0, 23198, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.18750000, 0.0000000, 0.46875000E-01, 0, 2023.2.13, CO2SM_Entropy = 0.14939499610008138974493 * CO2SM_Temperature - 0.17976441163457116453189E-003 * CO2SM_Temperature^2 + 0.77693937356230112414422E-007 * CO2SM_Temperature^3 - 133.27718047705354820209 * CO2SM_Pressure/CO2SM_Temperature + 730.35524319799355907890 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 41.102371874741407964393 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 5, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -80.0, -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 54, 0, 0, 0, 23198, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.18750000, 0.0000000, 0.0000000, 0, 2023.2.13, CO2SM_status = 0.00 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 6, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -80.0, -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 54, 0, 0, 0, 23198, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.18750000, 0.0000000, 0.0000000, 0, 2023.2.13, graph_error = 0.00 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.133E+08, 0.154E+08, 439., 0.999, 6, 1000., 0.154E+08, 0.154E+08, 987., 991., 0.211E+06, 0.154E+08, 0.555, 0.46875000E-01, 5812, 54, 0, 0, 0, 23198, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.23437500, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 176.55059769150571469254 * CO2SM_Temperature + 1.3198067554064116801982 * CO2SM_Pressure^2 - 0.18224852391170778820317 * CO2SM_Temperature^2 + 0.85541428308563406896284E-004 * CO2SM_Temperature^3 - 73901.888114777990267612 * CO2SM_Pressure/CO2SM_Temperature - 438640.43116398435086012 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 150., 176., 1.48, 0.995, 6, 89.2, 206., 108., 76.1, 80.6, 2.41, 176., 202., 0.31250000E-01, 5812, 54, 0, 0, 0, 23198, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.23437500, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = 0.62506866399844585036050 * CO2SM_Temperature - 0.75213429572650069340062E-003 * CO2SM_Temperature^2 + 0.32507143114294706824857E-006 * CO2SM_Temperature^3 - 557.63176358158557377465 * CO2SM_Pressure/CO2SM_Temperature + 3055.8068419703354265948 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 171.97232386939020898353 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 3, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.760E+06, 0.880E+06, 105., 0.999, 6, 771., 0.880E+06, 0.880E+06, 758., 762., 0.121E+05, 0.880E+06, 0.555, 0.46875000E-01, 5812, 54, 0, 0, 0, 23198, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.23437500, 0.0000000, 0.46875000E-01, 0, 2023.2.13, CO2SM_Enthalpy = 42.196605565648880542540 * CO2SM_Temperature + 0.31544138526528625598999 * CO2SM_Pressure^2 - 0.43558442616133292990543E-001 * CO2SM_Temperature^2 + 0.20444892042552407200205E-004 * CO2SM_Temperature^3 - 17662.975173862170777284 * CO2SM_Pressure/CO2SM_Temperature - 104837.57914973552396987 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 4, 0, 3, 1, 1, 1, T, 0, T, 0, F, 8.54, 10.0, 0.354, 0.995, 6, -140., 40.8, -58.0, -153., -148., 0.137, 10.0, 202., 0.46875000E-01, 5812, 54, 0, 0, 0, 23198, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.23437500, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_Entropy = 0.14939499610008138974493 * CO2SM_Temperature - 0.17976441163457116453189E-003 * CO2SM_Temperature^2 + 0.77693937356230112414422E-007 * CO2SM_Temperature^3 - 133.27718047705354820209 * CO2SM_Pressure/CO2SM_Temperature + 730.35524319799355907890 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 41.102371874741407964393 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 5, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -80.0, -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 54, 0, 0, 0, 23198, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.23437500, 0.0000000, 0.0000000, 0, 2023.2.13, CO2SM_status = 0.00 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 80, 6, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -80.0, -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 54, 0, 0, 0, 23198, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 80, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.23437500, 0.0000000, 0.0000000, 0, 2023.2.13, graph_error = 0.00 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.230E+08, 0.265E+08, 257., 1.00, 10, 0.450E+04, 0.265E+08, 0.265E+08, 0.446E+04, 0.448E+04, 0.681E+05, 0.265E+08, 0.460, 0.23437500, 8100, 54, 0, 0, 0, 32350, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 400, 0, 0.29687500, 1, 0, 0.0000000, 0.0000000, 1.1093750, 0.0000000, 0.34375000, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 2860.0917895466641311941 * CO2SM_Pressure + 0.41114065793325729747387E-001 * CO2SM_Temperature^2 - 0.24511079895081816504288 * CO2SM_Pressure^3 - 0.26137405569194172157924E-005 * CO2SM_Temperature^3 - 2.4652884476114720335715 * CO2SM_Pressure*CO2SM_Temperature + 0.19963575728343876535160E-004 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 1096476.5309965882916003 * CO2SM_Pressure/CO2SM_Temperature + 3416585.1440337863750756 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 0.14953162437187511590864 * (CO2SM_Temperature/CO2SM_Pressure)^2 - 391728.11327593511668965 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 249., 280., 0.837, 0.998, 10, -82.6, 331., -99.9, -122., -107., 0.720, 280., 654., 0.21875000, 8100, 54, 0, 0, 0, 32350, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 400, 0, 0.29687500, 1, 0, 0.0000000, 0.0000000, 1.1093750, 0.0000000, 0.25000000, 0, 2023.2.13, CO2SM_CO2_Entropy = 11.286303106626364822773 * CO2SM_Pressure - 0.71135645696710672614138E-001 * CO2SM_Pressure^2 + 0.31930373384546908215711E-007 * CO2SM_Temperature^3 - 0.74934168145713081141124E-002 * CO2SM_Pressure*CO2SM_Temperature + 0.61133486594534157755043E-007 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 3513.2415892659041674051 * CO2SM_Pressure/CO2SM_Temperature + 0.56266010599018123627957 * CO2SM_Temperature/CO2SM_Pressure + 10831.756094491300245863 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 0.23982487120224379312239E-002 * (CO2SM_Temperature/CO2SM_Pressure)^2 - 45.946159518132610344310 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 3, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.131E+07, 0.151E+07, 61.5, 1.00, 10, 0.336E+04, 0.151E+07, 0.151E+07, 0.332E+04, 0.333E+04, 0.389E+04, 0.151E+07, 0.460, 0.18750000, 8100, 54, 0, 0, 0, 32350, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 400, 0, 0.29687500, 1, 0, 0.0000000, 0.0000000, 1.1093750, 0.0000000, 0.25000000, 0, 2023.2.13, CO2SM_Enthalpy = 683.57834356311559531605 * CO2SM_Pressure + 0.98264975598204816475967E-002 * CO2SM_Temperature^2 - 0.58582886935247546755789E-001 * CO2SM_Pressure^3 - 0.62469898550104396943292E-006 * CO2SM_Temperature^3 - 0.58921808017509558208502 * CO2SM_Pressure*CO2SM_Temperature + 0.47714091123749270467649E-005 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 262064.18044725127401762 * CO2SM_Pressure/CO2SM_Temperature + 816583.44742147240322083 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 0.35738915949167997243574E-001 * (CO2SM_Temperature/CO2SM_Pressure)^2 - 93625.266079235429060645 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 4, 0, 3, 1, 1, 1, T, 0, T, 0, F, 14.2, 16.0, 0.200, 0.998, 10, -0.123E+04, 67.3, -364., -0.127E+04, -0.125E+04, 0.411E-01, 16.0, 654., 0.21875000, 8100, 54, 0, 0, 0, 32350, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 400, 0, 0.29687500, 1, 0, 0.0000000, 0.0000000, 1.1093750, 0.0000000, 0.26562500, 0, 2023.2.13, CO2SM_Entropy = 2.6974911255478333238500 * CO2SM_Pressure - 0.17001827070737211833329E-001 * CO2SM_Pressure^2 + 0.76315423564098926898215E-008 * CO2SM_Temperature^3 - 0.17909695634806262025396E-002 * CO2SM_Pressure*CO2SM_Temperature + 0.14611253869703294915436E-007 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 839.68488869059240187198 * CO2SM_Pressure/CO2SM_Temperature + 0.13447898158523036160616 * CO2SM_Temperature/CO2SM_Pressure + 2588.8518702175160797196 * (CO2SM_Pressure/CO2SM_Temperature)^2 - 0.57319514319895137084010E-003 * (CO2SM_Temperature/CO2SM_Pressure)^2 - 10.981394989522810234917 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 5, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -400., -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 54, 0, 0, 0, 32350, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 400, 0, 0.29687500, 1, 0, 0.0000000, 0.0000000, 1.1093750, 0.0000000, 0.0000000, 0, 2023.2.13, CO2SM_status = 0.00 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 6, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -400., -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 54, 0, 0, 0, 32350, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 13, 13, 0.0000000, 0, 400, 0, 0.29687500, 1, 0, 0.0000000, 0.0000000, 1.1093750, 0.0000000, 0.0000000, 0, 2023.2.13, graph_error = 0.00 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.78125000E-01, 1816, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.10937500, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.31250000E-01, 2047, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure^2 - 0.55969916790357242958320E-003 * CO2SM_Temperature^2 - 0.21077870265129327632947E-002 * CO2SM_Pressure^3 + 0.24061231665087056461711E-006 * CO2SM_Temperature^3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 3, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.294E+07, 0.377E+07, 97.0, 0.999, 6, 0.370E+04, 0.377E+07, 0.377E+07, 0.367E+04, 0.368E+04, 0.958E+04, 0.377E+07, 0.594, 0.46875000E-01, 1816, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.46875000E-01, 0, 2023.2.13, CO2SM_Enthalpy = 33.939370470319893513533 * CO2SM_Temperature + 0.60422203012027542179396 * CO2SM_Pressure^2 - 0.32955134326585151793854E-001 * CO2SM_Temperature^2 + 0.15818984973112021048900E-004 * CO2SM_Temperature^3 - 27406.221633511711843312 * CO2SM_Pressure/CO2SM_Temperature - 102521.29303853253077250 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 4, 0, 3, 1, 1, 1, T, 0, T, 0, F, 30.9, 31.9, 0.282, 0.997, 10, -952., 79.8, -348., -991., -976., 0.819E-01, 31.9, 0.130E+04, 0.62500000E-01, 2047, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_Entropy = - 0.93641315073096120791263 * CO2SM_Pressure + 0.12325698651689911966578 * CO2SM_Temperature + 0.41162867090195801034191E-001 * CO2SM_Pressure^2 - 0.13377131079761491578260E-003 * CO2SM_Temperature^2 - 0.50377313657987707676278E-003 * CO2SM_Pressure^3 + 0.57507723427902941834909E-007 * CO2SM_Temperature^3 - 0.24905720184968666150102E-003 * CO2SM_Pressure*CO2SM_Temperature - 86.824357622398892431193 * CO2SM_Pressure/CO2SM_Temperature - 0.48892818305477876716925E-001 * CO2SM_Temperature/CO2SM_Pressure - 27.885578090744829182768 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 5, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -400., -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.0000000, 0, 2023.2.13, CO2SM_status = 0.00 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 6, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -400., -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.0000000, 0, 2023.2.13, graph_error = 0.00 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.46875000E-01, 1816, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.62500000E-01, 1, 0, 0.0000000, 0.0000000, 0.32812500, 0.0000000, 0.46875000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.78125000E-01, 2047, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.62500000E-01, 1, 0, 0.0000000, 0.0000000, 0.32812500, 0.0000000, 0.78125000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure^2 - 0.55969916790357242958320E-003 * CO2SM_Temperature^2 - 0.21077870265129327632947E-002 * CO2SM_Pressure^3 + 0.24061231665087056461711E-006 * CO2SM_Temperature^3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 3, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.294E+07, 0.377E+07, 97.0, 0.999, 6, 0.370E+04, 0.377E+07, 0.377E+07, 0.367E+04, 0.368E+04, 0.958E+04, 0.377E+07, 0.594, 0.10937500, 1816, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.62500000E-01, 1, 0, 0.0000000, 0.0000000, 0.32812500, 0.0000000, 0.10937500, 0, 2023.2.13, CO2SM_Enthalpy = 33.939370470319893513533 * CO2SM_Temperature + 0.60422203012027542179396 * CO2SM_Pressure^2 - 0.32955134326585151793854E-001 * CO2SM_Temperature^2 + 0.15818984973112021048900E-004 * CO2SM_Temperature^3 - 27406.221633511711843312 * CO2SM_Pressure/CO2SM_Temperature - 102521.29303853253077250 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 4, 0, 3, 1, 1, 1, T, 0, T, 0, F, 30.9, 31.9, 0.282, 0.997, 10, -952., 79.8, -348., -991., -976., 0.819E-01, 31.9, 0.130E+04, 0.78125000E-01, 2047, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.62500000E-01, 1, 0, 0.0000000, 0.0000000, 0.32812500, 0.0000000, 0.93750000E-01, 0, 2023.2.13, CO2SM_Entropy = - 0.93641315073096120791263 * CO2SM_Pressure + 0.12325698651689911966578 * CO2SM_Temperature + 0.41162867090195801034191E-001 * CO2SM_Pressure^2 - 0.13377131079761491578260E-003 * CO2SM_Temperature^2 - 0.50377313657987707676278E-003 * CO2SM_Pressure^3 + 0.57507723427902941834909E-007 * CO2SM_Temperature^3 - 0.24905720184968666150102E-003 * CO2SM_Pressure*CO2SM_Temperature - 86.824357622398892431193 * CO2SM_Pressure/CO2SM_Temperature - 0.48892818305477876716925E-001 * CO2SM_Temperature/CO2SM_Pressure - 27.885578090744829182768 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 5, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -400., -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.62500000E-01, 1, 0, 0.0000000, 0.0000000, 0.32812500, 0.0000000, 0.0000000, 0, 2023.2.13, CO2SM_status = 0.00 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 6, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -400., -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.62500000E-01, 1, 0, 0.0000000, 0.0000000, 0.32812500, 0.0000000, 0.0000000, 0, 2023.2.13, graph_error = 0.00 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.31250000E-01, 1816, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.23437500, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.62500000E-01, 2047, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.23437500, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure^2 - 0.55969916790357242958320E-003 * CO2SM_Temperature^2 - 0.21077870265129327632947E-002 * CO2SM_Pressure^3 + 0.24061231665087056461711E-006 * CO2SM_Temperature^3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 3, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.294E+07, 0.377E+07, 97.0, 0.999, 6, 0.370E+04, 0.377E+07, 0.377E+07, 0.367E+04, 0.368E+04, 0.958E+04, 0.377E+07, 0.594, 0.46875000E-01, 1816, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.23437500, 0.0000000, 0.46875000E-01, 0, 2023.2.13, CO2SM_Enthalpy = 33.939370470319893513533 * CO2SM_Temperature + 0.60422203012027542179396 * CO2SM_Pressure^2 - 0.32955134326585151793854E-001 * CO2SM_Temperature^2 + 0.15818984973112021048900E-004 * CO2SM_Temperature^3 - 27406.221633511711843312 * CO2SM_Pressure/CO2SM_Temperature - 102521.29303853253077250 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 4, 0, 3, 1, 1, 1, T, 0, T, 0, F, 30.9, 31.9, 0.282, 0.997, 10, -952., 79.8, -348., -991., -976., 0.819E-01, 31.9, 0.130E+04, 0.62500000E-01, 2047, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.23437500, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_Entropy = - 0.93641315073096120791263 * CO2SM_Pressure + 0.12325698651689911966578 * CO2SM_Temperature + 0.41162867090195801034191E-001 * CO2SM_Pressure^2 - 0.13377131079761491578260E-003 * CO2SM_Temperature^2 - 0.50377313657987707676278E-003 * CO2SM_Pressure^3 + 0.57507723427902941834909E-007 * CO2SM_Temperature^3 - 0.24905720184968666150102E-003 * CO2SM_Pressure*CO2SM_Temperature - 86.824357622398892431193 * CO2SM_Pressure/CO2SM_Temperature - 0.48892818305477876716925E-001 * CO2SM_Temperature/CO2SM_Pressure - 27.885578090744829182768 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 5, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -400., -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.23437500, 0.0000000, 0.0000000, 0, 2023.2.13, CO2SM_status = 0.00 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 6, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -400., -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.23437500, 0.0000000, 0.0000000, 0, 2023.2.13, graph_error = 0.00 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.46875000E-01, 1816, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.10937500, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.46875000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.62500000E-01, 2047, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.10937500, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.78125000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure^2 - 0.55969916790357242958320E-003 * CO2SM_Temperature^2 - 0.21077870265129327632947E-002 * CO2SM_Pressure^3 + 0.24061231665087056461711E-006 * CO2SM_Temperature^3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 3, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.294E+07, 0.377E+07, 97.0, 0.999, 6, 0.370E+04, 0.377E+07, 0.377E+07, 0.367E+04, 0.368E+04, 0.958E+04, 0.377E+07, 0.594, 0.78125000E-01, 1816, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.10937500, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.78125000E-01, 0, 2023.2.13, CO2SM_Enthalpy = 33.939370470319893513533 * CO2SM_Temperature + 0.60422203012027542179396 * CO2SM_Pressure^2 - 0.32955134326585151793854E-001 * CO2SM_Temperature^2 + 0.15818984973112021048900E-004 * CO2SM_Temperature^3 - 27406.221633511711843312 * CO2SM_Pressure/CO2SM_Temperature - 102521.29303853253077250 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 4, 0, 3, 1, 1, 1, T, 0, T, 0, F, 30.9, 31.9, 0.282, 0.997, 10, -952., 79.8, -348., -991., -976., 0.819E-01, 31.9, 0.130E+04, 0.46875000E-01, 2047, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.10937500, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.78125000E-01, 0, 2023.2.13, CO2SM_Entropy = - 0.93641315073096120791263 * CO2SM_Pressure + 0.12325698651689911966578 * CO2SM_Temperature + 0.41162867090195801034191E-001 * CO2SM_Pressure^2 - 0.13377131079761491578260E-003 * CO2SM_Temperature^2 - 0.50377313657987707676278E-003 * CO2SM_Pressure^3 + 0.57507723427902941834909E-007 * CO2SM_Temperature^3 - 0.24905720184968666150102E-003 * CO2SM_Pressure*CO2SM_Temperature - 86.824357622398892431193 * CO2SM_Pressure/CO2SM_Temperature - 0.48892818305477876716925E-001 * CO2SM_Temperature/CO2SM_Pressure - 27.885578090744829182768 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 5, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -400., -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.10937500, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.0000000, 0, 2023.2.13, CO2SM_status = 0.00 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 6, 400, 6, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.00, 0.00, 0.00, 1.00, 0, -0.100E+31, 0.00, -400., -0.100E+31, -0.100E+31, 0.00, 0.00, 0.00, 0.0000000, 2, 46, 0, 0, 0, 7684, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.10937500, 1, 0, 0.0000000, 0.0000000, 0.28125000, 0.0000000, 0.0000000, 0, 2023.2.13, graph_error = 0.00 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.62500000E-01, 1816, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.15625000, 0.0000000, 0.78125000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.78125000E-01, 2047, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.15625000, 0.0000000, 0.78125000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure^2 - 0.55969916790357242958320E-003 * CO2SM_Temperature^2 - 0.21077870265129327632947E-002 * CO2SM_Pressure^3 + 0.24061231665087056461711E-006 * CO2SM_Temperature^3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 2, 80, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.148E+08, 0.154E+08, 439., 0.999, 6, 1000., 0.154E+08, 0.154E+08, 987., 991., 0.211E+06, 0.154E+08, 0.555, 0.15625000E-01, 1816, 22, 0, 0, 0, 3610, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 80, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.62500000E-01, 0.0000000, 0.31250000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 176.55059769150571469254 * CO2SM_Temperature + 1.3198067554064116801982 * CO2SM_Pressure^2 - 0.18224852391170778820317 * CO2SM_Temperature^2 + 0.85541428308563406896284E-004 * CO2SM_Temperature^3 - 73901.888114777990267612 * CO2SM_Pressure/CO2SM_Temperature - 438640.43116398435086012 +c:\Users\javal\Desktop\Internship\IDAES_core\CO2_example\ALAMO\alamo_run.alm, 2, 2, 80, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 166., 178., 1.49, 0.995, 6, 90.4, 207., 110., 77.3, 81.8, 2.44, 178., 188., 0.15625000E-01, 1816, 22, 0, 0, 0, 3610, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 80, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.62500000E-01, 0.0000000, 0.31250000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = - 1.1568396287182769466284 * CO2SM_Pressure + 0.68041349273895801452738 * CO2SM_Temperature + 0.14903303526011966312348E-001 * CO2SM_Pressure^2 - 0.79596105430869304540181E-003 * CO2SM_Temperature^2 + 0.33907015327313520096657E-006 * CO2SM_Temperature^3 - 190.84959428296468786357 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\workspace-idaes\CO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.93750000E-01, 1816, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.26562500, 0.0000000, 0.12500000, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 +c:\Users\javal\Desktop\Internship\IDAES_core\workspace-idaes\CO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.12500000, 2047, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.26562500, 0.0000000, 0.14062500, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure^2 - 0.55969916790357242958320E-003 * CO2SM_Temperature^2 - 0.21077870265129327632947E-002 * CO2SM_Pressure^3 + 0.24061231665087056461711E-006 * CO2SM_Temperature^3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES_core\workspace-idaes\CO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.46875000E-01, 1816, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.14062500, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 +c:\Users\javal\Desktop\Internship\IDAES_core\workspace-idaes\CO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.62500000E-01, 2047, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.14062500, 0.0000000, 0.78125000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure^2 - 0.55969916790357242958320E-003 * CO2SM_Temperature^2 - 0.21077870265129327632947E-002 * CO2SM_Pressure^3 + 0.24061231665087056461711E-006 * CO2SM_Temperature^3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045 diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_surrogate.json b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_surrogate.json new file mode 100644 index 00000000..4a8cf455 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_surrogate.json @@ -0,0 +1 @@ +{"surrogate": {"CO2SM_CO2_Enthalpy": " CO2SM_CO2_Enthalpy == 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure**2 - 0.13788428202598035365867 * CO2SM_Temperature**2 + 0.66186633129257225506559E-004 * CO2SM_Temperature**3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895", "CO2SM_CO2_Entropy": " CO2SM_CO2_Entropy == - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure**2 - 0.55969916790357242958320E-003 * CO2SM_Temperature**2 - 0.21077870265129327632947E-002 * CO2SM_Pressure**3 + 0.24061231665087056461711E-006 * CO2SM_Temperature**3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045"}, "input_labels": ["CO2SM_Pressure", "CO2SM_Temperature"], "output_labels": ["CO2SM_CO2_Enthalpy", "CO2SM_CO2_Entropy"], "input_bounds": {"CO2SM_Pressure": [7.460891, 34.993814], "CO2SM_Temperature": [306.215965, 999.971989]}} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_train_parity.pdf b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_train_parity.pdf new file mode 100644 index 0000000000000000000000000000000000000000..028158f12a13df472c313ae783b95a9ac8c4fdca GIT binary patch literal 29828 zcmb@uV{|6py7n8}wr$(?lTOmHosMnWPM%nuq+`2dTOHfB{q}#awf5QXIcJ>lemI}1 zYL1Efz6zsiUcXtC$`VqHtV|qml$Glw742}WBrGKM#y{W$1W1^bjV#QZN!b3DsE{zL zc{-SpFpC?x7}?lckO&FEnc12CQ<3L?G?4Ugkydpvaxo*}_*Ww-dpj2r_J8dw8@afc 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b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras.py new file mode 100644 index 00000000..09995a91 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras.py @@ -0,0 +1,242 @@ +""" + +SCO2 baseline cycle from the NETL baseline report + +Case Baseline620 - Turbine inlet temperature 893.15 K (620 C). +Case Basleine760 - Turbine inlet temperature 1033.15 K (760 C). + +""" +from pyomo.environ import (ConcreteModel, + Block, + Var, + Param, + Constraint, + SolverFactory, + TransformationFactory, TerminationCondition, + value, Expression, minimize, units) +from pyomo.network import Arc, SequentialDecomposition + +# Import IDAES libraries +from idaes.core import FlowsheetBlock, UnitModelBlockData +from idaes.models.unit_models import (Mixer, MomentumMixingType, + PressureChanger, Heater, + Separator, HeatExchanger) +from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption +from idaes.core.util.model_statistics import degrees_of_freedom +from idaes.core.util.initialization import propagate_state +from SCO2_properties_keras_surrogate import SCO2ParameterBlock +import idaes.logger as idaeslog + +def main(): + # Setup solver and options + solver = SolverFactory('ipopt') + outlvl = 0 + tee = True + + # Set up concrete model + m = ConcreteModel() + + # Create a flowsheet block + m.fs = FlowsheetBlock(dynamic=False) + + # Create the properties param block + m.fs.properties = SCO2ParameterBlock() + + # Add unit models to the flowsheet + m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True) + + m.fs.turbine = PressureChanger(dynamic=False, + property_package= m.fs.properties, + compressor=False, + thermodynamic_assumption=ThermodynamicAssumption.isentropic) + + m.fs.HTR_pseudo_shell = Heater(dynamic= False, + property_package= m.fs.properties, + has_pressure_change= True) + + m.fs.HTR_pseudo_tube = Heater(dynamic=False, + property_package= m.fs.properties, + has_pressure_change= True) + + m.fs.LTR_pseudo_shell = Heater(dynamic= False, + property_package= m.fs.properties, + has_pressure_change=True) + + m.fs.LTR_pseudo_tube = Heater(dynamic= False, + property_package= m.fs.properties, + has_pressure_change=True) + + m.fs.splitter_1 = Separator(property_package= m.fs.properties, + outlet_list= ["bypass", "to_cooler"]) + + m.fs.co2_cooler = Heater(dynamic= False, + property_package=m.fs.properties, + has_pressure_change= True) + + m.fs.main_compressor = PressureChanger(dynamic= False, + property_package= m.fs.properties, + compressor= True, + thermodynamic_assumption= ThermodynamicAssumption.isentropic) + + m.fs.bypass_compressor = PressureChanger(dynamic= False, + property_package= m.fs.properties, + compressor= True, + thermodynamic_assumption= ThermodynamicAssumption.isentropic) + + m.fs.splitter_2 = Separator(property_package= m.fs.properties, + ideal_separation= False, + outlet_list= ["to_FG_cooler", + "to_LTR"]) + + m.fs.FG_cooler = Heater(dynamic= False, + property_package= m.fs.properties, + has_pressure_change= True) + + m.fs.mixer = Mixer(property_package= m.fs.properties, + inlet_list=["FG_out", "LTR_out", "bypass"]) + + + # # Connect the flowsheet + m.fs.s01 = Arc(source=m.fs.boiler.outlet, + destination=m.fs.turbine.inlet) + m.fs.s02 = Arc(source=m.fs.turbine.outlet, + destination=m.fs.HTR_pseudo_shell.inlet) + m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet, + destination=m.fs.LTR_pseudo_shell.inlet) + m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet, + destination=m.fs.splitter_1.inlet) + m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, + destination=m.fs.co2_cooler.inlet) + m.fs.s06 = Arc(source=m.fs.splitter_1.bypass, + destination=m.fs.bypass_compressor.inlet) + m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet, + destination=m.fs.main_compressor.inlet) + m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, + destination=m.fs.mixer.bypass) + m.fs.s09 = Arc(source=m.fs.main_compressor.outlet, + destination=m.fs.splitter_2.inlet) + m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler, + destination=m.fs.FG_cooler.inlet) + m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR, + destination=m.fs.LTR_pseudo_tube.inlet) + m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, + destination=m.fs.mixer.LTR_out) + m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, + destination=m.fs.mixer.FG_out) + m.fs.s14 = Arc(source=m.fs.mixer.outlet, + destination=m.fs.HTR_pseudo_tube.inlet) + + # NETL Baseline - Case A + m.fs.boiler.inlet.flow_mol.fix(121.1) + m.fs.boiler.inlet.temperature.fix(685.15) + m.fs.boiler.inlet.pressure.fix(34.51) + + m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C + m.fs.boiler.deltaP.fix(-0.21) + + m.fs.boiler.initialize(outlvl=idaeslog.DEBUG) + + propagate_state(m.fs.s01) + + m.fs.turbine.ratioP.fix(1/3.68) + m.fs.turbine.efficiency_isentropic.fix(0.927) + m.fs.turbine.initialize(outlvl=outlvl) + + propagate_state(m.fs.s02) + m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15) + m.fs.HTR_pseudo_shell.deltaP.fix(-0.07) + + m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl) + + + propagate_state(m.fs.s03) + + m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15) + m.fs.LTR_pseudo_shell.deltaP.fix(-0.07) + m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl) + + + propagate_state(m.fs.s04) + m.fs.splitter_1.split_fraction[0, "bypass"].fix(0.25) + + m.fs.splitter_1.initialize(outlvl=outlvl) + + propagate_state(m.fs.s05) + m.fs.co2_cooler.outlet.temperature.fix(308.15) + m.fs.co2_cooler.deltaP.fix(-0.07) + m.fs.co2_cooler.initialize(outlvl=outlvl) + + + propagate_state(m.fs.s06) + m.fs.bypass_compressor.efficiency_isentropic.fix(0.85) + m.fs.bypass_compressor.ratioP.fix(3.8) + m.fs.bypass_compressor.initialize(outlvl=outlvl) + + propagate_state(m.fs.s07) + m.fs.main_compressor.efficiency_isentropic.fix(0.85) + m.fs.main_compressor.ratioP.fix(3.8) + m.fs.main_compressor.initialize(outlvl=outlvl) + + propagate_state(m.fs.s09) + + m.fs.splitter_2.split_fraction[0, "to_FG_cooler"].fix(0.046) + m.fs.splitter_2.initialize(outlvl=outlvl) + + propagate_state(m.fs.s10) + m.fs.FG_cooler.outlet.temperature.fix(483.15) + m.fs.FG_cooler.deltaP.fix(-0.06) + m.fs.FG_cooler.initialize(outlvl=outlvl) + + + propagate_state(m.fs.s11) + + m.fs.LTR_pseudo_tube.deltaP.fix(0) + m.fs.LTR_pseudo_tube.heat_duty[0].\ + fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0])) + m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl) + + # Add constraint heats of the LTR_pseudo shell and tube + m.fs.LTR_pseudo_tube.heat_duty[0].unfix() + m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] == + -m.fs.LTR_pseudo_tube.heat_duty[0]) + + propagate_state(m.fs.s08) + propagate_state(m.fs.s12) + propagate_state(m.fs.s13) + + m.fs.mixer.initialize(outlvl=outlvl) + + propagate_state(m.fs.s14) + + m.fs.HTR_pseudo_tube.heat_duty[0].\ + fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0])) + m.fs.HTR_pseudo_tube.deltaP.fix(-0.07) + m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl) + + m.fs.HTR_pseudo_tube.heat_duty[0].unfix() + m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] == + -m.fs.HTR_pseudo_tube.heat_duty[0]) + + TransformationFactory("network.expand_arcs").apply_to(m.fs) + + print("--------------------------------------------------------------------") + print("The degrees of freedom for the flowsheet is ", degrees_of_freedom(m)) + print("--------------------------------------------------------------------") + + solver.solve(m, tee=tee) + + # + from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity + # Print reports + for i in m.fs.component_objects(Block): + if isinstance(i, UnitModelBlockData): + i.report() + + # Converting units for readability + print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\ + -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\ + -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW) + return m + +if __name__ == "__main__": + m = main() diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb new file mode 100644 index 00000000..1bcd1806 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb @@ -0,0 +1,643 @@ +{ + "cells": [ + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - SCO2_flowsheet_optimization_keras_surrogate (Part 3)\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. \n", + "\n", + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory, TerminationCondition,\n", + " value, Expression, minimize, units)\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (Mixer, MomentumMixingType,\n", + " PressureChanger, Heater,\n", + " Separator, HeatExchanger)\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from SCO2_properties_keras_surrogate import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to get the total work done. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-08 10:28:35 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-08 10:28:35 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:28:36 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:28:36 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-08 10:28:36 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:28:36 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-08 10:28:37 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:28:37 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:28:37 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-08 10:28:37 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:28:38 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:28:39 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:28:39 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:28:39 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-08 10:28:39 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:28:39 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-08 10:28:39 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:28:40 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:28:40 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-08 10:28:40 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 51411\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2674\n", + "\n", + "Total number of variables............................: 5920\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 5669\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 5920\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 9.10e-01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 7.86e-09 7.53e-01 -1.0 9.10e-01 - 9.89e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.112\n", + "Total CPU secs in NLP function evaluations = 0.004\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.3854e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -1.0221e+06 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 719.28\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.5254e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 719.28 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.5254e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 543.23 750.68\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.0875e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.0875e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 396.40 579.39\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -3.1174e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 2.7059e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 396.40\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.0998e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 452.96\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 396.40 396.40 396.40\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 25836. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 396.40 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 579.39 452.96 543.23\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "641.5293430698576 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory('ipopt')\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True)\n", + "\n", + " m.fs.turbine = PressureChanger(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.splitter_1 = Separator(property_package= m.fs.properties,\n", + " outlet_list= [\"bypass\", \"to_cooler\"])\n", + "\n", + " m.fs.co2_cooler = Heater(dynamic= False,\n", + " property_package=m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.main_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.splitter_2 = Separator(property_package= m.fs.properties,\n", + " ideal_separation= False,\n", + " outlet_list= [\"to_FG_cooler\",\n", + " \"to_LTR\"])\n", + "\n", + " m.fs.FG_cooler = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.mixer = Mixer(property_package= m.fs.properties,\n", + " inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"])\n", + "\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet,\n", + " destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet,\n", + " destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet,\n", + " destination=m.fs.LTR_pseudo_shell.inlet)\n", + " m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet,\n", + " destination=m.fs.splitter_1.inlet)\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler,\n", + " destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(source=m.fs.splitter_1.bypass,\n", + " destination=m.fs.bypass_compressor.inlet)\n", + " m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet,\n", + " destination=m.fs.main_compressor.inlet)\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet,\n", + " destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(source=m.fs.main_compressor.outlet,\n", + " destination=m.fs.splitter_2.inlet)\n", + " m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler,\n", + " destination=m.fs.FG_cooler.inlet)\n", + " m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR,\n", + " destination=m.fs.LTR_pseudo_tube.inlet)\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet,\n", + " destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", + " destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline \n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1/3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0) \n", + " m.fs.LTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.LTR_pseudo_tube.heat_duty[0])\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.HTR_pseudo_tube.heat_duty[0])\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW)\n", + " return m\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb new file mode 100644 index 00000000..37ed4efe --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb @@ -0,0 +1,1058 @@ +{ + "cells": [ + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part -1)\n", + "\n", + "## 1. Introduction\n", + "This notebook illustrates the use of KerasSurrogate API leveraging TensorFlow Keras and OMLT package to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "### 1.1 Need for ML Surrogates\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebric, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebric equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "![image.png](attachment:image.png)\n", + "\n", + "The above flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train a tanh model from our data and then demonstrate that we can solve an optimization problem with that surrogate model. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random as rn\n", + "import tensorflow as tf\n", + "import tensorflow.keras as keras\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")\n", + "\n", + "# fix environment variables to ensure consist neural network training\n", + "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "np.random.seed(46)\n", + "rn.seed(1342)\n", + "tf.random.set_seed(62)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using CoolProp package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\" and change them to the variable names to be used in the property package. Further, the input variables are ***pressure***, ***temperature***, while the output variables are ***enth_mol***, ***entr_mol***, hence we slice them and create the input and output data. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(\"./500_Points_DataSet.csv\")\n", + "csv_data.columns.values[0:6] =[\"pressure\", \"temperature\",\"enth_mol\",\"entr_mol\",\"CO2_enthalpy\",\"CO2_entropy\"]\n", + "data = csv_data.sample(n=500)\n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = pd.DataFrame(data.iloc[:,2:4])\n", + "\n", + "# # Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns \n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogate with TensorFlow Keras\n", + "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", + "\n", + "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", + "\n", + "* Activation function: sigmoid, ***tanh***\n", + "* Optimizer: ***Adam***\n", + "* Number of hidden layers: 3, ***4***, 5, 6\n", + "* Number of neurons per layer: ***20***, 40, 60\n", + "\n", + "Important thing to note here is that we do not use ReLU activation function for the training as the flowsheet we intend to solve with this surrogate model is a NLP problem and using ReLU activation function will make it an MINLP. Another thing to note here is the network is smaller (4,20) in order to avoid overfitting. \n", + "\n", + "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", + "\n", + "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/250\n", + "13/13 - 2s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 2s/epoch - 190ms/step\n", + "Epoch 2/250\n", + "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 213ms/epoch - 16ms/step\n", + "Epoch 3/250\n", + "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 237ms/epoch - 18ms/step\n", + "Epoch 4/250\n", + "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 222ms/epoch - 17ms/step\n", + "Epoch 5/250\n", + "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 259ms/epoch - 20ms/step\n", + "Epoch 6/250\n", + "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 237ms/epoch - 18ms/step\n", + "Epoch 7/250\n", + "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 246ms/epoch - 19ms/step\n", + "Epoch 8/250\n", + "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 258ms/epoch - 20ms/step\n", + "Epoch 9/250\n", + "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 202ms/epoch - 16ms/step\n", + "Epoch 10/250\n", + "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 259ms/epoch - 20ms/step\n", + "Epoch 11/250\n", + "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 208ms/epoch - 16ms/step\n", + "Epoch 12/250\n", + "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 300ms/epoch - 23ms/step\n", + "Epoch 13/250\n", + "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 287ms/epoch - 22ms/step\n", + "Epoch 14/250\n", + "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 269ms/epoch - 21ms/step\n", + "Epoch 15/250\n", + "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 257ms/epoch - 20ms/step\n", + "Epoch 16/250\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 254ms/epoch - 20ms/step\n", + "Epoch 17/250\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 277ms/epoch - 21ms/step\n", + "Epoch 18/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 259ms/epoch - 20ms/step\n", + "Epoch 19/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 264ms/epoch - 20ms/step\n", + "Epoch 20/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 257ms/epoch - 20ms/step\n", + "Epoch 21/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 284ms/epoch - 22ms/step\n", + "Epoch 22/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 236ms/epoch - 18ms/step\n", + "Epoch 23/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 150ms/epoch - 12ms/step\n", + "Epoch 24/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 268ms/epoch - 21ms/step\n", + "Epoch 25/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 271ms/epoch - 21ms/step\n", + "Epoch 26/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 239ms/epoch - 18ms/step\n", + "Epoch 27/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 255ms/epoch - 20ms/step\n", + "Epoch 28/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 138ms/epoch - 11ms/step\n", + "Epoch 29/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 134ms/epoch - 10ms/step\n", + "Epoch 30/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 272ms/epoch - 21ms/step\n", + "Epoch 31/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 273ms/epoch - 21ms/step\n", + "Epoch 32/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 258ms/epoch - 20ms/step\n", + "Epoch 33/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 130ms/epoch - 10ms/step\n", + "Epoch 34/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 258ms/epoch - 20ms/step\n", + "Epoch 35/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 210ms/epoch - 16ms/step\n", + "Epoch 36/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 238ms/epoch - 18ms/step\n", + "Epoch 37/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 139ms/epoch - 11ms/step\n", + "Epoch 38/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 228ms/epoch - 18ms/step\n", + "Epoch 39/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 139ms/epoch - 11ms/step\n", + "Epoch 40/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 233ms/epoch - 18ms/step\n", + "Epoch 41/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 123ms/epoch - 9ms/step\n", + "Epoch 42/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 174ms/epoch - 13ms/step\n", + "Epoch 43/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0238 - mse: 0.0011 - val_loss: 9.4863e-04 - val_mae: 0.0232 - val_mse: 9.4863e-04 - 240ms/epoch - 18ms/step\n", + "Epoch 44/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0236 - mse: 0.0011 - val_loss: 9.8018e-04 - val_mae: 0.0230 - val_mse: 9.8018e-04 - 134ms/epoch - 10ms/step\n", + "Epoch 45/250\n", + "13/13 - 1s - loss: 0.0011 - mae: 0.0239 - mse: 0.0011 - val_loss: 9.5093e-04 - val_mae: 0.0233 - val_mse: 9.5093e-04 - 511ms/epoch - 39ms/step\n", + "Epoch 46/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.4785e-04 - val_mae: 0.0223 - val_mse: 9.4785e-04 - 342ms/epoch - 26ms/step\n", + "Epoch 47/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 48/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 234ms/epoch - 18ms/step\n", + "Epoch 49/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.2521e-04 - val_mae: 0.0218 - val_mse: 9.2521e-04 - 135ms/epoch - 10ms/step\n", + "Epoch 50/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7818e-04 - val_mae: 0.0231 - val_mse: 9.7818e-04 - 126ms/epoch - 10ms/step\n", + "Epoch 51/250\n", + "13/13 - 0s - loss: 9.9977e-04 - mae: 0.0232 - mse: 9.9977e-04 - val_loss: 9.4350e-04 - val_mae: 0.0221 - val_mse: 9.4350e-04 - 137ms/epoch - 11ms/step\n", + "Epoch 52/250\n", + "13/13 - 0s - loss: 9.8599e-04 - mae: 0.0229 - mse: 9.8599e-04 - val_loss: 9.0638e-04 - val_mae: 0.0230 - val_mse: 9.0638e-04 - 262ms/epoch - 20ms/step\n", + "Epoch 53/250\n", + "13/13 - 0s - loss: 9.8295e-04 - mae: 0.0228 - mse: 9.8295e-04 - val_loss: 9.0667e-04 - val_mae: 0.0215 - val_mse: 9.0667e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 54/250\n", + "13/13 - 0s - loss: 9.7266e-04 - mae: 0.0225 - mse: 9.7266e-04 - val_loss: 9.0391e-04 - val_mae: 0.0224 - val_mse: 9.0391e-04 - 272ms/epoch - 21ms/step\n", + "Epoch 55/250\n", + "13/13 - 0s - loss: 9.5234e-04 - mae: 0.0225 - mse: 9.5234e-04 - val_loss: 8.7426e-04 - val_mae: 0.0219 - val_mse: 8.7426e-04 - 280ms/epoch - 22ms/step\n", + "Epoch 56/250\n", + "13/13 - 0s - loss: 9.4315e-04 - mae: 0.0221 - mse: 9.4315e-04 - val_loss: 8.6742e-04 - val_mae: 0.0224 - val_mse: 8.6742e-04 - 330ms/epoch - 25ms/step\n", + "Epoch 57/250\n", + "13/13 - 0s - loss: 9.9226e-04 - mae: 0.0230 - mse: 9.9226e-04 - val_loss: 8.7793e-04 - val_mae: 0.0225 - val_mse: 8.7793e-04 - 131ms/epoch - 10ms/step\n", + "Epoch 58/250\n", + "13/13 - 0s - loss: 9.4137e-04 - mae: 0.0226 - mse: 9.4137e-04 - val_loss: 8.7477e-04 - val_mae: 0.0225 - val_mse: 8.7477e-04 - 141ms/epoch - 11ms/step\n", + "Epoch 59/250\n", + "13/13 - 0s - loss: 9.2474e-04 - mae: 0.0219 - mse: 9.2474e-04 - val_loss: 8.5320e-04 - val_mae: 0.0212 - val_mse: 8.5320e-04 - 269ms/epoch - 21ms/step\n", + "Epoch 60/250\n", + "13/13 - 0s - loss: 9.1133e-04 - mae: 0.0217 - mse: 9.1133e-04 - val_loss: 8.6082e-04 - val_mae: 0.0217 - val_mse: 8.6082e-04 - 146ms/epoch - 11ms/step\n", + "Epoch 61/250\n", + "13/13 - 0s - loss: 9.1801e-04 - mae: 0.0217 - mse: 9.1801e-04 - val_loss: 8.5403e-04 - val_mae: 0.0223 - val_mse: 8.5403e-04 - 252ms/epoch - 19ms/step\n", + "Epoch 62/250\n", + "13/13 - 0s - loss: 9.1987e-04 - mae: 0.0221 - mse: 9.1987e-04 - val_loss: 8.5714e-04 - val_mae: 0.0219 - val_mse: 8.5714e-04 - 156ms/epoch - 12ms/step\n", + "Epoch 63/250\n", + "13/13 - 0s - loss: 9.0862e-04 - mae: 0.0222 - mse: 9.0862e-04 - val_loss: 8.6160e-04 - val_mae: 0.0225 - val_mse: 8.6160e-04 - 133ms/epoch - 10ms/step\n", + "Epoch 64/250\n", + "13/13 - 0s - loss: 8.9349e-04 - mae: 0.0220 - mse: 8.9349e-04 - val_loss: 8.2851e-04 - val_mae: 0.0214 - val_mse: 8.2851e-04 - 236ms/epoch - 18ms/step\n", + "Epoch 65/250\n", + "13/13 - 0s - loss: 8.7848e-04 - mae: 0.0216 - mse: 8.7848e-04 - val_loss: 8.5189e-04 - val_mae: 0.0218 - val_mse: 8.5189e-04 - 182ms/epoch - 14ms/step\n", + "Epoch 66/250\n", + "13/13 - 0s - loss: 8.9773e-04 - mae: 0.0219 - mse: 8.9773e-04 - val_loss: 8.5650e-04 - val_mae: 0.0211 - val_mse: 8.5650e-04 - 166ms/epoch - 13ms/step\n", + "Epoch 67/250\n", + "13/13 - 0s - loss: 8.7443e-04 - mae: 0.0217 - mse: 8.7443e-04 - val_loss: 8.2545e-04 - val_mae: 0.0214 - val_mse: 8.2545e-04 - 371ms/epoch - 29ms/step\n", + "Epoch 68/250\n", + "13/13 - 0s - loss: 8.9141e-04 - mae: 0.0217 - mse: 8.9141e-04 - val_loss: 8.4471e-04 - val_mae: 0.0219 - val_mse: 8.4471e-04 - 192ms/epoch - 15ms/step\n", + "Epoch 69/250\n", + "13/13 - 0s - loss: 8.9507e-04 - mae: 0.0224 - mse: 8.9507e-04 - val_loss: 8.7916e-04 - val_mae: 0.0217 - val_mse: 8.7916e-04 - 216ms/epoch - 17ms/step\n", + "Epoch 70/250\n", + "13/13 - 0s - loss: 8.5737e-04 - mae: 0.0216 - mse: 8.5737e-04 - val_loss: 8.8807e-04 - val_mae: 0.0215 - val_mse: 8.8807e-04 - 200ms/epoch - 15ms/step\n", + "Epoch 71/250\n", + "13/13 - 0s - loss: 8.5560e-04 - mae: 0.0214 - mse: 8.5560e-04 - val_loss: 8.3750e-04 - val_mae: 0.0213 - val_mse: 8.3750e-04 - 141ms/epoch - 11ms/step\n", + "Epoch 72/250\n", + "13/13 - 0s - loss: 8.5576e-04 - mae: 0.0218 - mse: 8.5576e-04 - val_loss: 8.1156e-04 - val_mae: 0.0210 - val_mse: 8.1156e-04 - 271ms/epoch - 21ms/step\n", + "Epoch 73/250\n", + "13/13 - 0s - loss: 8.4688e-04 - mae: 0.0216 - mse: 8.4688e-04 - val_loss: 8.0221e-04 - val_mae: 0.0210 - val_mse: 8.0221e-04 - 416ms/epoch - 32ms/step\n", + "Epoch 74/250\n", + "13/13 - 0s - loss: 8.3636e-04 - mae: 0.0211 - mse: 8.3636e-04 - val_loss: 7.9384e-04 - val_mae: 0.0208 - val_mse: 7.9384e-04 - 360ms/epoch - 28ms/step\n", + "Epoch 75/250\n", + "13/13 - 0s - loss: 8.4758e-04 - mae: 0.0222 - mse: 8.4758e-04 - val_loss: 8.2932e-04 - val_mae: 0.0212 - val_mse: 8.2932e-04 - 186ms/epoch - 14ms/step\n", + "Epoch 76/250\n", + "13/13 - 0s - loss: 8.4142e-04 - mae: 0.0213 - mse: 8.4142e-04 - val_loss: 8.0552e-04 - val_mae: 0.0209 - val_mse: 8.0552e-04 - 179ms/epoch - 14ms/step\n", + "Epoch 77/250\n", + "13/13 - 0s - loss: 8.5035e-04 - mae: 0.0215 - mse: 8.5035e-04 - val_loss: 8.6014e-04 - val_mae: 0.0215 - val_mse: 8.6014e-04 - 148ms/epoch - 11ms/step\n", + "Epoch 78/250\n", + "13/13 - 0s - loss: 8.9015e-04 - mae: 0.0228 - mse: 8.9015e-04 - val_loss: 9.2548e-04 - val_mae: 0.0225 - val_mse: 9.2548e-04 - 263ms/epoch - 20ms/step\n", + "Epoch 79/250\n", + "13/13 - 0s - loss: 8.1577e-04 - mae: 0.0212 - mse: 8.1577e-04 - val_loss: 8.4703e-04 - val_mae: 0.0211 - val_mse: 8.4703e-04 - 273ms/epoch - 21ms/step\n", + "Epoch 80/250\n", + "13/13 - 0s - loss: 8.0555e-04 - mae: 0.0211 - mse: 8.0555e-04 - val_loss: 8.5652e-04 - val_mae: 0.0214 - val_mse: 8.5652e-04 - 198ms/epoch - 15ms/step\n", + "Epoch 81/250\n", + "13/13 - 0s - loss: 8.3478e-04 - mae: 0.0219 - mse: 8.3478e-04 - val_loss: 9.1057e-04 - val_mae: 0.0222 - val_mse: 9.1057e-04 - 143ms/epoch - 11ms/step\n", + "Epoch 82/250\n", + "13/13 - 0s - loss: 8.2593e-04 - mae: 0.0217 - mse: 8.2593e-04 - val_loss: 8.1172e-04 - val_mae: 0.0209 - val_mse: 8.1172e-04 - 146ms/epoch - 11ms/step\n", + "Epoch 83/250\n", + "13/13 - 0s - loss: 8.2887e-04 - mae: 0.0213 - mse: 8.2887e-04 - val_loss: 8.2033e-04 - val_mae: 0.0211 - val_mse: 8.2033e-04 - 123ms/epoch - 9ms/step\n", + "Epoch 84/250\n", + "13/13 - 0s - loss: 8.1454e-04 - mae: 0.0219 - mse: 8.1454e-04 - val_loss: 8.1589e-04 - val_mae: 0.0211 - val_mse: 8.1589e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 85/250\n", + "13/13 - 0s - loss: 8.0777e-04 - mae: 0.0212 - mse: 8.0777e-04 - val_loss: 7.8637e-04 - val_mae: 0.0208 - val_mse: 7.8637e-04 - 236ms/epoch - 18ms/step\n", + "Epoch 86/250\n", + "13/13 - 0s - loss: 7.8107e-04 - mae: 0.0213 - mse: 7.8107e-04 - val_loss: 7.8138e-04 - val_mae: 0.0212 - val_mse: 7.8138e-04 - 217ms/epoch - 17ms/step\n", + "Epoch 87/250\n", + "13/13 - 0s - loss: 7.9729e-04 - mae: 0.0210 - mse: 7.9729e-04 - val_loss: 7.3667e-04 - val_mae: 0.0204 - val_mse: 7.3667e-04 - 256ms/epoch - 20ms/step\n", + "Epoch 88/250\n", + "13/13 - 0s - loss: 7.5931e-04 - mae: 0.0205 - mse: 7.5931e-04 - val_loss: 7.5522e-04 - val_mae: 0.0210 - val_mse: 7.5522e-04 - 151ms/epoch - 12ms/step\n", + "Epoch 89/250\n", + "13/13 - 0s - loss: 7.6036e-04 - mae: 0.0211 - mse: 7.6036e-04 - val_loss: 7.5503e-04 - val_mae: 0.0207 - val_mse: 7.5503e-04 - 116ms/epoch - 9ms/step\n", + "Epoch 90/250\n", + "13/13 - 0s - loss: 7.6322e-04 - mae: 0.0204 - mse: 7.6322e-04 - val_loss: 7.7629e-04 - val_mae: 0.0203 - val_mse: 7.7629e-04 - 132ms/epoch - 10ms/step\n", + "Epoch 91/250\n", + "13/13 - 0s - loss: 7.5436e-04 - mae: 0.0208 - mse: 7.5436e-04 - val_loss: 7.4549e-04 - val_mae: 0.0210 - val_mse: 7.4549e-04 - 157ms/epoch - 12ms/step\n", + "Epoch 92/250\n", + "13/13 - 0s - loss: 7.8479e-04 - mae: 0.0208 - mse: 7.8479e-04 - val_loss: 8.0607e-04 - val_mae: 0.0208 - val_mse: 8.0607e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 93/250\n", + "13/13 - 0s - loss: 7.7194e-04 - mae: 0.0211 - mse: 7.7194e-04 - val_loss: 7.7994e-04 - val_mae: 0.0206 - val_mse: 7.7994e-04 - 213ms/epoch - 16ms/step\n", + "Epoch 94/250\n", + "13/13 - 0s - loss: 7.4802e-04 - mae: 0.0205 - mse: 7.4802e-04 - val_loss: 7.2386e-04 - val_mae: 0.0201 - val_mse: 7.2386e-04 - 333ms/epoch - 26ms/step\n", + "Epoch 95/250\n", + "13/13 - 0s - loss: 7.2616e-04 - mae: 0.0203 - mse: 7.2616e-04 - val_loss: 7.2728e-04 - val_mae: 0.0204 - val_mse: 7.2728e-04 - 127ms/epoch - 10ms/step\n", + "Epoch 96/250\n", + "13/13 - 0s - loss: 7.2310e-04 - mae: 0.0204 - mse: 7.2310e-04 - val_loss: 7.1349e-04 - val_mae: 0.0206 - val_mse: 7.1349e-04 - 235ms/epoch - 18ms/step\n", + "Epoch 97/250\n", + "13/13 - 0s - loss: 7.0905e-04 - mae: 0.0201 - mse: 7.0905e-04 - val_loss: 7.6242e-04 - val_mae: 0.0205 - val_mse: 7.6242e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 98/250\n", + "13/13 - 0s - loss: 7.1839e-04 - mae: 0.0200 - mse: 7.1839e-04 - val_loss: 7.7098e-04 - val_mae: 0.0202 - val_mse: 7.7098e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 99/250\n", + "13/13 - 0s - loss: 7.3924e-04 - mae: 0.0208 - mse: 7.3924e-04 - val_loss: 7.8554e-04 - val_mae: 0.0206 - val_mse: 7.8554e-04 - 393ms/epoch - 30ms/step\n", + "Epoch 100/250\n", + "13/13 - 0s - loss: 7.5556e-04 - mae: 0.0209 - mse: 7.5556e-04 - val_loss: 8.6021e-04 - val_mae: 0.0215 - val_mse: 8.6021e-04 - 230ms/epoch - 18ms/step\n", + "Epoch 101/250\n", + "13/13 - 0s - loss: 7.9288e-04 - mae: 0.0213 - mse: 7.9288e-04 - val_loss: 7.2968e-04 - val_mae: 0.0203 - val_mse: 7.2968e-04 - 158ms/epoch - 12ms/step\n", + "Epoch 102/250\n", + "13/13 - 0s - loss: 7.1861e-04 - mae: 0.0204 - mse: 7.1861e-04 - val_loss: 7.0941e-04 - val_mae: 0.0207 - val_mse: 7.0941e-04 - 191ms/epoch - 15ms/step\n", + "Epoch 103/250\n", + "13/13 - 0s - loss: 7.5092e-04 - mae: 0.0208 - mse: 7.5092e-04 - val_loss: 6.8788e-04 - val_mae: 0.0198 - val_mse: 6.8788e-04 - 233ms/epoch - 18ms/step\n", + "Epoch 104/250\n", + "13/13 - 0s - loss: 7.0460e-04 - mae: 0.0200 - mse: 7.0460e-04 - val_loss: 7.2570e-04 - val_mae: 0.0200 - val_mse: 7.2570e-04 - 125ms/epoch - 10ms/step\n", + "Epoch 105/250\n", + "13/13 - 0s - loss: 6.9255e-04 - mae: 0.0202 - mse: 6.9255e-04 - val_loss: 6.7411e-04 - val_mae: 0.0199 - val_mse: 6.7411e-04 - 236ms/epoch - 18ms/step\n", + "Epoch 106/250\n", + "13/13 - 0s - loss: 6.8175e-04 - mae: 0.0196 - mse: 6.8175e-04 - val_loss: 6.7593e-04 - val_mae: 0.0196 - val_mse: 6.7593e-04 - 161ms/epoch - 12ms/step\n", + "Epoch 107/250\n", + "13/13 - 0s - loss: 6.7018e-04 - mae: 0.0196 - mse: 6.7018e-04 - val_loss: 6.8702e-04 - val_mae: 0.0196 - val_mse: 6.8702e-04 - 149ms/epoch - 11ms/step\n", + "Epoch 108/250\n", + "13/13 - 0s - loss: 6.7955e-04 - mae: 0.0198 - mse: 6.7955e-04 - val_loss: 7.6778e-04 - val_mae: 0.0204 - val_mse: 7.6778e-04 - 239ms/epoch - 18ms/step\n", + "Epoch 109/250\n", + "13/13 - 0s - loss: 6.8953e-04 - mae: 0.0198 - mse: 6.8953e-04 - val_loss: 6.7251e-04 - val_mae: 0.0195 - val_mse: 6.7251e-04 - 278ms/epoch - 21ms/step\n", + "Epoch 110/250\n", + "13/13 - 0s - loss: 6.6819e-04 - mae: 0.0197 - mse: 6.6819e-04 - val_loss: 6.8310e-04 - val_mae: 0.0197 - val_mse: 6.8310e-04 - 171ms/epoch - 13ms/step\n", + "Epoch 111/250\n", + "13/13 - 0s - loss: 6.7136e-04 - mae: 0.0197 - mse: 6.7136e-04 - val_loss: 6.5858e-04 - val_mae: 0.0199 - val_mse: 6.5858e-04 - 184ms/epoch - 14ms/step\n", + "Epoch 112/250\n", + "13/13 - 0s - loss: 6.5784e-04 - mae: 0.0195 - mse: 6.5784e-04 - val_loss: 6.5838e-04 - val_mae: 0.0196 - val_mse: 6.5838e-04 - 234ms/epoch - 18ms/step\n", + "Epoch 113/250\n", + "13/13 - 0s - loss: 6.6861e-04 - mae: 0.0198 - mse: 6.6861e-04 - val_loss: 6.9871e-04 - val_mae: 0.0196 - val_mse: 6.9871e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 114/250\n", + "13/13 - 0s - loss: 6.6345e-04 - mae: 0.0196 - mse: 6.6345e-04 - val_loss: 6.8190e-04 - val_mae: 0.0196 - val_mse: 6.8190e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 115/250\n", + "13/13 - 0s - loss: 6.4121e-04 - mae: 0.0193 - mse: 6.4121e-04 - val_loss: 6.6493e-04 - val_mae: 0.0196 - val_mse: 6.6493e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 116/250\n", + "13/13 - 0s - loss: 6.5036e-04 - mae: 0.0194 - mse: 6.5036e-04 - val_loss: 6.5858e-04 - val_mae: 0.0191 - val_mse: 6.5858e-04 - 145ms/epoch - 11ms/step\n", + "Epoch 117/250\n", + "13/13 - 0s - loss: 6.4983e-04 - mae: 0.0194 - mse: 6.4983e-04 - val_loss: 7.0443e-04 - val_mae: 0.0198 - val_mse: 7.0443e-04 - 158ms/epoch - 12ms/step\n", + "Epoch 118/250\n", + "13/13 - 0s - loss: 6.4994e-04 - mae: 0.0195 - mse: 6.4994e-04 - val_loss: 6.3181e-04 - val_mae: 0.0193 - val_mse: 6.3181e-04 - 322ms/epoch - 25ms/step\n", + "Epoch 119/250\n", + "13/13 - 0s - loss: 6.6252e-04 - mae: 0.0199 - mse: 6.6252e-04 - val_loss: 6.3527e-04 - val_mae: 0.0191 - val_mse: 6.3527e-04 - 148ms/epoch - 11ms/step\n", + "Epoch 120/250\n", + "13/13 - 0s - loss: 6.4578e-04 - mae: 0.0193 - mse: 6.4578e-04 - val_loss: 6.3127e-04 - val_mae: 0.0189 - val_mse: 6.3127e-04 - 289ms/epoch - 22ms/step\n", + "Epoch 121/250\n", + "13/13 - 0s - loss: 6.1375e-04 - mae: 0.0191 - mse: 6.1375e-04 - val_loss: 6.5351e-04 - val_mae: 0.0192 - val_mse: 6.5351e-04 - 117ms/epoch - 9ms/step\n", + "Epoch 122/250\n", + "13/13 - 0s - loss: 6.4650e-04 - mae: 0.0196 - mse: 6.4650e-04 - val_loss: 8.0733e-04 - val_mae: 0.0210 - val_mse: 8.0733e-04 - 121ms/epoch - 9ms/step\n", + "Epoch 123/250\n", + "13/13 - 0s - loss: 6.5887e-04 - mae: 0.0198 - mse: 6.5887e-04 - val_loss: 6.2666e-04 - val_mae: 0.0191 - val_mse: 6.2666e-04 - 239ms/epoch - 18ms/step\n", + "Epoch 124/250\n", + "13/13 - 0s - loss: 6.1387e-04 - mae: 0.0189 - mse: 6.1387e-04 - val_loss: 6.1020e-04 - val_mae: 0.0188 - val_mse: 6.1020e-04 - 243ms/epoch - 19ms/step\n", + "Epoch 125/250\n", + "13/13 - 0s - loss: 6.1348e-04 - mae: 0.0191 - mse: 6.1348e-04 - val_loss: 6.1093e-04 - val_mae: 0.0193 - val_mse: 6.1093e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 126/250\n", + "13/13 - 0s - loss: 6.1374e-04 - mae: 0.0189 - mse: 6.1374e-04 - val_loss: 6.1062e-04 - val_mae: 0.0188 - val_mse: 6.1062e-04 - 172ms/epoch - 13ms/step\n", + "Epoch 127/250\n", + "13/13 - 0s - loss: 6.1279e-04 - mae: 0.0190 - mse: 6.1279e-04 - val_loss: 6.4391e-04 - val_mae: 0.0190 - val_mse: 6.4391e-04 - 176ms/epoch - 14ms/step\n", + "Epoch 128/250\n", + "13/13 - 0s - loss: 6.0951e-04 - mae: 0.0189 - mse: 6.0951e-04 - val_loss: 5.9592e-04 - val_mae: 0.0188 - val_mse: 5.9592e-04 - 333ms/epoch - 26ms/step\n", + "Epoch 129/250\n", + "13/13 - 0s - loss: 6.2194e-04 - mae: 0.0192 - mse: 6.2194e-04 - val_loss: 5.9344e-04 - val_mae: 0.0188 - val_mse: 5.9344e-04 - 180ms/epoch - 14ms/step\n", + "Epoch 130/250\n", + "13/13 - 0s - loss: 6.1795e-04 - mae: 0.0191 - mse: 6.1795e-04 - val_loss: 5.8880e-04 - val_mae: 0.0188 - val_mse: 5.8880e-04 - 139ms/epoch - 11ms/step\n", + "Epoch 131/250\n", + "13/13 - 0s - loss: 6.6297e-04 - mae: 0.0199 - mse: 6.6297e-04 - val_loss: 7.2306e-04 - val_mae: 0.0197 - val_mse: 7.2306e-04 - 75ms/epoch - 6ms/step\n", + "Epoch 132/250\n", + "13/13 - 0s - loss: 5.8788e-04 - mae: 0.0189 - mse: 5.8788e-04 - val_loss: 6.0686e-04 - val_mae: 0.0189 - val_mse: 6.0686e-04 - 81ms/epoch - 6ms/step\n", + "Epoch 133/250\n", + "13/13 - 0s - loss: 5.7425e-04 - mae: 0.0184 - mse: 5.7425e-04 - val_loss: 5.7895e-04 - val_mae: 0.0183 - val_mse: 5.7895e-04 - 135ms/epoch - 10ms/step\n", + "Epoch 134/250\n", + "13/13 - 0s - loss: 5.8783e-04 - mae: 0.0186 - mse: 5.8783e-04 - val_loss: 5.7846e-04 - val_mae: 0.0188 - val_mse: 5.7846e-04 - 134ms/epoch - 10ms/step\n", + "Epoch 135/250\n", + "13/13 - 0s - loss: 5.8541e-04 - mae: 0.0188 - mse: 5.8541e-04 - val_loss: 6.7887e-04 - val_mae: 0.0191 - val_mse: 6.7887e-04 - 73ms/epoch - 6ms/step\n", + "Epoch 136/250\n", + "13/13 - 0s - loss: 5.9158e-04 - mae: 0.0185 - mse: 5.9158e-04 - val_loss: 5.9231e-04 - val_mae: 0.0188 - val_mse: 5.9231e-04 - 83ms/epoch - 6ms/step\n", + "Epoch 137/250\n", + "13/13 - 0s - loss: 5.9616e-04 - mae: 0.0192 - mse: 5.9616e-04 - val_loss: 7.0218e-04 - val_mae: 0.0212 - val_mse: 7.0218e-04 - 87ms/epoch - 7ms/step\n", + "Epoch 138/250\n", + "13/13 - 0s - loss: 6.2132e-04 - mae: 0.0190 - mse: 6.2132e-04 - val_loss: 6.3436e-04 - val_mae: 0.0186 - val_mse: 6.3436e-04 - 70ms/epoch - 5ms/step\n", + "Epoch 139/250\n", + "13/13 - 0s - loss: 5.8416e-04 - mae: 0.0189 - mse: 5.8416e-04 - val_loss: 5.7793e-04 - val_mae: 0.0184 - val_mse: 5.7793e-04 - 159ms/epoch - 12ms/step\n", + "Epoch 140/250\n", + "13/13 - 0s - loss: 6.5695e-04 - mae: 0.0195 - mse: 6.5695e-04 - val_loss: 5.8062e-04 - val_mae: 0.0189 - val_mse: 5.8062e-04 - 80ms/epoch - 6ms/step\n", + "Epoch 141/250\n", + "13/13 - 0s - loss: 6.4168e-04 - mae: 0.0200 - mse: 6.4168e-04 - val_loss: 6.9879e-04 - val_mae: 0.0196 - val_mse: 6.9879e-04 - 82ms/epoch - 6ms/step\n", + "Epoch 142/250\n", + "13/13 - 0s - loss: 6.5517e-04 - mae: 0.0198 - mse: 6.5517e-04 - val_loss: 6.3928e-04 - val_mae: 0.0193 - val_mse: 6.3928e-04 - 76ms/epoch - 6ms/step\n", + "Epoch 143/250\n", + "13/13 - 0s - loss: 5.8456e-04 - mae: 0.0190 - mse: 5.8456e-04 - val_loss: 5.4596e-04 - val_mae: 0.0181 - val_mse: 5.4596e-04 - 158ms/epoch - 12ms/step\n", + "Epoch 144/250\n", + "13/13 - 0s - loss: 5.9458e-04 - mae: 0.0186 - mse: 5.9458e-04 - val_loss: 5.8598e-04 - val_mae: 0.0181 - val_mse: 5.8598e-04 - 82ms/epoch - 6ms/step\n", + "Epoch 145/250\n", + "13/13 - 0s - loss: 5.6787e-04 - mae: 0.0186 - mse: 5.6787e-04 - val_loss: 5.6263e-04 - val_mae: 0.0186 - val_mse: 5.6263e-04 - 82ms/epoch - 6ms/step\n", + "Epoch 146/250\n", + "13/13 - 0s - loss: 5.3545e-04 - mae: 0.0178 - mse: 5.3545e-04 - val_loss: 5.3802e-04 - val_mae: 0.0179 - val_mse: 5.3802e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 147/250\n", + "13/13 - 0s - loss: 5.2310e-04 - mae: 0.0177 - mse: 5.2310e-04 - val_loss: 5.4103e-04 - val_mae: 0.0179 - val_mse: 5.4103e-04 - 69ms/epoch - 5ms/step\n", + "Epoch 148/250\n", + "13/13 - 0s - loss: 5.2826e-04 - mae: 0.0176 - mse: 5.2826e-04 - val_loss: 5.9310e-04 - val_mae: 0.0181 - val_mse: 5.9310e-04 - 255ms/epoch - 20ms/step\n", + "Epoch 149/250\n", + "13/13 - 0s - loss: 5.3295e-04 - mae: 0.0179 - mse: 5.3295e-04 - val_loss: 5.4002e-04 - val_mae: 0.0176 - val_mse: 5.4002e-04 - 104ms/epoch - 8ms/step\n", + "Epoch 150/250\n", + "13/13 - 0s - loss: 5.1491e-04 - mae: 0.0174 - mse: 5.1491e-04 - val_loss: 5.9602e-04 - val_mae: 0.0179 - val_mse: 5.9602e-04 - 79ms/epoch - 6ms/step\n", + "Epoch 151/250\n", + "13/13 - 0s - loss: 5.2334e-04 - mae: 0.0179 - mse: 5.2334e-04 - val_loss: 5.2811e-04 - val_mae: 0.0178 - val_mse: 5.2811e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 152/250\n", + "13/13 - 0s - loss: 5.2768e-04 - mae: 0.0178 - mse: 5.2768e-04 - val_loss: 5.5139e-04 - val_mae: 0.0184 - val_mse: 5.5139e-04 - 76ms/epoch - 6ms/step\n", + "Epoch 153/250\n", + "13/13 - 0s - loss: 5.2962e-04 - mae: 0.0179 - mse: 5.2962e-04 - val_loss: 5.7462e-04 - val_mae: 0.0178 - val_mse: 5.7462e-04 - 75ms/epoch - 6ms/step\n", + "Epoch 154/250\n", + "13/13 - 0s - loss: 5.0260e-04 - mae: 0.0173 - mse: 5.0260e-04 - val_loss: 5.3387e-04 - val_mae: 0.0181 - val_mse: 5.3387e-04 - 75ms/epoch - 6ms/step\n", + "Epoch 155/250\n", + "13/13 - 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5.1410e-04 - 70ms/epoch - 5ms/step\n", + "Epoch 161/250\n", + "13/13 - 0s - loss: 4.8993e-04 - mae: 0.0171 - mse: 4.8993e-04 - val_loss: 5.1744e-04 - val_mae: 0.0171 - val_mse: 5.1744e-04 - 66ms/epoch - 5ms/step\n", + "Epoch 162/250\n", + "13/13 - 0s - loss: 4.8044e-04 - mae: 0.0169 - mse: 4.8044e-04 - val_loss: 5.1099e-04 - val_mae: 0.0168 - val_mse: 5.1099e-04 - 64ms/epoch - 5ms/step\n", + "Epoch 163/250\n", + "13/13 - 0s - loss: 4.9657e-04 - mae: 0.0171 - mse: 4.9657e-04 - val_loss: 4.9877e-04 - val_mae: 0.0171 - val_mse: 4.9877e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 164/250\n", + "13/13 - 0s - loss: 4.8858e-04 - mae: 0.0170 - mse: 4.8858e-04 - val_loss: 5.0099e-04 - val_mae: 0.0169 - val_mse: 5.0099e-04 - 62ms/epoch - 5ms/step\n", + "Epoch 165/250\n", + "13/13 - 0s - loss: 4.7747e-04 - mae: 0.0170 - mse: 4.7747e-04 - val_loss: 5.8449e-04 - val_mae: 0.0174 - val_mse: 5.8449e-04 - 66ms/epoch - 5ms/step\n", + "Epoch 166/250\n", + "13/13 - 0s - loss: 4.9897e-04 - mae: 0.0171 - mse: 4.9897e-04 - val_loss: 4.9512e-04 - val_mae: 0.0173 - val_mse: 4.9512e-04 - 168ms/epoch - 13ms/step\n", + "Epoch 167/250\n", + "13/13 - 0s - loss: 4.8695e-04 - mae: 0.0173 - mse: 4.8695e-04 - val_loss: 5.0306e-04 - val_mae: 0.0165 - val_mse: 5.0306e-04 - 78ms/epoch - 6ms/step\n", + "Epoch 168/250\n", + "13/13 - 0s - loss: 4.7948e-04 - mae: 0.0171 - mse: 4.7948e-04 - val_loss: 6.8895e-04 - val_mae: 0.0193 - val_mse: 6.8895e-04 - 64ms/epoch - 5ms/step\n", + "Epoch 169/250\n", + "13/13 - 0s - loss: 4.8055e-04 - mae: 0.0168 - mse: 4.8055e-04 - val_loss: 4.9053e-04 - val_mae: 0.0171 - val_mse: 4.9053e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 170/250\n", + "13/13 - 0s - loss: 4.5980e-04 - mae: 0.0168 - mse: 4.5980e-04 - val_loss: 5.2267e-04 - val_mae: 0.0170 - val_mse: 5.2267e-04 - 63ms/epoch - 5ms/step\n", + "Epoch 171/250\n", + "13/13 - 0s - loss: 4.6495e-04 - mae: 0.0168 - mse: 4.6495e-04 - val_loss: 4.6718e-04 - val_mae: 0.0165 - val_mse: 4.6718e-04 - 146ms/epoch - 11ms/step\n", + "Epoch 172/250\n", + "13/13 - 0s - loss: 4.6046e-04 - mae: 0.0168 - mse: 4.6046e-04 - val_loss: 4.6731e-04 - val_mae: 0.0166 - val_mse: 4.6731e-04 - 62ms/epoch - 5ms/step\n", + "Epoch 173/250\n", + "13/13 - 0s - loss: 4.6993e-04 - mae: 0.0168 - mse: 4.6993e-04 - val_loss: 4.8190e-04 - val_mae: 0.0167 - val_mse: 4.8190e-04 - 78ms/epoch - 6ms/step\n", + "Epoch 174/250\n", + "13/13 - 0s - loss: 4.8411e-04 - mae: 0.0172 - mse: 4.8411e-04 - val_loss: 5.0800e-04 - val_mae: 0.0164 - val_mse: 5.0800e-04 - 78ms/epoch - 6ms/step\n", + "Epoch 175/250\n", + "13/13 - 0s - loss: 4.5295e-04 - mae: 0.0164 - mse: 4.5295e-04 - val_loss: 6.2583e-04 - val_mae: 0.0182 - val_mse: 6.2583e-04 - 101ms/epoch - 8ms/step\n", + "Epoch 176/250\n", + "13/13 - 0s - loss: 5.3742e-04 - mae: 0.0183 - mse: 5.3742e-04 - val_loss: 5.6727e-04 - val_mae: 0.0187 - val_mse: 5.6727e-04 - 95ms/epoch - 7ms/step\n", + "Epoch 177/250\n", + "13/13 - 0s - loss: 5.3634e-04 - mae: 0.0182 - mse: 5.3634e-04 - val_loss: 4.6197e-04 - val_mae: 0.0157 - val_mse: 4.6197e-04 - 145ms/epoch - 11ms/step\n", + "Epoch 178/250\n", + "13/13 - 0s - loss: 4.8847e-04 - mae: 0.0169 - mse: 4.8847e-04 - val_loss: 4.6646e-04 - val_mae: 0.0160 - val_mse: 4.6646e-04 - 85ms/epoch - 7ms/step\n", + "Epoch 179/250\n", + "13/13 - 0s - loss: 4.3622e-04 - mae: 0.0160 - mse: 4.3622e-04 - val_loss: 5.3203e-04 - val_mae: 0.0164 - val_mse: 5.3203e-04 - 88ms/epoch - 7ms/step\n", + "Epoch 180/250\n", + "13/13 - 0s - loss: 4.7108e-04 - mae: 0.0165 - mse: 4.7108e-04 - val_loss: 4.6548e-04 - val_mae: 0.0161 - val_mse: 4.6548e-04 - 74ms/epoch - 6ms/step\n", + "Epoch 181/250\n", + "13/13 - 0s - loss: 4.3932e-04 - mae: 0.0164 - mse: 4.3932e-04 - val_loss: 4.4195e-04 - val_mae: 0.0157 - val_mse: 4.4195e-04 - 175ms/epoch - 13ms/step\n", + "Epoch 182/250\n", + "13/13 - 0s - loss: 4.3340e-04 - mae: 0.0159 - mse: 4.3340e-04 - val_loss: 4.5463e-04 - val_mae: 0.0158 - val_mse: 4.5463e-04 - 81ms/epoch - 6ms/step\n", + "Epoch 183/250\n", + "13/13 - 0s - loss: 4.2639e-04 - mae: 0.0162 - mse: 4.2639e-04 - val_loss: 4.3874e-04 - val_mae: 0.0156 - val_mse: 4.3874e-04 - 158ms/epoch - 12ms/step\n", + "Epoch 184/250\n", + "13/13 - 0s - loss: 4.4119e-04 - mae: 0.0159 - mse: 4.4119e-04 - val_loss: 4.7791e-04 - val_mae: 0.0169 - val_mse: 4.7791e-04 - 65ms/epoch - 5ms/step\n", + "Epoch 185/250\n", + "13/13 - 0s - loss: 4.4805e-04 - mae: 0.0164 - mse: 4.4805e-04 - val_loss: 4.6275e-04 - val_mae: 0.0163 - val_mse: 4.6275e-04 - 71ms/epoch - 5ms/step\n", + "Epoch 186/250\n", + "13/13 - 0s - loss: 4.4495e-04 - mae: 0.0163 - mse: 4.4495e-04 - val_loss: 4.4746e-04 - val_mae: 0.0155 - val_mse: 4.4746e-04 - 62ms/epoch - 5ms/step\n", + "Epoch 187/250\n", + "13/13 - 0s - loss: 4.7030e-04 - mae: 0.0167 - mse: 4.7030e-04 - val_loss: 5.6234e-04 - val_mae: 0.0169 - val_mse: 5.6234e-04 - 74ms/epoch - 6ms/step\n", + "Epoch 188/250\n", + "13/13 - 0s - loss: 4.4920e-04 - mae: 0.0160 - mse: 4.4920e-04 - val_loss: 4.2347e-04 - val_mae: 0.0154 - val_mse: 4.2347e-04 - 174ms/epoch - 13ms/step\n", + "Epoch 189/250\n", + "13/13 - 0s - loss: 4.1850e-04 - mae: 0.0159 - mse: 4.1850e-04 - val_loss: 4.5828e-04 - val_mae: 0.0156 - val_mse: 4.5828e-04 - 84ms/epoch - 6ms/step\n", + "Epoch 190/250\n", + "13/13 - 0s - loss: 4.2816e-04 - mae: 0.0159 - mse: 4.2816e-04 - val_loss: 4.2983e-04 - val_mae: 0.0155 - val_mse: 4.2983e-04 - 83ms/epoch - 6ms/step\n", + "Epoch 191/250\n", + "13/13 - 0s - loss: 4.1442e-04 - mae: 0.0156 - mse: 4.1442e-04 - val_loss: 4.5135e-04 - val_mae: 0.0154 - val_mse: 4.5135e-04 - 93ms/epoch - 7ms/step\n", + "Epoch 192/250\n", + "13/13 - 0s - loss: 4.1126e-04 - mae: 0.0159 - mse: 4.1126e-04 - val_loss: 4.2590e-04 - val_mae: 0.0151 - val_mse: 4.2590e-04 - 74ms/epoch - 6ms/step\n", + "Epoch 193/250\n", + "13/13 - 0s - loss: 4.1197e-04 - mae: 0.0155 - mse: 4.1197e-04 - val_loss: 4.2111e-04 - val_mae: 0.0151 - val_mse: 4.2111e-04 - 144ms/epoch - 11ms/step\n", + "Epoch 194/250\n", + "13/13 - 0s - loss: 4.0958e-04 - mae: 0.0157 - mse: 4.0958e-04 - val_loss: 4.1117e-04 - val_mae: 0.0149 - val_mse: 4.1117e-04 - 178ms/epoch - 14ms/step\n", + "Epoch 195/250\n", + "13/13 - 0s - loss: 3.9243e-04 - mae: 0.0153 - mse: 3.9243e-04 - val_loss: 4.1405e-04 - val_mae: 0.0150 - val_mse: 4.1405e-04 - 74ms/epoch - 6ms/step\n", + "Epoch 196/250\n", + "13/13 - 0s - loss: 4.0300e-04 - mae: 0.0153 - mse: 4.0300e-04 - val_loss: 4.3989e-04 - val_mae: 0.0150 - val_mse: 4.3989e-04 - 71ms/epoch - 5ms/step\n", + "Epoch 197/250\n", + "13/13 - 0s - loss: 4.0142e-04 - mae: 0.0154 - mse: 4.0142e-04 - val_loss: 4.3665e-04 - val_mae: 0.0151 - val_mse: 4.3665e-04 - 72ms/epoch - 6ms/step\n", + "Epoch 198/250\n", + "13/13 - 0s - loss: 3.9936e-04 - mae: 0.0153 - mse: 3.9936e-04 - val_loss: 4.2897e-04 - val_mae: 0.0149 - val_mse: 4.2897e-04 - 78ms/epoch - 6ms/step\n", + "Epoch 199/250\n", + "13/13 - 0s - loss: 4.0143e-04 - mae: 0.0153 - mse: 4.0143e-04 - val_loss: 4.0877e-04 - val_mae: 0.0148 - val_mse: 4.0877e-04 - 148ms/epoch - 11ms/step\n", + "Epoch 200/250\n", + "13/13 - 0s - loss: 3.9668e-04 - mae: 0.0152 - mse: 3.9668e-04 - val_loss: 4.3571e-04 - val_mae: 0.0150 - val_mse: 4.3571e-04 - 89ms/epoch - 7ms/step\n", + "Epoch 201/250\n", + "13/13 - 0s - loss: 3.9516e-04 - mae: 0.0154 - mse: 3.9516e-04 - val_loss: 5.1984e-04 - val_mae: 0.0161 - val_mse: 5.1984e-04 - 63ms/epoch - 5ms/step\n", + "Epoch 202/250\n", + "13/13 - 0s - loss: 4.5166e-04 - mae: 0.0161 - mse: 4.5166e-04 - val_loss: 5.4696e-04 - val_mae: 0.0182 - val_mse: 5.4696e-04 - 229ms/epoch - 18ms/step\n", + "Epoch 203/250\n", + "13/13 - 0s - loss: 4.5904e-04 - mae: 0.0166 - mse: 4.5904e-04 - val_loss: 4.1240e-04 - val_mae: 0.0150 - val_mse: 4.1240e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 204/250\n", + "13/13 - 0s - loss: 3.9851e-04 - mae: 0.0150 - mse: 3.9851e-04 - val_loss: 4.5210e-04 - val_mae: 0.0154 - val_mse: 4.5210e-04 - 76ms/epoch - 6ms/step\n", + "Epoch 205/250\n", + "13/13 - 0s - loss: 3.8760e-04 - mae: 0.0151 - mse: 3.8760e-04 - val_loss: 4.0982e-04 - val_mae: 0.0149 - val_mse: 4.0982e-04 - 74ms/epoch - 6ms/step\n", + "Epoch 206/250\n", + "13/13 - 0s - loss: 4.1937e-04 - mae: 0.0156 - mse: 4.1937e-04 - val_loss: 3.8857e-04 - val_mae: 0.0145 - val_mse: 3.8857e-04 - 170ms/epoch - 13ms/step\n", + "Epoch 207/250\n", + "13/13 - 0s - loss: 3.7173e-04 - mae: 0.0146 - mse: 3.7173e-04 - val_loss: 3.9353e-04 - val_mae: 0.0147 - val_mse: 3.9353e-04 - 74ms/epoch - 6ms/step\n", + "Epoch 208/250\n", + "13/13 - 0s - loss: 3.9673e-04 - mae: 0.0153 - mse: 3.9673e-04 - val_loss: 3.9003e-04 - val_mae: 0.0145 - val_mse: 3.9003e-04 - 65ms/epoch - 5ms/step\n", + "Epoch 209/250\n", + "13/13 - 0s - loss: 4.2359e-04 - mae: 0.0155 - mse: 4.2359e-04 - val_loss: 3.9027e-04 - val_mae: 0.0146 - val_mse: 3.9027e-04 - 83ms/epoch - 6ms/step\n", + "Epoch 210/250\n", + "13/13 - 0s - loss: 3.9302e-04 - mae: 0.0154 - mse: 3.9302e-04 - val_loss: 4.1320e-04 - val_mae: 0.0152 - val_mse: 4.1320e-04 - 71ms/epoch - 5ms/step\n", + "Epoch 211/250\n", + "13/13 - 0s - loss: 3.6641e-04 - mae: 0.0147 - mse: 3.6641e-04 - val_loss: 3.9564e-04 - val_mae: 0.0141 - val_mse: 3.9564e-04 - 73ms/epoch - 6ms/step\n", + "Epoch 212/250\n", + "13/13 - 0s - loss: 3.6259e-04 - mae: 0.0143 - mse: 3.6259e-04 - val_loss: 3.8787e-04 - val_mae: 0.0146 - val_mse: 3.8787e-04 - 155ms/epoch - 12ms/step\n", + "Epoch 213/250\n", + "13/13 - 0s - loss: 4.0665e-04 - mae: 0.0156 - mse: 4.0665e-04 - val_loss: 5.0910e-04 - val_mae: 0.0160 - val_mse: 5.0910e-04 - 81ms/epoch - 6ms/step\n", + "Epoch 214/250\n", + "13/13 - 0s - loss: 4.5758e-04 - mae: 0.0169 - mse: 4.5758e-04 - val_loss: 4.1241e-04 - val_mae: 0.0141 - val_mse: 4.1241e-04 - 96ms/epoch - 7ms/step\n", + "Epoch 215/250\n", + "13/13 - 0s - loss: 4.0666e-04 - mae: 0.0155 - mse: 4.0666e-04 - val_loss: 4.6639e-04 - val_mae: 0.0151 - val_mse: 4.6639e-04 - 77ms/epoch - 6ms/step\n", + "Epoch 216/250\n", + "13/13 - 0s - loss: 3.6615e-04 - mae: 0.0145 - mse: 3.6615e-04 - val_loss: 3.8294e-04 - val_mae: 0.0138 - val_mse: 3.8294e-04 - 194ms/epoch - 15ms/step\n", + "Epoch 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0.0142 - val_mse: 4.1371e-04 - 81ms/epoch - 6ms/step\n", + "Epoch 223/250\n", + "13/13 - 0s - loss: 3.7775e-04 - mae: 0.0149 - mse: 3.7775e-04 - val_loss: 3.8045e-04 - val_mae: 0.0142 - val_mse: 3.8045e-04 - 72ms/epoch - 6ms/step\n", + "Epoch 224/250\n", + "13/13 - 0s - loss: 3.5911e-04 - mae: 0.0145 - mse: 3.5911e-04 - val_loss: 3.5609e-04 - val_mae: 0.0134 - val_mse: 3.5609e-04 - 162ms/epoch - 12ms/step\n", + "Epoch 225/250\n", + "13/13 - 0s - loss: 3.5933e-04 - mae: 0.0144 - mse: 3.5933e-04 - val_loss: 3.5900e-04 - val_mae: 0.0134 - val_mse: 3.5900e-04 - 85ms/epoch - 7ms/step\n", + "Epoch 226/250\n", + "13/13 - 0s - loss: 3.6466e-04 - mae: 0.0144 - mse: 3.6466e-04 - val_loss: 3.5378e-04 - val_mae: 0.0135 - val_mse: 3.5378e-04 - 152ms/epoch - 12ms/step\n", + "Epoch 227/250\n", + "13/13 - 0s - loss: 3.5876e-04 - mae: 0.0144 - mse: 3.5876e-04 - val_loss: 3.6523e-04 - val_mae: 0.0133 - val_mse: 3.6523e-04 - 75ms/epoch - 6ms/step\n", + "Epoch 228/250\n", + "13/13 - 0s - loss: 3.4559e-04 - mae: 0.0142 - mse: 3.4559e-04 - val_loss: 3.5907e-04 - val_mae: 0.0139 - val_mse: 3.5907e-04 - 78ms/epoch - 6ms/step\n", + "Epoch 229/250\n", + "13/13 - 0s - loss: 3.4162e-04 - mae: 0.0142 - mse: 3.4162e-04 - val_loss: 4.2194e-04 - val_mae: 0.0141 - val_mse: 4.2194e-04 - 72ms/epoch - 6ms/step\n", + "Epoch 230/250\n", + "13/13 - 0s - loss: 3.6967e-04 - mae: 0.0146 - mse: 3.6967e-04 - val_loss: 3.7720e-04 - val_mae: 0.0138 - val_mse: 3.7720e-04 - 76ms/epoch - 6ms/step\n", + "Epoch 231/250\n", + "13/13 - 0s - loss: 3.3735e-04 - mae: 0.0136 - mse: 3.3735e-04 - val_loss: 3.3976e-04 - val_mae: 0.0129 - val_mse: 3.3976e-04 - 166ms/epoch - 13ms/step\n", + "Epoch 232/250\n", + "13/13 - 0s - loss: 3.3844e-04 - mae: 0.0141 - mse: 3.3844e-04 - val_loss: 3.8716e-04 - val_mae: 0.0135 - val_mse: 3.8716e-04 - 71ms/epoch - 5ms/step\n", + "Epoch 233/250\n", + "13/13 - 0s - loss: 3.6741e-04 - mae: 0.0145 - mse: 3.6741e-04 - val_loss: 3.8668e-04 - val_mae: 0.0136 - val_mse: 3.8668e-04 - 69ms/epoch - 5ms/step\n", + "Epoch 234/250\n", + "13/13 - 0s - loss: 3.4129e-04 - mae: 0.0139 - mse: 3.4129e-04 - val_loss: 3.4933e-04 - val_mae: 0.0133 - val_mse: 3.4933e-04 - 69ms/epoch - 5ms/step\n", + "Epoch 235/250\n", + "13/13 - 0s - loss: 3.2338e-04 - mae: 0.0137 - mse: 3.2338e-04 - val_loss: 3.4566e-04 - val_mae: 0.0133 - val_mse: 3.4566e-04 - 86ms/epoch - 7ms/step\n", + "Epoch 236/250\n", + "13/13 - 0s - loss: 3.1652e-04 - mae: 0.0134 - mse: 3.1652e-04 - val_loss: 3.9728e-04 - val_mae: 0.0136 - val_mse: 3.9728e-04 - 86ms/epoch - 7ms/step\n", + "Epoch 237/250\n", + "13/13 - 0s - loss: 3.2047e-04 - mae: 0.0136 - mse: 3.2047e-04 - val_loss: 3.3756e-04 - val_mae: 0.0130 - val_mse: 3.3756e-04 - 131ms/epoch - 10ms/step\n", + "Epoch 238/250\n", + "13/13 - 0s - loss: 3.3167e-04 - mae: 0.0138 - mse: 3.3167e-04 - val_loss: 3.3191e-04 - val_mae: 0.0126 - val_mse: 3.3191e-04 - 141ms/epoch - 11ms/step\n", + "Epoch 239/250\n", + "13/13 - 0s - loss: 3.2033e-04 - mae: 0.0134 - mse: 3.2033e-04 - val_loss: 3.2969e-04 - val_mae: 0.0128 - val_mse: 3.2969e-04 - 127ms/epoch - 10ms/step\n", + "Epoch 240/250\n", + "13/13 - 0s - loss: 3.5224e-04 - mae: 0.0141 - mse: 3.5224e-04 - val_loss: 3.9061e-04 - val_mae: 0.0148 - val_mse: 3.9061e-04 - 69ms/epoch - 5ms/step\n", + "Epoch 241/250\n", + "13/13 - 0s - loss: 3.9777e-04 - mae: 0.0153 - mse: 3.9777e-04 - val_loss: 3.7065e-04 - val_mae: 0.0137 - val_mse: 3.7065e-04 - 73ms/epoch - 6ms/step\n", + "Epoch 242/250\n", + "13/13 - 0s - loss: 3.2502e-04 - mae: 0.0138 - mse: 3.2502e-04 - val_loss: 3.3236e-04 - val_mae: 0.0124 - val_mse: 3.3236e-04 - 82ms/epoch - 6ms/step\n", + "Epoch 243/250\n", + "13/13 - 0s - loss: 3.0734e-04 - mae: 0.0133 - mse: 3.0734e-04 - val_loss: 3.2635e-04 - val_mae: 0.0126 - val_mse: 3.2635e-04 - 154ms/epoch - 12ms/step\n", + "Epoch 244/250\n", + "13/13 - 0s - loss: 3.2928e-04 - mae: 0.0137 - mse: 3.2928e-04 - val_loss: 3.2871e-04 - val_mae: 0.0125 - val_mse: 3.2871e-04 - 66ms/epoch - 5ms/step\n", + "Epoch 245/250\n", + "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 155ms/epoch - 12ms/step\n", + "Epoch 246/250\n", + "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 247/250\n", + "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 139ms/epoch - 11ms/step\n", + "Epoch 248/250\n", + "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 85ms/epoch - 7ms/step\n", + "Epoch 249/250\n", + "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 73ms/epoch - 6ms/step\n", + "Epoch 250/250\n", + "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 72ms/epoch - 6ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# selected settings for regression (best fit from options above)\n", + "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 4, 20\n", + "loss, metrics = \"mse\", [\"mae\", \"mse\"]\n", + "\n", + "# Create data objects for training using scalar normalization\n", + "n_inputs = len(input_labels)\n", + "n_outputs = len(output_labels)\n", + "x = input_data\n", + "y = output_data\n", + "\n", + "input_scaler = None\n", + "output_scaler = None\n", + "input_scaler = OffsetScaler.create_normalizing_scaler(x)\n", + "output_scaler = OffsetScaler.create_normalizing_scaler(y)\n", + "x = input_scaler.scale(x)\n", + "y = output_scaler.scale(y)\n", + "x = x.to_numpy()\n", + "y = y.to_numpy()\n", + "\n", + "# # Create Keras Sequential object and build neural network\n", + "model = tf.keras.Sequential()\n", + "model.add(\n", + " tf.keras.layers.Dense(\n", + " units=n_nodes_per_layer, input_dim=n_inputs, activation=activation\n", + " )\n", + ")\n", + "for i in range(1, n_hidden_layers):\n", + " model.add(tf.keras.layers.Dense(units=n_nodes_per_layer, activation=activation))\n", + "model.add(tf.keras.layers.Dense(units=n_outputs,activation=keras.activations.linear))\n", + "\n", + "# Train surrogate (calls optimizer on neural network and solves for weights)\n", + "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", + "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n", + " \".mdl_co2.h5\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", + ")\n", + "history = model.fit(x=x, y=y, validation_split=0.2, verbose=2, epochs=250, callbacks=[mcp_save])\n", + "\n", + "# Get the training and validation MSE from the history\n", + "train_mse = history.history['mse']\n", + "val_mse = history.history['val_mse']\n", + "\n", + "# Generate a plot of training MSE vs validation MSE\n", + "epochs = range(1, len(train_mse) + 1)\n", + "plt.plot(epochs, train_mse, 'bo-', label='Training MSE')\n", + "plt.plot(epochs, val_mse, 'ro-', label='Validation MSE')\n", + "plt.title('Training MSE vs Validation MSE')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('MSE')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: keras_surrogate\\assets\n" + ] + } + ], + "source": [ + "xmin, xmax = [7,306], [40,1000]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "\n", + "keras_surrogate = KerasSurrogate(\n", + " model,\n", + " input_labels=list(input_labels),\n", + " output_labels=list(output_labels),\n", + " input_bounds=input_bounds,\n", + " input_scaler=input_scaler,\n", + " output_scaler=output_scaler,\n", + ")\n", + "keras_surrogate.save_to_folder(\"keras_surrogate\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing Surrogates\n", + "\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 [==============================] - 0s 1ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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UYPTo0Q62miAIgiDEYTWUYdCgQejduzfWrFkDAPB6vViyZAm+/vprvPLKK/joo48wffp0AEC/fv2waNEiZGZmorKyEpWVlfjjH/8IIJxy/sgjj+DLL7/EO++8g7KyMowfP97W3yKGayw+H374YdTfy5cvR+vWrbF9+3YMGDAAwWAQS5cuxYoVKzBo0CAAwLJly/DLX/4Sn3/+Ofr27etEswmCIAjCdZx//vnYvXs3AGDKlCmR1/Pz8/Hoo4/ijjvuwHPPPYeUlBT4/X54PJ6I5Yjnlltuifx/586dsWTJElxyySU4ceIEmjdvbsvvEMM1Fp9YgsEgAKBly5YAgO3bt6O+vh6DBw+OHHP++eejY8eO2Lp1q+R5zpw5g+rq6qh/BEEQBJHIcBwXcV1t3LgRV1xxBdq1a4eMjAzceOONCAQCqK2tlT3H9u3bMWrUKHTs2BEZGRm4/PLLAQA//PCD5e2Xw5XCJxQKYcqUKejfvz969uwJILxXSkpKSpPI8jZt2uDw4cOS55o7dy78fn/kX4cOHaxsOkEQBEEwz969e1FQUICysjKMHDkSF154IVavXo3t27fjT3/6EwB5F9zJkycxdOhQZGZm4vXXX8d//vMfvP3224qfswPXuLqETJo0CXv27MFnn31m+FwzZ87E1KlTI39XV1czJX5SUlKi/g4GM3D8eDZatgzA76+RPM4oTmcaEARBEM7w0Ucf4auvvsK9996L7du3IxQK4emnn4bXG7aVrFq1Kur4lJQUNDQ0RL327bffIhAI4IknnojMqV988YU9P0AB1wmfyZMnY+3atfjXv/6F9u3bR15v27Yt6urqUFVVFWX1+emnn5r4HYWkpqYiNTXVyiYbIjs7G5MnT0ZdXR1WrEjDnDl+hEIeeL0c5s8PYuzYU6aLEKszDUhUEQRBsMGZM2dw+PDhqHT2uXPnYuTIkbjpppuwZ88e1NfX45lnnsGoUaOwefNmvPDCC1HnyM/Px4kTJ/DPf/4TvXv3Rnp6Ojp27IiUlBQ888wzuOOOO7Bnzx488sgjDv3KaFwjfDiOw1133YW3334bH3/8MQoKCqLeLywsRHJyMv75z39izJgxAIB9+/bhhx9+QHFxsRNNNo3s7GyUlwPTpwN8TalQyIMZM7JQWpoFoUYwQ1RYmWnAQvomQRAEEebDDz9Ebm4ukpKS0KJFC/Tu3RtLlizBuHHj4PV60bt3byxYsADz5s3DzJkzMWDAAMydOxc33XRT5Bz9+vXDHXfcgdLSUgQCgUg6+/Lly/E///M/WLJkCfr06YOnnnoKV199tYO/NoxrhM+kSZOwYsUK/P3vf0dGRkYkbsfv9yMtLQ1+vx8TJkzA1KlT0bJlS2RmZuKuu+5CcXFxXGR0ffddo+jhaWgA9u8HeMOXG0QFq+mbBEEQdqM2RMHsUAae5cuXY/ny5YrH3Xvvvbj33nujXrvxxhuj/n7++efx/PPPR7123XXX4brrrot6jeM4fY01EdcIH/6CDhw4MOr1ZcuWReoCLFy4EF6vF2PGjIkqYBgPdOsGeL3R4sfnA7p2bfybRAVBEIR7EIYySEGuf/NxjfBRoxLPO+88/OlPf4pEnMcT7dsDL70E3H572NLj8wFPP30CPl8NKivDxxw7dszZRhIEQRCaIFFjP64RPgQwYQIwdGjYvZWd/TPWrFmCl15yulUEQRAE4R5I+LiM9u3D/yorT9v2nVIp9ARBEAThNkj4xDFmCJYdOy7Ce++NBMd54fGEMGrUWvTps5OpNhIEQRCEWkj4xClGBAufQRAMZkTOAQAc58V7741Ely774ffXGM40sFpUqYFqChEEQSQWJHziBKHlBICsYFGCzzTYtAlYuDB6VxOO86J//3EYOFBfUJ5aUVVbW4tKPmpb4jxGiycGg0GsXLlS8TNq0v9JQBEEQbgDEj6MIzWhCjO4Yi0nxcVbI4KCh+O8OH68pWpLTXZ2Nvr2FU+hLyrKht45XI2o+tWvqvDaa68pnktrPSK1dY5iUUr/d0P9JIIgCCIMCR8b0GsNUDOhillOtmwphscTihI/Ph+Hu+4ajvz8JNWTr1gK/YsvNhZM1IuSqPL5rKlHZFX9IqqfRBAE4R5I+FiMEWuAmony+PHsJtYdwIvi4s3YurUYHOc9J1g8KCxso6XpAKJT6Lt2NS56eORElYyHyxGOHTsmK06pfhJBEERTPv74Y/zmN7/Bzz//HLWHphz5+fmYMmUKpkyZYlm7YmdMwmTMtAYEgxk4eDAfwWBG5LWWLQPweKL3svB4Qigq2oYpUxbhnXeCKCsLCxi9tG8PDBwIpKUFUFlZKfkvEAhoOu+ECUBZGbBpEwy30UrWrFmDZ599tsnv40XtmjVrHGoZQRCEfsaPHw+Px4M77rijyXuTJk2Cx+OJ7IwQT5DFx2b0pm9LZUD5/TUYNWpt5D2fj8O8edUYO/a6c1YKvynttiqOha9LJIddKe9K3xMrTsl1RRCE2+nQoQPefPNNLFy4EGlpaQCA06dPY8WKFejYsaPDrbMGEj42ojd9WykDqk+fnejSZT/69x+HoqJstG+fBSDL1LY7FcdiZcq7UOgcONDV8dR6giAIu+nTpw8OHDiANWvW4PrrrwcQtnJ37NgRBQUFkePOnDmD++67D2+++Saqq6tx8cUXY+HChbjkkksix7z//vuYMmUKDh06hL59+2LcuHFNvu+zzz7DzJkz8cUXXyAnJwf/7//9P8ydOxfNmjWz/seeg1xdNiElXoRuKynE4ng4zotDhxpNJX5/Dfr1qzMtBkcJMbebFd+h95opsWPHRVi0aApeeWUcFi6cgnffHaX6ewKBsMtPKrbHjmtDEET8UV4edv2Xl9v7vbfccguWLVsW+fvll1/GzTffHHXM9OnTsXr1arzyyivYsWMHunbtiqFDh+L48eMAgEOHDmH06NEYNWoUdu3ahVtvvRX3339/1DkOHDiAYcOGYcyYMdi9ezdWrlyJzz77DJMnT7b+Rwogi49NSIkXPsVcDj6OJ/bzb711Lerq7LdM2FV40Mg1kyNWUInpf15Y+v17o15XcvmxUJTRDVDdI4KIZulSYOLEcKar1xtO/rAr7vGGG27AzJkz8f333wMANm/ejDfffBMff/wxAODkyZN4/vnnsXz5cgwfPhwA8Oc//xkbNmzA0qVLcd999+H5559Hly5d8PTTTwMAunfvjq+++grz5s2LfM/cuXNx/fXXRwKXu3XrhiVLluDyyy/H888/j/POO8+W30vCxybExIvHE0LLlscVPxsbx9NItMuLt0BYOWkoud3MgK8zpHTNtFaO5o8Xz4RripiwlJusy8tzLa10zTJahAzVPSKIaMrLG0UPEP7v7beHM2rtsOK3atUKI0aMwPLly8FxHEaMGIGcnJzI+wcOHEB9fT369+8feS05ORmXXnop9u4NLw737t2LoqKiqPMWFxdH/f3ll19i9+7deP311yOvcRyHUCiEgwcP4pe//KUVP68JJHxsIla88NYAObEgnCj79NmJlJQzeOut30Udw3FefP11D1xwwTdR2UVWTRpWWWGE8EUO6+rq0K5dNWbM8KOhwRMVuF1bW4u6ujrJ6s5i4o8/b1nZWbz6KodQyCN4NwSPB7LCUo4dOy7Cu++ORKz1iOO86NnzGvz2t/64ncS1Chmqe0QQ0Xz3XXRNMyBc5mP/fnuEDxB2d/Eupz/96U+WfMeJEydw++234+67727ynp2B1CR8LCZWvHTpsh/Hj7dEy5bHoyZTMWsAP1FXVFRgzZo16NDhkIjLi8P69cOwYcOQKLeKVZOGEcuVFniRMG0aUFrK1xHyoH37LAQCDXjppZcUzyEm/rKzw1Wno2sIcRgxYq2ksFQSdbwVTMxl5vGE0Lt3s7gVPQAVcCQIo3TrJl7QtWtX+9owbNgw1NXVwePxYOjQoVHvdenSBSkpKdi8eTM6deoEAKivr8d//vOfiNvql7/8Jd59992oz33++edRf/fp0wfffPMNutr5w0Qg4WMxQuuFFHKuKeEKuanLiwMQtlqocTmZEVehx3JllNiUdzMmWmFhxuzsKqxZsxPBYIaiay32nMFgBr7++gJR1xl/bfLz+zd5L1Ghgo8E0RSrquRrwefzRdxWPp8v6r1mzZrhzjvvxH333YeWLVuiY8eOmD9/PmprazHhXCDSHXfcgaeffhr33Xcfbr31Vmzfvh3Lly+POs+MGTPQt29fTJ48GbfeeiuaNWuGb775Bhs2bNC1nZBeSPjYgNHVvpjV6Ouve2D9+mFRx8lZJ2LdEVI1a6RcZEYsV1ajt85Po6Bqgbw8da41qT3ShCIUADweDmvXBlBU1N911h6rAo+rqqqwatUqI00jiLjFqir5WsjMzJR874knnkAoFMKNN96ImpoaXHzxxfjHP/6BFi1aAAi7qlavXo17770XzzzzDC699FI8/vjjuOWWWyLnuPDCC/HJJ5/ggQcewGWXXQaO49ClSxeUlpZa/tuEkPBxAUKr0bFjx7BmzRpccME32LBhiGqXk3Aik8s8kprwjFqurMKsLCqtrrWmmWEe8OLH4wnhySercdVVrYz9OAfQEq8DoIkYlOPo0aOG2kYQ8Y6agq5mEmuRieWdd96J/P95552HJUuWYMmSJZLHjxw5EiNHjox6LTYt/pJLLsH69eslz1FWVibbJjMg4eMSYgWFXpeTkaws1iwXVmWYqXGtiWeGeTB06Ifo0eMbjB17HcwuIqkXLRYctW7Eo0ePYuXKlZrasWnTJtHX7arMTRAEAZDwcTVyLicp7MjKsgunfkswmIGTJ9MBhCAMaPZ4QujR4xumUtetSh2vr6+XfE+LkKG6RwRB2A0JH5fj99domuTtysqyAyd+S9O4nrD48XhCeOihCkyceB1TxffszrjSImSULHZ2iUcqpkgQiQUJH5ehdjKQOs6JrCyrUPNb9ExqsZ/hY1jE4no8Hg5jxqxChw7lmDjxOuTm5pr3AzUi9luNZlBpsd5odT1KWezsrHtExRQJIvEg4eMyzAgy5l1k4b2+POjQ4VDkPX6irKjw4uDBJBQUnEVeXkjVeZUoLw8X6urWzVgAn9oMs0OHDuGDDz6I/K0mk01uIpSaqJs1q3VcOKqdwKWuAX/fpbLW1FhvxNL65VyPyclnRC12dtY9SvQaRGTtMg+O45xuQkJgxnUm4eNCzBiIpHYjX7NmjeyEp3Xlyw+sK1akYfp0P0IhD7xeDvPnBzF27CnFgVU4MAvFWElJCaqqqrB+/fqIu4/fHJSf1IWiR20mm1idHl4osOwmVDMxy10DYdVvQJv1Rj6tP4Tk5Lqo+yL1Gb5NeXmXgLAesnaZQ3JyMgCgtrYWaWlpDrcm/qmtrQXQeN31QMInQRBaSeQmNQCyE56WlS8/sAaDGVi0aAo4LjwhhkIe3HdfJn788WX4/TWSA6twYG46aW+Lsj7ITep6s7/EzulWN6FZbqhY641SWv+FF+7G0qW3Rl2vLl32i3wmhAkT/oL27SuRkkIFH+0g0a1dZuHz+ZCVlYUjR44AANLT0+HxeBQ+RWiF4zjU1tbiyJEjyMrKalJkUQskfOIQMZdSdnY2SkpKsGrVKtlJDfCYlinFD5hKk6jUwMq/rjRpK72vJ/tL6pxTpizClCmLNBVvdMKdEOvS0noNlKxbSUnhoUMurb9Dhx8ioof/vvfeG4nhw98X+YwXxcWD43pPM7WQ+8l9tG3bFgAi4oewjqysrMj11gsJH8YwGgezdGnjLr9eb7gM+rmK4sjKygIgP6nV1DQ33Z1j1EWkNGkrva/n++XOWVDwPW6++cqo3YulJiMn3AlilqouXfZLXgOxuB+/vwYPPVSBRx5p16SKtVDgSV3bHj2+kbyG778/okmbWdnTzOmaQnb1F9o6xFw8Hg9yc3PRunVr2VIPhDGSk5MNWXp4SPgwhJxoUSIQCKCs7CwmTmwd2XU8FAJuv51Dbu4x1NWloEOHxkmouHgrtm4tjpochXE/sXEXRiYBo5lkSsJF6X093690zpycHFUZXHIxQ8LvN8udIGepErsGUrFeADBxog8TJniiqlgLizLyQfZi23yMGHEVKit9ePVVLtIfw0THAIVhI7aHhZpCdrif1IorQjs+n8+UiZmwFhI+jFBe3ih6AF60hPduUbL88APZwYP5CIXGRb3X0ODBiBHZALzwejPRq9dvsXv3hecmxhD69duMoqJtAHAuDkc87kKsvVosU3qKLfIoCRc1wkbr91uR9m/HxCpnqerTZycmT+6GQ4dSsWfPOwCi73lTF2IQfj/QvXv4PJUx3SAlJQW5ubmi23wAWTj/fH7jRQ4NDeH+JLaD/bXXvoWePfcCsFb4yKX7W1UFnEWkRJPT1i4rYdV9yGq74h0SPgwQCATw+edAKBTdwRsagG3bAkhLk8/k4h8cMStFeIUd/jsU8uDLL3ujccXtxdatxSgq2iYRq+FFfX3TuBW9limtxRaFKAkXNcJG6/cbEWux2DWxKlmqevbMQs+ewKFDNTh4MF/WRahmSwre5SK1x9CECcCll1bh6af/juTkuqiYH75tHTqUAwhvYmrVQK9k5VByl7JSidsqWLB2WQWr2WustisRIOHjMMLMJ49nSpNJYfPmV7Bnj3TmkxAxK4VY4KkQfnBXGwezZ08VJk5s28Sd9qtfHUF+fpKlD6iScNErrISTWuwEJ3VOrROh1dtr8O1RslSlpKTICmWt8VxqXC69erXA00+PwtGjR3HkiHTb+J3brRjoldopdi18Pg533TXc8n4thx1WGDNEOcuWC1az11htVyJAwsdh+E6tNGGp7fxCK4XYCluszgpvzVDj2nn99X8jFPpl1GsNDR4888wHKCj4nsnVSWlpKfx+v+h7sQOyVbvQW10DKLbds2YdRVlZEvLzz56Lnbkk0u5AIABAmzvP6AScnZ2Nuro6VVY0JwZ6/lqsWzfqXKwS8OKLHhQWtrG9LTx2WWEOHepgyNpFlgvCbZDwYQizXCtCK0XsxHbhhbsjMT6xE53c9//mN7/Bpk2bFCdw4aRldHsNtZ8vKSmJZKyJnUPrYGvF4GzHViHCdufmAoWF0scpiaSqqqqIBUZuAuZjZLRcZ6mCk3YiJuT69NmJWbOKUFPTBl27Gqsubkb77HCN8vc2Fo8nhOuvL0KfPqMU76vbLBesxjKx1C6WLXhmQMKHScwrfiUmZgYN+khSXEm5dlq0aBF5X+0ELmY9id0KQ+4Bssr64hRmxgwZRa1IUpqAhRWftazonYwpkfvuvLwQHNxuLYLVrlFArPgkT/ia9Ox5iWueLbWwGsukt11WCJREsOCR8GEIqx7KWDFjJMgY0DaBCx8MPUHRbn2weMyIGVIa3Gpra5Geni7bBr3XUWoC/uabHujR4xtdKflOZlA5nb2l5l4Cyq5RM4KtxRMajGfZKe0HF4tc/xRer6qqKpw9ezbq/eTk5IgbW6mfO33vzW6XVQLF7hIcTkDChxGseChjO6yZplQt4kmuxpAdQdFWILWHWOyGrkatVrGDm9I9VLMRqxakMgX/8Y9hWL9+iC5xboc1w87vVrvqVjtR3XDDDUhPTxetj8QXkDTyvPCiSUpc8Vl2esSVlv3ghIj1Tz31huT6uZP9Tg697bJKoFRVVUX+n1ULmVFI+DCCkYdSbICK7bBisT12dGClGkMsB0VLIb+HWNMNXY38LuGgpTQIqd2IVQuxrk1hcLxece7kZq9mf7eWVbfae5Ceni5bH8koQjFuprgysngTuzZ6+mxdXV0TIcpbmozee6viXszok2YJlEAgEIntY9VCZgYkfBjBSOePtSrs2VOF2bO7R3VYYf0ePR2Y35tJK2pTp1k0m0oVaVS7h5iZv8nofmVaEYpp3rX5zTc98I9/DIs6Ts+K2YxAb62TkJZ0fy0YCexVY4GVqo8Ui9brwf+/meJKzeJNjRtMSkAIP8t/X+x5Dh06hA8++EC0fUbuvRVuJbP6pJnPvrAPbdtWxKSFzAxI+DgM79NX6vz8cVIIH7bNm1NU1+8RduDRo0dH7T/FI6z9ohc7sprMgJ9AVqxIw/TpfoRCHni9HObPD2Ls2FNRg5CdpnOj+5VpRSimjx07hjVr1qBHj2+wfv0Q3StTMTGlZbNXHj2TkJZ0fyOodSeb6UIwOimrFVdKSC1ukpPrcPBgPioqcrFx42BVbrDJkydH/S28XmGrY7gwa+x5YkVP7P3g+13Pntegd+9mqu+9FW4ls/qkFeNQMJiBLVuKm7xul2XWakj4OIwwKFVuMpALXhUSCASQnX0cHk8L2fo9AIeKijwUFHwfeSUvL0821kQNcpMWS1lNYgiLSYa3cuDjkTy4775M/Pjjy/D7a1BSUgLAXpeN0f3K9BDbF4yKV7Oy9PROQmoz2fSiVsyYbZ1zOp1cznJx4YW7BbXEmrpJW7c+jPr6VNl71zT7zNPkPGLXbvPmYlGh5ffXYPjwNOTm6qvRZKZoNdIn+VgcK57948ezIba9THHxVubGbT2Q8GEMIxlXwpXfqFHRD+cvf7kX33zTA43ix4ONGwejZ889mDjxKrRq1Up2wjFr0jKaUWYl/G9TWkHxmSV2WrGUvsuuthgVr2bHcbESfKlFzFhtKbSrHozQvVZaWor6+nqMHg3ceOO/sXbttyIFVJtanfn35e6dVPaZ8Dyx127z5mJs2HAljMajxcJK3IswFsfvr8HgwRubiDwj7RFPaghF9nV0OyR84gihKImdoI4fz8Y331wQdXzjgOFXNSG5JfjYKFpWUGqEgNYNXaUwY78yPVi1jYdRWJmEAG1ixsgKXW6jVcA+IajkXisogOhecNFwqu6d+CTcSOy1CwYzsGHDYKhx72uFlcyw2KQHXvQAIQwevNHwPVezkHLz/nUkfOKY2AnKqUwaPZglFvSg1XoiJQSOHTsmGyukR0gqWcyssKixWkiSlUkI0CZm9Frn5Esb2CsE1bgbpTdN9gAIIdaVInXvxK4XxwHCGB/hZ6TcNGaMd05mJArh3VxN3YDeiCVfb8A+j9xCqrS01NULYRI+CYLcYFtVVYVcC8vVSpneY7M0ePTu/m4mZlhPli3bIBsrNHnyZJw6lS0r8MxaVRk9j5ODXGzNpF27TiEYzGBmEgK0r5D1BHcrlTZo0eJnR4SglJVJ7JoMHrwReXkVovsIxt47uesFQPLZlBJcgwdvNKV+mdNJGkI3l1LmlVGBIrWQktr70C2Q8EkgpAbbVatWWVZHZ9UqPlA4elCMHSw7darFPfeELT286AH4QofA0KHOWH7EHvrk5GRVn1eySCxf7sP06fICT421xcrKzU4jXjOpDTyeKRg1aq3jk5AWMWOW5UzKsjNhwl9sF4JKVia5a6J072Kvl1Ll5mAwiJUrVzYRJ0AIV165Ef37bzXlNzudpCEsp6GUeaVVoOjZX9FJ67xeSPgkGFKTuRUZH+XlwNSpzc+ZpcOD4rp1o3D77cWYMycnYgnhOC+mTWuOMWPCDxAvengaGsJ1Rlh5qPx+v+wExqd/y1kkgsEMzJnjjxF44pWsjYqW8nJg0yZ3DUw8SjWTpkxZhClTFjk2CWkVM2YIUClBXV+fYrsQVBL3sSUy+GcDUCcgorOe1Fullc6t1QJqdZyb2krwQqzIvNLan5Ws86yKIhI+DiP1oMS6h9wYSCYuYjzYt6+VqLjZvj2I9u1Pw+tt3NoCAHw+DhkZRxAIsLO1hZp2yJnFw5Wso4MvzaxkraYekZ7z6xmgzUBugi0o+N72YGsnd6+WE9QFBd/rro9kdlsAICcnJ0qwWCkg1J77hhtu0HxvrIxz01oJnsfMzKtogaL8G9RsQ/TOO9mOhyxIQcLHYcQeqBUr0s5ZA8KT1YIFJ5Gd3dzBVmonEAggM/NsExHj9XJo02Y/vN6uTV7/5JOl8PtrMHJk9MM/YsRarF0r/vCbiR4zrxJSK08rK1mrrUek9VrqHaDNQOl6xVoWrBQeavdPs6qvKsWZ3HzzlU0KkVp1PbTGvFghIBonbWuD8K3qT3orwZsVb6Q1plLNNkQtWx7H4sX3Mrs3IwkfBhB2gvJyROI+gPBkxbuBlEyFrFiFhBNDrIgZOXItvvtup+jr/AMrZ6a2cmsLq1Z1YitPK4Mk1dYj0notndiqg0fpesVaFqxE7f5pZlwHoWVpz54qHDyYj5YtA7LPiJ3XAtAe82LGpCdt0fRh7Fh3xrXpyVI04tLTu3m0mm2Ijh/PttSibRQSPoxhJMZFbOIuKyvD+vXrLWipNHL1hNSIG8C5Qof8w2iHb9rqIEm9WU9Kv92pNHKng0pjsTp9XNzC9ssmmVNOYNRlZcRlapVF02n0Pq9S114uo+urr37G00+vxcmT6QiFfhf1nlqBorQYYXlvRhI+NqF2Iu3WLWxuFIofnw/o2lXd97D4oEunRLJZxdnOdHorr4Eeq5Ka3+5kGrmZ18uouLVaAKq1sMVih+XXiHXUqMvUKoum05htBZbK6Ao/41kIhcbB4wkhtqaSFoEitRhhIe1fDhI+NqBlIm3fPvz+7beHLT0+H/Dii2xFxMczZqTTs+JyBNRbSdSYvXlYH9TkMDPo2y4BqCVzyk4Xj97vMctlaub1NyKCzbQOq3le9cYiij3jjQUhw+JHz7MstRhhzUIrhISPxWiZSPlB+aqrgG3bvIKdekOorNQ3qFkRsBuvBAIBfP45EApFX+OGBmDbtgDS0tQN9nKrYWE6r10oWUnUBCsWFHyP0tLSyOt2DWpm9l+zXSR2CUCtmVNuwajFzOj1NyKCrcqa5H+X3G/QY22Te8Y5zotrr12FZs1qTX+WWbXqk/CxGLUxO1J73+zZE/23Vr81q9sNsIZwUvR4pjSZZDZvfgV79qifFN10PdUEKwJAfX191OfsGNTM7L96XCRisSh+f1XkfTsEoJstbFIEgxk4eTLdsMVG7/U3IoJZiDFSqvgeW7tL6Rnv0KHc1f1JKyR8LEZtzE5Z2dlIxoZcB9Tjt3bLJGzXrtJCeDN1Zqa6HdeNxg2wbIFjdYK1OzWeRzoWpQUGDy6OVAK2QwCy7DZQCy8iX3qpIVLNHQhF7oXe/qbn+huJEzI7xkjrmKAUOiH2/lVXhd9j9Rm3GxI+FhIIBODz1WH+/DTMmOFHQ4MHPh+HefOC8PlOIRAIr1TDHbV1JNjMqh2VWUbtrtJmCoLoAaI1Ro68CH367LR0krHDAmdEXMn99mPHzlMlzll3m6od/OViUTZsuBIAZLdB0HId1MSJsOo2UENTK0njppocF8K1164ybHXQs3AyEidkVoyRljFBKXRC6v1t2xrbaHR8Y/35VgMJH4uIdV3dfXdGpKOdOFGDl14Kvz569N2YOLFFVLBZbMaG1GaerKL1wRCbWNatG4VZs4oi6a38ec1a/TcdIDxR193KScZqC5xRcSX223fsuAhz5nRFKNQtKpZBy3n1YkVpAbHBn3/OKiq8EIbMiK3sAQ82bGjcBbukpARZWVmRd9VcB6U4kWAwaM6PZQA5KwngRbNmtYaeN7GFkxq0WkDKy4HPP09BMJhhqvVEzTOjFIN45kwVdu7MEn3/yy9PRr1mZHyTG1/4/dKUcFo8kfCxCLEqm2Idbd++UJMYIKG5NPaBbteuGtOmWdlyadROQHIPBr/RoHBzwc2bU7BwYfRg2NDgQU1NG1gVsykWe2VHLRq7MFN8NArTxliGGTOyUFqaZXm2oZWlBYTPpPA5e/VVDvPnV2HIkGMApLYGAAAvtm0rwpAhG+HxeDQFGKuNE1GD05OIFvRYSWLHndjfK5Uddu+9/4eUlIDis6DWAtLYF7Mjm+RqEdBGUBuDKPX+nj3vQM+G6vy1jr0HUtc0NzfXFTGlJHwcpqDgbJMYIOFGlrEP9IwZfpSW2p/ernUCknswYunb11jtIj2IxV7ZVYvGbYit0rVmumlFb1VZPcQ+Z43iYwOA8CT2619/ik8/HQAguhrt1q3FKCrahpUrV2oKZlUbJxJrSYqFhUlEC1qsJCkpKRLjTnhhdeTIEaxatUryGi5f/pnqKsFyFhCpNPBYCzHQVECbJdT5/qJ0/YxaocRKI5g19rMECR+HycsLnavbw6GhwdNkI8umE47H9p3KzahtI4XaOCgzEftOr5eL2jaDaERqla41000tatPrzfpeqYlz27YibN1aHJlAOnUqw/ffFzQ5zkjBPCULSFZWlitT1eWQs7DwE29tbe05scFJCl9eEFq5711VVRVWrVolmQYutBCLCWixcdKo61bJQmUkhie2NIKVY7+TkPBxmGPHjuGqq4APPqjC669vi+qoyclnmjzQPh+Hrl09UqezBCPbaMihNg7KzIlV7jsTVfQouUqsznSLRW16vdbvlfqdUrtc86IHCE9yP/zQCXJVbvWQqFk2UhaWnJwcpKSk4KWXXlIUviUlJZFzWXUNz54NZ3uqcdGpsYya5bpVitHRG8MT+4xYNfY7DQkfhxEWsysQLCaFJlOAAxC2Bj39dC3at7d3p3aj22hIIZy4pDIygsEMbNoUdoeZ8aCpjb2Swk3xFGqRiskSFlt0Ip3a7Akt9nfyv0/se4qLt2LLlv5Rn+c4L/r12xxlBVLTHqUVfjykqpuJWuHLixJA+zVU+xwnJycDUNcXlSyjjYks4fecsJ4IXVmxiLlNrRr7nYaED4PEmkzDoofDm29+j8suOw+VlTW2+vat3kZDKpWdf33hQq/le2bFIjZAuC2eQgtqKxXbPSmbLQqkfmfs9wCIsvgA4UmsqGgbioq2qW6P2hW+m1PVrUKr8NVyDdVmPqrZcFlte8USWey2nmip8u1EGIJdkPBhEPGYAw/ef/8T7N37feQ1O3YftmIbDSFSGRmtWx+O8Zfbuzpy6zYA8YjVokBobSwoaHy+lIJI5VAKzk5Pr7Ls97CIltpSWsSGEdSMW5WVlVF/K/VFufb6/Ufg9baM9AcgHLqQkXEEgYB5wfpm4EQYgp3EpfD505/+hCeffBKHDx9G79698cwzz+DSSy+1tQ1GXCJqUz6t3n3Yqm00hEgFlv7wQ0dRf7nbfcsEW8gVzjS6HYJSjEoioaW2lFaxwRpS7d2yZRVGjozubyNGrMXatfK70TuB2pAAq+cgq4g74bNy5UpMnToVL7zwAoqKirBo0SIMHToU+/btQ+vWrW1rh9KDLrdZJSsBj2o7tZHOLyXyOnb8QSSwO+xbtqKgHZFYBAIB7NlTJbs7OGBsOwQzKvtKLaDMeAbsfo5YmdSdRE5MK4lCNShVr47HGEU9xJ3wWbBgAW677TbcfPPNAIAXXngB69atw8svv4z777/f1rZoedBjO2yiBDxKibz27SujXud9yytXQlDl1t64HyI+EFpkOO6XUe8ZKWIZO6koLWBKS0tx8mQLHDyYhIKCs1FVyvnziY0hZmQGWVkY0i74wGMlWJvs9YhpNRaz5ct9WLy4VZMK4DzxHKOolbgSPnV1ddi+fTtmzpwZec3r9WLw4MHYulV8T50zZ87gzJkzkb+rq6stb2csUuZ2t5l4tVJVVQVAehUU+/qPPyKmym181JQwgpWrdqc2VLX6e82wyMQGv0tNKnILmHXr2mL69CzV4kNNUUelXbvtLAxpNX6/n5l97+xA7neUlwP/+7/RW/DYVV3djcSV8Dl27BgaGhrQpk2bqNfbtGmDb7/9VvQzc+fOxezZs+1onihSwb1Cc7uTWLVjeiAQwKpVqyJ/S4k84eviBR21xf2wvDu6Vqxetduxoarc95aVndVsDdGCEZdyXl6e6u8X69vBYAbmzPGrTm1WEzf0888tsXbtKElrqN2FIfWi5RllYd87vsihk8RrvR2riCvho4eZM2di6tSpkb+rq6vRoUMHy7+Xf7iVStY7yYoVaZGdlM3eNd6sKrdaa0o4NZmbjdUVVRstSdmODJzvvJNtiytGTRXhWMzoH8ePZ0dl9wDy24AoWamSk+ti9lNrasWxqjCk2bD2jCp9DwuLpHitt2MVcSV8cnJy4PP58NNPP0W9/tNPP6Ft27ain0lNTUVqaqodzYtCuKp99VUuahBkYc+o8vJwLA0/kLJgiYpdoeutJ8SaqNHqrlLapdno/llOx3/YXSZfroqwnpIGFRVeHDyYL2sl1bsNiJSVqr4+VXR7G6EVR+kcTi+0hLD2jPKIPatGElnMwupaa0ZhLSElroRPSkoKCgsL8c9//hPXXHMNACAUCuGf//xn1IPPCtnZ2cjOju6wWgYhK1caYdNp9IqUBUsUv0Lv2fMa9O7dzLR6Qk6hVWSo3aVZr7vC6b15rBZ1VhO+n60RCo2TtZIa2QZEakdwLVacREmeMBO5Z9XJPmlFrTWluYUPgTh27DzFHeidXkiJEVfCBwCmTp2KcePG4eKLL8all16KRYsW4eTJk5EsL5aI7bBffnkSe/a8ozgIjR49WlOMgR7CplN1lii7Tb1+fw0OHXoVhw5Fvy412bO22uDRIzLU7tKs113hZKyAVlEnvK982528x433U52V1Ij4iLVS6bHixHvyhJkYWRBYGVdoVa01OSvWihVp5+LTPHj1VXkh4/RCSoq4Ez6lpaU4evQoZs2ahcOHD+NXv/oVPvzwwyYBz04j1WH9fuXP5uTkWL7CCJtOPZFd48Pp5NUYO/a6qOOEqwmnBYbYQ8riaoPHqMgwe9UeCASQmXkWXm9rxFaXra09hnfeScHFF/stu7daRJ3wvnrONZXj7L3Hwv6eliZuqeI4L3r2vAbFxWeQlZXVxO1hpvgwoz8EgxnYvDnFtL3x4gUjz6qVMUtaa61pGaOlFpHTp6sXMqwGXced8AHC6pZF15YQIwGEVltY5Eyn/PfHPhQsCgxWVxs8ZgQkmjVxCoV4bHXZnj13Y+TIC8FxXni9HF56yWP5vZWbxCsqvFH3leMaP6fmHpuxAo/u7xxGjtyMLl32i1qq9ux5B4cOhS1VUhtEmoWR/qBmbzynFzdOUF4OHD1q7FllwT1rxhitVciwGnQdl8InnlBbL8Qs9JhOWRUYrK42APY2AJTaHyk5uQ5Ll94q2DPNY9u9lZrEd+062eS+ClGKBzK6Am/a3z14772RmDJlkSXuRzuILash9gyzuLixmvBvDrv8PR4OHk9430QtzyoLYjF2saB3jNYqZFgNuibhwzh2b5apZ5sKPQJD7aq7pKQEZ8+e1ZUVwepqww0bAPKiQ6p2kpOBxl9//U4Ty4oQNUHeetstFXzNB/5bFTRshpVX7hxiZTWE9/nUqWwmFzdWEha4jXGO4QzXEK699m/o0KFc1bMqJxbtFEQHDyaZsgjUI2QmTAj3k/37w2MvC/2FhA9hGD0Cw8imhWpgzaIixE0bAOpNu7YSsRigsLvLWiuLUvA1H/hvRdCw1POiJVVa7BzBYBArV65UvM/5+TcjFOoYdT6nBbBRlISHWGYr4EWzZrVN7q9YP5OzhP/jH/ZazwoKzpq2CFQrZPiQCf67uncPv84P505m4pLwiQPsNqXGBkDqNWfq7fRK1aSVLCpPPhn+/LRpV6NXrxa62pAoGM0es6pvxlpWAFiemq02+FoOKatLbJ8WO07sedEaryT1zCn9pi+/fEtU7H344Wq8916q654lNW47LZmtYkhZwrdutT80IC8vZNjlpEXISIVMxOKUVZuEj8ux2+8eGwC5YMEJlJTUmFpDQs33y1WTlrOoCD//6quc6+IUnNhyQ6/7xuq+KZbSbRd6r4mY1UWYHuz1cliw4CSys5vrPl8sap9Bud8kJowuvHB3JP7LTc+SmphE3mI8a1YDZs/O01XoUcoSznH2xx4eO3bM0BitVcjoCZmwExI+LsbuoGKxAMh7703H99+/FDUYGK0hEQs/iUvta9a69WHU16cqFtNq2n77AnXNwqly/lrdN3r6Jgul/7Wg16UlvDdN04M9mDatOcaMUd8nzbzXcr+JlaB3oyjFJMZO8lOmZGgWuHKu9i5dzoiUjNDndlL7zMS6Q7WO0awLGa2Q8HEIM1budmct6d1XzOjDwE/2mzYBCxc2/f6lS28DxzUW07rqKvXtZyXLSwtWmobN2JRWb/VlK0v/s5BZE4sbq1SzHPSuFqWYRLUxeFIoudrXrg2XjFi3btQ5QaTO7WTldhluESxmQcLHIcxYududtaS0uaFVhB/4bHTq1PT3Any2RaNVYds28WwfMzY5jVdSUlJUuRGVMLqlhhUTJitp2MJFjNJ1+vDD1dizp9LxndKlEHuWAGeD3mMRq+ydk/MzcnJOyyY9GEWNcOrTZycmT+6G6urWqtxORrfLMGNBE0+Q8HEQIwODVVlLchamWD9/uHCbtZsbxj7wN94IvPZaeHUpJoIaGoCyMvFuLdZ+FrK8WODUqWysXTvK8Ka0Vm+poRWz6pdoQWxX99gJTeo6ARw4zou//OVWXHnlRkycyOZKnG/3u++OBMCLHw8OHOiKPn12Om5BkKrs7fH4MWrUp+jTZ6dkGYnS0lJb2rhlyyoAym4noyENZixo4g0SPi7EyjowcimvgL2bG4o98K+9xuHdd4/h1Ckv0tJCGDUqp4mvPD//bJPBhCe2/U7UzXEiQFkJKzalNbuv6LkeX311WlQcW+mS0Vp7q0+fnWjd+jD+8pdb0SgivNiw4Uo891w1HnnE9CaaQrhStbByticilp0kdtwQVvaOFfRifbK+vl7zd5r5rMaKRiMhDRUVXtG4SK0LmniDhI8LsboOjNJkYNfmhuIPvAerVq1DQcH3AKK3V+AtOCkpR2TP63TdHKcClKWQ2qNLjRtTacA3s6/EXreqqiqcPXs26pjk5GT4/X4Eg0G89NL7WLfu3wA6oFFQ6K9DZKVgra9PjWpjGA8efzwTJSU/W5YqbmTClov5cxKxcUOIUUHPw1v2rH5WjYQ0HDyYpCsuM94h4UMwi9gDHzsZi1lwdMa/2oodokZNQK8w1qRv3yJs3Vos6poqKSlBVlZW1GedKEAm/D4pq0p5ObB4cQZefHFKxH0EhGC0wKGVgrVly0CkjUJCIQ+efvrvePDBy9ClSxfN51VC6TdVVVVh1apVou/JxfwFg0FbK84LERs3hJgVl1hX1xrfftsmEkNkFUa2fSgoOOtIXCaLVm0hJHwIJhHGME2f7j+3V454HQ0pqwK/IjOSDeRW1Ab01tXVRcUAACH067cZRUXboq5pVlaWYxOZFpYuBW67DeA4YR2c8D5LY8asQocO5YZWulYJPb+/BldeuREbNlwJoKnV7bXXXrPMFSt3ztzc3ChhJHyW5OK4Vq5cqdheKzLtxMeNsK+L46THEK3s2HER5sxpbXnAvNyG0Wpq8OTlhXQX2xSip1AmS1btWEj4EJajVdXHxjDdc4/2OhqA/fucsYKWYMjYGADAi61bi1FUtM3WNhslEAigrOwsJk5sHQnQFsJx4lsNsET//lsBABs3DmZqk1O5yUkujkuuvVZk2smNG4D6yt5JSfLTYmM9sOhsUrMD5vVsGB1LSkqK5D0SZnopjdF6hAzLySIkfAjTUJPNogajdTQSHS3BkPEQA8BPEAcP5iMUGid6jB3mfTPo338revbcY0vygFnYUdxSDUrjhto2ZmVlyU7ymzenNKknJny+zHLfGC0ayFuL+I2ehaxZ0wKLF7eLVAu/4AKPovBkWchohYQPYRpSFhY7isdRnYowUoHKPh+HjIwjCASSovbT8fuPwONpYVoMgBO+fX7gF68tA9PcG1pQ6vNKZSNY7cNm3De7C6/qQW6S79tXPtjYjA1ljSK3xUQwmIFFi6YILFbRVbdZLPZpNiR8CEuxo3gc1akIIxzshNluHk8II0asxdq14WsyefJkAIgcO2pU0+und+J10rfftCZOCP36bW0Sr2Q1avo8f50qKipcFX+WnZ2NkpISyYBnJbQIc7vhF09yW9+orZ9mxoayRpB7/uQq2Nu9a7xTkPBxIXavqvV8X3k5sGULH2gafs0KX7jU/l3CWiKsZxiYhXCw0xJ7YXa9HSdN4lbWmVKzEtbixtGymSNLxGb3qUWLMLerD/HueeFGsfzWN8IJv7wc+OKLIP71r1cifUpr/TRWAn6lKtg3a2b/rvFOQcLHhdj9AGn9PuGKNxazTdpqaomwMuDYjRaXCcvuFa1o+S1qxa4aK44b996yE73C3EpycnLQ0JAbs1Fs9ITfeO/98HimRCzKeuqBsXD/Yy2jfHr8iRPsuyDNgoSPS3GyfoocsSveWMzeG0uulkhtbW3kNRYGHDfjdmuYEK2F59RYcfTuURZP11UrrIhtuZgjILYKdHxUPuaFZ//+41BUlB2J7bFz70cnEd/NkSB0Ilc1NewLr4LPV4lAIGDK9/GrF48n/KXCGJXXXnvNtO9JVEaPHs3EhpNmwgfhq/lNYStOQHLbC75/xe69JdYfhcfxZGdn44YbblDVblZEUry5jvmCh0L4CV9sPGOhOrUZ+P01+MUvKuDzVaKyshI+XyUWLDgBny/8vpZCiW6DLD6EqYQHEa7J1gdjxryFDh3KVfnCtQ6YrJjN45GcnJy4Ej1a0GLFEaI1zqhLly7MuGLVxDGx6jrWI7Rqa2uRnl4pGayck3MevN4WstXjgeisUrPgf49SxqoRgSkWWL9z590IBFqga9f4FD0ACR/CRPiMh1mzGjB7dl5UllDPnnubHC81cKoZWGNL6bNiNifsR+vAr/Z4IzvNa+2PLIhLLRmYLLQ3lthxIxgMNtlwNCkpKRKcXVtbi9deey3ynlSw8uzZt+Hhh3PR0CBe+Tk2q7Rdu2pMm2bO78nKmoY5c5pF6u3Mnx/E2LGnIsdYITBzck6jVy9TT8kcJHwIU4itGzFlir5qyzwsDqwEmwgnPLHJDmic8PROFEpWnIoKLw4ezHdtHSmrigrajZq93HgqKyuj/pYSqw0Nf44SRcJjxLJKZ8zwo7TU+HUrLwemTm0uuCcezJiRhdLSLMVzG7ECHTt2TPR88TQmk/AhTIGqLduHlDvCitgLt8Rp8IOylVuUSPXpFSv4faHGubKOFGWjKSN178Vr4nhMyYQyUuhRzGqutoCi1DHxFOtHwocgXIScO0Jr7IXYsWHLRRIKCs4iP9+5YnJuIRjMiNR/AdyX9aM3G00PdgZF21V9WLwmDoeuXZvuF6cVsV3mtWRZmf3sxlO8JAkfgnAJatwRWga72GPtqLKtBTeUzj9+PDsqkB9w115nRuKYtGJXULSd/fiaay6G31+BOXMa972KreKsl/btw22//fawpcfuLKt43gaIhA9BuAAr3RHCnc35STwsqjj86ldHHLH8sCbCpJCrIyWGXa5DPaLRyqrXPFb3IytjlcSEwKZNmwBsitoJXk3mqlomTAi3ff9+2JplJbYNUDxBwodwJfFWS0QOK90RcjubNzR48MwzH6Cg4Hvb/PssijA5lCwlfLFEwL4AUSOi0e2xeVZtgKq0H6CeKs5y8DurA2FLT/fu4df5eGwr+5LUNkCzZh2V3MPMbZDwIRzDiChhtZaIGcSu1q10R8jtbC60XNjh32dNhKntn3KWEr5Yol3ES3aWHqzaAFVuP0ArRKLczupCtD4Havuz1DZAZWVJKCxU/XVMQ8KHsBWt2wXI4UZRo4TYav2qqxrft8odoSSqrIQXepmZZwGwIcIAeXEdmyFjl6VEzoWVyNlZejZANSoErIrjiu1vUrE2Wp8Dqf4c25elnr/8/LOS53ZDPJ4QEj6EKagdRPLy8uJ28DWCnIvngw+qoo61apK1I8Yjlmih1xojR16EPn12OibCYmGpr8q5sOzMztKCXROing1Q5YRtMBjEypUrAWiP4zITJRebVtTce6lFUF7eJaLHuyUeTwgJH8IU4tn1ZDVKLp7XX9+GggJ72mKGqFIz2YkLPU/EheCECGMZJReWndlZanFyQlTbj6XGo9zcXEyePBlHjx5FfX19k8ytP/5xP9LTre2TdrnYxBatYs9fSkr/Jse51bVKwoeIYHR1RqJGH2rjbNxA7GQ3dSpwzz3R/UlO6AldCE4E2rJoslfjwhKiRTRalSTg1gkxFt7qAyAqc8tq0QPY52Izsmi1Kpjcakj4EADYMleyOPnYgZNxNkaRctU99RTw9NMcFi48iXvuaQ6APaHHZ9A0VmCO3hdJbNC3K6tQrQurtLQ06nNaLB5mW2rjJdbI6Wr0dj4f/P0QZpMJqaurQ2VlZZO+YLTIolOQ8ElwWEsfZkmAOYEbXTxyFhwA4DgP7r03Hb1770evXi0ir7Mg9ITCYtGiKeC4Rrfbffdl4scfX4bf3zQ2xi7XrloXltj+ZGox8/lmNdbIKYwIX7ufDz3ZZE4XWdQLCZ8EhqX0YdYEmJPoXVnqGWTNsFzIWXB4OM6L5cs/Q0HB9ygpKYm87rTQ49uu5FYQEzh290enr5UaWIw1shphrSYhZghfO++52nsSe5xTRRaNQMIngWGlhgtLAswp1JSHlxpgAf2DrJmWi9jJToiwP509e7bJ57QM6FYUpWTF7aaEmwoMxk7aACI72McTZtdqqq2tjfpb6p7HHuck7du7Q/DwkPAhHHc56BFg8RQHtGJF2jk3i3zKqlXF8MwUk/xkt21bEbZsKQagvz+JCT2rMgOdfgZiaaxt1NR65ib4STs2Lbtdu2pMm2be97BQyd2sMSk9Pd3U48wkGMzA5s0p6NsXSEsTjwfiYTmLl4QPAYANM7raySde4oB499706a0jsSVu291bDL+/BkOGbERR0TZD/cnuqsdOPwPiQdaNtY3kSEpSN5Q7sYWLWFr2jBl+lJZqEwhywsLpchrxMibJwYvXhQu98Ho5jBy5OapfilmtWbXSk/AhIrBgRpebfILBICoqvHERB6Q2pVuI2/YdY6E/acWpNssFWasRwllZWczW0RKLn2po8GjK8FIjLJx69sNjkvtT9+WIFa+x/VKq0CKrsVwkfAjHqajwRnz/cvVbVq5cGTdxQGrde2Zu8cEKycnJqo5zm9AzgtogazlY7R9SfVxthpdTNYHU9r9Dh85zZS0bLcj1SwCShRZZhYQP4SjhlVxrhELjVJVkdzoQ22yU3Ht2u3vswO/3M2udcBq9QdYsi0QjGV5O1gRS6z47dcpvuJaN0I3n8xlotEXI9UslUcQiJHwIQ+gN6BNLX1cT38JaEKoQvdeCd+8dOtQegAcdOhyyrI1WoGfSZUXUOBUUK9VXlPq3nQHfepC6Tnrip1ioCaR03kAgAJ+vEvPnp2HGDD8aGjzw+TjMmxeEz3cKgYDyvYl1482fnyZ7vFXI9fHYfunzcRgxorFfuiEjUggJnwRG76Cvp9Jt7Oe1xrcIcToIVYjRa8Fz4EBXUR95VVUV8xYffmV85MgRrFq1SvF4lqwTdgbFyvWVIUOORY6T6985OTloaMhlNqNReD2N7mDPek2g2IJ/d9/duKXFiRM1eOml8OtywkzMjTdjhh93351h+7im5ll49NEgAoEWyMg4EtnxnuXFqBQkfBIYPYO+3kq3QszYsoCFwFkzrgUgvxnhqlWrXBG3lJ2d7XhmjV7saI9yX9kAv7/xeKn+/dJLDZgzh9MtsO3AinYoLXacKG+hdksLuedBfK8rj6qYLisWEEr3Ljc3fK0//zwJwWCjOGNpMaoGEj42I7UXCo/dA5jW7zJS6TYWpZVCv34lCAZbo6DgLNLTj6uyJtiJWdfCjGvJCqxMvqxhRvByWDTl6RbYbkdKWIQtaO5MJZfa62ratN8iJ+e05OfsnCeEc1ajtTIbHs+UqJhMFhajaiHhYyN69kJhFbMq3UqtFHbsuAhz5pwfWdnOmqV/LyKrMXot3FI1mDCOkXsdTwLZLILBDMyZ43dtKrnUXlfCPe2cRDhnxVor1cRkslRdWoi7y4K6DL17obAIb63xeMIjjhG/rt9fg4KC7yOf5V0/jbV6PJg9Ow/BYIbiufbsqcJbbwWwfftPqKysRGVlJQIBa0vk67kWQjO1mdeSYBsj95oXTUISXSAfP54dGSd4+Iwvq597owQCAVRWVuKqqyqxbdtPeOutALZt+wlXXWXPuKUG4VykJ3vrtddeY+J3xEIWHwdRsz8Ty1jl19XrDtix4yLMnt1ddOsHq61oWq9FdnY2SktLsXLlSl2fdxKh6TtcgykJBQVnkZcXnpRZijdhEbl7LZa1xQcJuy2I1I6MOaM1gpxCyvq/Z0/03yy1X6+1ksWFPAkfh5CqdKkXp2KHrPDrqi3qBzROCnIBwna5AbReC78wmlXH551AOGDL9WGnB2zWYulikbrXSnWb3CSQ7Qh2ZzXjSwk3Wv/dJrzlIOHjAEqTtFbiKXYI0FfUz0jQKKEefiBmQWhKEfs8SFlW3fI8xOIGgcxj5PqqtQS5SQy6nXi51iR8HMDsSTp2kpEa6FlZPagZ0LQ+YBQgbC+sCE0xy86xY401ceSsUqw8D/GGWanlchYjozWCzICFHeGdwE3CWwoSPg5g5SRttgstFjMedqkBzchgFmslAkIoLt4adW6z3RtGr4WbB04WhKa8Zcd8y6oR3Hyv1WBWIc9Y1HzGqVhJt9atIjQIn+rqatUnzczM1NWYRMEqX6kdA71ZD7uVRc62bSvC1q3F2LKlP7ZuLcaoUWsBhAWVme4No9fCyOedjmFhwd8v/P1igr9Fi5+ZsEoBxu4166LJrEKeerB6oaeE0u8pLwc2bWKzynYio1r4ZGVlwePxyB7DcRw8Hg8aGhoMNyweEQ5Mcq4cvQOYXe4H1lcwW7cWWxZ7Yrbg0HMtWYlhYcXfLyX4J0z4iyNWKSv6iNOWBTn3lZlFTdUSDAaZsuiJEbsHl5sKK/JonYvckqmsWvhs2rTJynYkBFYPYCy4H4xgxsrWSvHHiuBQsnTYGcPCgr9f6p7X16fYbpWyqo84sdjQ6r6ya/wJBAJYuXIljh/PZ8aiF4vYHlxuKqzIk52djZKSElVV86XGIhbdt6qFz+WXX25lOxKGeHc/6EG4Qi4pKcHZs2ej3k9OTo6kfscOtrEPlZWDr1rBUVFRISk6zFyZs77itQu5e15Q8L2tVimWRKkRtLiveOwaf7Ts9efUpCu+Bxewf39Y+LDuvhSSlZWleEwwmIG1a0dFVXVet24UHn30MmRns1GFWoju4OaqqiosXboUe/fuBQBccMEFuOWWW5rUJiHshRX3g1qMrpB5K1pFRYVtRd6UBAcfoG21RciJzCoWB2yle+6EVcrtolSv+8rO8UfpvpeUlDhmKcvMPAuvt3VUVWmfj0NGxhEEAklMuC/NRLyCtgeBAHuiB9ApfL744gsMHToUaWlpuPTSSwEACxYswGOPPYb169ejT58+pjaSkCd2kpEa6FlYPcRixgo5Ozs76n2rB181gsOO1b4Trk1WB2w999zK54GVdH+j6OljRoSm1lR4ufuuxlJhNsKF3MiR0WPAiBFrsXYtGwU+YzFagqBlywC8Xi5G6AFdu5rYSBPRJXzuvfdeXH311fjzn/+MpKTwKc6ePYtbb70VU6ZMwb/+9S9TG0nIw+pkpAWjK+Sqqqqov61c5StNBnat9p1ybbLaj6Tuudg2EFY/D26Pt+Oxo48ZTYVnIc6MR+0CjCVXpxlB2H5/DebPD2LGjKyozVZZjWfSbfERih4ASEpKwvTp03HxxReb1jhCPaxORmoxskIOBAKqgu/MQmkysHO17zbXppmotdjk5eXZ/nzYJUrtKGtgZR9zMhXeDlgSZWKIBWFPnAhceCFwySXh19Q+Z+PHN6C0NBzH1LUru6IH0Cl8MjMz8cMPP+D888+Pev3QoUPIyFDeQZsgYjGyQlZbuRowz73BTwaHDrUH4EGHDoci79m92md9cLUK1i2dVotSO7MM5fqYkbgvJ1LhiUbEgrBDIaBv30bLj9JzVlVVBY/Hg7q6Ovh8lejePfx6ZWX4vyx6G3QJn9LSUkyYMAFPPfUU+vXrBwDYvHkz7rvvPlx33XWmNpBIDMxaIcvF1pSWlpr6AB440FX0u6xe7bMYYOwUsfcz1gJSV1eHSn4Ehv2DsJWilJUMMjMEaLy4Bt1Gt25h95aY+BGm30vdO7XWdtYsdrqEz1NPPQWPx4ObbropknqcnJyMO++8E0888YSpDSQSB6MrZKXYGj7jUK97QCgklL5L7rcI95JS+k4xWLd0OAULm/U6IUr1xJQpPQPBYFBTG8ywKKldLJDwN4dAIACfrw7z5zfGVgkRpt9L4cZd5gGdwiclJQWLFy/G3LlzceDAAQBAly5dkJ6ebmrjeMrKyvDII4/go48+wuHDh5GXl4cbbrgBDzzwQFTn3r17NyZNmoT//Oc/aNWqFe666y5Mnz7dkjYR1mBkhawmtsbI5Cgs5qXmu6R+i3A/MqXvlCLRRI0aWNis1wlRqjWmTK2LTA1migu1Cx8S/saJ7QO33JKLpUtvbWJxy84OAmAzJd0IhjYpTU9PR69evcxqiyTffvstQqEQXnzxRXTt2hV79uzBbbfdhpMnT+Kpp54CEN5LbMiQIRg8eDBeeOEFfPXVV7jllluQlZWFiRMnWt5GQh9mrt7UmMuNrlD4FFkrTPOsrYrcjJN7ONk94Wrti2pdZCUlJbIp4VaIC7ULHxI1xogda9q3rxS1uAEFCARCcXe9dQmf06dP45lnnsGmTZtw5MgRhGIchDt27DClcTzDhg3DsGHDIn937twZ+/btw/PPPx8RPq+//jrq6urw8ssvIyUlBRdccAF27dqFBQsWkPCxGDmzOR/4JlXYMiUlxbTVm57YGr2rXaXvEqZQx+4675b9bNyK24sHakVvTJnSdcrKykJubq4dPyFucLMbTmhxS06uQ319KpYt2+/qrDopdAmfCRMmYP369bj22mtx6aWXKm5eagXBYBAtW7aM/L1161YMGDAgqkMNHToU8+bNw88//4wWLeLPXMcCal1HckyePNm0AVZLnJBRq4Dcd+Xk5Ij+Jqd3k04E9JQTcHq3e6PoiY+LlyKLLOF2N5zfXyOatBFv1mhdwmft2rV4//330b9/f7Pbo4r9+/fjmWeeiVh7AODw4cMoKCiIOq5NmzaR96SEz5kzZ3DmzJnI39XV1Ra0OH4x44Eweg4tlav579JrFTBSJTvRLBFOodX1w0JQtBlojY9jIZPKzRYSKVjuI0pIjVGzZh1FPBn/dAmfdu3amVKv5/7778e8efNkj9m7d29UvaAff/wRw4YNw+9+9zvcdttthtswd+5czJ492/B5tOD21SVraFll8anNele7RlZ0tMK2B62uH7XCu6KiAgA7E5tR0cDCpsZut5BIYXQLCKeQGqPKypJQWOhQoyxAl/B5+umnMWPGDLzwwgvo1KmT7i+fNm0axo8fL3tM586dI/9fUVGB3/zmN+jXrx9eeumlqOPatm2Ln376Keo1/u+2bdtKnn/mzJmYOnVq5O/q6mp06NBB7U/QDCurS6vEl1z8ipWxLWraGggEIqnkRla7eu8LCytsu3FK5BspjSDVT/k4LVYsP2aIBq3XyYr7ycK1NAOjW2+oxUpRJTVGZWYeQSDQ9F651WKnS/hcfPHFOH36NDp37oz09HQkJydHvX/8uLqBvFWrVmjVqpWqY3/88Uf85je/QWFhIZYtWwavN1qVFhcX44EHHkB9fX2kPRs2bED37t1l43tSU1ORmpqqqg1mwELdA6vEl1z8itOxLbG/2YnVLgsrbDuxW+SbsVmvmn7KUryDGddNrYvMzkrRbsOurTfM2FdLDqkxasuWndiypem9davFTpfwue666/Djjz/i8ccfR5s2bSwPbv7xxx8xcOBAdOrUCU899RSOHj0aeY+35owdOxazZ8/GhAkTMGPGDOzZsweLFy/GwoULLW2bG7FCfMnFrwBwPLZF7LfIrXbNXKEIz2XXd7KAGf1Mi4XB6CCcKDFYelfprFSKZhE7tt4Q21dLWF1ZC3J9QOvmqqyJGjXoEj5btmzB1q1b0bt3b7PbI8qGDRuwf/9+7N+/H+1j7jDHcQAAv9+P9evXY9KkSSgsLEROTg5mzZrl2lR2seq+PCwqaLkHHvAwG9sSu9odPXq06ZtaunVV5DR6LEZGrqFUHz50qD38/r26z8saJBCtwyp3diAQwOefA6FQ9D1paAC2bQsgLU1b3+f7QEVFBZYt29DEchfv+//pEj7nn38+Tp06ZXZbJBk/frxiLBAAXHjhhfj000+tb5ANSFX35WHNnKz0wLMW2yJlps/JybHkurJ0r9yC3W5hsT4MAKtXX4u6ukaLBr8ocbNYtUIgsrCQcRor3NlCN5rHM6XJOLp58yvYs0e7Gy07OxuvvZZ6zjWXWOU1dAmfJ554AtOmTcNjjz2GXr16NYnxyczMNKVxhDSsmJN5k6nSAy/3nt0uHqfjjRIV1gs38n343XdHAmicXGItGsJFCWsLEDtIxCB9LRjdczAWfqxXGmO1zgnl5cDUqc1xzmmSUJY7XcKHr6J8xRVXRL3OcRw8Hg8aGhqMt4xwBbFm81mzjqKsLAn5+WeRl3cJqqq6wePxYOJEf5P3gEtsXzWTmd4ZzBCbUsIpGAwaKoAZG4OVknIGb731u6hjpCwarCxA7CTegvSFcWQVFV4cPJiEgoKzyMsLB9PoGaOschUZFVV8RlhOzs/Yty/UxHWWKJY7XcJn06ZNZreDcBCjK3HhoJCbi6h6D8IJKfY9JyAzvf2YITblhNPKlSsNWV6E8Q5r1qxBhw6HyKKhgNlWDacQxpHJ9TGWLHt6RZUwI8zj8WPw4I3weAZb0s9Zr2OkS/hcfvnlqo77wx/+gDlz5kT2LCLYy9xJNLcPmentx6jYVCOcjFpehJOamEVj8OCNOH48O/I+ER8BsGoruevpX2ILSqPWSb3EZoRxnBcbNw7G4MEbsXHjYFMtd1an3JuBod3ZlXjttdfwxz/+kYSPgFjXUKxpNXZDSz0opQDX1tYCUH7YWRNpRlAbixRPv9lp+GupJDaVrrldVjrhs3ns2DF06bIIx4+3REVFXpPJIZ4XB1JYUayOlSr2RvtY7G+WWlAatU7qQSojjOO8yMurwJQpi1Rb7pTurZkp91ZiqfDhU82JaPhOL6aMr7rK2LnVpgDfcMMN2LkzCwsXNn3Y+/cfh4ED4ysTSSkWyYl4o3hHeM3btavGjBl+NDR44PNxmDevGmPHXofa2lrU1dVFthIRYkaVbT1t5uEngb/+9SZdbjpWJnWzMLssAytV7AHjfSw7OxslJSVYtWqVJdYjvShlhPFiR6wvjx49Ospooebefvddo+jhaWgA9u9PIOFDSCOljLdt88p/UAG1D1V6ejr69s2G1xvdUX0+oKgoGy4aj1UjF4tEWAN/zadNA0pLwwNg164etG+fhUCgocnWM2LEWumAsOtJrbXHiADRawlgaVI3EzPbGntPpGIN7RAKZgRsZ2VlAWArjlBtRpgYOTk5mtxygUAAmZln4fW2RijUWNTY5+OQkXEEgUASM32dhI9DSCnj8vLzVH0+GAwaXjG2bx+2Mt1+e/i7fT7gxRfZUuZE/NC+fXTf0jKh9emzE6dOnRdxOW3cOBhpaacVXU5GBYheSwALW9O4CRZiDc0K2DbbQmmWi9HKgHThczZyZPS9HDFiLdauZStInISPQ3TrBlFrS2GhH7/+9WQcOXIEq1atkvz8ypUrARjvSBMmhP2v4ZU4iR6CTYLBjIjoAdS7nPQKEIoJsw+WSkyYEbBtdrq/mS5Gvg1mB+oL26a05QULGV8kfBwiLS2A+fN9MXEPQfh8pyDs30qp5masGGNX4gTBGna7DygmzD5Ycg2ZhdnWFbP6mV2WNSkBGd653vmML83C5+zZs3j88cdxyy23NNk3K5YbbriBqjiLIDQL3n13RuThOHGiBsKQBxbMvwTBAkYzw/RgZkwY61WrncTJEhNWZKrx6LUeWVFQEdBuWTP7mQoGMzBnjp+JjC/NwicpKQlPPvkkbrrpJsVjn3/+eV2NineEVhqph4Ml8y9B2IGUOCgtLYXf75fMDGPd8kILGHmcrATN2gbCVhZUVLKslZSURAK0rfjNx49nRwU9A85lfOlydQ0aNAiffPIJ8vPzTW4OwROP5t9EI97Sma1EbpD3+/3Izc0VzQwDsjR9j92WF5YWMCz3RycrQZv1m82wHllZUFHJspaVlWVpccWWLQPwejkmMr50CZ/hw4fj/vvvx1dffYXCwkI0a9Ys6v2rr77alMYlMnrNv1aabt0CCwN8vKYzW4EWcWAkHs0JywsrCxgW+2PsGCRl/XbLWGWm9cisfiO8dk7vseb312DkyPeYyPjSJXz+8Ic/AAAWLFjQ5D3apNQc9HZS1ky3dsPKAB+P6cxaBKWaY/lBWWmQN2Pis9vyYlbVarNgsT/G41hlVlvNinvKzs5GaWlpJAvY6T3WlDK+7EKX8AnFFqAhLEFvJxU+fCykDtoJiwN8PKBFUAJQfezkyZNRVnYWr77a1AR+113DkZ9vzARup7gSoqZqtdFJnQXLplFYb59TmGmd8fv9Tc5ttuDR8vywsMebLuHz17/+FaWlpUhNTY16va6uDm+++aaqwOdEQzhI8aX41aDUSeQ6nBs2iyPcgRWCsq6uDrm5ucjOFiuk6UFhYRu9zY3ACxArxZXcdwPhqtVDhvyM//43dC4dPrxwFG7VoVWksGLZJKzDaeuMFuSsd2bsP2k2uoTPzTffjGHDhqF169ZRr9fU1ODmm28m4ROD2kEqFj6bRQq5wdItm8URBGBtIc3s7GxLxZUSgUAAa9aEn/89e6SP0yJSyLKZGEgtfPnFs5np7kZxk8DWJXw4joPH42nyenl5uexEnajoHXxatWqluzO5ZbM4guCxupCmU1XKWRQpVFfI3axZs8b0dPdEQpPwueiii+DxeODxeHDFFVcgKanx4w0NDTh48CCGDRtmeiMTBeFuuEZVu9SWGF27Gm0loQeaaNiAqpRTXSGWURsroyXdnTJ9m6JJ+FxzzTUAgF27dmHo0KFo3rx55L2UlBTk5+djzJgxpjYwkZDbDVdrkLJTG5DGQ8Cl2dBEQ7ACS3WFiKYoZbrx8TJa0t3Nyp6Lp0QZTcLnoYceAgDk5+ejtLQU552nbidxwhh6g5TtNu1TwGUj/OpJaaJJpFUWYR1KFkWnstsI7agZG7Wmu+sdb/mFbHiPLT9CIQ+8Xg7z5wcxduwpVYKJRYuTrhifcePGAQj7pI8cOdIkvb1jx47GW0YAMB6kbKdpn4VYhtiHR2pCsPoh41dZmzYBCxc2nWj69x+HgQPdFRAoRIvrjtx80Zh9PdRYFLVmt5HlVj1OXCuz0t3lrDj8QjYYzMCiRVPAceE+Ewp5cN99mfjxx5fh99coLmRZrNekS/h89913uOWWW7Bly5ao1/mgZypgaB4UpKwN4UO2YkXauU3xtK9SzGpL377isVZFReFMIzlYmnyEQlFuoo0VlFqOTQTMdntqcV2pzW4jy616nLxWetPd1Vpx+LFHyUqoZiHLWj/RJXzGjx+PpKQkrF27Frm5uaIZXoR6+BVgRYUXsSE+FKSsnezsbJSXA9OnCy1lHsyYkYXS0ixFwWEmemOtWJt8hBaDOXNaR1Z/HOfFunWjMGtWUZTFQMuxiYKZ8TVGXFdKLnAWLLduwelrpbUYoBYrDo9ZVaRZQpfw2bVrF7Zv347zzz/f7PbEJXIrW+EK8NVXuSbxO04FKbsdlixlemKtnB5QxcjOzsbu3WLX1YOamjZRglLLsfGOFfE1RgszUnabNbDu1lWy4hw61B7Hj5/Cjh1H0LbtWQDO7/FlBbqET48ePTRVH050pHycFRXeqBVxKOQRjd+Rmzj1ukNYcqNYAWuWsniZaLRcV9bugVNYVT3a6cKMRDR2ZG+aFSgsZsUBQli9+lpwnBd//Svf/vA7bqoirQZdwmfevHmYPn06Hn/8cfTq1QvJyclR72dmZprSuHhCbFD79lv1VgmxiVOvO8QKN0ogEGBKDJOlzBq0XFe6B41YKVKcKsxINGJXmQCzAoVjrThACIAnyi0d234W9tgyC13CZ/DgwQCAQYMGRcX3UHCzNoyuiPW6Q8x2o+jdksNqaEKwBi3Xle5BNFZdDystiqy7b1hAS10do5hliRdacU6ebIa33vpd1PtWtZ8FdAmfTZs2md2OhCReVsRa40zszObRMiHEu/vPTLRcVxbcfCzdWxauh1pYKL7J0r2Twq0BwLwVJxjMcGX79aJL+Fx++eX49NNP8eKLL+LAgQN466230K5dO7z66qsoKCgwu41xjR0r4lgXlFMuqdGjRyMvL8/xQUoMu7Ko3DCIxxusZci5BRaqPLvl3rk9ANhI+91YlkKX8Fm9ejVuvPFGXH/99di5cyfOnDkDAAgGg3j88cfx/vvvm9rIeMfqFeCaNWusO7kGcnJymJ1Y7Mii0jKIE+bBYoYcy7BU5Zn1eye8BnIBwHaLA6kFltyiV679wn0khbh1oaZL+Dz66KN44YUXcNNNN+HNN9+MvN6/f388+uijpjWOcDcUGxCNlkGcxTLvRGJgVRZaPMJiVeLYBZaWcVgqgFluH0k3okv47Nu3DwMGDGjyut/vR1VVldE2xT3xtNmbFGKxAYR6YgfUigovDh5MQkHBWeTlhaPh3braItjHqVT5WEsFS5miUrD2DAqvn1yM1ujRowGw4xGwE13Cp23btti/fz/y8/OjXv/ss8/QuXNnM9oVt+jdcNRNSMUGzJp1tEllakIafkBNhD5DsImdWXlqXMFkRVaPUoxWTk5OwlqWdQmf2267Dffccw9efvlleDweVFRUYOvWrfjjH/+IBx980Ow2xg1GNxyNxerOqPf8UrEBZWVJKCw0o2WJg9l9hnAGNwe1G41BVPvblVzBLGSYacXJ+64mxZ5FV50d6BI+999/P0KhEK644grU1tZiwIABSE1NxR//+EfcddddZrcxbjB7GwW5Tnvs2DFVJkwzgtZiBZJUaucvfuGN/SihAEtbbxD6cEtmkhWYFdDPQoaZVg4cOIDXXntN8Tir7rvaFPt463Nq0CV8PB4PHnjgAdx3333Yv38/Tpw4gR49eqB58+Zmty+usKKEv9FOa0bQmpgAa9euGjNm+NHQ4IHPx+Hpp2uRlxdCZWWl5HnicWVhFNr2gS30rOBZz0yyErN+u50FAs0gEAioEj2AtRuYujnF3kp0CR+elJQU9OjRw6y2xD3xUrBQjNjBfto0oLSUjw3wIC3tDNOrXlZ93fHcZ+zCrHubyJYbp1GyXgSDQaYWTmJixon4pHjbY8ssDAkfQjt2BQuyMJELYwMqK9le9drh69Z7T2jbB2OYlSGXyJYbJ4gVCnLWi5UrVwJgV3SaEZ+kN14onvbYMgsSPg5gR8l6LRO52en1bk3XFw4aVvwGIxOwm7Y5YBHKkHMXUkJByXrBougMBjPw7rsjAeiPT9JibWRh0cs6JHziGDUrH7MngniYWKz8DTQBOwdlyLkDpUBmt1kvtm0rAi96eLTGJ2mxNubm5iZkppYWSPjEEVqtFGZPBHLn8/m0n88J7JgcaQI2F7UuAMqQcx65OBe1W2W4iWAwA1u3Fou8Y2wDUKV4oUQWNWog4RMn6LEgmD0RyJ2ve3ft53MCOyZHmoDNQ4sLoFu3bMqQcxClOBe5rTK8Xs70ncLtqLEjJuIAoF+/rbpFnBvrGbEGCZ84QK8FwexU6XhIvbbjN8TDdWIFLS4ApzLkYmMppFbr8Rhzwf8mJfcVf5zUVhnz5gVx4oR51h67MvSkstGKirZF/tZy391Yz4hFSPi4DLFVyuefpyAUin441VgQzJ4I5M4nU76HKeyYHOMpRd1tFYmdyJATBrWvWJGGOXP8CIU88Ho5zJ8fxNixp5i7TmbA943S0lJs3pwi6r7q338cBg5s6pqJvU8+3ym89FLj+0ZTw63O0OPFjFI22g033KDpvseTG9BJSPi4CKlVSjCYAY9nStQDEWtBkIr/MXsikDqfmzIN7Jgc4yFF3a11bfRmyBnpw3z25PTpQsusBzNmZKG0NAsMXR5TENshPHaM4qu5Z2e3ED1HdDmMxtfd4OqJzeCcNesoysqSkJ9/Fnl5lwC4RJfYVVuNmZCHhI+LkFp9xK4qfD4OL77oiQwaSvE/ZqdKi53PbXvC2JE+LvcdbrCkJFpdG6P1gBIptiv2nktZPnJyLlF1Pq0uMxYQ9oPcXJiyTyFVYzYHEj5xgrDGxV13DUdhYRsAbGUQOT1RuwW3WlISASPlCBI9tku8Do+48BET/iUlJdiyJVWTy0wtrO/6LhR0cvWMWBJ+LEPCJ47ga1zwq08gsVaZZuOU1SXRLCluQ+9iIp5iu/Sipg6PnPCXcusXFWXrdhe60XUmBgtWYLdAwifOSfRVpl7I6kJIYWQxEQ+xXVYjN7k3desbE49uypJywzjjBhc9QMInLjl27Fjk/30+YMGCDEyb1pypVSbr21qQ1cUdOBE0b3QxQduPGIN39fTvPw5FRdlISwvI7gUoN9lSlpQ25IRNVVUVVq1aFflbyn3IwmKRhE8csmbNmiav7dx5NwKBFkysMmm7BsIsnHABkMsqGrHJULj4sgK/vwb9+tUhJUWfZZYXwkpZUhQz04haKzgg7z5kYbFIwidByMk5jV69nG4FW8HWRHxg9eayYpDLKozaydCq4GG9llmhYG7XrhozZvjR0OCBz8dh3rxqjB17HTNuGVaIvYZS99QN7kMSPi4iHlYfFGwdP7BWm8luSyK5rNQJD7nVv5NjGi9qpk0DSkt5EetB+/ZZALIca5cbkLunbnAfkvBxEWJm/WPHjom6tliFgq3jB5YyTciSyCZiq/9160Zh1qwi5OcnMWNRIRGrHiWLjhuKLJLwcRmsDBR6ofgIZVizpMjhhJtJDLIksonY6r+hwYOamjaq089Zr7GTaChZdNxQZJGED2E7FB8hD0uWFLU4HbBOlkQ2EVv9i90XYYB0VVVV5HUlNxkLgbKJhhqLjlyRRRYg4UM4AuumZaetLiyJGiVYcDORJZFN+NX/unWjzgUPN70vcnsQKrnJKlXufiyXZebEIsIt9W7EUGvRUVOs0ilI+BCECCxZXVivecSKm4ksiWzSp89OzJpVhJqaNqL3ReoZ0+Mmk3KLKcVB2llbJh6Koxqx6LDgoifh43KctkzEMywMOk67kNTAkpuJdUtiopKXF0JurrbPqHGTCcc1I1tP2Okyc2tx1Ng5RMqiU1paCr/fL3kOFsZVEj4uhyXLRDzjhNWFBReSGsjNlLjEToZSFhc9C6+m21NwePFFT1S/4se/srKzmDOnNTjOA6DRLTZ5cjds2bJK4hsILcTTXEPCJw7Q0tHc7Ft2CqesLqy4kNRAbiY2sfp5F06GK1akYc4cP0IhD7xeDvPnBzF27ClD3yF0qdx113AUFrYRbcPu3WLPigfV1a11fS8hTrzMDSR8Egi3+padFGtOWl1YciGpgdxMbGHX856dnY3ycmD6dOFz4sGMGVkoLc3SvWs6D+9SycsLSR4j9azk55/Fnj3Gvt9qKF3ffkj4uBit7hc3+padFmtOWl3IhUQYwc7n3WnrpNSzIieWWMBIXBKhH6/yIQSLLF0KdOoEDBoU/u/SpU63yBqcFmv8SlKInVaXCROAsjJg06bwf1kLbCYIwNznJBjMwMGD+QgGMzR9Ts2zovfcViBVAZmFtsU7rrP4nDlzBkVFRfjyyy+xc+dO/OpXv4q8t3v3bkyaNAn/+c9/0KpVK9x1112YPn26c421CLcEvcYDLFhdyIXELhQzF8bIc6I2M0tNgLTcs8KadcUNe1rFK64TPtOnT0deXh6+/PLLqNerq6sxZMgQDB48GC+88AK++uor3HLLLcjKysLEiRMdaq01OG1WTjTMDNyliTJ+UOuGLSkpQVZWluh78XS/9T4nSplZRvb14sWS0v5Sdpb74L9LqQIylSCxDlcJnw8++ADr16/H6tWr8cEHH0S99/rrr6Ourg4vv/wyUlJScMEFF2DXrl1YsGBB3AkftwW9xgNmWF2cjlcizEWte3XVKvl06ni633qfE7nMLC37eomdd/Lkydi0CVi4sKl1pX//cRg40N5sJWEmXLt21Zgxw3+uqjWHefOqMXbsdXEliFnENcLnp59+wm233YZ33nkH6enpTd7funUrBgwYEKWShw4dinnz5uHnn39GixYtRM975swZnDlzJvJ3dXW1+Y03GTmzMutVfhMZp+OVCDah+x3GqgVddnY2+vYVP3dRUbbhrDO9bQKAadOA0lLeSuZB+/ZZALKijiUrsfm4QvhwHIfx48fjjjvuwMUXX4yysrImxxw+fBgFBQVRr7Vp0ybynpTwmTt3LmbPnm16m61GzKzshiq/VsHvxUODAEG4Eyvj6ViI1ZNrm1Q7yEpsDY4Kn/vvvx/z5s2TPWbv3r1Yv349ampqMHPmTNPbMHPmTEydOjXyd3V1NTp06GD691iB8IFRE/Acz9tbCPfiMXMQoNUW4Va0VlVmoa9bWQjTjUU2yUpsDY4Kn2nTpmH8+PGyx3Tu3BkfffQRtm7ditTU1Kj3Lr74Ylx//fV45ZVX0LZtW/z0009R7/N/t23bVvL8qampTc7rRtQEPLux5LgeEWbWIECrLcLNaKmqzFJfF7OAmCXKKEOSABwWPq1atUKrVq0Uj1uyZAkeffTRyN8VFRUYOnQoVq5ciaKiIgBAcXExHnjgAdTX1yM5ORkAsGHDBnTv3l3SzRVPqPWPu22CFg7ex44dU9xl2UxotUW4HbVVlVnu6yyJMqehKs/m4IoYn44dO0b93bx5cwBAly5d0P6cfB87dixmz56NCRMmYMaMGdizZw8WL16MhQsX2t5eJ2DZh20U4WBGDz5BaMPt5S9YFmV2wlodIjfjCuGjBr/fj/Xr12PSpEkoLCxETk4OZs2aFXep7HK40YethRUr0rBo0RR68AkmMDsWzqoYGyp/4X6U6hAR2nCl8MnPzwfHcU1ev/DCC/Hpp5860CJ20OLDdlPqe9hc748qbua2Bz+eg8sTkdiYuYoKLw4eTEJBwVnk5YUQDAaxcuVKxfOkpKRY6s6JZ2twokBVns3FlcKHMI7bUt/D5npP1Gtue/CVJkqAveByHhYyfliE/83iz1Ou6vtdWVmp6vv0unPi3Roc7yhVeSa0QcInAXHjXl9hcz0XJX7c+ODLT5QON04CCi6VR/55Yud+U0aT++Ctv35/DUaNWtskxodf9JGVWBskfBIQNwY7tm8PLFhwEvfemx558AcP3ojjx8MTi1hdErMxK7DabcKTgkvlUXqe3Ha/7UKvFTGREhxircSzZh1FWVkS8vPPIi/vEgCXJKy11QgkfBIQtwY73nNPcwwa9DP++98Qdu1KwuOPXylZl8QMzNw1WogbhSchjdLz5Ib7rbXYoVG0WBGFJGJmk3A8y80FCgsdbEycQMInAXFzsGOvXi3QogVQUiJfl8QMrNo12q3CkxBH6Xlyy/0uLS1FfX091qxpgcWL20UWFbNm/YiSkhq0bt3atEWFFisiizusE+6GhE+C4uZgRztX0FbsGu2E8KTgZGuRe55YX2gIrS/BYMa5khFhkR8KeTB7dh6CwUV44IFxjrSPxR3WCXdDwieBcWuwo90raCu+z07hScHJ9iD3PGm933bGsQgFsVzadF1dnWMCmsUd1vVACxA2IOFDuA67V9BWfZ9dwlOtW6GiogKA8so5kYJLzUTuflsVT6YVubRptXWJrBLQrFvOlKAFCDuQ8CFcid2uOje7BtXC74MmN/AmYnCpHVgVT6YVubTp+vp6VeewMrvPzc8hZUeyAwkfwrXY7apzq2tQK7EDLwWX2oMV8WR66NNnJ7p02Y/jx1uiZcvjEateTY111j0tVsREeQ4J6yDhQxCELBRcah+sZID5/TVNBMiGDRss+a5EtSKSy9g5SPgQRIKhZ8CNl+BS1mEhjsWOCTnRrYiJKvZYgYQPQSQQRgZcFiblRMDJOBa7JuREtiLSTuvOQ8KHIBIEsQH33XdHonXrw2jfXt0mmW4OLnUTdsaxqLW+xGLUMpSoVkTaad15vMqHEAThZviJTWzABbxYuvRW7NhxkerztW8PDBxIoide4K0v/fqNE52Q+/cfh5KSkqjXd+y4CIsWTcErr4zDokVTNPUfIbwV0ecL/50IVkS+ZIAQN2647GZI+BBEnMNPbNdff2mTARdoXNkHgxkOtI5ggbD1JRvemBmBt75kZWVFXpOyDPH9R2tczoQJQFkZsGlT+L9271pvF7E7rfPPIu20bj/k6iKIBCA7Oxt9+gCjRq3Fu++OROyaR2hqp4E3MZGL4aoUeELlXDUTJ16lKy4nEVLUaad1diDhQxAJQnZ2Nl5+uT+2bQtg5MicSJE8APD5ONx113BbiuQR7KImhquiIhcAB6Cx//CuGr/fb1tb3QjttM4GJHwIIoHIzs7GVVcBf/5z7Mreg8LCNk43j2AAMeuLMAB648bBEIoegMPgwRvJWmgQ2sfLPjwcx3FON4Ilqqur4ff7EQwGkZmZ6XRzCMIyyssTMzuLJhh9BAIBbNoE/O53Ta/NW28F4jb93A5oHy9zUDt/k8WHIBKURIiriIUmGHVIicOCAi+8Xg6hkNBNGt/p53YQe62lSgXQPl7mQMKHYIrycuC778Kl+904KZvVfrdfB1ahjSKVURKHI0c2FjlMhPRzu6GqztZD6ewEMyxdCnTqBAwaFP7v0qVOt0gbZrXf7deBcDdKoq9Pn52YMmUR3norENfp506gVCqAMAcSPi6lvDxc96K83OmWmEN5OTBxYmMF11AoHHzrlt9nVvvdfh3cRjCYgYMH82li0YjfX4N+/erI0mMycqUCnCae5hxydbmQpUsbJ0evN1x7w+2rru++iy5bD4Qzjvbvd4cZ3Yz2BwIBfP45EApFB0s0NADbtgWQlkbBo2ZCLgWCNfiqzkLx42RVZz7Wa8WKNEyf7kco5IHXy2H+/CDGjj3l2kQAsvi4jHi1CHTrBtGqsV27OtMerRhtPx9XsWXLK6Ll7DdvfgXPPvssAoGA6OfjaTVmB+RSUA9ZxexDqaqznfBj0pNPvoH77suMBLSHQh7cd18mnnzyDdkxiWXI4uMy3G4ZkcLNO38HAgH4fHWYPz8NM2b40dDggc/HYd68IHy+UwgElFdFfFwFP/DFWiL4gU8s/iIeLYBWUVVVBYA2ilQLWcXsp0+fnejSZT+OH295riikM/2RH2uUnhU3JgKQ8HEZvGUhdjdjt1hG5HDjzt+xGTB3350RGbBOnKjBSy+FX9eSHq1l4JOyAA4d6o7rZyeBQACrVq0CwJ5LgUW07tZO6Ce28KPfXyN6jZ0oEBmPzwoJH5fhZsuIGtxWWyZ2tSM1YGldFUmdJ5Z4tQBagfAe+P01GDx4IzZuHCxqWaMKxGQVs5PYfbzEcCqeRskK7UZI+LgQN1pGiDBm1ucJBALIzDwLr7d1TEE5DhkZRxAI0L5bUuzYcVFE9AAhDB68MeLCKS0tTejrxos+pZU+iUNzYbnPseJ+MwsSPi7FbZYRwtxYHKGLTVhQzuMJYcSItVi7NjyJJ3oFYjFiXTiAFxs3DkbPnnvOWdoSe6NNofWhXbvqmLi1aowde51rs3kI/ai1QrsBEj4EYQMVFV5TY3GEJnG51ZgbAw+thlw4yvCiZto0oLSUty570L59FoAsJ5tGEIYh4UPENaxs/XDwYJKlsTjxtBqzmngM1rQSsi4T8QbV8SHiFie3foitfVJQcFa2zo/aeAmKqzAOS7VSCIJV4nlMIosP4ThSO0Hz6IkncDLNW6z2ycSJIdlsPJazOuKReAvWJAiziecxiYQP4ShKO0HzaA3StSvNO3a1I1X75NFHg4rZeG4cQNwEy7VSCIJF4nVMIuFDOIra4FutQbp2FXqMXRVt3pyChQubBs4GAi0AaI+XYCVGKR6I5xWsECssqIT7obGkERI+RFxiZ6FH4STSt695gou2ojCfeJ/wYy2owWAGjh/PRsuWgSjrFpU5SAzidZNRo5DwIeIWJwo9miW4lGKU4jnwkNCP0NIjt88WlTmIf3gRHAxmYNGiKeC46E1Gf/zxZfj9NQkpgkn4EHGNE6m4ZggupRilRHHbEPqgfbaIeN5k1CgkfAjCAowKLjUxSiRqCCmoSCPBQ3WrmkLChyAsRk+wabxvRktYC012BE88bjJqFBI+BGEhRtL1aTNaAtCXjaM02R07dgxAeCuVgweTUFBwFnl5YfMiuUjjDzvrVrkhe4yED+Eo8R6kazRdn7YLSEzMyMaRm+zWrFkjG/yciAGv8Y4d29q4JROVhA/hKBSkay9uWI2xhBPXy8xsHKnJTin42e6AV6E7mKxQ7sTJavlaIeFDOA4NaPbgltUYKzh1vYxk46i1jLIU/Cx0B5MVyr3YVS3fDEj4EISAeLWIuGk1xgIsXC89AcpKFtRjx45hzZo1TAU/8221wwoVr8+3GHaGEQQCAWRmnoXX2xqhkCfyus/HISPjCAKBJKZEKwkfgjhHPFtE3LQaswotkx4L10tvNo6aCYbFTB+rrFCJWr3YrjACocVu5Mhoi92IEWuxdi17FjsSPgQBNlb4VmLX3mWsolXUsnK9rMzGYW2HeiusUIlevdiO3yQUVnJ9iqVCiV7lQwgi/pFb4ccDfF0gny/8dyLVBZISteXl4scHAgH4fJWYP78KPh8HIGyynzevCj5fJQKBgO52bNok/b1S+P01KCj43hJhYuW59bRl1Ki18HjCN8oMK5SaeCnhcYRxWOpTUpDFhyBg3QqfpXT9RK0LpMVtFVt36e67MyKr1xMnavDSS+HXtVoI4tmNaiZWWaFYimkinIeED0HAnErJUhWaS0pKcPbsWSQnJ8Pv9zd5P9bPrqfSs1oSsS6QFlEbe92l0sG1WAhYcqOyJMSlsKLeDIsxTYRzkPAhiHMYsYioqdAcDGZgwIAJuPhiv+S5jVR6JsRxevsPrYHSVoqTRK6bxVpME+EcJHwIQoBei4iSBYCvT7JwoVfW1WG00jMhjpNuPq1uVKvFCWuixk4rlB3Viwn2IeFDEBYTW58k3jLGzMCq+ipCt6HPB3TvHn69sjL8X6utG+FA6TrMn5+GGTP8aGjwnAuUDsLnO4VAQPz7WRMnVpLIVijCGUj4EITFiGWUJFoNHTmsCvx12m1oVaB0PJLov9/NuCFuLBYSPkTcYWVwsB7EMkoSqYaOHFYG/jrtNrQiUJrQhhsnZbfhRosdCR8irnB6lS9GbEZJItXQUYKFCslE/OLGSdmNuO36kfAh4gqnV/lS8Bkl/fuPQ1FRNk3q52ClQjIPWQjiD7dNyoT1kPAhCJvw+2vQr18dcnOdbgk7OJ1qHgtZCAgi/iHhQxAmYJalIBEtDqxVlCZRQxDxDQkfgjABsywFVlgcWAv2FiMRK0oTBOEMJHwIwiTMEg9mihAWg70JgiCchHZnJ4g4Rk0QdzCYoWvXcCvQu4O5GE67DZ3+foIgxCGLD0EkMGq30rADswsZOh2o7PT3EwQhjquEz7p16zBnzhzs3r0b5513Hi6//HK88847kfd/+OEH3Hnnndi0aROaN2+OcePGYe7cuUhKctXPlMQNsRpOQ6ts9bC0lYbZhQwbt8BwtnRAoj+PTmDV9ieEMVi6L65RBKtXr8Ztt92Gxx9/HIMGDcLZs2exZ8+eyPsNDQ0YMWIE2rZtiy1btqCyshI33XQTkpOT8fjjjzvYcnOgWA110CpbPSxtpWFmIUOrtsAg2IfuPZuwdl88HMdxzn29Os6ePYv8/HzMnj0bEySu1gcffICRI0eioqICbdq0AQC88MILmDFjBo4ePap6hV9dXQ2/349gMIjMzEzTfoNRKisr8RK/uY8MEydORC4ViiHOIddvgsEMLFo0pclWGmVlzlh8OnVqWshQa1vMOg/hPujes4md90Xt/O2K4OYdO3bgxx9/hNfrxUUXXYTc3FwMHz48yuKzdetW9OrVKyJ6AGDo0KGorq7G119/LXnuM2fOoLq6OuofQSQC/FYaHk94RLK7eGAgEEBlZSUqKyvh81Vi/vwq+HzcubZwePrpE5rbImc5IuIbuvdswuJ9cYWr6//+7/8AAA8//DAWLFiA/Px8PP300xg4cCD++9//omXLljh8+HCU6AEQ+fvw4cOS5547dy5mz55tXeMJgmGc2kpDynUr3MG8qqoGgYA21y1rW2AQ9kH3nk1YvC+OWnzuv/9+eDwe2X/ffvstQueu2AMPPIAxY8agsLAQy5Ytg8fjwd/+9jdDbZg5cyaCwWDk36FDh8z4aQTBBGpcvH5/DQYOtNcdIBWD5ffXoKDg+8gu5lr3VOO3wPD5wn87vQUGYQ+BQEDUajhvXhV8vkoEAgGHW5i4sPhMOmrxmTZtGsaPHy97TOfOnVFZWQkA6NGjR+T11NRUdO7cGT/88AMAoG3btvj3v/8d9dmffvop8p4UqampSE1N1dN8gmCeRAz2Zm0LDKthKVvGCWKth0Kr4YkTNeBD3BI98cNJWHsmHRU+rVq1QqtWrRSPKywsRGpqKvbt24df//rXAID6+nqUlZWhU6dOAIDi4mI89thjOHLkCFq3bg0A2LBhAzIzM6MEE0EkGok42CfKFhisZcs4Qayo9/trIhZDueMI6xGWYPH5gO7dw6+fs2U4tuhyRYxPZmYm7rjjDjz00EPo0KEDOnXqhCeffBIA8Lvf/Q4AMGTIEPTo0QM33ngj5s+fj8OHD+N///d/MWnSJLLoEAQRd5hd+4ggzITlEiyuED4A8OSTTyIpKQk33ngjTp06haKiInz00Udo0aIFAMDn82Ht2rW48847UVxcjGbNmmHcuHGYM2eOwy03ByrMRxCEEDNrHxGE2ai1sDlhiXON8ElOTsZTTz2Fp556SvKYTp064f3337exVfaRiLEaBJFIaI3VYTFbhiDcgGuED5GYsRoEkQjoidXhs2Vuvz1s6WEhW4Yg3IArChgSBBFfkOu2EalYHTU71E+YEK6Au2lT+L+JFthMEHogiw9BELZDrttGjMbqJEoGG0GYBQkfIuGhXe+dga5pGIrVMQZZDwmtkPAhEhqWUy4TBbOEp1sFLMXqGIOsh4RWSPgQCY3VKZdunYztwizh6XYBq7WyLfWraBLpt7oFli1xJHwIwiLcPhnbgVnCk+WaIWpRG6vjln6V6FtpJDosW+JI+BCERcTDZEywhxv6FW2lQQDsWuIonZ0gCIIwDSPp+QRhByR8CIIgCNOQS88nCBYg4UMQBEGYBp+eL4TS8wmWoBgfgiAIh4jH7CxKzydYh4QPkdCwnHJpJfE44boNt2Rn6UFrej5B2AkJHyKhYTnl0ipYmnDNEp5uFLBuyM4yAm2lQbAKCR8i4bFqcmd1MmZpwjVLeCaSgGW1XxGEWyDhQxAWkUiTsRHM+v2Jch2pXxGEMUj4EISF0ORDWAGr/Ypixwg3QMKHIAiCMAxLsWMEIQfV8SEIgiAMw1LsGEHIQcKHIAiCIIiEgYQPQRCEA1B2FkE4A8X4EESCQRMuG1B2FkE4AwkfgkgwaMJlB7rGBGE/JHwIIgGhCZcgiESFYnwIgiAIgkgYSPgQBEEQhqHYMcItkKuLIAiCMAzFjhFugYQPQRAEYQokagg3QK4ugiAIgiASBhI+BEEQBEEkDCR8CIIgCIJIGEj4EARBEASRMJDwIQiCIAgiYSDhQxAEQRBEwkDChyAIgiCIhIGED0EQBEEQCQMJH4IgCIIgEgaq3BwDx3EAgOrqaodbQhAEQRCEWvh5m5/HpSDhE0NNTQ0AoEOHDg63hCAIgiAIrdTU1MDv90u+7+GUpFGCEQqFUFFRgYyMDHg8HqebYzvV1dXo0KEDDh06hMzMTKeb41roOpoDXUfj0DU0B7qO5mDldeQ4DjU1NcjLy4PXKx3JQxafGLxeL9q3b+90MxwnMzOTHm4ToOtoDnQdjUPX0BzoOpqDVddRztLDQ8HNBEEQBEEkDCR8CIIgCIJIGEj4EFGkpqbioYceQmpqqtNNcTV0Hc2BrqNx6BqaA11Hc2DhOlJwM0EQBEEQCQNZfAiCIAiCSBhI+BAEQRAEkTCQ8CEIgiAIImEg4UMQBEEQRMJAwidB+de//oVRo0YhLy8PHo8H77zzTtT7HMdh1qxZyM3NRVpaGgYPHozvvvvOmcYyitI1HD9+PDweT9S/YcOGOdNYhpk7dy4uueQSZGRkoHXr1rjmmmuwb9++qGNOnz6NSZMmITs7G82bN8eYMWPw008/OdRiNlFzHQcOHNikT95xxx0OtZhNnn/+eVx44YWRAnvFxcX44IMPIu9TX1SH0nV0si+S8ElQTp48id69e+NPf/qT6Pvz58/HkiVL8MILL2Dbtm1o1qwZhg4ditOnT9vcUnZRuoYAMGzYMFRWVkb+vfHGGza20B188sknmDRpEj7//HNs2LAB9fX1GDJkCE6ePBk55t5778V7772Hv/3tb/jkk09QUVGB0aNHO9hq9lBzHQHgtttui+qT8+fPd6jFbNK+fXs88cQT2L59O7744gsMGjQIv/3tb/H1118DoL6oFqXrCDjYFzki4QHAvf3225G/Q6EQ17ZtW+7JJ5+MvFZVVcWlpqZyb7zxhgMtZJ/Ya8hxHDdu3Djut7/9rSPtcTNHjhzhAHCffPIJx3HhvpecnMz97W9/ixyzd+9eDgC3detWp5rJPLHXkeM47vLLL+fuuece5xrlUlq0aMH95S9/ob5oEP46cpyzfZEsPkQTDh48iMOHD2Pw4MGR1/x+P4qKirB161YHW+Y+Pv74Y7Ru3Rrdu3fHnXfeiUAg4HSTmCcYDAIAWrZsCQDYvn076uvro/rj+eefj44dO1J/lCH2OvK8/vrryMnJQc+ePTFz5kzU1tY60TxX0NDQgDfffBMnT55EcXEx9UWdxF5HHqf6Im1SSjTh8OHDAIA2bdpEvd6mTZvIe4Qyw4YNw+jRo1FQUIADBw7gf/7nfzB8+HBs3boVPp/P6eYxSSgUwpQpU9C/f3/07NkTQLg/pqSkICsrK+pY6o/SiF1HABg7diw6deqEvLw87N69GzNmzMC+ffuwZs0aB1vLHl999RWKi4tx+vRpNG/eHG+//TZ69OiBXbt2UV/UgNR1BJztiyR8CMIifv/730f+v1evXrjwwgvRpUsXfPzxx7jiiiscbBm7TJo0CXv27MFnn33mdFNcjdR1nDhxYuT/e/XqhdzcXFxxxRU4cOAAunTpYnczmaV79+7YtWsXgsEg3nrrLYwbNw6ffPKJ081yHVLXsUePHo72RXJ1EU1o27YtADTJVPjpp58i7xHa6dy5M3JycrB//36nm8IkkydPxtq1a7Fp0ya0b98+8nrbtm1RV1eHqqqqqOOpP4ojdR3FKCoqAgDqkzGkpKSga9euKCwsxNy5c9G7d28sXryY+qJGpK6jGHb2RRI+RBMKCgrQtm1b/POf/4y8Vl1djW3btkX5ZwltlJeXIxAIIDc31+mmMAXHcZg8eTLefvttfPTRRygoKIh6v7CwEMnJyVH9cd++ffjhhx+oPwpQuo5i7Nq1CwCoTyoQCoVw5swZ6osG4a+jGHb2RXJ1JSgnTpyIUtYHDx7Erl270LJlS3Ts2BFTpkzBo48+im7duqGgoAAPPvgg8vLycM011zjXaMaQu4YtW7bE7NmzMWbMGLRt2xYHDhzA9OnT0bVrVwwdOtTBVrPHpEmTsGLFCvz9739HRkZGJFbC7/cjLS0Nfr8fEyZMwNSpU9GyZUtkZmbirrvuQnFxMfr27etw69lB6ToeOHAAK1aswFVXXYXs7Gzs3r0b9957LwYMGIALL7zQ4dazw8yZMzF8+HB07NgRNTU1WLFiBT7++GP84x//oL6oAbnr6HhfdCSXjHCcTZs2cQCa/Bs3bhzHceGU9gcffJBr06YNl5qayl1xxRXcvn37nG00Y8hdw9raWm7IkCFcq1atuOTkZK5Tp07cbbfdxh0+fNjpZjOH2DUEwC1btixyzKlTp7g//OEPXIsWLbj09HTu//2//8dVVlY612gGUbqOP/zwAzdgwACuZcuWXGpqKte1a1fuvvvu44LBoLMNZ4xbbrmF69SpE5eSksK1atWKu+KKK7j169dH3qe+qA656+h0X/RwHMdZL68IgiAIgiCch2J8CIIgCIJIGEj4EARBEASRMJDwIQiCIAgiYSDhQxAEQRBEwkDChyAIgiCIhIGED0EQBEEQCQMJH4IgCIIgEgYSPgRBEARBJAwkfAiCIAiCSBhI+BAE4Rrq6uqcbkITWGwTQRDSkPAhCMIxBg4ciMmTJ2Py5Mnw+/3IycnBgw8+CH4nnfz8fDzyyCO46aabkJmZiYkTJwIAPvvsM1x22WVIS0tDhw4dcPfdd+PkyZOR8z733HPo1q0bzjvvPLRp0wbXXntt5L233noLvXr1QlpaGrKzszF48ODIZwcOHIgpU6ZEtfGaa67B+PHjI3/rbRNBEGxAwocgCEd55ZVXkJSUhH//+99YvHgxFixYgL/85S+R95966in07t0bO3fuxIMPPogDBw5g2LBhGDNmDHbv3o2VK1fis88+w+TJkwEAX3zxBe6++27MmTMH+/btw4cffogBAwYAACorK3Hdddfhlltuwd69e/Hxxx9j9OjR0LplodY2EQTBDrRJKUEQjjFw4EAcOXIEX3/9NTweDwDg/vvvx7vvvotvvvkG+fn5uOiii/D2229HPnPrrbfC5/PhxRdfjLz22Wef4fLLL8fJkyfx/vvv4+abb0Z5eTkyMjKivm/Hjh0oLCxEWVkZOnXqJNqeX/3qV1i0aFHktWuuuQZZWVlYvnw5AOhq03nnnWfoOhEEYR5k8SEIwlH69u0bET0AUFxcjO+++w4NDQ0AgIsvvjjq+C+//BLLly9H8+bNI/+GDh2KUCiEgwcP4sorr0SnTp3QuXNn3HjjjXj99ddRW1sLAOjduzeuuOIK9OrVC7/73e/w5z//GT///LPmNmttE0EQ7EDChyAIpmnWrFnU3ydOnMDtt9+OXbt2Rf59+eWX+O6779ClSxdkZGRgx44deOONN5Cbm4tZs2ahd+/eqKqqgs/nw4YNG/DBBx+gR48eeOaZZ9C9e/eIOPF6vU3cXvX19YbbRBAEO5DwIQjCUbZt2xb19+eff45u3brB5/OJHt+nTx9888036Nq1a5N/KSkpAICkpCQMHjwY8+fPx+7du1FWVoaPPvoIAODxeNC/f3/Mnj0bO3fuREpKSsRt1apVK1RWVka+q6GhAXv27FH8DWraRBAEG5DwIQjCUX744QdMnToV+/btwxtvvIFnnnkG99xzj+TxM2bMwJYtWzB58mTs2rUL3333Hf7+979HAonXrl2LJUuWYNeuXfj+++/x17/+FaFQCN27d8e2bdvw+OOP44svvsAPP/yANWvW4OjRo/jlL38JABg0aBDWrVuHdevW4dtvv8Wdd96Jqqoqxd+g1CaCINghyekGEASR2Nx00004deoULr30Uvh8Ptxzzz2RFHExLrzwQnzyySd44IEHcNlll4HjOHTp0gWlpaUAgKysLKxZswYPP/wwTp8+jW7duuGNN97ABRdcgL179+Jf//oXFi1ahOrqanTq1AlPP/00hg8fDgC45ZZb8OWXX+Kmm25CUlIS7r33XvzmN79R/A1KbSIIgh0oq4sgCMcQy6IiCIKwEnJ1EQRBEASRMJDwIQiCIAgiYSBXF0EQBEEQCQNZfAiCIAiCSBhI+BAEQRAEkTCQ8CEIgiAIImEg4UMQBEEQRMJAwocgCIIgiISBhA9BEARBEAkDCR+CIAiCIBIGEj4EQRAEQSQMJHwIgiAIgkgY/j8koO3uKDDgCwAAAABJRU5ErkJggg==", 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", 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", 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", 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WydAVjjJrINbuK8a8dUdgFoAgA5A3LB0jeiVpnSwiIiK/wBqiBqDEVCUGQwBgFoAn1x1lTRERUQNlNpuRl5eHlJQUhIWFITMzEx988AGA35qYtm3bhp49eyI8PBy33norTpw4AQBYvXo1FixYgG+++QYGgwEGgwGrV68W933x4kXcd999CA8PR4cOHfDxxx8rSpPle7ds2YLu3bsjLCwMt99+O86fP49NmzahS5cuiIiIwKhRo1BZWSl+rrq6Go8//jhiYmLQpEkT9OnTB/v27VPvZCnEgKgBKLpYIQZDFrWCgFMXK6U/QEREqvNlt4W8vDy8/fbbWL58OY4dO4YZM2bg97//PXbu3Clu86c//Ql//etfsX//fjRq1Ajjx48HAIwYMQJ//OMfccMNN6CkpAQlJSUYMWKE+LkFCxbgd7/7HQ4fPow777wTDz74IC5duqQ4bc8++yz+/ve/Y8+ePThz5gx+97vfYenSpVizZg02btyIzz77DK+88oq4/ezZs/Hhhx/irbfewsGDB9G+fXvk5OS49J1qYEDUAKRENUWQ3YjDYIMByVGcW4OIyBfW7itG78XbMWrFXvRevB1r9xV77buqq6uxaNEivPnmm8jJyUG7du0wduxY/P73v8frr78ubve///u/6Nu3L9LS0jB37lzs2bMHV69eRVhYGJo1a4ZGjRohLi4OcXFx4uSGADB27Fg88MADaN++PRYtWoQrV67g66+/Vpy+5557Dr1790b37t0xYcIE7Ny5E8uWLUP37t1x22234f7778fnn38OAKioqMCyZcvwwgsvYMiQIUhLS8OKFSsQFhaGN954Q72TpgADogYg3hiGvGHpCP51HoZggwGLhnVFvDHMySeJiMhTvu62cPLkSVRWVuKOO+5As2bNxH9vv/02CgsLxe0yMjLE/7YsaWFZ4sIR6881bdoUERERij4n9fnY2FiEh4ejXbt2Nq9Z9ldYWIhr166hd+/e4vuNGzfGTTfdhO+++07xd6qBnaobiBG9kpDdMRqnLlYiOSqcwRARkY846rbgjXvxlStXAAAbN25E69atbd4LDQ0Vg6LGjRuLr1smLjSbzU73b/05y2eVfE7q8waDweP9+QoDogYk3hjGQIiIyMcs3RasgyJvdltIS0tDaGgoiouL0bdv33rvW9cSyQkJCUFtba03kueS1NRUhISE4Msvv0Tbtm0B1C2/sW/fPkyfPt2naWFARERE5AFLt4Un1x1FrSB4vdtC8+bN8cQTT2DGjBkwm83o06cPTCYTvvzyS0RERIiBhSPJyckoKipCQUEB2rRpg+bNmyM0NNQr6XWkadOmmDJlCmbNmoXIyEgkJSVhyZIlqKysxIQJE3yaFgZEREREHvJ1t4U///nPiI6ORl5eHn744Qe0aNECN954I5588klFzVHDhw/HunXr0L9/f5SVlWHVqlUYO3asV9MsZ/HixTCbzXjooYfwyy+/oGfPntiyZQtatmzp03QYBEEQnG9G5eXlMBqNMJlMiIiI0Do5RETkoatXr6KoqAgpKSlo0qSJ1skhDzj6LZWW3xxlRkRERAGPARERERE5NXnyZJth/tb/Jk+erHXyPMY+REREROTUwoUL8cQTT0i+1xC6kjAgIiIiIqdiYmIQExOjdTK8hk1mREREFPAYEBERUUDT46zJ5Bo1fkM2mRERUUAKCQlBUFAQzp49i+joaISEhIhLXJB/EAQBNTU1uHDhAoKCghASEuL2vhgQERFRQAoKCkJKSgpKSkpw9uxZrZNDHggPD0dSUhKCgtxv+GJAREREASskJARJSUm4fv26Ltb2ItcFBwejUaNGHtfuMSAiIqKAZlmR3X5Vdgos7FRNREREAY8BEREREQU8BkREREQU8BgQERERUcBjQEREREQBjwERERERBTwGRERERBTwGBARERFRwGNARERERAGPAREREREFPAZEREREFPAYEBEREVHA0zQgWrZsGTIyMhAREYGIiAhkZWVh06ZN4vtXr17F1KlT0apVKzRr1gzDhw/HuXPnbPZRXFyM3NxchIeHIyYmBrNmzcL169dtttmxYwduvPFGhIaGon379li9erUvDo+IiIj8hKYBUZs2bbB48WIcOHAA+/fvx+233457770Xx44dAwDMmDEDn3zyCd5//33s3LkTZ8+exbBhw8TP19bWIjc3FzU1NdizZw/eeustrF69Gk8//bS4TVFREXJzc9G/f38UFBRg+vTpmDhxIrZs2eLz4yUiIiJ9MgiCIGidCGuRkZF44YUXcP/99yM6Ohpr1qzB/fffDwA4fvw4unTpgvz8fNxyyy3YtGkT7rrrLpw9exaxsbEAgOXLl2POnDm4cOECQkJCMGfOHGzcuBFHjx4Vv2PkyJEoKyvD5s2bFaervLwcRqMRJpMJERER6h40EREReYXS8ls3fYhqa2vxz3/+ExUVFcjKysKBAwdw7do1DBw4UNymc+fOSEpKQn5+PgAgPz8f6enpYjAEADk5OSgvLxdrmfLz8232YdnGsg851dXVKC8vt/lHREREDZPmAdGRI0fQrFkzhIaGYvLkyVi/fj3S0tJQWlqKkJAQtGjRwmb72NhYlJaWAgBKS0ttgiHL+5b3HG1TXl6Oqqoq2XTl5eXBaDSK/xITEz09VCIiItIpzQOiTp06oaCgAHv37sWUKVMwZswYfPvtt1onC/PmzYPJZBL/nTlzRuskERERkZc00joBISEhaN++PQCgR48e2LdvH1566SWMGDECNTU1KCsrs6klOnfuHOLi4gAAcXFx+Prrr232ZxmFZr2N/ci0c+fOISIiAmFhYbLpCg0NRWhoqMfHR0RERPqneQ2RPbPZjOrqavTo0QONGzfGtm3bxPdOnDiB4uJiZGVlAQCysrJw5MgRnD9/Xtxm69atiIiIQFpamriN9T4s21j2QURERKRpDdG8efMwZMgQJCUl4ZdffsGaNWuwY8cObNmyBUajERMmTMDMmTMRGRmJiIgIPPbYY8jKysItt9wCABg0aBDS0tLw0EMPYcmSJSgtLcVTTz2FqVOnirU7kydPxt///nfMnj0b48ePx/bt2/Gvf/0LGzdu1PLQiYiISEc0DYjOnz+P0aNHo6SkBEajERkZGdiyZQvuuOMOAMCLL76IoKAgDB8+HNXV1cjJycFrr70mfj44OBgbNmzAlClTkJWVhaZNm2LMmDFYuHChuE1KSgo2btyIGTNm4KWXXkKbNm2wcuVK5OTk+Px4iYiISJ90Nw+RXnEeIiIiIv/jd/MQEREREWmFAREREREFPAZEREREFPAYEBEpUGKqwp7Ciygxyc9uTkRE/kvziRmJ9G7tvmLMW3cEZgEIMgB5w9IxoleS1skiIiIVsYaIyIESU5UYDAGAWQCeXHeUNUVERA0MAyIiB4ouVojBkEWtIODUxUptEkRERF7BgIjIgZSopggy2L4WbDAgOSpcmwQREZFXMCAiciDeGIa8YekINtRFRcEGAxYN64p4o/zCwERE5H/YqZrIiRG9kpDdMRqnLlYiOSqcwRARUQPEgIhIgXhjGAMhIqIGjE1mREREFPAYEBEREVHAY0BEREREAY8BEREREQU8BkREREQU8BgQERERUcBjQEREREQBjwERERERBTwGRERERBTwGBARERFRwGNARERERAGPAREREREFPAZEREREFPAYEBEREVHAY0BEREREAY8BEZHGSkxV2FN4ESWmKq2TQkQUsBppnQCiQLZ2XzHmrTsCswAEGYC8YekY0StJ62QREQUc1hARaaTEVCUGQwBgFoAn1x1lTRERkQYYEBFppOhihRgMWdQKAk5drNQmQUREAYwBEZFGUqKaIshg+1qwwYDkqHBtEkREFMAYEBFpJN4Yhrxh6Qg21EVFwQYDFg3rinhjmMYpIyIKPOxUTaShEb2SkN0xGqcuViI5KpzBEBGRRhgQEWks3hjGQIiISGNsMiMiIqKAx4CIiIiIAh4DIiIiIgp4DIiIiIgo4DEgIiIiooCnaUCUl5eHXr16oXnz5oiJicHQoUNx4sQJm2369esHg8Fg82/y5Mk22xQXFyM3Nxfh4eGIiYnBrFmzcP36dZttduzYgRtvvBGhoaFo3749Vq9e7e3DIyIiIj+haUC0c+dOTJ06FV999RW2bt2Ka9euYdCgQaioqLDZbtKkSSgpKRH/LVmyRHyvtrYWubm5qKmpwZ49e/DWW29h9erVePrpp8VtioqKkJubi/79+6OgoADTp0/HxIkTsWXLFp8dKxEREemXQRAEwflmvnHhwgXExMRg586dyM7OBlBXQ9StWzcsXbpU8jObNm3CXXfdhbNnzyI2NhYAsHz5csyZMwcXLlxASEgI5syZg40bN+Lo0aPi50aOHImysjJs3rxZUdrKy8thNBphMpkQERHh2YESERGRTygtv3XVh8hkMgEAIiMjbV5/9913ERUVha5du2LevHmorPxt8cv8/Hykp6eLwRAA5OTkoLy8HMeOHRO3GThwoM0+c3JykJ+fL5uW6upqlJeX2/wjIiKihkk3M1WbzWZMnz4dvXv3RteuXcXXR40ahbZt2yIhIQGHDx/GnDlzcOLECaxbtw4AUFpaahMMARD/Li0tdbhNeXk5qqqqEBZWf5bgvLw8LFiwQNVjJCIiIn3STUA0depUHD16FLt377Z5/eGHHxb/Oz09HfHx8RgwYAAKCwuRmprqtfTMmzcPM2fOFP8uLy9HYmKi176PiIiItKOLJrNp06Zhw4YN+Pzzz9GmTRuH2958880AgJMnTwIA4uLicO7cOZttLH/HxcU53CYiIkKydggAQkNDERERYfOPiIiIGiZNAyJBEDBt2jSsX78e27dvR0pKitPPFBQUAADi4+MBAFlZWThy5AjOnz8vbrN161ZEREQgLS1N3Gbbtm02+9m6dSuysrJUOhIiIiLyZ5oGRFOnTsU777yDNWvWoHnz5igtLUVpaSmqqqoAAIWFhfjzn/+MAwcO4NSpU/j4448xevRoZGdnIyMjAwAwaNAgpKWl4aGHHsI333yDLVu24KmnnsLUqVMRGhoKAJg8eTJ++OEHzJ49G8ePH8drr72Gf/3rX5gxY4Zmx05ERET6oemwe4PBIPn6qlWrMHbsWJw5cwa///3vcfToUVRUVCAxMRH33XcfnnrqKZsmrNOnT2PKlCnYsWMHmjZtijFjxmDx4sVo1Oi3LlI7duzAjBkz8O2336JNmzaYP38+xo4dqzitHHZPRETkf5SW37qah0jPGBARERH5H7+ch4iIiIhICwyIiIiIKOAxICIiIqKAx4CIiIiIAh4DIiIiIgp4DIiIqJ4SUxX2FF5EialK66QQEfmEbtYyIyJ9WLuvGPPWHYFZAIIMQN6wdIzolaR1soiIvIo1REQkKjFVicEQAJgF4Ml1R1lTREQNHgMiIhIVXawQgyGLWkHAqYuV2iSIiMhHGBARkSglqimC7FbUCTYYkBwVrk2CiIh8hAEREYnijWHIG5aO4F/XGQw2GLBoWFfEG8M0ThkRkXexUzUR2RjRKwnZHaNx6mIlkqPCGQwRUUBgQERE9cQbwxgIEVFAYZMZERERBTwGRERERBTwGBARERFRwGNARERERAGPAREREREFPAZEREREFPAYEBEREVHAY0BEREREAY8BEQWcElMV9hRe5AruREQk4kzVFFDW7ivGvHVHYBaAIAOQNywdI3olaZ0sIiLSGGuIKGCUmKrEYAgAzALw5LqjrCkiIiIGRBQ4ii5WiMGQRa0g4NTFSm0SREREusGAiAJGSlRTBBlsXws2GJAcFa5NgoiISDcYEFHAiDeGIW9YOoINdVFRsMGARcO6clV3IiJip2oKLCN6JSG7YzROXaxEclQ4gyEiIgLAgIgCULwxjIEQERHZYJMZERERBTwGRERERBTwGBARERFRwGNARERERAGPAREREREFPAZEREREFPAYEBEREVHAY0BEREREAY8BEREREQU8BkREREQU8DQNiPLy8tCrVy80b94cMTExGDp0KE6cOGGzzdWrVzF16lS0atUKzZo1w/Dhw3Hu3DmbbYqLi5Gbm4vw8HDExMRg1qxZuH79us02O3bswI033ojQ0FC0b98eq1ev9vbhERERkZ/QNCDauXMnpk6diq+++gpbt27FtWvXMGjQIFRUVIjbzJgxA5988gnef/997Ny5E2fPnsWwYcPE92tra5Gbm4uamhrs2bMHb731FlavXo2nn35a3KaoqAi5ubno378/CgoKMH36dEycOBFbtmzx6fESERGRPhkEQRC0ToTFhQsXEBMTg507dyI7OxsmkwnR0dFYs2YN7r//fgDA8ePH0aVLF+Tn5+OWW27Bpk2bcNddd+Hs2bOIjY0FACxfvhxz5szBhQsXEBISgjlz5mDjxo04evSo+F0jR45EWVkZNm/erCht5eXlMBqNMJlMiIiIUP/giYiISHVKy29d9SEymUwAgMjISADAgQMHcO3aNQwcOFDcpnPnzkhKSkJ+fj4AID8/H+np6WIwBAA5OTkoLy/HsWPHxG2s92HZxrIPIiIiCmyNtE6AhdlsxvTp09G7d2907doVAFBaWoqQkBC0aNHCZtvY2FiUlpaK21gHQ5b3Le852qa8vBxVVVUICwurl57q6mpUV1eLf5eXl3t2gERERKRbuqkhmjp1Ko4ePYp//vOfWicFQF2Hb6PRKP5LTEzUOklERETkJboIiKZNm4YNGzbg888/R5s2bcTX4+LiUFNTg7KyMpvtz507h7i4OHEb+1Fnlr+dbRMRESFZOwQA8+bNg8lkEv+dOXPGo2MkIiIi/dI0IBIEAdOmTcP69euxfft2pKSk2Lzfo0cPNG7cGNu2bRNfO3HiBIqLi5GVlQUAyMrKwpEjR3D+/Hlxm61btyIiIgJpaWniNtb7sGxj2YeU0NBQRERE2PwjIiKihknxKDNX+tAoDR4effRRrFmzBv/+97/RqVMn8XWj0SjW3EyZMgWffvopVq9ejYiICDz22GMAgD179gCoG3bfrVs3JCQkYMmSJSgtLcVDDz2EiRMnYtGiRQDqht137doVU6dOxfjx47F9+3Y8/vjj2LhxI3JychSllaPMiIiI/I/S8ltxQBQUFASDweBwG0EQYDAYUFtbqyiRcvtbtWoVxo4dC6BuYsY//vGPeO+991BdXY2cnBy89tprYnMYAJw+fRpTpkzBjh070LRpU4wZMwaLFy9Go0a/9RnfsWMHZsyYgW+//RZt2rTB/Pnzxe9QggERERGR/1E9INq5c6fiL+/bt6/ibf0FAyIiIiL/o7T8VjzsviEGOURERESAB/MQlZWV4Y033sB3330HALjhhhswfvx4GI1G1RJHRERE5AtujTLbv38/UlNT8eKLL+LSpUu4dOkS/va3vyE1NRUHDx5UO41EREREXuXWWma33XYb2rdvjxUrVogdl69fv46JEyfihx9+wK5du1RPqNbYh4iIiMj/qN6p2lpYWBgOHTqEzp0727z+7bffomfPnqisrHQ9xTrHgIiIiMj/eHVx14iICBQXF9d7/cyZM2jevLk7uyQiklViqsKewosoMVVpnRQiaqDc6lQ9YsQITJgwAX/5y19w6623AgC+/PJLzJo1Cw888ICqCSSiwLZ2XzHmrTsCswAEGYC8YekY0StJ62QRUQPjVkD0l7/8BQaDAaNHj8b169cBAI0bN8aUKVOwePFiVRNIRIGrxFQlBkMAYBaAJ9cdRXbHaMQbpdchJCJyh1sBUUhICF566SXk5eWhsLAQAJCamorw8HBVE0dEga3oYoUYDFnUCgJOXaxkQEREqnJ7HiIACA8PR3p6ulppISKykRLVFEEG2ARFwQYDkqP48EVE6nIrILp69SpeeeUVfP755zh//jzMZrPN+5yLiIjUEG8MQ96wdDy57ihqBQHBBgMWDevK2iEiUp1bAdGECRPw2Wef4f7778dNN93kdNFXIiJ3jeiVhOyO0Th1sRLJUeEMhojIK9wKiDZs2IBPP/0UvXv3Vjs9RET1xBvDGAgRkVe5NQ9R69atOd8QERERNRhuBUR//etfMWfOHJw+fVrt9BCRBjjxIREFOreazHr27ImrV6+iXbt2CA8PR+PGjW3ev3TpkiqJIyLv48SHRERuBkQPPPAAfvrpJyxatAixsbHsVE3kpzjxIRFRHbcCoj179iA/Px+ZmZlqp4eIfIgTHxIR1XGrD1Hnzp1RVcW+BkT+zjLxoTVOfEhEgcitgGjx4sX44x//iB07duDnn39GeXm5zT8ikqa3zsuWiQ+Df2325sSHRBSoDIIgCM43sxUUVBdH2fcdEgQBBoMBtbW16qROR8rLy2E0GmEymRAREaF1csgP6bnzcompihMfElGDpLT8dqsP0eeff+52wogCkd47LwfKxIclpioUXaxASlTTgDheIlLOrYCob9++irZ79NFHsXDhQkRFRbnzNUQNBjsva0/PNXREpD23+hAp9c4777BPERHYeVlrcjV0eunLRUTa82pA5Eb3JKIGiZ2XteWoho6ICHCzyYyIXMdV27VjqaGzDopYQ0dE1rxaQ0REtuKNYchKbcVgyMdYQ0dEzrCGiIgCAmvoiMgRBkREpEveGCIfKNMLEJHrvBoQ/f73v+ckhkTkMg6RJyJfc2umagAoKyvD119/jfPnz8NsNtu8N3r0aFUSpyecqZrIN0pMVei9eHu9DtC75/Zn7Q4RucyrM1V/8sknePDBB3HlyhVERETYLOFhMBgaZEBERL7BSSyJSAtujTL74x//iPHjx+PKlSsoKyvD5cuXxX+XLl1SO41EFEA4iSURacGtgOinn37C448/jvBw3qCItFBiqsKewosNcqZlDpEnIi241WSWk5OD/fv3o127dmqnh4icCIQOxxwiT0S+pjgg+vjjj8X/zs3NxaxZs/Dtt98iPT0djRs3ttn2nnvuUS+FRCSSW5Mru2N0gwsaOESeiHxJcUA0dOjQeq8tXLiw3msGgwG1tbUeJYqIpLHDMRGRdygOiOyH1hOR73FNLiIi73CrU/Xbb7+N6urqeq/X1NTg7bff9jhRRCSNHY6JiLzDrYkZg4ODUVJSgpiYGJvXf/75Z8TExDTIJjNOzEh6UmKqYodjIiIFlJbfbtUQCYJgMxmjxY8//gij0ah4P7t27cLdd9+NhIQEGAwGfPTRRzbvjx07FgaDwebf4MGDbba5dOkSHnzwQURERKBFixaYMGECrly5YrPN4cOHcdttt6FJkyZITEzEkiVLlB+sH2rIQ7KpTrwxDFmprRgMERGpxKVh9927dxcDkwEDBqBRo98+Xltbi6KionoBiyMVFRXIzMzE+PHjMWzYMMltBg8ejFWrVol/h4aG2rz/4IMPoqSkBFu3bsW1a9cwbtw4PPzww1izZg2Aushw0KBBGDhwIJYvX44jR45g/PjxaNGiBR5++GFXDt8vBMKQbCIiIrW5FBBZRpoVFBQgJycHzZo1E98LCQlBcnIyhg8frnh/Q4YMwZAhQxxuExoairi4OMn3vvvuO2zevBn79u1Dz549AQCvvPIK7rzzTvzlL39BQkIC3n33XdTU1ODNN99ESEgIbrjhBhQUFOBvf/tbgwuIAmlINhERkZpcCoieeeYZAEBycjJGjBiBJk2aeCVR1nbs2IGYmBi0bNkSt99+O5577jm0atUKAJCfn48WLVqIwRAADBw4EEFBQdi7dy/uu+8+5OfnIzs7GyEhIeI2OTk5eP7553H58mW0bNlS8nurq6ttOo6Xl5d76QjVwyHZRERE7nFrpuoxY8YAqBtVJrXafVKSOk00gwcPxrBhw5CSkoLCwkI8+eSTGDJkCPLz8xEcHIzS0tJ6HbsbNWqEyMhIlJaWAgBKS0uRkpJis01sbKz4nlxAlJeXhwULFqhyHL7CIdlEDU+JqQpFFyuQEtWUDzZEXuRWQPT9999j/Pjx2LNnj83rls7Wao0yGzlypPjf6enpyMjIQGpqKnbs2IEBAwao8h1y5s2bh5kzZ4p/l5eXIzEx0avf6SnLkOwn1x1FrSBwSDaRn2OfQCLfcSsgGjt2LBo1aoQNGzYgPj5ecsSZN7Rr1w5RUVE4efIkBgwYgLi4OJw/f95mm+vXr+PSpUtiv6O4uDicO3fOZhvL33J9k4C6vkv2Hbj9AdeAImoY2CeQyLfcCogKCgpw4MABdO7cWe30OPTjjz/i559/Rnx8PAAgKysLZWVlOHDgAHr06AEA2L59O8xmM26++WZxmz/96U+4du2auOba1q1b0alTJ9nmMn/HNaCI/B/7BBL5llvzEKWlpeHixYsef/mVK1dQUFCAgoICAEBRUREKCgpQXFyMK1euYNasWfjqq69w6tQpbNu2Dffeey/at2+PnJwcAECXLl0wePBgTJo0CV9//TW+/PJLTJs2DSNHjkRCQgIAYNSoUQgJCcGECRNw7NgxrF27Fi+99JJNc1hDx3mJiPyPpU+gNfYJJPIet2aq3r59O5566iksWrRIcrV7pTM579ixA/3796/3+pgxY7Bs2TIMHToUhw4dQllZGRISEjBo0CD8+c9/FjtFA3UTM06bNg2ffPIJgoKCMHz4cLz88ss2UwIcPnwYU6dOxb59+xAVFYXHHnsMc+bMcemY/XWmavZBIPJfa/cV1+sTyOuXyDVKy2+3AqKgoN8qlqz7D6ndqVpP/DEgKjFVoffi7fVGne2e259V7kR+gsu0EHlGafntVh+izz//3O2Eke+wDwKR/2OfQCLfcKsPUd++fREUFIQVK1Zg7ty5aN++Pfr27Yvi4mIEBwernUZyE/sgEBERKeNWQPThhx8iJycHYWFhOHTokDijs8lkwqJFi1RNILnPMi9R8K/NmpyXiIiISJpbfYi6d++OGTNmYPTo0WjevDm++eYbtGvXDocOHcKQIUPEWaIbEn/sQ2TBPghERBSovNqH6MSJE8jOzq73utFoRFlZmTu7JC9iHwQiIiLH3Goyi4uLw8mTJ+u9vnv3brRr187jRBERERH5klsB0aRJk/CHP/wBe/fuhcFgwNmzZ/Huu+/iiSeewJQpU9ROIxEREZFXudVkNnfuXJjNZgwYMACVlZXIzs5GaGgonnjiCTz22GNqp5GIiIjIq9zqVG1RU1ODkydP4sqVK0hLS7OZHbqh8edO1URERIHKq52qLUJCQpCWlubJLoiIyAtKTFUouliBlKimHFRBpIBHAREREekP1zAkcp1bnarJO9xdlZ6r2RORRYmpSgyGAMAsAE+uO8r7A5ETrCHSCXef6JR8zlHVOavV9YG/A6mFaxgSuYcBkQ7IPdFld4x2eANT8jlHAROr1fWBvwOpybKGoXVQxDUMiZxjk5kOOHqi8+RzjqrOWa2uD/wdSG1cw5DIPawh0gF3n+icfc5RwCRAYLW6DrB5g7xhRK8kZHeM1uUahmweJr1iDZEOuPtE5+xzloDJmiVgcvQe+Q5/B/KWeGMYslJb6SroWLuvGL0Xb8eoFXvRe/F2rN1XrHWSiEQeTcwYSHwxMaO7q9I7+tzafcV4ct1R1AqCGDBZ9yGSe09vGvJTpT/9DkTuKjFVoffi7fVqtHfP7d/grmnSF6XlNwMihfx5pmpHAZO7QZgvBUKnY3/4HYg8safwIkat2Fvv9fcm3YKs1FYapIgChU9mqib/EG8Mky1kHb2nB+6OwPM3ev8diDzF0W+kd+xDRLrm7gg8ItIXjn4jvWMNEekanyqJGg49j34jYg0R6RqfKokaFj2OfiMCWENEDuhlZBefKomIyNsYEJEkvY3sYqdjIiLyJjaZUT3fnLmMuR9yOQkiIgocDIjIxtp9xRj62h7YT07FkV1ERNSQMSAikWXOH6mpOjmyi4iIGjIGRCSSmvMHqMskHNlFREQNGTtVk0hqzp8gA7D+0VuRmdjSJ2nQy8g2IiIKLKwhIpHUnD95w9J9FgxxJWwiItIKa4jIhlZz/kitWTZv3RF0jmvus4CMAhNrJYkIYEBEErSY80eq/5JZAIa+ugeLhze81e1JH/Q23xYRaYdNZjpWYqrCnsKLDWb+H0fHY+m/ZE8A50Ai75CqlWReIwpcrCHSqYb25OrseCz9l6wLKAvLHEhsziA1SdVKMq8RBS7WEOlQQ3tyVXo8I3olYf2jt8K+oohzIJE3SNVKMq8RBS4GRDrk6MnVH7lyPJmJLbF4OFe3J++TGlXJvEYUuNhkpkNS8wH585Orq8fD1e3JV5jXiMiCNUQ61NCeXN05nnhjGLJSW/ntMZP/YF4jIkDjgGjXrl24++67kZCQAIPBgI8++sjmfUEQ8PTTTyM+Ph5hYWEYOHAgvv/+e5ttLl26hAcffBARERFo0aIFJkyYgCtXrthsc/jwYdx2221o0qQJEhMTsWTJEm8fmsdG9ErC7rn98d6kW7B7bn+/7lANNLzjISKihkXTgKiiogKZmZl49dVXJd9fsmQJXn75ZSxfvhx79+5F06ZNkZOTg6tXr4rbPPjggzh27Bi2bt2KDRs2YNeuXXj44YfF98vLyzFo0CC0bdsWBw4cwAsvvIBnn30W//d//+f141PC0VD0hvbk2tCOh4iI3FdiqsKGw2fxyTc/6WLQkEEQpNY29z2DwYD169dj6NChAOpqhxISEvDHP/4RTzzxBADAZDIhNjYWq1evxsiRI/Hdd98hLS0N+/btQ8+ePQEAmzdvxp133okff/wRCQkJWLZsGf70pz+htLQUISEhAIC5c+fio48+wvHjxxWnr7y8HEajESaTCREREaocs5Kh9ZxFl4iIGpq1+4ox98MjsAQgBsBrk/AqLb9124eoqKgIpaWlGDhwoPia0WjEzTffjPz8fABAfn4+WrRoIQZDADBw4EAEBQVh79694jbZ2dliMAQAOTk5OHHiBC5fviz7/dXV1SgvL7f5pyYlQ9H1vrZXQ5s4koiI3Ke0TLCUf9a1MQKAeR8e0bQ80e0os9LSUgBAbGyszeuxsbHie6WlpYiJibF5v1GjRoiMjLTZJiUlpd4+LO+1bCm9TlZeXh4WLFjg+YHIcDYpnFzAlN0xWvx805BgVNTUalJ71NAmjiQiIve5UiZIlX8AYAY0nRhVtwGR1ubNm4eZM2eKf5eXlyMxMVG1/Tsbii4XMK36sggrvyiyec/XAYmjYI3NekREgcXVMkGq/APqmqy0nF5Gt01mcXFxAIBz587ZvH7u3Dnxvbi4OJw/f97m/evXr+PSpUs220jtw/o7pISGhiIiIsLmn5qcDUWXmkU3yACs2FVULxP5eiZrTyeOZFMbEZF/cXTfdrVMsJR/BqsyzgAgb3i6pg/Vuq0hSklJQVxcHLZt24Zu3boBqKul2bt3L6ZMmQIAyMrKQllZGQ4cOIAePXoAALZv3w6z2Yybb75Z3OZPf/oTrl27hsaNGwMAtm7dik6dOsk2l/mKo0nh4o1huK97a3x48Cfxtd7to/DF9xcl9+XLNZg8mTiSTW1EtjhwgrRgne8ASP63JT86u2+7UyZYyr+Dpy9DEIAeyS01z/+aBkRXrlzByZMnxb+LiopQUFCAyMhIJCUlYfr06XjuuefQoUMHpKSkYP78+UhISBBHonXp0gWDBw/GpEmTsHz5cly7dg3Tpk3DyJEjkZCQAAAYNWoUFixYgAkTJmDOnDk4evQoXnrpJbz44otaHHI98cYwyUxQYqrC+kM/2by2WyYYAuoyqa+qGi3R/ZPrjqJWEBRPHMmmNiJbfEDwHQaev7HOd5ZKGgG2/23Jj9kdo53et90tE+KNYcjN0M9voWlAtH//fvTv31/829JnZ8yYMVi9ejVmz56NiooKPPzwwygrK0OfPn2wefNmNGnSRPzMu+++i2nTpmHAgAEICgrC8OHD8fLLL4vvG41GfPbZZ5g6dSp69OiBqKgoPP300zZzFemRVBWko/kRBAHY9d8LPruZurPkQUNbXZw3WPIEHxB8h4Hnb+zznf1ILwtLflw6MlPRfbshLIOjaUDUr18/OJoGyWAwYOHChVi4cKHsNpGRkVizZo3D78nIyMAXX3zhdjq1INfpTI4A399M5Wq35DSkNdp4gyVPNbQHBL1i4GlLboSXlFpBQJDBoPi+7WqZoDe67VQd6Ow7XQcZfqvOlONKx2YtNJQ12pTMIUUNmxoDA6QGTvjrA4KeeToIRO9czYtS+U5OsMGAG9u2dPu+7W8DaHTbqZrqV0Hu+u8FsY02yFDXTGZ9nfvDzdTfq1UtU83zyT5wqVU76G6/C3JNQ6qZtudOXrTPdwYDgF/LEuv/ts6P1vft8JAgVNTUosRU5TCv+mMtum6W7tA7byzd4Y4SU5VkgGTJvHrPcP7M+gK3F2wwYPfc/izMGrgSUxV6L95er3D15Le3vqaZf7xj7b7iBnev9DQvWuc7AJL/bb8fpUGON64TTygtv1lD5Ges22j1UtsSCJ2L7ZvJrPHJPnB4o9+Pv/e78Ad6uVcqofR+6kpelNqnfb6T+2/rfSjti+Wv/eMYEPk5rW+m/lgt6g65jojzc7vgzox4XV/kpJ6G3PzS0Gl9r1TClfup0ryo1j3alSDHX68Tdqomt1j60sz9MDA6F8t1gNUyGPK3DosNQUMZGEDas79+XR2soSQvqjkAxJVBAP56nbCGiFzmqC+NVtWi3m6281UHWKXHESg1c3rkT80vpE9S129iZLjLzUzO8qKaTVeu3gP98TphQNSAeSNIcNSXBtCmWtRXwYG3L3BXOixyXhVt+UPzC+mT3PW77tEsl5qZrO/vWamtJLdRu+nK1Xugv10nbDJroNbuK0bvxdsxasVe9F68HWv3FauyX0eTemlRLerrOYHijWHISm3llZohpcfR0OdVIWrI5K7fyhqz4mYmpfd3bzRduXoP9KemfdYQNUDerEGQeuIIMgAvj+wuuzifJzVVzj7rr6MZ7AVCh0WihsSVxVGtObp+s1JbOa2BcfX+7s2abWf3Z39r2mdA1AB5M0iQa0e+KzNBcntPLghHn7VciE1Dgv0mOHB083AlyOGEfkTacmVxVPv7nbPr11kzkzv3d280Xdnfnyf0ScH4Pini9/hj0z4DIj/hSi2Lt2sQlD5xeHJBSH123odHkN0xGrv+e8HmQryve2t8dOis14IDNfpiOQsMG3KHxUCYp4oaDmf51dXFUaXud55cv3qoIZa6P6/4oggrvyjC4uF19zZ/rL1nQOQHXK1l8UUNgtQTh/2NxJMLQuqzZgCvbDuJf+4rtrkQPzp0FusezcKZS1WAAejRtqWHR/cbNap8lQaGDbHDor9VmVNgU5JfXV0cVe5+5+71q4caYrlzYL3IuB4CN1cxINI5d2tZnBWucu3fataAZHeMdvuCSIlqil+X1bFhHQxZ1AoCNh4uxcrdP6ha8KpV5etKYOgPQY5S/lhlTg2fo74/SvKrVEEvx1sBgNY1xI7OgeXelpXaSvPAzVUMiHTOk1oWucJVrv1b7RqQ3XP7u31BxBvDMOm2FPzfF0U2r1vSbH1KggAxGLL+fk8LXrWqfL35pKTn5ih/rDKnhs3RvW9CnxTJ/Hrw9GW0bPrbNWZfQ2PN8Ov/CELdNT57SCcUXawAIL0chie0fHiynAOpKVis721aB26uYkCkc2oXpo7av71RA+LJBTGuTwpW7i6qd+yzB3fCks0nxCBrQp/keoGTGgWvWufeW1Xcem+O8scqc2q4nN373thdVO9hy2AApq05VO+BcUSvJJRVXcPiTcch/Hr9TezTDuP6JAOoWxz18I9leH7Tca9dn1o/DFnu7au+LMLKXUUwQ3pYvz/VejMg0jm1C1Nn7d/eqAFRu618RK8k3NMtwWZ1ZqnAyVnBK3dDsX5drXOv9pOSPzRH6aGvA5GFs3ufWQAezk7BG1+cQq0gIOjXmh7B6n3LNQYAz/8aDFnee2N3Ecb1SRbz94Mrv3Lp+nQlwNHLw1C8MQxP3pmGcb1TcODUZdX7cPoaAyI/oGZhKtc3x0JPNSCA/LHbB1mufr/cDUXq9d1z+6ty7tV8UvKX5ii9VJlr/TRN2nPW9yfYYMC43ikY1zsFpy5W4uKVq3jsvQKbbSzXmADB4fXn6vXpSoCjx4ch+5G/equtVooBkZ9QqzD9y5YT9YIhS4AUBPi0BkRpIaXk2Ef0SkLnuObYd+oyeiW3RGai/FOK3A2lc1xz2b5QclPja8WfmqO0rjLXy9M0acv+wc2+v4/9XEAlpiqH15ij91y5Pl0NcPT2MKQ0/f7wUMKAyE+5k7m+OXMZHx78qd7r4rVlqPeWS1wp+NQupFzZn9wNZd+py7q60TjC5ihl9Pg0Tdqxf3ADIPsQ5+wacza5otLr09UAR28PQ0rS7y8PJQyI/JC7mevrU5ccvu+rwsJZIaU02LOerVquxqeiprbefo78ZKq3r2CDASkyN5TwEH0u+aeX5ig909vTdENlf816c7keT9k/uLk7fYmz608q+NpTeLHecbka4OjtYchZ+v3poYQBkZ/xJHPdlBzpdP++KCwcFVJK26Lth8/aNwPWCgKGvrqn3uiQElMVnt90vN7+Zg/uhMqaWsn0VtaY3TtQH9C6OUrv9PY07a8cBSn2D2j3dW+N9Yd+Eq/NuUM645G+qYq+R481CY6uMWfXn+V9R8flToCjp4chZ+n3p4cSBkR+xpPMlZnYEsNvbC3ZbGbhi8JCrpAKDwlS3BYtN3zWmtToELmRJj9XVOP5zfUDJS0LT39oc9c7vT1N+yNnawraX7PW9xcBQN6m44ABeCTbcVDkTzUJFkquUSXH5U6A487DkLfuKY7S708PJQyI/Iynmeuvv+uG0Vltsf/UZfRMbonjpb/4vLCQK6QqamoVBXtyQY3lvEiNJLHsR+r8BRmAFbuK6gVWnnQy95SnT8rWzYlSzYaBRElho2ZB0ZACWWeFudJlLJ7fdBz3ZCY4PB/+VJMAOF/c1ELpcalR2+tKTZ7atW9y6fenhxIGRH5GjcyVmfjbKKzMxJaaVL1KFVLORnUAdRf8pYqaes1kwQYD1j2ahcoaM8JDgnDfa3sk9yN1/sb3ScYKu4kdAeCVUd2Rm5Fg85ovCjtPn5Stb3wWeml+0IqjwkbNgkKPTT6Aa/nWeltnhbnSZSzMApwGNv5Uk6BkcVMLXx2XqzV5vqx901MTnyMMiPyQ2plLq34oUp0bHQV79v2GLEGRZTtLkFdiqsKEPil449fJGu33I9XZ8Q2JiR1vtJtgzFeFnSdPyvY3Pgu1boANqfYDULeg0LrQsaTB/vdxJd/abztncGenk67aX7ODbojFpqOlNvtVEgD4oiZBrfyrZHFTd0acucudmjxf1r75y32DAZGfaqidaeWCPal+Q0EG4JWR3dEjuaVs0PRwdt1Ea1JDaq1fc3bD8rSwc+WG4MkTpaMmDE9vgHIL+PrDjU6OmgWFs325Wktz4PRlCIKAnsmRipr55H4fpflWKo8v2XwCc4Z0xpJNJ2SvDalr9vVdheKyFa4EAK487LlayKr5QKNkcVNXRqR5yp2aPF/Vvum11lQKAyKdUnMYq17JHZNUsCd1wZsFoLT8qs3+7IOmN744hXG9U5ymxdkNy5OC01lVtv058OSJ0tGNWs018MwCMPfDIzBY9dvy1Y3O2bUgt5q51LZqFhRy0zkkR4W7XEsz98MjYpOwAcDcOzsjvbVRNviZM6SzGIAAvwU+S0dmKs63cnk8o3ULp7O121+zj2Sn4p7MBLcCACUPe64WsmrX3lmuUWeLm7p6XO5SsnySFv149FBr6goGRDrkaBir3iNspVy9ockV9M9t/A6LPv0OecPSkRgZ7tHTvqMblrsFZ4mpyqZws74hOJpiwN0nSvsbn3Va1V4DTwBs1nJSu8bMneYfR6uZS+UxRwWFqzU6ctM5AFA0T1aJqQr7T13C3HVHbPrHCQDyPq3btwHAo/1SsWxnoc3+rIMhi7r1uAyK862jPO5OYe6tAMDVQrbEVIUNh8+q3mSkdHFTX1AS8GjRj0frpjpXMSDSGWfDWPUeYTtjaQqQCxLkjkmuoLf+/LpHs7xWLezuE9abu+uPXqsVBBw4ddnpTd3dAsX6xhceEoTKGrMqa+A56zyrRo2Zo22cNf84W81cLo9JFRSuBuxyTZUZbVrIFgpDX9sjrpRu/dDjiADg1R2F9V63BIDWH7f0g1Oab7WqRXCVK4Ws1AADCzXuDfHG3xY31brDsJKAx9ddLfypozzAgEh3lAxj1XOE7Yijm5OSY7Jc8BsPl+C5jd/V+3xljdnmhh4EYEKfZNXSn90xGktHZiLo14JGSa3BG7uL6r0eBAAOpgYAHDfzKOGNG9/EPilY+UXdk3AQfq0hsno/yABFNWbOAkG5bZw1/zi7dqTymHUtkGW9Oneq+Z3d+KWCSevaNUdzgykRbDBg9uBOWLK5fl8fV2oG/GE0kNJCVm6AgWV7NYM9R9ebL2ft1lvfUn8Jsi0YEOmMkidxPUfYchzdnADlxxRvDENuRjwWffqd5A0xK7VVXTX27lNYufsH/N8XRVi5u8ina6VZyBXQE7NT0KNtS8mb+uGfyvDgyq901Txqf+wP92mHcX2Sseu/F+qadyw1MgLwccFZpLcxyt7AlTzdy23jrPlHyWrm1nlM7jd1p5rf2Y3fPlBXY+5zy7FavmtEryTc0026344rBaXeClV7SgtZuetvfm4X3JkRbxOAe6t/picdirXojOyNc+EPQbYFAyKdkbrYh3ZPwEeHzvpFhC3H0dO7q8ek5Ia4cvcPqnXkc7djoNwkkJZRb/bHMHtwJ8mOsVo2j0od+xu7izCuTzKyO0bbVBEJ+HVWYsjfwCXPCWxrluRqAOybf4IMwHirGkD7c2r4tQ1JgG0ec9Zsq6QGQqrgULruldQ8WfaCACwcegNahIXgu5Lyes1k1vNuWX+XN/vt6GlQh5JCVu53tA6GvBl0eNKhWIvOyN48F3oPsi0YEOmQ1MX+RE4nv4iw5cgFBy/bDZtXytENUe2OfO7uz1ngZn8MvuqA6ErhduD0Zdk0CRBkl02Ru4Fbzol1MCIA2PXfC4rWdrLuyLpiVxFWfFGEN6xqAB2tZg4A/7vx23pzTtkfV1Zqq3qB1+whnZwWos7Oq3WhoOShx3I+7spMQER443rD2C3zbnmbXodNOytknV1/3g46HF07zvbv687I/jYazFsYEOmU/cXuLxG2HLmb012ZCc4/7GCfrjwZutvM6Mn+nD3J2h+DtzsgujP8256zvjEWcjfw7I7RMBh+60MjwPW1nVZ+USTbKV/q2rEfyi7F+rhG9EpCWeU1LP41CHl+03G0CGss27G7rPIant983Ol5tQRN2R2j6w1ll3rosWx/T2aC28PY3WG99IsvC0q1a6I8fXByNz1Krh1HfN0Z2d9Gg3kLAyLyGV+1JavdkU/Jk6bSmgFfptueK0+Blm3tA4ggA2T7xtiTu4F7uraTqzdvuWOxT6v9b/r85uP1gq6XHugm+d2LN9Xf1v68OgtG7Y9Xq5oZm6kLrAJXC28VlN46XncfnNxNj9Jrx1mafdkZ2d9Gg3kLA6IApkW/AF/VdKkdfMntT+2buDeDRlcCCbk+X9P6t6/rOySR3sM/lTmc0di61sGTm6+rN29H/dfkmm3lzhWE+rVi9sPdLdva1zS4OneOFk0Y9aYukBmhpXZBqXT0oZr3K0dBhyfnXy6/vTyyu0s14r56gLScV2czkgcCBkQNnNxNpCEuw2BP7eDLfn/eKrS8FTS6EkjIjdh6eftJ/P3zkzaBnyW9WamtZJt1pCYbdXeggKtPz3LHYsn3UoWU3LnqkVy/Y7dU4Wc/BYGrtVpaNWHIFeaWkXHeKiidHa+3ao/kgg5Pzr+jvOMqbz9ASq1dl9GmhZh39xRebDDlgRIMiBowR50/6y3DsO6IOCJHi46TehvFooS/tbu7EkjYb2vNUeAndQOXym8fHTorOUpKKVeenu2PJQh1Ux9IrXEn9xm5eX0+/uYnvPf1mXqfn9innc2+Xa3V0qoJQ+57PfmtPPne5Khwr9eWSeVZT86/r5u73CW3dt3uuf1tZtEHgFE3JeKxAR10dwxqC9I6Ac48++yzMBgMNv86d+4svn/16lVMnToVrVq1QrNmzTB8+HCcO3fOZh/FxcXIzc1FeHg4YmJiMGvWLFy/ft3Xh+JTcjcRS+BhX5D/2hpQb1tfWLuvGL0Xb8eoFXvRe/F2rN1X7JPv9ZTlpmlNrUKrxFSFPYUXVf8NRvRKwu65/fHepFuwe25/h0GvZduncrvUe896Ekln5ALHyhozslJbedS3S+nnrY/7y3m348k705x+ztG5ijeGITkqHP+UCIYMAMbZTQhqKSSDDXUZxlkh6er2apH73szElh79Vvbs87ej43X04OEtnp5/V64zrcid14OnbWfRB4A1X5/BrXn+c292l1/UEN1www34z3/+I/7dqNFvyZ4xYwY2btyI999/H0ajEdOmTcOwYcPw5ZdfAgBqa2uRm5uLuLg47NmzByUlJRg9ejQaN26MRYsW+fxYfMXRTUTNZRg85c/DPb31JOjtzrSuVMPHGx1PhKmEXjpsKj1u+9pKuc/sP3VJsqP2AzcnSvZ7cbVPiFYT2nn7e+Xyt9z3apV/PD0Pvuov6S6582oWBMmyQQAw78Mjqtyb9doi4BcBUaNGjRAXF1fvdZPJhDfeeANr1qzB7bffDgBYtWoVunTpgq+++gq33HILPvvsM3z77bf4z3/+g9jYWHTr1g1//vOfMWfOHDz77LMICQnx9eH4hNwEeD9XVCM5KrxePwjrGiLAdwWWvzU72VO78HAWIKp5I1G6L08DP39pQgBcC0YNBoPk671To8R9WYb6GwAsHl63L1eOW+1C1ZXf3Bu/j7P8LfW9WuYfvQc1npA7rz2TIyUHCgB1/cg8vTfrdV4rwE8Cou+//x4JCQlo0qQJsrKykJeXh6SkJBw4cADXrl3DwIEDxW07d+6MpKQk5Ofn45ZbbkF+fj7S09MRGxsrbpOTk4MpU6bg2LFj6N69uxaH5HX2md2SwaetOSRmQut5UHb990K9CwPwfqc6vdQeeMKdm6ZcweQoQLRu1/f0RuLqTcnTwM8fpu93tbayR9uW9QoOgwG4sW1LlJiq6k0+OVfi6dqXT8ruTiipJlcegDypXSNl5M7r3CGdxVnnrdnPKu8qvbcI6D4guvnmm7F69Wp06tQJJSUlWLBgAW677TYcPXoUpaWlCAkJQYsWLWw+Exsbi9LSUgBAaWmpTTBked/ynpzq6mpUV1eLf5eXl6t0RL5jyewHTl3G4/88VC8T7p7bX1zQ0v7C2PXfC+i9eLvXo3h/qj1Qi6NgRC5ADA8JUu1G4u5NSUng56hw1fvTtqu1lfHGMCwenl7vt4w3huGTb36q94QtADhw6jLuyvTOlA2OyP3mZVXXxBmwvZkGV6dckDs3Ur+DXptf/IXUdflI31TAACz+9Lf5tQwA8oane3SO9d4ioPuAaMiQIeJ/Z2Rk4Oabb0bbtm3xr3/9C2Fh3juBeXl5WLBggdf27yvxxjBENnNtIjypm+e8dUcQHhKMnsmRDjOuOzcnf3v683T1amdNBlIBYkVNrWo3Em/dlPRcFa6EO7WVcnlXrjnN8rIn15g75H7zxZuOi3MNeetp3dGUC0EAJth1QHclYNdDrVdD9Uh2Ku7JTMDB05chCBCnDVDaaiD1G+i9RUD3AZG9Fi1aoGPHjjh58iTuuOMO1NTUoKyszKaW6Ny5c2Kfo7i4OHz99dc2+7CMQpPql2Qxb948zJw5U/y7vLwciYmJKh6J76gxkZ1ZAB57r8BhQedJgaj32gMLpcfoTpOYZTupQrbEVKXajcQbNyW9V4Ur4W5tpVTeddScBrh/jblLrk+ht5/WHU25sPFwKVbu/gH/90URVuwuwtwhnfFIdqrigF3rWq9AUDeowvUaTUc1fHpuEdD9sHt7V65cQWFhIeLj49GjRw80btwY27ZtE98/ceIEiouLkZWVBQDIysrCkSNHcP78eXGbrVu3IiIiAmlpabLfExoaioiICJt//srVIaRSw8kt5IbkOxrm31AoPUZH0wgoHapvP6RcyW+odKi+N4Z0e3NotLemIJCi1nBpS3Oa5bcOMgCLh6XXe1KW4uq1o+T8SP3mc4Z09tq0ERZy+eLMpSqs3P2D+J4gAHmfHsfrOwsVXyOOar0a8n1IC67c351tq+cpCXRfQ/TEE0/g7rvvRtu2bXH27Fk888wzCA4OxgMPPACj0YgJEyZg5syZiIyMREREBB577DFkZWXhlltuAQAMGjQIaWlpeOihh7BkyRKUlpbiqaeewtSpUxEaGqrx0fmOJxPZ2ZN6WtN727AalC4G6U6TmJJz5Og39GYnaSXND96qCteiGU6t2kpH59ida0yK3Eg2Z+kJDwlCRU2t15drkMsXkJn2Y/Gm47inW4Kia0SrWi+taNkM6OmyP/bb6rVFQPcB0Y8//ogHHngAP//8M6Kjo9GnTx989dVXiI6uW0/pxRdfRFBQEIYPH47q6mrk5OTgtddeEz8fHByMDRs2YMqUKcjKykLTpk0xZswYLFy4UKtD0owrmdBy8zx4+jKmrTnkdEi+3tuG1aDkGN1tElNK6jf0ZidppQGJWlXh1jd9AJLH1TmuOSpqanXfP8RZAebqNSa1fyUj2azFG8PqjVa0Xq5B7fMply96tG0puXCsAGDV7lN4MreL02tEat+zB3fC85uP6/4+5Gpwo3X/PGcziVsfiz+XBQZBkFq+j+yVl5fDaDTCZDL5dfOZO9buK653Q5PrQ6RkO3/m7BhLTFXi6DyLYIMBu+f291rhvafwIkat2Fvv9fcm3SKOInSHO8dSYqpyu3O8/U1/Qp8UrPiiqN52loJUz/1DXC3A3Ll2PvnmJzz2XkG91//+gPwiolrkT8v32ueL13cVIu9TiaHdBuDLubcrTo/9vq3PpSUfje8jv0yLr7maN7T6zexJ5VEAkscil5+1quVSWn7rvoaItKe0RsPfRou5w9kxatFp0FtPZO40g7pbFS5Vy/XG7iLJCeK8MSpK7QkvXa2xc+facTaSzZIW6+NSs2nblXMmObQ7OxWnL1Zgjd3yJ2ZB2eR/1t9vHfhbzuWqL4uwYlcRVnxRhDd2F+kieHYnb+ilO4J9HgVgE6hZH4tUfta6lksJBkSkiNKCTq9tw2pydozeCAydze/jjSDMl1XfcqOuHs5OwRtfnBKHaJvtPqfH6QLcLcBcvXacjWSTOq7sjtGq/KZqnbPHBnTAP/edUZQe62tAySSlK78oqrc+o9ajHt3JG3pqgrLOo3sKLzo8FuttlQSCepgqgQEROaWHjOpv1AwMlRQ+3gjCfFnbJXfTH9e7blV6S0fg+17bo6gfg1LemC7AVwWYZSSb1MSQcse1e25/j39TNc+Z0jxmfw1ILUZt/f16qVWx507e0OtQdVeOxdnvoZfaIwZE5JBeMqo/UiOQdKXw8UbtnK+aQZ3d9C3/L7WNJ0uaeKPg9GUBJvf7ODouT39Ttc+Zs/RIXQP2rKd4cGVGbF9zN2/osTuCK8firFO2XuYwY0BEsvSUUf2NWoGkHp50fdUMKjUsvMRUZfPdrvRjUJJmpU+5rga3vizApH4fZ8flyW/qjRowR+mRugbsBRsMOPxjGR5c+ZV4zVnPiK2XWhXA/bzh6+4ISvK80mNxFDw5a3rzJQZEJEsPhbE1bzfdqbV/NQNJPfUfcEStcxdvrD8s3D6YdKUfg5Lvc/aU625w640CzJXV6r1RS2X5/jmDO2PJZu/NX2RN6hqwFmwwYPaQTuLs1IDtjNiVNWbZAFsreu9r6UqeV3oscsGTnu5xDIjIhvUNV08Z1dtNd2ruX81AUq/9B5R0cLUvvJ39bdmvK8HkkZ9M9V5zNY86esrVUy2pNyffdOf75wzpjIzW3pm/yFq8MUx2Cob5uV1wZ0a87DVXWWNG8aUKNvu7wJt5Xip40tM9jgERiaRuuHrIqN4ulNTev9qBpN76D1jnE8sIb/sOrmWV18QJ8izNF+sP/ST7t6WQciWYLDFV4flN9eexmT24k1uBp9RnvF1LqrTGx34SRrMAzF3neBJGwPWaCLn0SF0jSzad8NlcOOP7pNiMGgPqrqk7M+IRbwzD+fKr9UbcBRsMCA8J0k1A62vu1tpq0TKgl3scAyICIB8U7J7bH7vn9tc0o3r7AlV7/9544tFLFbt9PpFqxRBXUf/1b7MAfHjwJ/F9qb8thZSnI1cAIKNNC9cPTIY3a0ldqfE5cPqy5HxMB09fFhff9GZ6tG4+t4yok7qmLOm2D4YWDeuKippaXTX7+4onNd5atQzo4R7HgIgAOL7hWS8y6i5P+ph4+wL1xv718sSjNiUdXB3195BjndfUGLmiFm/2xXGl5kJuQQG11hlwlh49NJ9LXVP26Qbq8t+6R7MQE9EEB05flqw50lsfPDV5WuOtpyYsX2NARAC0fRJ2Fix5+wL11v718MSjNql8YkDdhIBmQbqDqxLWeU2NkSuesM+P3ghuXa1x6ZkcWX8SRgA9kls6Tb8a6dFLIWl/TclN6LnxSAlWflEkNutazl1DLtwtv/ulihqXa8V8kef9AQMiAqC8cHH1ZuvsaUVp1a63L1B/vgH4cuJMuXxif+5ahDW22WZo9wSxz5C9IAPq5TVPR664S252Z7XPr6sPIJYmo3kfHoEZdau65w1Pr5ced5tKlKTHnXPt7bwpueK9AVixq8hm0dsgA/DKyO7okdxSMh2O+k75w6S09v36XKkVk8szDfGBzhku7qpQoCzu6mhxTiU3W/sbiKOFR5OjwnWxaKE/02riTCWLuNpv882Zyxj66h6bG3UQgBVjeiAspJHbhY6a0yXY50fr2i+1z687C7o6Ou+eLgKq9oKcvsqb9uke3ydZckSa3GLHcun0l0lpneVbR3lLLwvHehsXdyW3yD0VKGmXdnXtJG901PSXJzqlHB2PlkPClTw92m+TmdiyXsfYod0TMOntA24XOt6eLkGAdxaTBdyrcXF03j29ntRckNOXeVNqss43dhcpqn2TS2fnuOZ+MzpNLt++MrI7WjULdZi3tO4srzcMiEgRZxeOu2snqdlvyV+e6JRydjyuDlHXQ6BoPxu19dpkrhY6vpguwZ59nvf0nKrZLNE0JBgGg21Ha1evJ+v0eHJ+fV3Q2p9Hpf2d5NK579RlvwkU5Jo75ZoHlXy2IXc6d4QBESni7MJxZ+0kNTtq6mkCPTUoOR6lNzO9BYqWwsvTWaaVBOmuBCz2+TEIv9YQWW1jOb/eOqeeNk/ZB0OedCD2JKiR69tz8cpVn8wWrbT2Te4a6pXc0m8CBU/uo3rpLK8XDIhIEWcXjrtrJ6nVKbahVf0qOR4lNzMlgZVWtUeePp06+ry7AYt9ftz13wv1zi8ArwTfajVPAXV9s9Y9moXMxPqj0JTy5Pexz5uWmqvH3ivwWVCutFlX6hrKTGzpV4GC3H1UzfXIAgEDIlLM0YXj6VOKKyM/pDS0ql+lx+PsZuYssNKy9kiqRmZCn2S3P69WwGKdH6XOrzcWo1S7ecoMoLLG7FZaLDytPbCcu4OnL2PamkP1ZjNXs/bWk6Be7hryRqDgzYcP+/uoq+uRAXV5yfrvQMOAiFzi6KlLzRuIqwV1Q6v6deV4HP0mjgIrPTQzWvLMqt2nsHL3D/i/L4qwcneR2zU63ghY7M+vN4JvtZun1HoY8PSajjeGoWXTinqzbKtZe6tGUC93DanZx0uNdLqy1Isr17bemtW1EqR1AqhhiTeGiUNb9xReRImpyulnSkxVNtvKXczO9jWiVxJ2z+2P9ybdgnWPZiExMlzR9ytJkxasj2f33P5u3aAsgVWwoW7VMevAylEh7Gsrd//g8u9tYclz9s231tSsLXR0Tt3lSZqVpsfdPG1/fl3lzd/D3XuFr6mRzrX7itF78XaMWrEXvRdvx9p9xbLbunJt+8s59AXWEJHqXHnakNo2MTLc7afleGOY7Orr3ki/t6nxhCr3lC/X8TU8xLfPSf6wlpy1ElMVEiPDse7RLFTWmFVpTlGrecqTOcSccbe5x5u/h7/0HfQ0na7W+Hi6JqAez6EvMCBqAPQypNqSFqUXrty26x7NcrsJwNNmID00I3mDVGBlX1ABdcd732t7fBoE+tNaclKBhdRkf+5Qo3lKri+ep3na04DK02OTu8f5S99BT9PpatDiShDqadr0VP54ik1mfs6ValRfcKWqVm7byhqz200SnjYDeaMZSWlThdR23m66G9ErCesezYJ1i4avq8y90QRl2a8aCxNbeNK0oPR3VDvNgOd5Wq0mFVeOzfp8ObrHeSvvqM3TdLrT7Ki0yd2TtOmt/PEUa4j8mB5rM1x52nC0bVZqK7eeKL05lNsZqSclpU/WUtsB8EnTXUVNrVc7vSrhD0N/3W1a0LoJ1pu1E5b31awdsD5fgO26XFL3OFfzjlY1Gp7kcXebHZU2ubu7Tp3eyh9PMSDyY3ps+3V1dJSjbd3pP+NpfwV3Py+3bImSG4bUjWXeh0cAq0LMmzcbtZodPC1o1BzR4w3uzASth0LD02tCLn8c/rEMD678StVAT2pOJSXButK8Y78I6twhnfFI31SP0uwK+3QquWYs22R3jMbuuf299tDg6vWnx/LHUwyI/Jhe289dedrwRs2Ap/t054lTqtB76YFuim4YcvPI2JcE3rrZqNHpVetaEG9zdyZobxUargafatdOzB7SCc9vOq56oCd1vuy5e4+zv04FAHmbjgMG4JFs3wVFFjbBmeHX4MwuHXq+rvRa/niCAZEf8/ZoGk/T5kqtjCtpti8MpAoHT2sbXPm8XKEHQdlabZKjvQCbGiK5z6rFkwJTD7Ug3uTJTNDuFBrOgh13C0lPrgn7/CGX5w+cuozIZsoCNanjdLaeXJABbt/j5IKt5zcdxz2ZCarkVXfnCRIEIO/T44AAscZK79eVnssfdzEg8nP+0PdCTfaFwX3dW2P9oZ80fYJytLiikhuG3I0FgE9vNpYC09KhVWntQ0OsOrfmyUzQrhYazoIdXxaS9oW7fUBln+cNAB7/5yG3p9sY0StJPF/zPjwC+7MbBGD9o7e6vRxJSlRTm/5IFmYBquRVVwJVh8FZtwQAwIbDZ3V/XTW08ocBUQOg974XapEqDD48+JP4vlZPUI4KPaU3DLntfH2zcaf2oSFWnVvz9PiU5gElwY6vgk9n+cA+z1sWwvVkug3Ltvazl5uF35onPVmbLd4YhrlDOtc1k1lRI6+6Ot3IpYoayf2YAaz6sggrvyiSDJisZ5n3ZsdwJbXwFp7W8OsJAyLyG0r6F8gVDiWmKhw4fRmCIKBncqTqF6J9oQfAppZFyfdJbefLYNfd2oeGWHVuTY3jU/I7Kgl2fBF8Ks0H1nn+54pqTFtzyGHanR3nxsMlyM2IF8/Vk7ldMK5PsqoPBI/0TQUMEPs/Wf+WnhTUSgNV+07d9rezIAOwYldRvdeB39Lq6cSzznizFl7PfaIABkRkR8/Ru7P+BYB04bB2XzHmfnhEvMkYACwerv6FaLmR6/2il+OoX8hdmY7zgnXhGB4ShIqaWpSYqnSXh4Df8njTkGBU1NRK5nX768AXTQNKgh1fBJ+u1EJZN7N6Mt0GADy38Tss+vQ7m+vFWSDpzv3qkexU3JOZYPNbenrNKvntpDp1W6YWEn7dfnyfZKz4oqje/ufndsGdGfEAgN6Lt3utydSbtfB67xMFMCAiK3ovyKUKg6HdE/DRobOyhYPlIrS+9wqoG9burX4Xer7oHRUgcgXV4/88hIqa607zQrzR82VTvM1+jhugfjod9W/xdh8uJcGOt4MzpYW7dT7a9d8LNiPwDA46P9sfpzWp60Uuz8pNdaEkQLL+LdW4Zp39diWmKsk+QQKAV0d1R2TTUPH8vrG7qN65v/PXmjOlCxe7+2CrtBb+4OnLaNnUtf37Q19DBkQNlKsXhN4LcgupwuCJnE6yhYPcBW6GOh0p7en5olfaL8S+Q6uz/hCWfAZA13lIarQYYJtOQNtjUBrseDM4c1a42+ejOUM64/lNx20eOgwCxPMpxXKcGw+X4LmN39m8Z329yOVZqfvV3HVHAKEuyHAlGFfrmpX77aSCcItggwE3tm1p8z2Ozr2SYNWTB1sltfAGAzBtzSHZ8+zPy6wwIGpALBnxyE8msY1c6QWh54Lcnn1h4KhwOPKTSfL1IMArF6K7F70vOkkq7RfSNLSRov4g9jfeCX1SdJ2HHD39WtIpQND8GPQwSEKucJfKR9bzEVkoeeCIN4YhNyMeiz79TvJ6cZRnpX5L68omVwJZNQtq+99OLgi3fIerNYBKaqI8Ceid1cIH/ToxqeVw7PfvKBjzh76GDIh0yJ3CUe4pROkFoefo3d1gocRUheftRpRY5A1P98qF6M5F74umSlcC3h5tW7rcH8Is1FX123cU1WLGa7n9NQ0Jln36tU6nL64DPffVs5AKzCSnIJDoIKz0nDm6Xhw1DympyVAayCq5ZtVugrL0CXKnBtBRwHTg9GWPA3pHtfAXr1zFY+8VSO4fcF67qvdh+gyIdMadwtHRUwig7ILQa/TuSbAgdzN6dVR35GYkqJzS37hy0fuqqdKVgFdJXpArGB/OTsEbX5zSzYzXUiNmLE+7Fvbp9PZ1oPe+eo7I5aPZgzthyeYTbp0zuevFUZ61z6P2NRfW23qSBkD9JijrPkFSlARfUgGTZfCIPXcCerlaeEed55U+dOmhBlQOAyIdcbdwdNYRTukFobfo3dNgQe5mdGNb9+cyUcrRRW99w/NVU6WrAa+zvCB3bsf1TsG43im6mPFaan8fHTqLdY9mobLGjPCQIFTWmOul05vXgb/01ZMjl49G9ErCPd0S3D5nUteLszxr/zvt+u8F1adG8EYTlCcTc8qRGjwCeDaztxRnx6PXVgalGBDpiLuFo6PqY3cWN9XLjdnTYEHpzciXzRf1OqQO7uyzm4irBb2jvODs3LpzHtUODuX2V1ljRlZqK4ef9dZ1oEVfPbXzt1w+sq5FcGWmc3e+y8L6d/JGIKvG76XmxJyupBMAXh7ZHXdlqlsbbjmeA6cuA4a6JnZAv60MrmBApCPu9uORyoizB3dCRpsWuqjpcZca/Zqc3Yx82XwhdcNbsvkE5gzpjCWb3GtucJWaBb3aBZDa/dj02C/O12nyJH+7MzuxN64nV/Ks2oGsWr+XknR5Enw1DQmW7MfVI7mlVx745KbX0Fsrg6sMgiBIxJVkr7y8HEajESaTCREREarvXxwh9qOpXlu8KzcwuYzoD504pazdVyxZPe8OqenorSc5A+puIrvn9vfoHMmd6z2FFzFqxd5627836RYkR4X77U1ETWr+3t7Ynxp8lSZP8re7fRkdfR/vQY65+3tJDaixXg9R7nf0ZLCKN+6b3qS0/A6oGqJXX30VL7zwAkpLS5GZmYlXXnkFN910k9bJkpzXI6O167U7vnxq8xW1njikzkFiZLjqzReOzrWzTqJ6vZn4ktpPmK7uzxeFtq+eot2tcVCzL6Pl+/Q+Yacjvvq93GlykhpQE2QA1j2ahZiIJrKzWnvye/jTFC2uCtI6Ab6ydu1azJw5E8888wwOHjyIzMxM5OTk4Pz585qmS7IZZdMJ1S48uZtbianK4337SrwxDFmprdw+H3LnwDIM25rS6nBLPwnr8+jsXFtueMEGg/hd3mwek0qjP/D093Z3f2v3FaP34u0YtWIvei/ejrX7ilX5fk/S5AlLAG5NSf52VOC5833hIUEBfw9SakSvJOye2x/vTboFu+f2d2v+OLMAVNaYZX/Hg6cve/R7uJuv/EHABER/+9vfMGnSJIwbNw5paWlYvnw5wsPD8eabb2qaLndvPnrZvz9w1LnWnQBFruBUcq5dveG5y5eFe0PQEB4c7LkbgLtb4Ml9X0VNbcDfg1zhSvDl6LeSe88syE8+qjR9vnyw86WAaDKrqanBgQMHMG/ePPG1oKAgDBw4EPn5+ZKfqa6uRnV1tfh3eXm5V9Lm7U6WeuxY6muOzkFWaiuXm1TkmhOUnmtvN4/5+9BuLTTUZgB3mns8GS0k9X2uLPxKrnH2W0m91zM50uuDVfxVQAREFy9eRG1tLWJjY21ej42NxfHjMjMZ5+VhwYIFXk+bt4cqNoShkJ5SMkRc6flwVHBmpbbSxbluqIW7NzXkBwd3AnBPCjz77+M9yLsc/VZy76nxezTEfo8BMcrs7NmzaN26Nfbs2YOsrCzx9dmzZ2Pnzp3Yu7f+yB+pGqLExESvjjLzZrTt7f37AzXOgZIRFlqfa38cBaIHehyR1pBofV2QrUD6PTjKzEpUVBSCg4Nx7tw5m9fPnTuHuLg4yc+EhoYiNDTUF8kD4P1ouyFG865S4xwoedrV+lzzidw9DbUZQC+0vi7IFn+P+gIiIAoJCUGPHj2wbds2DB06FABgNpuxbds2TJs2TdvEkd/xh4LTH9KoRywkiAJXQAREADBz5kyMGTMGPXv2xE033YSlS5eioqIC48aN0zpp5If8oeD0hzQSEelFwAREI0aMwIULF/D000+jtLQU3bp1w+bNm+t1tCYiIqLAExCdqtXg7aU7iIiISH1Ky++AmZiRiIiISA4DIiIiIgp4DIiIiIgo4DEgIiIiooDHgIiIiIgCHgMiIiIiCngMiIiIiCjgMSAiIiKigMeAiIiIiAJewCzd4SnLhN7l5eUap4SIiIiUspTbzhbmYECk0C+//AIASExM1DglRERE5KpffvkFRqNR9n2uZaaQ2WzG2bNn0bx5cxgMBq2T43Pl5eVITEzEmTNnuJabB3gePcdzqA6eR3XwPKrDm+dREAT88ssvSEhIQFCQfE8h1hApFBQUhDZt2midDM1FRETwolcBz6PneA7VwfOoDp5HdXjrPDqqGbJgp2oiIiIKeAyIiIiIKOAxICJFQkND8cwzzyA0NFTrpPg1nkfP8Ryqg+dRHTyP6tDDeWSnaiIiIgp4rCEiIiKigMeAiIiIiAIeAyIiIiIKeAyIiIiIKOAxICLRrl27cPfddyMhIQEGgwEfffSRzfuCIODpp59GfHw8wsLCMHDgQHz//ffaJFbHnJ3HsWPHwmAw2PwbPHiwNonVsby8PPTq1QvNmzdHTEwMhg4dihMnTthsc/XqVUydOhWtWrVCs2bNMHz4cJw7d06jFOuTkvPYr1+/enly8uTJGqVYn5YtW4aMjAxx4sCsrCxs2rRJfJ950Tln51DrfMiAiEQVFRXIzMzEq6++Kvn+kiVL8PLLL2P58uXYu3cvmjZtipycHFy9etXHKdU3Z+cRAAYPHoySkhLx33vvvefDFPqHnTt3YurUqfjqq6+wdetWXLt2DYMGDUJFRYW4zYwZM/DJJ5/g/fffx86dO3H27FkMGzZMw1Trj5LzCACTJk2yyZNLlizRKMX61KZNGyxevBgHDhzA/v37cfvtt+Pee+/FsWPHADAvKuHsHAIa50OBSAIAYf369eLfZrNZiIuLE1544QXxtbKyMiE0NFR47733NEihf7A/j4IgCGPGjBHuvfdeTdLjz86fPy8AEHbu3CkIQl3+a9y4sfD++++L23z33XcCACE/P1+rZOqe/XkUBEHo27ev8Ic//EG7RPmpli1bCitXrmRe9IDlHAqC9vmQNUSkSFFREUpLSzFw4EDxNaPRiJtvvhn5+fkapsw/7dixAzExMejUqROmTJmCn3/+Wesk6Z7JZAIAREZGAgAOHDiAa9eu2eTJzp07IykpiXnSAfvzaPHuu+8iKioKXbt2xbx581BZWalF8vxCbW0t/vnPf6KiogJZWVnMi26wP4cWWuZDLu5KipSWlgIAYmNjbV6PjY0V3yNlBg8ejGHDhiElJQWFhYV48sknMWTIEOTn5yM4OFjr5OmS2WzG9OnT0bt3b3Tt2hVAXZ4MCQlBixYtbLZlnpQndR4BYNSoUWjbti0SEhJw+PBhzJkzBydOnMC6des0TK3+HDlyBFlZWbh69SqaNWuG9evXIy0tDQUFBcyLCsmdQ0D7fMiAiMjHRo4cKf53eno6MjIykJqaih07dmDAgAEapky/pk6diqNHj2L37t1aJ8WvyZ3Hhx9+WPzv9PR0xMfHY8CAASgsLERqaqqvk6lbnTp1QkFBAUwmEz744AOMGTMGO3fu1DpZfkXuHKalpWmeD9lkRorExcUBQL1RE+fOnRPfI/e0a9cOUVFROHnypNZJ0aVp06Zhw4YN+Pzzz9GmTRvx9bi4ONTU1KCsrMxme+ZJaXLnUcrNN98MAMyTdkJCQtC+fXv06NEDeXl5yMzMxEsvvcS86AK5cyjF1/mQAREpkpKSgri4OGzbtk18rby8HHv37rVp/yXX/fjjj/j5558RHx+vdVJ0RRAETJs2DevXr8f27duRkpJi836PHj3QuHFjmzx54sQJFBcXM09acXYepRQUFAAA86QTZrMZ1dXVzIsesJxDKb7Oh2wyI9GVK1dsIvGioiIUFBQgMjISSUlJmD59Op577jl06NABKSkpmD9/PhISEjB06FDtEq1Djs5jZGQkFixYgOHDhyMuLg6FhYWYPXs22rdvj5ycHA1TrT9Tp07FmjVr8O9//xvNmzcX+2IYjUaEhYXBaDRiwoQJmDlzJiIjIxEREYHHHnsMWVlZuOWWWzROvX44O4+FhYVYs2YN7rzzTrRq1QqHDx/GjBkzkJ2djYyMDI1Trx/z5s3DkCFDkJSUhF9++QVr1qzBjh07sGXLFuZFhRydQ13kQ83Gt5HufP755wKAev/GjBkjCELd0Pv58+cLsbGxQmhoqDBgwADhxIkT2iZahxydx8rKSmHQoEFCdHS00LhxY6Ft27bCpEmThNLSUq2TrTtS5xCAsGrVKnGbqqoq4dFHHxVatmwphIeHC/fdd59QUlKiXaJ1yNl5LC4uFrKzs4XIyEghNDRUaN++vTBr1izBZDJpm3CdGT9+vNC2bVshJCREiI6OFgYMGCB89tln4vvMi845Ood6yIcGQRAE34ReRERERPrEPkREREQU8BgQERERUcBjQEREREQBjwERERERBTwGRERERBTwGBARERFRwGNARERERAGPAREREREFPAZEREREFPAYEBGR36upqdE6CfXoMU1EJI8BERHpTr9+/TBt2jRMmzYNRqMRUVFRmD9/PiwrDSUnJ+PPf/4zRo8ejYiICDz88MMAgN27d+O2225DWFgYEhMT8fjjj6OiokLc72uvvYYOHTqgSZMmiI2Nxf333y++98EHHyA9PR1hYWFo1aoVBg4cKH62X79+mD59uk0ahw4dirFjx4p/u5smItIHBkREpEtvvfUWGjVqhK+//hovvfQS/va3v2HlypXi+3/5y1+QmZmJQ4cOYf78+SgsLMTgwYMxfPhwHD58GGvXrsXu3bsxbdo0AMD+/fvx+OOPY+HChThx4gQ2b96M7OxsAEBJSQkeeOABjB8/Ht999x127NiBYcOGwdWlHl1NExHpBxd3JSLd6devH86fP49jx47BYDAAAObOnYuPP/4Y3377LZKTk9G9e3esX79e/MzEiRMRHByM119/XXxt9+7d6Nu3LyoqKvDpp59i3Lhx+PHHH9G8eXOb7zt48CB69OiBU6dOoW3btpLp6datG5YuXSq+NnToULRo0QKrV68GALfS1KRJE4/OExGphzVERKRLt9xyixgMAUBWVha+//571NbWAgB69uxps/0333yD1atXo1mzZuK/nJwcmM1mFBUV4Y477kDbtm3Rrl07PPTQQ3j33XdRWVkJAMjMzMSAAQOQnp6O//mf/8GKFStw+fJll9PsapqISD8YEBGRX2ratKnN31euXMEjjzyCgoIC8d8333yD77//HqmpqWjevDkOHjyI9957D/Hx8Xj66aeRmZmJsrIyBAcHY+vWrdi0aRPS0tLwyiuvoFOnTmLQEhQUVK/57Nq1ax6niYj0gwEREenS3r17bf7+6quv0KFDBwQHB0tuf+ONN+Lbb79F+/bt6/0LCQkBADRq1AgDBw7EkiVLcPjwYZw6dQrbt28HABgMBvTu3RsLFizAoUOHEBISIjZ/RUdHo6SkRPyu2tpaHD161OkxKEkTEekDAyIi0qXi4mLMnDkTJ06cwHvvvYdXXnkFf/jDH2S3nzNnDvbs2YNp06ahoKAA33//Pf7973+LHZg3bNiAl19+GQUFBTh9+jTefvttmM1mdOrUCXv37sWiRYuwf/9+FBcXY926dbhw4QK6dOkCALj99tuxceNGbNy4EcePH8eUKVNQVlbm9BicpYmI9KOR1gkgIpIyevRoVFVV4aabbkJwcDD+8Ic/iEPZpWRkZGDnzp3405/+hNtuuw2CICA1NRUjRowAALRo0QLr1q3Ds88+i6tXr6JDhw547733cMMNN+C7777Drl27sHTpUpSXl6Nt27b461//iiFDhgAAxo8fj2+++QajR49Go0aNMGPGDPTv39/pMThLExHpB0eZEZHuSI3qIiLyJjaZERERUcBjQEREREQBj01mREREFPBYQ0REREQBjwERERERBTwGRERERBTwGBARERFRwGNARERERAGPAREREREFPAZEREREFPAYEBEREVHAY0BEREREAe//AZgQYx7jt1AvAAAAAElFTkSuQmCC", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "surrogate_scatter2D(\n", + " keras_surrogate, data_training, filename=\"keras_train_scatter2D.pdf\"\n", + ")\n", + "surrogate_parity(keras_surrogate, data_training, filename=\"keras_train_parity.pdf\")\n", + "surrogate_residual(keras_surrogate, data_training, filename=\"keras_train_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation\n", + "\n", + "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4/4 [==============================] - 0s 3ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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06dIFderUweXLlzFr1iw0a9ZMCksBAQHo2LEjRo4cia+++golJSUYO3YsXnvtNakXbvTo0Vi+fDmmTZuGkSNH4uDBg9iyZQt2794tvdekSZMwfPhw+Pj4oHPnzvjqq6+Qm5uLESNGAADs7e0REhKCSZMmoW7durCzs8NHH30EuVwOPz8/AEDPnj3RunVrDB06FAsXLkR6ejpmzpyJsWPHwtLSUis/o8LCQsyfP18r536a6dOnVxiwq8LT0xNnz54FAEyYMEEqb9KkCT777DOMHj0a33zzDSwsLGBvbw+ZTKbSgzly5Ejp702bNsXSpUvx4osvIicnB7Vq1XruNhIRkWE6ceIE9uzZIz23t7fHxx9/rMMWPRuDmJxgY2ODn376CT169ECrVq0QEhKCF154AUeOHJGCkImJCXbu3AlHR0d069YNgYGB8PLywqZNm6TzeHh4YPfu3Thw4ADat2+PxYsXY/Xq1VAoFFKdgQMHYtGiRZg9ezY6dOiA06dPY9++fUqTDb788kv06dMH/fv3R7du3eDi4iL1FAGAqakpdu3aBVNTU8jlcrz77rsYNmwY5s6dWw0/LcMlhJD+z+eXX35Bjx490LBhQ9SuXRtDhw7Fv//+iwcPHjzxHAkJCXjjjTfg7u6O2rVr4+WXXwbwaLiciIiMS0lJCSIiIpRC2+uvv46mTSfgpZdkiI7WYeOegUwY2uCuEcnOzoa9vT2ysrKU7nfLy8tDSkoKPDw8YGVlBaA09OhqNX1zc/NKdzUHBwcjMzMT27dvVzn2wgsvwN3dHcuXL4enpyfGjBmDgQMHom7duvj9998REhKCe/fuwcHBAVFRUZgwYYLSvYa5ublo3LgxFAoFRo8eDScnJ6SlpUGhUCAxMREdOnTQzAfWIXXXnoiI1EtKSsLmzZuVyj7++GM4ODjA3x+IjwfkciAuTrPvW9H3tyYYxFApPZ1MJtPIcKWuHDx4EH/++ScmTpyIhIQElJSUYPHixTAxKe0U3rJli1J9CwsLFBcXK5UlJSXh33//RWRkpLSUysmTJ6vnAxARkV6JiIhQKYuJmQ1vbxmCgoCwMCAysvRPQ2IQQ6VUs+Tn5yM9PR3//PMPTp06hXnz5qFv377o06cPhg0bhubNm6OwsBDLli3DlStX8P3332PVqlVK52jSpAlycnIQGxuLO3fu4MGDB3B3d4eFhYX0uujoaHz66ac6+pRERKQLhYWFKqHNyckJMTHhiI+XITKytCwoqLSnLShIB418DgxuVO327dsHV1dXNGnSBL169cKhQ4ewdOlS7NixA6ampmjfvj2WLFmCBQsWoG3btli/fr3KxAt/f3+MHj0aAwcOhJOTExYuXAgnJydERUVh69ataN26NSIjI7Fo0SIdfUoiIqpu0dHRmDdvnlLZ2bNvolGjDxEWVjosamg9bI/jPW56rCr3uJFx4LUnIlKvoqHR+HiZVu5jexLe40ZERESkRl5eHhYsWKBSHh4eDm9vw7yP7UkY3IiIiMggbd68WWlrTADo27evtIpAUJDh3cP2NAxuREREZHDUDY3Onj3b4HZCqCoGNyIiIjIY9+/fx5IlS1TKw8PDddCa6sfgRkRERAYhMjJS2qO8zNtvvw0vLy+VutHRj+5vq0nDpVwOhIiIiPReRESESmjz9g5XG9qA0tAWHw9p3baagsGNiIiI9Na9e/fU3s82Z074E0NZTVm37XEcKiUiIiK9pC6w9e/fH1eutH1qKKuJM0oBBjciIiLSQ+pCW9kEhLZta2YoqwwOlVKNFBwcjH79+knPX3nlFUyYMOG5zqmJcxAR0ZNlZGQ8MbQZO/a4UbUKDg7G2rVrAQDm5uZwd3fHsGHDMGPGDJiZae8/x59++gnm5uaVqnv48GF0794d9+7dg4ODwzOdg4iIqk5dYGvWbAjefbe5DlqjnxjcqNr16tULa9asQX5+Pvbs2YOxY8fC3Nwc06dPV6pXUFAACwsLjbxn3bp19eIcRESkXkUTEORy4N13ddAgPcWhUqp2lpaWcHFxQePGjTFmzBgEBAQgOjpaGt78/PPP0aBBA7Rq1QoAcO3aNbz99ttwcHBA3bp10bdvX6SmpkrnKy4uxqRJk+Dg4IB69eph2rRpEEIovefjw5z5+fkIDQ2Fm5sbLC0t0bx5c3z77bdITU1F9+7dAQB16tSBTCZDcHCw2nPcu3cPw4YNQ506dWBjY4PevXvjr7/+ko5HRUXBwcEBMTEx8PLyQq1atdCrVy/cvHlTqnP48GF07twZtra2cHBwwEsvvYSrV69q6CdNRKT/0tLS1IY2b+/wGjkr9Hmxx410ztraGv/++y8AIDY2FnZ2djhw4AAAoLCwEAqFAnK5HL/99hvMzMzw2WefoVevXjh79iwsLCywePFiREVF4bvvvoOXlxcWL16Mn3/+Ga+++mqF7zls2DDEx8dj6dKlaN++PVJSUnDnzh24ublh27Zt6N+/P5KTk2FnZwdra2u15wgODsZff/2F6Oho2NnZITQ0FK+//jouXLggDak+ePAAixYtwvfffw8TExO8++67mDJlCtavX4+ioiL069cPo0aNwsaNG1FQUIDjx4/X+O1aiIjKqAtsI0aMgLu7OwDjnYDwJAxupDNCCMTGxiImJgYfffQRbt++DVtbW6xevVoaIv3hhx9QUlKC1atXS4FmzZo1cHBwwOHDh9GzZ0989dVXmD59Ot566y0AwKpVqxATE1Ph+166dAlbtmzBgQMHEBAQAABo2rSpdLxsSLR+/fpK97iVVxbY/vjjD/j7+wMA1q9fDzc3N2zfvh3/+c9/AJQGz1WrVqFZs2YAgHHjxmHu3LkAgOzsbGRlZaFPnz7S8YoWkiQiqmk4AeHZcKiUEB0N+PuX/lkddu3ahVq1asHKygq9e/fGwIEDMWfOHABAu3btlO5rO3PmDP7++2/Url0btWrVQq1atVC3bl3k5eXh8uXLyMrKws2bN+Hr6yu9xszMDD4+PhW+/+nTp2FqaoqXX375mT/DxYsXYWZmpvS+9erVQ6tWrXDx4kWpzMbGRgplAODq6oqMjAwApQExODgYCoUCb7zxBr7++mulYVQioprozJkzDG3PgT1upLQtSHV0S3fv3h0rV66EhYUFGjRooDSb1NbWVqluTk4OOnXqhPXr16ucx8nJ6Znev6KhT214fBaqTCZTuv9uzZo1GD9+PPbt24fNmzdj5syZOHDgAPz8/KqtjURE1UVdYGvZciSWLXODtzeHRiuDPW5U7duC2Nraonnz5nB3d3/qEiAdO3bEX3/9hfr166N58+ZKD3t7e9jb28PV1RXHjh2TXlNUVISEhIQKz9muXTuUlJTgyJEjao+X9fgVFxdXeA4vLy8UFRUpve+///6L5ORktG7d+omf6XHe3t6YPn064uLi0LZtW2zYsKFKryciMgQV9bItW+ZWI/cU1RYGN0JQEBAXp5//pzNkyBA4Ojqib9+++O2335CSkoLDhw9j/PjxuH79OgDg448/RmRkJLZv346kpCR8+OGHyMzMrPCcTZo0wfDhwzFy5Ehs375dOueWLVsAAI0bN4ZMJsOuXbtw+/Zt5OTkqJyjRYsW6Nu3L0aNGoXff/8dZ86cwbvvvouGDRuib9++lfpsKSkpmD59OuLj43H16lXs378ff/31F+9zI6IaJT4+Xm1oi4kJR3R0zd1TVFsY3Eiv2djY4Ndff4W7uzveeusteHl5ISQkBHl5ebCzswMATJ48GUOHDsXw4cMhl8tRu3ZtvPnmm08878qVKzFgwAB8+OGH8PT0xKhRo5CbmwsAaNiwISIiIhAWFgZnZ2eMGzdO7TnWrFmDTp06oU+fPpDL5RBCYM+ePZVepNfGxgZJSUno378/WrZsiffffx9jx47FBx98UIWfEBGR/oqIiMD+/fuVykaNGoWYmHClW3T0tfNAH8nE4wtekd7Izs6Gvb09srKypJACAHl5eUhJSYGHhwesrKx02EKqbrz2RGQonjQBITq6NLSFhdXMwFbR97cmcHICERERacyWLVuUZteXKT9rNCioZga26sDgRkRERBqhrpftm2/GoFmz+uBqH5rB4EZERETPraJtq5o148QDTWJwIyIiome2evVq/PPPPyrlMTHh8PYunXhAmsPgZsA4r8T48JoTkT5R18v20UcfoU+futW6sLsx4XIgBqj8BuZkXMqueWWXHCEi0gYhRIWzRuvWrcu12bSIPW4GyNTUFA4ODtKelzY2NtIG7FQzCSHw4MEDZGRkwMHBAaamprpuEhEZiceX7li0aJG07mV5nDVaPRjcDJSLiwsASOGNjIODg4N07YmIqkP5/awTE1V72SZNmoTatWvroGXGicHNQMlkMri6uqJ+/fooLCzUdXOoGpibm7OnjYiqXVgYsGBBMXr2/EzlWDjX+Kh2DG4GztTUlF/mRESkFdHRpb1sPXuqHmNo0w0GNyIiIlJL3dBobGwofv2VW+7pCoMbERERKcnPz0dkZKRKeUxMOGeK6hiDGxEREUnULfMBlO6CwNFR3WNwIyIiIgDqQ9uMGTO4dqQeYXAjIiIyctu25eDcucUq5ZyAoH8Y3IiIiIxYRUOjDG36icGNiIjIyJTthqBQqIa2WbNmwcSEO2LqKwY3IiIiI7N06R0oFCtUytnLpv8Y3IiIiIxIREQEunZVLWdoMwwMbkREREZC3f1ss2fPhkwm00Fr6FkwuBEREdVwKSkpWLdunUq5t3c4mNkMC4MbERFRDcZZozULgxsREVENpS60MbAZNgY3IiKiGub06dPYsWOHSjlDm+FjcCMiIqpB1PWymZqaYubMmTpoDWkagxsREVENoS60eXuHIyhIB40hrWBwIyIiMmDR0cCWLUfQosVhlWMcGq15GNyIiIgMWGJiBFq0UC6rU6cOxo8fr5sGkVYxuBERERmQsn1Gw8JKQ9vj2MtWszG4ERERGZDISKBhwx+RmHhe5RhDW83H4EZERKTnyveyKRSqvWzu7u4YMWKEDlpG1c1E1w0gIiIiZdHRgL9/6Z9AaWiLj694aJShzXjIhBBC140g9bKzs2Fvb4+srCzY2dnpujlERFRN/P1Lg5qnJ1CnDtCz51LIZPdU6nFoVD9p8/ubPW5ERER6JiwMkMsBIUqHRh8PbW3btmVoM1IMbkRERDr0+LAoAAQFAXFxwODB6odG+/fvX40tJH3CyQlERETVrPxkg7L71yIjIe1woG4HBIBDo8TgRkREVO3Kh7WwMCA0FLh7tzTQqZuA4O/vj9dee00HLSV9w6FSIiKialI2LNq9e+k9bGFhpb1sdeoAyckVzxplaKMy7HEjIiKqJmU9bUDpPWxlFIoIKBSq9Tk0So9jcCMiIqomZfe0hYU9KlN3P1uvXr3g6+tbjS0jQ8HgRkREVE2Cgh5NQADUhzb2stGTMLgRERFVM84apWdlMJMTgoKC4O7uDisrK7i6umLo0KG4ceOGdHzOnDmQyWQqD1tbW6XzbN26FZ6enrCyskK7du2wZ88epeNCCMyePRuurq6wtrZGQEAA/vrrL6U6d+/exZAhQ2BnZwcHBweEhIQgJydHqc7Zs2fRtWtXWFlZwc3NDQsXLtTwT4SIiAyRutD2n//8h6GNKsVgglv37t2xZcsWJCcnY9u2bbh8+TIGDBggHZ8yZQpu3ryp9GjdujX+85//SHXi4uIwePBghISEIDExEf369UO/fv1w7tw5qc7ChQuxdOlSrFq1CseOHYOtrS0UCgXy8vKkOkOGDMH58+dx4MAB7Nq1C7/++ivef/996Xh2djZ69uyJxo0bIyEhAV988QXmzJmD//73v1r+KRERkb4SQlQ4NNq6dWsdtIgMkcHuVRodHY1+/fohPz8f5ubmKsfPnDmDDh064Ndff0XXrl0BAAMHDkRubi527dol1fPz80OHDh2watUqCCHQoEEDTJ48GVOmTAEAZGVlwdnZGVFRURg0aBAuXryI1q1b48SJE/Dx8QEA7Nu3D6+//jquX7+OBg0aYOXKlfjkk0+Qnp4OCwsLAEBYWBi2b9+OpKSkSn9G7lVKRFQzcGjUuHCv0sfcvXsX69evh7+/v9rQBgCrV69Gy5YtpdAGAPHx8QgICFCqp1AoEP//52anpKQgPT1dqY69vT18fX2lOvHx8XBwcJBCGwAEBATAxMQEx44dk+p069ZNCm1l75OcnIx791Q3CS6Tn5+P7OxspQcRERk2daFtxIgRDG30TAwquIWGhsLW1hb16tVDWloaduzYobZeXl4e1q9fj5CQEKXy9PR0ODs7K5U5OzsjPT1dOl5W9qQ69evXVzpuZmaGunXrKtVRd47y76HO/PnzYW9vLz3c3NwqrEtERPqtsLCwwqFRd3d3HbSIagKdBrewsDC1EwrKP8oPLU6dOhWJiYnYv38/TE1NMWzYMKgb6f35559x//59DB8+vDo/znObPn06srKypMe1a9d03SQiInoGERERmDdvnko5e9noeel0OZDJkycjODj4iXWaNm0q/d3R0RGOjo5o2bIlvLy84ObmhqNHj0Iulyu9ZvXq1ejTp49Kr5eLiwtu3bqlVHbr1i24uLhIx8vKXF1dlep06NBBqpORkaF0jqKiIty9e1fpPOrep/x7qGNpaQlLS8sKjxMRkf4ov1H809ZmGzt2LBwdHauxdVRT6TS4OTk5wcnJ6ZleW1JSAqD0vrDyUlJScOjQIURHR6u8Ri6XIzY2FhMmTJDKDhw4IAU/Dw8PuLi4IDY2Vgpq2dnZOHbsGMaMGSOdIzMzEwkJCejUqRMA4ODBgygpKZFWuZbL5fjkk09QWFgo3YN34MABtGrVCnXq1Hmmz0tERPqhLLDdvVu6v2hkZGlwy8nJweLFi1Xqs5eNNMkgZpUeO3YMJ06cQJcuXVCnTh1cvnwZs2bNwq1bt3D+/HmlXqpZs2bhu+++Q1paGkxNTZXOExcXh5dffhmRkZEIDAzEpk2bMG/ePJw6dQpt27YFACxYsACRkZFYu3YtPDw8MGvWLJw9exYXLlyAlZUVAKB37964desWVq1ahcLCQowYMQI+Pj7YsGEDgNKZqK1atULPnj0RGhqKc+fOYeTIkfjyyy+Vlg15Gs4qJSLSL9HRwJAhQE4O4OlZujl8WJj6zeEBhjZjpc3vb4PYOcHGxgY//fQTwsPDkZubC1dXV/Tq1QszZ85UCm0lJSWIiopCcHCwSmgDAH9/f2zYsAEzZ87EjBkz0KJFC2zfvl0KbQAwbdo05Obm4v3330dmZia6dOmCffv2SaENANavX49x48ahR48eMDExQf/+/bF06VLpuL29Pfbv34+xY8eiU6dOcHR0xOzZs6sU2oiISP9ERpaGtlq1gAULSnva1A2NTpkyRWUBeCJNMIgeN2PFHjciIv1S/r42ufw2vvnmG5U67GUjruNGRERUzaKjAX//0j/LBAUBcXGlQ6MMbaQLBjFUSkREVN0iI4H4+EeTD8qoGxr95JNPYGbGr1TSPva4ERERqVE6HFr6JwBcuXKlwgV1GdqouvC/NCIiIjWCgh71tHGvUdIX7HEjIiKjVv5eNnX3takLbbNnz2ZoI51gjxsRERm18veyAY/+3qjRKezcuVOlPgMb6RKDGxERGbWwsEdLfAClf1coIqAmszG0kc4xuBERkdF5fJ/R8rNG1e2CwMBG+oLBjYiIjI66pT727t2L48ePq9RlaCN9wuBGRERG5/HhUc4aJUPB4EZEREbnaUt9MLCRvmJwIyIio7R27VqkpqaqlDO0kT5jcCMiIqPDoVEyVAxuRERkVDg0SoaMwY2IiIzCokWLkJubq1LO0EaGhFteERGRwVO3VVV5ERERDG1UI7DHjYiIDF7ZumxDhgDr1ysvqMuhUapJGNyIiMjghYWVhracnEeL6nICAtVEHColIiKD88knQO3apX8CpUFt/XpALi8NcepCm62tLUMbGTyZEELouhGkXnZ2Nuzt7ZGVlQU7OztdN4eISG/Url3au2ZiAvz8M4dGSb9o8/ubPW5ERGRwxo8vDW0lJaVDo0BpYGNoo5qOPW56jD1uREQVi45+tN9oYqJqYGvevDmGDBmig5aRsdPm9zcnJxARkUEq22+UvWxkTBjciIjIIHHWKBkjBjciIjI46kJbly5d0KNHDx20hqj6MLgREZFB4dAoGTMGNyIi0jvlJx6ULfXBoVEiBjciItJDZVtYPWkXhL59+6JDhw7V3zgiHWJwIyIivRMWVhraQkMFIiLmqhxnLxsZKy7AS0REOhUdDfj7l/5ZJigIUCgicPo0QxtReexxIyIinXp8WBRQPzQ6bNgweHh4VHPriPQLe9yIiEinwsIebQ5fWFhY4axRhjYibnml17jlFREZE84apZqCm8wTEZFBevz+tehowNMT8PJSvqdNXWgbM2YMQxvRY9jjpsfY40ZEhs7fv/T+tVq1gPXrH93PBpQOjx44kItFixapvI6BjQwZe9yIiMgghYWVhracnEcL6rZqVdrrplBEMLQRVRFnlRIRkdYEBT3qaSvbBaGiBXUnT56MWrVq6aCVRIaDwY2IiLSqLKwBwJ07d7BixQqVOuxlI6ocBjciIqoWnDVK9PwY3IiISOvUhbYZM2bA3NxcB60hMlxVnpxgamqKjIwMlfJ///0XpqamGmkUERHpP3VbVT3uypUrFS6oy9BGVHVV7nGraPWQ/Px8WFhYPHeDiIjIMKjbqqo8Do0SaV6lg9vSpUsBADKZDKtXr1aa+VNcXIxff/0Vnp6emm8hERHppbCwR7NFH6cutM2aNQsmJlyFiuh5VHoB3rI94q5evYpGjRopDYtaWFigSZMmmDt3Lnx9fbXTUiPEBXiJyNCcOnUKO3fuVClnLxsZE21+f1e6xy0lJQUA0L17d/z000+oU6eORhtCRESGjUOjRNpX5XvcDh06pI12EBGRAatoAgIRaVaVg9vIkSOfePy777575sYQEZFh+eWXX/DHH3+olDO0EWlHlYPbvXv3lJ4XFhbi3LlzyMzMxKuvvqqxhhERkX6IjlbesqoMh0aJql+Vg9vPP/+sUlZSUoIxY8agWbNmGmkUERHpD3XLfnBolEg3Kj2r9GmSk5Pxyiuv4ObNm5o4HYGzSolIP5TvccvP34oLFy6o1GFoI3pEm9/fGltQ5/LlyygqKtLU6YiISIfK74oQFATExQGJiREMbUQ6VuWh0kmTJik9F0Lg5s2b2L17N4YPH66xhhERkW5ERwNDhgA5OY+GRzk0SqQfqhzcEhMTlZ6bmJjAyckJixcvfuqMUyIi0n+RkaWhrVYt4I03liIi4p5KHYY2It3gOm5ERKSkbCsrhSICBQWqxxnaiHSnysGtTEZGBpKTkwEArVq1Qv369TXWKCIi0p2goNL72R7HwEake1UObtnZ2Rg7diw2btyIkpISAICpqSkGDhyIFStWwN7eXuONJCKi6sG12Yj0W5VnlY4aNQrHjh3D7t27kZmZiczMTOzatQsnT57EBx98oI02EhGRFpSfOQqoD202NjYMbUR6pMrruNna2iImJgZdunRRKv/tt9/Qq1cv5ObmarSBxozruBGRNvn7ly6sK5eX3s/2OAY2omejze/vKg+V1qtXT+1wqL29PerUqaORRhERkfaFham/lw1gaCPSV1UeKp05cyYmTZqE9PR0qSw9PR1Tp07FrFmzNNo4IiJ6Po8Ph5anLrS1aNGCoY1Ij1V5qNTb2xt///038vPz4e7uDgBIS0uDpaUlWrRooVT31KlTmmupEeJQKRE9r/LDoXFxj8q5oC6R9ujVUGnfvn0hk8k02ggiItKOsjXZwsJKn3PWKJFh09gm86R57HEjIk1SF9p8fX3Rq1cvHbSGqObSq03mmzZtin///VelPDMzE02bNtVIo4iISLMqGhplaCMyLFUeKk1NTUVxcbFKeX5+Pq5fv66RRhER0bOLjgamTQNkMmDQIA6NEtUklQ5u0eWmJMXExCgtCVJcXIzY2Fh4eHhotnVERFRlkZFAcjIwZ45qaAsMDISPj48OWkVEmlDpodJ+/fqhX79+kMlkGD58uPS8X79+GDRoEA4cOIDFixdrraFBQUFwd3eHlZUVXF1dMXToUNy4cUOpTkxMDPz8/FC7dm04OTmhf//+SE1NVapz+PBhdOzYEZaWlmjevDmioqJU3mvFihVo0qQJrKys4Ovri+PHjysdz8vLw9ixY1GvXj3UqlUL/fv3x61bt5TqpKWlITAwEDY2Nqhfvz6mTp2KoqIijfwsiIieJDRUqA1t4eHhDG1EBq7Swa2kpAQlJSVwd3dHRkaG9LykpAT5+flITk5Gnz59tNbQ7t27Y8uWLUhOTsa2bdtw+fJlDBgwQDqekpKCvn374tVXX8Xp06cRExODO3fu4K233lKqExgYiO7du+P06dOYMGEC3nvvPcTExEh1Nm/ejEmTJiE8PBynTp1C+/btoVAokJGRIdWZOHEidu7cia1bt+LIkSO4ceOG0vsUFxcjMDAQBQUFiIuLw9q1axEVFYXZs2dr7edDRASU3st2+vRclXIOjRLVDAY7qzQ6Ohr9+vVDfn4+zM3N8eOPP2Lw4MHIz8+HiUlpHt25cyf69u0r1QkNDcXu3btx7tw56TyDBg1CZmYm9u3bB6B0htWLL76I5cuXAygNrG5ubvjoo48QFhaGrKwsODk5YcOGDVJwTEpKgpeXF+Lj4+Hn54e9e/eiT58+uHHjBpydnQEAq1atQmhoKG7fvg0LC4tKfUbOKiWiqlA3AWHQoEFo1aqVDlpDZLz0ah23uXNV/0+uvOroVbp79y7Wr18Pf39/mJubAwA6deoEExMTrFmzBsHBwcjJycH333+PgIAAqU58fDwCAgKUzqVQKDBhwgQAQEFBARISEjB9+nTpuImJCQICAhAfHw8ASEhIQGFhodJ5PD094e7uLgW3+Ph4tGvXTgptZe8zZswYnD9/Ht7e3lr5uRBRzRYd/WhNtqCgR+VFRUX4/PPPVeqzl42o5qlycPv555+VnhcWFiIlJQVmZmZo1qyZVoNbaGgoli9fjgcPHsDPzw+7du2Sjnl4eGD//v14++238cEHH6C4uBhyuRx79uyR6qSnpyuFKQBwdnZGdnY2Hj58iHv37qG4uFhtnaSkJOkcFhYWcHBwUKlTtg1YRe9Tdqwi+fn5yM/Pl55nZ2c/7UdCREYkMrJ0F4TIyEfBjQvqEhmXKq/jlpiYqPQ4d+4cbt68iR49emDixIlVOldYWBhkMtkTH2WBCQCmTp2KxMRE7N+/H6amphg2bBjKRnrT09MxatQoDB8+HCdOnMCRI0dgYWGBAQMGwFBGg+fPnw97e3vp4ebmpusmEZEe6d4dqFWr9E9AfWgbNWoUQxtRDVblHjd17OzsEBERgTfeeANDhw6t9OsmT56M4ODgJ9Ypv6ivo6MjHB0d0bJlS3h5ecHNzQ1Hjx6FXC7HihUrYG9vj4ULF0r1f/jhB7i5ueHYsWPw8/ODi4uLyuzPW7duwc7ODtbW1jA1NYWpqanaOi4uLgAAFxcXFBQUIDMzU6nX7fE6j89ELTtnWR11pk+fjkmTJknPs7OzGd6ISHLoEJCTA/z++wNERHyhcpyBjajm00hwA4CsrCxkZWVV6TVOTk5wcnJ6pvcrKSkBAGlo8cGDB9KkhDKmpqZKdR8fOgWAAwcOQC6XAwAsLCzQqVMnxMbGol+/ftJrY2NjMW7cOACl99KZm5sjNjYW/fv3BwAkJycjLS1NOo9cLsfnn3+OjIwM1K9fX3ofOzs7tG7dusLPZGlpCUtLy2f6eRBRzRcWBiQmcmiUyJhVObgtXbpU6bkQAjdv3sT333+P3r17a6xh5R07dgwnTpxAly5dUKdOHVy+fBmzZs1Cs2bNpLAUGBiIL7/8EnPnzsXgwYNx//59zJgxA40bN5YmA4wePRrLly/HtGnTMHLkSBw8eBBbtmzB7t27pfeaNGkShg8fDh8fH3Tu3BlfffUVcnNzMWLECACAvb09QkJCMGnSJNStWxd2dnb46KOPIJfL4efnBwDo2bMnWrdujaFDh2LhwoVIT0/HzJkzMXbsWAYzIqqS8hMS1IW2CRMmKC2ITkQ1W5WXA3l8dwQTExM4OTnh1VdfxfTp01G7dm2NNhAA/vzzT3z88cc4c+YMcnNz4erqil69emHmzJlo2LChVG/Tpk1YuHAhLl26BBsbG8jlcixYsACenp5SncOHD2PixIm4cOECGjVqhFmzZqkM1y5fvhxffPEF0tPT0aFDByxduhS+vr7S8by8PEyePBkbN25Efn4+FAoFvvnmG6Vh0KtXr2LMmDE4fPgwbG1tMXz4cERGRsLMrPJZmcuBEJG/P/DXX3cwbtwKlWPsZSPST9r8/jbYddyMAYMbEXHWKJHh0at13AAgMzMTf//9NwCgefPmKktjEBFR1ahbo01daAsLC+MtF0RGrErLgaSmpiIwMBCOjo7w9fWFr68vHB0d0adPH5U9QYmIqPJCQ0vXaAsNLd2eT11oCw8PZ2gjMnKV7nG7du0a/Pz8YG5ujk8//RReXl4AgAsXLmDlypWQy+U4ceIEGjVqpLXGEhHVVGU3rQwaFIF161SPc2iUiIAq3OMWEhKCv//+GzExMbCyslI69vDhQ/Tq1QstWrTA6tWrtdJQY8R73IiMR3S0+lmjs2bNUlnqiIj0m17c47Zv3z5s3rxZJbQBgLW1NT799FMMGjRIo40jIjIGp0+fRmLiDpVy9rIR0eMqHdzu3LmDJk2aVHi8adOmuHv3ribaRERUoz1tbTaAoY2I1Kt0cHN1dZXWPlPn3LlzT9zOiYiISpVtFq8utDGwEdGTVPrGiX79+mHKlCm4ffu2yrGMjAyEhoZK20QREVHFhg37BXPmMLQRUdVVenLCvXv34Ovri/T0dLz77rvw9PSEEAIXL17Ehg0b4OLigqNHj6Ju3brabrPR4OQEopqHC+oS1Xx6MTmhTp06OHbsGGbMmIFNmzYhMzMTAODg4IB33nkH8+bNY2gjIqpARbNGGdiIqCqeacsrIYQ0ZOrk5ASZTKbxhhF73Ihqiu+++w7Xrl1TKWdoI6qZ9KLHrTyZTIb69etrtCFERDURh0aJSJOeKbgREdHTVbRtFRHRs2JwIyLSgPJrs509+xmKi4tV6jC0EdHzYnAjItKAJ63NBjC0EZFmMLgREWlARbsgMLARkSZVKrgtXbq00iccP378MzeGiEjflR8SDQoqLeMEBCKqLpVaDsTDw6NyJ5PJcOXKleduFJXiciBE+sffv3RIVC4H4uIY2ohIlc6XA0lJSdHomxIRGaqwMCA0FLh7l7NGiaj6PfM9bgUFBUhJSUGzZs1gZsZb5YjIOAQFcQICEelOpTeZL/PgwQOEhITAxsYGbdq0QVpaGgDgo48+QmRkpMYbSESkT9T1sllZWTG0EVG1qHJwmz59Os6cOYPDhw/DyspKKg8ICMDmzZs12jgiIn0RHV3x0GhoaKgOWkRExqjKY5zbt2/H5s2b4efnp7RHaZs2bXD58mWNNo6ISB9wAgIR6YsqB7fbt2+r3ac0NzeXm80TUY0SHa3+frY2bdpgwIABOmgRERm7Kg+V+vj4YPfu3dLzsrC2evVqyOVyzbWMiEjHKlpQl6GNiHSlyj1u8+bNQ+/evXHhwgUUFRXh66+/xoULFxAXF4cjR45oo41ERNWKQ6NEpK+q3OPWpUsXnD59GkVFRWjXrh3279+P+vXrIz4+Hp06ddJGG4mIqo260PbKK68wtBGRXqjUzgmkG9w5gaj6CCEwd+5clXIGNiKqKp3vnJCdnV3pEzJgEJGh4dAoERmKSgU3BweHSs8YLS4ufq4GERFVJ3Wh7c0338QLL7ygg9YQET1ZpYLboUOHpL+npqYiLCwMwcHB0izS+Ph4rF27FvPnz9dOK4mINKy4uBifffaZSrm3dziY2YhIX1X5HrcePXrgvffew+DBg5XKN2zYgP/+9784fPiwJttn1HiPG5F2cGiUiLRJm9/fVZ5VGh8fDx8fH5VyHx8fHD9+XCONIiLSFnWhLT7+PXh7M7QRkf6rcnBzc3PD//73P5Xy1atXw83NTSONIiLStIcPH1a41+i+fQ0RFKSDRhERVVGVF+D98ssv0b9/f+zduxe+vr4AgOPHj+Ovv/7Ctm3bNN5AIqLnxaFRIqopnmkdt+vXr+Obb75BUlISAMDLywujR49mj5uG8R43ouenLrR9/PHHcHBwqP7GEJFR0Ob3Nxfg1WMMbkTP7u7du1i2bJlKOXvZiEjbdL4A7+MyMzPx7bff4uLFiwCANm3aYOTIkbC3t9do44iIngWHRomopqpyj9vJkyehUChgbW2Nzp07AwBOnDiBhw8fYv/+/ejYsaNWGmqM2ONGVHXqQltoaCisrKx00BoiMkZ6NVTatWtXNG/eHP/73/9gZlbaYVdUVIT33nsPV65cwa+//qrRBhozBjeiyrt27Rq+++47lXL2shFRddOr4GZtbY3ExER4enoqlV+4cAE+Pj548OCBRhtozBjciCqHQ6NEpE/06h43Ozs7pKWlqQS3a9euoXbt2hprGBFRZagLbTNnzoSpqakOWkNEpF1VDm4DBw5ESEgIFi1aBH9/fwDAH3/8galTp6psg0VEpC0XLlzA1q1bVcrZy0ZENVmVg9uiRYsgk8kwbNgwFBUVAQDMzc0xZswYREZGaryBRESP49AoERmrZ17H7cGDB7h8+TIAoFmzZrCxsdFow4j3uBGpoy60zZ49GzKZTAetISJSpVf3uJWxsbFBu3btNNkWIqIK/f7774iNjVUpZy8bERmTSge3kSNHVqqeuun4RETPo6KhUW9vhjYiMi6VDm5RUVFo3LgxvL29wV2yiKi6qAtt7GUjImNV6eA2ZswYbNy4ESkpKRgxYgTeffdd1K1bV5ttIyIjtmvXLiQkJKiUM7QRkTEzqWzFFStW4ObNm5g2bRp27twJNzc3vP3224iJiWEPHBFpVEREhNrQFhPD0EZExu2ZZ5VevXoVUVFRWLduHYqKinD+/HnUqlVL0+0zapxVSsZI3dDoxo3hkMmABQuAoCAdNIqIqAr0clapiYkJZDIZhBAoLi7WZJuIyAitWbMGaWlpKuXh4eHg6CgRUalKD5UCQH5+PjZu3IjXXnsNLVu2xJ9//only5cjLS2NvW1E9MwiIiIqDG1ERPRIpXvcPvzwQ2zatAlubm4YOXIkNm7cCEdHR222jYiMAGeNEhFVXqXvcTMxMYG7uzu8vb2fuEL5Tz/9pLHGGTve40Y12bx581BYWKhSztBGRIZOL+5xGzZsGLeUISKNUNfLZmFhgenTp+ugNUREhqNKC/ASET0vDo0SET27Z55VSkRUFRVtW8XQRkRUeQxuRKR16kKbu7s7RowYoYPWEBEZLgY3ItIqDo0SEWkOgxsRaQWHRomINI/BjYg0Tl1o8/HxQWBgoA5aQ0RUczC4EZFGcWiUiEh7GNyISCM4NEpEpH0MbkT0XKKjgcRE1dDWs2dPyOVyHbSIiKjmYnAjomcmhEBi4lyVcvayERFpB4MbET0TDo0SEVU/E103oLKCgoLg7u4OKysruLq6YujQobhx44ZSnS1btqBDhw6wsbFB48aN8cUXX6ic5/Dhw+jYsSMsLS3RvHlztVt5rVixAk2aNIGVlRV8fX1x/PhxpeN5eXkYO3Ys6tWrh1q1aqF///64deuWUp20tDQEBgbCxsYG9evXx9SpU1FUVPT8PwgiPaAutDVtOpihjYhIywwmuHXv3h1btmxBcnIytm3bhsuXL2PAgAHS8b1792LIkCEYPXo0zp07h2+++QZffvklli9fLtVJSUlBYGAgunfvjtOnT2PChAl47733EBMTI9XZvHkzJk2ahPDwcJw6dQrt27eHQqFARkaGVGfixInYuXMntm7diiNHjuDGjRt46623pOPFxcUIDAxEQUEB4uLisHbtWkRFRWH27Nla/ikRaVdJSYna0DZnTjhWrmyJ6GjA37/0vjciItI8mRBC6LoRzyI6Ohr9+vVDfn4+zM3N8c4776CwsBBbt26V6ixbtgwLFy5EWloaZDIZQkNDsXv3bpw7d06qM2jQIGRmZmLfvn0AAF9fX7z44otS4CspKYGbmxs++ugjhIWFISsrC05OTtiwYYMUHJOSkuDl5YX4+Hj4+flh79696NOnD27cuAFnZ2cAwKpVqxAaGorbt2/DwsKiUp8xOzsb9vb2yMrKgp2dnUZ+bkTPqqKhUW/vcERGAmFhQGQkEB8PyOVAXFw1N5CISE9o8/vbYHrcyrt79y7Wr18Pf39/mJubAwDy8/NhZWWlVM/a2hrXr1/H1atXAQDx8fEICAhQqqNQKBAfHw8AKCgoQEJCglIdExMTBAQESHUSEhJQWFioVMfT0xPu7u5Snfj4eLRr104KbWXvk52djfPnz1f4ufLz85Gdna30INIH6kLbBx98gPDwcAQFlYa0oKDS8CaXl/5JRESaZ1DBLTQ0FLa2tqhXrx7S0tKwY8cO6ZhCocBPP/2E2NhYlJSU4NKlS1i8eDEA4ObNmwCA9PR0pTAFAM7OzsjOzsbDhw9x584dFBcXq62Tnp4uncPCwgIODg5PrKPuHGXHKjJ//nzY29tLDzc3t8r+aIi0oqCgoMIFdV1cXFTKy4c4IiLSPJ0Gt7CwMMhksic+kpKSpPpTp05FYmIi9u/fD1NTUwwbNgxlI72jRo3CuHHj0KdPH1hYWMDPzw+DBg0CUNprZgimT5+OrKws6XHt2jVdN4mMWEREBObPn69SzgkIRES6o9PlQCZPnozg4OAn1mnatKn0d0dHRzg6OqJly5bw8vKCm5sbjh49CrlcDplMhgULFmDevHlIT0+Hk5MTYmNjlc7h4uKiMvvz1q1bsLOzg7W1NUxNTWFqaqq2TlnvgouLCwoKCpCZmanU6/Z4ncdnopadU10vRRlLS0tYWlo+8edBVB3U9bJNnDhRulcjOhrSfW3sXSMiqj467YpycnKCp6fnEx8V3chfUlICoPS+sPJMTU3RsGFDWFhYYOPGjZDL5XBycgIAyOVyKcyVOXDggLS6u4WFBTp16qRUp6SkBLGxsVKdTp06wdzcXKlOcnIy0tLSpDpyuRx//vmn0kzUAwcOwM7ODq1bt36mnxVRdcjJyalwaLR8aBsypHQSQmRkdbeQiMi4GcQCvMeOHcOJEyfQpUsX1KlTB5cvX8asWbPQrFkzKSzduXMHP/74I1555RXk5eVhzZo10nIdZUaPHo3ly5dj2rRpGDlyJA4ePIgtW7Zg9+7dUp1JkyZh+PDh8PHxQefOnfHVV18hNzcXI0aMAADY29sjJCQEkyZNQt26dWFnZ4ePPvoIcrkcfn5+AEq3+mndujWGDh2KhQsXIj09HTNnzsTYsWPZo0Z6q7IL6kZGAjk5QK1anIRARFTthAE4e/as6N69u6hbt66wtLQUTZo0EaNHjxbXr1+X6ty+fVv4+fkJW1tbYWNjI3r06CGOHj2qcq5Dhw6JDh06CAsLC9G0aVOxZs0alTrLli0T7u7uwsLCQnTu3FnlPA8fPhQffvihqFOnjrCxsRFvvvmmuHnzplKd1NRU0bt3b2FtbS0cHR3F5MmTRWFhYZU+d1ZWlgAgsrKyqvQ6oqqaM2eOyiMvL09t3R07hJDLS/8kIiJV2vz+Nth13IwB13Ejbbt9+za++eYblXJOQCAienba/P42iKFSItI87jVKRGR4GNyIjJC60DZz5kyYmprqoDVERFRZhrHAGRFpxLVr1yqcNVoW2rjfKBGR/mKPG5GRqGhoNCYmHN7ej9ZjK9tvNDKSa7QREekb9rgRGQF1oW327NnYuDEc8fHAtGmPyrnfKBGR/mKPG1ENdunSJWzcuFGlvGwCgkwGpT+B0l429rQREeknBjeiGqoys0YXLHi0dRUREek/BjeiGqiiCQjlcb9RIiLDw3vciGqQhISESoU2QHkSAhERGQb2uBHVEFVdUDcsjMOkRESGhsGNqAaobC/b48OjHCIlIjIsDG5EBuy3337DwYMHVcor6mXjGm1ERIaNwY3IQKnrZXN0dMTYsWMrfA2HR4mIDBuDG5EBquzQ6OM4PEpEZNgY3IgMyM6dO3Hq1CmV8sqENiIiMnxcDoTIQERERKiEtnbt2iE8PJwbwxMRGQn2uBEZgKcNjXLSARGRcWBwI9Jj69atQ0pKikr540OjnHRARGQcGNyI9JS6XrauXbsiJ+dV+Psrb1XFSQdERMaBwY1IDz1paNTfn8OiRETGisGNSI8sWbIE9+/fVykvPzTKYVEiIuPF4EakJ9T1svXp0wedOnVSKuOwKBGR8WJwI9IDz7qgLhERGRcGNyId+vTTT1FSUqJSztBGRETqMLgR6Yi6XrYhQ4agefPmOmgNEREZAgY3omomhMDcuXNVytnLRkRET8PgRlSNPvvsMxQXF6uUl21bVTZblJMPiIhIHQY3omqibmg0Lu4DxMS4AOC2VURE9HTcZJ5Iy4QQakPbpk3huHrVRdoYPiwMkMu5PhsREVVMJoQQum4EqZednQ17e3tkZWXBzs5O182hZ6AusAGlQ6NlOyDI5UBcXDU3jIiItEab398cKiXSEnWhbcKECbC3twfAHRCIiKjqGNyINKyoqAiff/65Svnjs0a5AwIREVUVgxuRBj1paJSIiOh5MbgRaYi60DZt2jRYW1vroDVERFQTMbgRPaf8/HxERkaqlLOXjYiINI3Bjeg5cGiUiIiqE4Mb0TNSF9pmzJgBc3NzHbSGiIiMAYMbURXl5uZi0aJFKuXsZSMiIm1jcCOqAg6NEhGRLnHLK6JKUhfaZs2aBW/v0l0QyrauIiIi0hYGN6KnyMzMVBvawsPDYWJiorQ5PBERkTZxqJToCdQFNkvLeoiOHgdv79KdD7h1FRERVRduMq/HuMm8bqkLbbNnz8ZLL8m4OTwREVVIm9/fHColekxGRkaFQ6MymQxhYaWhjT1sRERU3ThUSlSOusDm4eGBYcOGSc+5OTwREekKgxvR/1dRLxsREZG+YHAjo3fjxg3873//UylnaCMiIn3D4EZGTV0vW/v27dGvX7/qbwwREdFTMLiR0VIX2ry9w3n/GhER6S3OKqUaLzoaSjsbXLlypcL72RjaiIhIn7HHjWq88jsbJCaqBraAgAC89NJLOmgZERFR1TC4UY1XtrOBQsFZo0REZNgY3KjG8/L6CwrFBpVyb2+GNiIiMiwMblQjRUdX3Mt2+vQAbN/eBnI5F9IlIiLDwuBGNdKThkajo4Fbt7hlFRERGR4GN6pxLl++DIXiB5XysvvZuGUVEREZKgY3qlHULfMxYsQIuLu766A1REREmsXgRjUG9xolIqKajsGNDF5SUhI2b96sUs7QRkRENQ2DGxk0db1sY8eOhaOjow5aQ0REpF0MbmSwODRKRETGhsGNDM6ZM2ewfft2lXKGNiIiqukY3MigqOtlmzBhAuzt7XXQGiIiourF4EYGg0OjRERk7BjcSO8dPXoUMTExSmW1atXC5MmTddQiIiIi3WBwI72mrpdtypQpsLW11UFriIiIdIvBjfSSEAJz585VKefQKBERGTMTXTegqvLz89GhQwfIZDKcPn1a6djZs2fRtWtXWFlZwc3NDQsXLlR5/datW+Hp6QkrKyu0a9cOe/bsUTouhMDs2bPh6uoKa2trBAQE4K+//lKqc/fuXQwZMgR2dnZwcHBASEgIcnJyqtwWUu/w4cMqoc3FxYWhjYiIjJ7BBbdp06ahQYMGKuXZ2dno2bMnGjdujISEBHzxxReYM2cO/vvf/0p14uLiMHjwYISEhCAxMRH9+vVDv379cO7cOanOwoULsXTpUqxatQrHjh2Dra0tFAoF8vLypDpDhgzB+fPnceDAAezatQu//vor3n///Sq1hdSLiIjAkSNHlMpCQ0PxwQcf6KhFRERE+kMmhBC6bkRl7d27F5MmTcK2bdvQpk0bJCYmokOHDgCAlStX4pNPPkF6ejosLCwAAGFhYdi+fTuSkpIAAAMHDkRubi527dolndPPzw8dOnTAqlWrIIRAgwYNMHnyZEyZMgUAkJWVBWdnZ0RFRWHQoEG4ePEiWrdujRMnTsDHxwcAsG/fPrz++uu4fv06GjRoUKm2VEZ2djbs7e2RlZUFOzu75/756bOSkhJ8+umnKuXsZSMiIkOjze9vg+lxu3XrFkaNGoXvv/8eNjY2Ksfj4+PRrVs3KSgBgEKhQHJyMu7duyfVCQgIUHqdQqFAfHw8ACAlJQXp6elKdezt7eHr6yvViY+Ph4ODgxTaACAgIAAmJiY4duxYpduiTn5+PrKzs5UexmDv3r0qoa1Zs2YMbURERI8xiMkJQggEBwdj9OjR8PHxQWpqqkqd9PR0eHh4KJU5OztLx+rUqYP09HSprHyd9PR0qV7511VUp379+krHzczMULduXaU6T2uLOvPnz1c7i7ImU/d5Z8yYAXNzcx20hoiISL/ptMctLCwMMpnsiY+kpCQsW7YM9+/fx/Tp03XZXK2bPn06srKypMe1a9d03SStKS4urnBBXYY2IiIi9XTa4zZ58mQEBwc/sU7Tpk1x8OBBxMfHw9LSUumYj48PhgwZgrVr18LFxQW3bt1SOl723MXFRfpTXZ3yx8vKXF1dleqU3Uvn4uKCjIwMpXMUFRXh7t27T32f8u+hjqWlpcpnrIm2bdumNCEEAF544QW8+eabOmoRERGRYdBpcHNycoKTk9NT6y1duhSfffaZ9PzGjRtQKBTYvHkzfH19AQByuRyffPIJCgsLpR6bAwcOoFWrVtLQpFwuR2xsLCZMmCCd68CBA5DL5QAADw8PuLi4IDY2Vgpq2dnZOHbsGMaMGSOdIzMzEwkJCejUqRMA4ODBgygpKalSW4yVul62mTNnwtTUVAetISIiMiwGMTnB3d0dbdu2lR4tW7YEUHoDe6NGjQAA77zzDiwsLBASEoLz589j8+bN+PrrrzFp0iTpPB9//DH27duHxYsXIykpCXPmzMHJkycxbtw4AIBMJsOECRPw2WefITo6Gn/++SeGDRuGBg0aoF+/fgAALy8v9OrVC6NGjcLx48fxxx9/YNy4cRg0aJC0TEll2mJsCgsLKxwaZWgjIiKqHIOYnFAZ9vb22L9/P8aOHYtOnTrB0dERs2fPVlpfzd/fHxs2bMDMmTMxY8YMtGjRAtu3b0fbtm2lOtOmTUNubi7ef/99ZGZmokuXLti3bx+srKykOuvXr8e4cePQo0cPmJiYoH///li6dGmV2lJTREcDkZFAWBgQFKS+zrp165CSkqJU5ufnB4VCUQ0tJCIiqjkMah03Y2MI67j5+wPx8YBcDsTFqR5X18s2e/ZsyGSyamgdERFR9eM6bqRT0dGlAS06WvVYWFhpaAsLUy7Py8urcGiUoY2IiOjZsMdNj+lLj9vTetUet3LlSpWZt6+88gpefvllLbWQiIhIf2jz+7vG3ONG2hMW9ug+tqfh0CgREZH2cKiUKlQ2RAqU9rRVNPkAAHJycjg0SkREpGXscaMKRUaWDpFGRj45tH3xxRd48OCBUlnv3r3RuXNnLbeQiIjIuDC4UYUqM0RaUS8bERERaR6DG1UoKKjinrb79+9jyZIlKuUMbURERNrD4EZVtmrVKpW9WN9880288MILOmoRERGRceDkBHriOm2Pi4iIUAlt4eHhDG1ERETVgMGNlCYhVOTevXu8n42IiEjHOFRKT52EsGTJEty/f1+pLDg4GI0bN66G1hEREVEZ9rgZoceHRoOCKl6nLSIiQiW0hYeHM7QRERHpAIObEarM0Ghubi6HRomIiPQMh0qN0NOGRg8fPowjR44olY0dOxaOjo7V0DoiIiKqCIObEXrS+mzsZSMiItJfDG4EQP2Cuq+88gpefvllHbWIiIiIHsfgRjhw4ADi4uKUyqZMmQJbW1sdtYiIiIjUYXAzclu3bsWFCxeUyjg0SkREpJ8Y3IxUXl4eFixYoFTWs2dPyOVyHbWIiIiInobBzQhdunQJGzduVCqbMWMGzM3NddQiIiIiqgwGNyNUPrT5+flBoVDosDVERERUWQxuRmzUqFFo0KCBrptBRERElSQTQghdN4LUy87Ohr29PbKysmBnZ6fr5hAREVElaPP7m1teERERERkIBjciIiIiA8HgRkRERGQgGNyIiIiIDASDGxEREZGBYHAjIiIiMhAMbkREREQGgsGNiIiIyEAwuBEREREZCAY3IiIiIgPB4EZERERkIBjciIiIiAwEgxsRERGRgWBwIyIiIjIQZrpuAFVMCAEAyM7O1nFLiIiIqLLKvrfLvsc1icFNj92/fx8A4ObmpuOWEBERUVXdv38f9vb2Gj2nTGgjDpJGlJSU4MaNG6hduzZkMpmum6N12dnZcHNzw7Vr12BnZ6fr5tBjeH30G6+PfuP10W+avj5CCNy/fx8NGjSAiYlm70pjj5seMzExQaNGjXTdjGpnZ2fHX2x6jNdHv/H66DdeH/2myeuj6Z62MpycQERERGQgGNyIiIiIDASDG+kNS0tLhIeHw9LSUtdNITV4ffQbr49+4/XRb4Z0fTg5gYiIiMhAsMeNiIiIyEAwuBEREREZCAY3IiIiIgPB4EZERERkIBjcSCPy8/PRoUMHyGQynD59WunY2bNn0bVrV1hZWcHNzQ0LFy5Uef3WrVvh6ekJKysrtGvXDnv27FE6LoTA7Nmz4erqCmtrawQEBOCvv/5SqnP37l0MGTIEdnZ2cHBwQEhICHJycqrclpokKCgI7u7usLKygqurK4YOHYobN24o1dmyZQs6dOgAGxsbNG7cGF988YXKeQ4fPoyOHTvC0tISzZs3R1RUlEqdFStWoEmTJrCysoKvry+OHz+udDwvLw9jx45FvXr1UKtWLfTv3x+3bt1SqpOWlobAwEDY2Nigfv36mDp1KoqKip7/B6GnKnN9YmJi4Ofnh9q1a8PJyQn9+/dHamqqUh1eH+152jWaM2cOZDKZysPW1lbpPPwdpx2V+TckhMCiRYvQsmVLWFpaomHDhvj888+V6hjUvyFBpAHjx48XvXv3FgBEYmKiVJ6VlSWcnZ3FkCFDxLlz58TGjRuFtbW1+L//+z+pzh9//CFMTU3FwoULxYULF8TMmTOFubm5+PPPP6U6kZGRwt7eXmzfvl2cOXNGBAUFCQ8PD/Hw4UOpTq9evUT79u3F0aNHxW+//SaaN28uBg8eXKW21DRLliwR8fHxIjU1Vfzxxx9CLpcLuVwuHd+zZ48wMzMTK1euFJcvXxa7du0Srq6uYtmyZVKdK1euCBsbGzFp0iRx4cIFsWzZMmFqair27dsn1dm0aZOwsLAQ3333nTh//rwYNWqUcHBwELdu3ZLqjB49Wri5uYnY2Fhx8uRJ4efnJ/z9/aXjRUVFom3btiIgIEAkJiaKPXv2CEdHRzF9+nQt/5R052nX58qVK8LS0lJMnz5d/P333yIhIUF069ZNeHt7K9Xh9dGep12j+/fvi5s3byo9WrduLYYPHy7V4e847Xna9RFCiI8++ki0atVK7NixQ1y5ckWcPHlS7N+/XzpuaP+GGNzoue3Zs0d4enqK8+fPqwS3b775RtSpU0fk5+dLZaGhoaJVq1bS87ffflsEBgYqndPX11d88MEHQgghSkpKhIuLi/jiiy+k45mZmcLS0lJs3LhRCCHEhQsXBABx4sQJqc7evXuFTCYT//zzT6XbUtPt2LFDyGQyUVBQIIQQYvDgwWLAgAFKdZYuXSoaNWokSkpKhBBCTJs2TbRp00apzsCBA4VCoZCed+7cWYwdO1Z6XlxcLBo0aCDmz58vhCi9Xubm5mLr1q1SnYsXLwoAIj4+XghR+t+RiYmJSE9Pl+qsXLlS2NnZKV2zmuzx67N161ZhZmYmiouLpTrR0dFKdXh9qtfj1+hxp0+fFgDEr7/+KpXxd1z1efz6XLhwQZiZmYmkpKQKX2No/4Y4VErP5datWxg1ahS+//572NjYqByPj49Ht27dYGFhIZUpFAokJyfj3r17Up2AgACl1ykUCsTHxwMAUlJSkJ6erlTH3t4evr6+Up34+Hg4ODjAx8dHqhMQEAATExMcO3as0m2pye7evYv169fD398f5ubmAEqHuK2srJTqWVtb4/r167h69SqAp1+fgoICJCQkKNUxMTFBQECAVCchIQGFhYVKdTw9PeHu7q50Ddu1awdnZ2el98nOzsb58+c19WPQW+quT6dOnWBiYoI1a9aguLgYWVlZ+P777xEQECDV4fWpPuqu0eNWr16Nli1bomvXrlIZf8dVD3XXZ+fOnWjatCl27doFDw8PNGnSBO+99x7u3r0rvc7Q/g0xuNEzE0IgODgYo0ePVvplUl56errSf6QApOfp6elPrFP+ePnXVVSnfv36SsfNzMxQt27dp75P+feoiUJDQ2Fra4t69eohLS0NO3bskI4pFAr89NNPiI2NRUlJCS5duoTFixcDAG7evAmg4p9bdnY2Hj58iDt37qC4uPip18fCwgIODg5PrMPro3x9PDw8sH//fsyYMQOWlpZwcHDA9evXsWXLFqkOr4/2PekalZeXl4f169cjJCREqZy/47TrSdfnypUruHr1KrZu3Yp169YhKioKCQkJGDBggFTH0P4NMbiRirCwMLU325Z/JCUlYdmyZbh//z6mT5+u6yYblcpenzJTp05FYmIi9u/fD1NTUwwbNgzi/2+YMmrUKIwbNw59+vSBhYUF/Pz8MGjQIACl/0dJVafJ65Oeno5Ro0Zh+PDhOHHiBI4cOQILCwsMGDBAqkNVp8lrVN7PP/+M+/fvY/jw4dX5cWocTV6fkpIS5OfnY926dejatSteeeUVfPvttzh06BCSk5N19RGfi5muG0D6Z/LkyQgODn5inaZNm+LgwYOIj49X2dvNx8cHQ4YMwdq1a+Hi4qIyq6bsuYuLi/Snujrlj5eVubq6KtXp0KGDVCcjI0PpHEVFRbh79+5T36f8exiCyl6fMo6OjnB0dETLli3h5eUFNzc3HD16FHK5HDKZDAsWLMC8efOQnp4OJycnxMbGKp2jop+bnZ0drK2tYWpqClNT06dew4KCAmRmZir9H+njdR6fpWXs12fFihWwt7dXmhn4ww8/wM3NDceOHYOfnx+vzzPQ5DUqb/Xq1ejTp49Krwp/x1WNJq+Pq6srzMzM0LJlS6m+l5cXgNIZnq1atTK8f0OVvhuO6DFXr14Vf/75p/SIiYkRAMSPP/4orl27JoR4dLNs+Rt5p0+frjI5oU+fPkrnlsvlKjfuLlq0SDqelZWl9sbdkydPSnViYmLU3rj7pLbUdFevXhUAxKFDhyqsM3ToUKVZWdOmTRNt27ZVqjN48GCVG3fHjRsnPS8uLhYNGzZUuXH3xx9/lOokJSWpvXG3/Cyt//u//xN2dnYiLy/v2T6wgXn8+kyaNEl07txZqc6NGzcEAPHHH38IIXh9qltF/4auXLkiZDKZ2Llzp8pr+Duu+jx+fcq+l/7++2+pTtkEkuTkZCGE4f0bYnAjjUlJSVGZVZqZmSmcnZ3F0KFDxblz58SmTZuEjY2NynIgZmZmYtGiReLixYsiPDxc7VR5BwcHsWPHDnH27FnRt29ftVPlvb29xbFjx8Tvv/8uWrRooTRVvjJtqUmOHj0qli1bJhITE0VqaqqIjY0V/v7+olmzZtIvidu3b4uVK1eKixcvisTERDF+/HhhZWUljh07Jp2nbKr81KlTxcWLF8WKFSvUTpW3tLQUUVFR4sKFC+L9998XDg4OSrOnRo8eLdzd3cXBgwfFyZMnVabtl02V79mzpzh9+rTYt2+fcHJyqrHLTVTm+sTGxgqZTCYiIiLEpUuXREJCglAoFKJx48biwYMHQgheH22qzDUqM3PmTNGgQQNRVFSkch7+jtOOylyf4uJi0bFjR9GtWzdx6tQpcfLkSeHr6ytee+016TyG9m+IwY00Rl1wE0KIM2fOiC5dughLS0vRsGFDERkZqfLaLVu2iJYtWwoLCwvRpk0bsXv3bqXjJSUlYtasWcLZ2VlYWlqKHj16SP+3VObff/8VgwcPFrVq1RJ2dnZixIgR4v79+1VuS01x9uxZ0b17d1G3bl1haWkpmjRpIkaPHi2uX78u1bl9+7bw8/MTtra2wsbGRvTo0UMcPXpU5VyHDh0SHTp0EBYWFqJp06ZizZo1KnWWLVsm3N3dhYWFhejcubPKeR4+fCg+/PBDUadOHWFjYyPefPNNcfPmTaU6qamponfv3sLa2lo4OjqKyZMni8LCQs38QPRMZa6PEEJs3LhReHt7C1tbW+Hk5CSCgoLExYsXlerw+mhHZa9RcXGxaNSokZgxY0aF5+LvOM2r7PX5559/xFtvvSVq1aolnJ2dRXBwsPj333+V6hjSvyGZELzDlYiIiMgQcNoYERERkYFgcCMiIiIyEAxuRERERAaCwY2IiIjIQDC4ERERERkIBjciIiIiA8HgRkRERGQgGNyIiLRAJpNh+/btum6GksOHD0MmkyEzM1PXTSGiZ8TgRkT0HObMmSNtBE5EpG0MbkREREQGgsGNiIxaSUkJ5s+fDw8PD1hbW6N9+/b48ccfATwaWoyNjYWPjw9sbGzg7++P5ORkAEBUVBQiIiJw5swZyGQyyGQyREVFSee+c+cO3nzzTdjY2KBFixaIjo6uVJvK3jcmJgbe3t6wtrbGq6++ioyMDOzduxdeXl6ws7PDO++8gwcPHkivy8/Px/jx41G/fn1YWVmhS5cuOHHihOZ+WESkcwxuRGTU5s+fj3Xr1mHVqlU4f/48Jk6ciHfffRdHjhyR6nzyySdYvHgxTp48CTMzM4wcORIAMHDgQEyePBlt2rTBzZs3cfPmTQwcOFB6XUREBN5++22cPXsWr7/+OoYMGYK7d+9Wum1z5szB8uXLERcXh2vXruHtt9/GV199hQ0bNmD37t3Yv38/li1bJtWfNm0atm3bhrVr1+LUqVNo3rw5FApFld6TiPRclbakJyKqQfLy8oSNjY2Ii4tTKg8JCRGDBw8Whw4dEgDEL7/8Ih3bvXu3ACAePnwohBAiPDxctG/fXuXcAMTMmTOl5zk5OQKA2Lt371Pbpe5958+fLwCIy5cvS2UffPCBUCgU0vnNzc3F+vXrpeMFBQWiQYMGYuHChUrnvXfv3lPbQET6yUyHmZGISKf+/vtvPHjwAK+99ppSeUFBAby9vaXnL7zwgvR3V1dXAEBGRgbc3d2feP7yr7O1tYWdnR0yMjIq3b7yr3d2doaNjQ2aNm2qVHb8+HEAwOXLl1FYWIiXXnpJOm5ubo7OnTvj4sWLlX5PItJvDG5EZLRycnIAALt370bDhg2VjllaWuLy5csASgNQGZlMBqD03rinKf+6stdW5nXqXi+TyZ77fERk+HiPGxEZrdatW8PS0hJpaWlo3ry50sPNza1S57CwsEBxcbGWW/p0zZo1g4WFBf744w+prLCwECdOnEDr1q112DIi0iT2uBGR0apduzamTJmCiRMnoqSkBF26dEFWVhb++OMP2NnZoXHjxk89R5MmTZCSkoLTp0+jUaNGqF27NiwtLauh9cpsbW0xZswYTJ06FXXr1oW7uzsWLlyIBw8eICQkpNrbQ0TaweBGREbt008/hZOTE+bPn48rV67AwcEBHTt2xIwZMyo1DNm/f3/89NNP6N69OzIzM7FmzRoEBwdrv+FqREZGoqSkBEOHDsX9+/fh4+ODmJgY1KlTRyftISLNkwkhhK4bQURERERPx3vciIiIiAwEgxsRUTUbPXo0atWqpfYxevRoXTePiPQYh0qJiKpZRkYGsrOz1R6zs7ND/fr1q7lFRGQoGNyIiIiIDASHSomIiIgMBIMbERERkYFgcCMiIiIyEAxuRERERAaCwY2IiIjIQDC4ERERERkIBjciIiIiA8HgRkRERGQg/h8Bu7sJt+ciGQAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(\n", + " keras_surrogate, data_validation, filename=\"keras_val_scatter2D.pdf\"\n", + ")\n", + "surrogate_parity(keras_surrogate, data_validation, filename=\"keras_val_parity.pdf\")\n", + "surrogate_residual(keras_surrogate, data_validation, filename=\"keras_val_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the \"SCO2_properties_keras_surrogate_embedding.ipynb\" file." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate.py new file mode 100644 index 00000000..88b78cf3 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate.py @@ -0,0 +1,311 @@ +############################################################################## +# Institute for the Design of Advanced Energy Systems Process Systems +# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the +# software owners: The Regents of the University of California, through +# Lawrence Berkeley National Laboratory, National Technology & Engineering +# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia +# University Research Corporation, et al. All rights reserved. +# +# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and +# license information, respectively. Both files are also available online +# at the URL "https://github.com/IDAES/idaes-pse". +############################################################################## +""" +Surrogate property package for SCO2 cycle. + +Valid Pressure Range = 7.49 MPa to 35 MPa +Valid Temperature Range = 306.25 K to 1000 K +""" + +# Changes the divide behavior to not do integer division +from __future__ import division + +# Import Python libraries +import logging + +# Import Pyomo libraries +from pyomo.environ import Constraint, Param, \ + Reals, Set, value, Var, NonNegativeReals, units +from pyomo.opt import SolverFactory, TerminationCondition + +# Import IDAES cores +from idaes.core import (declare_process_block_class, + PhysicalParameterBlock, + StateBlockData, + StateBlock, + MaterialBalanceType, + EnergyBalanceType, + LiquidPhase, + Component) +from idaes.core.util.initialization import solve_indexed_blocks +from idaes.core.util.model_statistics import degrees_of_freedom +from idaes.core.util.misc import extract_data +from idaes.core.solvers import get_solver +from pyomo.util.check_units import assert_units_consistent +from idaes.core.surrogate.surrogate_block import SurrogateBlock +from idaes.core.surrogate.keras_surrogate import KerasSurrogate + +from pyomo.util.model_size import build_model_size_report + +# Some more information about this module +__author__ = "Javal Vyas" + + +# Set up logger +_log = logging.getLogger(__name__) + + +@declare_process_block_class("SCO2ParameterBlock") +class PhysicalParameterData(PhysicalParameterBlock): + """ + Property Parameter Block Class + + Contains parameters and indexing sets associated with properties for + supercritical CO2. + + """ + def build(self): + ''' + Callable method for Block construction. + ''' + super(PhysicalParameterData, self).build() + + self._state_block_class = SCO2StateBlock + + # List of valid phases in property package + self.Liq = LiquidPhase() + + # Component list - a list of component identifiers + self.CO2 = Component() + + @classmethod + def define_metadata(cls, obj): + obj.add_properties({ + 'flow_mol': {'method': None, 'units': 'kmol/s'}, + 'pressure': {'method': None, 'units': 'MPa'}, + 'temperature': {'method': None, 'units': 'K'}, + 'enth_mol': {'method': None, 'units': 'kJ/kmol'}, + 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}}) + + obj.add_default_units({'time': units.s, + 'length': units.m, + 'mass': units.kg, + 'amount': units.mol, + 'temperature': units.K}) + +class _StateBlock(StateBlock): + """ + This Class contains methods which should be applied to Property Blocks as a + whole, rather than individual elements of indexed Property Blocks. + """ + def initialize(blk, state_args=None, hold_state=False, outlvl=1, + state_vars_fixed=False, solver='ipopt', + optarg={'tol': 1e-8}): + + ''' + Initialisation routine for property package. + + Keyword Arguments: + flow_mol : value at which to initialize component flows + (default=None) + pressure : value at which to initialize pressure (default=None) + temperature : value at which to initialize temperature + (default=None) + outlvl : sets output level of initialisation routine + + * 0 = no output (default) + * 1 = return solver state for each step in routine + * 2 = include solver output infomation (tee=True) + state_vars_fixed: Flag to denote if state vars have already been + fixed. + - True - states have already been fixed by the + control volume 1D. Control volume 0D + does not fix the state vars, so will + be False if this state block is used + with 0D blocks. + - False - states have not been fixed. The state + block will deal with fixing/unfixing. + optarg : solver options dictionary object (default=None) + solver : str indicating whcih solver to use during + initialization (default = 'ipopt') + hold_state : flag indicating whether the initialization routine + should unfix any state variables fixed during + initialization (default=False). + - True - states varaibles are not unfixed, and + a dict of returned containing flags for + which states were fixed during + initialization. + - False - state variables are unfixed after + initialization by calling the + relase_state method + + Returns: + If hold_states is True, returns a dict containing flags for + which states were fixed during initialization. + ''' + if state_vars_fixed is False: + # Fix state variables if not already fixed + Fcflag = {} + Pflag = {} + Tflag = {} + + for k in blk.keys(): + if blk[k].flow_mol.fixed is True: + Fcflag[k] = True + else: + Fcflag[k] = False + if state_args is None: + blk[k].flow_mol.fix() + else: + blk[k].flow_mol.fix(state_args["flow_mol"]) + + if blk[k].pressure.fixed is True: + Pflag[k] = True + else: + Pflag[k] = False + if state_args is None: + blk[k].pressure.fix() + else: + blk[k].pressure.fix(state_args["pressure"]) + + if blk[k].temperature.fixed is True: + Tflag[k] = True + else: + Tflag[k] = False + if state_args is None: + blk[k].temperature.fix() + else: + blk[k].temperature.fix(state_args["temperature"]) + + # If input block, return flags, else release state + flags = {"Fcflag": Fcflag, "Pflag": Pflag, + "Tflag": Tflag} + + else: + # Check when the state vars are fixed already result in dof 0 + for k in blk.keys(): + if degrees_of_freedom(blk[k]) != 0: + raise Exception("State vars fixed but degrees of freedom " + "for state block is not zero during " + "initialization.") + + if state_vars_fixed is False: + if hold_state is True: + return flags + else: + blk.release_state(flags) + + def release_state(blk, flags, outlvl=0): + ''' + Method to relase state variables fixed during initialisation. + + Keyword Arguments: + flags : dict containing information of which state variables + were fixed during initialization, and should now be + unfixed. This dict is returned by initialize if + hold_state=True. + outlvl : sets output level of of logging + ''' + if flags is None: + return + + # Unfix state variables + for k in blk.keys(): + if flags['Fcflag'][k] is False: + blk[k].flow_mol.unfix() + if flags['Pflag'][k] is False: + blk[k].pressure.unfix() + if flags['Tflag'][k] is False: + blk[k].temperature.unfix() + + if outlvl > 0: + if outlvl > 0: + _log.info('{} State Released.'.format(blk.name)) + + +@declare_process_block_class("SCO2StateBlock", + block_class=_StateBlock) +class SCO2StateBlockData(StateBlockData): + """ + An example property package for ideal gas properties with Gibbs energy + """ + + def build(self): + """ + Callable method for Block construction + """ + super(SCO2StateBlockData, self).build() + self._make_state_vars() + + def _make_state_vars(self): + self.flow_mol = Var(domain=NonNegativeReals, + initialize=1.0, + units=units.kmol/units.s, + doc='Total molar flowrate [kmol/s]') + self.pressure = Var(domain=NonNegativeReals, + initialize=8, + bounds=(7.38, 40), + units=units.MPa, + doc='State pressure [MPa]') + + self.temperature = Var(domain=NonNegativeReals, + initialize=350, + bounds=(304.2, 760+273.15), + units=units.K, + doc='State temperature [K]') + + self.entr_mol = Var(domain=Reals, + initialize=10, + units=units.kJ/units.kmol/units.K, + doc='Entropy [KJ/kmol/K]') + + self.enth_mol = Var(domain=Reals, + initialize=1, + units=units.kJ/units.kmol, + doc='Enthalpy [KJ/ kmol]') + + inputs=[self.pressure,self.temperature] + outputs=[self.enth_mol,self.entr_mol] + self.keras_surrogate = KerasSurrogate.load_from_folder("keras_surrogate") + self.surrogate_enth = SurrogateBlock() + self.surrogate_enth.build_model( + self.keras_surrogate, + formulation=KerasSurrogate.Formulation.FULL_SPACE, + input_vars=inputs, + output_vars=outputs, + ) + + def get_material_flow_terms(self, p, j): + return self.flow_mol + + def get_enthalpy_flow_terms(self, p): + return self.flow_mol*self.enth_mol + + def default_material_balance_type(self): + return MaterialBalanceType.componentTotal + + def default_energy_balance_type(self): + return EnergyBalanceType.enthalpyTotal + + def define_state_vars(self): + return {"flow_mol": self.flow_mol, + "temperature": self.temperature, + "pressure": self.pressure} + + def model_check(blk): + """ + Model checks for property block + """ + # Check temperature bounds + if value(blk.temperature) < blk.temperature.lb: + _log.error('{} Temperature set below lower bound.' + .format(blk.name)) + if value(blk.temperature) > blk.temperature.ub: + _log.error('{} Temperature set above upper bound.' + .format(blk.name)) + + # Check pressure bounds + if value(blk.pressure) < blk.pressure.lb: + _log.error('{} Pressure set below lower bound.'.format(blk.name)) + if value(blk.pressure) > blk.pressure.ub: + _log.error('{} Pressure set above upper bound.'.format(blk.name)) diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb new file mode 100644 index 00000000..ddd415b2 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_keras_surrogate.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Changes the divide behavior to not do integer division\n", + "from __future__ import division\n", + "\n", + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, Param, \\\n", + " Reals, Set, value, Var, NonNegativeReals, units\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component)\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occuring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + " def build(self):\n", + " '''\n", + " Callable method for Block construction.\n", + " '''\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties({\n", + " 'flow_mol': {'method': None, 'units': 'kmol/s'},\n", + " 'pressure': {'method': None, 'units': 'MPa'},\n", + " 'temperature': {'method': None, 'units': 'K'},\n", + " 'enth_mol': {'method': None, 'units': 'kJ/kmol'},\n", + " 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}})\n", + "\n", + " obj.add_default_units({'time': units.s,\n", + " 'length': units.m,\n", + " 'mass': units.kg,\n", + " 'amount': units.mol,\n", + " 'temperature': units.K})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the SCO2_keras_surrogate.ipynb file) using the keras Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\",\n", + " block_class=_StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self): \n", + " self.flow_mol = Var(domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol/units.s,\n", + " doc='Total molar flowrate [kmol/s]')\n", + " self.pressure = Var(domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc='State pressure [MPa]')\n", + " self.temperature = Var(domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760+273.15),\n", + " units=units.K,\n", + " doc='State temperature [K]')\n", + " self.entr_mol = Var(domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ/units.kmol/units.K,\n", + " doc='Entropy [kJ/kmol/K]')\n", + " self.enth_mol = Var(domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ/units.kmol,\n", + " doc='Enthalpy [kJ/ kmol]')\n", + " \n", + " inputs=[self.pressure,self.temperature]\n", + " outputs=[self.enth_mol,self.entr_mol]\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + " \n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol*self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure}\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error('{} Temperature set below lower bound.'\n", + " .format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error('{} Temperature set above upper bound.'\n", + " .format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error('{} Pressure set below lower bound.'.format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error('{} Pressure set above upper bound.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would interm fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the varible with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + " def initialize(blk, state_args=None, hold_state=False, outlvl=1,\n", + " state_vars_fixed=False, solver='ipopt',\n", + " optarg={'tol': 1e-8}):\n", + "\n", + " '''\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output infomation (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating whcih solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states varaibles are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " relase_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " '''\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag,\n", + " \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\")\n", + " \n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " '''\n", + " Method to relase state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " '''\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags['Fcflag'][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags['Pflag'][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags['Tflag'][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info('{} State Released.'.format(blk.name))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, SCO2_flowsheet_keras_surrogate.ipynb. To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/500_Points_DataSet.csv b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/500_Points_DataSet.csv new file mode 100644 index 00000000..d963f97b --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/500_Points_DataSet.csv @@ -0,0 +1,501 @@ +CO2SM.Pressure,CO2SM.Temperature,CO2SM.CO2_Enthalpy,CO2SM.CO2_Entropy,CO2SM.Enthalpy,CO2SM.Entropy,CO2SM.status,graph.error +13.44352,853.1312,-368176.883626,8.552332,-87996.3871,2.044056,0,0 +31.863708,909.520515,-365486.064526,4.127018,-87353.2659,0.986381,0,0 +21.132666,713.351479,-376015.521854,-5.327858,-89869.8666,-1.273389,0,0 +21.981615,809.477728,-370818.552442,1.169161,-88627.7611,0.279436,0,0 +18.081246,960.589169,-362401.453621,12.388151,-86616.0262,2.960839,0,0 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+24.816531,663.089859,-378936.076446,-10.929035,-90567.8959,-2.612102,0,0 diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo.py new file mode 100644 index 00000000..037e781e --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo.py @@ -0,0 +1,241 @@ +""" + +SCO2 baseline cycle from the NETL baseline report + +Case Baseline620 - Turbine inlet temperature 893.15 K (620 C). +Case Basleine760 - Turbine inlet temperature 1033.15 K (760 C). + +""" +from pyomo.environ import (ConcreteModel, + Block, + Var, + Param, + Constraint, + SolverFactory, + TransformationFactory, TerminationCondition, + value, Expression, minimize, units) +from pyomo.network import Arc, SequentialDecomposition + +# Import IDAES libraries +from idaes.core import FlowsheetBlock, UnitModelBlockData +from idaes.models.unit_models import (Mixer, MomentumMixingType, + PressureChanger, Heater, + Separator, HeatExchanger) +from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption +from idaes.core.util.model_statistics import degrees_of_freedom +from idaes.core.util.initialization import propagate_state +from SCO2_properties_pysmo_surrogate import SCO2ParameterBlock +import idaes.logger as idaeslog + +def main(): + # Setup solver and options + solver = SolverFactory('ipopt') + outlvl = 0 + tee = True + + # Set up concrete model + m = ConcreteModel() + + # Create a flowsheet block + m.fs = FlowsheetBlock(dynamic=False) + + # Create the properties param block + m.fs.properties = SCO2ParameterBlock() + + # Add unit models to the flowsheet + m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True) + + m.fs.turbine = PressureChanger(dynamic=False, + property_package= m.fs.properties, + compressor=False, + thermodynamic_assumption=ThermodynamicAssumption.isentropic) + + m.fs.HTR_pseudo_shell = Heater(dynamic= False, + property_package= m.fs.properties, + has_pressure_change= True) + + m.fs.HTR_pseudo_tube = Heater(dynamic=False, + property_package= m.fs.properties, + has_pressure_change= True) + + m.fs.LTR_pseudo_shell = Heater(dynamic= False, + property_package= m.fs.properties, + has_pressure_change=True) + + m.fs.LTR_pseudo_tube = Heater(dynamic= False, + property_package= m.fs.properties, + has_pressure_change=True) + + m.fs.splitter_1 = Separator(property_package= m.fs.properties, + outlet_list= ["bypass", "to_cooler"]) + + m.fs.co2_cooler = Heater(dynamic= False, + property_package=m.fs.properties, + has_pressure_change= True) + + m.fs.main_compressor = PressureChanger(dynamic= False, + property_package= m.fs.properties, + compressor= True, + thermodynamic_assumption= ThermodynamicAssumption.isentropic) + + m.fs.bypass_compressor = PressureChanger(dynamic= False, + property_package= m.fs.properties, + compressor= True, + thermodynamic_assumption= ThermodynamicAssumption.isentropic) + + m.fs.splitter_2 = Separator(property_package= m.fs.properties, + ideal_separation= False, + outlet_list= ["to_FG_cooler", + "to_LTR"]) + + m.fs.FG_cooler = Heater(dynamic= False, + property_package= m.fs.properties, + has_pressure_change= True) + + m.fs.mixer = Mixer(property_package= m.fs.properties, + inlet_list=["FG_out", "LTR_out", "bypass"]) + + # # Connect the flowsheet + m.fs.s01 = Arc(source=m.fs.boiler.outlet, + destination=m.fs.turbine.inlet) + m.fs.s02 = Arc(source=m.fs.turbine.outlet, + destination=m.fs.HTR_pseudo_shell.inlet) + m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet, + destination=m.fs.LTR_pseudo_shell.inlet) + m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet, + destination=m.fs.splitter_1.inlet) + m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler, + destination=m.fs.co2_cooler.inlet) + m.fs.s06 = Arc(source=m.fs.splitter_1.bypass, + destination=m.fs.bypass_compressor.inlet) + m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet, + destination=m.fs.main_compressor.inlet) + m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet, + destination=m.fs.mixer.bypass) + m.fs.s09 = Arc(source=m.fs.main_compressor.outlet, + destination=m.fs.splitter_2.inlet) + m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler, + destination=m.fs.FG_cooler.inlet) + m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR, + destination=m.fs.LTR_pseudo_tube.inlet) + m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet, + destination=m.fs.mixer.LTR_out) + m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet, + destination=m.fs.mixer.FG_out) + m.fs.s14 = Arc(source=m.fs.mixer.outlet, + destination=m.fs.HTR_pseudo_tube.inlet) + + # NETL Baseline + m.fs.boiler.inlet.flow_mol.fix(121.1) + m.fs.boiler.inlet.temperature.fix(685.15) + m.fs.boiler.inlet.pressure.fix(34.51) + + m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C + m.fs.boiler.deltaP.fix(-0.21) + + m.fs.boiler.initialize(outlvl=outlvl) + + propagate_state(m.fs.s01) + + m.fs.turbine.ratioP.fix(1/3.68) + m.fs.turbine.efficiency_isentropic.fix(0.927) + m.fs.turbine.initialize(outlvl=outlvl) + + propagate_state(m.fs.s02) + m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15) + m.fs.HTR_pseudo_shell.deltaP.fix(-0.07) + + m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl) + + + propagate_state(m.fs.s03) + + m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15) + m.fs.LTR_pseudo_shell.deltaP.fix(-0.07) + m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl) + + + propagate_state(m.fs.s04) + m.fs.splitter_1.split_fraction[0, "bypass"].fix(0.25) + + m.fs.splitter_1.initialize(outlvl=outlvl) + + propagate_state(m.fs.s05) + m.fs.co2_cooler.outlet.temperature.fix(308.15) + m.fs.co2_cooler.deltaP.fix(-0.07) + m.fs.co2_cooler.initialize(outlvl=outlvl) + + + propagate_state(m.fs.s06) + m.fs.bypass_compressor.efficiency_isentropic.fix(0.85) + m.fs.bypass_compressor.ratioP.fix(3.8) + m.fs.bypass_compressor.initialize(outlvl=outlvl) + + propagate_state(m.fs.s07) + m.fs.main_compressor.efficiency_isentropic.fix(0.85) + m.fs.main_compressor.ratioP.fix(3.8) + m.fs.main_compressor.initialize(outlvl=outlvl) + + propagate_state(m.fs.s09) + + m.fs.splitter_2.split_fraction[0, "to_FG_cooler"].fix(0.046) + m.fs.splitter_2.initialize(outlvl=outlvl) + + propagate_state(m.fs.s10) + m.fs.FG_cooler.outlet.temperature.fix(483.15) + m.fs.FG_cooler.deltaP.fix(-0.06) + m.fs.FG_cooler.initialize(outlvl=outlvl) + + + propagate_state(m.fs.s11) + + m.fs.LTR_pseudo_tube.deltaP.fix(0) + m.fs.LTR_pseudo_tube.heat_duty[0].\ + fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0])) + m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl) + + # Add constraint heats of the LTR_pseudo shell and tube + m.fs.LTR_pseudo_tube.heat_duty[0].unfix() + m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] == + -m.fs.LTR_pseudo_tube.heat_duty[0]) + + propagate_state(m.fs.s08) + propagate_state(m.fs.s12) + propagate_state(m.fs.s13) + + m.fs.mixer.initialize(outlvl=outlvl) + + propagate_state(m.fs.s14) + + m.fs.HTR_pseudo_tube.heat_duty[0].\ + fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0])) + m.fs.HTR_pseudo_tube.deltaP.fix(-0.07) + m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl) + + m.fs.HTR_pseudo_tube.heat_duty[0].unfix() + m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] == + -m.fs.HTR_pseudo_tube.heat_duty[0]) + + TransformationFactory("network.expand_arcs").apply_to(m.fs) + + print("--------------------------------------------------------------------") + print("The degrees of freedom for the flowsheet is ", degrees_of_freedom(m)) + print("--------------------------------------------------------------------") + + solver.solve(m, tee=tee) + + # + from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity + # Print reports + for i in m.fs.component_objects(Block): + if isinstance(i, UnitModelBlockData): + i.report() + + # Converting units for readability + print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\ + -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\ + -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW) + return m + +if __name__ == "__main__": + m = main() diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb new file mode 100644 index 00000000..9ed41c53 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb @@ -0,0 +1,1404 @@ +{ + "cells": [ + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - SCO2_flowsheet_optimization_pysmo_surrogate (Part 3)\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. \n", + "\n", + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory, TerminationCondition,\n", + " value, Expression, minimize, units)\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (Mixer, MomentumMixingType,\n", + " PressureChanger, Heater,\n", + " Separator, HeatExchanger)\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from SCO2_properties_pysmo_surrogate import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to get the total work done. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-08 10:34:59 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-08 10:34:59 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:35:00 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:35:00 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-08 10:35:00 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:35:00 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-08 10:35:00 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:35:00 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:35:00 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-08 10:35:00 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:35:00 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:35:01 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:35:01 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:35:01 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-08 10:35:01 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:35:01 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-08 10:35:01 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:35:01 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-08 10:35:01 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-08 10:35:01 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 452\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 118\n", + "\n", + "Total number of variables............................: 178\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 59\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 178\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.12e+02 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 3.28e-01 1.12e-02 -1.0 1.32e+01 - 9.89e-01 1.00e+00h 1\n", + " 2 0.0000000e+00 5.45e-06 1.05e-06 -1.0 1.32e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 1.37e-08 2.83e-08 -2.5 2.87e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 3.4924596548080444e-10 1.3737007975578308e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 3.4924596548080444e-10 1.3737007975578308e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.4382e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 34.510 34.300\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -9.9927e+05 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 729.38\n", + " pressure pascal 34.300 9.3207\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.4056e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 729.38 489.15\n", + " pressure pascal 9.3207 9.2507\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.4056e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 535.47 736.02\n", + " pressure pascal 34.560 34.490\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.0929e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507 9.1807\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.0929e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet\n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 378.99 566.32\n", + " pressure pascal 34.620 34.620\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825. \n", + " temperature kelvin 354.15 354.15 354.15 \n", + " pressure pascal 9.1807 9.1807 9.1807 \n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -4.4513e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet\n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807 9.1107\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 2.2092e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet\n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 378.99\n", + " pressure pascal 9.1107 34.620\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.1041e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet\n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 460.04\n", + " pressure pascal 9.1807 34.886\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR\n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 378.99 378.99 378.99\n", + " pressure pascal 34.620 34.620 34.620\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 31903. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet\n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 378.99 483.15\n", + " pressure pascal 34.620 34.560\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 566.32 460.04 535.47\n", + " pressure pascal 34.560 34.620 34.886 34.560\n", + "====================================================================================\n", + "667.9424945058901 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory('ipopt')\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True)\n", + "\n", + " m.fs.turbine = PressureChanger(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.splitter_1 = Separator(property_package= m.fs.properties,\n", + " outlet_list= [\"bypass\", \"to_cooler\"])\n", + "\n", + " m.fs.co2_cooler = Heater(dynamic= False,\n", + " property_package=m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.main_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.splitter_2 = Separator(property_package= m.fs.properties,\n", + " ideal_separation= False,\n", + " outlet_list= [\"to_FG_cooler\",\n", + " \"to_LTR\"])\n", + "\n", + " m.fs.FG_cooler = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.mixer = Mixer(property_package= m.fs.properties,\n", + " inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"])\n", + "\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet,\n", + " destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet,\n", + " destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet,\n", + " destination=m.fs.LTR_pseudo_shell.inlet)\n", + " m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet,\n", + " destination=m.fs.splitter_1.inlet)\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler,\n", + " destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(source=m.fs.splitter_1.bypass,\n", + " destination=m.fs.bypass_compressor.inlet)\n", + " m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet,\n", + " destination=m.fs.main_compressor.inlet)\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet,\n", + " destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(source=m.fs.main_compressor.outlet,\n", + " destination=m.fs.splitter_2.inlet)\n", + " m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler,\n", + " destination=m.fs.FG_cooler.inlet)\n", + " m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR,\n", + " destination=m.fs.LTR_pseudo_tube.inlet)\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet,\n", + " destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", + " destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline \n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1/3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0) \n", + " m.fs.LTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.LTR_pseudo_tube.heat_duty[0])\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.HTR_pseudo_tube.heat_duty[0])\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW)\n", + " return m\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate.py new file mode 100644 index 00000000..46231590 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate.py @@ -0,0 +1,313 @@ +############################################################################## +# Institute for the Design of Advanced Energy Systems Process Systems +# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the +# software owners: The Regents of the University of California, through +# Lawrence Berkeley National Laboratory, National Technology & Engineering +# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia +# University Research Corporation, et al. All rights reserved. +# +# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and +# license information, respectively. Both files are also available online +# at the URL "https://github.com/IDAES/idaes-pse". +############################################################################## +""" +Surrogate property package for SCO2 cycle. + +Valid Pressure Range = 7.49 MPa to 35 MPa +Valid Temperature Range = 306.25 K to 1000 K + +""" + +# Changes the divide behavior to not do integer division +from __future__ import division + +# Import Python libraries +import logging + +# Import Pyomo libraries +from pyomo.environ import Constraint, Param, \ + Reals, Set, value, Var, NonNegativeReals, units +from pyomo.opt import SolverFactory, TerminationCondition + +# Import IDAES cores +from idaes.core import (declare_process_block_class, + PhysicalParameterBlock, + StateBlockData, + StateBlock, + MaterialBalanceType, + EnergyBalanceType, + LiquidPhase, + Component) +from idaes.core.util.initialization import solve_indexed_blocks +from idaes.core.util.model_statistics import degrees_of_freedom +from idaes.core.util.misc import extract_data +from idaes.core.solvers import get_solver +from pyomo.util.check_units import assert_units_consistent +from idaes.core.surrogate.surrogate_block import SurrogateBlock +from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate + +from pyomo.util.model_size import build_model_size_report + +# Some more information about this module +__author__ = "Javal Vyas" + + +# Set up logger +_log = logging.getLogger(__name__) + + +@declare_process_block_class("SCO2ParameterBlock") +class PhysicalParameterData(PhysicalParameterBlock): + """ + Property Parameter Block Class + + Contains parameters and indexing sets associated with properties for + supercritical CO2. + + """ + def build(self): + ''' + Callable method for Block construction. + ''' + super(PhysicalParameterData, self).build() + + self._state_block_class = SCO2StateBlock + + # List of valid phases in property package + self.Liq = LiquidPhase() + + # Component list - a list of component identifiers + self.CO2 = Component() + + + @classmethod + def define_metadata(cls, obj): + obj.add_properties({ + 'flow_mol': {'method': None, 'units': 'kmol/s'}, + 'pressure': {'method': None, 'units': 'MPa'}, + 'temperature': {'method': None, 'units': 'K'}, + 'enth_mol': {'method': None, 'units': 'kJ/kmol'}, + 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}}) + + obj.add_default_units({'time': units.s, + 'length': units.m, + 'mass': units.kg, + 'amount': units.mol, + 'temperature': units.K}) + +class _StateBlock(StateBlock): + """ + This Class contains methods which should be applied to Property Blocks as a + whole, rather than individual elements of indexed Property Blocks. + """ + def initialize(blk, state_args=None, hold_state=False, outlvl=1, + state_vars_fixed=False, solver='ipopt', + optarg={'tol': 1e-8}): + + ''' + Initialisation routine for property package. + + Keyword Arguments: + flow_mol : value at which to initialize component flows + (default=None) + pressure : value at which to initialize pressure (default=None) + temperature : value at which to initialize temperature + (default=None) + outlvl : sets output level of initialisation routine + + * 0 = no output (default) + * 1 = return solver state for each step in routine + * 2 = include solver output infomation (tee=True) + state_vars_fixed: Flag to denote if state vars have already been + fixed. + - True - states have already been fixed by the + control volume 1D. Control volume 0D + does not fix the state vars, so will + be False if this state block is used + with 0D blocks. + - False - states have not been fixed. The state + block will deal with fixing/unfixing. + optarg : solver options dictionary object (default=None) + solver : str indicating whcih solver to use during + initialization (default = 'ipopt') + hold_state : flag indicating whether the initialization routine + should unfix any state variables fixed during + initialization (default=False). + - True - states varaibles are not unfixed, and + a dict of returned containing flags for + which states were fixed during + initialization. + - False - state variables are unfixed after + initialization by calling the + relase_state method + + Returns: + If hold_states is True, returns a dict containing flags for + which states were fixed during initialization. + ''' + if state_vars_fixed is False: + # Fix state variables if not already fixed + Fcflag = {} + Pflag = {} + Tflag = {} + + for k in blk.keys(): + if blk[k].flow_mol.fixed is True: + Fcflag[k] = True + else: + Fcflag[k] = False + if state_args is None: + blk[k].flow_mol.fix() + else: + blk[k].flow_mol.fix(state_args["flow_mol"]) + + if blk[k].pressure.fixed is True: + Pflag[k] = True + else: + Pflag[k] = False + if state_args is None: + blk[k].pressure.fix() + else: + blk[k].pressure.fix(state_args["pressure"]) + + if blk[k].temperature.fixed is True: + Tflag[k] = True + else: + Tflag[k] = False + if state_args is None: + blk[k].temperature.fix() + else: + blk[k].temperature.fix(state_args["temperature"]) + + # If input block, return flags, else release state + flags = {"Fcflag": Fcflag, "Pflag": Pflag, + "Tflag": Tflag} + + else: + # Check when the state vars are fixed already result in dof 0 + for k in blk.keys(): + if degrees_of_freedom(blk[k]) != 0: + raise Exception("State vars fixed but degrees of freedom " + "for state block is not zero during " + "initialization.") + + if state_vars_fixed is False: + if hold_state is True: + return flags + else: + blk.release_state(flags) + + def release_state(blk, flags, outlvl=0): + ''' + Method to relase state variables fixed during initialisation. + + Keyword Arguments: + flags : dict containing information of which state variables + were fixed during initialization, and should now be + unfixed. This dict is returned by initialize if + hold_state=True. + outlvl : sets output level of of logging + ''' + if flags is None: + return + + # Unfix state variables + for k in blk.keys(): + if flags['Fcflag'][k] is False: + blk[k].flow_mol.unfix() + if flags['Pflag'][k] is False: + blk[k].pressure.unfix() + if flags['Tflag'][k] is False: + blk[k].temperature.unfix() + + if outlvl > 0: + if outlvl > 0: + _log.info('{} State Released.'.format(blk.name)) + + +@declare_process_block_class("SCO2StateBlock", + block_class=_StateBlock) +class SCO2StateBlockData(StateBlockData): + """ + An example property package for ideal gas properties with Gibbs energy + """ + + def build(self): + """ + Callable method for Block construction + """ + super(SCO2StateBlockData, self).build() + self._make_state_vars() + + def _make_state_vars(self): + + self.flow_mol = Var(domain=NonNegativeReals, + initialize=1.0, + units=units.kmol/units.s, + doc='Total molar flowrate [kmol/s]') + self.pressure = Var(domain=NonNegativeReals, + initialize=8, + bounds=(7.38, 40), + units=units.MPa, + doc='State pressure [MPa]') + + self.temperature = Var(domain=NonNegativeReals, + initialize=350, + bounds=(304.2, 760+273.15), + units=units.K, + doc='State temperature [K]') + + self.entr_mol = Var(domain=Reals, + initialize=10, + units=units.kJ/units.kmol/units.K, + doc='Entropy [kJ/ kmol / K]') + + self.enth_mol = Var(domain=Reals, + initialize=1, + units=units.kJ/units.kmol, + doc='Enthalpy [kJ/ kmol]') + + inputs=[self.pressure,self.temperature] + outputs=[self.enth_mol,self.entr_mol] + self.pysmo_surrogate = PysmoSurrogate.load_from_file("pysmo_poly_surrogate.json") + self.surrogate_enth = SurrogateBlock() + self.surrogate_enth.build_model( + self.pysmo_surrogate, + input_vars=inputs, + output_vars=outputs, + ) + + def get_material_flow_terms(self, p, j): + return self.flow_mol + + def get_enthalpy_flow_terms(self, p): + return self.flow_mol*self.enth_mol + + def default_material_balance_type(self): + return MaterialBalanceType.componentTotal + + def default_energy_balance_type(self): + return EnergyBalanceType.enthalpyTotal + + def define_state_vars(self): + return {"flow_mol": self.flow_mol, + "temperature": self.temperature, + "pressure": self.pressure} + + def model_check(blk): + """ + Model checks for property block + """ + # Check temperature bounds + if value(blk.temperature) < blk.temperature.lb: + _log.error('{} Temperature set below lower bound.' + .format(blk.name)) + if value(blk.temperature) > blk.temperature.ub: + _log.error('{} Temperature set above upper bound.' + .format(blk.name)) + + # Check pressure bounds + if value(blk.pressure) < blk.pressure.lb: + _log.error('{} Pressure set below lower bound.'.format(blk.name)) + if value(blk.pressure) > blk.pressure.ub: + _log.error('{} Pressure set above upper bound.'.format(blk.name)) \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb new file mode 100644 index 00000000..f7c263e1 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_pysmo_surrogate.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Changes the divide behavior to not do integer division\n", + "from __future__ import division\n", + "\n", + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, Param, \\\n", + " Reals, Set, value, Var, NonNegativeReals, units\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component)\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occuring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + " def build(self):\n", + " '''\n", + " Callable method for Block construction.\n", + " '''\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties({\n", + " 'flow_mol': {'method': None, 'units': 'kmol/s'},\n", + " 'pressure': {'method': None, 'units': 'MPa'},\n", + " 'temperature': {'method': None, 'units': 'K'},\n", + " 'enth_mol': {'method': None, 'units': 'kJ/kmol'},\n", + " 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}})\n", + "\n", + " obj.add_default_units({'time': units.s,\n", + " 'length': units.m,\n", + " 'mass': units.kg,\n", + " 'amount': units.mol,\n", + " 'temperature': units.K})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the SCO2_pysmo_surrogate.ipynb file) using the PySMO Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\",\n", + " block_class=_StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " \n", + " self.flow_mol = Var(domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol/units.s,\n", + " doc='Total molar flowrate [kmol/s]')\n", + " self.pressure = Var(domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc='State pressure [MPa]')\n", + "\n", + " self.temperature = Var(domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760+273.15),\n", + " units=units.K,\n", + " doc='State temperature [K]')\n", + "\n", + " self.entr_mol = Var(domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ/units.kmol/units.K,\n", + " doc='Entropy [kJ/ kmol / K]')\n", + " \n", + " self.enth_mol = Var(domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ/units.kmol,\n", + " doc='Enthalpy [kJ/ kmol]')\n", + " \n", + " inputs=[self.pressure,self.temperature]\n", + " outputs=[self.enth_mol,self.entr_mol]\n", + " self.pysmo_surrogate = PysmoSurrogate.load_from_file(\"pysmo_poly_surrogate.json\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.pysmo_surrogate,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol*self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure}\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error('{} Temperature set below lower bound.'\n", + " .format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error('{} Temperature set above upper bound.'\n", + " .format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error('{} Pressure set below lower bound.'.format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error('{} Pressure set above upper bound.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would interm fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the varible with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + " def initialize(blk, state_args=None, hold_state=False, outlvl=1,\n", + " state_vars_fixed=False, solver='ipopt',\n", + " optarg={'tol': 1e-8}):\n", + "\n", + " '''\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output infomation (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating whcih solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states varaibles are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " relase_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " '''\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag,\n", + " \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\")\n", + " \n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " '''\n", + " Method to relase state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " '''\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags['Fcflag'][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags['Pflag'][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags['Tflag'][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info('{} State Released.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, SCO2_flowsheet_pysmo_surrogate.ipynb. To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb new file mode 100644 index 00000000..5f7f7366 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb @@ -0,0 +1,605 @@ +{ + "cells": [ + { + "attachments": { + "image.png": { + "image/png": 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gAkANtaU4X1Y9fY6kHjZY6rfrYVsBAAAAAEAkCFDhG7//GZ9LT4s2bzU3v4nX7weES8PThh0PlkZ79bMtAAAAAAAgUgSoiJmO2xkPz+TH5zi//ykyb729E6P/rorPuDJz128t/TsRoKL66Jindeo3lpSe2bYFAAAAAABEgwAVMdFKz4kF8QkGNYiNR+Xo3PVbTGAZD8/mx+c4+pz8WBWLmqnozZFSsmKhNDvsQtsCAAAAAACiRYCKmGi1Z7yCwTWbk8zxYjVvQ+nz2hT7c9LfS4PYePx++rvp8wKq2tppT8u6r1+WtKxLbQsAAAAAAIgFASpiopWV8bhc3gWVXxXHHlZqCBuPINYdIx7H+mptfH43oCIb534oq1+/VtIOv1zqNGpmW1ETrVu9Wj577VV59PIhcvMxR8vnHTLMTde17bPXXjP7AAAAAABiR4CKmGi4qMFnrBMkuZ+PR7WnhrrxCCv1OCpex9LfLR6/H1CWTUvmysqnz5W0o66ServuaVtRE335xv/k5mOPlgk33iAzP3hflv78kyxKrmtuuq5tE2683uyj+wIAAAAAYkOAipi4y9JjrdJ0Px/rZe4axMYr1N32u8U4HEDguQTWY/07AWXZsmGNFDx9jqQedJY0aN/LtqKm0YrSp6+7Vp685mopWrnStpZP99F99WeoRgUAAACA6BGgIiauSjPWSZv0EnezjLHaMzigjDWsjFfoGfzzXMaPqlDw1NlSf/f9pFHXI2wLahoNQG898Xj5fOJ/bYtIm86d5W833CTDnnxKHvk219x0/W833Gi2Ofoz+rOEqAAAAPC79Zu2ysd5JfYe4B8EqIhacGWlC0Cj5YLYWC9zDw5yYw1143UJf/DPx/K7AWUpeP4ySUoSaXrA6bYFNdFLd94hhcuW2XsiRw4cJKPffkeOGjRIuh/WTxqmpJibrh816ILAttJ9HP1ZPQYAAADgZ1/9vllyfvhTVq3j3Bn+QoCKqAVPHhVLyBgcxKpYJqWat2H784gl1HVDAajA84v+WMHPiZn4UZ4NeXl2LXxF79wpm3//Tpox437CKiksrPTfXscxDa48/cuVV8kZN95k75Xvb6X76L6OHoMxUQEAAOBXWn36Sd4ms/7ewsAS8AsCVEQtuMIzlsrK0EvkY6kcXRwUdLoK0miEPqfQ+5Eo2mxXSnEJP8ozvUMHWTR8eNhB6rqvXpR108ZLatYltiV2hWvW2rX4eeq192TgDQ/IiZeOkFsfelbWrI3hG5IaSAPUiv7t9bL7V+66094TU1V60tAr7L3K6b5arerosbiUHwAAIPEtXLlZ7v+sZlXo6KX7rvL0q8Xb12uC34u2yIvfxRAswHMEqIhacGWlijb4DA06Y60cddZsTrJrkQv9XWIJdYN/Pw2auYwf5Vk8dmxYQeqfCz6VgucvkWb9h0jdlJa2NXofz/heep16hbQ86K+S3O0EOfXK2yXv9+2Xi0fr3Gvvk0tGPCT/eWOyvPvp13Ln4y9LZvYQmffzYrsHnPL+7b/94INtE0aZMU/DqDwNpdWqbkxUPda3H7xv1gEAAJCY9m26QR6dvtGEcu8uqBmVmoHq0x3HPs2ZW3MCR/3dNBS+8YP18v2yoCorJAwCVEQtuLJSRVtd6S5rb1s/sIw2YNSQ01WKptZNMseJ9tL70HA42lA3eCgA9/vFMkQBaoeKgtSSFYtk5dPnStrRw6V+q71ta/T+99EXcsTA62XWvJ9sS6DtgFPDr3Isi1aevvj2VLN+dN/9ZdzIKySza0f55Y/lcs09/zbt2Fnov/2sKZPtFpFDT4t+nNtDT/urXZPSY06xawAAAEhEJVu3FwtpMFcTKjW1+lRD1GAaNNaE302rhTU8Vfo7roqhQAveIUBF1Fxl5ZHNAv8bRRt8uoDx/JZ1zTLagNGFpV0bJW0LKz9cHd1zckMBnNI88LtFOxyA+930OXVt5I7Fm6WX5gwYIB8nJfnuVpbQMG1ryUapt2snaXP3L5Jy4mhTLRrrTatN1YlZB0r+l6/Iwveflh5d9jSX849+9HmzLRqffP29WWp4OumJ2+Si04+VcSOGmjatRi3rucRy09B34SPrzN+ycGoguNW/m96fPyhwCbv+Dd3fWy+dV7MOP9zczxs50tzPz8kx9/Xv7ui6tuk2pfvqff1ZpcfS+3pzgbc+pt7X56D0Obl9nG969tzhcYK5f/vUt9+WlE2BD1utO3Y0y2i07rinXRNZsnChXQMAAEAiWrDWnvCW0kAu0ccLLav61KkJVajvLdj+uzVvnCQHtg1kH0gsBKiISnBlZZeGgWW0EyS5cLJp3UCwGG3laHBYeWBKIKSIPtQN/Nx5LQMvkWiHA3Bhadv6Sdv+TrEMUYDYuRAskRTn5pqQruDpc2xL/P31uMMkrWkTyWi7m9w65CzTNmv+z2YZjZWFa8zylKP7mqXaf59Odg3hSE5Lk7ql/+71tgTK/Tvt38ssoxH8s4XLYx+eAQAAAN7RCtRjOtez9xK/UjO4+rRRvZ1/Nx2qIFFp9enCVdsv3z2mUz3zOyLxJG0tZddRC6SmpsrXX39t71Vs71mBb7FmdE82l8QH+3D1Frk8b7MJK4fuVses6z66byQ0qOw/N/BtzOSuyXLuohIThD7XMXlbCBoufQ76vPT5qEeWbTHVsY9mRPbtjoae2T8GntP8HvWk9+wSE8Tq89MgNBI3/LZZ/rtqi5y/Sx05sEnStr9Zzl47/53c89fHrMgBBxwgRUVF9h4i5SoA+/nsrS+4MtFJy8qSjmPGSEpmprm/8vHTpE69BpJ60Nnmfqy0ylTHPVVaearhqdLK09GPvSB/OeJgef2hm01bpHTCKDPmadeO8ugtQ+SA7nvJCZfeKh9+PlNa79Jcfpv6nN0zfpaOO13aPhz/ibCqklashlahanCq//btR4yQ6wedLxvWBn6nx777Xuo3bGTWI7WhuFiG7h/4/6hhkybyyMxZZr02S3p1jlkWZHeRtHpUAVS1AZ//Jjm/F8nW07vZFgConF718cuoUaZPzLBXiwAInNd/e1aJ3D9tw7bg9MB2yXLmftsrUxOFBqe3Td2wLUDV8PTY0tttU9dv+906Na8rlx/UwKwnmke/3LgtQG2bWkeuOSRQWdX1mbqc1ycYKlARlfKqPSOt+Aw+joaTbjiAaC5zd5Wseiy9qWjGZQ0eCkC54QCmF0f+rZf7PTQ8dX8n9zsD5dHwrNfMmdJjypRt4alqPvh52bT8J1k3+x3bEhutONWQVOml/A8++z8Tnj703P9M22EH7GuW0fjH4NOlfZtdJXfuIul71tXSKPNkE56q24edb5bYkQanLbOzzb97t4kTzb992q6BUFstmDHDrkVu4bff2LXSf/egYwIAACAxaRVj77bbC3MSddb60OrTfhmB3ym76/YwWAPIRKxC1erZHapPgyprkXgIUBEVdxl607qBCZuinSAp+BJ31dT+HxnNZe5VEeqqWILP4GO5Kl59PtEMUYCar7zg1ElKbiDNB/9HinPflA2LPretsXnqjuGm8lQnkbr6nidM5alWpp6ffaRcdd5f7F6Ra9qkkbz9xG1y7KEHmPtbtmw1laf6eHpsbFdWcOq07rR92IM/Fi6ya5Fbsmj7zwYfEwAAAIlLw0YdU9NJtLFQNfANHvv0sNLfx13evu9udU3FpvPid4lXifTJz9t/N62i1d8JiYsAFVFx1Z5aWalcABpp5aib7d4FsNFWjrrHDYS5gbDSBZaRBp8uvG1jvxxyoW6kY7zqc9Kw1D0n5X6/SP9OqPkqCk6DJe/SUVoMek4KP3hQ/lw637ZGT6tQ9fJ9Hfe0X+99TUXqA9ddbILOWHXp0E7e+tcoWf3167Log/Hmsn3C0x1peFpWcOr0sBNVqU9fe8WuRe7T1161a3rM/nYNAAAAicyMF9ppe1VjolWhfpK3qczqUye4YlMrULWiM1HsXH268zB+SCwEqIhKaJWmW/5u3/zCVWTfT1wQG1w5GgkX6HYJGh7QrUcexm4fCiB4GelxXJWpC4eV+/0IUBGqsuA0WP3Oh0r6WY/J6smPyubifNsam1svP1s+mnC3GfM0lsrTsjRp1NBczo+daYBa0b/9/kcdLaktWpj1PxYskJfuvMOsR+Ll0p/Rn1V6rP2POsqs11Q6Jlg4N2f33Xcvczu3+N7eeust8/cua1t5NwAAUDkd+zS4CjVRZq3XoPer37cHjMHVp05oFWoizcgfXH2qv0enFlSfJjomkapl9IQk1kmkNPzTSZa0zU0a5SaVinTSpv5zN5kwNniCJjdpk0605MLLyjy8dLOZNEona7qxTeDx7/xjszyzIjCp1BWtwntO+rj6+Mo9p+C2sibUKo8+tj6HU5rXkbt2Dzy+e55l/Z2YRKp6+HUSqWiseecO2fDdW9L8pFtsS+2WiJNIhePLN/4nT15ztb0n8pcrr5KThl5h71XszUcelv899KC9J3Lh/Q/IQSfHNyD3m3D7uYomSkT8hdvHOfR1ABSTSAFl0887c8/fHj5q5WnwJe63ZDXaIVT1I32++ryVPtdr+jbcKUBVWnl6/2fbLwe9oFcD318Kr9WnT3+z0d4r/RzUp8FOASqTSCUeKlARMVehGVxZGU2VpgaxrpLVhafKHTeSY7nL67sEJrQzXFVrJJfeu+cTfNm9rruT60iGA3BDAXRpuP13i7aaFShL0+NukuR2PWT11HG2BTWRBp7/N+AUe09MIKpVpZXRatXg8FSPUdPDUwAAgNoo0apQtfo0+HJ8nQyrrPBUaQVqcGCaCFWo7y3YPhYt1ac1BwEqIrZmc+CNLbg6VCeTUlqtGe4ESaGz3TvRXMbvQt3gKiIXgEYW6u48FICKZjiAsoLmWCa3AsqSfva/RItp18x42bagJjrjxpskbbfts+d/MGG83HrCcfLB+Kdl9icfy4biYnPT9Q/GB7Z9WLqPoz+rxwAAAEDNFDwWqoaTfp61Xie7cmOfavAbOvZpqOCxUM2l/7Zy1Y/0uQX/7XVoAtQMBKiImKus7N3ELIxAxWZgPdwqTbdfaIDa1n7zFG7laCC0DawHH8s9n0jCyvKek7sf7nHKe07RVrMCFUkf/IJsXPy9rJvzvm1BTdO4WTMZ/dakHSpRdVzTl++6U8ZeOFiG7p9pbrr+8l3bxzxV+jP6s3oMAAAA1Ew7VaH+4M8Z+Reu3LxDAHpYRr1yq0+dnatQ/fm7aSj8Sd72343q05qFABURK6vaU/VJCfzvFO4ESaGz3TuRXsI/b31gqT8XPBRAcKjr9qlMWUMBqEiHA3DhaOA57Ph3inZyK6A8dRo2leYXPC9rvnpZNubNsK2oaTQAveCee804pm5iKefcBYvMLZjuo/vqzxCeAgAA1HzZXe0JcCmdAd6PVajBAaMGvge2DS9gDP7dNKj0YxWqTopF9WnNRYCKiJRXWalcEOqC0cq4IDb0OJFe5u4C29CgUnVtFFmoW144HOlwAOUNBaAirWYFwlGvdVdpccGzUvDhQ7Jp+U+2FTWRjmN6+7vvy8A775aeRx0trTrsabeIWde2gXfeZfZhzFMAAIDaQyseg2etD55Yyg+0+jR47FMddqCy6lPHhK3ttgeSfqtCDVSfMvZpTUaAWoMU5+batarjwtPQak/lgkEXHlamvCBWw0sXYIZTOTpvQyCIDD2O2lbNGkaoW1E47I4TbqjrKlXLek7RTG4FhKNB16Mk7ZR7ZPWUR2XL+tW2FTWRVpQectppcvmjj8nt720fukHXte2Q006n6hQAAKAWCh4vVKshgwNLr723YHvVqAa9wYFoOILHefVbFerHeSVmfFaloXDwvwNqBgLUGmT+oEEy6/DDJT8nx7bEn6vALKva01WOukmmKuIqQsu6xF25ys1wKkeLbH/ggslgri2cia3KGwpABZ5nYD2cUNcFsaFDASgXDnMJP6pCk0MulEYHnC6FU5iZHwAAAKhtQqtQ/TJrvQa5OqyAE83l7X6tQi1r7NPgfwPUDPyL1jCFU6fKnAEDqixIraja01WOBio5Kw4HK7rEXbnj/176RlSZ8oYCUC7UdYFmRVxY6y77D+VC1XBC3fKGAlDudw63mhWIVOpJo6Xerp2k6JN/2xYAAAAAtcWZ+9nqn1JaFemHKtRPft4eMHZqXjfi6lMnu+uOVaha+ek1fQ76XJSpPg2qlEXNQYBaQ1VVkOqqPcuqrFSuSvPD1RUHg+VdKu9srxw1i3JpUOv2aVrG8CIuwAwn1HXhsPsdQrnnWtlwAMHPqbygOZJqViAaaec9LZs3rpM137xuWwAAAADUBjvPWh9GRVEVCq0+PaZzdOGp0oAyOHx9b4G3Vaih1ac6KZZWyqLmIUD1CQ07P05KkryRI819DT31/rT0dHNfTe/QwbS5QHTx2LHmvoak5Yl3kOoqK9vVL/t/neAJoCpS3mz3zrawspLL3LdXjW4fNzWUO1ZllaMVDQWgwh0OIDg8LWt4AhVJNWuoN9esMf/u0dz0/6ENeXn2SKjpmg9+Xjb8NF3Wz5tsWwB4paZfccAVFQAA+EvwGJxaherleKGh1aexTq4UWoX6rochamj16WEZVJ/WVASoCFtwZWV5l943tf9HVTZBUkWXuCtXTVpZ5WhFlZ6OC3UrCysrGgpAhTscgHuc8sJT5R4jnMmt4knDUwLU2qNOSktpMfh5KZr2jPz5W9VPMgfURJV9aRauiavicxztY6L58q0sH64O9Hux0r8RV1QAAOAvO1ehehMyxrP61AmtQtUKUDeBU3UKrT7VcV2pPq25CFA9plWhi4YPl7ZXXSX9tm6VDFuB2jI729zvW1Bg7qs+P/9s2nSbajdsmLnfY8oUc78saVlZ0m3iRLOP+7loBYeVlVV7VlQ5GghFA+vlhZXBl7lXFFi6ALKsy/edcELd4HC4vGO537myULeyoQCUq2aNpmLnpKZNzb97pDf9fwG1T712PaT5wPFS+OFDUrLqN9sKIFzTi+MTMrq+IVba/8RrEsKPiuL1nCr/khIAAIRneeEGc4uH7K7bT0o17POiCjX4EnsNdGOtPnVCq1C/+r36f7fQ6tN+UUyMhcRBgOqx4txccym+XmofT/EMTp1wKiuDL+EvLxysaLb7YG4yp4pOylzVaHmX3atwQl33GBWFw8odq6LnVNlQAMr9najYQXVouO+JknrCzbJ6yqOyZeM62wqgMvpe/1GRvRMjDWLjUc2qYWU0X76VJV7hsP6dqvuKCgAAaqq1GzbJxQ99Lk+9vyDmINXrWeu1+vT3ou2fN6KZeb88ZrKmoGEKqrsKVR8rtPpUnxNqLgJUj6VkZpqAs2FGhm2JTVUEp044lZUaProAsrzKURc+VhSeKjc+akUnZe4xyqtkVeGEuuEcR4VzGX9lQwEE0+cTjxNqoDJNsoZKg27HSdHUcbYFQGXiVe2px9F+o6K+I1zaJ1Y2TE44tC8OPKfYfz/9fOD6PgAAEB9vfvlbXILU4Bnhq7MKVR+rqqpPneCKT/N4C6svIP4kbxPVp7UMAarHNOTUwLPVwIG2JXp7jx9fJcGp4ypVKqqsVG581PJOOl0QW1nAuL3as+yTMhfEBi73L/9Y4YS64QwFoNxwAOWFuu6EVFX2nNzvF48TaiAczU65R+o0ayVFn02wLQAqou/PFX35Fi73Pu/6rVhonxivUFfFowpVf681m8vv8wAAQPRiDVK9qkL96veqqz51QqtQNRyujipUMylX6e/nUH1aOxCgeqyksNBM6qPLWGk1a1VyJ0fhBp/lnXCGc4m7ctWe5Z2UuWC1oopYp7JQ1x2rsufkfrfyjuNOSCv7Gyn3+8XjhBoIV/oFz8vmNSukeOb/bAuA8rgvy2IdbsW9z1d0RUW44h3qxuNLPPec4lHNCgCAV+565XvJHj3Z89vwJ2bYZ7Sj4CA1UqHjherYnVVJH0MrNJ2qqD51tPIzeOKm6qhC1ceg+rT2IUD1mI5/Or1DB7P0Mz3505Ojyqo9lQshy7vE0IWVlYWMrnK0vJOycANdVVmo604gKztW8HAAZQn3OKqyalagqqQP/o+snz9V1v/4qW0BUBbXX8X6RZe78iLWytHg5xFr8LktHI5xOAD3+cCtAwCQqKbPW2HX/KtJw+SoqlA15AuuQg2+tL4qaHVmcCVocJVovJkq1KBhCqq6ClWPrWO7OrFUn36clOTbm054jh0RoCIsLsAMp9rTBaxlnSjqcdxJX2WXyyv3eB+u3vlY7uSvdxOzqJAbT7WsE0V3whdOOOxCXVXWiaJ7Tm3C6B9cyBqPSzGBSNRNayvNL3hOiqY8Jn/+Ptu2Agjl+qvfbYVBtNyVF7Fyga6KPYwNHCvW4wR/wUmACgCoCXJu7e/pbczFve0z2U6D0z5ddpHbz+spN/x1X9samdAq1HerKEQNrT7V4LZtatVGT1rhWl1VqMHVp/qYNbX6dOX/uGIxFAGqxzJGjpS+BQXSbtgw2+JPkVRWutBTK1JCKzXdyZUexwWRFamo4tOd/IVznHb1A/+rl3Wi6I4TTjisKhoOwB0rnL9TZdWsQFWqn3GgpJ//lBROfkRKVi+xrQCc4DAw1mpP1zfo+30sIWPw84i173DH0uPEcqzg5xSPya0AAMB2ocFph1ZN7ZbIhVahVtWs9To8gDuuPmZVjH0aqrqqUBeu3LzDJFyHZdSLuvpU9du61Xc3nVsHZSNA9YHktDRz8zNXWdmlYeVvDoFKzsB66LhxkQSxyl3mHnpSpid7kRzLhZ5lnSi6oQBcoFkZ93hlnXBG8pwqq2YFqlqj/U+Tpv2vktVTHpOtJUEpCIBtoaeKpUpT39+DQ8ZYjhXcF8YSVob2OcHPL1LBw9DEWs0KAAAC4hmcBgutQv3q9/iOhRqoPt1+zAPb1q3y6lNHw+HgKtScufE/vwn+3czkXKW/H2oPAlSP6bgScwYMkKUT/D0rdqRVmu5S+NCTNHeiFc7l+8oFkaEnZe5kT59PZZfdq4pC3e3hsFlUqq39hin05NX9roHHqvw5KfecOOmEV1KOuloadDpMVk/9l20BoIInMCzry7dwhY7hHe1xVPxC3e3HUfE6Viy/GwAACNg1rVHcg1PHVGoGjUca7ypUrT51l7cHqk/DGNsujoKrUHWc0t+LdvzMEwutPg0e+1QfK5bqUyQeAlSPFefmmhBVZ+L3qzWl7xGRVFaqbcFnUGWKcidalc127wRf5h58YuZO9sINKlV5oa57Tq4atDLlhbruOK7aNRxcxg8/aPbXMSINU6Toi//YFvid9hn6xZu7OcFtfu5XEkFo/xXtTPyh1Z3RVo5qPxF8rNB+MRKhzyna44Q+JxXaxwIAgMho9Wm8g9NgwWN2atgZr/FCQ6tP9dL94IrQ6hBahRrPybLeW7D9d9OqWh13FbULAarH0rKypP2IEWbpV+7kKNxqT+UC0tDKG3escIPY4Mvcg0/S3IzG4R5HlRXqBp/8hXus8oYDiPQ4qrxqVqC6Nb/gBdmUnyfrvptkW+BnOuzLL6NGyfxBg8zNcfcXDR/u+6Fh/C70y7Vog0HX58R6xYELcAN9cWA92lDX9TnuS7xo+yDX7+nfyB0rlmpWAABQ9UKrUOM1Xmho9alXkytld7UflErFqwpVj7NwVXxm3kfiIkD1mAanOpGUnwNUd9IYSbWnO5EKDj2jucRdlTVpk5vRONzL7pULdcsKPSMJhwPPP7AefPLqTkAjeU6xnlAD8ZKUXF+aD/6PFH/3lmxY9LlthV9pOLrb+efbeztre9VVBKgxCP5y7chmgb4htCI1XC6IHbpb4CNX6Jdv4Qrui11/FW2o657Tti8Wo+yDgq+8cMeK5ncDAADVS8PNHSo1Y6xC1QA2tPrUq4AxdEb+F78LCiWi9MnP23+3Ts3r7jAZF2oPAlSPJcIl/NFUe7qKncBJaODn3YmWCw3DVdZJmTuWm10/HNsqbYJCT3fSGEmgq8o6eXXPyf3u4Qi+hJ+TTngtueWe0uKC56Twgwflz6XzbCv8qt2wYdIwI8Pe206DU92G6AV/uXZEauB9OvSKinC5Y/VJqbOtfwj+cjFcwX3xtuAzilA30C8H1gekxxbquuPo8+GKCgAAEoepQo3jrPU6GZUfqk+d4CpUrUANHrs0UqHVp8d0JjytrQhQPZYIk0gttieNkVRWKneC50JGNyGHaw+Xqxx1J2V6Eht8chupwMlj4DlFEw4rt7/7+eAT0kiOVV41K+CV+p0OlfSz/yWrJz8qm4vzbSv8qLwqVKpPY7f9C7/tl6ZHE3qGXnlR1hUV4Qq+8qKsKyrCFdx/an8VSx8UfOWFOw5XVAAAkBhCKzWjrUL1U/Wpo79b8Oz/sYyFGlp92qkFY5/WVgSoHtOTXL+f6LqTqkgqK5U76XQnkK5SpncTswibq/Z0J2U7VLxEUDmqz9+Fm+4Y0QwFoNzJqwuX3d9ITyAjeU7K7R/NiTBQFRr3OVua9L1QVk8ZV3qP/y/9LLQKlerT+HDBoPYZru8L/vItXKFXXrg+KJr3++ArL9xxogk9Q6+8cMtohgMIvvKCKyoAAEgs8apC/SRvk6+qT53srtt/t2irUKk+RTACVI/piW7fggIzDqpfuRMhd8IWrqb2/y53IhrNJe7KnXi6kzJ3khdpUKlCQ93gE9JIhJ68xvKcIv27AtWh6XE3SP3dM6XQhKjwq9AqVKpP48N9yea+XHPv05GGjKFXXoReURGu0CsvmtrCB+0TIw11Q6+82Naf2fZwBR47sK7H0L7d9e/RBLsAAKD6hc5anzPXdu5h0sD1q9+3B4x+mlxJK0WDq1Aj/d1UcPWpVrVSfVq7EaAiLHpyFGk46E7KtNol9EQrEnpC5kJUPSlzJ3muLRIu1NVq2OATUndZZbhCT16jHQpAuRNqwG+anaXhaR1Z89VLgQb4kqtCpfo0fkK/8Av98i1c7sqLLg0DP++OF+ll7sH9p/bFwf2i2xau0CsvQq+oCFdZV164vjTSvxMAAPBOcBWqVlxGMmu9Xvbvqk81iPVL9alz5n7bQwMT9i7eHohWJrT6VMNh1G4EqB5bPHasTO/QwSz9LJZqz+DwNPhEKxLuZ/SkbHpx4A09muAxONQNPiF1J7XhCj15jXYoABVN6ApUl/TBz8uff8yR9XPety3wG1eFSvVpfAR/ueben0OvqAiXC2Jdf+ECRu0X9Rausq5yOLJZ4ElFGlaGXnnhfsdIq0bLek7uWL/bEykAAOB/oVWo4Y4XqkFrcCB5WEY931SfOlqBqpWjTrjjvGooHPx3oPoUigDVYyWFhWYGfl36mTv5i4SGjC6YfDbfnURG94bqTsq0mifayahUcKhb1slfJIJPXt0Jqc6yHClXzQr4UZ2GTaX54Bek+JvXZOPPX9lW+MH0t96Ux68YKtcd0lfGvvqyuen645dfZrYhOmV94betD4qgcjT4y0P388FfvkUSWJZ15UXwFRXhCh0KQEU7HEBZV164LzbdYwAAgMQQPGt9uFWowRNHaQB7YFt/ntgG/27hVqGG/g2oPoUiQPVYy+xs6TZxorQaONC2+FO0l5m7apv/rgq8+UQTeir3+B+u3mJO8gInoZEfKzjUnVjgQl2ziJg7edXjuJPFaMLQ4BNqwI+Sd9tb0s+fIIUfPiybViyyrfBK3uzv5a4B2fLBvXdLne9nSd/UFDmxU0dz0/U6c2abbbqP7ovIuC/Xujba/hEp+Mu3cJUVxCq3HknlqLu8Prgvdv2p+wIvHMGBrnsewX1QJMFnWVdeuGNGOkQBAADwVuiM/C9+V/GHgoUrN+8QROowAH6rPnX09wquQs2ZW3EVqqk+DapUpfoUDgGqx1IyM02IGjyLsh9FG3yG/lw0l7grd/LqxBI4up91J6/RhsPbT14Dx9H7LpyNVDSVq0B1atD1SGl26j2yevJjsmX9atuK6vbG2DFy919Pl1Yb1smhLVrIXunp0qxBA6lXp4656bq26bbd1q81+7455p/2pxGOsqo9g798Czf43H75/o79gus7Iq0cVcF9qusX3VUZ4XDPfefnFOiDIgl1Q4cCUO5vpkFzJGEzAADwXnClZmWz1r+3YMfq0+CA0o+CfzcNSCuqQtVJsbRS1Tmm8/YxYlG7kdp4rHDqVDP+aXFurm3xp9CTrXC1DfkWKtLZ7p3QYDI0mI1EaBgb7bF2DnWjf05teE9GAmhyyIXSqNfpUjjlMduC6jTpX+Pkg6eflBM7ZMieDSv/Nqpjo0Zm3/dLf+btxx61raiMq6wM/XLNhYPhVle68VJD+xh33HADRg01t1ezbj+WC3X1OOFeel/eJIzuy81wQ93goQCCJ2HU5+OOzUz8AAAkFg1Bg2etL28sVK0+DZ5cyc/Vp44ZYqDd9svwy6tC1XD1k7zt2/Rngv8mqN34P8FjGqAuGj5c8nNybIv/xBJWhv5s6ElbJIKP5WY0jkZwqBs42YvuWO7k1YnX7wb4WerJo6XerntJ0Sf/ti2oDnop/hsPPSjH7L67NK0f/puN7nvMHnvIm488zOX8YXKVlTsFnxFexr8tYAzJut1xww0Yy6o+dVy/8+Hq8J5TWUMBKHfscIcDCB4KILgfVNFUswIAAH/I7rq9sqe8KtTg6tNOzevuEEz6WfDvVl4V6sd5JduqTzUUZuxTBCNA9Zheup+WleXrS/jLOmkLV3Blih4nlirN4IrPWMLK4J+N5Tgq+OejHQpAhVazAn6Wdt5TsnnjOin++nXbUr6nXntPBt7wgJx46Qi59aFnZc1aytKC6SSC4XjhhhukT/s9IgpPHf2ZA3dvJ89ff71tSVxVfbVGcGVl6JjW7su3cGfid2FkaMAY6aRNwWFlqMhD3cB+ocdyxwl3OIDyhgJQkVazAgAA/9CxPoMrLnPm2g8ilgaqwdWnh3VInIBRA9GKqlAD1afbQ1WdFIvqUwTj/waP6eRRPaZM8fUkUrFcXh6o8AysxxKeKjdpk4ol1A0OK2M5jorXsUKrWQG/05n5N/z8laybO9m27Ozca++TS0Y8JP95Y7K8++nXcufjL0tm9hCZ9/Niuwf06oNp6emydMIE27IznVF/y+qCsC7bL0/Hxo1la1Fhws/Or1dszDr88Cq7asMFg/p+Hvqe7PqycC7hDw5iQ/uG4H7R7VMRF0SW1Re7fjGcULe8oQCU64PCDXXLGwpAud83nOMAAAD/OXO/7R186Kz1n/y8Y/Wp38c+DRVahfpu0DAFWn2qbSpQfRpDEIIaiQDVYyWFhebmZ7GGjO5ErawTrUi45xE4+Yz+ObkTRRXLUADKnbzG+pyU/n3c8wL8rk5KC2k++Hkp/uI52fjLt7Z1O608ffHtqWb96L77y7iRV0hm147yyx/L5Zp7uPw/mPYB8wcNKjdI/fbNN6RVcuwf4PQY376Z2AGq0qFv5gwYUCVBarjVnpVVfG4PKrf3gcGObBboPMK5zL28IQWUaws31FXl9emujw7nOZU3FIByf6dwwmEAAOA/WnUZHIy6GelDq0+P6Zx4l7eHVqG6itPQ6lO9dF/HTQWCEaB6TCeQ0pPmvJEjbYt/DN2tjjkRKu9kK1zu52O5xF25k7LgYQGi5Y4Rr1A31uOoZzvWlRndGWMFiaNeu/0k/fynZPXkR6Rk5a+2NeCTrwPjbWp4OumJ2+Si04+VcSOGmjatRt26tfKQprYpL0jNm/OD7Nqwgb0XPT1G3pw59l7iq4og1VV7hl6+r4K/fKssHNxeyVr2xyz35Vs4l7nHO9Qtr093xwonQHXjt5Z1LPc3CreaFQAA+E/wrPWuCjW4+lQDVr3cPxGVVYUaWn16YFvOy7EzAlSU64pWdeW5jsllVs9EwgWn5Z20hcudvMZ6HOWOEeux3AlnPJ6TO+kEEknDfU+UpifcIqunPiZb/lxnW0VWFq4xy1OO7muWav99Otk1kXrdT5TkbifE7fZZ6W3hI+tMqKb0Uu+Pk5JMIKl0rFG9rzdX9a/Bm953X2BpCKf3p3foYO4rXdc2F9Dpvnpff1bpsfS+3tx4pvqYel+fg9Ln5PZxvunZ09xf9swztmW70CB1zepCaVwv9gpUPcbx0z7f9lwS8eb+fYPFM0h11Z7lfeHnvnyrrOKzokvcleszKjuOCzO1fyirL3b9oqos1K0oHFbhDgegz8mFteV9PnC/XzhhbEWyR0+ultvFD30uywvDHNwWAIBaQKsvg6tQX/zuzx3HPk3gyZU0IO0X9Py18pTqU4SDANVj7YYNk14zZ/p6DNRYachY3slfpPTkNXRG42joyXF5l1ZGwp28xjoUAJDIUrIul4bdT5DCyY/aFg1LO5rlv16aJNNnzZPNm7fICZfeatoQPg1T62/ZIrzDhCfcSbnK4yZRKu9LMdf+u61QKE+RPb8oL4h1X75VxgW6FV3lEG6oW1k4HG6oW9lQAMr9folyGb+Gpz8vDXzpAwAAAoKrUIMlcvWpc0znHatQg6tPg8NVIFjSVq6jrFVSU1Pl66+/tveqh1aqnLdos+TsFfsb0Z1/bDYnf278uGjpCeD1v202FbaxOndRiZzfsk7MzylcBxxwgBQVFdl74dPqLK3U0knL0rKybGvto1Vsqh9vfXFX8PQ5IiUbJfWQQWa2fZ0wSsc8VXXqJMmWLYG/+VN3DJfzs4806/G0dNzp0vbhtfZeYtBhXFylqpOclibtR4wwX6zp+vWH9JW+zVIktX5sl/Gv3rhRphUVyz2fTbMtice9jwXT97MWf/nLtr9XuP3c3rMC43np0Cn6RZhWS2b/WGLWyxtO5cPVW+TyvM3m/f7RjPJPHPrP3WTCw8ldy7+Ko/fsEtM/VrTPw0s3yyPLtsj5u9SRG9uU/XjaLz6zYosZdkevHCmP+33Lezx9LvqclPublEUfSx/zlOZ15K7dy34897yD/076d9O/3/we4VVTR9vXRequV76X6fNWyA1/3Vf6dNnFtgLwC73y45dRo0y/mOHDIc9Qs+hVCSrn1v5m6Wf6eWfu+dsrQqvKi9/9ucMkUuryPg0SPkBVeun+e0GTSCkNVo8NClerUtdn6lbLZ51I6RVwejWcfrbee/x42wpFBarH9HJD/Z8zXuO3+ZGehB2RWvaJWKTa1ovPJfx6CWM8jqP0OPE6FpDI0i/4j2wuXilrc/8nTZs0krefuE2OPfQAs03D09a7NK+y8LQm0PCv45gx0ufnn83VCXpftd9nH1m+fqNZj8WKDRslY5+u9l7i0+BU/17dJk7c4e8VrXCqPV3wWFGVpn5B5yovy7tcXrnH+XB1+cdyl9NXdOWF21bRpfeVDQWgdJsLTSuqHHVDAbSp4NzC9YmVVbMCAAB/Cx4vVNWE6lNHK02DL9Wn+hSVIUD1WHFurkn4dVmTaaVKPAxoHp+hAPQk8cjUOD2n9DpxeU5ATaAh6rq5U2X9/I+lS4d28ta/Rsnqr1+XRR+Ml9+mPkd4WobyglNn/5NPliUlO347Hg09xv4n/8XeS1zxDk6dyi7fVy701GpNNw5oKBdW6nFcIFkWd5l7ecdRLtSt6Djt6gf6sorCynDCYRXOcADuWBX9ncL53QAAgP+FzlqfyGOfhtLfrXfQZFH6u2kbUB4CVI/piaCeAKZkZtqWmileAWNFJ5GRCncMuspQfQpsVzetjTQf/B8p+uRx+XNxYCb+Jo0aSvs2u5p17EgvjSkvOHX6nHiSJKWmyaL1durzKCwsLjbH0GMlMr2EM97BqeMqKysa01r7IBdCupnoQ7nqzcr6hsombdLwMZxjudCzolDXhcOV9XvucSoKPsN5Tvp3cv11rBNJAQAAb7kq1JpUfeq4KlS9UX2KyhCgesxV0rTMzrYtAJDY6mf0lvRzn5SCjx6RksI/bCvKoiFgOEHg2XffLV/9+pus+bOCa6vLoT8z4/c/zDESnfaZ8Q5OnXCrNN0XguUFg5XNdu+4ALK8ak8XVOrzqehLyHBC3e3hsFmUS4fJUeWFuuEMBeC458Rl/AAAJDZ3aXvwxEs1hf5ux3SqZypRqT5FZQhQPaYzBuvl+zrTMgDUFI32P02aHjVMVk8ZJ1tLIg/9sKOM7vvKSUOvkPd+/TWiEFX3fb/0Z066fKg5BsoWbrWn2hZ82lAylAtiy5vt3gm+zL2sik8XPIZzBUdloW44QwGoykLPcENmxWX8AADUHNn71Je2cRoCz290iILqmjgKiY0A1WM6/uk3PXuamZgBoCZpeuTV0mCvfiZERexOGHK5HDlwkLz1c565JL8yC4rWmH2POG+gnHD5UNuKsrjwNJzKSheM6mRRZQlnLFWlj+UCTff4weZtCBy/suMot4/7mWCRhMOVhbruOOEMgVNZNSsAAACQSAhQAQBVptnp/5SkRs2k6Iv/2BbE4i9X/0Ouf+VVWdakqXyyfLn8WFAgqzdulE1btpibrmvbJ8tXyPKmqWbfv/zjWvvTKI+rrHTjiVbEhYdlhZ5aAarBYzhBrKpo0qaizYFlZZfdKxfqLi4j1HXPs7KhAFTgeQfWyxoOwIWh4TwnLuEHAABATUKA6rGMkSOl39atZgkANVH64OelJP8XWfvd27YFsdBL8W/M+Z8cfcNNsrlLV/m8qFj+u+gnc9P1zV32Kd12o9mHy/bD44LBcKo9XdVooLJzx3AwkkvclXu8sqo93bHcLPsV2VaBWkboGclQAKqi4QDCHQpAVVbNCgAAACQSAlQAQJVKqltPml/4vKz7/h1Z/+NnthWx0hn1L/3XE3L3Z9Pksdk/mJuuX/qvxxN+tv3q5qo0w6msVC6wDK1CDffyfcdVjoZe5q7BbHDlaGXchFVlhbqRDAWgtv1um3Y8TuDYgfVwjqUhqwtaQ/9OAAAAQKIhQPWYjn2qY6DqWKgAUFPVbZEhzQc9K6s/elD+XDLXtgL+EEllpXLVlaFVmttnuw/vOO7xQi9zDw4qw6kc1eO4oDU0rIxkKAC1fYxXs9jG3dfHCbeataIhCgAAAIBEkrS1lF2HB/JGjpRfRo2S9iNGVMtl/KmpqXYNiayoqMiuhW/W4YdL4dSp0mPKFEnLyrKttc/HSYETfx06A9Vv3fTnpeiNW6X5ybdK3aa72Nb4WDrudGn78Fp7D7WV9nNff/21vVe+vWdtMsvJXZOl/9ySbevhhIMPL90sjyzbIkc2qyOPZtjyz1L9524yQaO26bbKaFVn79mBx57RPXlboPrMii1y5x+bdzp+RW74bbP8d9UWubFNXTl/l+2P7Z7Tcx2Tw5r8SStY9e+hz0Wfk+Oekx5DjxUO3V9/Tp+PPocPV2+R+T3Cm+X2gAMOiKqvi9Rdr3wv0+etkBv+uq/06RLf9yQAsavucyXUbtmjJ5tlzq39zdLP9PPO3PPtt6RISF2fqVstn3UipcV98wcNklYDB8re48fbVigqUD3WMjtbOo4ZU22Blr5AuSX+DUhUjfucLY0PGRyYmZ8QGz7gqkgjqax0l7AHV1ZGeom7Cq4cDR6/1F12H87l+04bm0u6KlgV6VAAqrzhACIdCkCVV80KAAAAJBoCVI+lZGZKu2HDanVFIIDaJfW4G6Ve+15SqCEq4LHtAWoEwWDQBEmOCwkDoWj4x3L7Bg8H4GbTdwFkOFyw6YYjUMGBbrjPqbzhACIdCkC5x+QSfgAAACQ6AlSPFefmmhJpXQJAbZF25qOlPVBdKZr+om0BvBHJDPyOhox6Uy74dMGlG/czXO5xQytHVUTVnjbUdRNZqWjCYdW1UeDjYXCo636/dvXD/+joglgNmoPDZgAAACDREKB6LD8nx4wvoUsAqE3SBz8vm5bMk/Vz3rMtQPVzwV4k1Z7KhYOuujKaIFa5x3XPQ0PL7Zfdh38sF+rqcVwAG81QAMpVmbpQN5qhAFRwNWvwEAUAAABAoiFA9VjDjAxzGX9yWpptAYDaoU6DFGk++Hkp/vp12fDzdNsKVC8X7EUcfIZcxu8CxkgucVfucd3ziKb61HFh5fTiQLVoNEMBKPfYruo0mqEAHLc/FagAAABIZASoHtOZzXrNnGnGQa3pVq5cKY8//rj07t1bkpKSzK1Tp04yZMgQmTRpktlHt1UV95juBsB7ybvtJekDn5HVHz0sm5YvtK1A9XHBnps8KVxN7ScoV3nqwkZ3aX+4QidtCg4rI+VCXXeMaMPY0OEAoh0KQEXzewAAAAB+k7S1lF0HqsxLL71kgtKCggI54IADZNSoUXL88cebbRqejhgxQr7++mtzv6r+l5w1a5ZkZmbae1X3OH416/DDpXDqVOkxZUqtnrTsYxue9+Otz1fWTntait+7T5qffIvUaRxdRf7ScadL24fX2nuorVJTU7f1JxXZe9YmuxYI+XL2Srb3wvPh6i1yed5mE5h+1LWu9J5dYtond02OOGjsP3eTCT2f65gsz+RvMcc+f5c6cmObyFLdh5dulkeWbZEjm9WRobvVkewfA89pfg87RX8E3N9Hfx895n9XRfec3N/JCfe56GeFoqIie6/q3PXK9zJ93gq54a/7Sp8uu9hWAH6RN3Kk/FJ63tC+9Fwho3QdqErZoyebZc6t/c3Sz/TzDhJfdXzWiZTO0aPDTGqx397jx9tWKCpQPaYfCqalp8visWNtS82j4emZZ55pwtNjjjlGZsyYsS08Vbr+7rvvmpOlqtSjRw+7BsBvmvS9QBofeIYUTn7MtgDVJ5rKyuBL+N04qHoJfTTH6pMS+DimlZ6ukjXSy+6Vq/bU5xPLUADK/Zw+p2iHAlDu7wQAAOJHgzduiX9DYiFA9YGSwkJzq4l++uknE546jz1WdjjSokULefnll+09ALVR0xNHSL3WXWT1x0/YFqB6RDrJknKTNqmPilxYGd3Hqja2KFMnbYrHJfwa6rrL7qMNUN2xApNaRX+sSIc0AAAAAPyIANVjWhatl1Trsia6//777ZqY6tM999zT3tuZbgutQtXL7s8444xt45Yee+yx8tlnn9mt2+n4qjpEQPPmzc1+OraqVr6GS3/+pptu2vbz+pja5ujjuuegN30O+tz0cfRnNCgGELu0c5+ULZs2SvHXr9kWoOpFU1mpujQKLPXydhVNEKtcMKmXuysNHaOpZA0OdScWBI4V6diujhvjVY/jQt1onpOKNsQFAAAA/IIA1WM6C7+OR6nLmmjcuHF2TaR//8rHktHL+x0NJQ8//HBTmZqbmyv5+fmycOFCOfTQQ3cKRzXg1Me66667zNimGsRq5auGquHQn7/zzjvNzy9atMg8prY5oUMMzJkzR5o2bWrWdWiC114j7AHipcWFL8iGvK9l3dzAOFRAVYs24Av9uWiD2NDL3KMNYpULdV3oGe1zcr9bLBWxDpfxAwAAINERoHosPydHFg0fbib3qWnKqhSNxI033rht0ikdv1Qv87/44ovNNg1GXYWozuzvJgw57bTTzHLo0KFmqaFqZc/j3nvv3fbzl1xyiamE1WpZbQsOavXxndWrV5v9HnroIUlPT9/2uABil9Q4XZoPfl6Kv3hWNv7yrW0Fqka01Z4qNJyMNmQMrhxVsYSVoT8b7bFCQ89YnpOrZgUAAAASFR9pPVacm2smkKqJAWosNBx1Y6IGB5eOBqvTp083608++aRZqrL2feedd+xa2V599VW7trOcnBy7tqPu3bubpU6AtWrVqgqHJgAQuXpt95W088ZL4UcPScmqX2wrEH+xVHuGBq/RXi6vgp9Hl4bRh5Vt623/2VjC4dBQN5bfLZbwFQAAeCt4KLvQmxYrldXublqsVFU0M9BiKqC6EKB6LCUzU1pmZ9fIS/jbtGlj1yI3d+5cu1a+2bNnm6WrHi3PN998Y9fKFvzz7o3+vffeM/cLy5ncKzU11a4BqCqN9jtBmp00SgonPypbN661rUB8xRLuBYeeepzgwDFSwRWfsYS6wb9PLMdRbjgAFe1QAIpL+AEASFw6RN7bb79t7wW8+OKLpv2QQw4xSx0GL9hll11m2q+99lrbEn+XX365uTIUqC4EqB7T8LTbxIk1chIprcrs2LGjvScyebL/xzPUN/ngm459CsA7TfpdJo32PUkKJj9qW4D4iqXaM1DhGViPttLTCb7MPZZ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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part -1)\n", + "\n", + "## 1. Introduction\n", + "This notebook illustrates the use of the PySMO Polynomial surrogate trainer to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package. PySMO also has other training methods like Radial Basis Function and Kriging surrogate models, but we focus on Polynomial surrogate model. \n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "\n", + "### 1.1 Need for ML Surrogates\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebric, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebric equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "![image.png](attachment:image.png)\n", + "\n", + "The above flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train the model using polynomial regression for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoPolyTrainer, PysmoSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using CoolProp package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\" and change them to the variable names to be used in the property package. Further, the input variables are ***pressure***, ***temperature***, while the output variables are ***enth_mol***, ***entr_mol***, hence we slice them and create the input and output data. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(\"./500_Points_DataSet.csv\")\n", + "csv_data.columns.values[0:4]=[\"pressure\",\"temperature\",\"enth_mol\",\"entr_mol\"]\n", + "data = csv_data.sample(n=500)\n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# # Define labels, and split training and validation data\n", + "input_labels = list(input_data.columns)\n", + "output_labels = list(output_data.columns) \n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogates with PySMO\n", + "\n", + "IDAES builds a model class for each type of PySMO surrogate model. In this case, we will call and build the Polynomial Regression class. Regression settings can be directly passed as class arguments, as shown below. In this example, allowed basis terms span a 5th order polynomial, a variable product as well as a extra features are defined, and data is internally cross-validated using 10 iterations of 80/20 splits to ensure a robust surrogate fit. Note that PySMO uses cross-validation of training data to adjust model coefficients and ensure a more accurate fit, while we separate the validation dataset pre-training in order to visualize the surrogate fits.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a folder which contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "No iterations will be run.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 26156.366230\n", + "\n", + "------------------------------------------------------------\n", + "The final coefficients of the regression terms are: \n", + "\n", + "k | -502464.012637\n", + "(x_ 1 )^ 1 | -4896.649136\n", + "(x_ 2 )^ 1 | 857.309074\n", + "(x_ 1 )^ 2 | 236.735794\n", + "(x_ 2 )^ 2 | -2.283672\n", + "(x_ 1 )^ 3 | -8.252162\n", + "(x_ 2 )^ 3 | 0.003144\n", + "(x_ 1 )^ 4 | 0.159508\n", + "(x_ 2 )^ 4 | -2e-06\n", + "(x_ 1 )^ 5 | -0.001228\n", + "(x_ 2 )^ 5 | 0.0\n", + "x_ 1 .x_ 2 | 4.603417\n", + "\n", + "The coefficients of the extra terms in additional_regression_features are:\n", + "\n", + "Coeff. additional_regression_features[ 1 ]: -0.003097\n", + "Coeff. additional_regression_features[ 2 ]: 4.7e-05\n", + "Coeff. additional_regression_features[ 3 ]: -0.063913\n", + "Coeff. additional_regression_features[ 4 ]: 139048.007363\n", + "Coeff. additional_regression_features[ 5 ]: -71.706987\n", + "\n", + "Regression model performance on training data:\n", + "Order: 5 / MAE: 111.978134 / MSE: 34702.874291 / R^2: 0.999740\n", + "\n", + "Results saved in solution.pickle\n", + "2023-08-08 10:16:16 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "No iterations will be run.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.190163\n", + "\n", + "------------------------------------------------------------\n", + "The final coefficients of the regression terms are: \n", + "\n", + "k | -408.470578\n", + "(x_ 1 )^ 1 | -13.06054\n", + "(x_ 2 )^ 1 | 2.970997\n", + "(x_ 1 )^ 2 | 0.656992\n", + "(x_ 2 )^ 2 | -0.008065\n", + "(x_ 1 )^ 3 | -0.0229\n", + "(x_ 2 )^ 3 | 1.1e-05\n", + "(x_ 1 )^ 4 | 0.000444\n", + "(x_ 2 )^ 4 | -0.0\n", + "(x_ 1 )^ 5 | -3e-06\n", + "(x_ 2 )^ 5 | 0.0\n", + "x_ 1 .x_ 2 | 0.010388\n", + "\n", + "The coefficients of the extra terms in additional_regression_features are:\n", + "\n", + "Coeff. additional_regression_features[ 1 ]: -7e-06\n", + "Coeff. additional_regression_features[ 2 ]: 0.0\n", + "Coeff. additional_regression_features[ 3 ]: -0.000154\n", + "Coeff. additional_regression_features[ 4 ]: 274.423201\n", + "Coeff. additional_regression_features[ 5 ]: -0.164325\n", + "\n", + "Regression model performance on training data:\n", + "Order: 5 / MAE: 0.303688 / MSE: 0.282479 / R^2: 0.999317\n", + "\n", + "Results saved in solution.pickle\n", + "2023-08-08 10:16:40 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" + ] + } + ], + "source": [ + "# Create PySMO trainer object\n", + "trainer = PysmoPolyTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + ")\n", + "\n", + "var = output_labels\n", + "trainer.config.extra_features=['pressure*temperature*temperature','pressure*pressure*temperature*temperature','pressure*pressure*temperature','pressure/temperature','temperature/pressure']\n", + "# Set PySMO options\n", + "trainer.config.maximum_polynomial_order = 5\n", + "trainer.config.multinomials = True\n", + "trainer.config.training_split = 0.8\n", + "trainer.config.number_of_crossvalidations = 10\n", + "\n", + "# Train surrogate (calls PySMO through IDAES Python wrapper)\n", + "poly_train = trainer.train_surrogate()\n", + "\n", + "# create callable surrogate object\n", + "xmin, xmax = [7,306], [40,1000]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "poly_surr = PysmoSurrogate(poly_train, input_labels, output_labels, input_bounds)\n", + "# save model to JSON\n", + "model = poly_surr.save_to_file(\"pysmo_poly_surrogate.json\", overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing surrogates\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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/fx4RERGS7rG8vBx2ux0Oh4M28FaZ0tJS4Rl/881t+OCD/uC4ENhsTgwYsBOdOn0LAMjOzkZsbKyRRSU0pLS0FBUVFT6PR0RE0PM3IfRcCaOROn6H6Vgm1bh48SI2b96M7t27Izw8HADwwQcfoEWLFti5cyf69u0LjuOQnp6OpUuXolGjRgCAvLw8pKenu10rIyMDU6dOBQBUVFTg0KFDmDNnjnA8JCQE6enpyMvLAwAcOnQIlZWVbtdp27YtmjZtKoiwvLw8dOjQQRBg/O9MmjQJP/zwA2677Tav93X9+nVcv35d+Lu8vDyAWlIX106tqCgEhYVhSE2tQnKyE4D5OjX+XhyOBoIAAwCOC8EHH/RHy5a/wG6/7LcjJ8yNqxD3Bwlxc0HPlTATphJhs2bNwurVq3H16lV069YNO3fuFI793//9H06ePIl33nkHb7zxBqqrqzFt2jQMHToU+/fvBwCUlJS4CSMASEhIQHl5OX7//XdcunQJ1dXVXs/56aefhGtEREQgJiam1jklJSV+f4c/5ovFixdj0aJFMmpEH6xsNbp4MVYQYDwcF4KLFxvBbr9sUKmUQxYA6XjWk8PRABcvxqJRo1K3Z09C3FzQc1UG9R3GYKgImz17NpYsWeL3nGPHjqFt27YAgBkzZmDs2LE4efIkFi1ahBEjRmDnzp2w2WxwOp24fv063njjDdx4440AgHXr1qFz5844fvy4TxclS8yZMwfTp08X/i4vL0dKSoqBJarBylajRo1KYbM53YSYzeZEo0YXDSyVMjwtAL4GHzOKZa3xN7kgzAurz5U1wUPWQ+MwVIQ9/vjjGDVqlN9zWrRoIfw7Li4OcXFxuPHGG9GuXTukpKTgiy++QFpaGpKSkhAWFiYIMABo164dgJqVim3atEFiYmKtVYxnz55FdHQ06tati9DQUISGhno9JzExEQCQmJiIiooKlJWVuVnDPM/xXFHJX5M/xxuRkZGIjIz0Wx9GYjWrEQDY7ZcxYMDOWh21Ge/HtVP3N/iYUSxridjkgjAnrD5XFgUPWQ+Nw1ARFh8fj/j4eEXfdTprYpH4GKoePXqgqqoKBQUFaNmyJQDgv//9LwCgWbNmAIC0tDR8+OGHbtfJzc1FWloagJrZR+fOnbFv3z4MGjRI+J19+/YhOzsbANC5c2eEh4dj3759GDJkCADg+PHjOHXqlHCdtLQ0PPfcczh37hwaN24s/E50dDTat2+v6H5ZQE+rkZ4xaJ06fYuWLX/BxYuN0KjRRd07aLVnxawOPnqgpC6tOLkwG1pYhlh9rqwLHtash6xZDdXGFDFh+fn5+Oqrr3DnnXeiYcOGKCgowLx589CyZUtB+KSnp6NTp04YM2YMli9fDqfTicmTJ+Pee+8VrGMTJ07E6tWrMXPmTIwZMwb79+/Htm3bsGvXLuG3pk+fjpEjR6JLly644447sHz5cly5cgWjR48GANjtdowdOxbTp09Ho0aNEB0djSlTpiAtLQ3dunUDAPTp0wft27fHww8/jKVLl6KkpARz587F5MmTmbZ0iaGX1chXDBrgRPfueejaNR92+2VkZmaicePGqryAdvtlQzpmLVyIrA4+WqPUwmAll7QZ0coyZIbnyprgYW0CFwwhFqYQYVFRUcjJycGCBQtw5coVJCUloW/fvpg7d64gakJCQvDBBx9gypQp6NmzJ+rVq4f77rsPL7/8snCd1NRU7Nq1C9OmTcOKFSvQpEkTvP7668jIyBDOycrKwvnz5zF//nyUlJTg1ltvxe7du90C7ZctW4aQkBAMGTIE169fR0ZGBl599VXheGhoKHbu3IlJkyYhLS0N9erVw8iRI/H000/rUFvaoofVyFcMGhCCgwd7IC8vDQMG7ASwDYA5X0B+dnfhwgXhM7VciGYYfLRAqYXBSi5pMyK1bcu1DLH+XFkTPAB7E7hgCLEwhQjr0KGDsMLRH8nJydi+fbvfc+6++258+63/mUZ2drbgfvRGnTp1sGbNGqxZs8bnOc2aNavl+rQKelmNvHUIgDoLAqTmapN6nhy8zfzV7JBZH3z0QK6FwWiXNPEHvsSzElh8rmVlZQDYEzwAuxM4FgWrWphChBHBibcOgSfQzio2NhbZ2dmGxBp4s9j88MNNAXXIpaWlblY1FgcfvZDSYZeWlgqDIY+vyYUWQpzwjhruOc/nxdJzLS0txbZtNRZ8FgUPqxM4FgWrWpAII0QxymrEdwg7dvSH5w5banRWLLgw3WPeOAA24ZjUe/QVU2NUnJvRiHXYZWVlwkDoj6ysLMTHxzPRToIBtawdRk6wxHAtE6uCR+kETssAehYFq1qQCCNEMbJT4zuE/PyuyMtLY6qzCpTaMW828EJMzj1KjYXisbplR6zDrqqqknQdu92ue64mFoWDXqhp7TBLPbVs+QuGDNkOgENKyhnD+rRArYdap91gVbCqAYkwQhJGdmp2+2X06bMXXbvmW8q95j3mzYaMjN1o3/5Ht3u8cOGCpEHYnztn8ODBSE5ONs0ApRS5HbaaMUhKYTF3lCt6CEQrWzu8IeZ61XOyFOhEW6u0Gw6HQ/i3VUMsSIQRpsFq7jVfg46nAAOAnJwcAP4HYTF3TlxcnOUFGI+/DvvSpUvCv1lJEaDVCkE10FIgusYyWtna4YnYu5qVlaX7u6rW76n1TpWWlmLr1q1un1ltDABIhBEMwpK7TEsLgL9BR8lM0srBq/7gn5HUQPsDBw4AYHvFFSvWOc80Kv6QKxC9iTt/4pmlfiFQxN5Vu91uUMkCQ813Sk6YhZnbBokwgjl40/j58+drzYS8odULqEeiQG+DjtKZpNXcOVIEMABJVhpvsCpaWbDO+bN+qSUQfT1bT/FsRTe61d5VHq3eKX/vRGZmpqnbBokwgkliY2MNX+WkVaJAf0GwcmaSnpYKK7lzpLrAMjMzJV/TUzywOBCyYp3z1aa1FIi+xJ0V3ehWeldd0eKdEnsnXPdwNiMkwgimYaHzVXtg9CYuL1y4gJycHMkzSV8ixSrBq1KFredKR18DuS/xwNpAyKp1zuFogNOnUzQTiGqKO5ZXmbpOwKzoetVCXLL6TqgFiTCCEEGLTsDXICB1JikWL2G11BRSXGC+BnJ/Ipq1gZBF65x7Ljt31BgM1ZzksL7K1Gjrvh6oPRFk8Z1QExJhBCGCnp2AkpmkWFqKuLg4U3fsUqwk/gZy8SBod9Gq5sbwcmHNTVU7l507arwHak5yWF5lymPW99AfWu5SwNo7oTYkwghCBL07ATkzSSlpKZKSkjQppx5ItZL4G8h9iejw8AoUFjavZV2LiYnRfaBk1U3la/9WAKq9B1pOclhYZaoFrLlctbbwWSXMwhskwghCAlp3AkpnklaPl5B6f/4Gcm8i+pZbjmDdunGG5wfjYdVN5X3/VieGDn23VoZ3pQJRq0mOlosIjBRBrLpc1f4tlvcAVRMSYQQhES0TBSodhK0eLyH1/sQGclcRHR5eIQgwgJ38YCy6qXzV6803HwMQmLtbS+uflqtMjRZBLLpctRClrE5M1IZEGEH4QO+Ny2lPtdqI3V94eLhwrr+BPDMzE1VVVcjJyUFhYXNLWw/VQKpACiR/l5aDrJYWYtZEkNEuVy1FqdkFlhRIhBGED/SYiakxg7RivIRUERAfHy/5GRUXFwOwvvVQDfSyQmg1yIo9Y1+7ACi5J38iSGu3JQuJfbXaNzJYIBFGBARrAaJqo2XZA5lBWj1eQksRYHXroVqY+b0Ve8b8XqzekGOx8SeCysrKsG3bNtFrKHVbspLY1xUWRKHZIBFGKMbo2AizE8gMMhjiJbQsuxWth4R0C6o/pFpsxESQZyLhQH/PE9YW5bAoCs0AiTBCMWSGVg8lM0gzCywePS2prFgPrW49NhJ/kxN+VwqeQGOp5IogtWO3WHOrsyYKzQKJMEIVyAytnGCdQRYUFOBf//qX6HlqWVJZsB6S9Vh7pNSbGv2VHBGkRf/ImludNVFoFkiEEQETrCJCLeTMIM1gRZFSRgCSBBigriXV6Loh67HxqNVfSRVBavePrCb2ZU0UmgUSYUTABGKGNoOo0BqpM0gzWFGkljErK8vtb36DaABISTkdFB03WY+NIVC3mVQRxKdPUdtNx4JF1xcUaykfEmFEwCg1QysVFawIN7XKIXUGaQYritQyVlZWCv/+5pvbsGPHAAC2/33ixMCB1hYkZD02jkDdZlJFEH9cCzcdSxNTo2MtWRkPlEIijAgYpWZoJaKCFWuQ2uWQO4M0gxVFzsbbfwgwAAjBjh3WFiQUxGwcarjNpLzTfF46q7vpjLTMSe2HMzMzERMTo1u55EAijFCFQM3QUkUFK9mq1bBKKZ1BmsGKEsjG2zVYW5AYHcRsduuBEvSOpWI1dksLjGorUvthf/najF4EQyKMUIxaZuhARIXRW3YAyq1SSmeQZrCiBLLxdg3WXlVlpHWEFWuy3uhtsWE5dsuKiPXDLIZvACTCiABQq5NRKipYcMkFapVS0gEbbUWRgtKNt2uoiQnTUpCwYAkyKohZy9hCFurVH3r/NgksfRDrh1kYK3xBIowICDU6GSWighWXnBFWKTPEmMgpIy9GTp9uAgBISTmjqbvGSEuQ0UHMnqg5OAWrhY0wHn/9MAAmxgpfkAgjDEeJqGDFJWeUVcoMS8H9lTEszL3rqREjx2pd46GHHlJ9wDZylSlLLiq1JzJGxWuybn0jxAn0Gfrrh1kZK3xBIoxgArmighWXnJ5WKZasKL46TYfD4fa3rzI2btxYshjRcpA1wk3BiiDQenDSI16TrG/mR41nKNYPszBW+IJEGCGKVoNgIKKCJZecVlYpb/WelZUl5NgKCwurtexaj1m/nsvCtRxkA7UEmd0Co+VERi9xa4bceVZAy7au1jP01Q+zNFZ4g0QY4RctB0ElrhlWln1rbZVieYYvtdOMiYlBUlKSLr+lZJANdKcHVp+PVLQanIyK12Q5+NrM6NnW5T5Dqf0wy+EbJMIIv2gd56GHcNMCrcthlhm+ngOf2r8ViCXILM9HDC0GJyNicFhZqGNF9GrrSp6hv37Y4XBg69atwt9GL4LxBYkwQhYs5OVixbIgpxyBmPPVEh9quxT0HPi0+C21LEFms8BobcU1Il6T9eBrq6BlW1f6DH31WUlJSUxM2MUgEUZIxmyDDSsEYs5XS3xo4VLQc+AT+60LFy4AkN+pBmoJMqMFRg0rrjdBzz8DI2JwWFmoY2XUbOuu7YdvN1bfY9MXJMIISZhxsGGFQMz5agkdLVwKUjtNNSxwYr+Vk5MjfC4mJNW0BJnVAhPI4CRF0Osdr8l68LUVUKut+2o/wfoMSYQRkjDrYMMacq2JWswO1bJoSuk01bLAefut9PS9uHgxVjjOIyYk1YznE3s+Fy5cYMLloSZSBf3o0fciLi7O7Vwt64Ll4GsroFZf5K/9BOMzJBFGSILM/f7xZ+3hze1KrIlqzw7VtmiKdZpqWuBcf6uoKBl796YrFpJqCQGx58Nb6FheJRkI/gR9XFxcwKtjxTA6d57Z05TIQQtLla/2w2IAvVaQCCMkEaymYilItfYotSaqOTtUw6IpZ+BzHaCUWOC8/RYAvPHGCM1d41KENeD+fMLDK1BZGQmHo4GpVkkqgYUQBSNXS1shTYlc1OyLxNrP4MGDBUuqlcSsJyTCggSlMzZW8nKxjFRrjxxrolYzfDUsmnIGvuLiYgDKB2zX37pw4QJycnJ0cY17DrBiq4Lt9ssoKGgVVAtXWAlRMGpwtkqaEjG06ovE2o8ellQWIBEWBAQyY2MlL5dZ8LT2pKfvRXJysdAxS7UmalXvalk05f5uIAO252/p4RqXasHr1asXDhw4wIRVSG8oROEPjFo5roc7VKu+iNpPDSTCgoBAE64Gu8AS6+j4/RK9DcS5ufcCsLl1zFLN+VYKYFazw9XTNS4mrho2bAiAHauQnlglRCFQIWOUANfTHapFX2SV9hMoJMKCEDUSrgZLQKrUjg7wPhADNgC1O2a9A0+NDmBWu8PVS0hKFVfBOqs3+2o2NYSMUQLcCu5Qs7cfNSARFmSoYTYPpoBUqR0d4H0gdsW1Y3YNOgW0F60suJUD7XCNEJJSxVUwzeqNFvRqooaQYUGAmymRtpXajxqQCAsi1DKba72fJKuIdXSeAzHAgbeEAe4dsxFBp0YIYjU7XCOEpBxxFSyzehYEvRYoFTJGC3AW4xHFPCUPPfQQoqKifB43Y/tRComwIEIrszkL+0mKoVfch1guK1brRwyl9af2gG1Ex+xPXIWFuXehwTKrt9oAGaiQMVKAG+EO9dcfeG6c7Wt8sIKnRA1IhAURLGdf1xK94z74gTg19SRuvvmo6S0jgdafFTpaX+IqJibGklYhT6weA6pEyLDiVtPbHSonfYu/8cFqnhKlkAgLIljPvq4EKYODGu5TsY4uMzMTMTExtWaBVrCMWCEAWC5Sn4/ZxYcUgiEGVImQYcUtq7c7VGr6FhbGBzNAIiwI0CrhqtHL8qUODpmZmW5/K3GfinV0MTExSEpKQlJSEhMds1aYwfKpBqwMsGLoYaEKBhGuVMgY/fx5jHCHiokso8cHs0AiLAiwarI9qZ1+VVWV8O9ARITUjo6Vjlltgm1my/pzlOoWUtNCZWURbraFFUa7Q8VEltHjg1kgERYkBEOyPTELlxIRoWZHZ/a4GprZsoVUt5BaFiqriHDX97CsrMztmJnCB4y21oqJLNbGB1YhEUbIRuv9JJWIFSkzdCUiQq2OzgpxNTSzZRO9xJEVRLjU9zArKwt2ux0A25MjI8slRWSZzbpoBCTCCNloOQOTI1Z4pA5CSkWEGh2dFeJqaGbLJnqJIyuIcKnvod1uZ27zaBYt6VJEli/rohxYvHe1IBFGKEKrBq9kJaPUQYgVEWHmuBqa2bKHXuKIlfdHLdR+D7UUCixb0pWKLKmeEiNiH/WERBjBNFJWMooNQuHh4cLnWrhP5SBmtbtw4YLX7xk50zM6AJjwj57iyCoiXG0XrtZCQY4lXWurkdT3nE/bE2gZ9I591BsSYYTu+OskXEWI1Jmq2CBkt9uZSTcgZrXLycnx+V2jZnpGBwAT4mgpjqwowtV24eopFPzn5tI+W71R/YFVFoZ4QiKM0BWpZnUpL5ycBQKsCAQpriMW48VYqT/CN2rE3njDiiJcKxeu1kJB7PqVlZXCuVqKQSOetRUWhniDRBihK1LN6lJeODMODmJWOzPHixH6Iierf6Cw9A6pgVYuXK2FgtTrmzHsQQwrLAzxBokwwjD8CQ4pL5xZV8z4stpZ1dxOaIMZJyEsIdWFK6Wf4dFaKEi9vhnDHsSw2sIQHhJhhCGICQ6xF84z9sEXrHQoUuJqrGpu1wKzCnC1CYZ7VBO58W1SA+75rdG0FgpSr29k2IOW76ZVFoa4QiKMMAQpgsPfC8dxnKTfYWXFjD+rxYULF5CTkyPacbq6EIJFZHjD6kvWvUGiUx3kWg+lBty7bo2mtVCQmpvLiLAHPd5NrWIfjYJEGGEIYoJj8ODBiIuL8/rdiIgIybFlLCHW6Yh1nJ4uBCuJDDlYfcm6J8EoOrVESR3JDRVQWyhIteCFhf0xpOsR9uA5OZC6ul3Ou6ln7KMRkAgjDEGJ2d51hlpcXCx8bqVgdjmzaDOIDC0tOMESQ6e16CQrmzhilnvXXIT+UCoUpFrwPI8rCXvwFbTP/wbfFnxPDhoAgGrvptVjH0mEEYbhT3D4Chz1nO1bYSAWm+UqtfIZPbhqneU72GLotGjrLGdiZwnxzaq1z0Uo5bulpaWi54jdi7+gfeCPtuBvcpCWlqfqu2nltkcijFAVsYH/6tWrbn/7MqtLDRq1wkDsOdPjY8QA5VY+FgZXrffLtOqSdV+ItfWysjLZ+x1aYU9TPZBiuWdBKEiJPZXqhZDaFrxNDvLy0gA4AQTHuxkIphFhAwcOxOHDh3Hu3Dk0bNgQ6enpWLJkCZKTkwEACxcuxKJFi2p9LyoqCleuXBH+fueddzBv3jycOHECrVu3xpIlS/DnP/9ZOM5xHBYsWIB//vOfKCsrQ48ePfDaa6+hdevWwjkXL17ElClT8MEHHyAkJARDhgzBihUrUL9+feGcI0eOYPLkyfjqq68QHx+PKVOmYObMmVpUDTNIHfgfeughREVFuX2mVHhYZSD21oEHYvmQOrgWFRX5HGDVtJRp4TK26pJ1X4i19W3btgUkqq3k1tcCs6zMk/L8xe5FTlvwNTno3v1z5OWlBcW7GQimEWG9evXCk08+iaSkJPz666944oknMHToUBw8eBAA8MQTT2DixIlu3+nduzduv/124e+DBw9i+PDhWLx4Mfr3748tW7Zg0KBB+Oabb3DzzTcDAJYuXYqVK1di06ZNSE1Nxbx585CRkYEff/wRderUAQA8+OCDKC4uRm5uLiorKzF69GhMmDABW7ZsAQCUl5ejT58+SE9Px9q1a/H9999jzJgxiImJwYQJE/SoLkOQOluOioryOWNXEgArdSA22j0nF7WsfP46VKmuh0DQ0mVsloFRDTzbOgBwHFBQ0Ep4nkVFRQDkW2Ws4NbXAzOvzJMa3C+3LfiaHHTtmo+uXfOD4t0MBNOIsGnTpgn/btasGWbPno1BgwahsrIS4eHhqF+/vpsl6rvvvsOPP/6ItWvXCp+tWLECffv2xYwZMwAAzzzzDHJzc7F69WqsXbsWHMdh+fLlmDt3Lu6//34AwBtvvIGEhAS8//77GDZsGI4dO4bdu3fjq6++QpcuXQAAq1atwp///Ge89NJLSE5OxubNm1FRUYH169cjIiICN910Ew4fPoxXXnnF0iLME1/WF3+pFqQKDzlbFgFsuOfkooaVL9DBVQ03lNYuYzMPjHJp2fIXuGdnCcGOHf3RuHEJmjQpFkS13HZsBbe+FlhpZZ5YgDvvjZDbFsT37g3e9iMF04gwVy5evIjNmzeje/fuPlelvP7667jxxhvxpz/9SfgsLy8P06dPdzsvIyMD77//PgCgsLAQJSUlSE9PF47b7XZ07doVeXl5GDZsGPLy8hATEyMIMABIT09HSEgI8vPz8Ze//AV5eXno2bOn24uZkZGBJUuW4NKlS2jYsKHXMl+/fh3Xr18X/i4vL5deKYwhx/qSnZ0t/Fuq8Agk3w9gjtgXNdxtcrY50SrFhxwxKcVaaaWBkUfqpvYXL8bCNc6mhhCsWzcuoJWSVnHrq43VVuZJKaeStiDXIm2md1NrTCXCZs2ahdWrV+Pq1avo1q0bdu7c6fW8a9euYfPmzZg9e7bb5yUlJUhISHD7LCEhASUlJcJx/jN/5zRu3NjteFhYGBo1auR2Tmpqaq1r8Md8ibDFixd7jWszG3KtL64dnBzhYdXYF7lWPn9I6VC1rg+pz1SOtdJKA6PUPGCA9+cJBO4+DLb4OjmYpR2phdK24Msi7Znz0Uzvph4YKsJmz56NJUuW+D3n2LFjaNu2LQBgxowZGDt2LE6ePIlFixZhxIgR2LlzJ2w2m9t33nvvPVy+fBkjR47UrOxaMGfOHDdLXXl5OVJSUgwskTJ8WV9On26Cixd/F7W2aBnnY4bYFzVn3+LbP+lTH1KeqRxrpdxVgCwjNQ8Y8Mfz3LGjPzwtYoG6D4Mpvo7wj5S2INWalZycTKLLD4aKsMcffxyjRo3ye06LFi2Ef8fFxSEuLg433ngj2rVrh5SUFHzxxRdIS0tz+87rr7+O/v3717JoJSYm4uzZs26fnT17FomJicJx/jPXTv7s2bO49dZbhXPOnTvndo2qqipcvHjR7Trefsf1N7wRGRmJyMhIn8fNgvfZuhPbtw/1ObjI3dNNKWaJfVGz0/LXoWpZH4E8U9atlVrgTxADEARpp07fonHjEqxbNy5g96Fe7x3BPnLbgtVctUZhqAiLj49HfHy8ou86nU4AcIuhAmriug4cOIAdO3bU+k5aWhr27duHqVOnCp/l5uYKIi41NRWJiYnYt2+fILrKy8uRn5+PSZMmCdcoKyvDoUOH0LlzZwDA/v374XQ60bVrV+Gcp556Slg0wP9OmzZtfLoirUTtVVxOADZwXI3F0pu1Ra8XOlhiX6R2qFrWh9JnagZrpRb4EsT5+V1rLfXv1OlbVdyHNJASPEraArWLwDFFTFh+fj6++uor3HnnnWjYsCEKCgowb948tGzZspYVbP369UhKSsJ9991X6zqPPfYY7rrrLrz88svo168f3n77bXz99df4xz/+AQCw2WyYOnUqnn32WbRu3VpIUZGcnIxBgwYBANq1a4e+ffti/PjxWLt2LSorK5GdnY1hw4YJOcseeOABLFq0CGPHjsWsWbNw9OhRrFixAsuWLdO2ogIk0BQOvmKZrlyph3ff/avbud6sLXq80MES+yJ1JZTW9WH2TPh6pjXxZUHmBRjgLkjVch/SQErwUFvQH1OIsKioKOTk5GDBggW4cuUKkpKS0LdvX8ydO9fNfed0OrFx40aMGjUKoaGhta7TvXt3bNmyBXPnzsWTTz6J1q1b4/333xdyhAHAzJkzceXKFUyYMAFlZWW48847sXv3biFHGABs3rwZ2dnZ6N27t5CsdeXKlcJxu92OPXv2YPLkyejcuTPi4uIwf/58ptNTqJHCwVfmd4ejAVPWp2CJfVEimJUE/6sNK9ZKvTfO9iaI09LycPBgD7fzXAWplPQcRuTHM1tOPoIwClOIsA4dOmD//v2i54WEhOD06dN+z/nrX/+Kv/71rz6P22w2PP3003j66ad9ntOoUSMhMasvbrnlFvznP//xX2CGkLqkXew8bx0rC9Ynin1xh2U3FAvtBdB+42xveApiAG6WMECaIDUyP57e4pUgxGB5UmAKEUbojxp5o1iytrAsOoyC5XtlyVqpd4ya5wTBnyD1XP4PGJ8fT6p41WvLLCK4YX1SQCKMqIVaK9NYEz7UqbMNq9ZKrWPUxO7HnyCNi4uTnK5D7xWnYuKVT9rM2qBIWAsjLNpyIBFGuKH2rJ86UUIq/kS7w+FAZWUlwsLCUFFRgeLiYrfjWop5rWPUvN2364b2QOBbM6n5Xou5dhwOBwBp4pXFQZGHRRcWi2UyAiX1wOqqaxJhhBssrUwjgg9vA0hpaSm2bt0q/K235USPGDXPcqu9NZNa77XUGDNAXLyyOigCbLqwzLj/rRYofTasjm0kwgg3WFmZZiQ022QLJe4EtZ+h3jFqarvy1XqvpcaYAeLildVBEWDThWXG/W+1QOmzYXVsIxFGuMHKyjSjoNkmu0i1nGhlxQjUJSgXNduXFu+1WIyZw9EADRtewtixr6OyMqKWeGV1UHSFVWudWXeUUHNyJPfZsDq2kQgjAKi7ktHzRePjeXjCwsIQExPjdk1WBA0Ls02yxHlHquVELSuG2i5Bo1HTmic2AHqr99TUk27XYHVQdIVFa50ewlCLPkjtCa6SZ8PSqmseEmEEAPXcH3JiRlxh0bJkxGyTLHG+kWs5CXSwYm11rxK0WnHqbwAEILneWRwUXWHRWqe1MNSqD1J7gqv02eht0RaDRBghoMZgIvYCsRLH4Gumd+HCBQDGuSHUSpprReRaTtQYrFgWWFLQSkj6GwDF6r1Xr144cOCAcIy1QdEVI6x1Yn2T1sJQD2+AGhNcqc+GdYs2iTBCU1xf4IKCVkzEMUiZ6bHihlAjaa6VkGM5YdGKYQRGrBj1V+/x8fGSfoMVN6+e1jopfZOewlALb4CaE1wpz4Z1izaJMAKANjEAri8w4ARg+99/+lmWvN0XP6Pk8SZ0WBjAzRp8qzVSLSdmiDkyM74GQLF6j4mJYXpQ9IZe1jqpVig9hKFW3gC1J7hSng1LbckTEmGEJjEAni8wEFLrHNcXr6ysTHLmb6lIuS9fQsfoAZzVVVlGEIg7gfWYI7MhNcZMrN5ZHhQBNlxYnn1TevpeJCcXC4JM6x0ltPIGBDrBZeHZqAmJMEKTOCRvL7Anri/etm3bVA82F5tVigkdIwdwVtyhLBCoO4HlmCOz4e9ZqJ3l30iMdmF565tyc+8FYKtlFXfdP1Rqmfx5PrSOPQt0gmv0s1EbEmFELdSIQ/L2AgNO2Gzw+eJpGWzuzeLVsOElUaFj1P6FLLhDWUJOh2q1mTJr+HoWatS7WFjE1atXERUV5ffaag2+Rg7i3iex3kM55OwfCkjPo6elNyDQCa5ZBJYUSIQRbqgVh+TrBW7Z8hecPt0EgA0pKafVvwEv+LJ4jR37ul+h4zrDdEWPWZbR7lAzY7WZshyMzDEXaL1LFQdiWCF9i/dJ7B8EYhWXmkcPUNedr1W6FLNDIowQUCMOSSzpqxHB5r5ce5WVEX6FjtwZphqomTQ3mDH7IKwEFvY7DOS6csQBwE66Gy3wnIQBHHhLGKCOVVxqf6+WWArmyZE/SIQRAmrlVfJ80U6cOIE9e/YYFmzuz7WXmnqSqcBt6qgIpbC436ESpPQTwbBy2HUSVlSUjL1701W1iov19748AYDyPoj6rdqQCCME1IpD8vWiGRVsLubaM8IsTlsTEVph9pW1Yv2E2e/PH75cdqmpJ3HzzUdVnSyK9fdGeAKCERJhhIDWcUhGBpv7c+15m/FpKYJoayL2MbNINvvKWl+LesLDa56H2e/PH66W8LKyMmzbtk04pvZkkeJO2YBEGKFbHJLeL73UQNDk5GRdB1TamohdSktLce7cObfBz4jYKjH8JSE2+8pa7/FQIVi3bpywuMff/bkmY2ZZLPuCL29SUpLmoQlyAu/NPDFhGRJhhOZxSHqIPF8dRGZmJqqqqhAeHg673e61bEZ3HLQ1ERt4s1CyGFvlPwDfGhaOTp2+RePGJVi3blwtt+PUqcu93h8AFBY2x4YNucyI5UCRWu5ABJKUfG4sLPqwKiTCCADaBkxqLfLM7N4LhgBjs+DZPs+cScKOHf3B7/bASuyRlAB8K+wUUFkZ6dPt6Hl/BQWtsHz5VKbEsl7IFUhK8rlZZdEHi5AII3RBS/Ejdb811joIKwcYmx1+oPHcboul2COx9mO2jPWe4kDMrcrfX7C/R3IFUiCT4mCvay0gEUZYCjNZlqwcYGxmau97+ge+Yo880cPNrbT9sJpjjhcHRUVFyMnJEXWr9urVCwcOHDDFe6RHPJUcgaT0t8xQ12aDRBhhGcw2SzN7ALVV8b3vqbsI4PdJNCo+Rqz96L3qVw1iY2PdxIo/t2rDhg0BsPse8cLL4XBg69atoucH2l70EEis1rWZIRFGWAazzdLMHkCt1eze6FVYvgaasWNfR5MmxW7nGhkfI9Z+pOZ5Mrq+vf2eK77cquHh4cJx1t4jf3FaADQJl9BDILFY12aHRBhhGcwyS7PC1kRaLYZgYZGFr4HGU4CxYHkNNACfxVVvUmOWpFrMjMBXnBbgRM32QzbVwyX0Ekis1bXZIRFGWAazzNKssDWRVrnOWFlkIWWgYcXyGkgAPqur3qS0/eJid1HM4kKE2vGFf7SXQEU7b8F0jU3USyCxWNdmhUQYYSnMMktjWWApQYtcZ3ovspDqBrv33nuRm5trmOVVSYoBMViw6slFi3pQG9/xhTUoFe3+LMZaCCQz1LVZIRFGmB6pgyd1ENrgTywpXUEoJgq0yIou1w1mlOVVC0uqmFXvwoULzFlnzWBR9r4F0x8oFe1SLcauBNL/maGuzQqJMML0UAdhHGJiiV9B6AtfsUZiosDzumrFLMl1gxlleVW7LYtZ9fj6Zi3hMUtl8UbtLZicsNmgqmj3NwniV8iq0f+xXtdmhUQY4QZrK6WkwmKZggGpcVFyY7t8iYLw8AoUFjZnKhGvFeJjvImFtLS8WuexlvDYDHgKdQCqiXaxSZDUFbKEcZAIIwRYWJlGmAspcVFKYru8ufpuueWIsJegkYl4rRofw4uF/PyuyMtLw8GDPZCXl+ZWz2VlZZYZ1PWccHoKdbXCJVhZHKImZjUEKIVEGCHAyso0wlikdII8YnFRgQR8u1oQwsMrvG7mbETguFz3t9kGlby8NJ/1vG3bNktMwrROzSFVUGVmZiImJkZxGzBLWh6pBKMhgEQY4RUzbf+jFWYbPJXgeY9lZWXYtm2b6Pceeugh4d/+4qLkztR9LbIoLGwue8av5fOT+j3WBxXP+vb1vPLzu6JPn70ArDEJ0zo1h15xqmZJyyOVYDQEkAgjamHG5epSkTowsz54qoGUe/TVCUZFRfkdZC5cuICcnBzZM3XPwUvpdVh5fqwPKp77NfpazXfwYBq6ds03/fvviZZ9ndx2pXTSYJa0PHIJFkMAiTCiFlaMMwCkD8yZmZmoqqpy+4y1wVMNxASCWCcoZZBRMlP3dl2512FR/LA6qLju12i3X0ZaWh4OHuzhcZb5339vsNLXyZ00WD0tT6Di2ExeDBJhRC2sFmfAI3Vg9nTHsTp4qonnPaan78XevemqWAjUmqkrvQ4Lz89M1uWuXfNx8GAaXLO7W+H99wYrfZ3UvqmoqEg4l58shoeHw26317omS0JDLoGIY1as4FIhEUbUwmpxBt6QOjDLSRoKmLPj83aPrgKMR46FQK2ZeqDXYUX8sGJx8YXnYouBA639/vOw2Nf565t85d1jRVCoRSDimEUruD9IhBFesWqcAeB/YAbg9sLKTRoKmK9D9HWPNZsNK7MQqBWYHOh1jBI/nvv6sWJx8UVsbCyysrKwdetWANZ+/z1h6V7FJg2sCwq1UEscs2AFF4NEGCFg9TgDHn8rwPjl+fwL27LlL7IHT7N1iL4EgqtLUkknqJYQDeQ6Rogfb+4QFi0unni6tKyQhFYqrNyrv0lDQUEr5gWFmgQqjlmxgotBIowQCJbtf7yvAHN6zY80depyn4OnFptWG4EvgdCp07e4+eajTFgIlKK3+CktLUVRUZHbZ3w7adnyF0ydupzZ+rRqElpvsHqv/naK8G8hc/i9biD9tp5B7moaAlgPAeAhEUa4YXaBJQVvA7O3FWH8C+ttRmYGM7ccfM06rWAN1cvd5C0BqDfrqrd2onZ9Khk4g2USBrB7r74mDZWVkX4FBe9G9oeSMAm9g9zVfC6shwDwkAgLMsy0dFdLvO3n5moJA9xfWFcxYhYztxhSZ51ZWVlurioztBEjXOueCUB37OgP15g6z3ai5ubKrgQycLL+XNWE1Xv1NmlwOBoELCiUhEkYEeSu5lZRrIcAACTCggq5W3VYTbCJDcz+XtjBgwcDqFmdZBYztxisWgPUwMh740W6qwDjcW0nWm2ubLbVYYR43yRHUGgVJmFG6z9Liy58QSIsiJCzVYfZcq1IwdvA7HA4JK0IS05OFr4nx8zNupA1y7NTglH35k2k8+jtDjHjwBmM+Jo08DtGANIEhVbP20zWf7MtMCMRFoRIeaGsOpv2HJiTkpIkW0yKi4sBSJ+VWlHIEuL42vonUHeIXEFvpoGT8D5pkCooAG2ft5ms/2az8JMIC0LkvlBWn01LfRldO0R/s1L+PKsKWcI/niIdcKJ797yA9l6UI+h5zDRwBiNSRbWYoCgrK8O2bds0fd5GpXpRKqRYEVhSIBEWhMh5oWg2/QeBzLCsLmQJd6S4juS4Q5QIerOsDgtGtLCSa/m8jUj1EixeBBJhQYjYC1VWViZsYE2zaXeUvPAkZP+A9Rg5NfHlOho8eDCSk5MV36dUQW+W1WHBiBZWcq2ft55B7lLv2wpeBBJhQYq/F8p1A+vw8Os0mw4QErI1WH12K9WyFYgAkyvozbA6LNiRIqr9TV7KysqEf0sJk5ADK0HuVkmM7Q0SYUGM2FYdrp0DwAGw6TqbtorVJBjdQt6enedm51aLkdMjIFiKoGdl4CTEkSKqpU5ePPP5uaK03bEQ5G71UA4SYUGEnE7Xs3MAbACcGDv2dTRpUqxJ+VyxktVEzE3AixOziEoxpDw7q3asWj8/KYKehYGTkIYUUS3VdWm32zXJO2ckwRDKQSIsiJDSOfN5s7znOgpBZaW7kNNqNm21lYX+3AR8HiDAHKJSDLFnFwwdq1ZIFfSukOBiF7lWcr0nL0ZPhoMhlINEWJCh1kqbQIOL5WBWq4mcHD88ZhGVUvH27Bo2vGT5jlVLpAp6V6wg7q2I3Ez4ek9ejA6QD4ZQDskirLy8XPJFo6OjFRWGYAexziEuLk6XTl1qzASLrhdXy6Nr5mvA2oGmPL6e3dixr1u+Y1UbqYKeFYsxq+8ki0hdPMGCVUjvfisYVvhKFmExMTGw2Wx+z+E4DjabDdXV1QEXjDAeFlZWiXU8fKJCMdSwBCgZWII5Z5ivZ1dZGWH5jlVtpGxrw0q7MtqFZQbkLJ5Qsl2aFujZvuQmxjYzkkXYgQMHtCwHwShSXGhaItbx8PnMeLSyBEgdWDIzMxETEwPAuygLpngof88uNfWk4QLfbPgTLCy1K6vFc2qBnMUTcrdL0wK921cwLS6RLMLuuusuLctBMILUmYVeMxA5HY+WMzWpA4unVc5zts+CS0EvxJ4dpU5QD1bbldHWOZbdokp+1yjvhBHtywoCSwqKA/PLysqwbt06HDt2DABw0003YcyYMT7zlBBs4tpJFRWFoLAwDD17PoC4uGsAgPDw8FrPVO+OS0rHo+dMTc7A4jkAGO1S0Bt/z27w4MGIi4tzO98qs1u9YbFdGW2ds4pblIW8byy2L6ugSIR9/fXXyMjIQN26dXHHHXcAAF555RU899xz2LNnDzp16qRqIQltcO2k3IVFQzdhYUQnJbXjCQ8PB6DfTM3XwNK4cQkqKyNFA1aDIdBU6rPTa3VtMKBHu5JrVTLaOmcVtygLrrlg6LeMQpEImzZtGgYOHIh//vOfCAuruURVVRXGjRuHqVOn4t///reqhSS0gX+pxWasRnRSUjseLYNWXQcdPv+Sr4Fl3bpxfi1jwRRoysKgYWaUutC0dFXJsSrxsGQ9MdotGihGvSvB1G8ZhWJLmKsAA4CwsDDMnDkTXbp0Ua1whD4YPWP1hWfH4zk48akfAPVnar4GHW8DC8CJulzMJkwCjaVh5T6UYlQskVwXml6uKiVWJVasJ0a7Rc2M2fotM6JIhEVHR+PUqVNo27at2+enT59GgwYNVCkYoR8szVh9IWVwUtMS4G/QcR1YACcAaQLWLB2VVWJplGLk/csVO0YMknKsSmZIc2MVtJo4sPSO+7tHh8MBjuOE1emesCoWFYmwrKwsjB07Fi+99BK6d+8OAPj8888xY8YMDB8+XNUC8gwcOBCHDx/GuXPn0LBhQ6Snp2PJkiVITk4Wzvn444+xYMEC/PDDD6hTpw569uyJl19+Gc2bNxfO+eSTTzB9+nT88MMPSElJwdy5czFq1Ci331qzZg1efPFFlJSUoGPHjli1apUQ+wYA165dw+OPP463334b169fR0ZGBl599VUkJCQI55w6dQqTJk3CgQMHUL9+fYwcORKLFy92sx6yAiszVn9I3z9NfUuAt0Fn6tTluHixEcLDKwRXJA9rAlYuVomlUQor9y9V7Og5sEixKrEQSO6KGSaZgRIMEyep9+gPFu9fkSJ46aWXYLPZMGLECCFPU3h4OCZNmoQXXnhB1QLy9OrVC08++SSSkpLw66+/4oknnsDQoUNx8OBBAEBhYSHuv/9+TJ8+HZs3b4bD4cC0adMwePBgfPPNN8I5/fr1w8SJE7F582bs27cP48aNQ1JSEjIyMgAAW7duxfTp07F27Vp07doVy5cvR0ZGBo4fP47GjRsDqImJ27VrF9555x3Y7XZkZ2dj8ODB+PzzzwEA1dXV6NevHxITE3Hw4EEUFxdjxIgRCA8Px/PPP69J/QQKCzNWqfgbnNRecedr0Jk6dTlSU08CAPMCNhDMHksTKEbdP6suNClWJdZcWFL322TVUiIFo7cX0gM1ys7i/SsSYREREVixYgUWL16MgoICAEDLli0RFRWlauFcmTZtmvDvZs2aYfbs2Rg0aBAqKysRHh6OQ4cOobq6Gs8++yxCQmo6iSeeeAL333+/cM7atWuRmpqKl19+GQDQrl07fPbZZ1i2bJkgwl555RWMHz8eo0ePBgCsXbsWu3btwvr16zF79mw4HA6sW7cOW7ZswT333AMA2LBhA9q1a4cvvvgC3bp1w549e/Djjz9i7969SEhIwK233opnnnkGs2bNwsKFC5kNYjQ6MasUxAanuLg4JCUlqfZ7UgYdqwas6i0EWMvpZKQQYtWFJtWqxJqYkbrfpi9LCWttU4xg2RbN1z2a6f4D8o1FRUWhQ4cOapVFMhcvXsTmzZvRvXt3IUVB586dERISgg0bNmDUqFH47bff8OabbyI9PV04Jy8vD+np6W7XysjIwNSpUwHUqORDhw5hzpw5wvGQkBCkp6cjLy8PAHDo0CFUVla6Xadt27Zo2rQp8vLy0K1bN+Tl5aFDhw5u7smMjAxMmjQJP/zwA2677TZN6iUY0HtwkrKRuafljYe1jlkuetY1i+4UI4UQqy40M4Qu8Phzi8pxMbPYNv0RDNZrf/dotvtXJMKuXbuGVatW4cCBAzh37hycTqfbcd79pzazZs3C6tWrcfXqVXTr1g07d+4UjqWmpmLPnj3IzMzEI488gurqaqSlpeHDDz8UzikpKXETRgCQkJCA8vJy/P7777h06RKqq6u9nvPTTz8J14iIiKgV/JeQkICSkhK/v8Mf88X169dx/fp14W85m6YrgbXs+P5mm7zLQO/BSWzQ8RfjV1FRgdLSUiY6ZiXoWddGxWF5a3NGtTVXWBY7ZgldcHWLBrLHJisxglJQar01k6XP3z0CYNKN7w9FImzs2LHYs2cPhg4dijvuuEN0Y29fzJ49G0uWLPF7zrFjx4RVmDNmzMDYsWNx8uRJLFq0CCNGjMDOnTths9lQUlKC8ePHY+TIkRg+fDguX76M+fPnY+jQocjNzVVcRj1ZvHgxFi1apNvvBRq74S3bfmpqFZKTnaLf9XYt19mmv8B7vQcnf4OOXpuHKyHQjtUoIaDXTFbMwmG0EDJS7Hi2HYfD4Xbc6GB7qai9dyvrVhYl1luzWfr83SNgY9KN7w9FImznzp348MMP0aNHj4B+/PHHH6+1MtGTFi1aCP+Oi4tDXFwcbrzxRrRr1w4pKSn44osvkJaWhjVr1sBut2Pp0qXC+f/617+QkpKC/Px8dOvWDYmJiTh79qzb9c+ePYvo6GjUrVsXoaGhCA0N9XpOYmIiACAxMREVFRUoKytzs4Z5nvPll1/WugZ/zBdz5szB9OnThb/Ly8uRkpLit34CRelL5TvbvnvHJPXFde3wxTo6PQYnqSu8XGFphqxWx6q3ENAzDkuKhUPv+2dhZaGSzer5MrEwSPsjEBczq4slXFFivTVbUL/YPbLoxveHIhF2ww03qJIPLD4+HvHx8Yq+y7tAeffd1atXhYB8ntDQULdzPd2TAJCbm4u0tDQANZ1I586dsW/fPgwaNEj47r59+4RM0J07d0Z4eDj27duHIUOGAACOHz+OU6dOCddJS0vDc889h3PnzgkrKnNzcxEdHY327dv7vKfIyEhERkYqqg+90SrbvtSOTuvBSYqV0OFwYOvWrQDYmyEH4kIxUggEGoel1Prn7/npef8srCyU2nZiYmJUXQSjB4G4mFldLOGKGtZb1oPaxe6RVTe+LxSJsJdffhmzZs3C2rVr0axZM7XLVIv8/Hx89dVXuPPOO9GwYUMUFBRg3rx5aNmypSB8+vXrh2XLluHpp58W3JFPPvkkmjVrJgTCT5w4EatXr8bMmTMxZswY7N+/H9u2bcOuXbuE35o+fTpGjhyJLl264I477sDy5ctx5coVYbWk3W7H2LFjMX36dDRq1AjR0dGYMmUK0tLS0K1bNwBAnz590L59ezz88MNYunQpSkpKMHfuXEyePNk0IksqandMYtfTMxBe6rVYnyHLFYhGCoFABkmpFpysrCy3yZ/c55eZmYnGjRtrJoRYsiaxNrkIlEBECquLJQD1thdi+XlLvUezrVZXJMK6dOmCa9euoUWLFoiKihJWH/JcvKhuo4yKikJOTg4WLFiAK1euICkpCX379sXcuXMFUXPPPfdgy5YtWLp0KZYuXYqoqCikpaVh9+7dqFu3LoCa4P1du3Zh2rRpWLFiBZo0aYLXX39dSE8B1HTQ58+fx/z581FSUoJbb70Vu3fvdgu0X7ZsGUJCQjBkyBC3ZK08oaGh2LlzJyZNmoS0tDTUq1cPI0eOxNNPP61qvbCA2h2T2PXUTkGhBizPkJUKRKOEQCCDpFQLDm+9zMrKAuD7+Z0+3QQXL/7u1QLEklDSCtYnF0pR6mI2OkbQH2pMnFh/3rGxscjMzBTicPkyXbwY6/Y3AIwefa+q+SK1RJEIGz58OH799Vc8//zzSEhI0DzovUOHDti/f7/oecOGDcOwYcP8nnP33Xfj22/9K/vs7Gy3jWg9qVOnDtasWYM1a9b4PKdZs2a1XJ9WRO2OieWOzhcsz5BZFoi+UCMOy3NGn5aWh65d892uVVlZCcD383v33aEA2LMI6IUZ244v1HKxs7wyNFCBYYbn7RqD6M9qx+Jk3ReKRNjBgweRl5eHjh07ql0ewoSo3TGx3NF5Qy/hqCTeSY5ANHKZuppxaN5m9AcP9sDBg2kYOLC2mPL2/DgO4PcEZc0ioBdibYdP5eEJixaHQCxFLCyW0AMtJpNa9SmsW+3koEiEtW3bFr///rvaZSFMjNrZ9s2Qvd8VrYWj0tWOUgWi0cvU1YxD8zajr8F3R+36/K5cqYd33/2r23HWLAKBIiUnn1jbcc007wkr6QxcUVoeFhZL6IHak0kt+xQzWO2kokiEvfDCC3j88cfx3HPPoUOHDrViwqKjo1UpHBE8qJk4Vi+Ljp4z5EBWO0oRiCwkpFRrEPM2o+dx7ag9E+3yz8/haMCse1kN5GyErHRywUo6A7Uwu8Dyh1pB/Z5omfqC5RAQuSgSYX379gUA9O7d2+1zjuNgs9lQXV0deMkIplE7275as02pSV/VmKkbNUOWsoIpEIHI8gopKXjO6F1x7ahjYmLcnh+fVd2McYlykCq4efxZpVlPZ0CIo1c/pmZbsdI7qkiEHThwQO1yECZDixdXDbEiNemrWjN1KWVW0zInNRZC6fOxSqwFP6PPz++KgwfT4Bpg71lP/r5vlrhEpfh7R7ylgwlk+x+CXbS29KnVVrSy2hmJIhF21113STrvb3/7G55++mmfeZ0Ic8OyiZ4VMaF2XIScWAglz8fMsRberH99+uxF1675ksRUsARg84i9I/5WmLHyfhHso2ZbsWJ8niIRJpV//etfeOKJJ0iEEbrDiphQO9ZK61gIM8da8B30uXPn3Pb0lCqmtOjgWd4YOZB3hJX3yxMj6pvlZ8wCarcVq9WlpiKMq1nnTRC6w6KYUMMkr3UshNljLWJjYwMSU2p28EavOBUjkHeExffLiPpm/RmzIBD1bCss3K9cNBVhBGEUrIkJNU3yWscrWSEeioWOloUVp/4I5B1h7f0CjNmImuXNr1kRiHrmUWThfuVCIoywLCyJiUBN8lrGK5WWlsLhcGh2fYLdIHa574iZAqONWLnJ0mpRowWi3m3F6PtVCokwwtKwkvQ1UJO8VgGpUmePWm9abWVYC2IPRNCbJTDaCNHLqtDm0VsgGt1WWBLE/iARRlgKtfOXqYUaJnktOiupLrNg2bRaLlIyz7MWxB7o4MhqbBOPEaKXNaHtiVECUe8UPjysC2JXNBVhDz30EGXPJ3TF6NmXP1hyj3rDTB0XYHwQrlQrIotB7GYS1FLrOSsrC4Axopc1oe2KmEB03QNU775Rizgu1gWxJ4pFWFlZGb788kucO3cOTqfT7diIESMAAK+99lpgpSMIBbA0wJgl95TZOi4WgnClWhHFrKAOh8NnPi5Cej1XVlYCCEz0KhX2LAptHjGB6LkHqJ6B61Ljs4qKigBI69tZFsTeUCTCPvjgAzz44IP47bffEB0dDZvNJhyz2WyCCCOIYIdly5wrZuu4WFt5KGZF7NTpWzRuXIJTp5qiadNTaNKkWDjGiwe1MdpSqAVSrLVKXf+BCHsWV4vy+BKI4eEVKCxszsxqXcD3e8wLRSkCkWVB7A1FIuzxxx/HmDFj8PzzzyMqKkrtMhGEpTDDQGe2jqusrEz4t7+BuaysTHMrkxQror8yem4krgYsWArVRqyeXetRyWo8JavrzLBa1JtAvOWWI1i3bhxToQdSBLaUZ8SyIPaGorf/119/xaOPPkoCjCBMDi9mxDouPcSMHKqqqgCID8z8eVoiZkUUK2NMTIzqZTLrcn1/iNWz54bs3pC7T6vY6jqWLd2+BGJ4eIUgwIDa7dGId93XO9K4cYmb1dgfUgWx6wSO/56RExFFIiwjIwNff/01WrRooXZ5CIJQgFLXk6tI4V1mp083RUqKu8tMiZjRwx3GghtVzIrIQhnNslzfH1KstWoNpnIWqbBqSfQUiPzm64WFzf22x23btuluIfX1jrz++jgMHCjNSudNEDscDmzdutXtPNctzXiMtAhLFmE7duwQ/t2vXz/MmDEDP/74Izp06IDw8HC3cwcOHKheCQmCMViLtQnE9eT67vobeDzfcS3LJIfw8OsAOAA2l085hIfrZ+ERsyIa7eo126pXX+jlZjLbIhV/KF1EoLeF1FuZapBX90r7EiMtwpJF2KBBg2p99vTTT9f6zGazobq6OqBCEQSrsBhrE0iQut1uF77jb+Dhz1NapkDP80VlZSTcBRgA2FBZqW8Mjj/3h5ExKlYSFIA+aV5YsFxqiWd7BACOAwoKWhkmzr2ViUfNumfRIixZhHmmoSAILThzBvj5Z6B1a6BJE6NLUxvWVuV5otTqofXAo1XnZ6SVSWr6EcC4HHFWEBR6p3kx2nIpB6VW+ZYtfwHHuX5ijDj3jONq3LgEr78+DoD3uudzmlkpgauimLA33ngDWVlZiIyMdPu8oqICb7/9NqWoIGTBdyRbttTFzJl2OJ02hIRwWLrUgQce+N3wwElfsPZSB2L10HLg0aKeePeomJVJrhtVDmJB2Z7xKEbkiDODoJAiJPQMfjfL6jq5VnnXdnbxYixchQ5gjDjn36GioiLk5OSgSZNiDBzou+5dc5pZJYGrIhE2evRo9O3bF40bN3b7/PLlyxg9ejSJMEIyfEficDTA8uVTwXE1riWn04YZM6Lx66/rYbdfZm4pPYsvdSBWD60GHq3qydU96t8VKM+NKhd/bTIpKcnwlXOsCwo5QkLrFXtmSDfhilSrfFFRkXBunz59sGfPHqbEuWf796x7AAHnM2PZIqxIhHEc55aglefMmTOad3qEteBfJLGXRCv3nlJzPosvdaAdqxYuM63qySw7ERg1cWBBUEh5t1hy77OcbkIMf9Zmz4z4AHvi3LXu+VWcdvtl1azoLIlOT2SJsNtuuw02mw02mw29e/d2S45XXV2NwsJC9O3bV/VCEtbHiJfEcxbuawDwZoVj8aVW0rEGKmZ8DbR87IZW9WTmAVMPjK4fufs9Amy49+XUByurpJVam1nby9azrtS0orMmOl2RJcL4FZKHDx9GRkYG6tevLxyLiIhA8+bNMWTIEFULSAQHRrwkrh2ovwHA+6pCNl9quR1rIIO1lIFWy3pyLZO3AbGiogLFxcV+78HKGHm/Ui1X/JZNLLr3/cHSKmkpyYJ9LYph1YIMqGNFZ8EiLIYsEbZgwQIAQPPmzZGVlYU6depoUigiODFqZiY2APBWHQBu//ZXXtfzAG1FQKDWLLVy6/jq7LXu/FgaEHlYsZKwgtjqWBbd+/5gyY3qz9rsb3I5ePBgxMXF1bqeEXkOXetJTSu60RZhKSiKCRs5ciSAmgZ27ty5WukrmjZtGnjJiKDE3zJ/V1xf3KKiEBQWhiE1tQrJyTVtUc6LJTYAbNiQK2sm6XA0wMsvfyPJrakGLHQ0Rnb2LA2IAJui0EikuBnlDrgsiVyj3ai+rM0A/E4u4+LiDN+KzN+7opYVnfV3TJEI+/nnnzFmzBgcPHjQ7XM+YJ+StRJa4vri+usApQ5ygc4k+UBSsfJoKQKM7GjELIl6dvZGD4gAe6LQSKS6GeUMuCyJXFbcqN6szWLbE7GA2DvAWtyaFigSYaNGjUJYWBh27tyJpKQkryslCUIr+BdXrAOUOsipNZPUs0NmyRLAiiuJlQHRFRZEoZHIaRtSB1yWRK6RbV8sDIHFxUNieHuWLMetqYEiEXb48GEcOnQIbdu2Vbs8RJAh9UXydp6aHaAaM0m9OmSploDMzEzExMR4PaamSGOls2dFDPIoEYUsiWs1EGsbrivsAfkDrtEiV+22L+f5+wpDcE3xwNriIV/xX4C8kAazvQf+UCTC2rdvXyvwmCCUEEg8k9odYKAzSbHzL1y4oErnIdUSsG3bNr/XUctdw0pnz4oY5JErCllys6mFWNuIiYlR/P6zYPlUs+0ref7e6oXVFYH+7k/tkAYzTWYUibAlS5Zg5syZeP7559GhQ4daW4NER0erUjgiOFD6MgTaAYp1QnKv73k+4ERaWp5wnI8bU3MQlWIJ0MNdw0LsBitikEeuKGTJzRYocoSA0neBFcunWm1frU3vWVio4w1/7VvNZ2m2yYwiEZaeng4AuOeee9ziwSgwn9CbQDpAMXO+kuvz5+fnd0VeXhoOHuyBvLw0TQL0pVgCtHTXsJi1ngUxyBOIKPT33NSyqGqJHkLADJu3B9L2A9n0nuW2AdRu3+npe1V7lmqJWb1QJMIOHDigdjkIQjFS01p4Q0pnpeT6eXlpmrtJpCRp1NJdw8qMm0UxyKNEFIo9Ny0sqlqgddmMtHxq3faNjnXTEm/te+/edKSn78XevemqP8tAxKweKBJhd911F/7zn//g73//OwoKCvDuu+/ihhtuwJtvvonU1FS1y0gEKWfOAD//DLRuDTRpot/vyl0s4Hm+Xm4SMUuAHuVgQQSwIgZdf8sVuaLQ13M7fboJ7PZjwmeszOSNxEjLp1btiYVYNy3x1b6Tk4swdepyVePXzCBmFYmw7du34+GHH8aDDz6Ib7/9FtevXwcAOBwOPP/88/jwww9VLSQRPPABlVu21MXMmXY4nTaEhHBYutSBBx74HREREQGtqJSC3EGdP7+oqAg5OTm6uUnELAGsBaprCQtikCdQUejtuQHAu+8ORUUFe4OI3mhl+WQlmJuVWDet8Ncv2e2XMXRoNzdjjtJ6N4uYVSTCnn32WaxduxYjRozA22+/LXzeo0cPPPvss6oVjggu+IBKh6MBli+fCo6riTd0Om2YMSMav/66Hnb7ZWRnZ6tu+Qi0A46NjRW+r6ebxJ8lQKwc3lY4sx5rZBYCqcPaCzx4vG+nFWzPTAvLJ0vB3FafPIn1S7m5uarUs5iY9ZXhQe/3SZEIO378OHr27Fnrc7vdjrKyskDLRAQpfKcq9vJUVFSomoHdswP2FUMg1jG4tn1/4qisrCyg8ku1BIiVg48t8oT1WCMlBCqy9baSdOr0LSIiruPdd//q9rnre+D6/Kz4zPyh9r2ytDKVpVW+WrV7vl86fboJABtSUk67HVejnsXErK/+D9D3fVIkwhITE/HLL7+gefPmbp9/9tlnaNGihRrlIoIYvWeCri98INsOVVVVefm09m4S3s+TjpglwOFwYOvWrcLfvkSamVMgyCFQK4deVhJPcZ2SctqLW9KJK1fqweFoYOlnZiR6xxHxQsfhcAifsZDfS+1271nugoJWmtazFDHLQh+oSISNHz8ejz32GNavXw+bzYaioiLk5eXhiSeewLx589QuIxFkGDUTDDSGwDVfnr+O3DOvnhL8dXpJSUmiqTfMELCqFoFaOfSyknjGFnrLOwfY8O67f7X8MzMKveOIxDaw5n+T3/1CT1eZ2u0+NjYWmZmZ2LZtm6b1LDVHHSt9oCIRNnv2bDidTvTu3RtXr15Fz549ERkZiSeeeAJTpkxRu4xEEGLEqqdAA2LtdjsA8Y6cP09LF5e/75klYFULAu14te64XWMLAXe3zfbtQ4U4yWB6Znqid1C8VKETExOjagiGXNRq9/w2alrWsz9PAT8RZakPVCTCbDYbnnrqKcyYMQO//PILfvvtN7Rv3x7169dXu3xEEBNI/i8lqOUGldLBqBWHpgSrr77yRaAdr14dt7eYv4sXfw/ombGy8o91jAyKZ8Uy44kW7V7rehZryyz1gYpEGE9ERATat2+vVlkIwlDUcoNK6WDUikNTgtVXX/ki0I5Xr47bdSbPz9wDeWYsrfxjHbOGQmiJFu3e6MUHLPWBAYkwgrAaarhB5XQwRnS+RneARhFox6tnx+0phgJ5Ziyt/DMDZgyF0BKxdu8r1QPg38JqZKJdlvpAEmEEM2idhFUqarhBpXYwRnW+LO2xqBeBdrxGd9xqPDNWXV5GY/TWVyxZZjwRa/f+Uj0A7hZWo+vZFVb6QBJhBDMYtf2MWuJPTgfD36OenS9LHaBRBNrx6t1xq/nMWHZ5GY3RW18ZLfDFkNLupVhYja5nFvtAEmEEUxgRk6JWxyDlOlevXhVifQB9O1+jO0CjkNrxuuZp8ve5nh23ms+MJZcXiwsFjG73rFhmeOQkhZZjYTWynlnsA0mEEUGBlE5fjSXgYlnX//GPf9T6XM/O1+iBxgjEOt6ysjJs27bNLcGtN/hcTd7QsuNW67qsuLxoocAfsGiZ4RF7b/RO96CWcGetTZEIIyyD60taVBSCwsIwpKZWoV69S24DrJ7pIFwRC5A2euZrZeQ8V1ZzNQUKKy4vWijwByxaZlxhxcJqZeFOIoywBK4vaW3TeD46dYKPY9qmg/CFVPO9leOzWMTqgetaWF0DsVBYvb6lYDbR4IkeFlYrC3cSYYQl4F8+f6ZxAEwEJouZ7wcPHoy4uDhLxmexjFUD17V0eQViobBqfQcbeltYrSbcSYQRlsKfaRywMRGYLGa+j4uLM7Xby6ywFLiuJlq6vAKxUFi1voMRtSysvqyq/EImKwp3EmGEpRAzjbMQmMxKgDThjpWfCy+wfA1yFRUVKC4uDsj6KtdCYeX6DgbUtrBKsapaUbiTCCMshZhpnIXAZFYCpAl3rP5ctAxuVmKhsHp9Wx21LaxSrKpWFO4kwghmOHMG+PlnoHVroEkT5dfxZxpnJRcPK+Ug3LHyc9EyuFmphcLK9R0MaBWz6suqakXhTiKMMBTePbJlS13MnGmH02lDSAiHpUsdeOCB3xW7R/ylfDAqHQTLOYGCmWB8LmoHN8uxUMitbxYTuxLaIWZVtZpwJxFGGAbvHnE4GmD58qngOBsAwOm0YcaMaPz663rY7Zd1y/2i9SDLek6gQNBzoFT7t6z8XLyhRXCzHAuFnPq2cn4owjtSrKpWmiiRCCNUcwPKhe+ExV46Ke4RqS+fr6zneg2yVhwo9BwotfotKz4XX2gV3CzHQqFmnJC38wh2kDtpErOq8il8xK5jFkiEBTnr1gETJgBOJxASAvzjH8DYsTXH9BJnagRbBps1gyWkDoBqDJR6/pZVUTO4WU9XrtXyQwUDciZNPGJWVaul8CERFsScOfOHAANq/v/II0BGBvDxx77FmdqoFWxJAss7esfU+LJWaIGev2UV1Axu1mvyw0p+KIpPk4dSS6ZUq6oVngeJsCCltLQUX3wBOJ3uDbS6Gti16yL+9reGcDr5GK0aQXbLLcDtt2tTHqsFW7KC3jE1eloryDKiHDXfNz0GORbyQ1F8WmCIva9KFmy4Pg+j9gQOFBJhQYhrQLzNNrWWW2Lv3n1wOv/q9h2nE+jalcOyZVfw2GP1NSkXbWKtPnq679S0VgRj5mytMfMqUBbyQ1F8mnKkvK9yraqu57GyJ7ASSIQFIXyj9OWWSEk5XavDAwCOs2HatCjccsvPuOWWRkzPLgjvaOm+U8taEayZs7XGzHGTrOWHMqMV1kjXndT3Vcnvm31CRiIsyPHllhgwYCd27OgPoPaLs2nT50hNPcm8mddoWItX0HrgUMtaIWXmyoJlxIyY+X3VM2TB27trZius0a5ULd9Xs0/ITCPCBg4ciMOHD+PcuXNo2LAh0tPTsWTJEiQnJwvnbNu2Dc8//zz++9//Ij4+HtnZ2ZgxY4bbdT755BNMnz4dP/zwA1JSUjB37lyMGjXK7Zw1a9bgxRdfRElJCTp27IhVq1bhjjvuEI5fu3YNjz/+ON5++21cv34dGRkZePXVV5GQkCCcc+rUKUyaNAkHDhxA/fr1MXLkSCxevBhhYexVuTe3RKdO36Jx4xKsWzfO54vjb7B07cSKikJQWBiG1NQqJCfXrAKIiIiQ7PZg0T0ihtGdnid6DBxaWSu8We9Ys4wQ2mCEC1Xs3TXjoH/u3Dm3v/V2pWr5vpp9QsaeIvBBr1698OSTTyIpKQm//vornnjiCQwdOhQHDx4EAHz00Ud48MEHsWrVKvTp0wfHjh3D+PHjUbduXWH5a2FhIfr164eJEydi8+bN2LdvH8aNG4ekpCRkZGQAALZu3Yrp06dj7dq16Nq1K5YvX46MjAwcP34cjRs3BgBMmzYNu3btwjvvvAO73Y7s7GwMHjwYn3/+OQCguroa/fr1Q2JiIg4ePIji4mKMGDEC4eHheP755w2oPWU0aVKs6MVx7cT8WV+ys7NN6x4Rg7VUCloOHK4DoD9rhZKB0l/7Ufu3CPYwwoUqFvtltkG/tLQU27ZtE/7+/PM07N2bHpBFXImVXytLptknZKYRYdOmTRP+3axZM8yePRuDBg1CZWUlwsPD8eabb2LQoEGYOHEiAKBFixaYM2cOlixZgsmTJ8Nms2Ht2rVITU3Fyy+/DABo164dPvvsMyxbtkwQYa+88grGjx+P0aNHAwDWrl2LXbt2Yf369Zg9ezYcDgfWrVuHLVu24J577gEAbNiwAe3atcMXX3yBbt26Yc+ePfjxxx+xd+9eJCQk4NZbb8UzzzyDWbNmYeHChaYaIJS8OPzLKWZ9qaiosFS+F38YnUpBy4FDq4FSrP1YLWkj4R01n6UU8eCKr0mAmQZ91/v9/PM05ObeC6Bm5bsSi7gcK79elkwzr643jQhz5eLFi9i8eTO6d++O8PBwAMD169cRFRXldl7dunVx5swZnDx5Es2bN0deXh7S09PdzsnIyMDUqVMB1DTWQ4cOYc6cOcLxkJAQpKenIy8vDwBw6NAhVFZWul2nbdu2aNq0KfLy8tCtWzfk5eWhQ4cObu7JjIwMTJo0CT/88ANuu+02r/d1/fp1XL9+Xfi7vLxcQe2oj9JVi2Y022sBC0G8Ws8WtRA9Yu1Hr6SNasb2sRYnGExIFQ+ZmZkA/E8CzDjoOxwNkJubDl6A8cjtk+WsEk1KStLNkmnW1fWmEmGzZs3C6tWrcfXqVXTr1g07d+4UjmVkZGDatGkYNWoUevXqhV9++UWweBUXF6N58+YoKSlxE0YAkJCQgPLycvz++++4dOkSqqurvZ7z008/AQBKSkoQERFRa+ubhIQElJSUCOd4uwZ/zBeLFy/GokWLZNSI/ri+cGKYzWyvBUYH8WrpKtQaFtqPmrF9rMUJBhtSxUNVVRUA8UmAr0G/rKyMSTF98WIsPBdaAYG9U1ImmFrVg1Viig0VYbNnz8aSJUv8nnPs2DG0bdsWADBjxgyMHTsWJ0+exKJFizBixAjs3LkTNpsN48ePR0FBAfr374/KykpER0fjsccew8KFCxESUrvhscicOXMwffp04e/y8nKkpKSo/jtKG6XnC2e3F2HChGKfHY7ZffVqYLQ1kNISBIaasX2UZ4odpIgHpZMAPv6KNTHt7X4ADunpexW9U0ZPMM3ct7liqAh7/PHHa61M9KRFixbCv+Pi4hAXF4cbb7wR7dq1Q0pKCr744gukpaXBZrNhyZIleP7551FSUoL4+Hjs27fP7RqJiYk4e/as2/XPnj2L6Oho1K1bF6GhoQgNDfV6TmJionCNiooKlJWVuVnDPM/58ssva12DP+aLyMhIREZG+q0PNfDVeMvKytwCOF3x9sItWpQMh2M57PbLPjscM5rt1YQFaw7rnZAnLFvv1IrtM8pFTe5Q6eJB6iTALGLa834AJ+69dy969MhTdD2jJ5iA+fo2bxgqwuLj4xEfH6/ou87/bXjoGkMFAKGhobjhhhsAAG+99RbS0tKE30hLS8OHH37odn5ubi7S0tIA1HRAnTt3xr59+zBo0CDhd/bt2yessOzcuTPCw8Oxb98+DBkyBABw/PhxnDp1SrhOWloannvuOZw7d05YUZmbm4vo6Gi0b99e0f2qjbfGW12dhI4dpyIm5jw+/XSz2zGxF85fh2NWX70asGDNMRusznDVEk5GWRCMcIeyKPrkiAexSaRabcK1nsrKygSXKE94eDjsdjuAwOpM7H7kTGxYmGBaAVPEhOXn5+Orr77CnXfeiYYNG6KgoADz5s1Dy5YtBeFz4cIFvPvuu7j77rtx7do1bNiwAe+88w4+/fRT4ToTJ07E6tWrMXPmTIwZMwb79+/Htm3bsGvXLuGc6dOnY+TIkejSpQvuuOMOLF++HFeuXBFWS9rtdowdOxbTp09Ho0aNEB0djSlTpiAtLQ3dunUDAPTp0wft27fHww8/jKVLl6KkpARz587F5MmTdbF0yYF/+bdsqYuZM+1wOu0ICYnGokXjcf/9pfjtt9+wZ88eeuFkwrI1xwywNsMVE058Ik9AfJA0yoKgtzuU1Rg4sb6MX+zF42sSqZaYlroHoiuB1Jmv+8nKypJ1TZpgqoMpRFhUVBRycnKwYMECXLlyBUlJSejbty/mzp3rJmo2bdqEJ554AhzHIS0tDZ988olbktXU1FTs2rUL06ZNw4oVK9CkSRO8/vrrQnoKoKYhnj9/HvPnz0dJSQluvfVW7N692y3QftmyZQgJCcGQIUPckrXyhIaGYufOnZg0aRLS0tJQr149jBw5Ek8//bTGNSUP1z0kly+fCo7jN+y2Yf78RFy8+Lai5JhGB0yyMPtm1ZpDKENMOOXk5Lgd8zdIqjWhCaSd6+EOZTUGTqwv43M/+irXhQsXkJOTo5qY9rcHYnr6XiQnFwdUZ1L7WSVeqWAPN1EDU4iwDh06YP/+/X7PiYuLE9JI+OPuu+/Gt9/672z4JKK+qFOnDtasWYM1a9b4PKdZs2a1XJ+swb/IUjsTqS+ckQKEpdm33vmNKE2CdsgVTmLu+UAtCIG0cyPcoSykaXFFrC+T0v7V9g54ey58Tq9A6kzt/tgsG8GbpZ8zhQgjtEVOZyI1vsuoxs1apno1oDQJxuNLOAFAYWFz2YH6gVoQArEy6e0ONXoVHT8Yl5WVuX0eqHhQ2x3n7bkEklTVFTXfZVas/P5EluciM1/vBwv9HIkwgnz7jOHZubjGG/lDSZqEQM+zOv5i+woKWv3PjS/NuqOVBUGulUnv+E4jV9FJnXRkZWXBbrfLFg9quuO8p5D4A5YSXRstXOTE0fl7P1jo50iEEQCs69s3ersguUgdNAh98Jz18/FASqw7WlgQlJRD70mXkYt6pFoM7Xa75N0XtBLTtVNIcHDNbk8Lof7AXxydq8gy2gorBRJhhEAgqSSM9v97g7U4FCnoPTMzm0g1Am+iSKl1R20LgtJy6DnpYsXSrqQ/8OXyysrKQmVlJcLCwmrtngIod8e5PpeiouRaG23TO+qOmMhiIZeZGCTCCFEyMzO9djQ8rAQ4umKGGZBSzJ4w1AqIWXe8uZC1eE/kWJmMDKg22tKupD+Q6vJSO66Ify6pqSdx881HLeedUBMxkWWG1EokwghRYmJiNNkoWcvVK2aYAUnBs+M3e8JQqyBm3fFMWcGjxYAt1cqkd0A1S6volPQHUl1erucp6dP83T+rKw9ZQUxksWKF9QeJsCDGyHxeWq/SM8MMSAxvOYN49wQQmHCyikjVG6lJeH2hhbtZTjn0tFizsooOCKw/kDphUWo586wnLTPmWw0pIstoK6wYJMKCGCM7Sa1W6fGDpNjLyfpM0lvH7yrAeJQKJyuIVCPw9c7wAfs8WsfasWRl8gcrYiEQi4jUCYsSyxmPaz1p4XWwMlJEFstb55EIC3JY6STVwnOQnD//PE6cCEPz5lVITr4dwO2mmEn66vgBJwDvwknKgGsVkeqJnokZxa6jR6wdS1Yms6DUIiJ3wkKufv1RKrJY6OdIhBGa422AlJr7Sgnus0qgc2fNfkozfHX8ri7J0FAOS5aU44EHhksecK0iUl1hKQGtngOwmZ6RUahhMZRrRSNXv/ZIFU98/jdf12DhHSIRRmiK1AGSUiXUIGap6tTpW9x881HcdddYdO5sR5MmMQBiZP2GFUSqKywloKUB2Bj8WUKzsrLAcZzPFd6egzF/LdeJohwrmpKVs77KQnjHSpZgEmGEpkgZ+ChVwh9It1R5n90RxkKxdvqj17ZeUl1eUlfOsryVjhmwSh2RCCMMheInamM1S5XeGGlVNcOSeKsRyD6aSq/lijfXmJjljPWtdAj9IBFGGAq5bwg1McqqKjV1BQuBwFZGzefv71qDBw9GXFycX5eXL8sZTTwJV0iEEbriObMUc9/QoEVIxcjBzUoxKmZFzecvdq24uLhaqSSk9lU08SRcIRFG6IavmeWAATuxc+cAOJ02RSv+CAIwfnCjtmosaj5/JdcSE+J8LjmKGyRcIRFG6IK/mWWnTt9i/vyuuHw5Aa1a2RSt+CMIGtyCGzWfv9JrSRHiFDdIuEIijNAFsZllcrITlCi6NnomITUrVk1AS8hDTXGjtVBifSsdX1B/pD4kwghN4Qc+iv2SD0tJSFnGigloCWWoKW60Fkosb6XjDaV7YxL+IRFGaIrrAHnDDeWYNcuO6mqK/ZICS0lIWYfSegQvau6jqeWenFK/w+qENJC9MQnfkAgjNIcfIB9/HMjKAn75BRT7RRCELPy5wjIzM2Gz2QLeokbLVa5WWUFLKTbUhUQYIZtA4gKaNKn5jyAIQip6usK0FEEsCizX/ryoKASFhWFITa1CcrITQO3+3OhVyFaDRBghC4pTIgjzYfaAanKFaYNrf+6vXrOzs4Xv0CpkdSERRsiC4pSMgzY5J5RgtomTN8HIb3pttCvM7GLWE/5exOrV9Z4pxYa6kAgjCBNAm5wTSlFzb0WtEROMRrrCrLw6UG69mjXFBouQCCM0gZ+58phthsgSRs/+CevAupgXE4xGusKs7BJVUq9mS7HBKiTCCE3Iycmp9ZkZZ4hGwi9VF5ulsrqknWALs4l5f9ucGekKM1s9SkGKi9HsKTZYhUQYoRtmnCEaCb+k/cSJKrz5Jgen0yYcCw3lMGXKfWjePIyELSEJM61qE9vmzEhXmJnqUQ5i9WqVFBusQSKMIBgmNjYWsbHAP/4BPPIIUF0NhIYCf/+7DZ07JxhdPMJEmGlVm5jQMdIVZqZ6lItYvZLAUh8SYQQhAaNXRY0dC2Rk8IluA8+1ZvT9EPpjplVtSoWOEleY3HfBTPVIsA+JMEIWnp1cMKRNYGWJv1qJbgO9HxJw5sVoV55UxITO4MGDERcX5/YdJe1OzopHV8xSj/4oLS1FWVkZAPF+nOK8tINEGCEL17iALVvq4umn7XA6bUyutFILq+VGC+R+WBGkhHS03A9RS/wJnbi4OCQlJQX8G4GseDTz6kCpSVqzsrIQHx9P77KGkAgjZBMbG4szZ4CZMwFnzc4WllghRIhjNUEaDJgpoPrq1atuf/sSOp7nBYqUFY9WWh0oNUmr3W5nol1YGRJhhCJ+/vkPAcZjhRVCLMKC+48vg2f+N71hoS7MiFnqJCoqyu1vX26yyspKFBcX+7yO3HYgZcWjmcSsVKy60tNMkAgjFNG6NRAS4i7ExAJnzTBDZA0W3H9Sy6A1LNQFoR/+3GRbt24VzlMjc73UhQBWa1dWXulpFkiEEYpo0sQzbQKHJUvK8cADw1FUFILCwjCkplYhOblGpZlthsgKerj/xIJy/V1bz4UZ5AoNHqQmRFUrc32wrngM1vtmCRJhhGLc0ybY0KRJDNati8GECTUWspCQGqE2dqzRJSV8Ecg2NqxvgUOYFyluMrUz11thxaMSgvW+WSFE/BSC8E2TJsDdd9f8/8wZCAIMqPn/I4/UfG4lHI4GKCxsDoejgdFFCQhfg5iU+wrku2phledA1IZ3k7ni6SbzJ9SUYrdfRmrqyaATIsF63yxAljBCNbwF61dX11jK1MhvZRSusWz+rD9miXkLdE9Kh6MBfvjhJkUBvWoF1pMVztpIcZOpEc9kpRWPWnHmTE3f3rq1uftxViERRqiGt2D90NCaDO9mxnUPx6efbgyOq9nDkeNCsGvXAMyf31W3PRzViMEKZE9KV/EDcAD++K7rAOht0FIrsN6KGygTtRFzk6kRz2TFFY9SkCoq160DFiyo6SNCQjgsXerAAw/8bsk6MQoSYYRq1A7WB/7+d2vMnmJjY3HkiDdLnw2XLydAj/5ITeuPkj0pPcVPjQCrEWI2mxMLFhRhwoThPjtoqYHSRUVFQhm9QcvqrYvUxLI8asQzBaOY8Cc+y8rKsG3bNjgcDbB8eYIw6XQ6bZgxIxq//roedvtlWoWsEiTCCFVRe49DljDS0qeV9UfO8/ImfgAbMjJ2o337HzFhwnBVspjn5OQA8G0Ro2X11sVVHJSVlaGqqgoAUFIShlOnItG06XXUq3cJubm5wnfMnLneSMQElNhkx8hVyFZykZIII1RHrT0OWcMIS1+g8VtSEHte/LV9iZ/27X9UVAa5qTH464u5oYI5fscKxMbGorS0FNu2bQPgywIsfh1qB4HB2mSHjyfdsqUuZs60W8ZFSiKMIGSgt6UvkPgttctQUVGBG24ox6xZdlRX29xyw8ntAD0H1rS0PHTtmu/XouHpQpk//zxOnAhD8+ZVSE6+HcDtpu2ICXekbqvjbSNvwJpxXHrDUg4xPp60xkU61VIuUhJhBCETvS19SuK3tCgDADz+OJCV5Z4bDoiRdS1vA+vBgz1w8GAaBg70H+fm2sEmJQGdO8u9E8JMiFmAw8PDVXGBE95hJYcYL8pZdpEqhfKEEYRJGDsWOHECOHCg5v9GJcF1zQ2nBO+xZQCgf64xgm285QsDOBQVJQOo2b6otLRU/4IFESzlEJOSP85skAgjCBMRqABiAe8Daw2BJtskrIXdfhnp6XtRswqXx4a9e9MFsW5G6wehDN5FyvcfSl2kZ87UTGZZSCRO7kiCIHTBV2C9K2af1RLqk5xcDNd8dAClJAlmAnWRrlsHprbWIxFGEIQu8IH1NXnActCy5S/Iz++KgwfTANDmwYR3WFulZ3XMsIuAkrQkpaWlOHGiChMmNBYWONVsrcfh1lvP6ZZw2xMSYQRB6IZrJ2e3X0afPnvRtWu+11ktpRggALZW6QUDVtxFgF9dWVjYHE7nSLdj1dU2rFr1EVJTTxqyupJEGEEQumLFTl4Jau2jaUU8BTgrq/SCBau1O/49E7OqGhFfSCKMIAjdsVonLxfPfTR9Ja41Y94jNYiNjUVmZqaQsBWgzPjBjFouUhatqiTCCIJwgyw02uNav/72BA3mlX8xMTFGF4HQCLnbDqlpPWfNqkoijCAIAU8LjS+C1UKjNlrtCWoFzBAgTkgn0G2H1OxvWLKqkggjCELAc6bpy00WzBYaNRHLAB7MUOygdbDytkOBQiKMICyC2m5Ef24yQh2snn4h0DYZbAOyVbHytkOBQiKMICyA2oHectxkrgNtUVEICgvDkJpaheTkmqzWZK3wDYuBwmpBiw8IT6w+6VACiTCCsABqB3pLdZO5DrT+fpcGWt+wFiisFrT4gPDEqEkHy/GFJMIIwkKoFegtdcbKD6Biv0sDrX9YChRWG1p8QLii9aTDl2U+MzMTVVVVCA8Ph91ur/U9oyz2JMIIwkKoFegtd8ZKAebyYHlmrjbUNghPtJp0+LfM5zNpmScRRhAWQs2YCzkzVor1kEcwrfwzW9ugGEfzYkbLPIkwgvCC3GSCrBBozIWn5cXXjNXbeVYNMNeKYBnIzdQ2tIxxpCTI+mEm6yuJMIL4H4EmE2SFQGIuArHQWDXAnAgcs7QNrSwpwb5SVG/3u5msryTCCALWSyYoFnPhz9IXyP1ZOcCcCAwztQ21LSnBvlJUb/e7mayvJMIIAuZPJihnprluHTBhAuB0AiEhwD/+AYwdq3EBiaDDzIsPtLKkBPNKUb0nr2axvppOhF2/fh1du3bFd999h2+//Ra33nqrcOzIkSOYPHkyvvrqK8THx2PKlCmYOXOm2/ffeecdzJs3DydOnEDr1q2xZMkS/PnPfxaOcxyHBQsW4J///CfKysrQo0cPvPbaa2jdurVwzsWLFzFlyhR88MEHCAkJwZAhQ7BixQrUr19fVlnUwOl0MisMzERlZSXq1auH5s2vonnzq0In6XQCJSVhTJqxXZEy07x69SpOnKjChAkcnE7e0gc88giHW289h+bNw2R3lGYeaAltMfPiA60sKWaKVbICZrC+mk6EzZw5E8nJyfjuu+/cPi8vL0efPn2Qnp6OtWvX4vvvv8eYMWMQExODCRMmAAAOHjyI4cOHY/Hixejfvz+2bNmCQYMG4ZtvvsHNN98MAFi6dClWrlyJTZs2ITU1FfPmzUNGRgZ+/PFH1KlTBwDw4IMPori4GLm5uaisrMTo0aMxYcIEbNmyRXJZ1KCiogKFhYVwOp2qXTNYqa6uRo8ePQAAvXodw++/1wVgA8dxqK4uxw8/VOLaNWPLKIa/way0tBT/+Mc/UFjYHE7nSLdj1dU2rFr1EVJTT8p2t5p5oCW0x8zPXQtLiplilQh9MJUI++ijj7Bnzx5s374dH330kduxzZs3o6KiAuvXr0dERARuuukmHD58GK+88oogfFasWIG+fftixowZAIBnnnkGubm5WL16NdauXQuO47B8+XLMnTsX999/PwDgjTfeQEJCAt5//30MGzYMx44dw+7du/HVV1+hS5cuAIBVq1bhz3/+M1566SUkJydLKkugcByH4uJihIaGIiUlBSEhIeJfInxSUVGBsrIy4e/qahuqq8MQElKJ8vIKVFW1qSX8zQQvksQGASVWVTMPtAThD7UtKWaKVTIjZrTMm0aEnT17FuPHj8f777+PqKioWsfz8vLQs2dPt8rNyMjAkiVLcOnSJTRs2BB5eXmYPn262/cyMjLw/vvvAwAKCwtRUlKC9PR04bjdbkfXrl2Rl5eHYcOGIS8vDzExMYIAA4D09HSEhIQgPz8ff/nLXySVxRvXr1/H9evXhb/Ly8t91kdVVRWuXr2K5ORkr/VByCMkJARhYX+8DjX/5ACEwWazIz4+nqkXVyp8AH50dI3ookGAIIzFLLFKZsSMlnlTiDCO4zBq1ChMnDgRXbp0wYkTJ2qdU1JSgtTUVLfPEhIShGMNGzZESUmJ8JnrOSUlJcJ5rt/zdU7jxo3djoeFhaFRo0Zu54iVxRuLFy/GokWLvFeCB9XV1QDYUvRWJSQkBCEhIQgPDze6KLJwD8BvjP79b0OnTt/SIEAQPtDLkmKGWCWzwpLAkoKhImz27NlYsmSJ33OOHTuGPXv24PLly5gzZ45OJTOGOXPmuFnqysvLkZKS4vc7NptN62IFPYHWsRGJX8+c+UOAATWpNlxXYdEgoC+Uhd0caGVJMaObjNAHQ0XY448/jlGjRvk9p0WLFti/fz/y8vIQGRnpdqxLly548MEHsWnTJiQmJuLs2bNux/m/ExMThf97O8f1OP9ZUlKS2zn8KszExEScO3fO7RpVVVW4ePGi6O+4/oY3IiMja90joQ9SY+rkdpJGpIMoLS3FF18ATqf7QEGrsIxByyzshPpo8QzM6CYj9MHQaO74+Hi0bdvW738RERFYuXIlvvvuOxw+fBiHDx/Ghx9+CADYunUrnnvuOQBAWloa/v3vf6OyslK4fm5uLtq0aSO4/9LS0rBv3z63MuTm5iItLQ0AkJqaisTERLdzysvLkZ+fL5yTlpaGsrIyHDp0SDhn//79cDqd6Nq1q+SyBCujRo2CzWaDzWZDeHg4EhIScO+992L9+vWyVnlu3LgRMTExqpUrLCwMjRs3RlxcXK3/YmJi0KBBAzz44IOyOsna1ijgkUdqPtcKfsA/eHATbDb3+qRVWMYgloXd4Wjgdh5hTWJjY5GUlOTzPxJgwYkpltQ1bdoUN998s/DfjTfeCABo2bIlmvzPv/PAAw8gIiICY8eOxQ8//ICtW7dixYoVbu69xx57DLt378bLL7+Mn376CQsXLsTXX3+N7OxsADVup6lTp+LZZ5/Fjh078P3332PEiBFITk7GoEGDAADt2rVD3759MX78eHz55Zf4/PPPkZ2djWHDhiE5OVlyWYKZvn37ori4GCdOnMBHH32EXr164bHHHkP//v1RVVVlWLnCwsIQERHh9b/Q0FDZAvrnn/8QYDzV1cAvv6hYaA/4gZwPwOeFGAXgG4+/HFEEQQQnpgjMl4LdbseePXswefJkdO7cGXFxcZg/f75bSoju3btjy5YtmDt3Lp588km0bt0a77//vpAjDKjJQ3blyhVMmDABZWVluPPOO7F7924hRxhQkw4jOzsbvXv3FpK1rly5UlZZjMbIzWQjIyMFt+wNN9yATp06oVu3bujduzc2btyIcePG4ZVXXsGGDRvwf//3f2jUqBEGDBiApUuXon79+vjkk08wevRoAH/Eay1YsAALFy7Em2++iRUrVuD48eOoV68e7rnnHixfvrzWYgo9aN26xgXpKsRCQ4FWrfT5fSUB+BSToh2UI4ogCE9sHMdxRheC8E55eTnsdjscDgeio6Pdjl27dg2FhYVITU11E4hS8NxM1hdaxKiMGjUKZWVlQloQV2699VYkJyfjww8/xPLly9GxY0ekpqbi//7v//C3v/0N99xzD1599VVUVFTgtddew/z583H8+HEAQP369VG/fn2sX78eSUlJaNOmDc6dO4fp06cjJiZGcGErQUldu24GPmuWHdXVNoSGcliyRNvNwIuLi/GPf/xD9LzBgwcjLi7O7TM9Y1KMWKxgFK7PxF9M2IQJE9xiUQmCMC/+xm9XLGMJI6QjNfZE7xiVtm3b4siRIwCAqVOnCp83b94czz77LCZOnIhXX30VERERsNvtsNlstRY6jBkzRvh3ixYtsHLlStx+++347bff3LaV0hJPkfvoow0Ea9Rvv10Gr5GMDMSOi4szbMAP5r0rKT0IQRiDkd4ff5AII5iB4zjBvbh3714sXrwYP/30E8rLy1FVVYVr167h6tWrfpPTHjp0CAsXLsR3332HS5cuCcH+p06dQvv27XW5D88X3Vc6iGALxC4tLf3f3pWNVdu70oxQehCC0BcjvT9imCIwnwgOjh07htTUVJw4cQL9+/fHLbfcgu3bt+PQoUNYs2YNAP/C5cqVK8jIyEB0dDQ2b96Mr776Cu+9957o9wjt4TvBVat2CwKMh9+7cvXq1SgtLTWohISWlJaWori42Od/9NwJLWHV+wOQJYxghP379+P777/HtGnTcOjQITidTrz88stC/q5t27a5nR8RESHsGsDz008/obS0FC+88IKQ5Pbrr7/W5wYIv2i5dyXrBHuiTpatEERw4nA0wMWLsWjUqNRwqzSJMEJ3rl+/jpKSElRXV+Ps2bPYvXs3Fi9ejP79+2PEiBE4evQoKisrsWrVKgwYMACff/451q5d63aN5s2b47fffsO+ffvQsWNHREVFoWnTpoiIiMCqVaswceJEHD16FM8884xBd2kMrA/4LO5dqXU2+2BP1Ol5374GQCsKcII9/C2OMQISYYTu7N69G0lJSQgLC0PDhg3RsWNHrFy5EiNHjkRISAg6duyIV155BUuWLMGcOXPQs2dPLF68GCNGjBCu0b17d0ycOBFZWVkoLS0VUlRs3LgRTz75JFauXIlOnTrhpZdewsCBAw28W30xw4DPUnC6XtnsrSqw5MLaAEgEF74SJvPbuRkBiTBCVzZu3IiNGzeKnjdt2jRMmzbN7bOHH37Y7e/XXnsNr732mttnw4cPx/Dhw90+C7YsLGYY8FkJThfLZs93zmSlCRwWB0AiuPCXMJlEGKEbrLusCEJvWOyc5cLqEnweK9QxYW5YTJhMIiwIMYPLysyQyDUfLHbOcjBD8LtYHV+4cIH6HUJTWIxJJREWpFBHpx0kcs0Hi52zHMwQ/C5Wxzk5OQBolSShPq4TXn8xqUZMjEmEEYQG0CDijhmsgywtGAgEPYPfpbhAXZFSxxR/R6gNyxNjEmEEEWQYsW8jy52gK6wsGFCKnsHvUl2gDz30kNvfZq9jwpwY3bf4gkQYQQQRRu7byGonKAWzxCvpGfwu1WIVFRWF7OxsFBUVCS5HgK2EmSzC+kILQh1IhBFEkHDmzB8CDOD3bQQyMvSziLGGL/enp0AwS7wSqwsMYmNj3QQF5QvzjxkWWhDqQCKMIIKA0tJSfPEF4HS6d9jV1UB+finq1jW3pUopvJvU1UrjTyCwHq9khgUGlC9MHJb3OiTUhUQYQVgcflbtcDSAzTa1lpXk88834ejRy0E7q3a10lhBIChZYKCG60uqe5HyhanHhQsXan1GbkpzQSKMsBSffPIJevXqhUuXLiEmJkbSd5o3b46pU6di6tSpmpbNKPjBVcxKQrNq8woET7eqr+B3b+5XNVxfctyLrLpMzYhrjJ0rwTqhMiMkwghdGTVqFDZt2oRHHnmk1qbckydPxquvvoqRI0dK2tqIkI9V0jBohVkFQiCrTwN1fUm1HvICUGwyQEmMA4cmVOaBRBihOykpKXj77bexbNky1K1bFwBw7do1bNmyBU2bNjW4dNaHpRQBrm6woqIQFBaGITW1CsnJNasH9HatmCGmyhdGWT6kWg89heL8+edx4kQYmjevQnLy7QBuJ1caEXSQCCN0p1OnTigoKEBOTg4efPBBADVm9aZNmyI1NVU47/r165gxYwbefvttlJeXo0uXLli2bBluv/124ZwPP/wQU6dOxenTp9GtWzeMHDmy1u999tlnmDNnDr7++mvExcXhL3/5CxYvXox69eppf7OET1zdYP7cWXq7VshaKA3eYiVmPXS1bLk+x6QkoHNnnQprciidh3UJET+FINRnzJgx2LBhg/D3+vXrMXr0aLdzZs6cie3bt2PTpk345ptv0KpVK2RkZODixZrO/fTp0xg8eDAGDBiAw4cPY9y4cZg9e7bbNQoKCtC3b18MGTIER44cwdatW/HZZ58hOztb+5sk/CIWDO9wNHA7T0/s9stITT0ZlAOew9EAhYXNhfr3BW/ZmjFjOF58sRyhoRwAIDSUw4svlmPGjOEUm6QC33xzG5Yvn4pNm0Zi+fKp+Oab24wuEqEiZAkjDMmg/tBDD2HOnDk4efIkAODzzz/H22+/jU8++QQAcOXKFbz22mvYuHEj7rvvPgDAP//5T+Tm5mLdunWYMWMGXnvtNbRs2RIvv/wyAKBNmzb4/vvvsWTJEuF3Fi9ejAcffFAIum/dujVWrlyJu+66C6+99hrq1Kmjzw0TPmEhGN4M2yrpgdz8XbzAevxxICsL+OUXoFUrG5o0iQEQo0uZrQjfzqywWpfwD4mwIMeoDOrx8fHo168fNm7cCI7j0K9fP8TFxQnHCwoKUFlZiR49egifhYeH44477sCxY8cAAMeOHUPXrl3drpuWlub293fffYcjR45g8+bNwmccx8HpdKKwsBDt2rXT4vYIGbAQDG+WbZW0JNABv0mT4E36qzZ8ezxwAFi2zHyrdQnpkAgLYozOoD5mzBjBLbhmzRpNfuO3337DI488gkcffbTWsWBZBMC6lYeVYHgrCywpsGCRJP4gNjYW3brVTI75Phpwn6BQrJj5IREWxPz8s/vLDdRkUP/lF31EWN++fVFRUQGbzYaMjAy3Yy1btkRERAQ+//xzNGvWDABQWVmJr776SnAttmvXDjt27HD73hdffOH2d6dOnfDjjz+iVatW2t0I45jBykPB8MahJMCe0IcmTWq8E488UtM3h4Zy6NevZoLiz3VMz8o8kAgLYlq3rj3LCg0F9NIroaGhgmsxNDTU7Vi9evUwadIkzJgxA40aNULTpk2xdOlSXL16FWP/5y+dOHEiXn75ZcyYMQPjxo3DoUOHauUXmzVrFrp164bs7GyMGzcO9erVw48//ojc3FxJCSqtghmsPCylzggmXEX6DTeUY9YsO6qrbQgN5bBkSTkeeGC44SI9mBk7tsY7wcfb1a3bAydOdMXTTzcGx9kA1Fgsd+0agPnzu6J58zB6ViaCRFgQU3uWBfz97/rGdURHR/s89sILL8DpdOLhhx/G5cuX0aVLF3z88cdo2LAhgBp34vbt2zFt2jSsWrUKd9xxB55//nmMGTNGuMYtt9yCTz/9FE899RT+9Kc/geM4tGzZEllZWZrfG0GYBQqwZxv3eLtYHDnizYthw+XLCSD9ZS5sHMdxRheC8E55eTnsdjscDkctsXLt2jUUFhYiNTU14BV+Z87wnS4F1npDzbom/kCN7XIIIhg5cwZo1qy2F+PECerDWcHf+O0KWcIIWtVEGIIZYtUIgkVY8GIQ6kAijCAIwyCBRRDKcI8VIwFmVkiEEQRBEIQJIS+G+aFtiwiCIAiCIAyARBhBEARBEIQBkAgzObS4VXuojgmCIAgtIBFmUvjkpv5WlhHqwNexZ0JZgiAIgggECsw3KWFhYYiKisL58+cRHh6OkBDS01rgdDpx/vx5REVFISyMXheCIAhCPWhUMSk2mw1JSUkoLCzEyZMnjS6OpQkJCUHTpk1hs9mMLgpBEARhIUiEmZiIiAi0bt2aXJIaExERQZZGgiAIQnVIhJmckJAQ2kqHIAiCIEwITe8JgiAIgiAMgEQYQRAEQRCEAZAIIwiCIAiCMACKCWMYPkloeXm5wSUhCIIgCEIq/LgtluybRBjDXL58GQCQkpJicEkIgiAIgpDL5cuXYbfbfR63cbQnC7M4nU4UFRWhQYMGQZWjqry8HCkpKTh9+jSio6ONLo6pobpUB6pHdaB6VAeqR3XQsh45jsPly5eRnJzsN8URWcIYJiQkBE2aNDG6GIYRHR1NHYxKUF2qA9WjOlA9qgPVozpoVY/+LGA8FJhPEARBEARhACTCCIIgCIIgDIBEGMEckZGRWLBgASIjI40uiumhulQHqkd1oHpUB6pHdWChHikwnyAIgiAIwgDIEkYQBEEQBGEAJMIIgiAIgiAMgEQYQRAEQRCEAZAIIwiCIAiCMAASYYRh/Pvf/8aAAQOQnJwMm82G999/3+04x3GYP38+kpKSULduXaSnp+Pnn382prAMI1aPo0aNgs1mc/uvb9++xhSWYRYvXozbb78dDRo0QOPGjTFo0CAcP37c7Zxr165h8uTJiI2NRf369TFkyBCcPXvWoBKziZR6vPvuu2u1yYkTJxpUYjZ57bXXcMsttwiJRNPS0vDRRx8Jx6ktSkesLo1sjyTCCMO4cuUKOnbsiDVr1ng9vnTpUqxcuRJr165Ffn4+6tWrh4yMDFy7dk3nkrKNWD0CQN++fVFcXCz899Zbb+lYQnPw6aefYvLkyfjiiy+Qm5uLyspK9OnTB1euXBHOmTZtGj744AO88847+PTTT1FUVITBgwcbWGr2kFKPADB+/Hi3Nrl06VKDSswmTZo0wQsvvIBDhw7h66+/xj333IP7778fP/zwAwBqi3IQq0vAwPbIEQQDAODee+894W+n08klJiZyL774ovBZWVkZFxkZyb311lsGlNAceNYjx3HcyJEjufvvv9+Q8piZc+fOcQC4Tz/9lOO4mvYXHh7OvfPOO8I5x44d4wBweXl5RhWTeTzrkeM47q677uIee+wx4wplUho2bMi9/vrr1BZVgK9LjjO2PZIljGCSwsJClJSUID09XfjMbreja9euyMvLM7Bk5uSTTz5B48aN0aZNG0yaNAmlpaVGF4l5HA4HAKBRo0YAgEOHDqGystKtTbZt2xZNmzalNukHz3rk2bx5M+Li4nDzzTdjzpw5uHr1qhHFMwXV1dV4++23ceXKFaSlpVFbDADPuuQxqj3SBt4Ek5SUlAAAEhIS3D5PSEgQjhHS6Nu3LwYPHozU1FQUFBTgySefxH333Ye8vDyEhoYaXTwmcTqdmDp1Knr06IGbb74ZQE2bjIiIQExMjNu51CZ9460eAeCBBx5As2bNkJycjCNHjmDWrFk4fvw4cnJyDCwte3z//fdIS0vDtWvXUL9+fbz33nto3749Dh8+TG1RJr7qEjC2PZIIIwiLM2zYMOHfHTp0wC233IKWLVvik08+Qe/evQ0sGbtMnjwZR48exWeffWZ0UUyNr3qcMGGC8O8OHTogKSkJvXv3RkFBAVq2bKl3MZmlTZs2OHz4MBwOB959912MHDkSn376qdHFMiW+6rJ9+/aGtkdyRxJMkpiYCAC1VvucPXtWOEYoo0WLFoiLi8Mvv/xidFGYJDs7Gzt37sSBAwfQpEkT4fPExERUVFSgrKzM7Xxqk97xVY/e6Nq1KwBQm/QgIiICrVq1QufOnbF48WJ07NgRK1asoLaoAF916Q092yOJMIJJUlNTkZiYiH379gmflZeXIz8/382PT8jnzJkzKC0tRVJSktFFYQqO45CdnY333nsP+/fvR2pqqtvxzp07Izw83K1NHj9+HKdOnaI26YJYPXrj8OHDAEBtUgSn04nr169TW1QBvi69oWd7JHckYRi//fab20yjsLAQhw8fRqNGjdC0aVNMnToVzz77LFq3bo3U1FTMmzcPycnJGDRokHGFZhB/9dioUSMsWrQIQ4YMQWJiIgoKCjBz5ky0atUKGRkZBpaaPSZPnowtW7bg//2//4cGDRoIsTV2ux1169aF3W7H2LFjMX36dDRq1AjR0dGYMmUK0tLS0K1bN4NLzw5i9VhQUIAtW7bgz3/+M2JjY3HkyBFMmzYNPXv2xC233GJw6dlhzpw5uO+++9C0aVNcvnwZW7ZswSeffIKPP/6Y2qJM/NWl4e3RkDWZBMFx3IEDBzgAtf4bOXIkx3E1aSrmzZvHJSQkcJGRkVzv3r2548ePG1toBvFXj1evXuX69OnDxcfHc+Hh4VyzZs248ePHcyUlJUYXmzm81SEAbsOGDcI5v//+O/e3v/2Na9iwIRcVFcX95S9/4YqLi40rNIOI1eOpU6e4nj17co0aNeIiIyO5Vq1acTNmzOAcDoexBWeMMWPGcM2aNeMiIiK4+Ph4rnfv3tyePXuE49QWpeOvLo1ujzaO4zjtpR5BEARBEAThCsWEEQRBEARBGACJMIIgCIIgCAMgEUYQBEEQBGEAJMIIgiAIgiAMgEQYQRAEQRCEAZAIIwiCIAiCMAASYQRBEARBEAZAIowgCIIgCMIASIQRBEEQBEEYAIkwgiAIBVRUVBhdhFqwWCaCIHxDIowgCALA3XffjezsbGRnZ8NutyMuLg7z5s0Dv7Nb8+bN8cwzz2DEiBGIjo7GhAkTAACfffYZ/vSnP6Fu3bpISUnBo48+iitXrgjXffXVV9G6dWvUqVMHCQkJGDp0qHDs3XffRYcOHVC3bl3ExsYiPT1d+O7dd9+NqVOnupVx0KBBGDVqlPC30jIRBMEGJMIIgiD+x6ZNmxAWFoYvv/wSK1aswCuvvILXX39dOP7SSy+hY8eO+PbbbzFv3jwUFBSgb9++GDJkCI4cOYKtW7fis88+Q3Z2NgDg66+/xqOPPoqnn34ax48fx+7du9GzZ08AQHFxMYYPH44xY8bg2LFj+OSTTzB48GDI3c5XbpkIgmAH2sCbIAgCNZanc+fO4YcffoDNZgMAzJ49Gzt27MCPP/6I5s2b47bbbsN7770nfGfcuHEIDQ3F3//+d+Gzzz77DHfddReuXLmCDz/8EKNHj8aZM2fQoEEDt9/75ptv0LlzZ5w4cQLNmjXzWp5bb70Vy5cvFz4bNGgQYmJisHHjRgBQVKY6deoEVE8EQagHWcIIgiD+R7du3QQBBgBpaWn4+eefUV1dDQDo0qWL2/nfffcdNm7ciPr16wv/ZWRkwOl0orCwEPfeey+aNWuGFi1a4OGHH8bmzZtx9epVAEDHjh3Ru3dvdOjQAX/961/xz3/+E5cuXZJdZrllIgiCHUiEEQRBSKRevXpuf//222945JFHcPjwYeG/7777Dj///DNatmyJBg0a4JtvvsFbb72FpKQkzJ8/Hx07dkRZWRlCQ0ORm5uLjz76CO3bt8eqVavQpk0bQSiFhITUck1WVlYGXCaCINiBRBhBEMT/yM/Pd/v7iy++QOvWrREaGur1/E6dOuHHH39Eq1atav0XEREBAAgLC0N6ejqWLl2KI0eO4MSJE9i/fz8AwGazoUePHli0aBG+/fZbRERECK7F+Ph4FBcXC79VXV2No0ePit6DlDIRBMEGJMIIgiD+x6lTpzB9+nQcP34cb731FlatWoXHHnvM5/mzZs3CwYMHkZ2djcOHD+Pnn3/G//t//08Igt+5cydWrlyJw4cP4+TJk3jjjTfgdDrRpk0b5Ofn4/nnn8fXX3+NU6dOIScnB+fPn0e7du0AAPfccw927dqFXbt24aeffsKkSZNQVlYmeg9iZSIIgh3CjC4AQRAEK4wYMQK///477rjjDoSGhuKxxx4T0j5445ZbbsGnn36Kp556Cn/605/AcRxatmyJrKwsAEBMTAxycnKwcOFCXLt2Da1bt8Zbb72Fm266CceOHcO///1vLF++HOXl5WjWrBlefvll3HfffQCAMWPG4LvvvsOIESMQFhaGadOmoVevXqL3IFYmgiDYgVZHEgRBwPtqRIIgCC0hdyRBEARBEIQBkAgjCIIgCIIwAHJHEgRBEARBGABZwgiCIAiCIAyARBhBEARBEIQBkAgjCIIgCIIwABJhBEEQBEEQBkAijCAIgiAIwgBIhBEEQRAEQRgAiTCCIAiCIAgDIBFGEARBEARhACTCCIIgCIIgDOD/A+dC3Ni7tGIhAAAAAElFTkSuQmCC", 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lBJ48eRIdO3ZEcnIyevXqhR07dmDo0KHIzMxE06ZNAQDLli3DnDlzcOXKlTIX0+fm5kKr1cJgMFht4H3r1i2kp6cjLCwMjRo1Kt+HB9MPpKMmr66urvDz8yv3eUszadIkXLp0CZ988gmKi4tx6dIl7Ny5E4sWLULfvn2xZcsWODuXftPuypUrER8fj5ycnEq/Rku3+1kTEVHN0ev1yu/0gwd7YOvWoRBCA0kyYtiwBEREHAIAjBkzBoGBgZXyu8/R729zdWYmzFx2djZWr16N3r17Ky0Dtm7ditatWyMhIQFhYWFo1aoVpk6diuzsbOV5ycnJiI6OVp0rJiYGycnJAEzNT1NTU1VjNBoNoqOjlTGpqakoLCxUjenQoQNatGihjElOTkbXrl2VACa/Tm5uLo4dO2b3feXn5yM3N1f1VRXkH8iPPvrI7tf7779fZf9n4ObmhqCgIDRr1gwRERF49tln8b///Q87duxQlofffvttdO3aFZ6enggNDcU///lPXL9+HYBpSfmRRx6BwWBQZtUWLlwIAPjss8/Qs2dPeHl5ISgoCA899JBqFpOIiBoGefbr1KlTMBi8cPRoJ2zZYgpgACCEBlu3DoXB4AUAWL9+fZX+7rOlToWwOXPmwNPTE35+fjh//jz+97//Kcd+//13nDt3Dl9++SU+/fRTrFy5Eqmpqaoly6ysLFUwAoCmTZsiNzcXf/75J65evYri4mKbY7KyspRzuLq6Wi2FWY6xdQ75mD2LFi2CVqtVvkJDQ8v4yZRPWbc5qs7tkO677z50794dmzZtAmAKv++++y6OHTuGVatW4ZtvvsHs2bMBAL1798aSJUvg7e2tTCU/88wzAIDCwkK8/PLL+OWXX7B582acPXsWkyZNqrb3QURENS8tLU2ZbHjjjatYsiQeGzb8HZaxRwgNMjKaqx6rzt99NRrC5s6dq8xk2Ps6efKkMn7WrFk4dOgQdu3aBScnJzz88MOQV1ONRiPy8/Px6aefom/fvrj33nuxYsUK7NmzB6dOnaqpt1gu8+bNg8FgUL4yMjJq+pKqVYcOHXD27FkAQHx8PPr3749WrVrhvvvuwyuvvIL169cDMC2VarVaSJKEoKAgBAUFoXHjxgCAyZMnY/DgwWjdujV69eqFd999Fzt27FBm0YiIqH7T6/X4/PPPAQAGg5dq9suWjRtH4+DBHtV1eSo12jH/6aefLnWWonXr1srf/f394e/vj3bt2qFjx44IDQ3Fjz/+CJ1Oh+DgYDg7O6Ndu3bK+I4dOwIw3anYvn17BAUFWd3FeOnSJXh7e8Pd3R1OTk5wcnKyOSYoKAgAEBQUhIKCAuTk5KhmwyzHWN5RKZ9THmOLm5sb3NzcHH4e9ZkQQunJlZSUhEWLFuHkyZPIzc1FUVERbt26hZs3bzrcISA1NRULFy7EL7/8gmvXrinF/ufPn0enTp2q5X0QEVHNMZ/JSkmJQmnzTfKyZHj4GWi1eVV8dWo1OhMWEBCADh06OPyyV8Qu/3LNz88HAPTp0wdFRUVIS0tTxvz2228AgJYtWwIAdDoddu/erTpPYmIidDodANMMS2RkpGqM0WjE7t27lTGRkZFwcXFRjTl16hTOnz+vjNHpdPj1119VtUiJiYnw9vZmEHDgxIkTCAsLw9mzZzF06FB069YNGzduRGpqKv79738DcDxNfOPGDcTExMDb2xurV6/GgQMH8NVXX5X6PCIiqrv0ej1OnjyJxMRErFq1G++/fwwGgxcMBi8kJ+vKdA4hNMjO9q3iK7VWJ/aOTElJwYEDB3D33XejSZMmSEtLw/PPP4/w8HAl+ERHRyMiIgKTJ0/GkiVLYDQaMX36dAwYMECZHXvsscfw/vvvY/bs2Zg8eTK++eYbrF+/Htu2bVNea+bMmZg4cSJ69uyJu+66C0uWLMGNGzfwyCOPAAC0Wi2mTJmCmTNnwtfXF97e3njiiSeg0+nQq1cvAMDAgQPRqVMnTJgwAYsXL0ZWVhaee+45TJ8+vUHPdDnyzTff4Ndff8WMGTOQmpoKo9GIt956S9mYXF6KlLm6ulptI3Ty5Eno9Xq8/vrrSj3dzz//XD1vgIiIqpVer8fly5exfv16GAxeSEmJwv79Opjml+5DZGSqw2VIc5JkhK9vdukDK1mdCGEeHh7YtGkTXnjhBdy4cQPBwcEYNGgQnnvuOSXUaDQabN26FU888QT69esHT09PDB48GG+99ZZynrCwMGzbtg0zZszA0qVL0bx5cyxfvhwxMTHKmNjYWFy5cgULFixAVlYW7rjjDuzcuVNVaP/OO+9Ao9Fg1KhRyM/PR0xMDD744APluJOTExISEvD4449Dp9PB09MTEydOxEsvvVQNn1btl5+fj6ysLKsWFUOHDsXDDz+Mo0ePorCwEO+99x6GDRuGffv2YdmyZapztGrVCtevX8fu3bvRvXt3eHh4oEWLFnB1dcV7772Hxx57DEePHsXLL79cQ++SiIiqSlpamlL3Zd52ooQGqak9AQgAjreek1tVVPdSJFCH+4Q1BFXVJ+zixYv46KOPSh03bdo0BAcHl+vcpZk0aRJWrVoFAHB2dkaTJk3QvXt3PPTQQ5g4caIy8/XOO+/gzTffRE5ODvr164dx48bh4YcfxrVr15RavMcffxxffvkl9Ho9XnjhBSxcuBBffPEFnn32WVy8eBERERGYN28ehg8fjkOHDuGOO+6o0DWzTxgRUe1h3vfLYPDCkiXxZZ7xUjNi9OgNCA29oApglfG7r6x9whjCarGqCmHmP8COxMXFVUnD1rqGIYyIqObJy49XrlzBnj17AADp6a2watXECpxNYMCARPTpk2x1pDJ+95U1hNWJ5UiqXH5+foiLi6uRjvlERETlZW/yIDMzGGVZcjQxjZMkI6Kjk1QBbMCAAQgLC6v2330MYQ0UAxYREdUFer0emZmZqscMBi9kZIQiMTEa6gBmL5AZMWBAEkJCMuHrm21V/9W+ffsa+b3IEEZERES1jl6vx++//47t27erHrddiC+TYB3EjJg6dTmaN79o83XGjx9fYxMTDGFERERUq5jf/WjOYPByEMBk5kHMiOHDE6wC2MiRI+Hv71/jpTcMYURERFTj9Ho9rly5goyMDOzfv191zGDwQna2H65c8S3jnZASYmJ2olOn4zZbT4SEhNSKshyGMCIiIqpRloX3cujy9dUjLa2N2exX2Ro6SJJRCWDh4eEICAhAkyZNEBoaWuOzX+YYwoiIiKhG6PV6FBQUID09XXnMvOZLkowwNdKSZ78c3QVZcvejefPV+++/v9J7XlYWhjAiIiKqdrbaTljWfJW9Cav9ux/t7UFdGzCEERERUbW7fPmy6nuDwQvHjnWuQPd767sfa0vhfWkq0uefqNb69ttvIUkScnJyyvycVq1aYcmSJVV2TUREpKbX67F+/Xrl+4MHe2DJknjs2hWDstV9mcZIku27H0NCQhAcHFyrAxjAEEbVbNKkSZAkCY899pjVsenTp0OSJEyaNKn6L4yIiKqFZfNV67YTpXW/F7jrrh8xceJKxMcvQUTEIeXIyJEj69SWe1yOpGoXGhqKtWvX4p133oG7uzsA0/6Ma9asQYsWLWr46oiIqKrY6v+VkhJV7tovW3s+ArWn9URZcSaMql1ERARCQ0OxadMm5bFNmzahRYsW6NGjh/JYfn4+nnzySQQGBqJRo0a4++67ceDAAdW5tm/fjnbt2sHd3R39+/fH2bNnrV7vhx9+QN++feHu7o7Q0FA8+eSTuHHjRpW9PyIismYrgBkMXti/X1eGZxsRGXkAM2YsUQWwBx54ANOmTcO0adPq1AyYjCGMcOECsGeP6c/qMnnyZHzyySfK9x9//DEeeeQR1ZjZs2dj48aNWLVqFQ4ePIg2bdogJiYG2dnZAICMjAyMHDkSw4YNw+HDhzF16lTMnTtXdY60tDQMGjQIo0aNwpEjR7Bu3Tr88MMPiIuLq/o3SUREAExLkLY64GdkhMJxFCkJX8OGbVfuehw8eDDi4uJw5513Ijg4uE7Uf9nC5cgGbsUKYNo0wGgENBrgo4+AKVOq/nXHjx+PefPm4dy5cwCAffv2Ye3atfj2228BADdu3MCHH36IlStXYvDgwQCA//73v0hMTMSKFSswa9YsfPjhhwgPD8dbb70FwLQB66+//oo33nhDeZ1FixZh3LhxiI+PBwC0bdsW7777Lu655x58+OGHaNSoUdW/WSKiBkTu/ZWRkYFbt24BAPLy1F3rDQYvfP99X6Sm9rR7HkkyYsoU9V2PgwcPRnh4eJ0MXLYwhDVgFy6UBDDA9Oc//gHExADNm1ftawcEBGDIkCFYuXIlhBAYMmQI/P39leNpaWkoLCxEnz59lMdcXFxw11134cSJEwCAEydOICoqSnVenU49rf3LL7/gyJEjWL16tfKYEAJGoxHp6eno2LFjVbw9IqIGRQ5eOTk5qrsebTl4sAe2bBkG2wX46oarcgAbOXJknav3KguGsAbs9OmSACYrLgbOnKn6EAaYliTlZcF///vfVfIa169fxz/+8Q88+eSTVsd4EwAR0e2z1XRVZr79kFabp9wJae8OyAceSEBAgN6q4Wp9DGAAQ1iD1rataQnSPIg5OQFt2lTP6w8aNAgFBQWQJAkxMTGqY+Hh4XB1dcW+ffvQsmVLAEBhYSEOHDigLC127NgRW7ZsUT3vxx9/VH0fERGB48ePo011vSkiogZEr9fj1KlTqsfk4JWZGYykpOi/7nw0onPn42jZMt3unZCSZET79qdV4WvMmDEIDAyslwEMYAhr0Jo3N9WA/eMfphkwJyfgP/+pnlkwAHByclKWFp2cnFTHPD098fjjj2PWrFnw9fVFixYtsHjxYty8eRNT/ipae+yxx/DWW29h1qxZmDp1KlJTU7Fy5UrVeebMmYNevXohLi4OU6dOhaenJ44fP47ExES7/+dGRESls3W3o/m+j/LSookGx451wbFjnS0elwlERycpAay+Lj9aYghr4KZMMdWAnTljmgGrrgAm8/b2tnvs9ddfh9FoxIQJE5CXl4eePXvi66+/RpMmTQCYlhM3btyIGTNm4L333sNdd92F1157DZMnT1bO0a1bN3z33XeYP38++vbtCyEEwsPDERsbW+XvjYiovtHr9bhy5Qqys7ORmJioOla2pqsSTCHMPIhZ9/5qCAEMACQhRFn2B6AakJubC61WC4PBYBVWbt26hfT0dISFhfEOvyrGz5qIGjq9Xo/Lly/bLbo3GLywbdsD+O23DmU63z337EFAwBUAQGjoBdUS5Pjx4xEeHn77F12DHP3+NseZMCIiIrLJUfgyGLyQkRGK9PRWf7WasH+3o+VjbduettrvEagfAaw8GMKIiIjIir27Hg0GL6SkRP3V6d5xz/fIyJ+Rk+ODtLQ2kJciu3f/RQlg/fv3R9u2bQEArq6uDWIJ0hxDGBEREVkpKCiweszU42soyrbhjhH9+u2FVpuHCxeCkZHRAqGh51UzYM2aNUNwcHDlXXQdwxBGREREVnJyclTfX7gQXI4AJjBgQMndjs2bX8To0S3h7t4Bzs5d4OXlBQ8Pjwa19GgLQ1gdx/sqqh4/YyJqSNLS0nDz5k2cP39eecxxl3tL1nc7xsbGokOHshXtNyQMYXWU3FeroKAA7u7uNXw19Zs8JW/Zy4yIqL6x1fvLYPAqRwADRo/egC5dTqgeCwgIqKxLrFcYwuooZ2dneHh44MqVK3BxcYFGU5bpYSovo9GIK1euwMPDA87O/NeFiOovvV6P06dPqx4zGLyQmhqBsgYwSTIiNPSC8v0DDzyA1q1bN7iC+7Lib5U6SpIkBAcHIz09HefOnavpy6nXNBoNWrRoAUkq23+EiIjqArnxamFhIfLy8qyar6q73zui3nRbrgPjEmTpGMLqMFdXV7Rt29bmHSxUeVxdXTnTSER1nl6vR0FBAXJycvDHH39g3759dseaivAdLUGWBK/o6CSEhGRabbqt1Wor9frrI4awOk6j0bCLOxEROWSv55dMbrxq+rsWiYnRsB/ATIX3toKXOVdX19u86vqPIYyIiKies7diYjB44fvv+yI1NRIlrSdsdbk36ddvDyIjD9kNXrGxsdBqtQ2y8WpFMIQRERHVU/K2Q1euXFE97rjrvf0ZMFsBbMyYMfDx8WHwqgCGMCIionrIVrsJoDwF9+bUzVfl7YYYvG4PQxgREVE9Y6/f16lTbbF9+xCUreu9TGDAgERV89WGvt1QZWEIIyIiqkdsBbB9+3RITByAsvb7MjHigQe2oX3706olyMGDBzf47YYqC0MYERFRHSe3nzAYDFi3bp3q2O7d92Lv3n4oPYCJv740Ss+viIhDVqMYwCoPQxgREVEdJRfer1+/3ubxsgUwgQceSED79qZu+dnZvlatJ/r374+AgAAEBgayBqwSMYQRERHVIeZNV22FL7nn16lT7fDrr91Q+gyYhIAAvRK6bLWf6Ny5M8NXFWAIIyIiqiMcNV113HbCPkkywtc3GwDQs2dPNGnSBL6+vkrHe94BWXUYwoiIiOoAvV6PzMxMm8fKt8+jqe4LgNV+jxEREbzrsRoxhBEREdVSjgrugZKlxy1bhqJss18SRo/+UvkuNPSC3e73VPUYwoiIiGqhsiw9JifrytV0VZKMDoMX93usXgxhREREtZC9/R4PHuxRjpkv67YTcgDr3r270m7C2dmZdz7WAIYwIiKiWkKv1+PKlSsoLCzEtWvXVMfkpcetW8sawIDRo79EaOgFm20noqKiWP9VwxjCiIiIagFHy48V2e/RfOnR1vIjlx5rHkMYERFRDZEL7wHg6tWrVsfLX3hvYrn0KIuNjYVWq2XbiVqCIYyIiKgGOJr5Akz7PSYlRZdr9gswonfvZERFpagC2MiRIxESEsLgVcswhBEREdUAe4X3QPk33JYkI0aN2mD3zkcGsNqJIYyIiKgayUuQlsuPBoMXsrP94OKSj8TEaNgPYMLimEB0dBK6dDlhNXLMmDG867EWYwgjIiKqYqU1XVUX3hvhqP4rMvJnHDwYqYwdMCAJffokK8dHjhwJf39/1n3VAQxhREREVai0pqvWhff2A5gkGdGv317067fXZtsJAPD392friTqCIYyIiKgKOWq6Wr62E+o7Htn1vu5jCCMiIqoCjmq/5KarjgOYqfZLkozQ6azveLRl/PjxXIKsQxjCiIiIKokcvHJycrB+/Xqr4+Wb/ZIQE7MTnTodtxm+5J5fMtaA1T0MYURERJWgLLVf5Vl+lCSjzQDGnl/1B0MYERFRJbCs/ZJbTmRmBleg6aqp7QR7ftVvDGFEREQVYL7lEKDedki97GjZ16s0bDvRUDCEERERlVNpS4/qZceyBjCB8PAzGD58q9UMGGe/6ieGMCIionLQ6/XIzMy0ecxg8MKxY51LWXo0IjIyVdVwtXPnY9DpktG8+UWr0bGxsQxg9RRDGBERURnZmwEzGLzw/fd9kZoaCVOzVftLkJKEUhuumgsICKici6dahyGMiIjIDvO6r5ycHFy5csVqzMGDPbBlyzCoQ5f9JUghNMjO9kVY2DmH4Ss2NhYBAQGcBavHGMKIiIhscFT3JbtwIfivLYfKXngvSUb4+mbbPc5NtxsOhjAiIqK/mM982ep0n53tB19fPQAgJSUK+/f3RtkCWEn3e/Oth+S7HmW8+7FhKU/Tkho1fPhwtGjRAo0aNUJwcDAmTJigKoxcuHAhJEmy+vL09FSd58svv0SHDh3QqFEjdO3aFdu3b1cdF0JgwYIFCA4Ohru7O6Kjo3H69GnVmOzsbIwbNw7e3t7w8fHBlClTcP36ddWYI0eOoG/fvmjUqBFCQ0OxePHiSv5EiIioMskzXx999BE++ugjbNq0STl28GAPLFkSj1WrJuKdd2bgnXfisX9/H5QtgBnRt+/3mDhxJeLjlyAi4pByRN5sW/5iAGtY6kwI69+/P9avX49Tp05h48aNSEtLw+jRo5XjzzzzDC5evKj66tSpE/7+978rY/bv348HH3wQU6ZMwaFDhzBixAiMGDECR48eVcYsXrwY7777LpYtW4aUlBR4enoiJiYGt27dUsaMGzcOx44dQ2JiIhISEvD9999j2rRpyvHc3FwMHDgQLVu2RGpqKt58800sXLgQH330URV/SkREVFG26r0Aey0nyt71furU5bj//m9t1oBxs+2GTRJCiJq+iIrYsmULRowYgfz8fLi4uFgd/+WXX3DHHXfg+++/R9++fQGYihxv3LiBhIQEZVyvXr1wxx13YNmyZRBCICQkBE8//TSeeeYZAIDBYEDTpk2xcuVKjB07FidOnECnTp1w4MAB9OzZEwCwc+dOPPDAA7hw4QJCQkLw4YcfYv78+cjKylL+BZs7dy42b96MkydPlvk95ubmQqvVwmAwwNvbu8KfFRERWbMsurfc61FefrxxwwMbNvzd1ikckpcezWe+ZKz7qt/K+vu7TtaEZWdnY/Xq1ejdu7fNAAYAy5cvR7t27ZQABgDJycmYOXOmalxMTAw2b94MAEhPT0dWVhaio6OV41qtFlFRUUhOTsbYsWORnJwMHx8fJYABQHR0NDQaDVJSUvB///d/SE5ORr9+/VT/hxMTE4M33ngD165dQ5MmTWxec35+PvLz85Xvc3Nzy/6hEBFRmZVWdL9vnw6JidEwzXgZUb6u90aMHr0BoaEXbN79OH78eISHh1fgqqm+qTPLkQAwZ84ceHp6ws/PD+fPn8f//vc/m+Nu3bqF1atXY8qUKarHs7Ky0LRpU9VjTZs2RVZWlnJcfszRmMDAQNVxZ2dn+Pr6qsbYOof5a9iyaNEiaLVa5Ss0NNTuWCIiqjjLfR7NmQLYAJT8itSg9ABmWlSSJCOGD09Aly4nbG68HRcXxwBGihoNYXPnzrVZTG/+Zb58N2vWLBw6dAi7du2Ck5MTHn74YdhaTf3qq6+Ql5eHiRMnVufbuW3z5s2DwWBQvjIyMmr6koiIGgSDwQvp6a1w4ULwXwGs7LNevXvvw9Sp/7VZeG+OWw+RpRpdjnz66acxadIkh2Nat26t/N3f3x/+/v5o164dOnbsiNDQUPz444/Q6XSq5yxfvhxDhw61mo0KCgrCpUuXVI9dunQJQUFBynH5seDgYNWYO+64Qxlz+fJl1TmKioqQnZ2tOo+t1zF/DVvc3Nzg5uZm9zgREVW+8m62LUlGDB68DR4ef9pdcpSNGTMGPj4+bD1BNtVoCAsICKjwdgxGoxEAVDVUgKmua8+ePdiyZYvVc3Q6HXbv3o34+HjlscTERCXEhYWFISgoCLt371ZCV25uLlJSUvD4448r58jJyUFqaioiIyMBAN988w2MRiOioqKUMfPnz0dhYaFSs5aYmIj27dvbrQcjIqLqV97Nth0V2wOmO/kDAwOh1WoZvKhUdaIwPyUlBQcOHMDdd9+NJk2aIC0tDc8//zzCw8OtZsE+/vhjBAcHY/DgwVbneeqpp3DPPffgrbfewpAhQ7B27Vr8/PPPSusISZIQHx+PV155BW3btkVYWBief/55hISEYMSIEQCAjh07YtCgQXj00UexbNkyFBYWIi4uDmPHjkVISAgA4KGHHsKLL76IKVOmYM6cOTh69CiWLl2Kd955p2o/KCIiUt31aM5gMKCwsBDOzs4oKioCAGRn+5Wy2bbMiN69kxEVleJw5qtz584MXlRmdSKEeXh4YNOmTXjhhRdw48YNBAcHY9CgQXjuuedUy3dGoxErV67EpEmT4OTkZHWe3r17Y82aNXjuuefw7LPPom3btti8eTO6dOmijJk9ezZu3LiBadOmIScnB3fffTd27tyJRo0aKWNWr16NuLg43H///dBoNBg1ahTeffdd5bhWq8WuXbswffp0REZGwt/fHwsWLFD1EiMiospXlq2GALn9RCu4uORDkowOgphAZOTP6Ndvr83wZd7xnjNfVF51tk9YQ8A+YURE5XPx4sVSG2Ob14BJkhEdO57A8eOdYL0UacSAAUno0yfZ7rni4uIYvMhKve4TRkREVBbm+z1qtXkwGLywZcswyIFLCA2OH++Eu+76ET/9FAXAFMx0OttLj5z5osrEEEZERPWS5YzXsGEJSE9vAesZLwk//RSFAQOSEBKSCV/fbLt1X2wzQZWJIYyIiOoFvV6P9PR0ANZ3PQqhwZYtQ2H/7kcNkpKiER+/xCqAyXc8BgQEMIBRpWIIIyKiOs+yID8jI9RGsb3juyCF0CA729cqhPGOR6oqDGFERFRnWLafyMnJQVFREf744w/lMXkZsrwkyQhf32wAJbVfrPuiqsQQRkREdUJp7ScMBi9kZIRaNF91RPz1VVIzJs+C+fv7q3ZOIaoKDGFERFQr6fV6XLlyBdnZ2SgqKkJennqZ0PzOx7S0NuUIXyZy/6/sbF+HxfhEVYUhjIiIap3SZr0s73w0dbwsewCTJKPSgJXhi2oKQxgREdU6ly9ftnpMnvlyccm3uvOxfIRq6dEWV1fXcp6TqPwYwoiIqFbR6/VYv3696rF9+3RISor+K3AZUZ5ZL1PdlwTAiMjIVJtbEMXGxkKr1QJgE1aqPgxhRERUY8zvdpQ32L527ZpqzO7d92Lv3n4o6fGlQUmwkll+b05CTMxOdOp03Obs15gxY9ChQ4fbeyNEFcAQRkRENSItLQ2ff/65wzH79uksAphMgjp42QtgpvovewEMAAIDA8t6yUSViiGMiIiqnV6vdxjA5HYTiYnRcDTDZZ8poFm2nujSpQsCAgLg7OyMJk2aIDAwkEuPVGMYwoiIqNqZN1yVyYX3mZnBZvVf5SdJRkRH294Hsnfv3uz/RbUGQxgREdW4fft0f8162ar3KhtJMmLUqA0IDb1gd+mRdz1SbcIQRkRENcoUwAagLPVd9sjLjl26nLA6Jt/5yLseqbZhCCMiohpjMHghKclR3VdpjOjdOxlRUSmq2S/u/Uh1AUMYERFVCcvNts2lp6cDALKz/SpY+2XE6NH2lx5DQkIYvqjWYwgjIqJKV9q2QzJfX/1f2w6VdcNtyeHS45gxY3jHI9UZDGFERFTp7M2AAeqNt7XaPHTrdgS//NId1r2/zAn07fs9WrdOt7rjkUuPVFcxhBER0W2zXHq8evWq6rgcvH7/vRX27u0L012QRtx1VwqOHOkGdVG+UP0pt5zo0yfZ5mv7+/uz7QTVSQxhRER0W0pbejx4sIfZhtvmM10a/PRTL9jqhh8TsxOhoedRWOhqNfNliW0nqK5iCCMiottiufRovtwIwCyAAba3H7J4pJRthgBT7ZePjw+XIKlOYwgjIqJKYz7rJUlG6HTJ5br70XKbIXMDBw5EkyZNEBAQwOBF9QJDGBER3ZacnBwAphkw81kvITRITtYBMMJUA1YaI6ZMWY7mzS8qj7DonuozhjAiIiozvV6Py5cvo6ioSHns9OnTAGz3/BJCg+bNz+PChVA4bshqxPDhCaoABrDfF9VvDGFERFQmpRXgu7jkw7rFhMCFCy3+etz8rkcBoGTJ0lbHewYwqu8YwoiIqExs9f4yL8I/frwz7Bfem8LXAw9sRfv28syZr907H/39/RnAqN5jCCMiogoxbbwdDbnnV+n7P0ooLnZRQhfbTlBDxxBGRERWLGu/rl+/juzsbOW4KYANgHnPr9IJhIaedzgiNjaWdz9Sg8EQRkREKqXVfhkMXkhKikbpM1/y7JhpKbJ791+Uwvv+/fujbdu2qtG8A5IaGoYwIqIGxnKLIUsGg8Hiey9kZIQCAEJDM5CREVpq7y+531dgYBYyMlogNPS86s7HJk2acKshavAYwoiIGpDSZrksHTzYA1u2DEPJrFdZar+AUaM2oEuXEwBg1XYCAFxcXMp8DUT1VdnbGBMRUZ1na4uh9PRWMBi8rMbKzVfVoUsD6xAmVN9JkhGhoRccXkdAQEDZL5qonuJMGBFRAyJ3twestxgaNiwBERGHlOO2mq/aEhn5Mw4ejFSdx/LOx9jYWGi1WgCs/SKSMYQREdVzcg2YwWDA+vXrAdjeYmjr1qFwdc2Hj881FBa6oaDAGdbNV9UkyYh+/faiX7+9Nvt+sekqkX0MYURE9ZTcZkIOXubsbTG0YcPfIQcvSSqt/ksgOjrJYd8vBjAi+xjCiIjqIXsF+HKH+99/bwX7s1ymx0whzdFMmISQkEzlO3mzbRmXHYkcYwgjIqpH5KXHq1evWh0zrwErbZmxhLzXo/VYSTLC17ekgStnvYjKhyGMiKiecNR+4sKFYGzZMhQlN8WXJYDJJABGdO16BEePdrNZgB8bG8sARlRODGFERPWEvQas8gzY7XUl0qB9+98QHf2NzQJ8+c5HIio7hjAionpK7nSvngEzJy8zlnVp0lR8b6sAnxtuE5UfQxgRUT2krv+yRaBjx+O4664DcHEpwG+/tcP3399r93zmDVg7duyI5s2bo0mTJvDx8WEBPlEFMYQREdVype31aBmCLHuA2SbhxImO6NNnH5o3vwgvr+vYu7efxXNKWlWY13917NgRXbt2vb03RUQMYUREtZWtPl9yiwlfX71qWTAuLk75e1k73QMarFgxVemUP2xYgqqDfnR0EkJCMq3qv5yd+auDqDLw3yQiolrI1p2OjrYZyszMVMKRr68ekmQsUxCTO+WHh59BRMQhhIefsVl4by4wMPA23x0RAQxhRES1kuXyo2WLCfPwpNXmYdOmTcpYrTYP0dFJSEwcANubbasfE0KD7GxfpejeVvgaM2YM67+IKhlDGBFRLWevxYQQGmRkNIdWe8LqOSEhF+GoG75aSdNVy673ADvfE1UVhjAiolqstCL7jRtHw2BIQkjIRVWdmK0lSdP3gDrMCQwYULL/o7+/P4KDg6vo3RCROYYwIqIa4uiuR3nbodKK7IXQKMuOJcX0pkBmWWg/bFgCAFgV3/fpk1zp742ISscQRkRUAywL7+3d9Vi2IvuSDbfNA5lOl4wpU5ajsNBVVWjvqPieTVeJqg9DGBFRNdPr9cjMzFS+d3TXo1abh2HDEsyK8o1/PcteKCsJZPv398H+/ToMH56AsLBzygitNg+DB3dFUFCk8piLiwsCAgJY+0VUjRjCiIiqka0ZMPOaL8u7HmWSBAhh+rNbtyM4cqTbX88pbcsh9flGjhyJkJAQhi2iWuB2dnMlIqJysqwBy8gItVpqlO96TE9vhQsXgq1C2pEj3XD33XshSUaU7P0Isz9hdb7sbF8ApsJ7BjCi2oEzYURENaSk9YQlIzZuHP1X8DLCVmuKvXv7mj0uATCib9+9+OGHvlahTpJKWlAQUe1R5hCWm5tb5pN6e3tX6GKIiOobyzsg5bse7beeMM1uCSEvMVovOdou1Negdet09OyZipSUKOzfrwOgsdr3kYhqjzKHMB8fH0iSo7oDQAgBSZJQXFx82xdGRFTXpaWl4fPPP7d5zF7riXbtfsNvv3WweFRecpTMen1ZjPhrtkurzcPAgUmIikqxeQck734kqj3KHML27NlTlddBRFRv2Np4G1C3obDXeuK339rB1hIkIPDAAwnYvn2IjWPq2a7+/fujSZMmylEXFxdotVp2vieqZcocwu65556qvA4ionrB1sbbALBvnw5JSdGqNhQ6XTL27+9jMdLe/VIaFBW52Dw+evQGdOlSsnVR27Zt2fWeqA6ocGF+Tk4OVqxYgRMnTP/id+7cGZMnT4ZWq620iyMiqq3Ma70yMzVIT3dGWFgRXF0vW43dt0+n2kxbbkMxZcpypXarNJJkRIsW521uRRQaekE1lkuORHVDhULYzz//jJiYGLi7u+Ouu+4CALz99tt49dVXsWvXLkRERFTqRRIR1Sbms13WjVZTYP6fwAsXgpGYGA3LXl5CaFBY6IrhwxMc7g0JQJk5a978os2tiORlSPYAI6pbKhTCZsyYgeHDh+O///0vnJ1NpygqKsLUqVMRHx+P77//vlIvkoioNpFnwEprtFrSgsI6YMmF9GFh5/Dnn43+CmrWrSVGjdqA0NALStCKiDhkd9shBjCiuqXCM2HmAQwAnJ2dMXv2bPTs2bPSLo6IqDaRlyAdba5t3hjV/gyXQEREKgBTkEtKsh3Ahg1LUNV6yR59dDB8fHxUj7HonqjuqVAI8/b2xvnz59Ghg/o26oyMDHh5eVXKhRER1Sa2Cu5t3+FomuGy14JC7gOWmnonDh6MhE6XbHPcqFHqYvuRI0fC39+fYYuoHqnQtkWxsbGYMmUK1q1bh4yMDGRkZGDt2rWYOnUqHnzwwcq+RgDA8OHD0aJFCzRq1AjBwcGYMGGCagNcAPj666/Rq1cveHl5ISAgAKNGjcLZs2dVY7799ltERETAzc0Nbdq0wcqVK61e69///jdatWqFRo0aISoqCj/99JPq+K1btzB9+nT4+fmhcePGGDVqFC5duqQac/78eQwZMgQeHh4IDAzErFmzUFRUVCmfBRFVP8vthuR2E9HRSSjZVBsAJKSkRKGgwNni8ZLj5gX6yck6q3G2iu1DQkIQHBzMAEZUn4gKyM/PF08++aRwdXUVGo1GaDQa4ebmJuLj48WtW7cqcspSvf322yI5OVmcPXtW7Nu3T+h0OqHT6ZTjv//+u3BzcxPz5s0TZ86cEampqaJfv36iR48eqjEeHh5i5syZ4vjx4+K9994TTk5OYufOncqYtWvXCldXV/Hxxx+LY8eOiUcffVT4+PiIS5cuKWMee+wxERoaKnbv3i1+/vln0atXL9G7d2/leFFRkejSpYuIjo4Whw4dEtu3bxf+/v5i3rx55XrPBoNBABAGg6EiHxkRVaLMzEyxcOFCsXDhQjF8+P+EJBULQPz1p+nv6i+jxZ/2v3r3/kF1vuHD/6e81pEjR8TVq1dr+u0TUTmU9fe3JISt3stlc/PmTaSlpQEAwsPD4eHhUUnRsHRbtmzBiBEjkJ+fDxcXF2zYsAEPPvgg8vPzodGYJvi2bt2Kv/3tb8qYOXPmYNu2bTh69KhynrFjxyInJwc7d+4EAERFReHOO+9Ulh2MRiNCQ0PxxBNPYO7cuTAYDAgICMCaNWswevRoAMDJkyfRsWNHJCcno1evXtixYweGDh2KzMxMNG3aFACwbNkyzJkzB1euXCnz7eO5ubnQarUwGAzcCoqohl28eBEfffQRDAYvLFkS7/BuxvKQJCPi45cAgM1i+2nTprHnF1EdU9bf37f1XxEPDw907doVXbt2rdYAlp2djdWrV6N3795wcXEBAERGRkKj0eCTTz5BcXExDAYDPvvsM0RHRytjkpOTER0drTpXTEwMkpOTAZiWG1JTU1VjNBoNoqOjlTGpqakoLCxUjenQoQNatGihjElOTkbXrl2VACa/Tm5uLo4dO1YFnwgRVRf7tV4VIwSQltYGWm0ewsLOWe3xyJ5fRPVXhQrzb926hffeew979uzB5cuXYTSq6xkOHjxYKRdnac6cOXj//fdx8+ZN9OrVCwkJCcqxsLAw7Nq1C2PGjME//vEPFBcXQ6fTYfv27cqYrKwsVTACgKZNmyI3Nxd//vknrl27huLiYptjTp48qZzD1dXV6s6kpk2bIisry+HryMfsyc/PR35+vvJ9eTZNJ6KKMW+6mpOTY1W7KW/5k56eDsBeMb7468teOHN0XIMtW0raWsgF+ADveCSq7yoUwqZMmYJdu3Zh9OjRuOuuu0rd2NueuXPn4o033nA45sSJE8pdmLNmzcKUKVNw7tw5vPjii3j44YeRkJAASZKQlZWFRx99FBMnTsSDDz6IvLw8LFiwAKNHj0ZiYmKFr7E6LVq0CC+++GJNXwZRg6DX63HlyhWsW7dOKbJ3cclHYaEbfH31VjNSMq02T9Uw1URgwIAk3LjhadYB37Thtom8Abc9prYWWm0e/P39ufxI1EBUKIQlJCRg+/bt6NPHcs+z8nn66acxadIkh2Nat26t/N3f3x/+/v5o164dOnbsiNDQUPz444/Q6XT497//Da1Wi8WLFyvjP//8c4SGhiIlJQW9evVCUFCQ1V2Mly5dgre3N9zd3eHk5AQnJyebY4KCggAAQUFBKCgoQE5Ojmo2zHKM5R2V8jnlMbbMmzcPM2fOVL7Pzc1FaGiow8+HiMrPXsd7OThJkhHR0UkICbloM5AFBmZBXU2rQVJSNOLjl6BTp2NYvvxRWHbINwUzWxtzA3JbCyJqWCoUwpo1a1Yp/cACAgIQEBBQoefKS6Dy8t3NmzeVgnyZk5OTaqzl8iQAJCYmQqfTATBN/UdGRmL37t0YMWKE8tzdu3cjLi4OgKn2zMXFBbt378aoUaMAAKdOncL58+eV8+h0Orz66qu4fPkyAgMDldfx9vZGp06d7L4nNzc3uLm5VejzIKKys9fx3rx1hLzXoyQZodMlIyoqxWEXfLlJ640bnrAOYABgxIABSTY64xsxfHiC3Zk3Iqq/KhTC3nrrLcyZMwfLli1Dy5YtK/uarKSkpODAgQO4++670aRJE6SlpeH5559HeHi4EnyGDBmCd955By+99JKyHPnss8+iZcuW6NGjBwDgsccew/vvv4/Zs2dj8uTJ+Oabb7B+/Xps27ZNea2ZM2di4sSJ6NmzJ+666y4sWbIEN27cwCOPPAIA0Gq1mDJlCmbOnAlfX194e3vjiSeegE6nQ69evQAAAwcORKdOnTBhwgQsXrwYWVlZeO655zB9+nSGLKJaxHGRfUkg27+/D/bv12HAgCQkJUXbbcKamRnyV/d7S6blyj59ktGly1FkZDTHzZvu8PD4U7UlEcBCfKKGpEIhrGfPnrh16xZat24NDw8P5e5DWXZ25U6re3h4YNOmTXjhhRdw48YNBAcHY9CgQXjuueeUUHPfffdhzZo1WLx4MRYvXgwPDw/odDrs3LkT7u7uAEzF+9u2bcOMGTOwdOlSNG/eHMuXL0dMTIzyWrGxsbhy5QoWLFiArKws3HHHHdi5c6eq0P6dd96BRqPBqFGjkJ+fj5iYGHzwwQfKcScnJyQkJODxxx+HTqeDp6cnJk6ciJdeeqlSPxciuj22i+zt0djc39Gc7YBmVAIYYKopu/9+z79WE7QAguDs7AxfX18EBASwEJ+oAalQn7Do6GicP38eU6ZMQdOmTa2K3idOnFhpF9iQsU8YUdWQe34BtmvC1EX1lhwdszZ69Hqr/R/j4uIYtojqsbL+/q7QTNj+/fuRnJyM7t27V/gCiYiqi3kbCoPBgMuXLyvHIiIOITz8DLKzfeHiUoDjxztj//7eDs5mr92EdTiztf3Q+PHjGcCICEAFQ1iHDh3w559/Vva1EBFVOlsbb1vSavOg1ebBYPD6ay9HRzNdGowevR6Zmc2QnKyDEBq7S5o6XbJS7zVy5EiEhIQwgBGRokIh7PXXX8fTTz+NV199FV27drWqCePSGRHVBPMZL9nVq1dV38s9wXx99QCg/F2rzStTN3x5dqtLlxOIikpRZtBWrJhq8VwjoqJSlO8YwIjIUoVC2KBBgwAA999/v+pxIQQkSUJxcfHtXxkRUTlYzniZhy2t1vSYuv7LCNOMl6kNxbBhCQgMzILtXl7yUqOpXYVMnkEDoGrgKp9PPhYbG8sARkRWKhTC9uzZU9nXQUR0W65cuaL83TxsyYEoPPyMRU+wkqAlhGnrINM9RuoC/fDwNPTv/w2OH++M5GQd9u/vg+RkHYYNS0BExCHlHOa1ZZabcFe0HyIR1W8VCmH33HNPmcb985//xEsvvaTsg0ZEVBX0ej3WrVsHwLoBqxAabN06FKNGbSxlqVFj1gW/ZLuh339vjf79v1Hqv8zPaWu/R0vc/5GI7KlQCCurzz//HM888wxDGBFVKfM6MFt1XXL7ibL3BFM/9/z5FjbPyf0eieh2lO+/RuVUgRZkRES3JTMzGJabZcvF9MOGJUCSjH89alT93d4G25JkRIsW583GljzO/R6J6HZU6UwYEVF1Mhi8/to2yLzFhMDdd+9FdrYfwsPPID5+iVK3BUD5e1paG5sbeQ8bloDmzS86LLwnIqoIhjAiqlMctaGw3WJCwt69fbF37z2qIv3sbD+4uORDDmyWTVsLC11VBfaOCu+53yMRVQRDGBHVGaU1XrW9F2RJd3u5oN5UKWF+F6QRkZGp6NdvL8LCzlmdd+DAgfDx8YGPj4/VMRbeE1FFMYQRUZ1hOQOm7gVm6tlluWxou0hfJi9bapCaeidSUyMxfHhJ6wl2uSeiqlSlIWz8+PHsnk9ElUKv16u639vqBRYRcchqWdG6k70jpn5hcusJf39/BjAiqjIVDmE5OTn46aefcPnyZRiN6ruGHn74YQDAhx9+eHtXR0QE293wbfUCc3XNR2hohlKvlZ3th+joJCQlRZvNjAGObwwvaT1BRFSVKhTCtm7dinHjxuH69evw9vaGJJXciSRJkhLCiIhuh1yEb7n/Y0ZGqM1lxg0b/g5JMqJbtyM4cqSbsj1RZGQqwsLS4eOTg+PHO2P/fh3UNWHm2HqCiKpHhULY008/jcmTJ+O1116Dh4dHZV8TETVQ5nc+GgwGpQu+uYMHe2DLlqF2zyGEBr/80h226r3M94rU6fbByakIe/f2MxtrxPDhbD1BRNWjQiHsjz/+wJNPPskARkSVprQ7Hw0GL2RkhGLr1qEovc+05ewWYLlXZHKyDvHxS9CzZyoyMpoDAEJDL7D1BBFVmwqFsJiYGPz8889o3bp1ZV8PETUQ8qyXwWBAYWEhrl27pjpufuejupFq5ZC3HQoLO4cRI4LQpEkTAB0BAC4uLggICGBRPhFVqTKHsC1btih/HzJkCGbNmoXjx4+ja9eucHFxUY0dPnx45V0hEdV5lg1Wc3JysH79ervjze98NG0pJMH27FbFmW871LZtW+79SETVrswhbMSIEVaPvfTSS1aPSZKE4uLi27ooIqo/SltmBNSzXgAsZr3KOvtlq8jeNm47RES1QZlDmGUbCiIiey5cAE6fBtq2BZycHDdYtZz16tz5eAWXHW0FMMtgZsTo0RtY+0VEtUKFasI+/fRTxMbGws3NTfV4QUEB1q5dyxYVRA2QvOS4Zo07Zs/WwmiUoNEILFhQMjNu2WDVvIeXiQbHjnWBrfAkSajg8qQRQElD1y5dTgAABgwYgLCwMG47REQ1RhLC1LqwPJycnHDx4kUEBgaqHtfr9QgMDORyZCXJzc2FVquFwWDgzgNUq8lLjgaDF5YsiVfNZEmSEfHxSwDA5jH7s16mIKbedNvUBV/d60uepbd9ntGj18PT86bVpttxcXEMX0RUJcr6+7tCM2FCCFWDVtmFCxeg1WorckoiqsMuX74MwNSh3lYT1exsXwCSnX0cTTNV1iT067cHgYFXlS746rskTf//KElAt25H8Msv3azOI0lGZelx5MiR8Pf3B8BNt4modihXCOvRowckSYIkSbj//vvh7Fzy9OLiYqSnp2PQoEGVfpFEVHvp9XrlTkdfX73V7Jb5XYi2jkVHJyExMRq2AtT3398DeSnReunS9D+CQmhw5Eg3TJ26HMePd0Zysk61n6Q8++Xv7887IImoVilXCJPvkDx8+DBiYmLQuHFj5ZirqytatWqFUaNGVeoFElHtZt56Ii2tDcwLHCyD0LBhCTY33e7S5ShSUqJUAcp8j0chNDaDmkwIDQoLXTFwYBKiolKQne1rtfzI4nsiqm3KFcJeeOEFAECrVq0QGxuLRo0aVclFEVHtJBff5+TkoKioCACUJqvyptrqzvRAYGAW0tNbwddXj4iIQ0ptl3lI0mrzEBWVgpCQTNy82QjXr3vh++/vtXh1e3s9qmfbRozo+VfjVcDZ2Rk+Pj5cfiSiWqlCNWETJ04EYPo/4MuXL1u1r2jRosXtXxkR1Qrmne0/+mi7qp9XdrYfXFzyUVjYCjdueNgostdgxYqpSu1X797JiIpKQVjYOdUodZsKOWjZClwCkiRU4yxn29h4lYjqigqFsNOnT2Py5MnYv3+/6nG5YJ93RxLVD+aNVk1BKd5Gm4iSMGRdZC9U7Sf27++D/ft1GD7ctAwJlMygWdZ62dv/cdQo092OLi4FKCx0tVp2JCKqKyoUwiZNmgRnZ2ckJCQgODjY5p2SRFT3yfVe1kHJPGiVFMibarnkIGbvrkcNtmwZisDALBQWutmZQbPN/G5He1j7RUR1RYVC2OHDh5GamooOHTpU9vUQUS1kq/WELUJolL5cLi4FZkuRljRYvnwqSsKavS2HxF9f1nc79u/fn7VfRFSnVSiEderUCVevXq3sayGiWkSv1yv/nttqPWGL5UzVsGEJ2LJFXaxfwnxWzWhV6/XXGSFJAqNGrbeaAevcuTMDFxHVaRUKYW+88QZmz56N1157DV27doWLi4vqOLu7E9Vttjbd1umSzbrUmwcl6wJ5eX/I8PAzmDp1OZYvfxS2Z7pkplqv3Fxv7Nql7jUohAaenjdVDVc540VE9UGFQlh0dDQA4L777lPVg7Ewn6h+MO/9ZbnfY0nNl0xg9OgvlZkqy/HDhiVg+PCtFueA1TkMBh906XIUiYkD7TZ7ZcNVIqpPKhTC9uzZU9nXQUS1kGVBvr36LgDKDJjl+K1bhyI+fgni45cgJSXKzmyahKSkaHTpctRmQ1fe/UhE9VGFQtg999yDvXv34j//+Q/S0tKwYcMGNGvWDJ999hnCwsIq+xqJqJJcuACcPg20bQs0b176+LIW5N+86Y70dNu9wuS9I319s5GcLAcwwHJ5Uh5nr6ErwDsfiah+qVAI27hxIyZMmIBx48bh0KFDyM/PBwAYDAa89tpr2L59e6VeJBFVnNxsdc0ad8yerYXRKEGjEVi82ICHHvrTYX2V7YJ8yzsZBbZvHwJ7dzrKy4mlBzoBFxfTMuiIET3Rtm1b1VHWgRFRfVOhEPbKK69g2bJlePjhh7F27Vrl8T59+uCVV16ptIsjotsjF9gbDF5YsiQeQpjCkdEoYdYsb/zxx8fQavPQu3dvBAUFwcXFBVqtVrkrUqvNs1oe7NbtCI4c6Wanvkt9p6PlcqLjOywlFBaaZrqaNGnC2i8iqvcqFMJOnTqFfv36WT2u1WqRk5Nzu9dERJVELrC3NQslL/9ptXlWu1+Ys7U8eN993yA72xc3bnhiw4a/WzzDdKejiYTQ0AwA1oHO3owZEVFDUaEQFhQUhDNnzqBVq1aqx3/44Qe0bt26Mq6LiCqRvWXFo0c7q+qu5NYSvr56VS2WVpunFN7Lm3GHhZ2DweBl87zHjnXGyZMdlS2OBgxIQp8+yapAl5kZgqSkaJszZs7OFfpPExFRnVKh/9I9+uijeOqpp/Dxxx9DkiRkZmYiOTkZzzzzDJ5//vnKvkYiuk1abR6io5OQmDgA5nckpqbeidTUSAwfngAAqmXH6OgkaLUGAEBoaAbS0tpY3bUYEXHI5nlPnOhk9r3mr+NAnz7JSqALCzuHLl2O2izADwwMrOqPhIioxlUohM2dOxdGoxH3338/bt68iX79+sHNzQ3PPPMMnnjiicq+RiKqIIPBoPw9JOQi7G2KbQpXpr8DpqVKdbASf/1Zsk/k1q2m/R8lydaWQ9bfyy0ozJuu2sICfCJqKCoUwiRJwvz58zFr1iycOXMG169fR6dOndC4cePKvj4iqiC9Xo9169Yp37u45MPeptq2i+UlO38veU7J/o/29n5Uj5dr0Nh0lYiogiFM5urqik6dOlXWtRBRJbLV9d5eYLLdxb40AvZ6ftnCwnsiIrXy/BeXiOogyy72psAkYJoVg9nWQgmQJONfY4SNMxmV46Y/Swte6vHsfE9EpMZbkIjqOdtNUiWMHv0lPD1vqori//yzkXLHovmMmRyi5DsbXVwKsGLFVKs9HuXZNMvxloX3RETEEEZU79lqTyFJRmXDbZnB4GUWwABTADPigQe2oX370wCgal9ha49HW6HLVvji9kNERAxhRHWOvA2RPfLdhXLjZHuBSQ5H4eHhSEtLszNjpkFAgN5ue4qyhq4xY8bAx8dHdX1ERA0dQxhRLWYZuHJycrB+/XoHzzAZP368apyjTbHT0tIA2J8xc3EpUNWUye0pwsPPKD2/LMXGxkKr1QJg6CIisochjKiWkYOXwWDARx9tV5YAAXk50KvUDvc3b95UndPeOMtjtmbMCgvdHG55ZNnzi6GLiKhsGMKIahF5w21AbisRr2z9Y6rRkpRwBMDmEqEluT2FrXG2jsXHL1FmzAAgIyPU5gyZfJw9v4iIKoYhjKgWkZcerdtKlAQgeTnQssO9+RKhzPI85uMA2DwWH78EYWHnVAFNbjdhq6aMiIgqhiGMqBayXSRfwtYx8yVCR+eRxwGSg2OwCoFCGDF69HqruyqJiKhi2KyVqBaSi+TtMR0zWj0mLxFeu3bNwXkEMjNDbB6Tz2HvTklPz5tWAYztJoiIKoYhjKgWkttKlIQk6+7z5h3uLZcI9+zZo5wnOjoJ6g74ps20AVi9RnR0ErTaPIcBDQBGjhyJadOmIS4ujkX4REQVxOVIompy4QJw+jTQti3QvHnp4y3bSgCwajFhr+2E+R2PISEXYbnFkLzsGBFxSNUlPykpGu7utxARcchhbzEW4xMR3T6GMKIqJLebWLPGHbNna2E0StBoBBYvNuChh/4stZ2DZR8uy6VAW326LO94jI5Osnt3o2WXfPPCfUe9xYiI6PZxOZKoisjtJt588wvMmuUNo9E0G2U0Spg1yxtvvvkF3n//fej1emX81atXy/06DzzwgPJ3W3dDJiVFK0EMUC9dOi7cN4W8sLBzrAMjIqoCnAkjqoCyLC3K7SYcBR2tNg8FBQWq/mBlNWbMGAQGBqo66tt7rZCQTFX/LzlU2euSb177Zd6IFWAzViKiysKZMKJy+te/gJYtgfvuM/25YoXj8aUVuQNwuBekPT4+PlZhKDMzGOoi/JLXsjWrZXkDgL3aL/MvBjAiosrBmTCicnjzTWD27JLvjUbgH/8AYmLsz4iVtoG2rSVI88J6ADa3HJI36JafL9d3qYvwBSIiUq3O379/f+UOStZ+ERHVDIYwojJKTMzB7NlaWN5pWFwMpKTo4e4Ou7NEjoLOpk2bVGPVnerFX1/WWw5ZbuRtu7eXhNTUO3HwYKTquY0aNVKNsrcRN2u/iIiqDkMYURksWnQZ8+f7wzKAAaYlvH37VuHo0TyHfbPsBR1z1tsVScpr2tuaSH7ejRseVvVdMsvnuru7Iy4uzuEyKGu/iIiqFkMYkQN6vR5HjmRj/vxwO9sICaXBKVD+2i7zZUd7dyuqXs3G1kT29ni09dyMjObQak8AsD9rR0RE1YMhjMgO+Y7F9PRWEKKtjRFGDBiQhD59kpVHzOu7Sms3YdnPa9iwBISHn7EbogDrgn5bG30LYURUVDJSUnRlfq9ERFT96szdkcOHD0eLFi3QqFEjBAcHY8KECcjMzFSNWb9+Pe644w54eHigZcuWePPNN63O8+233yIiIgJubm5o06YNVq5caTXm3//+N1q1aoVGjRohKioKP/30k+r4rVu3MH36dPj5+aFx48YYNWoULl26pBpz/vx5DBkyBB4eHggMDMSsWbNQVFR0+x8EVZsrV64AwF/F8dZ3N06dulwVwABTfddHH32Ejz76yKrWy5ytfl5btgwFAKu7FeXXtizoB4CMjFCbezyGhmZYXTNgRGjohdLfOBERVYs6MxPWv39/PPvsswgODsYff/yBZ555BqNHj8b+/fsBADt27MC4cePw3nvvYeDAgThx4gQeffRRpfYFANLT0zFkyBA89thjWL16NXbv3o2pU6ciODgYMTExAIB169Zh5syZWLZsGaKiorBkyRLExMTg1KlTCAwMBADMmDED27Ztw5dffgmtVou4uDiMHDkS+/btAwAUFxdjyJAhCAoKwv79+3Hx4kU8/PDDcHFxwWuvvVYDnx6Vl16vx7p16wAAaWltoK4FM4Wh5s0vluucI0eOBGAKavY2yE5JicLAgUl2tysCgPT0VnBxycfx452RnGw92yVJprA1fHjCX8HOtEw5fHgC73wkIqpFJCGEKH1Y7bNlyxaMGDEC+fn5cHFxwUMPPYTCwkJ8+eWXypj33nsPixcvxvnz5yFJEubMmYNt27bh6NGjypixY8ciJycHO3fuBABERUXhzjvvVBpnGo1GhIaG4oknnsDcuXNhMBgQEBCANWvWYPTo0QCAkydPomPHjkhOTkavXr2wY8cODB06FJmZmWjatCkAYNmyZZgzZw6uXLlS5jvOcnNzodVqYTAY4O3tXSmfG5XNxYsX8dFHH8Fg8MKSJfFWzUzj45fYDTSWdV6yadOmAYBy3nfeiYflZLT5uXv27AkA+PnnnwHYumvS+iYBOWzJd0GarsX6jszY2Fh06NChfB8KERGVSVl/f9eZ5Uhz2dnZWL16NXr37g0XFxcAQH5+vtVt9+7u7rhw4QLOnTsHAEhOTkZ0dLRqTExMDJKTTUtKBQUFSE1NVY3RaDSIjo5WxqSmpqKwsFA1pkOHDmjRooUyJjk5GV27dlUCmPw6ubm5OHbsmN33lZ+fj9zcXNUX1azStvWxdPBgDyxZEo9VqyZiyZJ4HDzYQzl29epVpKenAzDdKdm7d7LV883P/fPPPysBzPZdk9ZGj96gBDD5dWxtO6TVah28ayIiqg51KoTNmTMHnp6e8PPzw/nz5/G///1PORYTE4NNmzZh9+7dMBqN+O233/DWW28BMM1qAEBWVpYqGAFA06ZNkZubiz///BNXr15FcXGxzTFZWVnKOVxdXeHj4+NwjK1zyMfsWbRoEbRarfIVGhpa1o+GbsOFC8CePaY/LZWl273MVp3X1q1DYTB4ATAtQyYmJirjo6JSHJ7bYPBCenorZWbN0V2T8nPLWvPF/l9ERDWvRmvC5s6dizfeeMPhmBMnTijLJrNmzcKUKVNw7tw5vPjii3j44YeRkJAASZLw6KOPIi0tDUOHDkVhYSG8vb3x1FNPYeHChdBo6kbWnDdvHmbOnKl8n5ubyyBWRfR6PQoKCrBmjTtmz9bCaJSg0QgsXmzAQw/9qXSjL63bvbnS9oi0RadLRnKyzurclndOyhtwO7pr0vy6+vfvj4CAAKv/WQDY/4uIqLao0RD29NNPY9KkSQ7HtG7dWvm7v78//P390a5dO3Ts2BGhoaH48ccfodPpIEkS3njjDbz22mvIyspCQEAAdu/erTpHUFCQ1V2Mly5dgre3N9zd3eHk5AQnJyebY4KCgpRzFBQUICcnR/ULznKM5R2V8jnlMba4ubnBzc3N4edBt09uPVFS72Va2jMaJcya5Y0//vhYFZrKuq1PaZthAyX1YpmZwUhKilZ6e/XuvQ9RUSnQavNgMHiZFdSbglxSUjQiIlKRmnqn1et27vwrBg5MVF1X27ZtERwcfDsfExERVbEanSIKCAhAhw4dHH7ZWzYxGk3LOPn5+arHnZyc0KxZM7i6uuKLL76ATqdDQEAAAECn0ynBTJaYmAidznSHmaurKyIjI1VjjEYjdu/erYyJjIyEi4uLasypU6dw/vx5ZYxOp8Ovv/6Ky5cvq17H29sbnTp1qtBnRbfHfMlRbqhannove7VVADBw4EBljKPNsM3rxRITB6h6e5nf5ZiSEgXLfzWF0CAsLB22WmVYBjCAy41ERHVBnWhRkZKSggMHDuDuu+9GkyZNkJaWhueffx7h4eFK8Ll69So2bNiAe++9F7du3cInn3yCL7/8Et99951ynsceewzvv/8+Zs+ejcmTJ+Obb77B+vXrsW3bNmXMzJkzMXHiRPTs2RN33XUXlixZghs3buCRRx4BYCponjJlCmbOnAlfX194e3vjiSeegE6nQ69evQCYfil36tQJEyZMwOLFi5GVlYXnnnsO06dP50xXNbK35LhgQTGAss1clWbMmDEIDAzErl27ANifNSutsF4Of3l5jbF/v60mqyVtJxwtjY4cORIhISFcbiQiqgPqRAjz8PDApk2b8MILL+DGjRsIDg7GoEGD8Nxzz6lCzapVq/DMM89ACAGdTodvv/0Wd911l3I8LCwM27Ztw4wZM7B06VI0b94cy5cvV3qEAaZb969cuYIFCxYgKysLd9xxB3bu3KkqtH/nnXeg0WgwatQo5OfnIyYmBh988IFy3MnJCQkJCXj88ceh0+ng6emJiRMn4qWXXqriT4pkjpYcX3wxBPHxXuWq97InMDAQfn5+iIuLw5UrV7Bu3Tqbe0SWVlgvSUb8/nsY9u7tB1t3PvbunQytNq/UpVEGMCKiuqPO9glrCNgnrOLkPl/p6a2watVEq+MTJ65EWJipdYm9Xlp9+vSBh4eH1U4Hzs7O8PLygouLCwICApTQI7+mLbb6jZUQCAjIwpUrQbC3QbijvmSy8ePHIzw83OEYIiKqemX9/V0nZsKIKqosS462Zq4AKDsglCYuLs7m7JNchO/iko/CQjdERychMTEa1qWYkt0AJnfnN7++MWPGWN31yDseiYjqHoYwqtcqY8mxNHKhvzlb3e0lyYi+fffaWXK0FcAEpk5drtoeacyYMejYsWOlXTsREdUchjCq90qro7K3zVBF2SvCF0KDH37oi65df8Gvv3aHOnjZ2oZIwMvruuoRef9SIiKq+xjCqEGwt+Ro2RR12LAE1bY/5soa1hwV4Quhwa+/3gFT6DLCtDRpbx/Ikkav/fv3R+fOnbnkSERUjzCEUYNlb5uh8PAzqtYSls1VLcPa1atXVX/aqkOzJkGSBPr23YPvv+9ve4RZ7VqTJk0YwIiI6hmGMKrT5F5gluRA5Ii9Zq0ZGc2Rnf2nRVf7ktkqy7C2adMm1Tks69DszXQJoUFg4BU7gU1duyZvVE9ERPUHQxjVWXIvsIqyd+fkhg2jYb1MaLu5qr1lSbkO7dSptti+fajNMfKG25Y3Duh0ycoWRjKtVlvh90lERLUTQxjVWbZmwGwZOXIk/P39le9zcnKwfv16m3dOmrrm2e5qb06SjHBxKUB6eiu7NWJpaW2wffsQm+cxv0uzrHtTEhFR/cIQRvWev7+/ajPr4OBgxMXFKSEuLu4UVq9OwY0bntiw4e8OzlTSaqJbtyNYsWKq3YJ+ud7M9vasRkyZom49Ye/GARn3giQiqn8YwqhOsFX7VZa6L3ssi9zDws7BYPCyUZ9VEryio5MQEpIJF5cCJYABtgv67d0hKQc28wDmSGxsrKorPxER1R8MYVTrlbX2q6L9vuRZJlvLk3LwMl8mTE9vZbOg37xGzPYdktYzYP3790dAQIBVB3z5uhi+iIjqL4YwqvXKUvtVnn5flvz8/DBmzBisX7++TPVZ9gr6LWvEbHXqt5wBa9u2rWqplIiIGg6GMKrzytLvqzTmM1HmS4rm35sfV7egMKJFi3NYvnwqAHUILC3QsdaLiKjhYgijOs9evy9HLSQcKcusWkTEIfz5ZyNlQ+5z58JUr20eAm1dw8iRIxESEsLlRiKiBsxRS2+iWslg8EJ6eisYDF4ASpYHzZl3my/PbJO9WTX5tQYMGKCMkwOYLXIItIcBjIiIOBNGdYq9WaphwxKQkDAMRqMEJyeBN97IxUMPPVju4vbSZtW8vLyUcY7+H8Y8BFr2KWPBPRERAQxhVIc4qv2KiDiEBQuikJfXFG3aSGje3AeAT7lfw17RvRyozMeVbMBtSb3lkGWfMiIiIoDLkVSHOJqlAgBX18to3/4inJwu4uLFi9Dr9WU+t2WbCnl507yzPQBcv35dGTd8eAJMQUy5GvTuvQ8zZiwp852ZRETUcHEmjGo9OSCVNktluZE2AMTFxZVp6c/Pz0/VRX/Bgis4e9YZrVoVwcOjLdavN4WqXbt2Kf3IwsPPYMaMJcjIaA4ACA29wC2HiIiozBjCqNYzD0jNmuVizhwtioslq1kqW8q6v6T8OrLgYCAy0vT3ixdLZrsq0o+MbSiIiMgWhjCqE+SA9PTTQGwskJKix759qypl5snWlkjmDAbDX3867kfWs2dPeHl5wdnZGb6+vtBqtSzCJyIiuxjCqMqUFm4qGlCaNwecnApw9GjlBDDzLZEstz4q+d6r1DsnIyIiWIBPRERlxhBGVaKs+z2WtWarMpS2CbjlUmO3bkdw5Eg31T6SZblzkoiIqCwYwqhKlLUWqzw1W46Utnl3aaHQ1lLjL790ByAp3yclRSM6OglJSdGqmjAW4xMRUUUwhFGdV5ZiecuwZxnabC01ygFMJoQGISGZiI9f4nA/SCIiorJgCKNKJS/5mS/zVaWKbN5tGdp0umQ4ORUBEFAHL/X38tKjvf0giYiIyoMhjCpNWevAKoPc9qG0YnnL9hC2Qtv+/X1gK4B16nQcJ050LPPSI1tREBFReTCEUaWprPquspB7h509W4TPPhMwGksClJOTwBNPDEarVs5WRf+2lx0By6VHQMKddx5ATMzXNpceuR8kERHdLoYwqrP8/Pzg5wd89BHwj38AxcWAkxPwn/9IiIxsavM5trru21La0iP3gyQiotvFEEY1ylbtmLysV9YeY1OmADExwJkzQJs2pj5i5vR6vfI68t6Q5kuSJUxLkrzrkYiIqgNDGFULey0kbO33WFbjx49HeHg4AFPwsgxfgO1mrE2aXMOUKctx6FAPpKZGAijpAxYSklmmux5Z/0VERLeLIYyqXEX2WyyLzz//3GazV/OmrPaasZpmvQTMA1ifPsk2X4f1X0REVBUYwqhKVaSFRHlYLlnau0PT8jpMhfjqRqxduhy1eU0hISEMXUREVOkYwqhKldZC4nbl5OSoCuSvXLmiOi4vg9644eGwGN/8mgYMGICwsDAAnPUiIqKqwxBGlcZWnZStuxErc7/F9evXK0uSer0e69atU47t26dDYmI0AA0AI6x7gZUwv6awsDDe+UhERFWOIYwqjdy7S14izMzUID3dGZ6eeVi0yBvFxRI0GoGhQ8t356HB4IWMjFAAQGhohtVz5dczX5o0BbABKAldpiAmSUKpTRPC9DjvhiQioprAEEaVSl66W7ECmDYNMBoBjQZ44w2gZ0/Ay+syEhLKXpR/8GAPbNkyFKYQBQACw4dvdVjYbzB4ISkpGtazXhqMGrUenp43lVkv7gFJREQ1hSGMKt2FCyUBDDD9OXcucPYs4ORkVI2117pCPqYOYAAglVrYb78rvhGhoRdUz2P4IiKimsIQRpXu9OmSACYrLjY1U+3ataRurLTWFaYlSOswJYQGKSlRGDgwyebr2+6KLzBgQBJDFxER1RoMYVTp2rY1LUGaBzEnJ1M3e/M9H196KRBClLSJ2Lp1KFxd8xEamoG0tDbYunWo3dfYv1+HqKgUVajKyckBAKSltfmr3ksmMGBAot0+YJbYiJWIiKoDQxhVuubNbe3nWNLR3s/PD0eOWM+WCaHBhg1/VxXN26duc6HX67F+/XqlH5j6uQJduhxVPXvMmDHw8fGxOitbUhARUXVhCKMqUdp+jqbZMgGj0bplRGmbawOmlhIuLgVIT2+Fo0dzEBRUBMC0hGn9fHVgGzNmDDp27Fih90VERFRZGMKoytjbz1E+9vbbNzBjhuMmqjLLlhLduh3BihVTIYQGn35qqicDethcwrTsSxYYGFixN0RERFSJGMKoxjz1VGN063YaH3xwCBs2jIb18qOpuapctD9ypCf27MmAi0uBEsAA08zZli1DIUm2ZtHUPcBiY2O53EhERLVC6VMQRFWoQ4fG6NLlBIYPT4Cpq705UwCbMmU5IiIOISzMBb6+2Th/vqXNJUdbM2qjR29Q3XGp1Wor/T0QERFVBGfCqFYIDz9j83EhNCgsNN2t+NZbOdi6Nf6vsGW5BZHRaiZMkkx9wYiIiGojhjCqFbKz/WBrYlau55LveiwJWRIslysBWPUdY18wIiKqrRjCqFaw12A1OtrUYDU9vZWN5Ua5x5jpu4iIQwgPP8OtiIiIqE5gTRjVClptHoYNS4AkyXVhRlWDVTmk2WZq9GoweEGrzUNY2Dm7AYyNWImIqLbgTBjVKPNQ5GgmSw5p6iXJEkKoe4FZGjlyJEJCQnhnJBER1RoMYVRj9Ho9CgoKEBsbi8LCQqvjzs7OKCoqwqZNmwCUhLSMjOZWLS0se4FZ8vf3ZwAjIqJahSGMaoRer8f777+vfG8weCE72w++vnrVbNaYMWNUz9Nq86DVnkBBQUK5ivC5DElERLUNQxhVOnmGyx5XV1fV8YMHe1gFKrm314ULQHp6K6tw5mjpcuTIkfD391e9HmfBiIiotmEIo0plOcNljzzDZdl6QghTkX14+BmkpbXBSy91gNHY0SqcAfKsmPXsl7+/P4KDgyvpHREREVUN3h1JlcpyBsxg8EJ6eisYDF6qx4uKTBtuZ2f7WRXaC6FBRkZzbN06VNngWw5nluchIiKqqzgTRpVCXoK8evWq8pijZca8PNMMlq3+YKZWFJLNcOboDkgiIqK6hCGMbputIvuMjFBs2TIU8mSr+TKjVpuHxMREAKYlxejoJCQlRavCWmhohs1w5ugOSCIiorqEIYxum70ie0u2ZrIOHuyhBDDAiOjoJGW2zLwvWHm2IeKdkEREVBcwhFGlsd7f0ZKAi0uBg/EaJCZGQ6s1IDQ0o1x3QMp4JyQREdUVDGFUaWwV2atJKCx0LWW8Bhs2/F1VQ8Y7IImIqD7i3ZFUaRzv72hd0+Xikm93PO+GJCKi+o4hjMrswgVgzx7Tn7bY2oRb/rtlTdfBgz2wYsXUv2bChM3zyTVkRERE9RGXI8khufXEmjXumD1bC6NRgkYjsHixAQ899KdVEbxlHRcAq5oug8FLdeckIMEU2FDmuyFZfE9ERHUdQxjZJbeeMBi8sGRJPIQwNU41GiXMmuWNP/74GADQrdsoGAxeSsiy7GRvWdOVkhIF60lYDXS6fUhO1tm8G9K8EJ/F90REVB8whJFdcusJe13tU1KizEJTvNW2QrYYDF5ITtbZOGJEVFQKoqJSbN4NyUJ8IiKqb+pcTVh+fj7uuOMOSJKEw4cPq44dOXIEffv2RaNGjRAaGorFixdbPf/LL79Ehw4d0KhRI3Tt2hXbt29XHRdCYMGCBQgODoa7uzuio6Nx+vRp1Zjs7GyMGzcO3t7e8PHxwZQpU3D9+vVyX0tdYbvg3qgEMMAUyhIShuGOO4Y6PJe9Oyh7905WZtDCws6xKz4REdV7dS6EzZ49GyEhIVaP5+bmYuDAgWjZsiVSU1Px5ptvYuHChfjoo4+UMfv378eDDz6IKVOm4NChQxgxYgRGjBiBo0ePKmMWL16Md999F8uWLUNKSgo8PT0RExODW7duKWPGjRuHY8eOITExEQkJCfj+++8xbdq0cl1LXWJZcC9JRvTunWwVpoxGCV999SsA0wbd06ZNU75GjhwJwHagkyTTLBgREVFDUqeWI3fs2IFdu3Zh48aN2LFjh+rY6tWrUVBQgI8//hiurq7o3LkzDh8+jLffflsJSEuXLsWgQYMwa9YsAMDLL7+MxMREvP/++1i2bBmEEFiyZAmee+45/O1vfwMAfPrpp2jatCk2b96MsWPH4sSJE9i5cycOHDiAnj17AgDee+89PPDAA/jXv/6FkJCQMl1LXWOr4N58JgxQF9L7+PjYXD6UA115O+GzEJ+IiOqbOhPCLl26hEcffRSbN2+Gh4eH1fHk5GT069dP9cs6JiYGb7zxBq5du4YmTZogOTkZM2fOVD0vJiYGmzdvBgCkp6cjKysL0dHRynGtVouoqCgkJydj7NixSE5Oho+PjxLAACA6OhoajQYpKSn4v//7vzJdiy35+fnIz89Xvs/NzS3fh1TF5KCUne0HX199hbcVctQJH7Duhs9CfCIiqo/qRAgTQmDSpEl47LHH0LNnT5w9e9ZqTFZWFsLCwlSPNW3aVDnWpEkTZGVlKY+Zj8nKylLGmT/P3pjAwEDVcWdnZ/j6+qrGlHYttixatAgvvvii7Q+hFjDfF1IOXfHxS+yGKUcs76A0FxISwtBFRET1Xo3WhM2dOxeSJDn8OnnyJN577z3k5eVh3rx5NXm5VW7evHkwGAzKV0ZGRo1eT2amBunprWAweFnt8yh3tAeAsLBzAKCMBYCrV69Cr9cr5yrrcuL48eMZwIiIqEGo0Zmwp59+GpMmTXI4pnXr1vjmm2+QnJwMNzc31bGePXti3LhxWLVqFYKCgnDp0iXVcfn7oKAg5U9bY8yPy4+Z1zNdunQJd9xxhzLm8uXLqnMUFRUhOzu71Ncxfw1b3NzcrN5jTVmxApg2LRBG40RIkhE6nXUhvtzRPi2tjdUMGbAJABAXFwc/Pz/4+fkhLi5OaXthC5cdiYioIanREBYQEICAgIBSx7377rt45ZVXlO8zMzMRExODdevWISoqCgCg0+kwf/58FBYWwsXFBQCQmJiI9u3bK8t/Op0Ou3fvRnx8vHKuxMRE6HSmvlVhYWEICgrC7t27ldCVm5uLlJQUPP7448o5cnJykJqaisjISADAN998A6PRWK5rqc0uXACmTTPd7QiYwtb+/TpIktGqEN/FpcDmDFl4+BlotXmq0MWARUREVKJOtKho0aIFunTpony1a9cOABAeHo7mzZsDAB566CG4urpiypQpOHbsGNatW4elS5eqCvGfeuop7Ny5E2+99RZOnjyJhQsX4ueff0ZcXBwAQJIkxMfH45VXXsGWLVvw66+/4uGHH0ZISAhGjBgBAOjYsSMGDRqERx99FD/99BP27duHuLg4jB07VmmdUZZrqc1OnwaMVvtqa6DTJVvtBVlY6GZ3hoyIiIjsqxOF+WWh1Wqxa9cuTJ8+HZGRkfD398eCBQtULSF69+6NNWvW4LnnnsOzzz6Ltm3bYvPmzejSpYsyZvbs2bhx4wamTZuGnJwc3H333di5cycaNWqkjFm9ejXi4uJw//33Q6PRYNSoUXj33XfLdS21Wdu2gEajDmJyLy/LjvYGg5fNGTJ7ez4SERGRiSSEEDV9EWRbbm4utFotDAYDvL29q+U1zTfsnjNHi+JiCZJkRHR0EkJCLsLXV291V6Otuybl7YumTZvG7YaIiKhBKevv73ozE0a3T96wW/bkk17IzvZFZmYIkpKibYYsoPS+X0RERGStTtSEUfWwvHNRq82Dr2+2EsCAksJ7uRWF+Vhbez7m5ORU6TUTERHVVQxh5JCtDbfLU3hfVFRUFZdFRERU5zGEkUP2Nty2LLw3GLxUzVplcosOIiIiUmNNGDmk1eYhOjoJiYnRAMybsZo65Pv66m02a5VrxrRabQ1ePRERUe3FEEYOHTzYA0lJJQEsOjoJALBkSbwSukz319pu1kpERES2MYSRXbb2izQV6QPmocuSXDPGEEZERGQfQxjZZa8ovzRs1kpERFQ6FuaTwtXVVfW9vaJ8wP5jck0YZ8GIiIgc40wYKfz8/PDAAw9g+/btAEqK8i0btQKwKsS316zVMtgRERGRCUMYKfR6Pf7880/le7ko37QEaSrKl+96tBW6tNo8jBw5Ev7+/gBMAczPz6/a3wcREVFdwBDWgF24AJw+bdqw291dvWWRZVE+YCrK79LlKLTaPOXLUkhICIMXERFRGTCENVArVgDTpgFGI6DRCMyZkwM3t5Ljjjrly+HLfNYL4MwXERFReTCENUAXLpQEMAAwGiW8/noYpkwJRmGhG3x99fD11cNUbG8exNR3PXLWi4iIqOIYwhoYvV6PH38EjEZ1eBJCg+XLp0LdlFVSjZHMvh04cCADGBER0W1gCGtA9HpT3ZfB4AVJirdYbhQwb8Bq2qZIHcLMlyMbN25cbddNRERUH7FPWANSUFAAwHQX47BhCUoPMNOfksVo6x8N8yaszs7M70RERLeDv0kbqIiIQ0qbCReXAqxYMdVhN3zLJqyBgYHVdalERET1EkNYA6HX63H16lUApvYT2dl+8PXVIyzsHABg2LAEi5YUaqNGbUCXLicwcuRIFuQTERFVAoawBkCuBQNMDVgtu91HRBxSZsYyMppjw4bRMF+OlCQjQkMvAOAdkURERJWFIawBkGvBLBuwCqHB1q1DERiYpbSmKChoBHV9WMky5JgxYxjAiIiIKglDWANirwFrST2YXKBfEsIkybRFEcA6MCIiosrEENaA+PrqIUlGq9YU5lsTWRJCgz59JuLee8FZMCIiokrEFhUNiGVripKZL/ucnASiovwYwIiIiCoZZ8IamNJbU4i//pQgSUa89dZNNG/OxqxERESVjSGsAdJq85R+X9HRSUhMHICSGTEJkiTw9tt/4P77PdC1a5Mau04iIqL6jCGsgQsJuQjr7Ykk3HFHM3TtWjPXRERE1BCwJqwBcHV1tXtMLtY35+QEtGlT1VdFRETUsHEmrAHw8/NDXFyc0i/MUrNmuZgzR4viYglOTsB//gM0b17NF0lERNTASEIIUfowqgm5ubnQarUwGAzw9vau0te6cAE4c8Y0A8YARkREVHFl/f3NmTACYApeDF9ERETVhzVhRERERDWAIYyIiIioBjCEEREREdUAhjAiIiKiGsAQRkRERFQDGMKIiIiIagBDGBEREVENYAgjIiIiqgEMYUREREQ1gCGMiIiIqAYwhBERERHVAO4dWYvJe6vn5ubW8JUQERFRWcm/t+Xf4/YwhNVieXl5AIDQ0NAavhIiIiIqr7y8PGi1WrvHJVFaTKMaYzQakZmZCS8vL0iSVOHz5ObmIjQ0FBkZGfD29q7EK6w7+BnwMwD4GQD8DAB+BgA/A6BqPwMhBPLy8hASEgKNxn7lF2fCajGNRoPmzZtX2vm8vb0b7L9sMn4G/AwAfgYAPwOAnwHAzwCous/A0QyYjIX5RERERDWAIYyIiIioBjCENQBubm544YUX4ObmVtOXUmP4GfAzAPgZAPwMAH4GAD8DoHZ8BizMJyIiIqoBnAkjIiIiqgEMYUREREQ1gCGMiIiIqAYwhBERERHVAIawOurDDz9Et27dlCZzOp0OO3bsUI7funUL06dPh5+fHxo3boxRo0bh0qVLqnOcP38eQ4YMgYeHBwIDAzFr1iwUFRVV91upNK+//jokSUJ8fLzyWH3/HBYuXAhJklRfHTp0UI7X9/cv++OPPzB+/Hj4+fnB3d0dXbt2xc8//6wcF0JgwYIFCA4Ohru7O6Kjo3H69GnVObKzszFu3Dh4e3vDx8cHU6ZMwfXr16v7rVRIq1atrH4OJEnC9OnTATSMn4Pi4mI8//zzCAsLg7u7O8LDw/Hyyy+r9u6r7z8HgGmbnPj4eLRs2RLu7u7o3bs3Dhw4oByvb5/B999/j2HDhiEkJASSJGHz5s2q45X1fo8cOYK+ffuiUaNGCA0NxeLFiyvnDQiqk7Zs2SK2bdsmfvvtN3Hq1Cnx7LPPChcXF3H06FEhhBCPPfaYCA0NFbt37xY///yz6NWrl+jdu7fy/KKiItGlSxcRHR0tDh06JLZv3y78/f3FvHnzauot3ZaffvpJtGrVSnTr1k089dRTyuP1/XN44YUXROfOncXFixeVrytXrijH6/v7F0KI7Oxs0bJlSzFp0iSRkpIifv/9d/H111+LM2fOKGNef/11odVqxebNm8Uvv/wihg8fLsLCwsSff/6pjBk0aJDo3r27+PHHH8XevXtFmzZtxIMPPlgTb6ncLl++rPoZSExMFADEnj17hBAN4+fg1VdfFX5+fiIhIUGkp6eLL7/8UjRu3FgsXbpUGVPffw6EEGLMmDGiU6dO4rvvvhOnT58WL7zwgvD29hYXLlwQQtS/z2D79u1i/vz5YtOmTQKA+Oqrr1THK+P9GgwG0bRpUzFu3Dhx9OhR8cUXXwh3d3fxn//857avnyGsHmnSpIlYvny5yMnJES4uLuLLL79Ujp04cUIAEMnJyUII0w+uRqMRWVlZypgPP/xQeHt7i/z8/Gq/9tuRl5cn2rZtKxITE8U999yjhLCG8Dm88MILonv37jaPNYT3L4QQc+bMEXfffbfd40ajUQQFBYk333xTeSwnJ0e4ubmJL774QgghxPHjxwUAceDAAWXMjh07hCRJ4o8//qi6i68iTz31lAgPDxdGo7HB/BwMGTJETJ48WfXYyJEjxbhx44QQDePn4ObNm8LJyUkkJCSoHo+IiBDz58+v95+BZQirrPf7wQcfiCZNmqj+XZgzZ45o3779bV8zlyPrgeLiYqxduxY3btyATqdDamoqCgsLER0drYzp0KEDWrRogeTkZABAcnIyunbtiqZNmypjYmJikJubi2PHjlX7e7gd06dPx5AhQ1TvF0CD+RxOnz6NkJAQtG7dGuPGjcP58+cBNJz3v2XLFvTs2RN///vfERgYiB49euC///2vcjw9PR1ZWVmqz0Gr1SIqKkr1Ofj4+KBnz57KmOjoaGg0GqSkpFTfm6kEBQUF+PzzzzF58mRIktRgfg569+6N3bt347fffgMA/PLLL/jhhx8wePBgAA3j56CoqAjFxcVo1KiR6nF3d3f88MMPDeIzMFdZ7zc5ORn9+vWDq6urMiYmJganTp3CtWvXbusauYF3Hfbrr79Cp9Ph1q1baNy4Mb766it06tQJhw8fhqurK3x8fFTjmzZtiqysLABAVlaW6j+48nH5WF2xdu1aHDx4UFXzIMvKyqr3n0NUVBRWrlyJ9u3b4+LFi3jxxRfRt29fHD16tEG8fwD4/fff8eGHH2LmzJl49tlnceDAATz55JNwdXXFxIkTlfdh632afw6BgYGq487OzvD19a0zn4Ns8+bNyMnJwaRJkwA0jH8PAGDu3LnIzc1Fhw4d4OTkhOLiYrz66qsYN24cADSInwMvLy/odDq8/PLL6NixI5o2bYovvvgCycnJaNOmTYP4DMxV1vvNyspCWFiY1TnkY02aNKnwNTKE1WHt27fH4cOHYTAYsGHDBkycOBHfffddTV9WtcnIyMBTTz2FxMREq//zayjk/8sHgG7duiEqKgotW7bE+vXr4e7uXoNXVn2MRiN69uyJ1157DQDQo0cPHD16FMuWLcPEiRNr+Oqq34oVKzB48GCEhITU9KVUq/Xr12P16tVYs2YNOnfujMOHDyM+Ph4hISEN6ufgs88+w+TJk9GsWTM4OTkhIiICDz74IFJTU2v60sgGLkfWYa6urmjTpg0iIyOxaNEidO/eHUuXLkVQUBAKCgqQk5OjGn/p0iUEBQUBAIKCgqzujpK/l8fUdqmpqbh8+TIiIiLg7OwMZ2dnfPfdd3j33Xfh7OyMpk2bNojPwZyPjw/atWuHM2fONJifg+DgYHTq1En1WMeOHZVlWfl92Hqf5p/D5cuXVceLioqQnZ1dZz4HADh37hySkpIwdepU5bGG8nMwa9YszJ07F2PHjkXXrl0xYcIEzJgxA4sWLQLQcH4OwsPD8d133+H69evIyMjATz/9hMLCQrRu3brBfAayynq/VfnvB0NYPWI0GpGfn4/IyEi4uLhg9+7dyrFTp07h/Pnz0Ol0AACdTodff/1V9cOXmJgIb29vq19otdX999+PX3/9FYcPH1a+evbsiXHjxil/bwifg7nr168jLS0NwcHBDebnoE+fPjh16pTqsd9++w0tW7YEAISFhSEoKEj1OeTm5iIlJUX1OeTk5KhmC7755hsYjUZERUVVw7uoHJ988gkCAwMxZMgQ5bGG8nNw8+ZNaDTqX2lOTk4wGo0AGtbPAQB4enoiODgY165dw9dff42//e1vDe4zqKz3q9Pp8P3336OwsFAZk5iYiPbt29/WUiQAtqioq+bOnSu+++47kZ6eLo4cOSLmzp0rJEkSu3btEkKYbklv0aKF+Oabb8TPP/8sdDqd0Ol0yvPlW9IHDhwoDh8+LHbu3CkCAgLq1C3ptpjfHSlE/f8cnn76afHtt9+K9PR0sW/fPhEdHS38/f3F5cuXhRD1//0LYWpP4uzsLF599VVx+vRpsXr1auHh4SE+//xzZczrr78ufHx8xP/+9z9x5MgR8be//c3mbeo9evQQKSkp4ocffhBt27attbfl21JcXCxatGgh5syZY3WsIfwcTJw4UTRr1kxpUbFp0ybh7+8vZs+erYxpCD8HO3fuFDt27BC///672LVrl+jevbuIiooSBQUFQoj69xnk5eWJQ4cOiUOHDgkA4u233xaHDh0S586dE0JUzvvNyckRTZs2FRMmTBBHjx4Va9euFR4eHmxR0ZBNnjxZtGzZUri6uoqAgABx//33KwFMCCH+/PNP8c9//lM0adJEeHh4iP/7v/8TFy9eVJ3j7NmzYvDgwcLd3V34+/uLp59+WhQWFlb3W6lUliGsvn8OsbGxIjg4WLi6uopmzZqJ2NhYVX+s+v7+ZVu3bhVdunQRbm5uokOHDuKjjz5SHTcajeL5558XTZs2FW5ubuL+++8Xp06dUo3R6/XiwQcfFI0bNxbe3t7ikUceEXl5edX5Nm7L119/LQBYvS8hGsbPQW5urnjqqadEixYtRKNGjUTr1q3F/PnzVW0FGsLPwbp160Tr1q2Fq6urCAoKEtOnTxc5OTnK8fr2GezZs0cAsPqaOHGiEKLy3u8vv/wi7r77buHm5iaaNWsmXn/99Uq5fkkIs3bCRERERFQtWBNGREREVAMYwoiIiIhqAEMYERERUQ1gCCMiIiKqAQxhRERERDWAIYyIiIioBjCEEREREdUAhjAiIiKiGsAQRkT1yr333ov4+Piavowqt3DhQtxxxx01fRlEdBsYwoiIapGCgoJqfT0hBIqKiqr1NYnIhCGMiOqNSZMm4bvvvsPSpUshSRIkScLZs2dx9OhRDB48GI0bN0bTpk0xYcIEXL16VXnevffeiyeeeALx8fFo0qQJmjZtiv/+97+4ceMGHnnkEXh5eaFNmzbYsWOH8pxvv/0WkiRh27Zt6NatGxo1aoRevXrh6NGjqmv64Ycf0LdvX7i7uyM0NBRPPvkkbty4oRxv1aoVXn75ZTz88MPw9vbGtGnTAABz5sxBu3bt4OHhgdatW+P5559HYWEhAGDlypV48cUX8csvvyjvc+XKlTh79iwkScLhw4eV8+fk5ECSJHz77beq696xYwciIyPh5uaGH374AUajEYsWLUJYWBjc3d3RvXt3bNiwobL/ERGRGYYwIqo3li5dCp1Oh0cffRQXL17ExYsX4eXlhfvuuw89evTAzz//jJ07d+LSpUsYM2aM6rmrVq2Cv78/fvrpJzzxxBN4/PHH8fe//x29e/fGwYMHMXDgQEyYMAE3b95UPW/WrFl46623cODAAQQEBGDYsGFKWEpLS8OgQYMwatQoHDlyBOvWrcMPP/yAuLg41Tn+9a9/oXv37jh06BCef/55AICXlxdWrlyJ48ePY+nSpfjvf/+Ld955BwAQGxuLp59+Gp07d1beZ2xsbLk+q7lz5+L111/HiRMn0K1bNyxatAiffvopli1bhmPHjmHGjBkYP348vvvuu3Kdl4jKoVK2ASciqiXuuece8dRTTynfv/zyy2LgwIGqMRkZGQKAOHXqlPKcu+++WzleVFQkPD09xYQJE5THLl68KACI5ORkIYQQe/bsEQDE2rVrlTF6vV64u7uLdevWCSGEmDJlipg2bZrqtffu3Ss0Go34888/hRBCtGzZUowYMaLU9/Xmm2+KyMhI5fsXXnhBdO/eXTUmPT1dABCHDh1SHrt27ZoAIPbs2aO67s2bNytjbt26JTw8PMT+/ftV55syZYp48MEHS702IqoY55oMgEREVe2XX37Bnj170LhxY6tjaWlpaNeuHQCgW7duyuNOTk7w8/ND165dlceaNm0KALh8+bLqHDqdTvm7r68v2rdvjxMnTiivfeTIEaxevVoZI4SA0WhEeno6OnbsCADo2bOn1bWtW7cO7777LtLS0nD9+nUUFRXB29u73O/fHvPXPHPmDG7evIkBAwaoxhQUFKBHjx6V9ppEpMYQRkT12vXr1zFs2DC88cYbVseCg4OVv7u4uKiOSZKkekySJACA0Wgs12v/4x//wJNPPml1rEWLFsrfPT09VceSk5Mxbtw4vPjii4iJiYFWq8XatWvx1ltvOXw9jcZUYSKEUB6Tl0Ytmb/m9evXAQDbtm1Ds2bNVOPc3NwcviYRVRxDGBHVK66uriguLla+j4iIwMaNG9GqVSs4O1f+f/J+/PFHJVBdu3YNv/32mzLDFRERgePHj6NNmzblOuf+/fvRsmVLzJ8/X3ns3LlzqjGW7xMAAgICAAAXL15UZrDMi/Tt6dSpE9zc3HD+/Hncc8895bpWIqo4FuYTUb3SqlUrpKSk4OzZs7h69SqmT5+O7OxsPPjggzhw4ADS0tLw9ddf45FHHrEKMRXx0ksvYffu3Th69CgmTZoEf39/jBgxAoDpDsf9+/cjLi4Ohw8fxunTp/G///3PqjDfUtu2bXH+/HmsXbsWaWlpePfdd/HVV19Zvc/09HQcPnwYV69eRX5+Ptzd3dGrVy+l4P67777Dc889V+p78PLywjPPPIMZM2Zg1apVSEtLw8GDB/Hee+9h1apVFf5siMgxhjAiqleeeeYZODk5oVOnTggICEBBQQH27duH4uJiDBw4EF27dkV8fDx8fHyU5bvb8frrr+Opp55CZGQksrKysHXrVri6ugIw1Zl99913+O2339C3b1/06NEDCxYsQEhIiMNzDh8+HDNmzEBcXBzuuOMO7N+/X7lrUjZq1CgMGjQI/fv3R0BAAL744gsAwMcff4yioiJERkYiPj4er7zySpnex8svv4znn38eixYtQseOHTFo0CBs27YNYWFhFfhUiKgsJGFePEBERGXy7bffon///rh27Rp8fHxq+nKIqA7iTBgRERFRDWAIIyIiIqoBXI4kIiIiqgGcCSMiIiKqAQxhRERERDWAIYyIiIioBjCEEREREdUAhjAiIiKiGsAQRkRERFQDGMKIiIiIagBDGBEREVENYAgjIiIiqgH/D1RTyrqXlZ/PAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_training, filename=\"pysmo_poly_train_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_training, filename=\"pysmo_poly_train_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_training, filename=\"pysmo_poly_train_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation\n", + "\n", + "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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v3LhxA8XFxZW2yczMFB/DyckJXl5eVbap7DEMx0xZsmQJdDqd+MVCrURERMpl1yBs9uzZ0Gg0VX6dPXtWbD9jxgycPHkS+/btg1arxciRIyEIAoDSLX4KCwvxwQcfoFu3bujZsyfee+89HDp0CGlpafZ6irUyZ84c6PV68euPP/6wd5eIiIjISuxarHX69OkYPXp0lW2aNm0q/r9hw4Zo2LAhmjdvjlatWiE4OBjff/89IiMjERgYiHr16qF58+Zi+1atWgEoXanYokULBAQEVFjFeO3aNXh6esLV1RVarRZarbbSNgEBAQCAgIAA3LlzB7m5uUajYeXblF9RaXhMQ5vKODs7w9nZucrzQURERMpg1yDM19cXvr6+Zt23pKQEQGkeFQA8+OCDuHv3LtLT0xEWFgYA+P333wEATZo0AQBERkZiz549Ro+zf/9+REZGAgCcnJwQERGBAwcOYMCAAeLPOXDgABISEgAAERERcHR0xIEDBzBo0CAAQFpaGi5duiQ+TmRkJF5++WVkZWXBz89P/Dmenp5o3bq1Wc+XiEgNsrOzcefOHZPHnZyc4OPjY8MeEVmPRjDM50nY0aNHcezYMTz00ENo0KAB0tPTMXfuXFy7dg2//vornJ2dUVJSggceeAD169fH8uXLUVJSgkmTJsHT0xP79u0DUFqi4r777sOkSZMwZswYHDx4EFOmTMHu3bsRHR0NoLRExahRo/D222+jU6dOWL58ObZs2YKzZ8+KeV0TJ07Enj17kJycDE9PT0yePBkAcOTIEQClJSratWuHoKAgJCUlITMzEyNGjMDYsWOxePHiGj/vvLw86HQ66PV6bltERIqXnZ2N1atXV9suISGBgRhJWk0/v2Wxd6SbmxtSUlIwf/585OfnIzAwEDExMXjhhRfE6TsHBwfs3LkTkydPRvfu3eHu7o5HH30Ur7/+uvg4oaGh2L17N6ZOnYoVK1agUaNGePfdd8UADACGDBmC69evY968ecjMzES7du2wd+9eo0T7ZcuWwcHBAYMGDUJhYSGio6OxZs0a8bhWq8WuXbswceJEREZGwt3dHaNGjcKiRYtscLaIiOSpqhEwc9oRSZ0sRsLUiiNhRKQmV69exbp168Tv9XoP5OT4wNs7GzrdTfH2+Ph4BAYG2qOLRDWiqJEwIiJSlxMn2mPnzr4QBAdoNCXo128XOnQ4ae9uEVmUrOqEERGR8un1HmIABgCC4ICdO/tCr/ewc8+ILItBGBERSUpOjg/Kb+giCA7IyfG2U4+IrINBGBERSYq3dzY0mhKj2zSaEnh755i4B5E8MQgjIiJJ0eluol+/XWIgZsgJK5ucT6QETMwnIiJJcHJyEv/focNJhIWdR06ON7y9c4wCsLLtiOSMQRgREUmCj48PEhISWDGfVINBGBERSQYDLFITBmFkVdwHjoiIqHIMwshquA8ckTLx4orIMhiEkdWUf5M2tQUJ94Ejkg9eXBFZDoMwsgluQUKkDNxkm8hyWCeMrI5bkBApl17vgYyMEP49E5mBI2FkdVVtQcLii3Un5/wcOfedOMJNVFcMwsjqDFuQlA3EuAWJZcg5P0fOfSfTI9xhYedVeXHFCwoyB4MwsjrDFiTlr5jV+EZtaXJe/CDnvhNHuMviBQWZi0EY2URVW5CQZch5akjOfVcrjnD/g4sVyFwMwshqyu/vptPdrDT44j5wdSfnqSE5913NOMJtmqlR3fI4hUkMwshquA+c7ch5akjOfVcjpW2ybelAqKajupzCJIBBGFkZ3zxsQ85TQ3Luuxop6eLK0oFQbUZ11ZgTyZG/ihiEESmAnKeG5Nx3tVLKB2VNA5wrV66YbFt2xM/cUV015ERy5K9yDMKIFELOix/k3HdSDlOjUSkpKVXeb8iQIQDMG9VVS06kGkf+aoJBGJGMyXnxg5z7TspTl9GooqIiAOaN6qoxJ1INI381xSCMSMbknJ8j576TstR0NMrU6E29ev98lNZ2sYLaciLVMvJXUwzCiGROzkGKnPtOylGT0aiqRm+8vLzMvqBQW06kGkf+qsIgjIiIVK260aiajN7U5YJCTTmRahv5q45D9U2IiIiUyzAapdGUAECF0aiqRm/MVVlOZGjoxQoBmNJyIqs712rDkTAiIlKlmhaetcbojdxzIutS80tNI3/VYRBGRESqVF0gdOPGDaSkpFgtb0uqAVZ1zKn5xdXQlWMQRkSkcKxUblpVz1tpWzRZijk1v+Q+8mctDMKIiBSMlcrNx8CherWp+aXm82QKgzAiMokjKPLHSuV1w9e3aaz5VXcMwoioUhxBUR5WKidLYs2vumOJCiKqVGUjKBkZIdDrPapsR9JkatSi/O+TqKYMq0bLUnPNL3NwJIyIqsURFPnjqAVZmtqq/VsDgzAiqhLzPpSBlcrJGljzq24YhFGtMVlbXTiCogwctSBLYc0vy2EQRrXCZG314QiKcnDUgiyBpTssh0EY1UpNk7CZrK0cHEGRN45akDUwwLIMBmFUJ6ZqDpGycARFvjhqQSRdDMLIbFwxp2wcQVEOBlhE0sQgjMzCFXPKxxEUIiLrYhBGZuGKOXVggEVEZD0Mwsgsllgxx1IXRESkZgzCyCzVrZi7ceMGANOBFEtdqAMDbSLL4N+SMjEIo1opm4Rd1Yq5lJQU8f+VBVIsdaF8DLSJLKP835KpVen8W5IfBmFUK2WTtW/cuIGUlJRqc8BqEkix1IXyVLYBeGW/YwbaRFUr+zdS1ap0/i3JD4MwqjVTV1rmBlIsdaF8/B0T1R1XpSsPgzCyCHM/ZPmmonz8HRNZBlelKw+DMKqzunzI8k1F+dTwO2bSNNkC93FVHgZhVGd1+ZDlm4ryKf13zAUIZCvcx1V5GIRRndXlQ5ZvKsqn9N8xFyCQLXEfV2VhEEZ1Zs6HbE1LXXBfQmVQywcHFyCQLZjax5Xkh0EYma0ugRT3JVQ+tW0AzgUIZC01/RtRyt+SmjAII7PVNZBigGUdUkkSt1agLZXnV54aFiCQffCiVbkYhFGd8I9eWqSWJG7pnyG151eW0hcgkH3xvVaZHKpvQkRyofTtoCpLgs/ICIFe71FlO1sw5EZqNCUAoLgFCERkeRwJI1IwJW8HJcUkeLUsQCDLkuoUO1kfgzAihZJikGIpUkqCV9sCBLIsbs6tbgzCiBRISkGKNUgpCZ5J01QX3Jxb3RiEESmQlIIUa5BaErwlAyxOTamT0i+cqHIMwogUSGpBiqUptQo/p6bUS+kXTlQ5BmFECqTUIKUsJSbBc2pKvZR+4USVYxBGssapG2NK3w5KLUnwnJpSHzVcOFFFDMJItqRcuNNelJ4krvTnZ8CpKXVS4uguVY1BGMmW0guTmkvuAUh1lP78AE5NqRk351YX2VTM79+/Pxo3bgwXFxcEBgZixIgRuHLlinh8wYIF0Gg0Fb7c3d2NHmfr1q1o2bIlXFxc0KZNG+zZs8fouCAImDdvHgIDA+Hq6oqoqCicO3fOqE1OTg6GDx8OT09PeHl5IS4uDrdu3TJq8/PPP6Nbt25wcXFBcHAwkpKSLHxGqDxT1dOJ5IbV99WDm3Orm2xGwnr16oXnnnsOgYGB+PPPP/Hss89i8ODBOHLkCADg2WefxYQJE4zu07t3bzzwwAPi90eOHMGwYcOwZMkS9O3bF5s2bcKAAQNw4sQJ3HfffQCApKQkrFy5Ehs2bEBoaCjmzp2L6Oho/Pbbb3BxcQEADB8+HFevXsX+/ftRVFSEp59+GvHx8di0aRMAIC8vD3369EFUVBTWrl2LX375BWPGjIGXlxfi4+NtcbpUR8mFSUmdODWlDmqZYqfKaQRBEOzdCXPs2LEDAwYMQGFhIRwdHSsc/+mnn9CuXTt8/fXX6NatGwBgyJAhyM/Px65du8R2Xbp0Qbt27bB27VoIgoCgoCBMnz4dzz77LABAr9fD398fycnJGDp0KM6cOYPWrVvj2LFj6NixIwBg7969+Ne//oXLly8jKCgIb731Fp5//nlkZmaKVy+zZ8/G9u3bcfbs2Ro/x7y8POh0Ouj1enh6epp9rqTIEgn1V69exbp166DXe2D58sQKUzeJicuh091EfHw8AgMDLdZ3ImsxvKarw9c0kbTV9PNbNiNhZeXk5GDjxo3o2rVrpQEYALz77rto3ry5GIABQGpqKqZNm2bULjo6Gtu3bwcAZGRkIDMzE1FRUeJxnU6Hzp07IzU1FUOHDkVqaiq8vLzEAAwAoqKi4ODggKNHj+Lf//43UlNT0b17d6Ph4+joaCxduhR//fUXGjRoYInTIFuWTqhnEjMpBaemiNRFVkHYrFmzsHr1ahQUFKBLly5GI1pl3b59Gxs3bsTs2bONbs/MzIS/v7/Rbf7+/sjMzBSPG26rqo2fn5/R8Xr16sHb29uoTWhoaIXHMBwzFYQVFhaisLBQ/D4vL6/SdnJn6YR6JjGTUnBqitSC5YVK2TUImz17NpYuXVplmzNnzqBly5YAgBkzZiAuLg4XL17EwoULMXLkSOzatQsajcboPp9++ilu3ryJUaNGWa3v1rBkyRIsXLjQ3t2wOVNVwWuK9XVISdTwwUPqxp0h/mHXIGz69OkYPXp0lW2aNm0q/r9hw4Zo2LAhmjdvjlatWiE4OBjff/89IiMjje7z7rvvom/fvhVGtAICAnDt2jWj265du4aAgADxuOG2svkW165dQ7t27cQ2WVlZRo9x9+5d5OTkGD1OZT+n7M+ozJw5c4ymS/Py8hAcHGyyvRLUJaFe6YVJiYiUiDtD/MOuQZivry98fX3Num9JSenS7bLTd0BpXtehQ4ewY8eOCveJjIzEgQMHkJiYKN62f/9+MYgLDQ1FQEAADhw4IAZdeXl5OHr0KCZOnCg+Rm5uLo4fP46IiAgAwMGDB1FSUoLOnTuLbZ5//nkUFRWJOWv79+9HixYtqswHc3Z2hrOzsxlnQ57qWhWcUzdERPLFnSFkkhN29OhRHDt2DA899BAaNGiA9PR0zJ07F2FhYRVGwd5//30EBgbi0UcfrfA4zzzzDHr06IHXX38dsbGx+Pjjj/Hjjz+Kq5E0Gg0SExPx0ksvITw8XCxRERQUhAEDBgAAWrVqhZiYGIwbNw5r165FUVEREhISMHToUAQFBQEAnnzySSxcuBBxcXGYNWsWTp8+jRUrVmDZsmXWPVEyY4mEegZYRNLFvB+qChdVySQIc3NzQ0pKCubPn4/8/HwEBgYiJiYGL7zwgtHIUUlJCZKTkzF69GhotdoKj9O1a1ds2rQJL7zwAp577jmEh4dj+/btYo0wAJg5cyby8/MRHx+P3NxcPPTQQ9i7d69YIwwANm7ciISEBPTu3RsODg4YNGgQVq5cKR7X6XTYt28fJk2ahIiICDRs2BDz5s1jjbBymFBPpFzM+6Hq8DNAJkFYmzZtcPDgwWrbOTg44I8//qiyzeOPP47HH3/c5HGNRoNFixZh0aJFJtt4e3uLhVlNuf/++/HNN99U3WGVY0I9kXLJPe+Ho3jWx88AmQRhpCxMqCdSDznm/XAUz3bUvjMEgzCyOSbUE6mHHPN+5D6KJzdq3rScQRjZBQMsInWQc96PHEfx5IA7Q/yDQRgREVmNnPN+5DiKJwecDfkHgzAiIrIqueb9yHkUT+rUEGDVhEP1TYiIiOpGp7uJ0NCLsgnAgH9G8TSa0uLgchrFI3ngSBgRkQTJvUSCUvJ+5DqKR/LAIIyISGLKl0gwxVolEiwRACop70fNq/fIuhiEERFJTPnAxVSdKmuUSLBkACiHAKsyShnFI+ljEEZEJGFV1amyBnsGgFKhpFE8kjYGYUREEmXvOlW2DgClhAEW2QJXRxIRSVRVdaqszVQAqNd7WP1nE6kFR8KIiCTKnnWqWKhUXeS+GleuGIQREUmUPavNs1CpenDDcvthEEZEJGH2qlMl5+2GqHa4Ybn9MAgjIpKY8qUPTNWpsnaJBBYqVRd7LwRRIwZhREQSY88SCVIJAMn2mAdoewzCiIgkyF65N6yRJR22TpZnHqDtMQgjWeJKHiLr4d+O/dkjWZ55gLbHIIxkx9776hERWZu9kuWZB2hbLNZKslPTNx2u5CEiubNH0Vyd7iZCQy8yALMBjoSR7JkapicikjtbJMtLZcNyNaaZMAgjWVPz3nZEpHy2SJaXwmIMtaaZMAgj2WJNG5IyNV7Vk+XZKlne3q/F8n8rpmY4lJZmwiCMZIs1bUiq1HpVT9ahtmR5Nc1wMDGfZMswTF8Wa9qQFFR2VZ+REVIhmVppV/VkPWpJlrfHQgR74kgYyZa1huk5jUSWpKarerIcqSTL25raZjgYhJHslH3TqWqY3pw3J04jqYu1A27mLdqfXC+qpJAsbw9qq9rPIIxkx5pvTqxBph62CLjVdlUvNXK/qJJin6xNbVX7GYSRLNnqzYk1yJTLFqux1HZVLzVqXXEnd2paiMAgjMgE5vKoh7V+12q7qpcy/j1LW/n0EZ3uZqV/J3q9vtL7ynXUkEEYUSWYy6Me1v5dq+mqXqr49yx9ptJMcnNzsWXLFvH7zZs3V3p/qU4pV4dBGFElmMujHtb4Xdf0ql5pK9ukin/P8lCTIEppU8oMwogqwVwe9bDG71qtK9ukin/PyqDEKWUWayWqhCGXx1AMlrk8ylXd7/rGjRvIzs6u9eP6+PggMDDQ5BcDMNvh37P8KbWIK0fCiMqwZg0ykq6qftcpKSkA5JtzQqWYmydvSp1SZhBGVAankdTDVN6WYYshpeScqBlz85RDqVPKDMKIymGApQ6GgPvKlSviaJcSc07UjBdVyqHUci8MwkhW5LoFCUmTj4+P+HpiGQNl4vuBcihxSplBGMmG3LcgIWlTas4JkZwpfUqZQRjJBrcgIWtSas4JkZwpfUqZQZhKKG0aj7k7ZGlKzTkhkjs5fTbVFoMwFVDaNB5zd8halJhzQkTSxSBMBZQ2jcfcHbIkpeecEJF0MQhTGSVM4zF3Rz1sMY2u9JwTIpIuBmEqUt00Xm5ubpX3l8oHEXN31MGW0+hSeF0TkfowCFOR6qbxtmzZUu1jSCVvjLk7yqe0aXQiovIYhKlIbabxpPiBx9wd9VLCNDoRUXkMwlSkptN4Uv3AY+6OOnE1LAHKK7NDBDAIU53qpvGk/oHHN1n14WpYUlqZHSIDBmEqUNNpPIAfeCQ9XA1LSswP5MgeAQzCVKEm03h6vR6bN2/mBx5Jhl6vB8DVsGRMqukStcGRPTJgEKYSNf1D5gceWUpdrvSzs7OxefNm8XuuhiVA+ukSNaXEkT0yD4MwqoAfeFRXdb3Sr+zDh6thSYnpEkoY2SPzMQgjACz/QJZl6St9U/d/4oknOF2jIkpLl1DKyJ6cSC0Xj0EYAWD5B7Keul7pV3V/Ly8vK/WapEhp6RJKHNmTMinm4jEIIxEDLLK0ul7pc6TA8qQ2ElBbSkqXUNrIntTVdOTdlrl4DMKIyGrqeqXPkQLLkuJIQE0oNV1CaSN7cmMqzcGWGIQRkdXU9UqfIwWWJddVeUpOl1DSyJ6cSGVBBIMwIrKaul7pc6TAeqTyIVRTcgywTFHqyJ5cSCnNgUEYEVmVOVf6ZT98qro/P6TMI6UPITVS8sieHEgpzYFBGBFZXF2v9PkhZV1S+hBSK752S9ljoYiU0hxqHITl5eXV+EE9PT3N6gwRKYMlgih+SFmPlD6ESL3stVBESmkONQ7CvLy8oNFoqmwjCAI0Gg2Ki4vr3DEikjcGUdIlpQ8hUi9bl4yQYppDjYOwQ4cOWbMfRERkQ1yVR1Jj7ZIRUkxzqHEQ1qNHD2v2g4iIrIyr8kiqbLVaV2oj9GYn5ufm5uK9997DmTNnAAD33nsvxowZA51OZ7HOERGR5UhxJIBIzat1HapvUtGPP/6IsLAwLFu2DDk5OcjJycEbb7yBsLAwnDhxwtJ9BAD0798fjRs3houLCwIDAzFixAhcuXLFqM0XX3yBLl26wMPDA76+vhg0aBAuXLhg1Obw4cPo0KEDnJ2d0axZMyQnJ1f4WW+++SZCQkLg4uKCzp0744cffjA6fvv2bUyaNAk+Pj6oX78+Bg0ahGvXrhm1uXTpEmJjY+Hm5gY/Pz/MmDEDd+/etci5ICIyl4+PDwIDA01+MQAjW6tqta7SmRWETZ06Ff3798eFCxeQkpKClJQUZGRkoG/fvkhMTLRwF0v16tULW7ZsQVpaGrZt24b09HQMHjxYPJ6RkYHHHnsMDz/8ME6dOoUvvvgCN27cwMCBA43axMbGolevXjh16hQSExMxduxYfPHFF2KbzZs3Y9q0aZg/fz5OnDiBtm3bIjo6GllZWUbPf+fOndi6dSu++uorXLlyxejnFBcXIzY2Fnfu3MGRI0ewYcMGJCcnY968eVY5N0RERHJlWK1bllpW62oEQRBqeydXV1ecPHkSLVu2NLr9t99+Q8eOHVFQUGCxDpqyY8cODBgwAIWFhXB0dMQnn3yCYcOGobCwEA4OpbHlzp078dhjj4ltZs2ahd27d+P06dPi4wwdOhS5ubnYu3cvAKBz58544IEHxGWzJSUlCA4OxuTJkzF79mzo9Xr4+vpi06ZNYhB49uxZtGrVCqmpqejSpQs+//xz9O3bF1euXIG/vz8AYO3atZg1axauX79e43yLvLw86HQ66PV6lv0gUjG5b7pNVJmrV69i3bp1AKrOCYuPj0dgYKA9u1prNf38NisnzNPTE5cuXaoQhP3xxx/w8PAw5yFrJScnBxs3bkTXrl3h6OgIAIiIiICDgwPWr1+P0aNH49atW/jwww8RFRUltklNTUVUVJTRY0VHR4ujd3fu3MHx48cxZ84c8biDgwOioqKQmpoKADh+/DiKioqMHqdly5Zo3LixGISlpqaiTZs2YgBm+DkTJ07Er7/+ivbt21f6vAoLC1FYWCh+X5vabESkTHLddJuoOlIsGWFrZgVhQ4YMQVxcHF577TV07doVAPDdd99hxowZGDZsmEU7WNasWbOwevVqFBQUoEuXLti1a5d4LDQ0FPv27cMTTzyB8ePHo7i4GJGRkdizZ4/YJjMz0ygwAgB/f3/k5eXh77//xl9//YXi4uJK25w9e1Z8DCcnJ3h5eVVok5mZWeXPMRwzZcmSJVi4cGENzwaRNHCUxrrkuuk2UXW4UMTMIOy1116DRqPByJEjxWRzR0dHTJw4Ea+88kqNH2f27NlYunRplW3OnDkjjrjNmDEDcXFxuHjxIhYuXIiRI0di165d0Gg0yMzMxLhx4zBq1CgMGzYMN2/exLx58zB48GDs37+/2kKzUjBnzhxMmzZN/D4vLw/BwcF27BFR1ThKY1ty23SbqDpqf18wKwhzcnLCihUrsGTJEqSnpwMAwsLC4ObmVqvHmT59OkaPHl1lm6ZNm4r/b9iwIRo2bIjmzZujVatWCA4Oxvfff4/IyEi8+eab0Ol0SEpKEtv/97//RXBwMI4ePYouXbogICCgwirGa9euwdPTE66urtBqtdBqtZW2CQgIAAAEBATgzp07yM3NNRoNK9+m/IpKw2Ma2lTG2dkZzs7OVZ4PIinhKI3tqHkZP5FS1WkDbzc3N7Rp08bs+/v6+sLX19es+5aUlK6kMORQFRQUiAn5Blqt1qht+elJANi/fz8iIyMBlAaXEREROHDgAAYMGCDe98CBA0hISABQmnvm6OiIAwcOYNCgQQCAtLQ0XLp0SXycyMhIvPzyy8jKyoKfn5/4czw9PdG6dWuzni+R1HGUxrq46TaR8pgVhN2+fRurVq3CoUOHkJWVJQY5BpauFXb06FEcO3YMDz30EBo0aID09HTMnTsXYWFhYuATGxuLZcuWYdGiReJ05HPPPYcmTZqIifATJkzA6tWrMXPmTIwZMwYHDx7Eli1bsHv3bvFnTZs2DaNGjULHjh3RqVMnLF++HPn5+Xj66acBADqdDnFxcZg2bRq8vb3h6emJyZMnIzIyEl26dAEA9OnTB61bt8aIESOQlJSEzMxMvPDCC5g0aRJHukiROEpjfdx0W96YO0mVMSsIi4uLw759+zB48GB06tTJ6vlWbm5uSElJwfz585Gfn4/AwEDExMTghRdeEIOahx9+GJs2bUJSUhKSkpLg5uaGyMhI7N27F66urgBKk/d3796NqVOnYsWKFWjUqBHeffddREdHiz9ryJAhuH79OubNm4fMzEy0a9cOe/fuNUq0X7ZsGRwcHDBo0CAUFhYiOjoaa9asEY9rtVrs2rULEydORGRkJNzd3TFq1CgsWrTIqueJyF44SmN93HRbvpg7SaaYFYTt2rULe/bswYMPPmjp/lSqTZs2OHjwYLXthg4diqFDh1bZpmfPnjh5suopkoSEBHH6sTIuLi5488038eabb5ps06RJkwpTn2RfvBK1Ho7S2AY33ZanmuZEMndSfcwKwu655x6b1AMjspTyV6KmEsh5JWoejtJYDzfdVh5T7z+kPmYFYa+//jpmzZqFtWvXokmTJpbuE5HFlb3CrCqBnFei5uMojXWwlpKycAELlWVWENaxY0fcvn0bTZs2hZubm1iR3iAnh1MQJE1MILcsjtLYBgMsZeD7D5VnVhA2bNgw/Pnnn1i8eDH8/f1lUQiVCGACuaVxlIao5vj+Q+WZFYQdOXIEqampaNu2raX7Q2RVTCC3PAZYRDXD9x8qz6H6JhW1bNkSf//9t6X7QmR1hgRyjaa0th0TyInIVvj+Q+WZNRL2yiuvYPr06Xj55ZfRpk2bCjlhnp6eFukckTUwgZyIbKlsTmRV7z/MnVQfs4KwmJgYAEDv3r2NbhcEARqNBsXFxXXvGZEVmUogJyKyNOZOkilmBWGHDh2ydD+IrKqmV5i8EiUia2CARZXRCIIgWOvB//Of/2DRokVo2LChtX6EouXl5UGn00Gv13OK1wJYMZ+IiGyhpp/fVg3CPD09cerUKTRt2tRaP0LRGIQRERHJT00/v81aHVlTVozviIiIiGTNqkEYEREREVWOQRgRERGRHTAIIyIiIrIDs0pUEKkBV1NSXfE1RERVsWoQ9tRTT3FVH8lSdnY2Vq9eLX6v13sgJ8cH3t7ZRkVeExIS+CFKlSr/GjKFryEi9TI7CMvNzcUPP/yArKwslJSUGB0bOXIkAOCtt96qW++I7KTs6MWJE+2xc2dfCIKDuNdbhw4nK7QjKqv8a8NUIM/XEJF6mRWE7dy5E8OHD8etW7fg6ekJjUYjHtNoNGIQRiR3er2HGIABgCA4YOfOvggLO89tj6jGqgrkiUi9zArCpk+fjjFjxmDx4sVwc3OzdJ+IJCMnx0cMwAwEwQE5Od4MwqhGGMiXYn4cUUVmBWF//vknpkyZwgCMFM/bOxsaTYlRIKbRlMDbO8eOvSI5YSDP/DgiU8wqUREdHY0ff/zR0n0hkhyd7ib69dsFjaY079EwlaSWD0+qO0MgX5baAvnK8uMyMkKg13tU2Y5I6Wo8ErZjxw7x/7GxsZgxYwZ+++03tGnTBo6OjkZt+/fvb7keEtlZhw4nERZ2Hjk53vD2zmEARrViCOTL54Sp9XXE/Diif9Q4CBswYECF2xYtWlThNo1Gg+Li4jp1ikhqdLqbqv3QpLpjIF+K+XFExmochJUvQ0GkZE5OThZtR+pT/rVhKpBX02uI+XHqwYUYNWNWYv4HH3yAIUOGwNnZ2ej2O3fu4OOPP2aJCpI9Hx8fJCQk8E2EzMbXUEVc6KIOXIhRc2YFYU8//TRiYmLg5+dndPvNmzfx9NNPMwgjRVD7mwPVHV9Dxpgfpw4sVFxzZgVhgiAYFWg1uHz5MnQ6XZ07RUREysT8OHXhQoyq1SoIa9++PTQaDTQaDXr37o169f65e3FxMTIyMhATE2PxThIRkXwxP06duBCjerUKwgwrJE+dOoXo6GjUr19fPObk5ISQkBAMGjTIoh0kIiJ5Y36cOnEhRvVqFYTNnz8fABASEoIhQ4bAxcXFKp0iIiJlYYClPlyIUT2zcsJGjRoFoDSpLisrq0L5isaNG9e9Z0RERCRbXIhRPbOCsHPnzmHMmDE4cuSI0e2GhH0WayUiIpI+a9fz4kKMqpkVhI0ePRr16tXDrl27EBgYWOlKSSIiIpIua9Xz4kKMmjMrCDt16hSOHz+Oli1bWro/REREZAPWqufFhRg1Z1YQ1rp1a9y4ccPSfSE7unwZOHcOCA8HGjWyd2/IHNwmhIjMZel6XnyvqRmzgrClS5di5syZWLx4Mdq0aQNHR0ej456enhbpHNnGe+8B8fFASQng4ACsWwfExVnmsRnc2Qa3CSEic7Gel/2YFYRFRUUBAB5++GGjfDAm5svL5cvAkSP/BGBA6b/jxwPR0XUPmqwZ3JExbhNCROZiPS/7MSsIO3TokKX7QTZWNkAqr7gYOH++bkHY5cvWC+6oatwmhIhqg/W87Meh+iYV9ejRAw4ODnjnnXcwe/ZsNGvWDD169MClS5eg1Wot3UeysPIBUnlarQAPj2vIzs426/Gzs7Px/ffZFR6/uBg4ejTb7Mel6pmaVtDrPezcMyKSKkM9L42m9E2b9bxsx6yRsG3btmHEiBEYPnw4Tp48icLCQgCAXq/H4sWLsWfPHot2kizr3DnTAZhGU4LY2F3Ytat05KS2OUSG3CS93gMaTWKFK6vvvtuA06dvMjfJSjitQETmYD0v+zBrJOyll17C2rVr8c477xgl5T/44IM4ceKExTpH1hEeDjg4COVuLcHgwVuQmLjcaOqqtjlEhvbVXVnVNTfp8mXg0KHSf+kfhmmFsjitQESVqayeV2joxQoBGOt5WY9ZI2FpaWno3r17hdt1Oh1yc3Pr2ieyskaNgKQkPWbM8DTKG7rvvjMW/TmWvrIylGDYtMkVM2fqUFKigYODgKQkPZ588m+WYAC3CbEHlgYhuWI9L/szKwgLCAjA+fPnERISYnT7t99+i6ZNm1qiX2Qlhg+MPn1u4M8/91t96NlUpeTaKjvNuXx5IgShdFVuSYkGM2Z44s8/34dOx2lOgNMKtsTSICR3fF3al1lB2Lhx4/DMM8/g/fffh0ajwZUrV5Camopnn30Wc+fOtXQfyULKf2DodJDNB7ThSq26nCe1lmDgNiH2wdIgRFQXZgVhs2fPRklJCXr37o2CggJ0794dzs7OePbZZzF58mRL95EsRAkfBFxKXTlOK9gfS4MQUW2ZFYRpNBo8//zzmDFjBs6fP49bt26hdevWqF+/vqX7R2SEOU+mMcCyn7pWHGdeGZE6mRWEGTg5OaF169aW6gtRjTDniaSmLqVBmFdGpF51CsJI+WqbQ1TT9nXNTbJUwj+RJdRlmpx5ZUTqxSCMKhg4cCAaNmxo1hQIc5NIjSw1Tc68MiJ1YRBGFTRs2BCBgYFm358BFqlRXafJ65pXRqRkSs2bZBBGsmCraU6i2rBkaRBuOUVUOSXnTTIIUxE5BzKc5iQpsuTrkuVXyFbkNqqk5LxJBmEqIvdARqr9InWz1OuS5VfIFuQ+qqS0vEkGYSojxT8qIirF8itkbXIeVVJi3iSDMCIiO+KWU2QvchtVUmLeJIMwIiI7knuaAMmTHEeVlJg3ySCMiFRHaonJDLDI1uQ4qqTEvEkGYUSkKnJPTCayBLmOKiktb9Kh+iZERMpR04RjKSYmE1mKYVRJoykBAEmPKlWWNxkaerFCX+WYN8mRMCJSNVOrw4jkoC5T63IZVVJy3iSDMCJSLbmtDiMqy5ypdbmuxpVjgFUTDMKISJXkuDqMqCxzan4peVRJjhiEEZEqyXF1GJEptRnVZYAlHUzMJyJVMqwOK0sOq8OIyjM1qqvXe9i5Z1QdBmFEpEpyWh1GVJWqRnVJ2mQThPXv3x+NGzeGi4sLAgMDMWLECFy5csWozZYtW9CuXTu4ubmhSZMmePXVVys8zuHDh9GhQwc4OzujWbNmSE5OrtDmzTffREhICFxcXNC5c2f88MMPRsdv376NSZMmwcfHB/Xr18egQYNw7do1ozaXLl1CbGws3Nzc4OfnhxkzZuDu3bt1PxFEVCdlE447dDiJxMTlGDUqGYmJy42mb6SWmExkCkd15Us2OWG9evXCc889h8DAQPz555949tlnMXjwYBw5cgQA8Pnnn2P48OFYtWoV+vTpgzNnzmDcuHFwdXVFQkICACAjIwOxsbGYMGECNm7ciAMHDmDs2LEIDAxEdHQ0AGDz5s2YNm0a1q5di86dO2P58uWIjo5GWloa/Pz8AABTp07F7t27sXXrVuh0OiQkJGDgwIH47rvvAADFxcWIjY1FQEAAjhw5gqtXr2LkyJFwdHTE4sWL7XD2iMiAicmkNEqsJK8WGkEQBHt3whw7duzAgAEDUFhYCEdHRzz55JMoKirC1q1bxTarVq1CUlISLl26BI1Gg1mzZmH37t04ffq02Gbo0KHIzc3F3r17AQCdO3fGAw88IC77LSkpQXBwMCZPnozZs2dDr9fD19cXmzZtwuDBgwEAZ8+eRatWrZCamoouXbrg888/R9++fXHlyhX4+/sDANauXYtZs2bh+vXrNb7CzsvLg06ng16vh6enp0XOGxERKcPVq1exbt068fvS1ZEVa37Fx8cjMDDQHl1UrZp+fstmOrKsnJwcbNy4EV27doWjoyMAoLCwEC4uLkbtXF1dcfnyZVy8eBEAkJqaiqioKKM20dHRSE1NBVC6jPf48eNGbRwcHBAVFSW2OX78OIqKiozatGzZEo0bNxbbpKamok2bNmIAZvg5eXl5+PXXX00+r8LCQuTl5Rl9EUlRdnY2rl69avIrOzvb3l0kUjwlV5JXC9lMRwLArFmzsHr1ahQUFKBLly7YtWuXeCw6OhpTp07F6NGj0atXL5w/fx6vv/46gNKrhZCQEGRmZhoFRgDg7++PvLw8/P333/jrr79QXFxcaZuzZ88CADIzM+Hk5AQvL68KbTIzM8U2lT2G4ZgpS5YswcKFC2txRohsj3svEkkDp9blz64jYbNnz4ZGo6nyyxD8AMCMGTNw8uRJ7Nu3D1qtFiNHjoRhNnXcuHFISEhA37594eTkhC5dumDo0KEASkez5GDOnDnQ6/Xi1x9//GHvLhFVwL0XiaTDx8cHgYGBJr8YgEmbXUfCpk+fjtGjR1fZpmnTpuL/GzZsiIYNG6J58+Zo1aoVgoOD8f333yMyMhIajQZLly7F4sWLkZmZCV9fXxw4cMDoMQICAiqsYrx27Ro8PT3h6uoKrVYLrVZbaZuAgADxMe7cuYPc3Fyj0bDybcqvqDQ8pqFNZZydneHs7Fzl+SAiIiJlsOsQka+vL1q2bFnll6m57JKS0uW4hYWFRrdrtVrcc889cHJywkcffYTIyEj4+voCACIjI8XAzGD//v2IjIwEUDpsGxERYdSmpKQEBw4cENtERETA0dHRqE1aWhouXboktomMjMQvv/yCrKwso5/j6emJ1q1bm3WuiKRKr/dARkYIC0MSEdWSLHLCjh49imPHjuGhhx5CgwYNkJ6ejrlz5yIsLEwMfG7cuIFPPvkEPXv2xO3bt7F+/Xps3boVX331lfg4EyZMwOrVqzFz5kyMGTMGBw8exJYtW7B7926xzbRp0zBq1Ch07NgRnTp1wvLly5Gfn4+nn34aAKDT6RAXF4dp06bB29sbnp6emDx5MiIjI9GlSxcAQJ8+fdC6dWuMGDECSUlJyMzMxAsvvIBJkyZxpIsUhRtgExGZTxZBmJubG1JSUjB//nzk5+cjMDAQMTExeOGFF4yCmg0bNuDZZ5+FIAiIjIzE4cOH0alTJ/F4aGgodu/ejalTp2LFihVo1KgR3n33XbFGGAAMGTIE169fx7x585CZmYl27dph7969Ron2y5Ytg4ODAwYNGoTCwkJER0djzZo14nGtVotdu3Zh4sSJiIyMhLu7O0aNGoVFixZZ+UwR2Q43wCYiqhvZ1glTA9YJIyky1CbKyAjBhg2jKhwfNSoZoaEXWZuIiFRL0XXCiMj+uFUKEVHdMAgjoloxLJapbgNsFogkIqoapyMljNORJFXZ2dliHbArVxxw4UI9hITcRVBQaUDGApFEpGY1/fyWRWI+WVbZD9DK8AOUqlP29REYCERE2LEzREQyxSBMZcpvOVO64asPvL2zjVa0ccsZIiIi62IQpjJlR8CqqvGkhC1nOOJHRERSxiBMpZRe44mbTBPZDy+AiGqGQZhK5eT4iAGYgSA4ICfHu05BmFTefLnJNJF9SPkCSCrvT0QGDMJUylDjqWwgVtcaT1J+8yUi2ygf5JjKO7X1BRDfn0iKGISplKHGU/mcsLqMgnH0iYjKktLeolINDuWCo4jWwSBMxTp0OImwsPPIyfGGt3eOInLBTDH1hktE1iHlvFMpBYdywFX11sMgTOV0upt2f0O0tqrecPV6fZX35dUdkXmslXdaV1IODqVKTavqbY1BmMrUdCsZS2w5I4XRp+recDdv3lztY/Dqjqj2rJF3aglSDQ7lgAGs5TEIUxkfHx8kJCRYfW5fKsP9lnjD5dWdMeaGUE1YI+/UEqQaHMoBA1jLYxCmQtb+gJTC1ZJhJI9vuJbFFWZUG1LMO5VqcCgHfD+1PAZhZHFSuFoqO+J3zz15mDVLh+JiDbRaAUuX5uHJJ4chNzcXW7ZssUl/lIIrYKk65VMZTOWdWiLlwVxSDA7lgAGs5TEII4up6eiTrd58DSMx06cDQ4YA588DzZpp0KiRFwAvm/SBSG1slfJQW3IIDuWAAaxlMQgji6nJ6JO98oUaNSr9IlI6KeTs1fbxbdFnqQaHcqSGVfW2wiCMLErOo09SWM1JVBdyzNmzZZ+l8pzlxpar6tWGQRhZjZxGn6SymlNuGLhKixxz9ljJXvo4img9DMJItQxXbdWt5uTVXeUYuEqf3IJkvqakiwGWdTAII9UyXN0dOgQsW1ZxNeeDD45Cz55886mMFMqQUNXkFtDwNUVq5FB9EyLl8vHxQZcuPnAo95eg1QKdO/swACvHMCpYVRmSsu3IPkwFNHq9BwDgxo0buHr1Kq5evYrs7Gx7dlVU3WuKSIk4Ekaq16gRsG4dMH48UFxcGoC9/bZ88tlsyTB6eOHCXXz4oYCSEo14TKsVMHnyowgJqcfg1c6qq9WXkpJidEwKifosBEpqxJEwIgBxccCFC8ChQ6X/xsXZu0fS5ePjg4gIf6xbp4FWW3pbaeCqQUSEv90/zOmfgKasqgIaKSS9GwqBGvrNQqCkBhwJI/ofOa3mlIK4OCA62lCGhOdOSkxVNgeAjIwQySbqsxAoqQ2DMCIyGwNXaSmbi1c+oElPb4blyxMll6jPSvakZgzCiIgUonw9pxs3biAlJUXSKw9Zg4rUjEEYEZGCVBasVJeoXxvW2GKIARapFYMwIiKFs9TKQzlui0QkZVwdSUSkcJZaeVjZFkMZGSFi/TFT7YiochwJIyJSqKoS9csGYOYkvcutIj+RFDEII7uyRn4JEZWyVtK7lBP9ieSEQRjZDfNLiKzPGn87lkz0J1IzBmFkNzXNG5F6fglH80htuMUQkWUwCCOqA47mkRqZqsjPUTCi2mEQRlQHShnNI6otqW4xxJFpkhMGYUREVCNS32Ko/Mi0Xu+BnByfCntlcmSapIJBGBER1YjUtxgq26+qSmhwZJqkgkEYERHVmBxGkFhCg+SCFfOJiEhRqiqhQSQlDMLIbmqaN2Kv/BIikidDCY2yWEKDpIjTkWQ3Us8vMYepRGAish2W0CC5YBBGdlU2wLp8GTh3DggPBxo1smOnaqHsKF1VicAczSOyLamW0CAqi0EYScJ77wHx8UBJCeDgAKxbB8TF2btX1TOM5l24cBeLFvlBEDQASvNPdu/uh3nzOiMkpJ6sRvOIlMJUCQ0iqWBOGNnd5cv/BGBA6b/jx5feLgc+Pj7Iy/NHSYnG6PbiYg1u3vRnAEZkI8wzJbnhSBjZ3blz/wRgBsXFwPnzlpuWtHYV7fDw0hG8ss9DqwWaNTP7IUnFWPXdPErMMyVlYxBGdmftAMYW+zs2alQ6hTp+fGkAqdUCb78tn9w2kg5Wfa8bnhOSEwZhZHfWDmBstb9jXBwQHV06gtesGQMwMg+rvhOpB4MwkgSlBDCNGsm37yQtrPpOpHwMwkgyGMAQ/aOqqu8MwoiUgUEYEdUJk8itw1D1vWwgxqrvRMrCIIyIzGaLRQ9qxarvRMrHIIxUh1sLWU75ETBT55ZJ5OZh1XciZWMQRqpS1WozqhueW+tg1Xci5WLFfFI8Q3VsU6vN9HoPo3ZUe9WdW6o5Vn0nUg+OhJHiGapoHzoELFtWcbXZgw+OQs+eLPJYF1zJZzms+k6kHgzCSBV8fHzQpUvllfk7d/YBP8/qhiv5LIsBFpE6cDqSVMNQmV+rLf2eWwtZjmEln0ZTGuFyJR8RUfU4EkaqopTK/FLElXxERLXDIIxUh5X5Lad8criplXxMIiciqohBGBGZjUnkZC1ld2K4csUBGRn1EBp6F0FBpVPefF2REjAII6I64QchWVrZnRiqqj/HnRhI7hiEERGRWaw1WmV4TFP158LCzkOnu8mdGEj2GIQREVGt2WK0ivXnSOlYooKIiGqtutEqw24JdRmtMtSfK4v150hJZBeEFRYWol27dtBoNDh16pTRsZ9//hndunWDi4sLgoODkZSUVOH+W7duRcuWLeHi4oI2bdpgz549RscFQcC8efMQGBgIV1dXREVF4dy5c0ZtcnJyMHz4cHh6esLLywtxcXG4detWrftCRCR3VY1W1RXrz5HSyS4ImzlzJoKCgircnpeXhz59+qBJkyY4fvw4Xn31VSxYsADr1q0T2xw5cgTDhg1DXFwcTp48iQEDBmDAgAE4ffq02CYpKQkrV67E2rVrcfToUbi7uyM6Ohq3b98W2wwfPhy//vor9u/fj127duHrr79GfHx8rfqiJJcvA4cOlf5LROpi7dGqDh1OIjFxOUaNSkZi4nJuCk+KIqucsM8//xz79u3Dtm3b8Pnnnxsd27hxI+7cuYP3338fTk5OuPfee3Hq1Cm88cYbYoC0YsUKxMTEYMaMGQCAF198Efv378fq1auxdu1aCIKA5cuX44UXXsBjjz0GAPjggw/g7++P7du3Y+jQoThz5gz27t2LY8eOoWPHjgCAVatW4V//+hdee+01BAUF1agvSvHee0B8fOlWQA4OpRXp4+Ls3Ssi5ZNKCQfDaFX5nDBLjlaZqj9HphUXF6OoqMje3VAsR0dHaA3br9SBbIKwa9euYdy4cdi+fTvc3NwqHE9NTUX37t2NikJGR0dj6dKl+Ouvv9CgQQOkpqZi2rRpRveLjo7G9u3bAQAZGRnIzMxEVFSUeFyn06Fz585ITU3F0KFDkZqaCi8vLzEAA4CoqCg4ODjg6NGj+Pe//12jvlSmsLAQhYWF4vd5eXm1O0k2dvnyPwEYUPrv+PGlFelZDJXIeqRWwoG7JUiHIAjIzMxEbm6uvbuieF5eXggICIBGozH7MWQRhAmCgNGjR2PChAno2LEjLly4UKFNZmYmQkNDjW7z9/cXjzVo0ACZmZnibWXbZGZmiu3K3s9UGz8/P6Pj9erVg7e3t1Gb6vpSmSVLlmDhwoWVnwQJOnfOeDNsACguLt0SiEGY8l2+XPoaCA/n79vWpFjCwZKjVTXdYYE7MVRkCMD8/Pzg5uZWpwCBKicIAgoKCpCVlQUACAwMNPux7BqEzZ49G0uXLq2yzZkzZ7Bv3z7cvHkTc+bMsVHP7GPOnDlGI3V5eXkIDg62Y4+qFh5eOgVZNhDTakv3ZCRl4zS0NFiqhINUpjYNuBODeYqLi8UAjOfGulxdXQEAWVlZ8PPzM3tq0q5B2PTp0zF69Ogq2zRt2hQHDx5EamoqnJ2djY517NgRw4cPx4YNGxAQEIBr164ZHTd8HxAQIP5bWZuyxw23lY1sr127hnbt2oltDNGvwd27d5GTk1Ptzyn7Myrj7Oxc4TlKWaNGpR++48eXjoBptcDbb3NURMmys7Nx4cJdxMf7oaSk9Aq7dBpaQLt2WQgJqcc3fxsyJMWXDcRqmxRv7tSmtUer+DqqPUMOWGUpO2R5hvNcVFQkzyDM19cXvr6+1bZbuXIlXnrpJfH7K1euIDo6Gps3b0bnzp0BAJGRkXj++edRVFQER0dHAMD+/fvRokULcfovMjISBw4cQGJiovhY+/fvR2RkJAAgNDQUAQEBOHDggBh05eXl4ejRo5g4caL4GLm5uTh+/DgiIiIAAAcPHkRJSUmt+qIUcXGlOWDnz5eOgDEAUy7Dh3VGRghKSkYZHSsu1mDVqs8RGnqRW8nYkCWS4s2d2uRolXRxCtI2LHGeZZET1rhxY6Pv69evDwAICwtDo/996j/55JNYuHAh4uLiMGvWLJw+fRorVqzAsmXLxPs988wz6NGjB15//XXExsbi448/xo8//iiWjtBoNEhMTMRLL72E8PBwhIaGYu7cuQgKCsKAAQMAAK1atUJMTAzGjRuHtWvXoqioCAkJCRg6dKhYOqMmfVGSRo0YfKmB4cO2utEXbiVjW5ZKijdnapMBFlHdyK5OmCk6nQ779u1DRkYGIiIiMH36dMybN8+oJETXrl2xadMmrFu3Dm3btsUnn3yC7du347777hPbzJw5E5MnT0Z8fDweeOAB3Lp1C3v37oWLi4vYZuPGjWjZsiV69+6Nf/3rX3jooYeMaoDVpC9EcsUCmtKj091EaOjFOv0OWJ2e7Gn06NHQaDTQaDRwdHSEv78/HnnkEbz//vsoKb8CrArJycnw8vKyXkctTBYjYeWFhIRAEIQKt99///345ptvqrzv448/jscff9zkcY1Gg0WLFmHRokUm23h7e2PTpk1V/pya9IVIrliSQHlsUe+LqCoxMTFYv349iouLce3aNezduxfPPPMMPvnkE+zYsQP16skyZKmS8p4REdkEC2jaj7WS4hlcq1vZVbKVsXaOn7Ozs7h47Z577kGHDh3QpUsX9O7dG8nJyRg7dizeeOMNrF+/Hv/3f/8Hb29v9OvXD0lJSahfvz4OHz6Mp59+GsA/+Vrz58/HggUL8OGHH2LFihVIS0uDu7s7Hn74YSxfvrxCySlbYxBGRCQz1kyKZ3CtTmVXyVbF1gtvHn74YbRt2xYpKSkYO3YsHBwcsHLlSoSGhuL//u//8J///AczZ87EmjVr0LVrVyxfvhzz5s1DWloagH9yyIuKivDiiy+iRYsWyMrKwrRp0zB69OgK+0fbGoMwIiIZYlK8fNl7xKkyNV1QY4+FNy1btsTPP/8MAEbVDUJCQvDSSy9hwoQJWLNmDZycnKDT6aDRaCqUgxozZoz4/6ZNm2LlypVi3rchULMHBmFERCrG6vS2JdURJykTBEGcXvzyyy+xZMkSnD17Fnl5ebh79y5u376NgoKCKuujHT9+HAsWLMBPP/2Ev/76S0z2v3TpElq3bm2T51EZBmFEVCP8sFYm1vuyLSmPOEnVmTNnEBoaigsXLqBv376YOHEiXn75ZXh7e+Pbb79FXFwc7ty5YzIIy8/PR3R0NKKjo7Fx40b4+vri0qVLiI6Otvt5ZhBGRDXCD2vl4u+MpOrgwYP45ZdfMHXqVBw/fhwlJSV4/fXX4eBQWmFry5YtRu2dnJxQXFxsdNvZs2eRnZ2NV155RdwK8Mcff7TNE6gGgzAiqjF+WBNZll7vgZwcH3h7Z6t+QURhYSEyMzONSlQsWbIEffv2xciRI3H69GkUFRVh1apV6NevH7777jusXbvW6DFCQkJw69YtHDhwAG3btoWbmxsaN24MJycnrFq1ChMmTMDp06fx4osv2ulZGlNMsVYiIiI5OXGiPZYvT8SGDaOwfHkiTpxob+8u2dXevXsRGBiIkJAQxMTE4NChQ1i5ciU+++wzaLVatG3bFm+88QaWLl2K++67Dxs3bsSSJUuMHqNr166YMGEChgwZAl9fXyQlJcHX1xfJycnYunUrWrdujVdeeQWvvfaanZ6lMY1QWdVTkoS8vDzodDro9Xp4enrauztGpLi6R8kuXwbOnQPCw7lFFJGcXb16FevWrYNe74HlyxMrbP+VmLgcOt1NxMfHIzAwsFaPffv2bWRkZCA0NNRol5fa9Ks65vRLqao63zX9/OZ0JNUaV/fY1nvvAfHxQEkJ4OAArFtXunE6EcmXOXt1WhMX3tgHgzCqNa7usZ3Ll/8JwIDSf8ePB6KjOSJGJGeGvTrLj4TZa69OLryxDwZhRBJ27tw/AZhBcTFw/jyDMCI5MowkVbdXpz1GnBhg2R6DMCIJCw8vnYIsG4hptUCzZvbrE5mHeZQEVBxxmjfvOi5cqIeQkLsICnoAwAN8LagIgzAiCWvUqDQHbPz40hEwrRZ4+22OgskN8yiprLK/48BAICLCjp0hu2IQRiRRhpGTf/0LOHrUoczVcgmuXuXIiZyUHwEzVRuKeZRE6sIgjEiCTI2cnD5t/D1HTuTnxIn2FfKAOnQ4ae9uEZEdsFgrkQRxBaoy6fUeYgAGlJYk2LmzL/R6Dzv3jIjsgSNhVGusJ0NkHqnVhlIzLpQgKWAQRrXGejJE5pFabSi14kIJ9Tl8+DB69eqFv/76C15eXjW6T0hICBITE5GYmGi1fnE6kszi4+ODwMBAk1984yKqyFAbSqMprTlSvjYU2Qan+6Vn9OjR0Gg0mDBhQoVjkyZNgkajwejRo23fMSvjSBgRkQ116HASYWHnkZPjDW/vHAZgRP8THByMjz/+GMuWLYOrqyuA0v0ZN23ahMaNG9u5d9bBkTAiIisrnx+p091EaOjFCgEY8yhJzTp06IDg4GCkpKSIt6WkpKBx48Zo3769eFthYSGmTJkCPz8/uLi44KGHHsKxY8eMHmvPnj1o3rw5XF1d0atXL1y4cKHCz/v222/RrVs3uLq6Ijg4GFOmTEF+fr7Vnl9lOBJGRGRlzKMkqpkxY8Zg/fr1GD58OADg/fffx9NPP43Dhw+LbWbOnIlt27Zhw4YNaNKkCZKSkhAdHY3z58/D29sbf/zxBwYOHIhJkyYhPj4eP/74I6ZPn270c9LT0xETE4OXXnoJ77//Pq5fv46EhAQkJCRg/fr1Nnu+HAkjkiCuQFUe5lGSnFy+DBw6VPqvLT311FP49ttvcfHiRVy8eBHfffcdnnrqKfF4fn4+3nrrLbz66qt49NFH0bp1a7zzzjtwdXXFe++9BwB46623EBYWhtdffx0tWrTA8OHDK+STLVmyBMOHD0diYiLCw8PRtWtXrFy5Eh988AFu375ts+fLkTAiCeLICRHZy3vvAfHxpXvWOjiUbp0WF2ebn+3r64vY2FgkJydDEATExsaiYcOG4vH09HQUFRXhwQcfFG9zdHREp06dcObMGQDAmTNn0LlzZ6PHjYyMNPr+p59+ws8//4yNGzeKtwmCgJKSEmRkZKBVq1bWeHoVMAgjkigGWES2YWobKTW6fPmfAAwo/Xf8eCA62nZ71o4ZMwYJCQkAgDfffNMqP+PWrVsYP348pkyZUuGYLRcBMAgjIiJVKTuNX9U2Umqc7j937p8AzKC4GDh/3nZBWExMDO7cuQONRoPo6GijY2FhYXBycsJ3332HJk2aAACKiopw7NgxsZ5Xq1atsGPHDqP7ff/990bfd+jQAb/99huaNWtmvSdSAwzCiIhIVQzT/Rcu3MWiRX4QBA2A0t0Ldu/uh3nzOiMkpJ4qR6PDw0unIMsGYlotYMtYRavVilOLWq3W6Ji7uzsmTpyIGTNmwNvbG40bN0ZSUhIKCgoQ97850wkTJuD111/HjBkzMHbsWBw/fhzJyclGjzNr1ix06dIFCQkJGDt2LNzd3fHbb79h//79NSrkaylMzCciItXx8fFBXp4/Sko0RrcXF2tw86a/KgMwoHS0a9260sALKP337bdtNwpm4OnpCU9Pz0qPvfLKKxg0aBBGjBiBDh064Pz58/jiiy/QoEEDAKXTidu2bcP27dvRtm1brF27FosXLzZ6jPvvvx9fffUVfv/9d3Tr1g3t27fHvHnzEBQUZPXnVpZGEATBpj+RaiwvLw86nQ56vd7ki5GIiMxz+TLQpEnFUZ8LF2wfdFjC7du3kZGRgdDQULi4uNTpsS5fLp2CbNZMnufCFqo63zX9/OZIGBERqZJURn2kqFEjoGdPngtrY04YERGpVlxc6co/jvqQPTAIIyIiVWvUiMEX2QenI4mIiIjsgEEYERERkR0wCCMiIlIQFj2wDUucZwZhRERECuDo6AgAKCgosHNP1MFwng3n3RxMzCciIlIArVYLLy8vZGVlAQDc3Nyg0WiquRfVliAIKCgoQFZWFry8vCpU9a8NBmFEREQKERAQAABiIEbW4+XlJZ5vczEIIyIiUgiNRoPAwED4+fmhqKjI3t1RLEdHxzqNgBkwCCMiIlIYrVZrkSCBrIuJ+URERER2wCCMiIiIyA4YhBERERHZAXPCJMxQCC4vL8/OPSEiIqKaMnxuV1fQlUGYhN28eRMAEBwcbOeeEBERUW3dvHkTOp3O5HGNwP0NJKukpARXrlyBh4eHagvu5eXlITg4GH/88Qc8PT3t3R3Z4/m0LJ5Py+L5tCyeT8uqzfkUBAE3b95EUFAQHBxMZ35xJEzCHBwc0KhRI3t3QxI8PT35JmJBPJ+WxfNpWTyflsXzaVk1PZ9VjYAZMDGfiIiIyA4YhBERERHZAYMwkjRnZ2fMnz8fzs7O9u6KIvB8WhbPp2XxfFoWz6dlWeN8MjGfiIiIyA44EkZERERkBwzCiIiIiOyAQRgRERGRHTAIIyIiIrIDBmEkCV9//TX69euHoKAgaDQabN++3ei4IAiYN28eAgMD4erqiqioKJw7d84+nZWB6s7n6NGjodFojL5iYmLs01mJW7JkCR544AF4eHjAz88PAwYMQFpamlGb27dvY9KkSfDx8UH9+vUxaNAgXLt2zU49lraanM+ePXtWeH1OmDDBTj2Wtrfeegv333+/WEA0MjISn3/+uXicr83aqe58Wvq1ySCMJCE/Px9t27bFm2++WenxpKQkrFy5EmvXrsXRo0fh7u6O6Oho3L5928Y9lYfqzicAxMTE4OrVq+LXRx99ZMMeysdXX32FSZMm4fvvv8f+/ftRVFSEPn36ID8/X2wzdepU7Ny5E1u3bsVXX32FK1euYODAgXbstXTV5HwCwLhx44xen0lJSXbqsbQ1atQIr7zyCo4fP44ff/wRDz/8MB577DH8+uuvAPjarK3qzidg4demQCQxAIRPP/1U/L6kpEQICAgQXn31VfG23NxcwdnZWfjoo4/s0EN5KX8+BUEQRo0aJTz22GN26Y/cZWVlCQCEr776ShCE0teio6OjsHXrVrHNmTNnBABCamqqvbopG+XPpyAIQo8ePYRnnnnGfp2SuQYNGgjvvvsuX5sWYjifgmD51yZHwkjyMjIykJmZiaioKPE2nU6Hzp07IzU11Y49k7fDhw/Dz88PLVq0wMSJE5GdnW3vLsmCXq8HAHh7ewMAjh8/jqKiIqPXZ8uWLdG4cWO+Pmug/Pk02LhxIxo2bIj77rsPc+bMQUFBgT26JyvFxcX4+OOPkZ+fj8jISL4266j8+TSw5GuTG3iT5GVmZgIA/P39jW739/cXj1HtxMTEYODAgQgNDUV6ejqee+45PProo0hNTYVWq7V39ySrpKQEiYmJePDBB3HfffcBKH19Ojk5wcvLy6gtX5/Vq+x8AsCTTz6JJk2aICgoCD///DNmzZqFtLQ0pKSk2LG30vXLL78gMjISt2/fRv369fHpp5+idevWOHXqFF+bZjB1PgHLvzYZhBGp0NChQ8X/t2nTBvfffz/CwsJw+PBh9O7d2449k7ZJkybh9OnT+Pbbb+3dFUUwdT7j4+PF/7dp0waBgYHo3bs30tPTERYWZutuSl6LFi1w6tQp6PV6fPLJJxg1ahS++uore3dLtkydz9atW1v8tcnpSJK8gIAAAKiwoufatWviMaqbpk2bomHDhjh//ry9uyJZCQkJ2LVrFw4dOoRGjRqJtwcEBODOnTvIzc01as/XZ9VMnc/KdO7cGQD4+jTByckJzZo1Q0REBJYsWYK2bdtixYoVfG2aydT5rExdX5sMwkjyQkNDERAQgAMHDoi35eXl4ejRo0bz9GS+y5cvIzs7G4GBgfbuiuQIgoCEhAR8+umnOHjwIEJDQ42OR0REwNHR0ej1mZaWhkuXLvH1WYnqzmdlTp06BQB8fdZQSUkJCgsL+dq0EMP5rExdX5ucjiRJuHXrltGVREZGBk6dOgVvb280btwYiYmJeOmllxAeHo7Q0FDMnTsXQUFBGDBggP06LWFVnU9vb28sXLgQgwYNQkBAANLT0zFz5kw0a9YM0dHRduy1NE2aNAmbNm3CZ599Bg8PDzGXRqfTwdXVFTqdDnFxcZg2bRq8vb3h6emJyZMnIzIyEl26dLFz76WnuvOZnp6OTZs24V//+hd8fHzw888/Y+rUqejevTvuv/9+O/deeubMmYNHH30UjRs3xs2bN7Fp0yYcPnwYX3zxBV+bZqjqfFrltWmxdZZEdXDo0CEBQIWvUaNGCYJQWqZi7ty5gr+/v+Ds7Cz07t1bSEtLs2+nJayq81lQUCD06dNH8PX1FRwdHYUmTZoI48aNEzIzM+3dbUmq7DwCENavXy+2+fvvv4X//Oc/QoMGDQQ3Nzfh3//+t3D16lX7dVrCqjufly5dErp37y54e3sLzs7OQrNmzYQZM2YIer3evh2XqDFjxghNmjQRnJycBF9fX6F3797Cvn37xON8bdZOVefTGq9NjSAIgrkRIxERERGZhzlhRERERHbAIIyIiIjIDhiEEREREdkBgzAiIiIiO2AQRkRERGQHDMKIiIiI7IBBGBEREZEdMAgjIiIisgMGYURERER2wCCMiMgMd+7csXcXKpBin4jINAZhREQAevbsiYSEBCQkJECn06Fhw4aYO3cuDDu7hYSE4MUXX8TIkSPh6emJ+Ph4AMC3336Lbt26wdXVFcHBwZgyZQry8/PFx12zZg3Cw8Ph4uICf39/DB48WDz2ySefoE2bNnB1dYWPjw+ioqLE+/bs2ROJiYlGfRwwYABGjx4tfm9un4hIGhiEERH9z4YNG1CvXj388MMPWLFiBd544w28++674vHXXnsNbdu2xcmTJzF37lykp6cjJiYGgwYNws8//4zNmzfj22+/RUJCAgDgxx9/xJQpU7Bo0SKkpaVh79696N69OwDg6tWrGDZsGMaMGYMzZ87g8OHDGDhwIGq7nW9t+0RE0sENvImIUDrylJWVhV9//RUajQYAMHv2bOzYsQO//fYbQkJC0L59e3z66afifcaOHQutVou3335bvO3bb79Fjx49kJ+fjz179uDpp5/G5cuX4eHhYfTzTpw4gYiICFy4cAFNmjSptD/t2rXD8uXLxdsGDBgALy8vJCcnA4BZfXJxcanTeSIiy+FIGBHR/3Tp0kUMwAAgMjIS586dQ3FxMQCgY8eORu1/+uknJCcno379+uJXdHQ0SkpKkJGRgUceeQRNmjRB06ZNMWLECGzcuBEFBQUAgLZt26J3795o06YNHn/8cbzzzjv466+/at3n2vaJiKSDQRgRUQ25u7sbfX/r1i2MHz8ep06dEr9++uknnDt3DmFhYfDw8MCJEyfw0UcfITAwEPPmzUPbtm2Rm5sLrVaL/fv34/PPP0fr1q2xatUqtGjRQgyUHBwcKkxNFhUV1blPRCQdDMKIiP7n6NGjRt9///33CA8Ph1arrbR9hw4d8Ntvv6FZs2YVvpycnAAA9erVQ1RUFJKSkvDzzz/jwoULOHjwIABAo9HgwQcfxMKFC3Hy5Ek4OTmJU4u+vr64evWq+LOKi4tx+vTpap9DTfpERNLAIIyI6H8uXbqEadOmIS0tDR999BFWrVqFZ555xmT7WbNm4ciRI0hISMCpU6dw7tw5fPbZZ2IS/K5du7By5UqcOnUKFy9exAcffICSkhK0aNECR48exeLFi/Hjjz/i0qVLSElJwfXr19GqVSsAwMMPP4zdu3dj9+7dOHv2LCZOnIjc3Nxqn0N1fSIi6ahn7w4QEUnFyJEj8ffff6NTp07QarV45plnxLIPlbn//vvx1Vdf4fnnn0e3bt0gCALCwsIwZMgQAICXlxdSUlKwYMEC3L59G+Hh4fjoo49w77334syZM/j666+xfPly5OXloUmTJnj99dfx6KOPAgDGjBmDn376CSNHjkS9evUwdepU9OrVq9rnUF2fiEg6uDqSiAiVr0YkIrImTkcSERER2QGDMCIiIiI74HQkERERkR1wJIyIiIjIDhiEEREREdkBgzAiIiIiO2AQRkRERGQHDMKIiIiI7IBBGBEREZEdMAgjIiIisgMGYURERER2wCCMiIiIyA7+HzacbTcR2AnFAAAAAElFTkSuQmCC", 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yvoqKilBUVCS9LigoMPsMhBDIycmBs7MzQkJCOIJTTXTFeXNychAaGsoCvUREVC3sKiCbMWMGli9fjjt37iA2Nhbbtm2Tzv33v//FxYsXsXHjRnzxxRcoLS3F5MmTMWjQIOzbtw8AkJubaxAkAUBgYCAKCgrw119/4ebNmygtLTXZ5syZM9I1XF1d4ePjU6ZNbm6uxc/RnTNnwYIFmD9/vlXP4t69e7hz5w6Cg4Ph6elp1XuocgICApCdnY179+5J/wAgIiKqSjYdVpk5cyZkMpnFL10gBADTp0/H8ePHsXv3bjg7O2PUqFHQzbhqNBoUFRXhiy++QNeuXfH4449j5cqV2L9/P86ePWurW6yQWbNmQa1WS1+XL18227a0tBQAOI1WA3TPWPfMiYiIqppNR8imTp2KMWPGWGzTrFkz6Xt/f3/4+/ujZcuWaNOmDUJCQvDjjz9CqVRCoVCgXr16aNmypdS+TZs2ALQrHlu1aoWgoKAyqyGvXr0Kb29veHh4wNnZGc7OzibbBAUFAQCCgoJQXFyM/Px8g1Ey4zbGKzN119S1McXNzQ1ubm4Wn4cxTqFVPz5jIiKqbjYNyAICAhAQEFCp9+oS2XU5V126dMG9e/eQmZmJiIgIAMAff/wBAGjatCkAQKlU4rvvvjO4TlpaGpRKJQDtSEh0dDT27t2LAQMGSJ+zd+9eJCcnAwCio6Ph4uKCvXv3YuDAgQCAs2fP4tKlS9J1lEolXn/9dVy7dk1amZmWlgZvb29ERkZW6n6JiIjowahUKouVCVxdXeHn51eDPbrPLnLIMjIycOTIETz22GNo2LAhMjMzMWfOHEREREhBUFxcHKKiojBu3DgsWbIEGo0GEydORK9evaRRswkTJmD58uV44YUXMG7cOOzbtw8bNmzA9u3bpc+aMmUKRo8ejU6dOuHRRx/FkiVLcPv2bYwdOxYAIJfLkZSUhClTpsDX1xfe3t6YNGkSlEolYmNjAQC9e/dGZGQkRo4ciUWLFiE3NxcvvfQSJk6cWOERMCIiInpwKpVKqqBgSXJysk2CMrtYmufp6YnU1FT07NkTrVq1QlJSEtq1a4cDBw5IAY6TkxO2bt0Kf39/dOvWDQkJCWjTpg3WrVsnXSc8PBzbt29HWloa2rdvj3feeQeffvqpVIMMAIYMGYK3334bc+fORYcOHXDixAns3LnTIEl/8eLF6Nu3LwYOHIhu3bohKCgIqamp0nlnZ2ds27YNzs7OUCqVGDFiBEaNGoVXXnmlBp5W7TZmzBgpP9DFxQWBgYHo1asXPvvsswqV71i1alWZhRVERETmWFuz01a1Pe22DpkjsFTH5O7du8jKykJ4eDjc3d0rfG1bDduOGTMGV69exeeff47S0lJcvXoVO3fuxIIFC9C1a1ds2bIF9eqVP3C7atUqpKSkID8/v8r7aOxBnzUREdleTk4OPv74Y+m1Wu2FvDw/+PqqIJcXSsfHjx8PhULxQJ/lMHXI6MHYetjWzc1NWtzQuHFjREVFITY2Fj179sSqVavw1FNP4d1338Xnn3+O//73v/D19UW/fv2waNEiNGjQAN9//700haxLuJ83bx5efvllfPnll1i6dCnOnj2L+vXr44knnsCSJUukXD4iIqJjxzpi69a+EMIJMpkG/fptQ1TUcZv2yS6mLKlq1cZh2yeeeALt27eXpn6dnJywbNky/Pbbb1i9ejX27duHF154AQDQuXNnLFmyBN7e3sjJyUFOTg6mTZsGACgpKcGrr76KX375BZs3b8aFCxfKXclLRESOQ632koIxABDCCVu39oVa7WXTfnGEjGqN1q1b4+TJkwAg7Z4AAGFhYXjttdcwYcIEfPDBB3B1dYVcLodMJitTRmTcuHHS982aNcOyZcvwyCOP4NatW2jQoEGN3AcREdVeeXl+UjCmI4QT8vJ8DaYuaxpHyKjWEEJIU5B79uxBz5490bhxY3h5eWHkyJFQqVS4c+eOxWscPXoU/fr1Q2hoKLy8vNC9e3cA2lp0REREvr4qyGSGi8hkMg18ffNs1CMtBmRUa5w+fRrh4eG4cOEC+vbti3bt2mHTpk04evQo3n//fQCWp1Fv376N+Ph4eHt7Y82aNThy5Ai+/fbbct9HRESOQy4vRL9+26SgTJdDZsvRMYBTllRL7Nu3D7/++ismT56Mo0ePQqPR4J133pE2Td+wYYNBe1dX1zJbGZ05cwYqlQoLFy5ESEgIAODnn3+umRsgIqJaTX+rwaio44iIOI+8PF/4+uYZBGO22pKQARnVuKKiIuTm5pYpe9G3b1+MGjUKp06dQklJCd577z3069cPhw4dwooVKwyuERYWhlu3bmHv3r1o3749PD09ERoaCldXV7z33nuYMGECTp06hVdffdVGd0lERLWJn58fkpOTa22lfk5ZUo3buXMnFAoFwsLC0KdPH+zfvx/Lli3Dv//9bzg7O6N9+/Z499138eabb+Lhhx/GmjVrsGDBAoNrdO7cGRMmTMCQIUMQEBCARYsWISAgAKtWrcLGjRsRGRmJhQsX4u2337bRXRIRUW3j5+cHhUJh9stWwRjAwrC1WnUVhrV1HTJ7w8KwREQ1pzbvN2ktFoYlq9T2YVsiInJM1g4YDBkyBHK5vE79rmJA5qDqyg8wERHVHcYDBea2N1q/fr30fV2ZzWFARkRERLWOtdsb1ZWyRgzIiIiIqFYxt72Rq2sRQkIu27xmWHVgQEZEREQ2kZmZabADy82bNwGY397om2/+WWs2A69qDMiIiIioxmVmZuKrr74yeU63vZFxUAbcHy2LiDhfp0bKWIeMiIiIapzx3sRXrihw+HAsrlxRlNneyJhuM/C6hCNkREREZFPffvt3/PJLewAyAALt2/+Cf/zj34iIOI/Ll5vgm28GQX8MqTZsBl7VOEJGRERENUalUiEnJ0fKF7tyRaEXjAGADL/80l4aKXv44dPo37/2bQZe1ThCRnXK999/jx49euDmzZvw8fGx6j1hYWFISUlBSkpKtfaNiMiRqVQqXLt2DRs2bDA4fulSU9wPxnRkSE9X4p//TAVQOzcDr2oMyKhGjRkzBqtXr8YzzzxTZsPwiRMn4oMPPsDo0aOxatUq23SQiIiqnLkEfrXaC/XqFQMQMA7KfvvtIfTunQa5vBCJiYnw9/cv835W6id6ACEhIVi3bh0WL14MDw8PANr9IteuXYvQ0FAb946IiKqKSqXC9evXDSrr6+gXftUGZMa0iftyeSH8/f2hUCiqvb+2xBwyqnFRUVEICQlBamqqdCw1NRWhoaHo2LGjdKyoqAjPPfccGjVqBHd3dzz22GM4cuSIwbW+++47tGzZEh4eHujRowcuXLhQ5vMOHjyIrl27wsPDAyEhIXjuuedw+/btars/IiK6vy+lcTCmVnvhp5+isGVLX72yFtpkfn11MXHfEgZkhCtXgP37tX/WlHHjxuHzzz+XXn/22WcYO3asQZsXXngBmzZtwurVq3Hs2DE0b94c8fHxyMvT/gW9fPkyEhMT0a9fP5w4cQJPPfUUZs6caXCNzMxM9OnTBwMHDsTJkyexfv16HDx4EMnJydV/k0REDszUlkbHjnXE4sWT8d13/VA2BJEBqNuJ+5YwIHNwK1cCTZsCTzyh/XPlypr53BEjRuDgwYO4ePEiLl68iEOHDmHEiBHS+du3b+PDDz/EW2+9hSeffBKRkZH45JNP4OHhgZX/6+SHH36IiIgIvPPOO2jVqhWGDx+OMWPGGHzOggULMHz4cKSkpKBFixbo3Lkzli1bhi+++AJ3796tmZslIiJcuaLAli39UDaBX0sm0+Cppz7F6NGrkJKyxKASf11J3LeEOWQO7MoVYPx4QPO/unsaDfDMM0B8PNCkSfV+dkBAABISErBq1SoIIZCQkGCQsJmZmYmSkhJ06dJFOubi4oJHH30Up0+fBgCcPn0aMTExBtdVKpUGr3/55RecPHkSa9askY4JIaDRaJCVlYU2bdpUx+0REZGeY8c6YsuWvjAXjAHaEbEmTXIAwCCJvy4l7lvCgMyBnTt3PxjTKS0Fzp+v/oAM0E5b6qYO33///Wr5jFu3buGZZ57Bc889V+YcFxAQEVU/3Ubh5ifltCNjumAMgEMk8RtjQObAWrQAnJwMgzJnZ6B585r5/D59+qC4uBgymQzx8fEG5yIiIuDq6opDhw6hadOmAICSkhIcOXJEqhfWpk0bbNmyxeB9P/74o8HrqKgo/P7772heUzdFREQGTG0Ufp8G/ftvMwjGAMeYojTGgMyBNWkCfPyxdpqytFQbjH30Uc2MjgGAs7OzNP3o7OxscK5+/fp49tlnMX36dPj6+iI0NBSLFi3CnTt3kJSUBACYMGEC3nnnHUyfPh1PPfUUjh49WqZ+2YwZMxAbG4vk5GQ89dRTqF+/Pn7//XekpaVh+fLlNXKfRER1jUqlMpm0r6MfUJneKFyDv/1tO1q1OmeQuD9kyBAEBAQ4xBSlMQZkDi4pSZszdv68dmSspoIxHW9vb7PnFi5cCI1Gg5EjR6KwsBCdOnXCrl270LBhQwDaKcdNmzZh8uTJeO+99/Doo4/ijTfewLhx46RrtGvXDgcOHMDs2bPRtWtXCCEQERGBIUOGVPu9ERHVRbpyFuUZPHgwAEgbhetqjulWUOon7QPaxV4RERHV0md7IBNCmKrGRrVAQUEB5HI51Gp1mcDl7t27yMrKQnh4ONzd3W3UQ8fAZ01Ejsx4NOzGjRsGdSTVai/k5fnB11dVZrRLvwaZtl3ZrY8GDx6MRo0a1alRMUu/v83hCBkRERGZVN5omH61feORL7lcjuTk5HKnNutSIPYgGJARERERANOjYfr0R8MA6G19BAjhhK1b+yIi4rw0AsZgy3oMyIiIiKjCo2FKZXqZ1ZNC3N9/kiqGARkRERGVmVosbzTs8GFlmdWTjrb/ZFViQGbnuCaj+vEZE5GjsWY0DHCCUnkI6elKgxwyjo5VDgMyO6Wr21VcXAwPDw8b96Zu0/2r0bhWGhFRXaSrrG/NaFhMTAZiYjJMrp6kimFAZqfq1asHT09PXL9+HS4uLnBy4j7x1UGj0eD69evw9PREvXr860JE9qkihVxNV9a3PBpmKhBzxGr7D4K/YeyUTCaDQqFAVlYWLl68aOvu1GlOTk4IDQ2FTGZuU1wiotrL2kKuuoLZpirrlzcapr8ZOMByFpXBgMyOubq6okWLFhb/1UMPztXVlSOQRGS3rP0dUVJSAsB8ZX1Lo2HBwcEMwB4QAzI75+TkxOrxRERkNXOV9fVFRR1HRMR5jobVIAZkREREDsJSZX3jPFm5vJCjYTWIARkREZEDMLV6Ur+yvo+PD7c6siEGZERERA7A1OpJ48r6DLZshwEZERGRHbKmlIV+gGVu9SQr69cODMiIiIjshC4Iy8/Px4YNG8pt/+STT0rFw8tbPUm2xYCMiIjIDlhbT0xHrfbCBx+c/t9qSu0xS6snWcjVthiQERER2QFL05NqtRcuXw4BAISEXEZmZnOzqynHju1lULYCYLJ+bcCAjIiIyA7p6ollZyuQlhYHQJcbJv73p3Z3EePVlP7+/lAoFLboMlnAgIyIiMjO6NcT0wZg+lu7ld3mzXg1JdU+DMiIiIhqMV0i/40bNwCUrSdmKgAzxtWUtR8DMiIiolrKVCK/qXpiZWkgk4GrKe0IAzIiIqJayjiRX632wu3bngA0uJ8zBuhPW+oCMHOrKal2YkBGRERkB8rmjWmDMplMg7i4PZDL8wEAISFXpACMgZj9YEBGRERUy5nKG5PJBAYO3GAQgFmD9cZqJwZkRERENqBfdf/evXtlzru4uKCkpASA+X0o69e/A7m8ED169EDDhg3x119/SZX5AaBevXrw8fGRXrPeWO3FgIyIiKiGVbTqfnn7ULZo0YK1xexcecs0iIiIqIqZq7qvVnshKysMarWXwXHdPpQymQYAuHKyDuIIGRERkY2p1V7IyIjB4cNK6BL19bc7AizvQ0n2jwEZERGRDRmuntQy3u5IRy4vNBmIMVHf/jEgIyIispGyqyfv09/uKDExscyG4DpM1K8bGJARERFVI91qSn26bZAsVd3XT9rnhuB1HwMyIiKiamK8mlKt9kJenh98fVWQy02vntRRKtOZJ+ZAGJARERFVE/2RMf1cMf2k/bi4PUhL6wXDTcI1iInJqPH+ku0wICMiIqoEU1OR+vQT7Y1zxfST9rt0SQcApKXFQX+Fpf7oGJP26z4GZERERBVkbWHXwYMHAzBfaV+XtN+lSzoefviUyZIWQ4YMYdK+A2BARkREVEGWRsb06bZEKq/SPmC+pIVcLn/A3pI9sJtK/f3790doaCjc3d2hUCgwcuRIZGdnS+dffvllyGSyMl/169c3uM7GjRvRunVruLu7o23btvjuu+8MzgshMHfuXCgUCnh4eCAuLg7nzp0zaJOXl4fhw4fD29sbPj4+SEpKwq1btwzanDx5El27doW7uztCQkKwaNGiKn4iRERUW5irsK/zIJX2OV3pGOxmhKxHjx548cUXoVAo8Oeff2LatGkYNGgQDh8+DACYNm0aJkyYYPCenj174pFHHpFeHz58GMOGDcOCBQvQt29frF27FgMGDMCxY8fw8MMPAwAWLVqEZcuWYfXq1QgPD8ecOXMQHx+P33//He7u7gCA4cOHIycnB2lpaSgpKcHYsWMxfvx4rF27FgBQUFCA3r17Iy4uDitWrMCvv/6KcePGwcfHB+PHj6+Jx0VERDXEXLK+MUuV9s3VGWONMcchE0IIW3eiMrZs2YIBAwagqKgILi4uZc7/8ssv6NChA/7zn/+ga9euALTz8Ldv38a2bdukdrGxsejQoQNWrFgBIQSCg4MxdepUTJs2DQCgVqsRGBiIVatWYejQoTh9+jQiIyNx5MgRdOrUCQCwc+dO/O1vf8OVK1cQHByMDz/8ELNnz0Zubq70L5uZM2di8+bNOHPmjNX3WFBQALlcDrVaDW9v70o/KyIiqpjyEvbVajXWr18PtdoLS5aklJmKTElZIhV0TU1NLffzxo8fzzpjdUhlfn/bzQiZvry8PKxZswadO3c2GYwBwKeffoqWLVtKwRgApKenY8qUKQbt4uPjsXnzZgBAVlYWcnNzERcXJ52Xy+WIiYlBeno6hg4divT0dPj4+EjBGADExcXByckJGRkZ+Mc//oH09HR069bNYJg5Pj4eb775Jm7evImGDRtWxWMgIqJqYG3CPlB+sr6531HGOC1JdhWQzZgxA8uXL8edO3cQGxtrMNKl7+7du1izZg1mzpxpcDw3NxeBgYEGxwIDA5Gbmyud1x2z1KZRo0YG5+vVqwdfX1+DNuHh4WWuoTtnLiArKipCUVGR9LqgoMBkOyIiqj7WJuwD5Sfry+VyJCcnl1seg9OSZNOk/pkzZ5pMxNf/0p/imz59Oo4fP47du3fD2dkZo0aNgqkZ12+//RaFhYUYPXp0Td7OA1uwYAHkcrn0FRISYusuERE5PHMJ+7qq+3Fxeywm6/v5+UGhUJj9YjBGgI1HyKZOnYoxY8ZYbNOsWTPpe39/f/j7+6Nly5Zo06YNQkJC8OOPP0KpVBq859NPP0Xfvn3LjHQFBQXh6tWrBseuXr2KoKAg6bzumP5c/tWrV9GhQwepzbVr1wyuce/ePeTl5Rlcx9Tn6H+GKbNmzTKYUi0oKGBQRkRkQ+YS9o2Px8XtQXBwdplkfU5FkrVsGpAFBAQgICCgUu/VaLT/GtGf4gO0eWD79+/Hli1byrxHqVRi7969SElJkY6lpaVJAV14eDiCgoKwd+9eKQArKChARkYGnn32Weka+fn5OHr0KKKjowEA+/btg0ajQUxMjNRm9uzZKCkpkfIH0tLS0KpVK4v5Y25ubnBzc6vE0yAioqpmrrp+o0a5ZY7v2RNnkMjv7+/PqUiqELuoQ5aRkYHly5fjxIkTuHjxIvbt24dhw4YhIiKizOjYZ599BoVCgSeffLLMdZ5//nns3LkT77zzDs6cOYOXX34ZP//8M5KTkwEAMpkMKSkpeO2117Blyxb8+uuvGDVqFIKDgzFgwAAAQJs2bdCnTx88/fTT+Omnn3Do0CEkJydj6NChCA4OBgD861//gqurK5KSkvDbb79h/fr1WLp0aZkFBUREVLuoVCrcuHEDgPmE/cuXQ80m8gPa2RxORVJF2UVSv6enJ1JTUzFv3jzcvn0bCoUCffr0wUsvvWQwoqTRaLBq1SqMGTMGzs7OZa7TuXNnrF27Fi+99BJefPFFtGjRAps3b5ZqkAHACy+8gNu3b2P8+PHIz8/HY489hp07d0o1yABgzZo1SE5ORs+ePeHk5ISBAwdi2bJl0nm5XI7du3dj4sSJiI6Ohr+/P+bOncsaZEREtZjx6koXlyIAGuiPXchkGoSEXCq36j5RRdltHTJHwDpkREQ1Q6VSITs7W6oZpp8jBggAMos5ZPrFYFlTjBymDhkREVFVMR4Zu3JFYZAjpgvGkpI+RZMmOQAsV90nqgwGZERE5ND0a4QdO9YRW7b0hXGKtRBOKCkxXDFpbjNwosqwi6R+IiKi6qZbVWnqV2NFcsRY6oIqgyNkREREML2qEihb7NXcRuAAq+5T5TEgIyIigultkADD3DHgflkLoqrEKUsiIiJoc8L69dtmsA1S//7bDIIxourCETIiIqozVCrVA23kbc3qSeaIUXVgQEZERHVCZmYmvvrqq3LbJScnWwzKzK2eTExMRHBwMHPEqFpwypKIiOyeSqUqE4yp1V7IygqDWu1lcNx4BM3aES8GY1SdOEJGRER2zzjIOnRIiT174kxW0jfm5+eH5OTkB5rqJHpQDMiIiKhOOXRIibS0XgBkALRFXbdu7YuIiPNmC7ky2CJb45QlERHVGWq1F9LS4qALxnSEcEJenq9tOkVkBY6QERGRXdJfUXnjxg0A2uKuD1ppn8gWGJAREZHdMd4QXK32Ql5eGFxcikwUdxWIi9vDfSepVmNARkREdsd4Q/CtW/tKCfzt2p3EyZPt/heUadCr1x506ZJuu84SWYEBGRER2S3dhuC6ETEhnHDyZDskJX2KkhJXk8VdWdiVaiMGZEREZLdMbQguhBNKSlwRHn6xTPsRI0ZwRSXVSgzIiIioVrK0DZIuid/UhuD6CfyJiYnw9/cHwFpiVLsxICMiohpRkX0mTSft+8HXV2UwBanbEFw/h6xfv21SG39/fygUimq6I6Kqw4CMiIiqnXGAZY5un0lLSfvGVfet2RCcqLZjYVgiIqp2lkbGLLUzlbS/dWvfMvtTyuWFCA+/yGCM7BZHyIiIqMaZm4LU5YbpF3o1lbSfl+drVfDFFZVkLxiQERFRjbI0BZmammrQtiJJ+8aYxE/2hAEZERFVK5VKJY14mZuC1N/423j0jEn75AgYkBERUbUxTuYvbwrS3OgZk/aprmNARkRE1cY4Sd/UXpO6KcjyRs8YiFFdxoCMiIgqxZq6Yvr0R78AAUBmMAWZlRVW4QR+Ju1TXcGAjIiIKszaumIxMTEAyuaOATIAGiQlfYomTXIAVDyBn0n7VJewDhkREVWYtXXFMjIyAJjOHQO0e07q6BL4ZTINAJhN4Nd9MRijuoQjZERE9MB0KyNdXIpQUuImrZDUP25p9EuHCfzkqBiQERHRAzGXG9au3UmcPNlOWjFp/Fp/9EufuQR+5otRXcaAjIiIKs10bpg2Gf+XX9obvD55sh2Skj5FSYlrmdGvIUOGQC6Xm/0c5otRXceAjIiIKs10bpiOzOCVENqcsfDwi2VaBgQEMOAih8aAjIiIKs3UykhzzK2Y5OgXEQMyIiIyw1KdMd1WSMZbG+lyyIxxyyMiyxiQERFRGdbWGQMMV0Zev+6H777rV6bNwIHf4OGHT1d1N4nqDNYhIyKiMoxHxtRqL2RlhUGt9jI43qtXLwDakbLw8Ito1eqcVEdMRybTICTkisExrpgkMsQRMiIissjcht8AEB4ejuTkZGRnZyM1NbXMFKbxVGViYiKCg4OZM0ZkhAEZEZGDsGbvSeNAqbwNvwHAz8/P4LqWirv6+/szGCMygQEZEZEDsDYnLDk52SBgMlXWwtSG38ZTkCzuSlQxDMiIiByAqZywvDw/aYsjc+3K2/Bbx8/PD8nJyRUegSMiLQZkRER1hDVlKgDLOWHGyssJ08dgi6jyGJAREdUB1k5JWpMTZowbfhNVPwZkRER1gLVTkswJI6qdGJAREdUxlqYkmRNGVDsxICMisiPm8sR0OWLlTUkyJ4yodmJARkRkJ6zJEzM3Jfn775GIjPwdcnkhc8KIaiFunUREZCes2c5INyVpSGDXrj5YsiQFx451BHB/qyPjYIw5YUS2wREyIiI7ZC5PzHhKEhAAZADKTl8mJibC399fuiZzwohshwEZEZGdKS9PTDcl+fvvkdi1q4/Be/VXVPr7+0OhUNjiFojICKcsiYjsjKXSFTpyeSEiI38HYDh9aWpFJRHZHgMyIiI7oFKppJWUpvLETAVamZnNoZuu1DK/opKIbItTlkREtZhKpcK1a9ewYcMG6Zg1pSt005r6AZlMBkREnK/J7hORlRiQERHVUpbKXJRXuiIjI6bcivxcUUlUezAgIyKqpcxth+TiUoSSEjf4+qoQHn6xzPvUai8cPqwsc1x/WnPw4MFcUUlUizAgIyKyA/plLnSlLIy3RdLJy/ODqRRhpTJdGh3z8fGp9j4TkfWY1E9EVAvpJ/Ebl7kwriumXxgWMFccVoOYmIxq7jURVRYDMiKiWkaXO5aamgrAdJkLHf1yF7rK/QDQr982KSiTyTTo398w6Z/5Y0S1C6csiYhqGePcMReXIshkGpNBmS4vzFTl/pSUJSaT/ocMGcL8MaJahiNkRES12LFjHbFy5VN6uWOQ/tQFXgBMVu4HYHK/yoCAgBrpOxFZjyNkRES1lOncMQ2GDfsarq4l0shXVlaYxRIX+ntWcr9KotqJARkRUS2in8xvOnfMCa6uJQblLnRJ/Ppt9UtccM9KotrP6oCsoKDA6ot6e3tXqjNERI7MuBBseYFWjx49sH//fqsq9xNR7WZ1QObj4wOZTGaxjRACMpkMpaWlD9wxIiJHY5zMX16gpZ8LZqlyP1dUEtV+Vgdk+/fvr85+EBGREbXaCw0b3kRS0qcoKXEtE2j5+PggOTm5TCCnjzljRPbB6oCse/fu1dkPIiLSY6qMhfE2SQy2iOqOSif15+fnY+XKlTh9+jQA4KGHHsK4ceMgl8urrHNERI7IeHWlroxFRMR5adVkcHAwgzGiOqRSdch+/vlnREREYPHixcjLy0NeXh7effddRERE4NixY1XdRwBA//79ERoaCnd3dygUCowcORLZ2dkGbXbt2oXY2Fh4eXkhICAAAwcOxIULFwzafP/994iKioKbmxuaN2+OVatWlfms999/H2FhYXB3d0dMTAx++ukng/N3797FxIkT4efnhwYNGmDgwIG4evWqQZtLly4hISEBnp6eaNSoEaZPn4579+5VybMgorrN1OpK/Yr8/v7+DMaI6phKBWSTJ09G//79ceHCBaSmpiI1NRVZWVno27cvUlJSqriLWj169MCGDRtw9uxZbNq0CZmZmRg0aJB0PisrC3//+9/xxBNP4MSJE9i1axdu3LiBxMREgzYJCQno0aMHTpw4gZSUFDz11FPYtWuX1Gb9+vWYMmUK5s2bh2PHjqF9+/aIj4/HtWvXDO5/69at2LhxIw4cOIDs7GyDzyktLUVCQgKKi4tx+PBhrF69GqtWrcLcuXOr5dkQUd1iai9K/dWVRFT3yIQQovxmhjw8PHD8+HG0bt3a4Pjvv/+OTp064c6dO1XWQXO2bNmCAQMGoKioCC4uLvjmm28wbNgwFBUVwclJG2du3boVf//736U2M2bMwPbt23Hq1CnpOkOHDkV+fj527twJAIiJicEjjzwiLT3XaDQICQnBpEmTMHPmTKjVagQEBGDt2rVSQHjmzBm0adMG6enpiI2NxY4dO9C3b19kZ2cjMDAQALBixQrMmDED169ft3rFU0FBAeRyOdRqNUuJENViKpWqShLrc3Jy8PHHHwMwnUMWFXUcADB+/HjWFSOqxSrz+7tSOWTe3t64dOlSmYDs8uXL8PLyqswlKyQvLw9r1qxB586d4eLiAgCIjo6Gk5MTPv/8c4wZMwa3bt3Cl19+ibi4OKlNeno64uLiDK4VHx8vjeoVFxfj6NGjmDVrlnTeyckJcXFxSE9PBwAcPXoUJSUlBtdp3bo1QkNDpYAsPT0dbdu2lYIx3ec8++yz+O2339CxY0eT91VUVISioiLpdUVqvxGRbRjXDjMnOTm53KBM/x9rLGNB5FgqFZANGTIESUlJePvtt9G5c2cAwKFDhzB9+nQMGzasSjuob8aMGVi+fDnu3LmD2NhYbNu2TToXHh6O3bt3Y/DgwXjmmWdQWloKpVKJ7777TmqTm5trECQBQGBgIAoKCvDXX3/h5s2bKC0tNdnmzJkz0jVcXV3h4+NTpk1ubq7Fz9GdM2fBggWYP3++lU+DiGqSuVEwXVV9HbXaC3l5fvD1VRkEUZZG0HT8/PxYxoLIQVUqIHv77bchk8kwatQoKVHdxcUFzz77LBYuXGj1dWbOnIk333zTYpvTp09LI3HTp09HUlISLl68iPnz52PUqFHYtm0bZDIZcnNz8fTTT2P06NEYNmwYCgsLMXfuXAwaNAhpaWnlFrWtDWbNmoUpU6ZIrwsKChASEmLDHhERYP0omKVpRmsx2CJyTJUKyFxdXbF06VIsWLAAmZmZAICIiAh4enpW6DpTp07FmDFjLLZp1qyZ9L2/vz/8/f3RsmVLtGnTBiEhIfjxxx+hVCrx/vvvQy6XY9GiRVL7r776CiEhIcjIyEBsbCyCgoLKrIa8evUqvL294eHhAWdnZzg7O5tsExQUBAAICgpCcXEx8vPzDUbJjNsYr8zUXVPXxhQ3Nze4ublZfB5EVPOMR6xMjYKVV6qCiMiSB9pc3NPTE23btq30+wMCAgy2/qgIjUa7AkmXc3Xnzh0pmV/H2dnZoK3xFCYApKWlQalUAtAGmtHR0di7dy8GDBggvXfv3r1ITk4GoM1Vc3Fxwd69ezFw4EAAwNmzZ3Hp0iXpOkqlEq+//jquXbuGRo0aSZ/j7e2NyMjISt0vEdUO5kbBLJWqYEBGROWpVEB29+5dvPfee9i/fz+uXbsmBTw6VV2LLCMjA0eOHMFjjz2Ghg0bIjMzE3PmzEFERIQUBCUkJGDx4sV45ZVXpCnLF198EU2bNpWS6CdMmIDly5fjhRdewLhx47Bv3z5s2LAB27dvlz5rypQpGD16NDp16oRHH30US5Yswe3btzF27FgAgFwuR1JSEqZMmQJfX194e3tj0qRJUCqViI2NBQD07t0bkZGRGDlyJBYtWoTc3Fy89NJLmDhxIkfAiOyYpVGw8jYCJyKypFIBWVJSEnbv3o1Bgwbh0Ucfrfb8LE9PT6SmpmLevHm4ffs2FAoF+vTpg5deekkKcJ544gmsXbsWixYtwqJFi+Dp6QmlUomdO3fCw8MDgDbxf/v27Zg8eTKWLl2KJk2a4NNPP0V8fLz0WUOGDMH169cxd+5c5ObmokOHDti5c6dBkv7ixYvh5OSEgQMHoqioCPHx8fjggw+k887Ozti2bRueffZZKJVK1K9fH6NHj8Yrr7xSrc+JiKqXpVGw8PCLFjcCJyKypFJ1yORyOb777jt06dKlOvpE/8M6ZERVq7L1wnT1wdRqLyxZklJmFCwlZYlBLpmpUhWsHUbkOGqsDlnjxo1rpN4YEVFVMV4paa48haV6YXJ5ocEoGKCBUplepo2pUTHWDiMiSyoVkL3zzjuYMWMGVqxYgaZNm1Z1n4iIqpz+yJil8hTl1QvTFWzNyIhBeroShw93QXq60uAaiYmJ8Pf3l97D2mFEVJ5KBWSdOnXC3bt30axZM3h6ekqV8HXy8pjESkS1U2XKU5ga3UpPV5q9hr+/P6cniahCKhWQDRs2DH/++SfeeOMNBAYG2kXRVSIiwHJivrmATFdBPzs7G6mpqSxxQURVrlIB2eHDh5Geno727dtXdX+IiKpVZctT+Pn5SdOZLHFBRFXNqfwmZbVu3Rp//fVXVfeFiKja6RLzZTJt/cTKlKeoimsQEemr1AjZwoULMXXqVLz++uto27ZtmRwylmggotpMl5hvqjyFJfq5ZJauwRWVRFRRlQrI+vTpAwDo2bOnwXEhBGQyGUpLSx+8Z0RE1chceQpLdLlklallRkRkSaUCsv3791d1P4iIqpW1o1bltWOwRUTVoVKV+q31f//3f3jllVcM6vGQ9Vipn6hqVbZSPxFRRVTm93e1BmTe3t44ceIEmjVrVl0fUacxICMiIrI/lfn9XalVltaqxliPiIiIqM6o1oCMiIiIiMrHgIyIiIjIxhiQEREREdlYpcpeEBFVlYqsfOQqSSKqq6o1IBsxYgRXBxKRWSqVCsuXL5deq9VeyMvzg6+vyqBoa3JyMgAYtDUnOTmZQRkR2Z1KB2T5+fn46aefcO3aNWg0GoNzo0aNAgB8+OGHD9Y7IqrT9Ee7jh3riK1b+0IIJ2lvyKio42Xa6ZgL3iyNoBER1VaVCsi2bt2K4cOH49atW/D29oZMJpPOyWQyKSAjIrKGWu0lBWMAIIQTtm7ti4iI8ya3N7IUvBER2aNKJfVPnToV48aNw61bt5Cfn4+bN29KX3l5eVXdRyKq4/Ly/KRgTEcIJ+Tl+QLQjsjfuHEDgPngTa32qtlOExFVoUqNkP3555947rnn4OnpWdX9ISIH5OurgkymMQjKZDINfH21/8DbsGGDdNxS8FbRzcKJiGqLSo2QxcfH4+eff67qvhCRg5LLC9Gv3zbIZNp8VN00pKkAy8WlSGqnox+8ERHZI6tHyLZs2SJ9n5CQgOnTp+P3339H27Zt4eLiYtC2f//+VddDInIIUVHHERFxHnl5vvD1zSs3dwwQAGQWgzciInthdUA2YMCAMsdeeeWVMsdkMhlKS0sfqFNE5Jjk8kKzgZVx7hggA6BBUtKnaNIkp8b6SERUHawOyIxLWxARPShXV1er25rKHQOcUFJieI2KXJOIqLaoVFL/F198gSFDhsDNzc3geHFxMdatW8eyF0RkFT8/PyQnJ5usHZafn2+QzF9e4n9iYiKCg4NZFJaI7FKlkvrHjh0LtVpd5nhhYSHGjh37wJ0iIsfh5+cHhUJR5svHx6dMW6Uy3Wziv7+/P4MxIrJblRohE0IYFIPVuXLlCuRy+QN3iohIn2EyvwadOx9CTEwGE/mJqM6oUEDWsWNHyGQyyGQy9OzZE/Xq3X97aWkpsrKy0KdPnyrvJBE5rrLJ/E5IT1ciJibDoB1zx4jInlUoINOttDxx4gTi4+PRoEED6ZyrqyvCwsIwcODAKu0gETm28grBMneMiOqCCgVk8+bNAwCEhYVhyJAhcHd3r5ZOERHplJfMz9wxIqoLKpVDNnr0aADaVZXXrl0rUxIjNDT0wXtGRIT7VfyNNxNn/hgR1SWVCsjOnTuHcePG4fDhwwbHdcn+LAxLRKaoVCqTJS50XF1dTY52WVPFn4jInlUqIBszZgzq1auHbdu2QaFQmFxxSUSkT6VSYfny5eW2S05Ohp+fX5kkfXNV/JnMT0R1QaUCshMnTuDo0aNo3bp1VfeHiOoo45ExtdoLeXl+8PVVGQRaunaWisbqmBtRIyKyN5UKyCIjI3Hjxo2q7gsRVYErV4Bz54AWLYAmTSr+/spOK1aEfl0xXU5YVNTxMu0YbBGRo6hUQPbmm2/ihRdewBtvvIG2bdvCxcXF4Ly3t3eVdI6IKmblSmD8eECjAZycgIULgU6drA/OKjqtWBnGdcWEcMLWrX0REXGeuWFE5LAqFZDFxcUBAJ544gmD/DEm9RPVHOORsCtX7gdjgPbPF17Qfu/kBHz8MZCUZPmaFZ1WrIzy6ooRETmiSgVk+/fvr+p+EFEFGI+Effwx0KzZ/WDMmEYDPPMMEB9v/TSmtdOKFVVeXTEiIkdUqc3Fu3fvDicnJ3zyySeYOXMmmjdvju7du+PSpUtwdnau6j4SkR5TI2HPPCPw11/X4eQkzL6vtBTIyFBBpVKV+xnmphXVaq8H7r+urpi5TcKJiBxRpQKyTZs2IT4+Hh4eHjh+/DiKiooAAGq1Gm+88UaVdpCIDJ07V3YkrLRUhg0btqNv361SoAMYBmcymQaHDq3G8uXLyw3KLE0rVoWoqONISVmC0aNXISVlSZWMvBER2bNKTVm+9tprWLFiBUaNGoV169ZJx7t06YLXXnutyjpHRGW1aKGdptQPynRTfuHhF9GoUS4uXQrFX3954ODBriar25eXA1Yd04qsK0ZEZF6lArKzZ8+iW7duZY7L5XLk5+c/aJ+IyIImTbQ5Y888o52GdHYWSEjQBlvGeV9xcXsQHJxdbnV7XakLXTmb6tiuiHXFiIjMq1RAFhQUhPPnzyMsLMzg+MGDB9GsWbOq6BcRmaALnP72NyAjwwkXLtRDvXoXcOLEcZN5X3v2xCElZUm5wZipUhfVsV0Rgy0iItMqFZA9/fTTeP755/HZZ59BJpMhOzsb6enpmDZtGubMmVPVfSQilF8jrLLlJMordcFpRSKi6lepgGzmzJnQaDTo2bMn7ty5g27dusHNzQ3Tpk3DpEmTqrqPRISayfuyVOoiMTER/v7+nFYkIqoGlQrIZDIZZs+ejenTp+P8+fO4desWIiMj0aBBg6ruHxFZqSJ5X/pbn+m+L6+Cvr+/PxQKRc3cDBGRg6lUQKbj6uqKyMjIquoLET0ga/O+UlNTyxxjBX0iItt5oICMiGofc3lfpujni7GCPhGR7TAgI3IQiYmJAO6PjpnKF6vqUhdERGQdBmREDsLf31/63ly+WErKEqSkLDE55Xnjxg0m9BMRVRMGZEQOwtXVVVqpaSlfLDz8oslRMd3IWnJyMoMyIqIqxoCMyE5YW/tr8ODB8PHxKfNePz8/5OTkALCuRIZxPTKd8spvEBFRxTEgI7ITVbn1UHklMizVIyMioqrHgIzIjlTlVKF+iQwXl2KUlLhBrfYCAIv1yIiIqOoxICNyIMbTnnJ5ITIzmxuMhimV6axHRkRUw5zKb0JEdYVu2lNXAsPUasv0dCUAjcH7WI+MiKh6cYSMyA6oVKoqyR0DtEFZeastO3c+hPR0JeuRERHVEAZkRLWcSqXC8uXLy21XmXIU5lZbxsRkICYmo9wtmIiIqGpwypKolrO2zERlylHoVlvKZNopSv3RMLm80GRNMmvLbxARkfU4QkZkZ8zVB8vPz7f4PnPTmpY2JE9MTDSo8M9K/URE1YMBGZEdsVQfbMOGDeW+XzetaWq1palpyeDgYAZgREQ1gAEZkZ0wt/+kqfpg5VXZr8ois0RE9OAYkBHZCUv7T+oHXdZW2WewRURUezCpn8hO6FZE6jO1/6SpUTRdBX4iIqqdGJAR2QlLKyJ1LI2iERFR7WU3AVn//v0RGhoKd3d3KBQKjBw5EtnZ2QZtNmzYgA4dOsDT0xNNmzbFW2+9VeY633//PaKiouDm5obmzZtj1apVZdq8//77CAsLg7u7O2JiYvDTTz8ZnL979y4mTpwIPz8/NGjQAAMHDsTVq1cN2ly6dAkJCQnw9PREo0aNMH36dNy7d+/BHwQ5HP0E/Kio40hJWYLRo1chJWVJmalIa0bRiIio9rGbHLIePXrgxRdfhEKhwJ9//olp06Zh0KBBOHz4MABgx44dGD58ON577z307t0bp0+fxtNPPw0PDw8kJycDALKyspCQkIAJEyZgzZo12Lt3L5566ikoFArEx8cDANavX48pU6ZgxYoViImJwZIlSxAfH4+zZ8+iUaNGAIDJkydj+/bt2LhxI+RyubQVzaFDhwAApaWlSEhIQFBQEA4fPoycnByMGjUKLi4ueOONN2zw9MieWZOAr1arsX79emkUzTiHjIVdiYhqN5kQQti6E5WxZcsWDBgwAEVFRXBxccG//vUvlJSUYOPGjVKb9957D4sWLcKlS5cgk8kwY8YMbN++HadOnZLaDB06FPn5+di5cycAICYmBo888ohUGV2j0SAkJASTJk3CzJkzoVarERAQgLVr12LQoEEAgDNnzqBNmzZIT09HbGwsduzYgb59+yI7OxuBgYEAgBUrVmDGjBm4fv261YU1CwoKIJfLoVar4e3tXSXPjeqmnJwcfPzxx9Jr7SrLsnXFxo8fD4VCYYsuEhE5jMr8/rabKUt9eXl5WLNmDTp37gwXFxcAQFFREdzd3Q3aeXh44MqVK7h48SIAID09HXFxcQZt4uPjkZ6eDkBbEuDo0aMGbZycnBAXFye1OXr0KEpKSgzatG7dGqGhoVKb9PR0tG3bVgrGdJ9TUFCA3377zex9FRUVoaCgwOCL7ItKpUJOTo7ZL5VKVS2fa6quGKvsExHZD7uZsgSAGTNmYPny5bhz5w5iY2Oxbds26Vx8fDwmT56MMWPGoEePHjh//jzeeecdANrRg7CwMOTm5hoESQAQGBiIgoIC/PXXX7h58yZKS0tNtjlz5gwAIDc3F66urvDx8SnTJjc3V2pj6hq6c+YsWLAA8+fPr8ATodqkOvecLA/rihER2TebjpDNnDkTMpnM4pcuEAKA6dOn4/jx49i9ezecnZ0xatQo6GZcn376aSQnJ6Nv375wdXVFbGwshg4dCkA7ymUPZs2aBbVaLX1dvnzZ1l2iCqjOPSet4efnB4VCYfaLwRgRUe1l0xGyqVOnYsyYMRbbNGvWTPre398f/v7+aNmyJdq0aYOQkBD8+OOPUCqVkMlkePPNN/HGG28gNzcXAQEB2Lt3r8E1goKCyqyGvHr1Kry9veHh4QFnZ2c4OzubbBMUFCRdo7i4GPn5+QajZMZtjFdm6q6pa2OKm5sb3NzcLD4PIiIiqntsOnQUEBCA1q1bW/wyl/Oi0WiX9hcVFRkcd3Z2RuPGjeHq6oqvv/4aSqUSAQEBAAClUikFaTppaWlQKpUAtFM60dHRBm00Gg327t0rtYmOjoaLi4tBm7Nnz+LSpUtSG6VSiV9//RXXrl0z+Bxvb29ERkZW6lmR/VGrvZCVFcairEREVC67yCHLyMjAkSNH8Nhjj6Fhw4bIzMzEnDlzEBERIQVBN27cwDfffIPHH38cd+/exeeff46NGzfiwIED0nUmTJiA5cuX44UXXsC4ceOwb98+bNiwAdu3b5faTJkyBaNHj0anTp3w6KOPYsmSJbh9+zbGjh0LAJDL5UhKSsKUKVPg6+sLb29vTJo0CUqlErGxsQCA3r17IzIyEiNHjsSiRYuQm5uLl156CRMnTuQImIOwdvsiIiIiwE4CMk9PT6SmpmLevHm4ffs2FAoF+vTpg5deeskgwFm9ejWmTZsGIQSUSiW+//57PProo9L58PBwbN++HZMnT8bSpUvRpEkTfPrpp1INMgAYMmQIrl+/jrlz5yI3NxcdOnTAzp07DZL0Fy9eDCcnJwwcOBBFRUWIj4/HBx98IJ13dnbGtm3b8Oyzz0KpVKJ+/foYPXo0XnnllWp+UlQbVGQTcCIiIsCO65A5AtYhsy+6WmBZWWFYvXp0mfOjR69CePhF1gIjIqrjHKYOGVFtxu2LiIioohiQEVUR3QKU8jYBZ3FWIiIyxinLWoxTlvZHpVJJdcays51w4UI9hIXdQ3CwNjhjcVYiorqvMr+/7SKpn8iYfuBjiq0CH/3PVCiA6Oga7wIREdkhBmRkd4y3KNJupO0HX1+VwSrG6tiiiIiIqDowICO7oz8yZqneV3Z2ttlRNE4dEhFRbcKAjOxWefW+UlNTLb6/KkbQauvUKRER2RcGZGRXVCoVbty4AQDIy/OTgjEdIZyQl+drVQHWB93k23jq1JwRI0YgIiLigT6LiIjqNpa9ILuhC4B0I1+2rvdlHNCZ27vyq6++gkqlqpE+ERGRfeIIGdkN4wBIV+/LOIfMFtsT6eeyARr06rUHXbqkS+d1+WycwiQiIlMYkJFdi4o6joiI88jL84Wvb57JYMzcKsyqYpzLBjghLa0X7t51Q7NmF+DrqzLIZ+PqTyIiMsaAjOyeXF5oNtCytAqzqpjKZQNk+OGHbvjhh+5lPvdBc9eIiKjuYQ4Z2S1zOVv6502twjTXvrJ8fVUANCbOyKr1c4mIqO7gCBnZJWtGvspbhalbrfmgeV1yeSF69dqDtLRe0AVhxiqy+pOIiBwPAzKyO+XVH9PRrcLUD8r0V2FWZV6XLoF/z564/32egH5wVpOrP4mIyP4wICO7Y239Mbm8ENOnn8dbbzUvdxVmZfK6XF1dDV536ZKOhx8+hbw8X2RnB0vBmS1XfxIRkX1gQEZ2QxcAlTfypW/EiCKUlCyxuAqzsvz8/DBixAh89dVX0jHdAoPw8ItScFbVn0tERHUPAzKyG35+fkhOTkZxcTHk8mzMnx9s1QiUpVWYOrp8MgDIz8/HvXv3AAAuLi6Qy+UGbfVzziIiIqQ+3bhxw2Aa1JrPJSIiAhiQkZ3RBULjx+dArb4/8gUAWVlhla41Vt6+l8b0c850fxpPYZpjbTsiInIcDMjIbulGoCpSa6yiRWLNtTeVc6Y/gmcOK/UTEZEpDMjILulGmcpbceni4iK9p7zAzTj4qkxRWQZbRERUGQzIyC7pRqP27wcWLy674rJLl9F4/PH7I1nlBW7GwVdc3B69EhbmS2sQERFVBVbqJ7vl5+eH2Fg/OBn9FDs7AzExfvDz85NG0iyVyjAVrOkHY8btiYiIqhoDMrJrTZoAH3+sDcIA7Z8ffaQ9DtwfSRszpgtkMsPtjXSlMswFa8bbIbG4KxERVRcGZGT3kpKACxeA/fu1fyYlGZ738/NDq1b10a/fNiko0y+Voatrpk8m06BXrz0m2xMREVU15pBRndCkyf1RMXOioo4jIuI88vJ84eJSjJISN6jVXpDLC9Gv3zaTCfws7kpERDWBARk5FLm8EJmZzU0GX7pgTT/4MlfclbXEiIioKjEgI4dS3mrLsWN7oV69elZX6iciIqoKDMiozlKpVFLZC93WSOVtTO7v7w+FQlHjfSUiIsfGgIzqJJVKheXLl5c5XpGNyYmIiGoKV1lSnWS8fZFa7YWsrDAAMLvakoiIyFY4QkZ1nqktkFJSlphcPclkfSIisgUGZPTA9HO1TLFlEry5JP6UlCUID7+IxMRE+Pv727yfRETk2BiQ0QMxl6tlLDk52SbBDpP4iYjIHjAgowdiaWRM3/Xr120yisYkfiIisgcMyKhGrF+/vtw21TGKZq4KP5P4iYioNmFARrWGtaNtphjnsenqjgEwW4WfiIiotmBARnbPOI9NrfZCXp4ffH29uAUSERHZBQZkZPf0R8ZMlbiIijoOAAYrKgGuqiQiotqDARnVGeXtU8kVlUREVFuxUj/VGZZKXBAREdVmDMjogdSmHCxdiQt9LHFBRET2gFOW9ED8/PyQnJxscYVkfn4+NmzYIL2+n3SvqtIVjyxxQURE9ooBGT0w/cT4K1eAc+eAFi2AJk20x/RH0Swl3VfFaBtLXBARkT1iQEZVZuVKYPx4QKMBnJyAjz8GkpLuj6JduHAPr7zSCELIAGjzu7Zv74e5c2MQFlavylY8mitxQUREVFsxh4yqxJUr94MxQPvnM89ojwPaoKygIBAajczgfaWlMhQWBj5QMGbtyFptyncjIiLSxxEyqjDjqvgA8OOPrtBoDIOq0lLg/Pn7U5ctWmhHzjR6effOzkDz5pavrc9U7TBr8thYc4yIiGozBmRUIcZV8XXUai/IZCkGZSeMg60mTbTTmM88ow3WnJ2Bjz66H7CZr7hvmPxvas9LBltERGTPGJBRhZgbhTJe4ejsLPDRRzIp2NJJSgLi47UjZ82bw+C8tRX3H2TPSyIiotqIARlVGf0VjpMmPYno6ECT7Zo0QZlATV95FfeJiIjqGgZkVKV0KxyDgzXlNzbDUsV9BmRERFQXMSCjalWZJH1dxX39oIwV94mIqC5jQEbVxtwCAGPGSfqsuE9ERI6GARk9EEvbIBmPjJlra2oEjRX3iYjIkTAgo0qztBLyQdrqsOI+ERE5ClbqpwrRVbs3txJSrfYyaGdNW+NrW9sHIiKiuoIjZFQhuqr4+/cDixeXXQnZpctoPP64tl1OTg4A61dNsuI+ERE5KgZkVGF+fn6IjTW9DVJMjB+M46WKrJpksEVERI6IU5ZUKbptkJydta+Nt0HSp1s1KZNpozeumiQiIjLEETKqNEvbIBnjqkkiIiLzGJDRA7G0DZJx8r25VZNM0iciIkfHgIyqjXGSvlqtRklJCQAgN7ceLl1yQ7NmpSguLkZOTg4T9omIyGExIKNqpQuwVCoV1q9fD8ByTTLjqv1ERESOgEn9VO1UKhWys7MBAFeuKCzWJLNU8oKIiKiu4ggZVSv9/SyPHeuILVv6wvjfAaZqkhERETkSjpBRtbqfP6at1m/qR85cTTIiIiJHYXcBWVFRETp06ACZTIYTJ04YnDt58iS6du0Kd3d3hISEYNGiRWXev3HjRrRu3Rru7u5o27YtvvvuO4PzQgjMnTsXCoUCHh4eiIuLw7lz5wza5OXlYfjw4fD29oaPjw+SkpJw69atCvfFkZiq1g+wJhkRERFghwHZCy+8gODg4DLHCwoK0Lt3bzRt2hRHjx7FW2+9hZdffhkff/yx1Obw4cMYNmwYkpKScPz4cQwYMAADBgzAqVOnpDaLFi3CsmXLsGLFCmRkZKB+/fqIj4/H3bt3pTbDhw/Hb7/9hrS0NGzbtg3/+c9/MH78+Ar1xZauXAH279f+WVN01foNaZCU9Gm5m4wTERHVdTIhhLB1J6y1Y8cOTJkyBZs2bcJDDz2E48ePo0OHDgCADz/8ELNnz0Zubq5U12rmzJnYvHkzzpw5AwAYMmQIbt++jW3btknXjI2NRYcOHbBixQoIIRAcHIypU6di2rRpALSlGgIDA7Fq1SoMHToUp0+fRmRkJI4cOYJOnToBAHbu3Im//e1vuHLlCoKDg63qizUKCgogl8uhVqvh7e39wM8PAFauBMaP12555OSkrbaflFQll4ZKpZKmKLOznZCVVQ9y+TUcPrwBgOXVlTrjx4+HQqGomg4RERHZQGV+f9tNUv/Vq1fx9NNPY/PmzfD09CxzPj09Hd26dTMoMhofH48333wTN2/eRMOGDZGeno4pU6YYvC8+Ph6bN28GAGRlZSE3NxdxcXHSeblcjpiYGKSnp2Po0KFIT0+Hj4+PFIwBQFxcHJycnJCRkYF//OMfVvXFlKKiIhQVFUmvCwoKKvaQynHlyv1gDND++cwz2mr7lqrsW8M4ef9+4NUQ/fp1RFTUcVbrJyIiMsMupiyFEBgzZgwmTJhgEAjpy83NRWBgoMEx3evc3FyLbfTP67/PXJtGjRoZnK9Xrx58fX3L/Rz9zzBlwYIFkMvl0ldISIjZtpVx7pzhZuAAUFqq3fqoIkxNeRon75srayGXFyI8/KLZYIxV+4mIyBHZNCCbOXMmZDKZxa8zZ87gvffeQ2FhIWbNmmXL7la7WbNmQa1WS1+XL1+u0uu3aKGdptTn7Kzdh9JaK1cCTZsCTzyh/XPlSu3o2I0bNwAAly+HlEne15W1sCQxMZFFYYmIyGHZdMpy6tSpGDNmjMU2zZo1w759+5Ceng43NzeDc506dcLw4cOxevVqBAUF4erVqwbnda+DgoKkP0210T+vO6afx3T16lUpVy0oKAjXrl0zuMa9e/eQl5dX7ufof4Ypbm5uZe6xKjVpos0Ze+YZ7ciYszPw0UfWTVeqVCpcuHAP48c3gkYjA6AdbRs/XuC331ZDLi/UqzNmyJqyFsHBwQzGiIjIYdk0IAsICEBAQEC57ZYtW4bXXntNep2dnY34+HisX78eMTExAAClUonZs2ejpKQELi4uAIC0tDS0atVKytlSKpXYu3cvUlJSpGulpaVBqVQCAMLDwxEUFIS9e/dKAVhBQQEyMjLw7LPPStfIz8/H0aNHER0dDQDYt28fNBpNhfpiK0lJ2pyx8+e1I2PWBmPLly9HVlYYNJrRBuc0Gpk0+mWqzphxWYvExET4+/sbtOEelkRE5OjsIqk/NDTU4HWDBg0AABEREWjyv4jiX//6F+bPn4+kpCTMmDEDp06dwtKlS7F48WLpfc8//zy6d++Od955BwkJCVi3bh1+/vlnqRyFTCZDSkoKXnvtNbRo0QLh4eGYM2cOgoODMWDAAABAmzZt0KdPHzz99NNYsWIFSkpKkJycjKFDh0rlOKzpiy01aVKxJH5dfpiudIX+lKRu9MtcnbGBA7/Bww+fll77+/tzFSUREZERu0jqt4ZcLsfu3buRlZWF6OhoTJ06FXPnzjWoD9a5c2esXbsWH3/8Mdq3b49vvvkGmzdvxsMPPyy1eeGFFzBp0iSMHz8ejzzyCG7duoWdO3fC3d1darNmzRq0bt0aPXv2xN/+9jc89thjBjXGrOmLPZLLC9Gv3zapnpj+6JepOmMymQYhITVY7IyIiMhO2VUdMkdTHXXIKiMnJ8cg4FSrvUyWrmCdMSIiojpeh4xqD7m80GTZCmvqjLGsBRERUVkMyKhKmQvWEhMTuZKSiIjIjDqTQ0a1m7+/P4MxIiIiMxiQUY3gVCUREZF5nLKkclU0mDKuNcY6Y0RERJYxIKNy+fn5ITk5GdnZ2UhNTS23PWuNERERVQwDMrKKn5+fVCBWR1v+wg++viqzm4UTERFR+RiQUaVYU3OMiIiIrMOAzEGoVKoyI1zZ2U7IyqqH8PB7CAurZ3Wel1rtJQVjACCEE7Zu7YuIiPMcKSMiIqoEBmQOQLc5uD5TI1yffdbFqqDM1L6VQjghL8+XARkREVElsOyFAzCV+2VqhOvChXtWXc/cvpW+vnlV02EiIiIHw4DMAZkb4bpwwfKAqa78haVNxvXbERERkXU4ZemAdCNc+kGZTKaBt/c15OQYjqbp1xDTlb/QjbjNnXsdFy7UQ1jYPQQHPwLgEdYcIyIiqgQGZA5IN8JlnEN2+PBxHD5ctn1ycrJBUKajUADR0TXVayIiorqLAZmDioo6joiI88jL84Wvbx7k8kKzdcWMc9CIiIioajEgc2ByeaEUeLGuGBERke0wqZ/MrrpUq71s3DMiIiLHwIDMAZS36tFSXTEiIiKqfgzIHIBudWRiYqLJ86wrRkREZFsMyByEn58f/P39TZ4rr64YERERVS8m9RMA06suiYiIqGYwICOJ/qpLfay8T0REVL0YkJFJiYmJ8Pf3Z+V9IiKiGsCAzIFUZKTL398fCoWiGntDREREOkzqdyB+fn4YPHiwrbtBRERERhiQORgfHx+D12q1F7KywlgEloiIyIY4Zelg9KctLW2XxER+IiKimiMTQghbd4JMKygogFwuh1qthre3d5VdV6VS4cKFe3j00UbQaGTScWdngYyMawgLq8dEfiIiokqqzO9vTlk6ID8/PxQUBBoEYwBQWipDYWEggzEiIqIaxoDMQbVoATgZ/dd3dgaaN7dNf4iIiBwZAzIH1aQJ8PHH2iAM0P750Ufa40RERFSzmNTvwJKSgPh44Px57cgYgzEiIiLbYEDm4Jo0YSBGRERka5yyJCIiIrIxBmRERERENsaAjIiIiMjGGJARERER2RgDMiIiIiIbY0BGREREZGMMyIiIiIhsjAEZERERkY0xICMiIiKyMQZkRERERDbGgIyIiIjIxriXZS0mhAAAFBQU2LgnREREZC3d723d73FrMCCrxQoLCwEAISEhNu4JERERVVRhYSHkcrlVbWWiIuEb1SiNRoPs7Gx4eXlBJpNV+joFBQUICQnB5cuX4e3tXYU9tC98Dlp8Dlp8Dlp8DvfxWWjxOWg9yHMQQqCwsBDBwcFwcrIuO4wjZLWYk5MTmjRpUmXX8/b2dui/XDp8Dlp8Dlp8Dlp8DvfxWWjxOWhV9jlYOzKmw6R+IiIiIhtjQEZERERkYwzIHICbmxvmzZsHNzc3W3fFpvgctPgctPgctPgc7uOz0OJz0Krp58CkfiIiIiIb4wgZERERkY0xICMiIiKyMQZkRERERDbGgIyIiIjIxhiQ2akPP/wQ7dq1kwrWKZVK7NixQzp/9+5dTJw4EX5+fmjQoAEGDhyIq1evGlzj0qVLSEhIgKenJxo1aoTp06fj3r17NX0rVWrhwoWQyWRISUmRjjnCs3j55Zchk8kMvlq3bi2dd4RnoPPnn39ixIgR8PPzg4eHB9q2bYuff/5ZOi+EwNy5c6FQKODh4YG4uDicO3fO4Bp5eXkYPnw4vL294ePjg6SkJNy6daumb6XSwsLCyvw8yGQyTJw4EYDj/DyUlpZizpw5CA8Ph4eHByIiIvDqq68a7C/oCD8PgHYLn5SUFDRt2hQeHh7o3Lkzjhw5Ip2vq8/hP//5D/r164fg4GDIZDJs3rzZ4HxV3ffJkyfRtWtXuLu7IyQkBIsWLap4ZwXZpS1btojt27eLP/74Q5w9e1a8+OKLwsXFRZw6dUoIIcSECRNESEiI2Lt3r/j5559FbGys6Ny5s/T+e/fuiYcffljExcWJ48ePi++++074+/uLWbNm2eqWHthPP/0kwsLCRLt27cTzzz8vHXeEZzFv3jzx0EMPiZycHOnr+vXr0nlHeAZCCJGXlyeaNm0qxowZIzIyMsR///tfsWvXLnH+/HmpzcKFC4VcLhebN28Wv/zyi+jfv78IDw8Xf/31l9SmT58+on379uLHH38UP/zwg2jevLkYNmyYLW6pUq5du2bws5CWliYAiP379wshHOfn4fXXXxd+fn5i27ZtIisrS2zcuFE0aNBALF26VGrjCD8PQggxePBgERkZKQ4cOCDOnTsn5s2bJ7y9vcWVK1eEEHX3OXz33Xdi9uzZIjU1VQAQ3377rcH5qrhvtVotAgMDxfDhw8WpU6fE119/LTw8PMRHH31Uob4yIKtDGjZsKD799FORn58vXFxcxMaNG6Vzp0+fFgBEenq6EEL7Q+rk5CRyc3OlNh9++KHw9vYWRUVFNd73B1VYWChatGgh0tLSRPfu3aWAzFGexbx580T79u1NnnOUZyCEEDNmzBCPPfaY2fMajUYEBQWJt956SzqWn58v3NzcxNdffy2EEOL3338XAMSRI0ekNjt27BAymUz8+eef1df5avT888+LiIgIodFoHOrnISEhQYwbN87gWGJiohg+fLgQwnF+Hu7cuSOcnZ3Ftm3bDI5HRUWJ2bNnO8xzMA7Iquq+P/jgA9GwYUODvxszZswQrVq1qlD/OGVZB5SWlmLdunW4ffs2lEoljh49ipKSEsTFxUltWrdujdDQUKSnpwMA0tPT0bZtWwQGBkpt4uPjUVBQgN9++63G7+FBTZw4EQkJCQb3DMChnsW5c+cQHByMZs2aYfjw4bh06RIAx3oGW7ZsQadOnfDPf/4TjRo1QseOHfHJJ59I57OyspCbm2vwLORyOWJiYgyehY+PDzp16iS1iYuLg5OTEzIyMmruZqpIcXExvvrqK4wbNw4ymcyhfh46d+6MvXv34o8//gAA/PLLLzh48CCefPJJAI7z83Dv3j2UlpbC3d3d4LiHhwcOHjzoMM/BWFXdd3p6Orp16wZXV1epTXx8PM6ePYubN29a3R9uLm7Hfv31VyiVSty9excNGjTAt99+i8jISJw4cQKurq7w8fExaB8YGIjc3FwAQG5ursH/bHXndefsybp163Ds2DGDfAid3Nxch3gWMTExWLVqFVq1aoWcnBzMnz8fXbt2xalTpxzmGQDAf//7X3z44YeYMmUKXnzxRRw5cgTPPfccXF1dMXr0aOleTN2r/rNo1KiRwfl69erB19fXrp6FzubNm5Gfn48xY8YAcJy/EwAwc+ZMFBQUoHXr1nB2dkZpaSlef/11DB8+HAAc5ufBy8sLSqUSr776Ktq0aYPAwEB8/fXXSE9PR/PmzR3mORirqvvOzc1FeHh4mWvozjVs2NCq/jAgs2OtWrXCiRMnoFar8c0332D06NE4cOCArbtVoy5fvoznn38eaWlpZf7150h0/+IHgHbt2iEmJgZNmzbFhg0b4OHhYcOe1SyNRoNOnTrhjTfeAAB07NgRp06dwooVKzB69Ggb9842Vq5ciSeffBLBwcG27kqN27BhA9asWYO1a9fioYcewokTJ5CSkoLg4GCH+3n48ssvMW7cODRu3BjOzs6IiorCsGHDcPToUVt3jf6HU5Z2zNXVFc2bN0d0dDQWLFiA9u3bY+nSpQgKCkJxcTHy8/MN2l+9ehVBQUEAgKCgoDKrqnSvdW3swdGjR3Ht2jVERUWhXr16qFevHg4cOIBly5ahXr16CAwMdJhnoc/HxwctW7bE+fPnHernQaFQIDIy0uBYmzZtpOlb3b2Yulf9Z3Ht2jWD8/fu3UNeXp5dPQsAuHjxIvbs2YOnnnpKOuZIPw/Tp0/HzJkzMXToULRt2xYjR47E5MmTsWDBAgCO9fMQERGBAwcO4NatW7h8+TJ++uknlJSUoFmzZg71HPRV1X1X1d8XBmR1iEajQVFREaKjo+Hi4oK9e/dK586ePYtLly5BqVQCAJRKJX799VeDH7S0tDR4e3uX+YVWm/Xs2RO//vorTpw4IX116tQJw4cPl753lGeh79atW8jMzIRCoXCon4cuXbrg7NmzBsf++OMPNG3aFAAQHh6OoKAgg2dRUFCAjIwMg2eRn59vMHKwb98+aDQaxMTE1MBdVJ3PP/8cjRo1QkJCgnTMkX4e7ty5Aycnw19zzs7O0Gg0ABzv5wEA6tevD4VCgZs3b2LXrl34+9//7pDPAai6//5KpRL/+c9/UFJSIrVJS0tDq1atrJ6uBMCyF/Zq5syZ4sCBAyIrK0ucPHlSzJw5U8hkMrF7924hhHZZe2hoqNi3b5/4+eefhVKpFEqlUnq/bll77969xYkTJ8TOnTtFQECA3S1rN0V/laUQjvEspk6dKr7//nuRlZUlDh06JOLi4oS/v7+4du2aEMIxnoEQ2tIn9erVE6+//ro4d+6cWLNmjfD09BRfffWV1GbhwoXCx8dH/Pvf/xYnT54Uf//7300uc+/YsaPIyMgQBw8eFC1atKj1y/uNlZaWitDQUDFjxowy5xzl52H06NGicePGUtmL1NRU4e/vL1544QWpjaP8POzcuVPs2LFD/Pe//xW7d+8W7du3FzExMaK4uFgIUXefQ2FhoTh+/Lg4fvy4ACDeffddcfz4cXHx4kUhRNXcd35+vggMDBQjR44Up06dEuvWrROenp4se+Eoxo0bJ5o2bSpcXV1FQECA6NmzpxSMCSHEX3/9Jf7v//5PNGzYUHh6eop//OMfIicnx+AaFy5cEE8++aTw8PAQ/v7+YurUqaKkpKSmb6XKGQdkjvAshgwZIhQKhXB1dRWNGzcWQ4YMMai95QjPQGfr1q3i4YcfFm5ubqJ169bi448/Njiv0WjEnDlzRGBgoHBzcxM9e/YUZ8+eNWijUqnEsGHDRIMGDYS3t7cYO3asKCwsrMnbeGC7du0SAMrcmxCO8/NQUFAgnn/+eREaGirc3d1Fs2bNxOzZsw3KEzjKz8P69etFs2bNhKurqwgKChITJ04U+fn50vm6+hz2798vAJT5Gj16tBCi6u77l19+EY899phwc3MTjRs3FgsXLqxwX2VC6JUsJiIiIqIaxxwyIiIiIhtjQEZERERkYwzIiIiIiGyMARkRERGRjTEgIyIiIrIxBmRERERENsaAjIiIiMjGGJARERER2RgDMiKqUx5//HGkpKTYuhvV7uWXX0aHDh1s3Q0iqiIMyIiIapHi4uIa/TwhBO7du1ejn0lEZTEgI6I6Y8yYMThw4ACWLl0KmUwGmUyGCxcu4NSpU3jyySfRoEEDBAYGYuTIkbhx44b0vscffxyTJk1CSkoKGjZsiMDAQHzyySe4ffs2xo4dCy8vLzRv3hw7duyQ3vP9999DJpNh+/btaNeuHdzd3REbG4tTp04Z9OngwYPo2rUrPDw8EBISgueeew63b9+WzoeFheHVV1/FqFGj4O3tjfHjxwMAZsyYgZYtW8LT0xPNmjXDnDlzUFJSAgBYtWoV5s+fj19++UW6z1WrVuHChQuQyWQ4ceKEdP38/HzIZDJ8//33Bv3esWMHoqOj4ebmhoMHD0Kj0WDBggUIDw+Hh4cH2rdvj2+++aaq/xMRkRkMyIiozli6dCmUSiWefvpp5OTkICcnB15eXnjiiSfQsWNH/Pzzz9i5cyeuXr2KwYMHG7x39erV8Pf3x08//YRJkybh2WefxT//+U907twZx44dQ+/evTFy5EjcuXPH4H3Tp0/HO++8gyNHjiAgIAD9+vWTAqfMzEz06dMHAwcOxMmTJ7F+/XocPHgQycnJBtd4++230b59exw/fhxz5swBAHh5eWHVqlX4/fffsXTpUnzyySdYvHgxAGDIkCGYOnUqHnroIek+hwwZUqFnNXPmTCxcuBCnT59Gu3btsGDBAnzxxRdYsWIFfvvtN0yePBkjRozAgQMHKnRdIqqkyu2fTkRUO3Xv3l08//zz0utXX31V9O7d26DN5cuXBQBx9uxZ6T2PPfaYdP7evXuifv36YuTIkdKxnJwcAUCkp6cLIYTYv3+/ACDWrVsntVGpVMLDw0OsX79eCCFEUlKSGD9+vMFn//DDD8LJyUn89ddfQgghmjZtKgYMGFDufb311lsiOjpaej1v3jzRvn17gzZZWVkCgDh+/Lh07ObNmwKA2L9/v0G/N2/eLLW5e/eu8PT0FIcPHza4XlJSkhg2bFi5fSOiB1fPlsEgEVF1++WXX7B//340aNCgzLnMzEy0bNkSANCuXTvpuLOzM/z8/NC2bVvpWGBgIADg2rVrBtdQKpXS976+vmjVqhVOnz4tffbJkyexZs0aqY0QAhqNBllZWWjTpg0AoFOnTmX6tn79eixbtgyZmZm4desW7t27B29v7wrfvzn6n3n+/HncuXMHvXr1MmhTXFyMjh07VtlnEpF5DMiIqE67desW+vXrhzfffLPMOYVCIX3v4uJicE4mkxkck8lkAACNRlOhz37mmWfw3HPPlTkXGhoqfV+/fn2Dc+np6Rg+fDjmz5+P+Ph4yOVyrFu3Du+8847Fz3Ny0mahCCGkY7rpU2P6n3nr1i0AwPbt29G4cWODdm5ubhY/k4iqBgMyIqpTXF1dUVpaKr2OiorCpk2bEBYWhnr1qv5/eT/++KMUXN28eRN//PGHNPIVFRWF33//Hc2bN6/QNQ8fPoymTZti9uzZ0rGLFy8atDG+TwAICAgAAOTk5EgjW/oJ/uZERkbCzc0Nly5dQvfu3SvUVyKqGkzqJ6I6JSwsDBkZGbhw4QJu3LiBiRMnIi8vD8OGDcORI0eQmZmJXbt2YezYsWUCmsp45ZVXsHfvXpw6dQpjxoyBv78/BgwYAEC7UvLw4cNITk7GiRMncO7cOfz73/8uk9RvrEWLFrh06RLWrVuHzMxMLFu2DN9++22Z+8zKysKJEydw48YNFBUVwcPDA7GxsVKy/oEDB/DSSy+Vew9eXl6YNm0aJk+ejNWrVyMzMxPHjh3De++9h9WrV1f62RCR9RiQEVGdMm3aNDg7OyMyMhIBAQEoLi7GoUOHUFpait69e6Nt27ZISUmBj4+PNMX3IBYuXIjnn38e0dHRyM3NxdatW+Hq6gpAm5d24MAB/PHHH+jatSs6duyIuXPnIjg42OI1+/fvj8mTJyM5ORkdOnTA4cOHpdWXOgMHDkSfPn3Qo0cPBAQE4OuvvwYAfPbZZ7h37x6io6ORkpKC1157zar7ePXVVzFnzhwsWLAAbdq0QZ8+fbB9+3aEh4dX4qkQUUXJhH6yARERWeX7779Hjx49cPPmTfj4+Ni6O0Rk5zhCRkRERGRjDMiIiIiIbIxTlkREREQ2xhEyIiIiIhtjQEZERERkYwzIiIiIiGyMARkRERGRjTEgIyIiIrIxBmRERERENsaAjIiIiMjGGJARERER2RgDMiIiIiIb+39gGqV+oaitJQAAAABJRU5ErkJggg==", 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", 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", 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", 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pKVOmmPQpAQBmycnJ0YwZM2zaHn74YTVs2NCkihzjqi4vsXr1atWvX19NmjRRr169tH79es2cOVMffvihfH191a5dO02fPl0vvvii2rZtq8WLF9uNzXbp0kXDhw/Xvffeq/DwcE2dOlXh4eFauHCh3n33XbVu3VopKSl66aWXTPqUAAAzbNmyxSb0+Pn5aeLEiVUu9EiSxXDGddEeJCcnR6GhocrOzra5b9f58+d14MABRUdHV4kxSrgO5x4AHLs4RSI3N9fa9uc//1ldunRxeS0lfX9fiqEuAABQZllZWXr99ddt2h5//PEqf2sjgg8AACiTVatWacuWLdbnV111lYYMGWJdHqUqI/gAAIBSycvLU0pKik1bVVibpywIPgAA4Ir27t2rJUuW2LRVlbV5yoLgAwAASmQYhp599lmbtqq2Nk9ZEHwAAIBDP//8s958802btmuueVh33FH1LlMvLYIPAACwM3v2bP3yyy82bc8/P0EdO/ppwACTinICgg8AALAqLCzU888/b9NWs2ZNNW8+Vh07SvHxJhXmJAQfAAAgSfr444+Vnp5u0xYXF6fGjRtLkvr2NaMq5+KWFXCauLg49evXz/r81ltv1ZgxYyp0TGccAwBwZUlJSXahZ9KkSdbQ4yno8fECcXFxWrRokaQL909p1KiRBg0apAkTJqhatcr7I/DBBx/Iz8+vVPtu2LBB3bp108mTJ21W/SzLMQAAZXf06FG98cYbNm2//NJUs2YNNKmiykXw8RK9evXSggULlJeXp48++kgjR46Un5+fEhISbPbLz8+Xv7+/U96zdu3aVeIYAADHkpKS7NpWrRquCRPqmVCNazDU5SUCAgIUGRmpxo0ba8SIEerevbtSU1Otw1PPP/+8GjRooBYtWkiSDh06pL/97W8KCwtT7dq1deeddyozM9N6vKKiIo0dO1ZhYWGqU6eOnn76aV16v9tLh6ny8vI0fvx4RUVFKSAgQM2bN9ebb76pzMxMdevWTZJUq1YtWSwWxcXFOTzGyZMnNWjQINWqVUvBwcHq3bu3fvjhB+v2hQsXKiwsTGlpaWrVqpVq1KihXr166ejRo9Z9NmzYoE6dOql69eoKCwvTjTfeqJ9++slJv9MAUPUZhuEw9EyenCgfn3oeMZenJAQfLxUUFKT8/HxJ0tq1a7Vnzx6tWbNGK1asUEFBgXr27KmaNWvqs88+06ZNm6wB4uJrXn75ZS1cuFDz58/X559/rt9++03//e9/L/uegwYN0pIlSzRz5kzt3r1br7/+umrUqKGoqCi9//77kqQ9e/bo6NGjeuWVVxweIy4uTl9//bVSU1OVnp4uwzD0l7/8RQUFBdZ9cnNz9dJLL+mtt97Sp59+qoMHD+rJJ5+UdOFqhX79+umWW27Rt99+q/T0dD3yyCNucX8ZAHCGjz/+2G5BwtOnIxQTk6jYWPe/autKGOryMoZhaO3atUpLS9Pf//53nThxQtWrV9e8efOsQ1xvv/22iouLNW/ePGsgWLBggcLCwrRhwwb16NFD//znP5WQkKC77rpLkjR37lylpaWV+L579+7Vf/7zH61Zs0bdu3eXJDVt2tS6/eKQVkRERIl39v3hhx+UmpqqTZs2qUuXLpKkxYsXKyoqSsuXL9c999wjSSooKNDcuXPVrFkzSdKoUaOsf8lzcnKUnZ2t22+/3brdne4xAwAV4aiXZ/36cRo7tob69vWMq7auhB4fk6SmSl26XPjpCitWrFCNGjUUGBio3r17695779XkyZMlSddee63NvJ4dO3Zo3759qlmzpmrUqKEaNWqodu3aOn/+vPbv36/s7GwdPXpUnTt3tr6mWrVq6tixY4nv/80338jX11e33HJLuT/D7t27Va1aNZv3rVOnjlq0aKHdu3db24KDg62hRpLq16+v48ePS7oQsOLi4tSzZ0/dcccdeuWVV2yGwQDAE509e9Zh6ElMTNSGDTW8IvBcRI+PSVJSpPT0Cz9d8QeuW7dumjNnjvz9/dWgQQObq7mqV69us++ZM2fUoUMHLV682O444eHh5Xr/oKCgcr2uPC69CsxisdjMP1qwYIFGjx6t1atXa9myZZo4caLWrFmjG264wWU1AoCrOAo8hw9fpzfeuNOEasxHj49J4uPl0rHU6tWrq3nz5mrUqNEVL2Fv3769fvjhB0VERKh58+Y2j9DQUIWGhqp+/fr68ssvra8pLCzU1q1bSzzmtddeq+LiYm3cuNHh9os9TkVFRSUeo1WrViosLLR5319//VV79uxR69atL/uZLhUTE6OEhARt3rxZbdu21TvvvFOm1wOAO3AUetLSJun2270z9EgEH9P07Stt3lw1x1MfeOAB1a1bV3feeac+++wzHThwQBs2bNDo0aP1888/S5Ief/xxpaSkaPny5crIyNBjjz2mU6dOlXjMJk2aaPDgwRoyZIiWL19uPeZ//vMfSVLjxo1lsVi0YsUKnThxQmfOnLE7xtVXX60777xTw4YN0+eff64dO3bowQcfVMOGDXXnnaX7S3zgwAElJCQoPT1dP/30kz7++GP98MMPzPMB4FG+/fbbEoe2Nm+2VMnvHlch+MBOcHCwPv30UzVq1Eh33XWXWrVqpaFDh+r8+fMKCQmRJI0bN04DBw7U4MGDFRsbq5o1a+qvf/3rZY87Z84c3X333XrsscfUsmVLDRs2TGfPnpUkNWzYUElJSYqPj1e9evU0atQoh8dYsGCBOnTooNtvv12xsbEyDEMfffRRqRc5DA4OVkZGhvr3769rrrlGjzzyiEaOHKlHH320DL9DAFB1JSUl2V1l279/fyUmJppUUdViMS5dfMXL5eTkKDQ0VNnZ2dYveUk6f/68Dhw4oOjoaAUGBppYIVyNcw/AHRiGYXeZuiSlpSVq82YTCnKxkr6/L8XkZgAA3Nw///lPZWdn27WnpSV6/Lo8ZUXwAQDAjTmay/P4448rLCxMjG7ZY44PAABu6OTJkyVctZVY4kKwoMcHAAC34yjw+PgEaNWqeIa2roDgU0bMBfc+nHMAVUlJa/Ns3mzRP/5hQkFuhuBTShcvl87NzXXpKsQwX25uriT7FaEBwJU+//xzrV271q6dCcxlQ/ApJV9fX4WFhVnv+RQcHMwdvT2cYRjKzc3V8ePHFRYWJl9fX7NLAuClHPXydO3aVd26dWMCcxkRfMogMjJSkqzhB94hLCzMeu4BwJUutzYPgad8CD5lYLFYVL9+fUVERKigoMDscuACfn5+9PQAMIWjXh5JWro0US++6OJiPAjBpxx8fX35MgQAVBpHoWfEiBGKiIigp6eCCD4AAFQRJ06c0GuvvWbXzn22nIfgAwBAFVDS0Bahx7kIPgAAmMxR6HnppUlavJirh52N4AMAgElmzZql3377za49KSlRy5dLffu6viZPR/ABAMAEjnp5Tp5spDfffEgJCYSeykLwAQDAhUpam2fy5ETVqCGdPm1CUV6E4AMAgIuUNIE5P/9C6Bk92sUFeSGCDwAALuAo9AwaNEjR0dGSpOefd3VF3ongAwBAJdq3b58WL15s185l6uYg+AAAUElYm6fq8TG7AAAAPJGj0LN06STFxBB6zESPDwAATnS5Xh46esxH8AEAwEkchR4/Pz9NmDDBhGrgCMEHAIAKKmltnrS0RG3ebEJBKBHBBwCACihpaCstLVHx8S4uBldE8AEAoJwchZ77779fV199NfN5qiiCDwAAZbRr1y69++67du1cpl71EXwAACgD1uZxb6zjAwBAKTkKPUuWsDaPO6HHBwCAK2BtHs9Bjw8AAJdRUuihl8c90eMDAIADxcXFmjJlil07c3ncG8EHAIBLMIHZczHUBQDAHzgKPYsXD1BaGqHHE9DjAwCApEWLdioz83279piYRKWliVWYPQTBBwDg9a40tNW3ryurQWXyyKGu2bNnq0mTJgoMDFTnzp21ZcsWs0sCAFRRjkLPpEmTmM/joTwu+Cxbtkxjx45VYmKitm3bpnbt2qlnz546fvy42aUBAKqQpKQkh6EnLS1RFovFhIrgCh4XfKZPn65hw4bpoYceUuvWrTV37lwFBwdr/vz5ZpcGAKgiShraWrKEO6p7Oo+a45Ofn6+tW7cqISHB2ubj46Pu3bsrPT3d4Wvy8vKUl5dnfZ6Tk1PpdQIAzHGltXkY3fJ8HhV8fvnlFxUVFalevXo27fXq1VNGRobD1yQnJ5eY/AEAniE1Vdq+nbV54IFDXWWVkJCg7Oxs6+PQoUNmlwQAcDJHoWfx4vtZm8cLeVSPT926deXr66tjx47ZtB87dkyRkZEOXxMQEKCAgABXlAcAcLGtW7dqxYoVdu2szeO9PCr4+Pv7q0OHDlq7dq369esn6cJ47tq1azVq1ChziwMAuExphrZYm8c7eVTwkaSxY8dq8ODB6tixozp16qR//vOfOnv2rB566CGzSwMAuIij0DNp0iQuU4fnBZ97771XJ06c0KRJk5SVlaXrrrtOq1evtpvwDADwPNxcFFdiMQzDMLuIqiQnJ0ehoaHKzs5WSEiI2eUAAEqJ0OPdSvv97XE9PgAA73KltXmAPyL4AADcFr08KCuCDwDALTkKPffff7+uvvpqE6qBuyD4AADcyvz5W3XokP3aPPTyoDQIPgAAt8HQFiqK4AMAcAuOQg9r86CsCD4AgCqNXh44k9ffpBQAUHUReuBs9PgAAKqcoqIiPffcc3btBB5UFMEHAFCl0MuDykTwAQBUCSXdUX3gwIFq2rSpCRXBExF8AACm27Jli7ZvX2XXHhOTKDIPnIngAwAwFUNbcCWCDwDANKzNA1cj+AAAXI5eHpiFdXwAAC5F6IGZCD4AAJcoLCx0GHrS0hIVE0PogWsw1AUAqHSX6+WhoweuRPABAFQqR6FnwIABuuaaa0yoBt6O4AMAqBSffvqp1q9fb9fOXB6YieADAHC6koa2mMsDsxF8AABO5Sj0XHfdJN15J2vzwHwEHwCAU3CZOtwBl7MDACqM0AN3QY8PAKDcCgsL9fzzz9u1E3hQVRF8AADlQi8P3BHBBwBQZo5Cz8aNA7RuHWvzoGoj+AAASm3Dhg3auHGjXXtaWqLi400oCCgjgg8AoFS47QQ8AcEHAHBFjkIPc3ngjgg+AIASsQIzPA3BBwDgEFdtwRMRfAAANgoKCvTCCy/YtRN44AkIPgAAq5J6eZYuZQIzPAPBBwAgyXHoadbsAb32WnO9+KIJBQGVgOADAF5uzZo12rx5s117WlqiNm+WHnzQhKKASkLwAQAvVtLQFgsSwlMRfADAS11ubR7m88BTEXwAwMtwmTq8mY/ZBQAAXIfQA29Hjw8AeIH8/HwlJyfbtRN44G0IPgDg4bjtBPA7gg8AeDDHa/M8qAcfbGZCNYD5CD4A4GZSU6WUFCk+Xurb1/E+n3zyiTZt2mTXztAWvB3BBwDcTEqKlJ5+4aej4MMEZqBkXNUFAG4gNVXq0uXCz/h4KTZWDhcYdBR6Jk9OVFoaoQeQytHj4+vrq6NHjyoiIsKm/ddff1VERISKioqcVhwA4II/9vJs3mzf03O5CcwlhSTAG5U5+BiG4bA9Ly9P/v7+FS4IAGAvPv73eT2XutLQVknzgABvVOrgM3PmTEmSxWLRvHnzVKNGDeu2oqIiffrpp2rZsqXzKwQAqG9f+wBT0to8F28uCsBeqYPPjBkzJF3o8Zk7d658fX2t2/z9/dWkSRPNnTvX+RUCgJe63NVb3FwUKB+LUdLYVQm6deumDz74QLVq1aqsmkyVk5Oj0NBQZWdnKyQkxOxyAHixLl0uzOuJjZVND46j0DN48GA1adLEdcUBVUxpv7/LPMdn/fr1FSoMAFA6l87rYW0eoOLKHHyGDBly2e3z588vdzEAgN/9cV4Pa/MAzlHm4HPy5Emb5wUFBdq5c6dOnTql2267zWmFAQAucBR6CDxA+ZQ5+Pz3v/+1aysuLtaIESPUrBn3fgEAZ6GXB3A+p6zc7OPjo7Fjx1qv/AIAVAx3VAcqh9Pu1bV//34VFhY663AA4JXy8vKUkpJi104vD+AcZQ4+Y8eOtXluGIaOHj2qlStXavDgwU4rDAC8DUNbQOUrc/DZvn27zXMfHx+Fh4fr5ZdfvuIVXwCACy5dnNBR6BkyZIiioqJMqA7wXKzjAwAmuHjT0fffX6vt2z+32x4TkygyD+B85Z7jc/z4ce3Zs0eS1KJFC7u7tQMAShYfL23fztAW4GplDj45OTkaOXKklixZouLiYkmSr6+v7r33Xs2ePVuhoaFOLxIAPI2j0EPgASpfmS9nHzZsmL788kutXLlSp06d0qlTp7RixQp9/fXXevTRRyujRgDwGElJSSxICJiozDcprV69utLS0nTTTTfZtH/22Wfq1auXzp4969QCXY2blAKoLI4CT2BgoMaPH29CNYBnqbSblNapU8fhcFZoaKjH3rEdACqCtXmAqqPMwWfixIkaO3as3nrrLUVGRkqSsrKy9NRTT+kf//iH0wsEAHfG2jxA1VLmOT5z5szRF198oUaNGql58+Zq3ry5GjVqpM2bN+v1119X+/btrQ9nyczM1NChQxUdHa2goCA1a9ZMiYmJys/Pt9nv22+/1c0336zAwEBFRUVp6tSpTqsBAMrKUeh5+OGHCT2Aicrc43PnnXfKYrFURi0lysjIUHFxsV5//XU1b95cO3fu1LBhw3T27Fm99NJLki6M7fXo0UPdu3fX3Llz9d1332nIkCEKCwvTI4884tJ6AXi3NWvWaPPmzXbtMTGJatjQhIIAWJV5cnNVMW3aNM2ZM0c//vijpAs9Uc8884yysrLk7+8vSYqPj9fy5cuVkZFR6uMyuRlARVzu5qJ9+7q4GMCLVNrk5qZNm+qrr75SnTp1bNpPnTql9u3bW4NIZcvOzlbt2rWtz9PT09W1a1dr6JGknj176sUXX9TJkydLnHidl5envLw86/OcnJzKKxqAR+MydaDqK3PwyczMVFFRkV17Xl6efv75Z6cUdSX79u3TrFmzrMNc0oUJ1tHR0Tb71atXz7qtpOCTnJxc4v/QAKA0mMAMuI9SB5/U1FTrr9PS0mwuaS8qKtLatWvtgseVxMfH68UXX7zsPrt371bLli2tzw8fPqxevXrpnnvu0bBhw8r0fo4kJCTY3HE+JyeHmwICKDVHoad69ep68sknTagGwJWUOvj069dPkmSxWDR48GCbbX5+fmrSpIlefvnlMr35uHHjFBcXd9l9mjZtav31kSNH1K1bN3Xp0kVvvPGGzX6RkZE6duyYTdvF5xcvu3ckICBAAQEBZaobAM6fP+/wP2708gBVW6mDz8X7ckVHR+urr75S3bp1K/zm4eHhCg8PL9W+hw8fVrdu3dShQwctWLBAPj62V+LHxsbqmWeeUUFBgfz8/CRduLKiRYsWLKwIwKkY2gLcl1tc1XX48GHdeuutaty4sRYtWiRfX1/rtou9OdnZ2WrRooV69Oih8ePHa+fOnRoyZIhmzJhRpsvZuaoLwOU4Cj3Dhg1TgwYNTKgGwEWVdlXXs88+e9ntkyZNKushr2jNmjXat2+f9u3bp6uuuspm28XcFhoaqo8//lgjR45Uhw4dVLduXU2aNIk1fAA4xerVq/Xll1/atdPLA7iXMvf4xMTE2DwvKCjQgQMHVK1aNTVr1kzbtm1zaoGuRo8PgEsxtAVUfZXW47N9+3aHbxYXF6e//vWvZT0cAFRprM0DeBanzfH57rvvdMcddygzM9MZhzMNPT4AJHp5AHdTaT0+JcnOzlZ2drazDgcApnEUekJCQvTEE0+YUA0AZypz8Jk5c6bNc8MwdPToUb311lvq3bu30woDAFcraW2etLREObjnKAA3VObgM2PGDJvnPj4+Cg8P1+DBg5WQkOC0wgDAlUoa2kpLS1R8vIuLAVBp3GIdH1dijg/gfRyFnuHDh1vv9weg6qvUOT6nTp3Svn37JEnNmzdXWFhYuYoEADOlpaXpiy++sGuPiUkUmQfwTGUKPpmZmRo5cqTS0tKsCwdaLBb16tVLr776qpo0aVIZNQKA03HVFuCdSh18Dh06pBtuuEF+fn6aMmWKWrVqJUnatWuX5syZo9jYWH311Vd2KysDQFXD2jyA9yr1HJ+hQ4dq3759SktLU2BgoM22c+fOqVevXrr66qs1b968SinUVZjjA3iuy01g5qotwL05fY7P6tWrtWzZMrvQI0lBQUGaMmWK7rvvvvJVCwCVzFHoCQyspw8/HM5VW4AXKXXw+eWXXy47h6dp06b67bffnFETADjNuXPnNHXqVLv2i0Nb48e7uiIAZip18Klfv7527dpV4hyenTt3KjIy0mmFAUBFlTS0FRPDfB7AW5U6+PTr109PPvmk1q5dq/DwcJttx48f1/jx49WvXz9n1wcA5cLaPAAcKfXk5pMnT6pz587KysrSgw8+qJYtW8owDO3evVvvvPOOIiMj9cUXX6h27dqVXXOlYnIz4N5WrFihrVu32rVz1Rbg2Zw+ublWrVr68ssvNWHCBC1dulSnTp2SJIWFhen+++/XCy+84PahB4B7Y20eAFdSrltWGIahEydOSJLCw8NlsVicXphZ6PEB3BNr8wDerVJvWWGxWBQREVHu4gDAWejlAVAW5Qo+AFAVOAo9derU0ahRo0yoBoA7IPgAcDu5ubmaNm2aXTu9PACuhOADwK2wNg+AiiD4AHAbjkJPy5aP6d57wx3sDQD2ShV8Zs6cWeoDjh49utzFAIAjK1eu1Ndff23XPnlyomJjpXvvNaEoAG6pVMFnxowZpTqYxWIh+ABwqssNbcXGihuMAiiTUgWfAwcOVHYdAGDnSmvz9O3rymoAeIJyz/HJz8/XgQMH1KxZM1WrxlQhAM7D2jwAKotPWV+Qm5uroUOHKjg4WG3atNHBgwclSX//+9+VkpLi9AIBeBdHoadBgwaEHgBOUebgk5CQoB07dmjDhg0KDAy0tnfv3l3Lli1zanEAvEdubm6JQ1vDhg0zoSIAnqjMY1TLly/XsmXLdMMNN9jco6tNmzbav3+/U4sD4B1YmweAq5S5x+fEiRMO79N19uxZj7pZKQDXcBR65s37u/LzE5m8DMDpyhx8OnbsqJUrV1qfXww78+bNU2xsrPMqA+DR1qxZ4zD0pKUl6uefa2v9ehOKAuDxyjzU9cILL6h3797atWuXCgsL9corr2jXrl3avHmzNm7cWBk1AvAwl7tqKyZGSklhfR4AlcNiGIZR1hft379fKSkp2rFjh86cOaP27dtr/PjxuvbaayujRpfKyclRaGiosrOzFRISYnY5gMe50to8AFAepf3+Llfw8WQEH6BysDYPgMpU2u/vUg115eTklPqNCQsALuUo9Fx77bW66667lJr6+9AWk5kBVLZSBZ+wsLBSX7FVVFRUoYIAeI5z585p6tSpdu1/7OVJSZHS0y/8JPgAqGylCj7r/3B5RWZmpuLj4xUXF2e9iis9PV2LFi1ScnJy5VQJwO1cbmjrj7088fFMZgbgOmWe4/OnP/1JDz/8sAYMGGDT/s477+iNN97Qhg0bnFmfyzHHB6g4R6FnzJgxCg0NlSR16XKhlyc2Vtq82dXVAfBEpf3+LvM6Punp6erYsaNde8eOHbVly5ayHg6AB9m4cWOJV21dDD3Shd6d2Fh6eQC4XpmDT1RUlP71r3/Ztc+bN09RUVFOKQqA+0lKSnLY4+voqq2+fS/09DCnB4CrlXkBwxkzZqh///5atWqVOnfuLEnasmWLfvjhB73//vtOLxBA1cfaPADcRbnW8fn555/12muvKSMjQ5LUqlUrDR8+3CN6fJjjA5Qea/MAqCpYwLCcCD5A6TgKPR07dlSfPn1MqAaAt3PqAoaXOnXqlN58803t3r1bktSmTRsNGTLEZvIiAM9UmrV5AKCqKnOPz9dff62ePXsqKChInTp1kiR99dVXOnfunD7++GO1b9++Ugp1FXp8gJIxtAWgqqq0oa6bb75ZzZs317/+9S9Vq3ahw6iwsFAPP/ywfvzxR3366acVq9xkBB/AMUeh54knnuDvCYAqodKCT1BQkLZv366WLVvatO/atUsdO3ZUbm5u+SquIgg+gK3169c7/A8NvTwAqpJKW8AwJCREBw8etGs/dOiQatasWdbDAajCkpKSrhh6UlMvrMScmurKygCgfMo8ufnee+/V0KFD9dJLL6lLly6SpE2bNumpp56yu40FAPdV2rV5uMkoAHdS5uDz0ksvyWKxaNCgQSosLJQk+fn5acSIEUpJSXF6gQBcq6wTmLnJKAB3Uu51fHJzc7V//35JUrNmzRQcHOzUwszCHB94M0eh5/rrr9df/vIXE6oBgNKr1HV8JCk4OFjXXntteV8OoArJzc3VtGnT7NqZwAzA05Q6+AwZMqRU+82fP7/cxQBwPdbmAeBNSh18Fi5cqMaNGysmJkbc5QLwDI5Cz9ixY7lCE4DHKnXwGTFihJYsWaIDBw7ooYce0oMPPqjatWtXZm0AKsmmTZv0ySef2LXTywPA05V6HZ/Zs2fr6NGjevrpp/W///1PUVFR+tvf/qa0tDR6gAA3kpSU5DD0xMQQegB4vjItYBgQEKABAwZozZo12rVrl9q0aaPHHntMTZo00ZkzZyqrRgBOUtLaPBd7eliIEICnK/dVXT4+PrJYLDIMQ0VFRc6sCYCTlWYCMwsRAvAGZerxycvL05IlS/TnP/9Z11xzjb777ju9+uqrOnjwoGrUqFFZNQKoAEehJzLyFrv5PPHxUmwsCxEC8Gyl7vF57LHHtHTpUkVFRWnIkCFasmSJ6tatW5m1AaiAc+fOaerUqXbtJU1g7tuXnh4Anq/UKzf7+PioUaNGiomJkcViKXG/Dz74wGnFmYGVm+EJWJsHgLdx+srNgwYNumzgAWC+1FRp+3b70PPUU095zG1lAKAiyrSAIYCq68svv9T27avt2unlAYDflfuqLgBVB0NbAFA6BB/AzZW0Ng8AwB7BB3BTzz33nMM1tAg9AFCyMq3jUxXk5eXpuuuuk8Vi0TfffGOz7dtvv9XNN9+swMBARUVFObyUF/AESUlJdqFn167ehB4AuAK3Cz5PP/20GjRoYNeek5OjHj16qHHjxtq6daumTZumyZMn64033jChSqBynD9/3uHQVlpaoh54oJMJFQGAe3Groa5Vq1bp448/1vvvv69Vq1bZbFu8eLHy8/M1f/58+fv7q02bNvrmm280ffp0PfLIIyZVDDhPSROYY2ISRUcPAJSO2wSfY8eOadiwYVq+fLnD9UjS09PVtWtX+fv7W9t69uypF198USdPnlStWrUcHjcvL095eXnW5zk5Oc4vHqggR6Fn/PjxCgwMNKEaAHBfbjHUZRiG4uLiNHz4cHXs2NHhPllZWapXr55N28XnWVlZJR47OTlZoaGh1kdUVJTzCgcqaNu2bSVetUXoAYCyMzX4xMfHy2KxXPaRkZGhWbNm6fTp00pISHB6DQkJCcrOzrY+Dh065PT3AMojKSlJ//vf/2zafH19mcAMABVg6lDXuHHjFBcXd9l9mjZtqnXr1ik9PV0BAQE22zp27KgHHnhAixYtUmRkpI4dO2az/eLzyMjIEo8fEBBgd1zAbCVNYN682YRiAMCDmBp8wsPDFR4efsX9Zs6cqeeee876/MiRI+rZs6eWLVumzp07S5JiY2P1zDPPqKCgQH5+fpKkNWvWqEWLFiXO7wGqmmeffVaO7huclpao+HgTCgIAD1Pqu7NXJZmZmYqOjtb27dt13XXXSZKys7PVokUL9ejRQ+PHj9fOnTs1ZMgQzZgxo0xXdXF3dpjFUS/PnXfeaf0zDgAomdPvzl7VhYaG6uOPP9bIkSPVoUMH1a1bV5MmTeJSdlR5586dc7jYJnN5AMD53LLHpzLR4wNX4uaiAOAcXtfjA7gb1uYBANcj+AAutnXrVq1YscKunV4eAKh8brGAIeApkpKS7EKPv79/mUNPaqrUpcuFnwCA0qPHB3CRklZgLo+UFCk9/cLPvn0rWhkAeA+CD1DJpk6dqnPnztm1x8SUf2grPv5C6GFtHwAoG4IPUIkc9fLcfffdatOmTYWO27cvPT0AUB7M8QEqwblz50oc2iop9DBvBwAqHz0+gJOVd20e5u0AQOUj+ABO5Cj0xMfHl+pGuMzbAYDKR/ABnOCbb77Rhx9+aNdelqu2mLcDAJWP4ANUkKNenlq1amn06NEmVAMAuByCD1ABzlybBwBQ+Qg+QDnMmjVLv/32m107oQcAqjaCD1BGjnp57rnnHrVu3dqEagAAZUHwAUrp/PnzevHFF+3a6eUBAPdB8AFKobxr8wAAqhaCD3AFjkLPhAkT5OfnZ0I1AICKIPgAJcjIyNCyZcvs2unlAQD3RfABHHDUy9OiRQvdd999JlQDAHAWblIKXKKktXkuDT3cVBQA3A89PsD/mTdvng4fPmzXXtLQFjcVBQD3Q/AB5LiXZ9CgQYqOji7xNdxUFADcD8EHXq0ia/NwU1EAcD8EH3gt1uYBAO9D8IFXYm0eAPBOBB94lT179mjp0qV27TExiSLzAIDnI/jAazjq5WnZsqXuvfdeE6oBAJiB4AOv4Cj0pKUliuk8AOBdCD7waEuWLNHevXvt2tPSErkMHQC8EMEHHstRL8+QIUMUFRVFTw8AeCmCDzxORdbmAQB4NoIPPApr8wAALofgA4/hKPQ888wzqlaNP+YAgAv4RoDb27t3r5YsWWLXTi8PAOBSBB+4NUe9PB06dNDtt99uQjUAgKqO4AO35Sj00MsDALgcgg/czttvv639+/fbtRN6AABXQvCBW3HUyzN06FBdddVVJlQDAHA3PmYXAHOlpkpdulz4WZWdP3++xKEtQg8AoLTo8fFyKSlSevqFn337ml2NY6zNAwBwFoKPl4uPvxB6qup9q1ibBwDgTHx7eLm+fatmT8+ePXu0dOlSu3Z6eQAAFUHwQZXjqJfn+uuv11/+8hcTqgEAeBKCD6oU1uYBAFQmgg+qhH//+986cOCAXTuhBwDgTAQfmCo1Vdq+3b6X5+GHH1bDhg1NqAgA4MlYxwemSE2VunY95zD0JCYmEnoAAJWCHh+YYvv2JP3pT/btDG0BACoTwQcux9o8AACzMNQFp7nS7S8yMjJKvGqL0AMAcAW+beA0l7v9haPA06lTJ/Xu3dtF1QEAQPCBE/3x9hepqb//uqQJzAAAuBrBB07zx9tfdOkiXXPNIm3fnmm3H6EHAGAWgg8qRc+e9r08w4YNU4MGDUyoBgCACwg+cKrz58/rxRdftGunlwcAUBUQfOA0jiYwS4QeAEDVQfCBUzgKPRMnTpSvr68J1QAA4BjBB+WWmirNnfuTOndeaLeNXh4AQFVE8EG5bd+epM6dbdt69eqlzpc2AgBQRRB8UC4lrcAMAEBVRvBBmXz++edau3atXTuhBwDgDgg+KDVHvTyPP/64wsLCXF8MAADlQPDBFeXn5ys5OdmunV4eAIC7IfjgsmbOnKmTJ0/atLVs2VL33nuvSRUBAFB+BB+UyNHQ1j/+8Q/5+PiYUA0AABVH8IGdw4cPa968eXbtDG0BANydW/3XfeXKlercubOCgoJUq1Yt9evXz2b7wYMH1adPHwUHBysiIkJPPfWUCgsLzSnWTSUlJdmFnv79+xN6AAAewW16fN5//30NGzZML7zwgm677TYVFhZq586d1u1FRUXq06ePIiMjtXnzZh09elSDBg2Sn5+fXnjhBRMrdx+szQMA8HQWwzAMs4u4ksLCQjVp0kRJSUkaOnSow31WrVql22+/XUeOHFG9evUkSXPnztX48eN14sQJ+fv7l+q9cnJyFBoaquzsbIWEhDjtM1Rln332mdatW2fXTugBALiL0n5/u8VQ17Zt23T48GH5+PgoJiZG9evXV+/evW16fNLT03XttddaQ48k9ezZUzk5Ofr+++9LPHZeXp5ycnJsHt4kKSnJLvQ8/vjjhB4AgEdyi+Dz448/SpImT56siRMnasWKFapVq5ZuvfVW/fbbb5KkrKwsm9Ajyfo8KyurxGMnJycrNDTU+oiKiqqkT+EcqalSly4XflZEQUFBiUNbLEgIAPBUpgaf+Ph4WSyWyz4yMjJUXFwsSXrmmWfUv39/dejQQQsWLJDFYtG7775boRoSEhKUnZ1tfRw6dMgZH63SpKRI6ekXfpbXkiVL7OY9tWrVil4eAIDHM3Vy87hx4xQXF3fZfZo2baqjR49Kklq3bm1tDwgIUNOmTXXw4EFJUmRkpLZs2WLz2mPHjlm3lSQgIEABAQHlKd8U8fEXQk98fPlez9o8AABvZmrwCQ8PV3h4+BX369ChgwICArRnzx7ddNNNki4M1WRmZqpx48aSpNjYWD3//PM6fvy4IiIiJElr1qxRSEiITWByd337XniU1fHjxzVnzhy7dnp5AADexC0uZw8JCdHw4cOVmJioqKgoNW7cWNOmTZMk3XPPPZKkHj16qHXr1ho4cKCmTp2qrKwsTZw4USNHjnSrHp3K4KiX58EHH1SzZs1MqAYAAPO4RfCRpGnTpqlatWoaOHCgzp07p86dO2vdunWqVauWJMnX11crVqzQiBEjFBsbq+rVq2vw4MF69tlnTa7cXKzNAwDA79xiHR9X8pR1fPbu3aslS5bYtNWsWVNjx441qSIAACpPab+/3abHB6XnqJenbdtx6t+/hgnVAABQdXApjwcpLCx0GHomT07Uyy8TegAAoMfHQ3zyySfatGmTTVufPn105EhHxcaW//J3AAA8CcHHAzjq5Zk0aZIsFouk8l3+DgCAJyL4uLGTJ09q5syZdu1ctQUAgGMEHzc1e/Zs/fLLLzZtw4YNU4MGDUyqCACAqo/g44ZYmwcAgPIh+LiRI0eO6F//+pdNW5s2bXT33XebVBEAAO6F4OMmXn31f/r11202bU8//bSCgoJMqggAAPdD8KniiouLNWPGDJ05c8ba5ufnpwkTJphYFQAA7ongU4UdO3ZMc+fOtWlr3nygHnigqUkVAQDg3gg+VVRaWpq++OIL6/P69etr2LBh1rV5AABA2RF8qpj8/HwlJyfbtN19991q06aNSRUBAOA5CD5VyL59+7R48WKbNiYwAwDgPASfKuLtt9/W/v37rc/btWunfv36mVcQAAAeiOBjstOnT2v69Ok2bUOGDFFUVJRJFQEA4LkIPib6+uuvtXLlSutzHx8fTZgwQb6+viZWBQCA5yL4mMDR2jzdu3fXjTfeaGJVAAB4PoKPizlam2f06NGqVauWSRUBAOA9CD4udOnaPA0aNNDDDz/M2jwAALgIwcdFFi5cqJ9++sn6/J577lHr1q1NrAgAAO9D8HGRkydPWn/N2jwAAJiD4OMigwcP1tmzZ7lMHQAAExF8XKR27dqqXbu22WUAAODVfMwuAAAAwFUIPgAAwGsQfAAAgNcg+AAAAK9B8HGR1FSpS5cLPwEAgDkIPi6SkiKlp1/4CQAAzEHwcZH4eCk29sJPAABgDtbxcZG+fS88AACAeejxAQAAXoPgAwAAvAbBBwAAeA2CDwAA8BoEHwAA4DUIPgAAwGsQfAAAgNcg+AAAAK9B8AEAAF6D4AMAALwGwQcAAHgNgg8AAPAaBB8AAOA1uDv7JQzDkCTl5OSYXAkAACiti9/bF7/HS0LwucTp06clSVFRUSZXAgAAyur06dMKDQ0tcbvFuFI08jLFxcU6cuSIatasKYvFYnY5lS4nJ0dRUVE6dOiQQkJCzC4HJeA8uQfOk3vgPLmHsp4nwzB0+vRpNWjQQD4+Jc/kocfnEj4+PrrqqqvMLsPlQkJC+AfADXCe3APnyT1wntxDWc7T5Xp6LmJyMwAA8BoEHwAA4DUIPl4uICBAiYmJCggIMLsUXAbnyT1wntwD58k9VNZ5YnIzAADwGvT4AAAAr0HwAQAAXoPgAwAAvAbBBwAAeA2Cj5dbuXKlOnfurKCgINWqVUv9+vWz2X7w4EH16dNHwcHBioiI0FNPPaXCwkJzivVyeXl5uu6662SxWPTNN9/YbPv222918803KzAwUFFRUZo6dao5RXqpzMxMDR06VNHR0QoKClKzZs2UmJio/Px8m/04T1XD7Nmz1aRJEwUGBqpz587asmWL2SV5reTkZF1//fWqWbOmIiIi1K9fP+3Zs8dmn/Pnz2vkyJGqU6eOatSoof79++vYsWPlfk+Cjxd7//33NXDgQD300EPasWOHNm3apPvvv9+6vaioSH369FF+fr42b96sRYsWaeHChZo0aZKJVXuvp59+Wg0aNLBrz8nJUY8ePdS4cWNt3bpV06ZN0+TJk/XGG2+YUKV3ysjIUHFxsV5//XV9//33mjFjhubOnasJEyZY9+E8VQ3Lli3T2LFjlZiYqG3btqldu3bq2bOnjh8/bnZpXmnjxo0aOXKkvvjiC61Zs0YFBQXq0aOHzp49a93niSee0P/+9z+9++672rhxo44cOaK77rqr/G9qwCsVFBQYDRs2NObNm1fiPh999JHh4+NjZGVlWdvmzJljhISEGHl5ea4oE//no48+Mlq2bGl8//33hiRj+/bt1m2vvfaaUatWLZtzMn78eKNFixYmVIqLpk6dakRHR1ufc56qhk6dOhkjR460Pi8qKjIaNGhgJCcnm1gVLjp+/Lghydi4caNhGIZx6tQpw8/Pz3j33Xet++zevduQZKSnp5frPejx8VLbtm3T4cOH5ePjo5iYGNWvX1+9e/fWzp07rfukp6fr2muvVb169axtPXv2VE5Ojr7//nszyvZKx44d07Bhw/TWW28pODjYbnt6erq6du0qf39/a1vPnj21Z88enTx50pWl4g+ys7NVu3Zt63POk/ny8/O1detWde/e3drm4+Oj7t27Kz093cTKcFF2drYkWf/ubN26VQUFBTbnrGXLlmrUqFG5zxnBx0v9+OOPkqTJkydr4sSJWrFihWrVqqVbb71Vv/32myQpKyvLJvRIsj7PyspybcFeyjAMxcXFafjw4erYsaPDfThPVc++ffs0a9YsPfroo9Y2zpP5fvnlFxUVFTk8D5wD8xUXF2vMmDG68cYb1bZtW0kX/m74+/srLCzMZt+KnDOCj4eJj4+XxWK57OPifARJeuaZZ9S/f3916NBBCxYskMVi0bvvvmvyp/B8pT1Ps2bN0unTp5WQkGB2yV6ptOfpjw4fPqxevXrpnnvu0bBhw0yqHHA/I0eO1M6dO7V06dJKfZ9qlXp0uNy4ceMUFxd32X2aNm2qo0ePSpJat25tbQ8ICFDTpk118OBBSVJkZKTd1Q4XZ9JHRkY6sWrvU9rztG7dOqWnp9vdq6Zjx4564IEHtGjRIkVGRtpd4cB5co7SnqeLjhw5om7duqlLly52k5Y5T+arW7eufH19HZ4HzoG5Ro0apRUrVujTTz/VVVddZW2PjIxUfn6+Tp06ZdPrU6Fz5ozJSHA/2dnZRkBAgM3k5vz8fCMiIsJ4/fXXDcP4fXLzsWPHrPu8/vrrRkhIiHH+/HmX1+yNfvrpJ+O7776zPtLS0gxJxnvvvWccOnTIMIzfJ83m5+dbX5eQkMCkWRf7+eefjauvvtq47777jMLCQrvtnKeqoVOnTsaoUaOsz4uKioyGDRsyudkkxcXFxsiRI40GDRoYe/futdt+cXLze++9Z23LyMio0ORmgo8Xe/zxx42GDRsaaWlpRkZGhjF06FAjIiLC+O233wzDMIzCwkKjbdu2Ro8ePYxvvvnGWL16tREeHm4kJCSYXLn3OnDggN1VXadOnTLq1atnDBw40Ni5c6exdOlSIzg42BpgUfl+/vlno3nz5saf/vQn4+effzaOHj1qfVzEeaoali5dagQEBBgLFy40du3aZTzyyCNGWFiYzdWrcJ0RI0YYoaGhxoYNG2z+3uTm5lr3GT58uNGoUSNj3bp1xtdff23ExsYasbGx5X5Pgo8Xy8/PN8aNG2dEREQYNWvWNLp3727s3LnTZp/MzEyjd+/eRlBQkFG3bl1j3LhxRkFBgUkVw1HwMQzD2LFjh3HTTTcZAQEBRsOGDY2UlBRzCvRSCxYsMCQ5fPwR56lqmDVrltGoUSPD39/f6NSpk/HFF1+YXZLXKunvzYIFC6z7nDt3znjssceMWrVqGcHBwcZf//pXm/9UlJXl/94YAADA43FVFwAA8BoEHwAA4DUIPgAAwGsQfAAAgNcg+AAAAK9B8AEAAF6D4AMAALwGwQcAKsmGDRtksVh06tQps0sB8H8IPgDc1uTJk3XdddeZXQYAN0LwAeDxCgoKzC4BQBVB8AFgmuLiYiUnJys6OlpBQUFq166d3nvvPUm/DxOtXbtWHTt2VHBwsLp06aI9e/ZIkhYuXKikpCTt2LFDFotFFotFCxculCRZLBbNmTNHffv2VfXq1fX8889fto6L75WWlqaYmBgFBQXptttu0/Hjx7Vq1Sq1atVKISEhuv/++5Wbm2t9XV5enkaPHq2IiAgFBgbqpptu0ldffVU5v1kAnKPCdxgDgHJ67rnnjJYtWxqrV6829u/fbyxYsMAICAgwNmzYYKxfv96QZHTu3NnYsGGD8f333xs333yz0aVLF8MwDCM3N9cYN26c0aZNG7s7OksyIiIijPnz5xv79+83fvrpp8vWcfG9brjhBuPzzz83tm3bZjRv3ty45ZZbjB49ehjbtm0zPv30U6NOnTo2NxYdPXq00aBBA+Ojjz4yvv/+e2Pw4MFGrVq1jF9//dXmuCdPnqyc30AAZUbwAWCK8+fPG8HBwcbmzZtt2ocOHWoMGDDAGho++eQT67aVK1cakoxz584ZhmEYiYmJRrt27eyOLckYM2ZMqWtx9F7JycmGJGP//v3WtkcffdTo2bOnYRiGcebMGcPPz89YvHixdXt+fr7RoEEDY+rUqTbHJfgAVUc1s3qaAHi3ffv2KTc3V3/+859t2vPz8xUTE2N9/v/+3/+z/rp+/fqSpOPHj6tRo0aXPX7Hjh3LXNMf36tevXoKDg5W06ZNbdq2bNkiSdq/f78KCgp04403Wrf7+fmpU6dO2r17d5nfG4BrEHwAmOLMmTOSpJUrV6phw4Y22wICArR//35JF8LERRaLRdKFuUFXUr169TLXdOl7/fH5xbbSvDeAqovJzQBM0bp1awUEBOjgwYNq3ry5zSMqKqpUx/D391dRUVElV+pYs2bN5O/vr02bNlnbCgoK9NVXX6l169am1ATgyujxAWCKmjVr6sknn9QTTzyh4uJi3XTTTcrOztamTZsUEhKixo0bX/EYTZo00YEDB/TNN9/oqquuUs2aNRUQEOCC6i/0KI0YMUJPPfWUateurUaNGmnq1KnKzc3V0KFDXVIDgLIj+AAwzZQpUxQeHq7k5GT9+OOPCgsLU/v27TVhwoRSDSn1799fH3zwgbp166ZTp05pwYIFiouLq/zC/09KSoqKi4s1cOBAnT59Wh07dlRaWppq1arlshoAlI3FMAzD7CIAAABcgTk+AADAaxB8AHi84cOHq0aNGg4fw4cPN7s8AC7EUBcAj3f8+HHl5OQ43BYSEqKIiAgXVwTALAQfAADgNRjqAgAAXoPgAwAAvAbBBwAAeA2CDwAA8BoEHwAA4DUIPgAAwGsQfAAAgNcg+AAAAK/x/wHMk0G0xPAGDAAAAABJRU5ErkJggg==", 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", 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", 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", 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uzz//XOHh4Y4gUJrY2FilpKRo165duuiii1SvXj2FhIRUWpkLq1Onju655x498MADatiwoVq0aKE//elPysrK0vjx46ukDK4QWgAANZ4vbvs/9thjioqKUlJSkn744QfVr19fl156qR566KFy3Ya56aabtHr1al155ZXKyMjQ0qVLNWbMmEovd4H58+crPz9fo0aN0qlTpxQfH68PP/xQDRo0qLIyFGUzxpiyF6ueMjMzFRERIbvdrvDwcF8XBwDgZWfPnlVKSori4uJUu3ZtXxcHbijtvfP0+k3vIQAAYAmEFgAALOzuu+926lZd+O/uu+/2dfG8ijYtAABY2KOPPqrp06e7nFfTmk74NLTk5eVpzpw5Wr58uY4ePapmzZppzJgx+uMf/+jorw4AAEoWHR2t6OhoXxejSvg0tCxYsECLFi3Siy++qE6dOmnbtm0aO3asIiIiNHnyZF8WDQAAVDM+DS2bN2/W9ddfr6FDh0q60Cf9tdde01dffeXLYgEAqpnqPlIriquMzsk+DS29e/fW3//+d33//fdq27atdu/erU2bNunpp592uXxOTo5ycnIc/8/MzKyqogIAfCA4OFgBAQE6cuSIoqKiFBwcTPMBCzDG6Pjx4y4fF1ARPg0tiYmJyszMVPv27RUYGKi8vDzNmzdPI0eOdLl8UlKS5s6dW8WlBAD4SkBAgOLi4pSWlqYjR474ujhwg81m00UXXaTAwEDvrdOXg8utWLFCDzzwgJ588kl16tRJu3bt0tSpU/X0009r9OjRxZZ3VdMSExPD4HIAUMMZY5Sbm+uzZ/HAfbVq1SoxsHg6uJxPQ0tMTIwSExM1ceJEx7THH39cy5cv13fffVfm6xkRFwAA67HkiLhZWVkKCHAuQmBgIA2uAABAMT5t03Lttddq3rx5atGihTp16qSdO3fq6aef1rhx43xZLAAAUA359PbQqVOnNGvWLL355ps6duyYmjVrphEjRuiRRx5RcHBwma/n9hAAANZjyTYtFUVoAQDAeizZpgUAAKC8CC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASfB5afv75Z91+++1q1KiRQkND1aVLF23bts3XxQIAANVMkC83fvLkSfXp00dXXnml1q5dq6ioKB04cEANGjTwZbEAAEA15NPQsmDBAsXExGjp0qWOaXFxcT4sEQAAqK58entozZo1io+P1y233KLo6Gh1795dS5Ys8WWRAABANeXT0PLDDz9o0aJFuvjii/Xhhx/qnnvu0eTJk/Xiiy+6XD4nJ0eZmZlOfwAAwD/YjDHGVxsPDg5WfHy8Nm/e7Jg2efJkbd26VVu2bCm2/Jw5czR37txi0+12u8LDwyu1rAAAwDsyMzMVERHh9vXbpzUtTZs2VceOHZ2mdejQQYcPH3a5/MyZM2W32x1/qampVVFMAABQDfi0IW6fPn20f/9+p2nff/+9WrZs6XL5kJAQhYSEVEXRAABANePTmpZp06bpiy++0BNPPKGDBw/q1Vdf1d///ndNnDjRl8UCAADVkE9Dy2WXXaY333xTr732mjp37qzHHntMCxcu1MiRI31ZLAAAUA35tCFuRXnakAcAAPiOJRviAgAAlBehBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWEJQeRfMzMws90rDw8M9KgwAAEBJyh1a6tevL5vNVuoyxhjZbDbl5eVVuGAAAACFlTu0rF+/vjLLAQAAUKpyh5b+/ftXZjkAAABKVe7QUlRGRob++c9/6ttvv5UkderUSePGjVNERITXCgcAAFDAo95D27ZtU+vWrfXMM8/oxIkTOnHihJ5++mm1bt1aO3bs8HYZAQAAZDPGGHdfdMUVV6hNmzZasmSJgoIuVNbk5uZqwoQJ+uGHH/Tpp596vaCuZGZmKiIiQna7nR5LAABYhKfXb49CS2hoqHbu3Kn27ds7Tf/mm28UHx+vrKwsd1fpEUILAADW4+n126PbQ+Hh4Tp8+HCx6ampqapXr54nqwQAACiVR6Fl+PDhGj9+vFauXKnU1FSlpqZqxYoVmjBhgkaMGOHtMgIAAHjWe+jPf/6zbDab7rjjDuXm5kqSatWqpXvuuUfz58/3agEBAAAkD9u0FMjKylJycrIkqXXr1goLC/NawcqDNi0AAFiPp9dvj8dpkaSwsDB16dKlIqsAAAAoF49Cy9mzZ/X8889r/fr1OnbsmPLz853mM1YLAADwNo9Cy/jx4/XRRx/p5ptvVs+ePct8kCIAAEBFeRRa3n33Xb3//vvq06ePt8sDAADgkkddnps3b854LAAAoEp5FFqeeuopzZgxQz/++KO3ywMAAOCSR7eH4uPjdfbsWbVq1UphYWGqVauW0/wTJ054pXAAAAAFPAotI0aM0M8//6wnnnhCjRs3piEuAACodB6Fls2bN2vLli3q1q2bt8sDAADgkkdtWtq3b6/s7GxvlwUAAKBEHoWW+fPn6/7779eGDRv066+/KjMz0+kPAADA2zx69lBAwIWsU7QtizFGNptNeXl53ildGXj2EAAA1lOlzx5av369Jy8DAADwmEehpX///uVa7t5779Wjjz6qyMhITzYDAADg4FGblvJavnw5bVwAAIBXVGpo8aC5DAAAgEuVGloAAAC8hdACAAAsgdACAAAsoVqFlvnz58tms2nq1Km+LgoAAKhm3A4tubm5evTRR/XTTz+Vueztt99e7kFjtm7dqhdeeEFdu3Z1t0gAAMAPuB1agoKC9OSTTyo3N7fMZRctWlSuMVpOnz6tkSNHasmSJWrQoIG7RQIAAH7Ao9tDV111lTZu3Oi1QkycOFFDhw7VoEGDvLZOAABQs3g0Iu6QIUOUmJiovXv3qkePHqpTp47T/Ouuu67c61qxYoV27NihrVu3lrlsTk6OcnJyHP9n4DoAAPyHR6Hl3nvvlSQ9/fTTxea588DE1NRUTZkyRevWrVPt2rXLXD4pKUlz5851r7AAAKBG8Ogpz97y1ltv6YYbblBgYKBjWl5enmw2mwICApSTk+M0z1VNS0xMDE95BgDAQjx9yrNHbVpeeuklp/BQ4Ny5c3rppZfKvZ6BAwdq79692rVrl+MvPj5eI0eO1K5du5wCiySFhIQoPDzc6Q8AAPgHj2paAgMDlZaWpujoaKfpv/76q6Kjo8t9e8iVAQMG6JJLLtHChQvLXNbTpAYAAHynSmtajDGy2WzFpv/000+KiIjwZJUAAAClcqshbvfu3WWz2WSz2TRw4EAFBf3v5Xl5eUpJSdE111xToQJt2LChQq8HAAA1k1uhZdiwYZKkXbt2afDgwapbt65jXnBwsGJjY3XTTTd5tYAAAACSm6Fl9uzZkqTY2FgNHz68XN2UAQAAvMGjcVpGjx4t6UJvoWPHjik/P99pfosWLSpeMgAAgEI8Ci0HDhzQuHHjtHnzZqfpBQ10K9J7CAAAwBWPQsuYMWMUFBSkd999V02bNnXZk6gmSLNnKyX9jOIi66hpRKiviwMAgF/zKLTs2rVL27dvV/v27b1dnmpj5dbDmrl6r/KNFGCTkm7souGXcdsLAABf8Wiclo4dOyo9Pd3bZak20uzZjsAiSflGemj1PqXZs31bMAAA/JhHoWXBggV68MEHtWHDBv3666/KzMx0+rO6lPQzjsBSIM8YHUrP8k2BAACAZ7eHBg0aJEm66qqrnNqz1JSGuHGRdRRgk1NwCbTZFBsZ5rtCAQDg5zwKLevXr/d2OaqVphGhSrqxix5avU95xijQZtMTN3amMS4AAD7kUWjp37+/PvvsM73wwgtKTk7WqlWr1Lx5c7388suKi4vzdhl9YvhlLdSvbZQOpWcpNjKMwAIAgI951KbljTfe0ODBgxUaGqqdO3cqJydHkmS32/XEE094tYC+1DQiVAmtGxFYAACoBjwKLY8//rgWL16sJUuWqFatWo7pffr00Y4dO7xWOAAAgAIehZb9+/erX79+xaZHREQoIyOjomUCAAAoxqPQ0qRJEx08eLDY9E2bNqlVq1YVLhQAAEBRHoWWO++8U1OmTNGXX34pm82mI0eO6JVXXtH06dN1zz33eLuMAAAAnvUeSkxMVH5+vgYOHKisrCz169dPISEhmj59uu677z5vlxEAAEA2Y4wpezHXzp07p4MHD+r06dPq2LGj6tat682ylSkzM1MRERGy2+0KDw+v0m0DAADPeHr99qimpUBwcLA6duxYkVUAAACUi0dtWgAAAKoaoQUAAFgCoaWKpdmztTk5XWn2bF8XBQAAS6lQmxa4Z+XWw5q5eq/yjRRgk5Ju7KLhl7XwdbEAALAEalqqSJo92xFYJCnfSA+t3keNCwAA5URoqSIp6WccgaVAnjE6lJ7lmwIBAGAxhJYqEhdZRwE252mBNptiI8N8UyAAACyG0FJFmkaEKunGLgq0XUgugTabnrixs5pGhPq4ZAAAWAMNcavQ8MtaqF/bKB1Kz1JsZBiBBQAANxBaqljTiFDCCgAAHuD2EAAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCSzWVZs/W5uR0pdmzfV0UAACqhSBfFwDFrdx6WDNX71W+kQJsUtKNXTT8sha+LhYAAD5FTUs1k2bPdgQWSco30kOr91Hj4gFqqwCgZvFpaElKStJll12mevXqKTo6WsOGDdP+/ft9WSSfS0k/4wgsBfKM0aH0LN8UyKJWbj2sPvM/0W1LvlSf+Z9o5dbDvi4SAKCCfBpaNm7cqIkTJ+qLL77QunXrdP78eV199dU6c+aML4vlU3GRdRRgc54WaLMpNjLMNwWyIGqrAKBm8mmblg8++MDp/8uWLVN0dLS2b9+ufv36+ahUvtU0IlRJN3bRQ6v3Kc8YBdpseuLGzmoaEerrollGabVVTSNClWbPVkr6GcVF1uG4AoCFVKuGuHa7XZLUsGFDl/NzcnKUk5Pj+H9mZmaVlKuqL3LDL2uhfm2jdCg9S7GRYVxY3VRQW1U4uBTUVtHIGQCsq9o0xM3Pz9fUqVPVp08fde7c2eUySUlJioiIcPzFxMRUerl81TaiaUSoElo3IrB4oKC2KtB24T5bQW2VJG4bAYCF2YwxpuzFKt8999yjtWvXatOmTbroootcLuOqpiUmJkZ2u13h4eFeL1OaPVt95n9S7Bf7psQrCRMWkGbPdqqt2pycrtuWfFlsudfuvFwJrRv5oIQA4J8yMzMVERHh9vW7WtwemjRpkt599119+umnJQYWSQoJCVFISEiVlausthGo3ppGhDq9T6XdNgIAVH8+vT1kjNGkSZP05ptv6pNPPlFcXJwvi1MMPXlqlpJuGxFAAcAafFrTMnHiRL366qt6++23Va9ePR09elSSFBERodBQ319IKtKThx4q1RONnAHAunzapsVms7mcvnTpUo0ZM6bM13t6T8xdRdtGlIUeKgAAlMySbVqqSRvgMhVtG1GakgY269c2il/1AABUQLXp8lxTMAw/AACVg9DiZTTeBQCgchBavMxVD5UHh7RTSvoZBjEDAKACqsU4LTVN4R4qe37K0IK139EoFwCACqKmpZI0jQhVbGSYFnzwHcPGAwDgBYSWSkSjXAAAvIfQUomqW6PcNHu2NienU9MDALAkQkslqk7DxvvqadUAAHhLtXnKsyeqakTcinJ3RN3K2D5PqwYAVBeWHBHXX7gzom5l4GnVAICagNtDfqC6ta0BAMAThBY/UJ3a1gAA4CluD/mJwgPe+aptDQAAFUFo8SO+blsDAEBFcHsIfo2xawDAOqhpgd9aufWwZq7ey3OhAMAiqGmBX0qzZzsCi8RzoQDACggt8Es8FwoArIfQAr/E2DUAYD2EFvglxq4BAOuhIS78FmPXAIC1EFrg1xi7BgCsg9tDAADAEggtNRgDpwEAahJuD9VQDJwGAKhpqGmpgRg4DQBQExFaaiAGTgMA1ETcHqqBCgZOKxxcyho4Lc2erZT0M6oTHKgz5/IUF1mHXjUAgGqF0FIDFQyc9tDqfcozpsyB0wq3fylAOxgAQHVjM8aYshernjIzMxURESG73a7w8HBfF6faSbNnlzlwWpo9W33mf1LsdpJ0oXZmU+KV1LgAALzK0+s3NS01WHkGTnPV/qVAQTsYQgsAoDqgIa6fc/XgwAI8QBAAUJ0QWvxc0QcHFuABggCA6obbQ3B6cGBYcICyzuXzAEEAQLVDaIEk/3twYEEXb7p2A4B1EFrgd3jEAQBYE21a4Fd4xAEAWBehBX6FRxwAgHURWuBXXHXxpms3AFgDoQV+pWgXb7p2A4B10BAXfqdwF2+6dgOAdRBa4Jf8rYs3ANQE3B4CAACWQGjxoTR7tjYnp9PdFgCAcuD2kI8wwBkAAO6hpsUHGOAMAAD3EVp8gAHOAABwH6HFBxjgDAAA9xFafIABzgAAcB8NcX2EAc4AAHAPocUH0uzZSkk/o7jIOkpo3ajEeQQZAAD+h9BSyYqGkNK6OvtjN2hCGgCgvAgtlahoCJkxpL0WrP2uWFfnfm2jJKlYN+iZb+xVnZAg9WjZoEZe0P0xpAEAPEdD3EriaiyWwoGlQEFXZ1fdoPMlTXp1p/rM/0Qrtx6uknJXFcaqAQC4i9BSSVyGECMV6ens6Orsqht04dfVtAv60k0pjFUDAHALoaWSlDQWS+KQ9i67OhftBl1UTbqgp9mzteSzlGLTA2xirBoAQIlo01JJCkLIQ6v3Kc8YR0AZflkLXXdJM5ddnQu6QW8/dFKTV+x0qomoSYPPpaSfkXExfULfVjWy7Q4AeJM/d2AgtFSiksZiKahZcaVpRKh+2y1UZ87lFgs8np6c1e0EL6iFKhzKAiSN7RvrqyLBT1S3zwJQkpLO1bI6MNT0c9xmjHH1o9cSMjMzFRERIbvdrvDwcF8Xx+vS7NkVHnyupBPc1yf2yq2HXdZCAZWF3mrFpdmzte3QCdlsNo96Kfr6e6Qy+WLfCra59ye7FnzwnVPP0y7NI1QnOFA3/G1zsVr4TYlXljmkRnXj6fWb0FKDpdmz1Wf+J8VO8Aevaef0gfDVie2NUAaUR0mfhYIve3+0cuthJb6x13Gr1iZp/k3l/y544dNkzV/7nUwZ3yO+DjaebN8XF//C2yyJzSa5umK/duflio0Ms9Q57un1m9tDNVhJT5Oev/Y7xxdV4bFiqvrELu02GUrm64tAdVOe41Hak9X96RgWHKs6wYFOgUWSjC6MFVWe74IXNiYrae13jv+X9D3i61/+nmy/pOEYKvM7sug2S+IqsBS0d/SXc5zQUoO5bDtS5P9SzTyxayp/v59dVHkvSq4+CzWpcXuB0t7/wsfKJrlsDJ9vVOZ3QZo9W/MLBZYCRb9HKnLx98Z57On2fXHxd7XN0gTowjheRds7+sM5TpdnN6XZs7U5Od0SY6a4epr0jCHtXXbFLu+JbaX9r2nKGpBv5dbD6jP/E9225MtqNSBhZZ0z7gxQWN2frF74GHl6vEp7/4seq5Kuj+UZdqCk3n9FX1vaxd/T/XCHp9svabiKyrz4lzZOV1GBNpvenNhbr915uTYlXukI6ZV1jle373xqWtzg66pOT7jqwVQ/tFa5eia589wkVL6yvoSrukq7LGn2bP1rU4r++d+BBL19zrj7i7i6Plm9aA2IdCFUuHO8yqpVKOmXfOEaF9t/t1fWcXFVayVdaCxa+LWe1G6Vp3akvLUwntaulTRcRWWeL662+eCQduravL72/JShP32w36ks3WIaOL2+4Jj0axulTYlXeu0cr47f+YSWcvLFfc7ylKk8H96ibUfK8+XtznOTrNjjoDqUwV2lfQlXt/vZRRt5St7/zHhyUfJlOypX51xpNSDuPH+srPe/pGO1+t4EpZ7Ils0mXVrO3kNFL7ABuvD9cF23ZtqcnO7YP08u/mXthzsX0WLltEnjyjmsQkUDriffLyVtM6F1oxLH9pIqL1hUx2ueRGgpt+p4UajIiVral7e7z01yZ/+rQ3KvDmUoj6JffGVdBCrjfrYnX74F54+rWwje/MyUdDwkOV08SytnWfvmrXBb7EfANe3V5aIInThzrtS2DAXPHyt6nhYtV1kBrqRj1S2mQbFf7eVR9AL76ffHHT1XCpfV3Yt/afvhyUW0YPtLP0/Rkk9TtOSzCzV/5fnMexpwK/L9Unibrj7/RadLlVfDWt2ueQUILeVUnRryVXYCLu25SYUnu7v/VZncS7rYVEYZKqPWpqQvvtIGLCz66/fBa9p59UJb3i/f0hoVevszU9bFc3zfOI3rG+fRr1NvhVtX51xBzxubSm4UW1jh8/TT74+7LFdZtRrevj1WcCEt6zNVeLmywmRpwXxzcnq5LqKuPo//+CylUnpMFt1WSceifZN6OnMur9zfESWde0WnT+gbV2nBojpd8wojtJSTL+5zlqSyE3BJJ+uD17Qrdm/Vne1VVXIv7WLjaRk8HZ3SE+W9CBQ1/LIWysg+r/n/rRWbv/Y7/Xo6R2NdXLQrWobSlNTmIcAmxznjzaBX2sVzyWcp+sdnKU7jj5S33YS7+1/SPpUW4owuhJaC42X7b4IpqZZq+6GTJZarPKGkMm6Plecz5c7npKT9iIusUyzg2eTc+NfVdmIahlXK94472xr2t81ljmdToLTgU3T6Pz5LqbRgUZ2ueYURWtxQXRryVXYCLulkdfXcJHcuPlWR3Mu62HhShtJGFa6MmqOKBKsF/x3sS7rw5f73z1L0j3JWh3ujDJLrWp8J/eI0tk9cpTboLikcGJXdKDXPGO348aQa1LlwLru7/6XtU0khrnD5nv9ddzWqG+I4D0t6/pjKGLLAF212yvpMefI5Kfd+FOpxU9J2Vt+b4PXvHXe2Jf1vfJXy7HtJ597WQyeL14BLuqtvK/1zU0qlBIvqcs0rrFp0ef7rX/+q2NhY1a5dW7169dJXX33l6yKVqGlEqBJaN6q0N6883csKLgqV2X1z+GUttCnxSpfd6gr235OuieP7xjm69lVGucvqYePusSutW+32H4t/iXjjadyedrks6aJduMyVXYYChc+fz2depYf+r2Op1efe6E5ZJzhQJTwk3RFKpJK7l058dafjXN77k73c+1/WPhU954oKkBTT8H+fq6YRofptt2Yuz9MeLRtUeXfcspT1mfK063FRrrpaGyPHekraTta5/FLL50mX3vJuy9UFtqx9L+mzd1ms6/d+bN9Yl9/V3lLZ1zx3+bymZeXKlfrDH/6gxYsXq1evXlq4cKEGDx6s/fv3Kzo62tfFq1LeqEKtCFcNv9y9mJf0C6Jot867Cv3y9qby1KS4c+xK+nKa9+43enfv0WLLe+MC4mm1bGm/6N2tDvdG1bCr86eybhEWnF+lPZRk0qs7dTonV8Mva6EZQ9or6f3iA6RJF47fnz7YrxlD2utPa8u+HVqefSp8zu35OcOxXunCr+Ub/ra52Oe9pPO0OlbZl/aZ8lYNa53gwFLb1ZW2nYTWjdSvbZS2Hzop2aQeLS80Pva01q882zqUnqWw4ACXzwoqq4dbSY2mb+jeXG/s+Nmx7LDuzZzatfkDnz97qFevXrrsssv0l7/8RZKUn5+vmJgY3XfffUpMTCz1tTXp2UO+fjaKux/ezcnpum3Jl8Wmv3bn5Upo3chpWlXvmzcfxlh0uPLSeLsnkifPZlq59bBmvrFX+UWme3q8vf18qMo4F1ytU3LdwLVgWynpZ1yev4UVPM+lrP33ZJ92p550tHMo72uKbrM6VdmXpaKfSVfP5XG1ntK246oHV8Ez2Aqvs7zvQXn3ydN9L/oe+/oa4W2WfPbQuXPntH37ds2cOdMxLSAgQIMGDdKWLVuKLZ+Tk6OcnBzH/zMzM6uknFXBl93LPLnn7M6vp6reN2/VQqXZs7Xgg/IFFkl67nfd9dtuzTzaliuetE9wdPHcdEj/2PSD8k3FbsN5u41EZTTuK+m22H1XtdFznxx0mlZw3rlq1FlYwblcnv33ZJ/OnMsrVivkzmfCF21XKqIin0lXz+UJsEmr700o1l27pO2UOIxDkW258x6Ud5883fei73F17YJc1XwaWtLT05WXl6fGjRs7TW/cuLG++674xSIpKUlz586tquJVKV92L/Pkw+DOF7Uv9s0bX+ruPA8k0GZTj1j3x7uoDE0jQvXQ0A4a2ze2Wv4a9/atzZLOr4EdovWX9QddnndNI0KVOKS9y1q0AMntIOXN8UhqKk8/kyUNwZB1rmjkKHk7Lteh4k9Ndvc9KO8+eeP7yB/PGVeqRUPc8po5c6bsdrvjLzU11ddF8pqqaFxbEk8bXZbUWLcoX+5bRZT3eSCeXOSqQnVrQFeYN8tW0vnVLaZBqefd7/u31sz/+9+zuAJs0l1XtNLnM6/y6BafO/tk1c+EL3jjWUAlrSNxSHvLvAecMxf4tE3LuXPnFBYWplWrVmnYsGGO6aNHj1ZGRobefvvtUl9fk9q0FPDVvWpvtgMpidXuw0vFj8uw7s301s4jjmHBJ/RtpbF9Yy2zPzVZSedXWeedL89LK34mfMEb308lrcNq74HVylsST6/f1aIhbs+ePfX8889LutAQt0WLFpo0aZJfNcStDmrKh8HbXDWI4zgBVcsbnzs+u9WHJRviStIf/vAHjR49WvHx8erZs6cWLlyoM2fOaOzYsb4umt+xWuO+qlL0uHCcgKrnjc8dn13r83loGT58uI4fP65HHnlER48e1SWXXKIPPvigWONcAADg33x+e6giuD0EAID1eHr9tlTvIQAA4L8ILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBJ8Pox/RRQM5puZmenjkgAAgPIquG67Oyi/pUPLqVOnJEkxMTE+LgkAAHDXqVOnFBERUe7lLf3sofz8fB05ckT16tWTzWbzeD2ZmZmKiYlRamqq3z/DiGNxAcfhAo7DBRyHCzgOF3Ac/sfTY2GM0alTp9SsWTMFBJS/pYqla1oCAgJ00UUXeW194eHhfn8CFuBYXMBxuIDjcAHH4QKOwwUch//x5Fi4U8NSgIa4AADAEggtAADAEggtkkJCQjR79myFhIT4uig+x7G4gONwAcfhAo7DBRyHCzgO/1PVx8LSDXEBAID/oKYFAABYAqEFAABYAqEFAABYAqEFAABYQo0NLYsWLVLXrl0dA94kJCRo7dq1jvlnz57VxIkT1ahRI9WtW1c33XSTfvnlF6d1HD58WEOHDlVYWJiio6P1wAMPKDc3t6p3xavmz58vm82mqVOnOqb5y7GYM2eObDab01/79u0d8/3lOEjSzz//rNtvv12NGjVSaGiounTpom3btjnmG2P0yCOPqGnTpgoNDdWgQYN04MABp3WcOHFCI0eOVHh4uOrXr6/x48fr9OnTVb0rHouNjS12PthsNk2cOFGS/5wPeXl5mjVrluLi4hQaGqrWrVvrsccec3omjD+cD9KFIeWnTp2qli1bKjQ0VL1799bWrVsd82vqcfj000917bXXqlmzZrLZbHrrrbec5ntrv/fs2aMrrrhCtWvXVkxMjP70pz+5X1hTQ61Zs8a899575vvvvzf79+83Dz30kKlVq5bZt2+fMcaYu+++28TExJiPP/7YbNu2zVx++eWmd+/ejtfn5uaazp07m0GDBpmdO3ea999/30RGRpqZM2f6apcq7KuvvjKxsbGma9euZsqUKY7p/nIsZs+ebTp16mTS0tIcf8ePH3fM95fjcOLECdOyZUszZswY8+WXX5offvjBfPjhh+bgwYOOZebPn28iIiLMW2+9ZXbv3m2uu+46ExcXZ7Kzsx3LXHPNNaZbt27miy++MJ999plp06aNGTFihC92ySPHjh1zOhfWrVtnJJn169cbY/znfJg3b55p1KiReffdd01KSop5/fXXTd26dc2zzz7rWMYfzgdjjLn11ltNx44dzcaNG82BAwfM7NmzTXh4uPnpp5+MMTX3OLz//vvm4YcfNqtXrzaSzJtvvuk03xv7bbfbTePGjc3IkSPNvn37zGuvvWZCQ0PNCy+84FZZa2xocaVBgwbmH//4h8nIyDC1atUyr7/+umPet99+aySZLVu2GGMuvIkBAQHm6NGjjmUWLVpkwsPDTU5OTpWXvaJOnTplLr74YrNu3TrTv39/R2jxp2Mxe/Zs061bN5fz/Ok4zJgxw/Tt27fE+fn5+aZJkybmySefdEzLyMgwISEh5rXXXjPGGPPNN98YSWbr1q2OZdauXWtsNpv5+eefK6/wlWjKlCmmdevWJj8/36/Oh6FDh5px48Y5TbvxxhvNyJEjjTH+cz5kZWWZwMBA8+677zpNv/TSS83DDz/sN8ehaGjx1n7/7W9/Mw0aNHD6bMyYMcO0a9fOrfLV2NtDheXl5WnFihU6c+aMEhIStH37dp0/f16DBg1yLNO+fXu1aNFCW7ZskSRt2bJFXbp0UePGjR3LDB48WJmZmfr666+rfB8qauLEiRo6dKjTPkvyu2Nx4MABNWvWTK1atdLIkSN1+PBhSf51HNasWaP4+Hjdcsstio6OVvfu3bVkyRLH/JSUFB09etTpWERERKhXr15Ox6J+/fqKj493LDNo0CAFBAToyy+/rLqd8ZJz585p+fLlGjdunGw2m1+dD71799bHH3+s77//XpK0e/dubdq0SUOGDJHkP+dDbm6u8vLyVLt2bafpoaGh2rRpk98ch6K8td9btmxRv379FBwc7Fhm8ODB2r9/v06ePFnu8lj6gYll2bt3rxISEnT27FnVrVtXb775pjp27Khdu3YpODhY9evXd1q+cePGOnr0qCTp6NGjTl9GBfML5lnJihUrtGPHDqd7swWOHj3qN8eiV69eWrZsmdq1a6e0tDTNnTtXV1xxhfbt2+dXx+GHH37QokWL9Ic//EEPPfSQtm7dqsmTJys4OFijR4927IurfS18LKKjo53mBwUFqWHDhpY6FgXeeustZWRkaMyYMZL863ORmJiozMxMtW/fXoGBgcrLy9O8efM0cuRISfKb86FevXpKSEjQY489pg4dOqhx48Z67bXXtGXLFrVp08ZvjkNR3trvo0ePKi4urtg6CuY1aNCgXOWp0aGlXbt22rVrl+x2u1atWqXRo0dr48aNvi5WlUpNTdWUKVO0bt26Yr8g/E3BL0dJ6tq1q3r16qWWLVvq3//+t0JDQ31YsqqVn5+v+Ph4PfHEE5Kk7t27a9++fVq8eLFGjx7t49L5xj//+U8NGTJEzZo183VRqty///1vvfLKK3r11VfVqVMn7dq1S1OnTlWzZs387nx4+eWXNW7cODVv3lyBgYG69NJLNWLECG3fvt3XRcN/1ejbQ8HBwWrTpo169OihpKQkdevWTc8++6yaNGmic+fOKSMjw2n5X375RU2aNJEkNWnSpFhPgYL/FyxjBdu3b9exY8d06aWXKigoSEFBQdq4caOee+45BQUFqXHjxn5zLIqqX7++2rZtq4MHD/rVOdG0aVN17NjRaVqHDh0ct8oK9sXVvhY+FseOHXOan5ubqxMnTljqWEjSjz/+qP/85z+aMGGCY5o/nQ8PPPCAEhMT9bvf/U5dunTRqFGjNG3aNCUlJUnyr/OhdevW2rhxo06fPq3U1FR99dVXOn/+vFq1auVXx6Ewb+23tz4vNTq0FJWfn6+cnBz16NFDtWrV0scff+yYt3//fh0+fFgJCQmSpISEBO3du9fpjVi3bp3Cw8OLfeFXZwMHDtTevXu1a9cux198fLxGjhzp+Le/HIuiTp8+reTkZDVt2tSvzok+ffpo//79TtO+//57tWzZUpIUFxenJk2aOB2LzMxMffnll07HIiMjw+kX6CeffKL8/Hz16tWrCvbCe5YuXaro6GgNHTrUMc2fzoesrCwFBDhfCgIDA5Wfny/J/84HSapTp46aNm2qkydP6sMPP9T111/vl8dB8t77n5CQoE8//VTnz593LLNu3Tq1a9eu3LeGJNXcLs+JiYlm48aNJiUlxezZs8ckJiYam81mPvroI2PMhe6MLVq0MJ988onZtm2bSUhIMAkJCY7XF3RnvPrqq82uXbvMBx98YKKioizXndGVwr2HjPGfY3H//febDRs2mJSUFPP555+bQYMGmcjISHPs2DFjjP8ch6+++soEBQWZefPmmQMHDphXXnnFhIWFmeXLlzuWmT9/vqlfv755++23zZ49e8z111/vsotj9+7dzZdffmk2bdpkLr744mrftbOovLw806JFCzNjxoxi8/zlfBg9erRp3ry5o8vz6tWrTWRkpHnwwQcdy/jL+fDBBx+YtWvXmh9++MF89NFHplu3bqZXr17m3LlzxpiaexxOnTpldu7caXbu3Gkkmaefftrs3LnT/Pjjj8YY7+x3RkaGady4sRk1apTZt2+fWbFihQkLC6PLc4Fx48aZli1bmuDgYBMVFWUGDhzoCCzGGJOdnW3uvfde06BBAxMWFmZuuOEGk5aW5rSOQ4cOmSFDhpjQ0FATGRlp7r//fnP+/Pmq3hWvKxpa/OVYDB8+3DRt2tQEBweb5s2bm+HDhzuNTeIvx8EYY9555x3TuXNnExISYtq3b2/+/ve/O83Pz883s2bNMo0bNzYhISFm4MCBZv/+/U7L/Prrr2bEiBGmbt26Jjw83IwdO9acOnWqKnejwj788EMjqdi+GeM/50NmZqaZMmWKadGihaldu7Zp1aqVefjhh526pvrL+bBy5UrTqlUrExwcbJo0aWImTpxoMjIyHPNr6nFYv369kVTsb/To0cYY7+337t27Td++fU1ISIhp3ry5mT9/vttltRlTaNhDAACAasqv2rQAAADrIrQAAABLILQAAABLILQAAABLILQAAABLILQAAABLILQAAABLILQAAABLILQAfmjAgAGaOnWqr4tR6ebMmaNLLrnE18UA4CWEFgCWc+7cuSrdnjFGubm5VbpNAMURWgA/M2bMGG3cuFHPPvusbDabbDabDh06pH379mnIkCGqW7euGjdurFGjRik9Pd3xugEDBui+++7T1KlT1aBBAzVu3FhLlizRmTNnNHbsWNWrV09t2rTR2rVrHa/ZsGGDbDab3nvvPXXt2lW1a9fW5Zdfrn379jmVadOmTbriiisUGhqqmJgYTZ48WWfOnHHMj42N1WOPPaY77rhD4eHhuuuuuyRJM2bMUNu2bRUWFqZWrVpp1qxZjqfILlu2THPnztXu3bsd+7ls2TIdOnRINptNu3btcqw/IyNDNptNGzZscCr32rVr1aNHD4WEhGjTpk3Kz89XUlKS4uLiFBoaqm7dumnVqlXefosAlIDQAviZZ599VgkJCbrzzjuVlpamtLQ01atXT1dddZW6d++ubdu26YMPPtAvv/yiW2+91em1L774oiIjI/XVV1/pvvvu0z333KNbbrlFvXv31o4dO3T11Vdr1KhRysrKcnrdAw88oKeeekpbt25VVFSUrr32Wke4SE5O1jXXXKObbrpJe/bs0cqVK7Vp0yZNmjTJaR1//vOf1a1bN+3cuVOzZs2SJNWrV0/Lli3TN998o2effVZLlizRM888I0kaPny47r//fnXq1Mmxn8OHD3frWCUmJmr+/Pn69ttv1bVrVyUlJemll17S4sWL9fXXX2vatGm6/fbbtXHjRrfWC8BDnj0TEoCVFX3S92OPPWauvvpqp2VSU1OdnoDcv39/07dvX8f83NxcU6dOHTNq1CjHtLS0NCPJbNmyxRjzv6fHrlixwrHMr7/+akJDQ83KlSuNMcaMHz/e3HXXXU7b/uyzz0xAQIDJzs42xhjTsmVLM2zYsDL368knnzQ9evRw/H/27NmmW7duTsukpKQYSWbnzp2OaSdPnjSSzPr1653K/dZbbzmWOXv2rAkLCzObN292Wt/48ePNiBEjyiwbgIoL8mVgAlA97N69W+vXr1fdunWLzUtOTlbbtm0lSV27dnVMDwwMVKNGjdSlSxfHtMaNG0uSjh075rSOhIQEx78bNmyodu3a6dtvv3Vse8+ePXrllVccyxhjlJ+fr5SUFHXo0EGSFB8fX6xsK1eu1HPPPafk5GSdPn1aubm5Cg8Pd3v/S1J4mwcPHlRWVpZ+85vfOC1z7tw5de/e3WvbBFAyQgsAnT59Wtdee60WLFhQbF7Tpk0d/65Vq5bTPJvN5jTNZrNJkvLz893a9u9//3tNnjy52LwWLVo4/l2nTh2neVu2bNHIkSM1d+5cDR48WBEREVqxYoWeeuqpUrcXEHDhrrgxxjGt4FZVUYW3efr0aUnSe++9p+bNmzstFxISUuo2AXgHoQXwQ8HBwcrLy3P8/9JLL9Ubb7yh2NhYBQV5/2vhiy++cASQkydP6vvvv3fUoFx66aX65ptv1KZNG7fWuXnzZrVs2VIPP/ywY9qPP/7otEzR/ZSkqKgoSVJaWpqjhqRwo9ySdOzYUSEhITp8+LD69+/vVlkBeAcNcQE/FBsbqy+//FKHDh1Senq6Jk6cqBMnTmjEiBHaunWrkpOT9eGHH2rs2LHFLvqeePTRR/Xxxx9r3759GjNmjCIjIzVs2DBJF3oAbd68WZMmTdKuXbt04MABvf3228Ua4hZ18cUX6/Dhw1qxYoWSk5P13HPP6c033yy2nykpKdq1a5fS09OVk5Oj0NBQXX755Y4Gths3btQf//jHMvehXr16mj59uqZNm6YXX3xRycnJ2rFjh55//nm9+OKLHh8bAOVHaAH80PTp0xUYGKiOHTsqKipK586d0+eff668vDxdffXV6tKli6ZOnar69es7bqdUxPz58zVlyhT16NFDR48e1TvvvKPg4GBJF9rJbNy4Ud9//72uuOIKde/eXY888oiaNWtW6jqvu+46TZs2TZMmTdIll1yizZs3O3oVFbjpppt0zTXX6Morr1RUVJRee+01SdK//vUv5ebmqkePHpo6daoef/zxcu3HY489plmzZikpKUkdOnTQNddco/fee09xcXEeHBUA7rKZwjd2AcCLNmzYoCuvvFInT55U/fr1fV0cABZHTQsAALAEQgsAALAEbg8BAABLoKYFAABYAqEFAABYAqEFAABYAqEFAABYAqEFAABYAqEFAABYAqEFAABYAqEFAABYAqEFAABYwv8Dy9AaUBUCbhEAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_validation, filename=\"pysmo_poly_val_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_validation, filename=\"pysmo_poly_val_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_validation, filename=\"pysmo_poly_val_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the \"SCO2_properties_pysmo_surrogate_embedding.ipynb\" file." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 96ad891057745b3cead4afc21ce59bd81a979e3a Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Mon, 21 Aug 2023 10:10:18 -0400 Subject: [PATCH 03/75] Encorporated changes --- .../hda_flowsheet_with_distillation.ipynb | 1735 +++++++++-------- ...flowsheet_with_distillation_exercise.ipynb | 186 +- ...flowsheet_with_distillation_solution.ipynb | 1649 ++++++++-------- .../{ALAMO => }/500_Points_DataSet.csv | 0 .../ALAMO/SCO2_alamo_surrogate.ipynb | 107 +- .../SCO2_flowsheet_alamo_surrogate.ipynb | 156 +- .../SCO2_example/ALAMO/alamo_run.trc | 6 + .../SCO2_example/ALAMO/alamo_train_parity.pdf | Bin 29828 -> 29828 bytes .../ALAMO/alamo_train_residual.pdf | Bin 47095 -> 47095 bytes .../ALAMO/alamo_train_scatter2D.pdf | Bin 67174 -> 67174 bytes .../SCO2_example/ALAMO/alamo_val_parity.pdf | Bin 22863 -> 22863 bytes .../SCO2_example/ALAMO/alamo_val_residual.pdf | Bin 27075 -> 27075 bytes .../ALAMO/alamo_val_scatter2D.pdf | Bin 33248 -> 33248 bytes .../surrogates/SCO2_example/CO2_flowsheet.png | Bin 0 -> 85113 bytes .../SCO2_example/OMLT/500_Points_DataSet.csv | 501 ----- .../OMLT/SCO2_flowsheet_keras_surrogate.ipynb | 84 +- .../OMLT/SCO2_keras_surrogate.ipynb | 580 +++--- .../SCO2_example/PySMO/500_Points_DataSet.csv | 501 ----- .../SCO2_flowsheet_pysmo_surrogate.ipynb | 354 ++-- .../PySMO/SCO2_pysmo_surrogate.ipynb | 171 +- 20 files changed, 2642 insertions(+), 3388 deletions(-) rename idaes_examples/notebooks/docs/surrogates/SCO2_example/{ALAMO => }/500_Points_DataSet.csv (100%) create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/CO2_flowsheet.png delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/500_Points_DataSet.csv delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/500_Points_DataSet.csv diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb index c0a0128c..a8ed1642 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb @@ -26,6 +26,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -82,6 +83,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -93,6 +95,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -131,6 +134,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -168,6 +172,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -214,6 +219,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -238,6 +244,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -261,6 +268,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -283,6 +291,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -310,6 +319,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -335,6 +345,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -383,6 +394,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -414,6 +426,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -423,6 +436,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -442,6 +456,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -478,6 +493,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -485,6 +501,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -553,6 +570,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -594,6 +612,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -612,6 +631,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -653,6 +673,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -674,6 +695,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -690,6 +712,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -723,6 +746,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -763,6 +787,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -788,6 +813,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -820,6 +846,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -839,6 +866,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -888,6 +916,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -908,6 +937,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -928,6 +958,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -948,6 +979,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -963,34 +995,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2023-07-10 13:46:02 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" ] } ], @@ -1011,6 +1043,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1072,6 +1105,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1100,6 +1134,99 @@ ] }, { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " '_run_impl',\n", + " 'adj_lists',\n", + " 'all_cycles',\n", + " 'arc_to_edge',\n", + " 'cache',\n", + " 'cacher',\n", + " 'calculation_order',\n", + " 'check_tear_set',\n", + " 'check_value_fix',\n", + " 'combine_and_fix',\n", + " 'compute_err',\n", + " 'create_graph',\n", + " 'cycle_edge_matrix',\n", + " 'edge_to_idx',\n", + " 'fixed_inputs',\n", + " 'generate_first_x',\n", + " 'generate_gofx',\n", + " 'idx_to_edge',\n", + " 'idx_to_node',\n", + " 'indexes_to_arcs',\n", + " 'load_guesses',\n", + " 'load_values',\n", + " 'node_to_idx',\n", + " 'options',\n", + " 'pass_edges',\n", + " 'pass_single_value',\n", + " 'pass_tear_direct',\n", + " 'pass_tear_wegstein',\n", + " 'pass_values',\n", + " 'run',\n", + " 'run_order',\n", + " 'scc_calculation_order',\n", + " 'scc_collect',\n", + " 'select_tear_heuristic',\n", + " 'select_tear_mip',\n", + " 'select_tear_mip_model',\n", + " 'set_guesses_for',\n", + " 'set_tear_set',\n", + " 'solve_tear_direct',\n", + " 'solve_tear_wegstein',\n", + " 'source_dest_peer',\n", + " 'sub_graph_edges',\n", + " 'tear_diff_direct',\n", + " 'tear_set',\n", + " 'tear_set_arcs',\n", + " 'tear_upper_bound',\n", + " 'tree_order']" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(seq)" + ] + }, + { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1108,7 +1235,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1125,6 +1252,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1133,7 +1261,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1155,6 +1283,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1165,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1189,6 +1318,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1197,7 +1327,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1206,6 +1336,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1214,7 +1345,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": { "scrolled": false }, @@ -1223,116 +1354,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-07-10 13:46:06 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:07 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:07 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:07 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:08 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:08 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:08 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-10 13:46:08 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-10 13:46:08 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:08 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:08 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:09 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:09 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:09 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:09 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", - "2023-07-10 13:46:09 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:09 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:10 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:10 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:10 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:10 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:11 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:11 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:11 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:11 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:11 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:11 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:12 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:12 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:12 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", - "2023-07-10 13:46:12 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", - "2023-07-10 13:46:12 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", - "2023-07-10 13:46:12 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:13 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-10 13:46:13 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:13 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:14 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:14 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:14 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:14 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:14 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:14 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-10 13:46:14 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-10 13:46:15 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:15 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:15 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:15 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-10 13:46:16 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:16 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:16 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:16 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:16 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:17 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:17 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:17 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-10 13:46:17 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-10 13:46:17 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:17 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:17 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:18 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-10 13:46:18 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:18 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:18 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:19 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:19 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:19 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:19 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:19 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-10 13:46:19 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-10 13:46:19 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:19 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:20 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:20 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-10 13:46:20 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:20 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:21 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:21 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:21 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:21 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:21 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:21 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-10 13:46:21 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-10 13:46:21 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:22 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-10 13:46:22 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:22 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-10 13:46:22 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:51 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-07-27 11:23:52 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-27 11:23:56 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:56 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:56 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:56 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:56 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:58 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-27 11:23:58 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:58 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:58 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:58 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:58 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:00 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-27 11:24:00 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:00 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:00 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-27 11:24:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:02 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:02 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:04 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:04 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:04 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-27 11:24:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", "WARNING: Wegstein failed to converge in 3 iterations\n", - "2023-07-10 13:46:22 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:22 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:23 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:23 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:23 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:23 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", - "2023-07-10 13:46:23 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:23 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:23 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:23 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:23 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:23 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", - "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", - "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", - "2023-07-10 13:46:24 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" ] } ], @@ -1341,6 +1478,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1352,7 +1490,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1391,7 +1529,13 @@ "component keys that are not exported as part of the NL file. Skipping.\n", "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", "tol=1e-06\n", - "max_iter=200\n", + "max_iter=200\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "\n", "******************************************************************************\n", @@ -1458,7 +1602,7 @@ "Number of equality constraint Jacobian evaluations = 10\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.026\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.025\n", "Total CPU secs in NLP function evaluations = 0.001\n", "\n", "EXIT: Optimal Solution Found.\n" @@ -1475,7 +1619,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": { "tags": [ "testing" @@ -1490,6 +1634,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1510,616 +1655,616 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:26 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", - "2023-07-10 13:46:27 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", - "2023-07-10 13:46:27 [INFO] idaes.init.fs.D101: Begin initialization.\n", - "2023-07-10 13:46:27 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", - "2023-07-10 13:46:27 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:27 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:27 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:27 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:28 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", - "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:29 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", - "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", - "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:30 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", - "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", - "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", - "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:31 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", - "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", - "2023-07-10 13:46:32 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", - "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", - "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:34 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", - "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", - "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:35 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", - "2023-07-10 13:46:36 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", - "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:37 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", - "2023-07-10 13:46:38 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", - "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:39 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", - "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", - "2023-07-10 13:46:40 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", - "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:41 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", - "2023-07-10 13:46:42 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", - "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:43 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", - "2023-07-10 13:46:44 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", - "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:45 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", - "2023-07-10 13:46:46 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:47 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:48 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:49 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", - "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:50 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", - "2023-07-10 13:46:51 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:51 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:51 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", - "2023-07-10 13:46:51 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", - "2023-07-10 13:46:52 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:52 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", - "2023-07-10 13:46:52 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:52 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", - "2023-07-10 13:46:53 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", - "2023-07-10 13:46:53 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" ] } ], @@ -2160,6 +2305,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2170,7 +2316,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -2197,6 +2343,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2205,7 +2352,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": { "tags": [ "testing" @@ -2219,7 +2366,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -2329,8 +2476,8 @@ "Number of equality constraint Jacobian evaluations = 10\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.129\n", - "Total CPU secs in NLP function evaluations = 0.024\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.057\n", + "Total CPU secs in NLP function evaluations = 0.006\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -2338,10 +2485,10 @@ { "data": { "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.27869677543640137}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.14362859725952148}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" ] }, - "execution_count": 53, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -2352,7 +2499,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "metadata": { "tags": [ "testing" @@ -2367,6 +2514,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2377,7 +2525,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -2434,7 +2582,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "metadata": { "tags": [ "testing" @@ -2460,6 +2608,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2468,7 +2617,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -2509,6 +2658,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2517,7 +2667,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -2558,6 +2708,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2571,7 +2722,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -2603,6 +2754,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2615,6 +2767,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2639,6 +2792,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2647,7 +2801,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -2655,6 +2809,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2663,7 +2818,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -2676,6 +2831,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2690,7 +2846,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 63, "metadata": { "tags": [ "exercise" @@ -2703,7 +2859,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 64, "metadata": { "tags": [ "solution" @@ -2716,6 +2872,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2732,7 +2889,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -2761,6 +2918,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2774,7 +2932,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 66, "metadata": { "tags": [ "exercise" @@ -2790,7 +2948,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 67, "metadata": { "tags": [ "solution" @@ -2808,6 +2966,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2816,7 +2975,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -2828,6 +2987,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2841,7 +3001,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 69, "metadata": { "tags": [ "exercise" @@ -2854,7 +3014,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 70, "metadata": { "tags": [ "solution" @@ -2867,6 +3027,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2875,7 +3036,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -2885,6 +3046,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2896,7 +3058,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -2929,7 +3091,13 @@ "component keys that are not exported as part of the NL file. Skipping.\n", "WARNING: model contains export suffix\n", "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", "tol=1e-06\n", "max_iter=200\n", @@ -3025,8 +3193,8 @@ "Number of equality constraint Jacobian evaluations = 34\n", "Number of inequality constraint Jacobian evaluations = 34\n", "Number of Lagrangian Hessian evaluations = 33\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.347\n", - "Total CPU secs in NLP function evaluations = 0.047\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.310\n", + "Total CPU secs in NLP function evaluations = 0.050\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -3038,7 +3206,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 73, "metadata": { "tags": [ "testing" @@ -3053,6 +3221,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3063,7 +3232,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -3120,7 +3289,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 75, "metadata": { "tags": [ "testing" @@ -3147,6 +3316,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3155,7 +3325,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -3191,6 +3361,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb index 95cb3a76..a8a353b7 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_exercise.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "header", @@ -26,7 +26,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -59,7 +58,7 @@ "example, toluene will be reacted with hydrogen gas at high temperatures\n", " to form benzene via the following reaction:\n", "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", "\n", "\n", "This reaction is often accompanied by an equilibrium side reaction\n", @@ -83,7 +82,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -95,7 +93,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -118,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +131,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -146,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "tags": [ "exercise" @@ -158,7 +154,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -177,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -186,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -205,7 +200,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -214,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -230,7 +224,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -241,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -254,7 +247,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -265,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -277,7 +269,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -286,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -305,7 +296,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -316,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -331,7 +321,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -349,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "tags": [ "exercise" @@ -361,7 +350,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -370,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -393,7 +381,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -403,7 +390,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -412,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -423,7 +409,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -442,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -460,7 +445,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -468,7 +452,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -480,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "tags": [ "exercise" @@ -493,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -521,7 +504,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -533,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "tags": [ "exercise" @@ -545,7 +527,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -556,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -564,7 +545,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -580,7 +560,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "tags": [ "exercise" @@ -592,7 +572,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -601,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -614,7 +593,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -623,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -631,7 +609,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -647,7 +624,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -665,7 +642,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -676,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -684,7 +660,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -693,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -710,7 +685,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -726,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -743,7 +717,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -754,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -763,7 +736,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -781,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": { "tags": [ "exercise" @@ -796,7 +768,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -805,7 +776,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -817,7 +788,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -826,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -838,7 +808,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -847,7 +816,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -859,7 +828,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -868,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -888,7 +856,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -902,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": { "tags": [ "exercise" @@ -914,7 +881,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -927,7 +893,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -943,7 +909,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -952,7 +917,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -961,7 +926,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -970,7 +934,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -979,7 +943,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -990,7 +953,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1014,7 +977,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1023,19 +985,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "def function(unit):\n", - " if hasattr(unit,\"fix_initialization_state\"):\n", - " unit.fix_initialization_state(outlvl=idaeslog.INFO)\n", - " else:\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " unit.initialize(outlvl=idaeslog.INFO)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1044,7 +1002,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": { "scrolled": false }, @@ -1054,7 +1012,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1066,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1078,7 +1035,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1099,7 +1055,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -1139,7 +1095,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1150,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1177,7 +1132,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1186,7 +1140,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -1194,7 +1148,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1205,7 +1158,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -1242,7 +1195,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1251,7 +1203,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -1259,7 +1211,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1268,7 +1219,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -1276,7 +1227,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1290,7 +1240,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -1304,7 +1254,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1317,7 +1266,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1342,7 +1290,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1351,7 +1298,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -1359,7 +1306,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1368,7 +1314,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -1381,7 +1327,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1396,7 +1341,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": { "tags": [ "exercise" @@ -1408,7 +1353,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1425,7 +1369,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -1454,7 +1398,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1468,7 +1411,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": { "tags": [ "exercise" @@ -1483,7 +1426,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1492,7 +1434,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -1504,7 +1446,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1518,7 +1459,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": { "tags": [ "exercise" @@ -1530,7 +1471,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1539,7 +1479,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -1549,7 +1489,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1561,7 +1500,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -1569,7 +1508,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1580,7 +1518,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -1617,7 +1555,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1626,7 +1563,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -1646,7 +1583,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1685,9 +1621,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb index 5dcbdc8a..19cb655d 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 71, "metadata": { "tags": [ "header", @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 73, "metadata": { "tags": [ "exercise" @@ -159,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 74, "metadata": { "tags": [ "solution" @@ -191,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -200,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ @@ -228,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -255,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -300,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -330,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -363,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 82, "metadata": { "tags": [ "exercise" @@ -376,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 83, "metadata": { "tags": [ "solution" @@ -403,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -445,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 85, "metadata": {}, "outputs": [], "source": [ @@ -475,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -513,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 87, "metadata": { "tags": [ "exercise" @@ -526,7 +526,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 88, "metadata": { "tags": [ "solution" @@ -542,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -582,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 90, "metadata": { "tags": [ "exercise" @@ -595,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 91, "metadata": { "tags": [ "solution" @@ -623,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -647,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 93, "metadata": { "tags": [ "exercise" @@ -660,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 94, "metadata": { "tags": [ "solution" @@ -682,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -704,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ @@ -728,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ @@ -757,7 +757,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -782,7 +782,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ @@ -815,7 +815,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -843,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ @@ -870,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 102, "metadata": { "tags": [ "exercise" @@ -886,7 +886,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 103, "metadata": { "tags": [ "solution" @@ -911,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ @@ -932,7 +932,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -953,7 +953,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -974,41 +974,41 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 107, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2023-07-10 13:48:13 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap]\n" ] } ], @@ -1043,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 108, "metadata": { "tags": [ "exercise" @@ -1056,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 109, "metadata": { "tags": [ "solution" @@ -1090,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 110, "metadata": {}, "outputs": [], "source": [ @@ -1115,7 +1115,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -1141,7 +1141,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -1174,7 +1174,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -1207,15 +1207,12 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ "def function(unit):\n", - " if hasattr(unit,\"fix_initialization_state\"):\n", - " unit.fix_initialization_state(outlvl=idaeslog.INFO)\n", - " else:\n", - " unit.initialize(outlvl=idaeslog.INFO)" + " unit.initialize(outlvl=idaeslog.INFO)" ] }, { @@ -1228,7 +1225,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 115, "metadata": { "scrolled": false }, @@ -1237,116 +1234,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-07-10 13:48:18 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:19 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:19 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:19 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:19 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:20 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:20 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-10 13:48:20 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-10 13:48:20 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", - "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:20 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:21 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:21 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:21 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:21 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:21 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:21 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:21 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:21 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", - "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", - "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", - "2023-07-10 13:48:22 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:22 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-10 13:48:23 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:23 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:23 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:23 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:23 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:24 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:24 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:24 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-10 13:48:24 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-10 13:48:24 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:24 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:24 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:24 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-10 13:48:25 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:25 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:25 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:25 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:25 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:25 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:26 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:26 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-10 13:48:26 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-10 13:48:26 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:26 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:26 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:26 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-10 13:48:26 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:27 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:27 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:27 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:27 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:27 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:27 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:27 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-10 13:48:27 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-10 13:48:27 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:28 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:28 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:28 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-10 13:48:28 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:28 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:29 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:29 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:29 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:29 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:29 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:29 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-10 13:48:29 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-10 13:48:29 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:29 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-10 13:48:30 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:30 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-10 13:48:30 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:47 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:47 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:48 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:49 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-06-26 12:37:54 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:54 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:54 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:54 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:56 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:56 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-06-26 12:37:56 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-06-26 12:37:57 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:57 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:57 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:57 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:57 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:58 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:58 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-06-26 12:37:58 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-06-26 12:37:58 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:58 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:58 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:59 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-06-26 12:37:59 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:00 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:00 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:00 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:00 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-06-26 12:38:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:02 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:02 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:04 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:04 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:04 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-06-26 12:38:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", "WARNING: Wegstein failed to converge in 3 iterations\n", - "2023-07-10 13:48:30 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:30 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:30 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:31 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:31 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:31 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", - "2023-07-10 13:48:31 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:31 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:31 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:32 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", - "2023-07-10 13:48:32 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", - "2023-07-10 13:48:32 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", - "2023-07-10 13:48:32 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" + "2023-06-26 12:38:04 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:04 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" ] } ], @@ -1367,7 +1370,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -1403,7 +1406,13 @@ "component keys that are not exported as part of the NL file. Skipping.\n", "WARNING: model contains export suffix\n", "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", "tol=1e-06\n", "max_iter=200\n", @@ -1473,8 +1482,8 @@ "Number of equality constraint Jacobian evaluations = 10\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.038\n", - "Total CPU secs in NLP function evaluations = 0.003\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.036\n", + "Total CPU secs in NLP function evaluations = 0.000\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -1510,616 +1519,616 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 117, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:33 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", - "2023-07-10 13:48:34 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", - "2023-07-10 13:48:34 [INFO] idaes.init.fs.D101: Begin initialization.\n", - "2023-07-10 13:48:34 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", - "2023-07-10 13:48:34 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:35 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", - "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:36 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", - "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", - "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:37 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", - "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", - "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", - "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:38 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:39 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", - "2023-07-10 13:48:40 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", - "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", - "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:41 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:42 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:43 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:44 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:45 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", - "2023-07-10 13:48:46 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", - "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", - "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:47 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", - "2023-07-10 13:48:48 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", - "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", - "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:49 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", - "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:50 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", - "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", - "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", - "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:51 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", - "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:52 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", - "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", - "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", - "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:53 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", - "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:54 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:55 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", - "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:56 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", - "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", - "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", - "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:57 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", - "2023-07-10 13:48:58 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:58 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", - "2023-07-10 13:48:58 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", - "2023-07-10 13:48:58 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" ] } ], @@ -2171,7 +2180,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 118, "metadata": {}, "outputs": [], "source": [ @@ -2207,7 +2216,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -2317,8 +2326,8 @@ "Number of equality constraint Jacobian evaluations = 10\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.072\n", - "Total CPU secs in NLP function evaluations = 0.013\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.068\n", + "Total CPU secs in NLP function evaluations = 0.009\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -2326,10 +2335,10 @@ { "data": { "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.1654343605041504}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.16938281059265137}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" ] }, - "execution_count": 49, + "execution_count": 119, "metadata": {}, "output_type": "execute_result" } @@ -2350,7 +2359,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 120, "metadata": {}, "outputs": [ { @@ -2415,7 +2424,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 121, "metadata": {}, "outputs": [ { @@ -2465,7 +2474,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 122, "metadata": {}, "outputs": [ { @@ -2520,7 +2529,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -2599,7 +2608,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 124, "metadata": {}, "outputs": [], "source": [ @@ -2616,7 +2625,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ @@ -2644,7 +2653,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 126, "metadata": { "tags": [ "exercise" @@ -2657,7 +2666,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 127, "metadata": { "tags": [ "solution" @@ -2687,7 +2696,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 128, "metadata": {}, "outputs": [], "source": [ @@ -2730,7 +2739,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 129, "metadata": { "tags": [ "exercise" @@ -2746,7 +2755,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 130, "metadata": { "tags": [ "solution" @@ -2773,7 +2782,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 131, "metadata": {}, "outputs": [], "source": [ @@ -2799,7 +2808,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 132, "metadata": { "tags": [ "exercise" @@ -2812,7 +2821,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 133, "metadata": { "tags": [ "solution" @@ -2834,7 +2843,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 134, "metadata": {}, "outputs": [], "source": [ @@ -2856,7 +2865,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 135, "metadata": {}, "outputs": [ { @@ -2985,8 +2994,8 @@ "Number of equality constraint Jacobian evaluations = 34\n", "Number of inequality constraint Jacobian evaluations = 34\n", "Number of Lagrangian Hessian evaluations = 33\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.440\n", - "Total CPU secs in NLP function evaluations = 0.054\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.522\n", + "Total CPU secs in NLP function evaluations = 0.078\n", "\n", "EXIT: Optimal Solution Found.\n" ] @@ -3008,7 +3017,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 136, "metadata": {}, "outputs": [ { @@ -3073,7 +3082,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 137, "metadata": {}, "outputs": [ { diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/500_Points_DataSet.csv b/idaes_examples/notebooks/docs/surrogates/SCO2_example/500_Points_DataSet.csv similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/500_Points_DataSet.csv rename to idaes_examples/notebooks/docs/surrogates/SCO2_example/500_Points_DataSet.csv diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb index 53bdba26..187b9b05 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb @@ -1,11 +1,6 @@ { "cells": [ { - "attachments": { - "image.png": { - "image/png": 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" - } - }, "cell_type": "markdown", "metadata": {}, "source": [ @@ -25,13 +20,40 @@ "\n", "### 1.2 Supercritical CO2 cycle process\n", "\n", - "![image.png](attachment:image.png)\n", - "\n", - "The above flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature. Thus, surrogate should have pressure and temperature as the inputs.\n", + "The below flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", "\n", "In this example, we will train a model using AlamoTrainer for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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sekxsQoLEJSdLXX4lhYdl5m2R/6zLkueWLZfjzhsoa9atk0eeeEIaN25s9wAAoHoprm98mL4R8BTzPl27Xp7N/EOOO/dc8z599KmneJ8CqHL0c23Mww/K+jVrZODxx8iq156STR+8KluW/273QLQQrHpMVnq6LBw82CwBr1m8ebO8um69vLpmrZxx6aWydsMGGf3QQ9KgQQO7BwAA1Yt/33g6fSPgSeZ9utb3Pj3tkktk3caNcs8jj/A+BVDl6efcg/eMlg3r1splZ54iGyZNkJz0CZKX+ZvdA5EiWPWYvIwMWTNxolkCXrFgU65MWLNO3t2YI3+9+hpZnZUlt6WlSWxsrN0DAIDqxb9vHGj7xtvpGwFPMe/T1WvlnQ3Zct6VV5r36R333MP7FEC1o597d99+m2StWSXXXnCebP74Ddn4zvOyefGvdg+Ei2DVYxJSUsw4qzocALC/zc3JkedXrZaPtm6TwSNHyrLVq2XkLbfYrQAAVD+ub5y8ZYtc/ve/m77x7/SNgKeY9+nK1fJh3hYZVPgd9o81a+TG22+3WwGgevvHyBGy+o9l8o8rB8ufX78vWW+Mk9yFP9utCBXBqsdosNpuzBhpmppqW4CKl7ExW55ZvlK+zN8tN9x1tyxatkyG3nCD3QoAQPWjfeO4Fatk2s5dcn1h37j4j+Vy7fXX260AvMD3Pl0pU//Ml+vuvFOWLF8uw0aMsFsBAP6GXXuNLFv0m4waOVxiZn4h6/79hOTM+8luRbAIVj1me2amGQYgPyfHtgAVZ9aGjfLkHytkZs1YueOhh2Te4sUy+Mor7VYAAKqfmRs27Okbby/sG39dskQup28EPEXfp08sW174Pq0ptz3woMz//XcZcvXVdisAoDRDBg+WRb/OlUfvuk3q/fq9rJn4mOT8/IPdirIQrHqMjq/6Y/fusmLsWNsClL/v12fJY0uXybz69eWBp56S2fPny4WXXGK3AgBQ/XyftUEez1wm8xvEyf2ub7z4YrsVgBfod9jH9TtsvQZy/xNPFL5PF8hFl15qtwIAQnHBBRfIvNk/yjMPPyDxS3+WVS88JJtmf2e3oiQEq0A1Nr3wpPHhJUtlaeMm8vSECTJjzs9yzrnn2q0AAFQ/0zdslIeW/C5LExvLUy9NkO8z5si59I2Ap3yzPsv3HbbwffrEiy/KDz//LOf99a92KwAgEgMGDJCMGd/JS08/IU3WLJLl4++V3J++sVsRKGb18hUFS5f/IX369LFNAMIVExMjz/TuZe9511eFJ43XLl5i1vM//1xOOOEEs14dfVn430z1KygwSwBAdFWWvvHrjRtl28XPm/Xr+hZU674R1U9leZ9+mbVBBv2+VOoXfm/LT0+XE/7yF7sFiL7Ue6aYZfrd/c0SqI50mMrpiYmyvVYtuaDJAdKkT39p2DPFbvWu2XdcIQUVdI5PxSpQTeQXfqhM3bBRRs1fIJsPOti2CieOAIBqy/SNG7Mlbf7Cwr6xjW2lbwS8RN+nU9ZvMN9h8w4+WOo3aGDa+/XrZ5YAgPIXV/jZ+97bb8rB27NlyT9vk83ffyEFu/Lt1uqNYNVjdGxVHWNVx1oFomH7rl3y2YaNcve8X2VX+w4y+fMv5MMpvl9fAQCojrRv/CI7x9c3tmsvH37+uXzwxRd2KwAvMN9hs/Q77PzC77Dt7XfYqRIbG2v3AABUpGOOOUa+mPyBfP7Rh9Kh5g5Z9PitkvftJ7J7x3a7R/VEsOoxWmadl5Eh2zMzbQsQnrz8fPl4w0a5a+6vUrdLV/nq2+/kvx99JL16ef8yLwAAyoP2jZ9m55i+sfZhXegbAQ8y32GzNsqdv8yTOocdVvg+/VYmffIJ71MA8Aj9PJ486V359qsvpWvDWrLgsZsl7+vJkr81z+5RvRCsekzT1FRpN2aMJKR4f8wKeFPOn3/Khxuy5c45v0jjI3vITxkZ8np6uhxxxBF2DwAAqhftGydv9PWNCd2P9PWNkybRNwIeou/TD9ZvMO/TxO7dZfacOfLGe+/xPgUAj9LP5/++/prM+ekn6dGskcx/5B+yedp7sjM32+5RPRCsekxccrK0HjGCYBUhy9qxQ97bkC13zJ4jB/3f/8lvixbJxDfflEMPPdTuAQBA9eLrGzeavrG16xvfeIO+EfAQ8z7N2rDX+/Tlt9/mfQoAlYR+Xr8+8SVZ9NtvckzbljL34X9I3pRJsmPjertH1Uaw6jE6DICOr6pLIBhrtm2T/xaeNN4/b74c2r+/rFq1Sp59+WVJSkqyewAAUL1o3zhpY7bcN/dXOfSEE3x940T6RsBLzHfY9VnmfdrxeN932OdffZX3KQBUUvr5PeG58ebz/IQu7WXRU6Nk82dvy/b1a+weVRPBqsdkpafLwsGDzRIozcqtW+Wtwi+jYxb/Lt1PPU1WrV4tTzz3nLRo0cLuAQBA9bJC+8asDfLP3xZL99NOl9Vr1sgTz9I3Al5i3qfrsuSfiwrfp6efYd6nT/7rX7xPAaCK0M/z8U+MldWrVsmZRyVL5gsPyeaP35Btq5fbPaoWglWPqZuUZIYDiE1IsC3A3jLztsjr67NkfOYfcuy558nqtWvl0aeekiZNmtg9AACoXnx94wZ51vaNa9avl0eeeIK+EfAQ8z5dt968T/uec46sKVznOywAVF36+T7m4Ydk3erVcl7K/8mq156STR+8KltXLLV7VA0Eqx7TfNAg6TF7thlnFfC3ePNmeXXtOnl59Ro59eJLZO2GDXLvI49IXFyc3QMAgOrF9I3r1ssra9YW9o0X+/rGhx+mbwQ8xLxP16yTVwq/w55y4UXmfXr/Y4/xPgWAakI/7x+8Z7RkrV0jl515iqx/9wXJSZ8geZmL7B6VG8Eq4HELNuXKhMITxnc2ZMt5V11tvozeMXq01KpVy+4BAED14usb1+3pG9dkZdE3Ah5j3qer18jbWRvl3CuvlDWF32HvvO8+3qcAUE3p5//dt98mG9aukWvOP0c2f/S6ZL/7L9m85Fe7R+VEsOoxmWlpMj0xUVaMHWtbUF3NzcmRf61aI5O3bJVBI0bKH2vWyI233Wa3IhKzP/9MXhhxg9zW7xj55uCDzE3XtU23AQC8yfSNq7Vv3CKDR9q+8dZb7VYAXmDepytXy+S8LXLZDSNk+dq1ctMdd9itAACI3Pz3v8vq5cvkxiGXyY5p70nWG+Mkd+HPdmvlQrDqQfmFX0b0huopY2O2jFuxUqbt3CXDCr+ELv7jDxnG0BBRsX75chlz2aXy/r33yO45s+XoBg2kd7fDzU3Xd83+Ud4bnSaPX3Sh2RcA4A3aN45fscr0jdfdeVdh37hcht5wg90KwAvMd9jlK2Tan/ky9LbbZHHhd6nhhSfOAACUZPjQa+WPJYtk1MjhIjO/kHX/fkJy5v1kt1YOBKseo2Osdps61SxRvczasFGeWLZcfqhZU2574EH5dckSueLqq+1WROq7Sf+VtDNOk7iNG6Rf40Q5NCFBEurUkVqF/9560/VOjRtLStMmErchS9JOP1Wmv/WmfTQAYH/QvvHJP1bIzJqxcttDD5m+8fIrr7RbAXiB731a+B22Rk259f4H5Nfff5crhw61WwEAKNuQwYNl8a9z5dG7bpO6876TtS8/Ljk//2C3ehvBqsfUTUqShJQUs0T18P36LHl86TKZV7+BPPDkk5Ixf4FcdOmldiuiYfq778h7jz4iKS2aS8fYmra1ZJ3q1ZWUli3k/ccfk+nvvG1bAQAV5fusDfLPzD9kXoPCvvHpp2X2/Ply4cUX260AvMB8h/09U+bVqy/3jRkjGQsWyMUUhwAAInDBBRfIrxk/ydMP3S/xv8+RVS88JLkZ39mt3kSw6jFZ6emyZORIyZk2zbagqppe+GX04SVLJbNxE3nqpZdkxpw5cu7AgXYroiUvO1vevO9e6dEoXg6sX9+2lk337ZHQSN68/z5zDABA+ft240Z5+Hdf3/ik9o0Zc+Scc86xWwF4wTfr1stDi3+XpQmJ8sQLL8iMn3+W8/52vt0KAEDkBgwYIBk/fC8vPf2ENF69SJaPv1c2//SN3eotBKsek5eRYSauIlitur7K2iD3/bZY1jZvIS++/rp8NXOmnH7GGXYrou31u++STi1byQF169qW4Gm42qFpU/nPHUwaBgDl6auNG+W+RYtlzYHN5cX/FPaNP/wgp59+ut0KwAu+XLfe9x22WXN5qfA77Nc//ihnnn223QoAQPTp98Efvv5SXp/wgjTPWSnLnh4lm2d5Ky8jWPWYuORkaZqaylAAVcyuggKZmrVB0hb8JrkHHSxv/u9/8vn06XLiiSfaPRCqhYMHy/bMTHuveAu+/14WFZ6cH1Y71raE7vC4BuYYeiwAQPRo3zgtO8fXN7Yu7BvTC/vGb76hbwQ8RN+nX6xbL6PmL5Tcg9uY77BffPednHjSSXYPAADKn34//PrzTyX9zdfl4O3Z8vuY22XLD1OkYNcuu8f+Q7DqMRqqdpk0icmrqogdu3bLZ1kb5K5582Vnu/bywaefyuSpU+XYY4+1eyBcWtU9o21bmTdgQIkB689ffC5JjRMkxt4Phz724Lg4mf3hB74GAEBEtG/8IjtH7v51gew8pJ2vb5wyhb4R8BDzHXZ9ltxd+B12d4eO8uFnn8lHhd+9eJ8CAPYn7Ye+mPyBfPrh+9IuZrssevwW2fLdZ7L7zx12j4pHsOox+Tk5JiTSJSqvLfn58vGGjXLX3HlS+7Au8uX06ZL+ySdy1FFH2T0QLToucUkB69yvvpQDdxfYe+FrUaeOzPt2ur0HAAiH9o2fZueYvrFW58Nk2jffyKSPPqJvBDzEfIddn7XnO+y0wu+wk/gOCwDwGO2XJk96V6Z/OU0Oa1BDFjx6s2z55iPJ35pn96g4MauXryhYuvwP6dOnj23C/pSZlibLRo+WNqNGSVLhOiqXmJgYOeWg1jJl9Rq55Pzz5R933CGdOnWyW73ly8LXqvT/a5XRyieeKPYHCK36bjdmjBlOY0SvHnJq8wOlXmwtuzU8W/N3yieF/03HzpptWwAAwfLvGy+7+GK58ZZbPNs3pt4zxSzT7+5vlkB1oe/Tk1u1lGlr18nFHv8OOz0x0XwH7JudLbEJCbYViD76BMBX/Kefu/p5q5+7XrVgwQJ5+LHH5d+vvCzNjzlZVkz9UAoKIi+yCgbBqscQrFZu+qV02NVXm5PGtm3b2lZvcl9KqyoNV9PGPS0XdzksoqEA1O7C22vzfpUXflvsawAABK0y9Y2cRKO60vfpNZdfLjffeWel+Q5LsIryRp8AVJ5g1Vm6dKk8+vg/ZfwzTxOsVmcu7OKLAspTXkaGuYy+siqpYjUhJcWEqjoRnFasnta8mdSNDX/yKrUtf6d8TMUqAFR5nEQD3kewiopCnwBUvmB1fyBYBVAp6biq/mOq+geqzt2nnCRHFC4PqF/P1xCm9Vu3SUZ+vtz3xVTbAgCoijiJBryPYBUVhT4BIFgNBpNXeYxWEOokPGsmTrQtAEqjgWqP2bOl29Spe4Wqqsuxx8m6mEgHAhBZvWO7dOnzf/YeAAAAAAAAwarnuMuzA2c3B7C30gJVp9uJJ0lmdrZEMrKKPvaPzVuk+5ln+RoAAAAAAAAK1fDFBvAKDYt04ipdAijZoRMmlBioOp2OPlo6HtVb5m7fYVtC90vuZmnfs6c5FgAAAAAAgEPFqsdooJqUlkawCkTJ+ffcK4vWrpV1W7faluDpY37bsEEueuhh2wIAAAAAAOBDsOoxDAUARFdcYqL89Y475afcvJDCVd33p+xNcv6dd5ljAAAAAAAA+KshEvnELogeJq8Coq/vuefJWTfeKFNXrZYF27fb1pLNz8uTqStXyek33CB9zxtoWwEAAAAAAIpQseoxsQkJ5gYguv7vvIEyevLHsqXJATJtfZYs2LhRcnbskJ27dpmbri8sbJu6Zq1sOeBAGf3RJ3LsRRfbRwMAAAAAAOyNYNVjWo8YIX2zs804qwCi64CDDpK///s1OTtttNTo1l2+25wnX/8yV2Zk/GzWYw4/Qv5y731y43/eMPsCAAAAAACUhGAVQLXT/cST5Monn5KHvp4u5yQdIqc2aWrWr3x6nNkGAAAAAABQFoJVj1kxdqzMaNvWLAGUr5xp08zNTRoHAAAAAAAQLIJVj8nPyZHtmZlmCaB8LRs92q7tvQ4AAAAAAFAWglWPaZqaKl0mTZLmgwbZFgDlwVWrOlStAgAAAACAUNQQKbCr8IK45GQTrtZNSrItAMpDcRWqVK0CAAAAAIBgUbHqMVpBp+OravUcgPIRWK3qULUKAAAAAACCRbDqMRr2LBk5knAHKEelVaZStQoAAAAAAIJBsOoxOgRAQkoKQwEA5USrUnWCOH2PFXfTieOKq2YFAAAAAADwR7DqMTppVbepU5m8CignOo5x76VL99zajRljbv5t+uMGAAAAAABAaQhWPUar5fQGoGLMGzDA3AAAAAAAAEJBsOoxOnHV9MREyUxLsy0AylNsQoK5AQAAAAAAhIJgFUC11jc729wAAAAAAABCQbDqMa1HjJAes2czxioAAAAAAADgYQSrHqOXJOvkOjo7OYDyN6NtW3MDAAAAAAAIBcGqx2Slp8vCwYPNEkD5256ZaW4AAAAAAAChIFj1mLyMDFkzcaJZAih/XSZNMjcAAAAAAIBQEKx6TEJKihlnVYcDAFD+mqammhsAAAAAAEAoCFY9RoPVdmPGEPQAFWTF2LHmBgAAAAAAEAqCVY/RsR51GID8nBzbAqA8LRk50twAAAAAAABCQbDqMTq+6o/du1NBB1QQrRLXGwAAAAAAQCgIVgFUa92mTjU3AAAAAACAUBCsekxSWpr0KygwSwDlT4fdYOgNAAAAAAAQKoJVANXa9MREcwMAAAAAAAgFwarH6NiqOsaqjrUKAAAAAAAAwJsIVj1GL0nOy8iQ7ZmZtgVAeeoxe7a5AQAAAAAAhIJg1WOapqZKuzFjmKUcqCBxycnmBgAAAAAAEAqCVY/RgKf1iBEEq0AFWTh4sLkBAAAAAACEgmDVY3QYAB1fVZcAyp++3xjTGAAAAAAAhIpg1WOy0tNN9ZwuAZQ/rRDXGwAAAAAAQCgIVj2mblKSGQ4gNiHBtgAoTzqmsd4AAAAAAABCQbDqMc0HDTIzlFNBB1QMHXaDoTcAAAAAAECoCFYBVGs/du9ubgAAAAAAAKEgWPWYzLQ0mZ6YKCvGjrUtAAAAAAAAALyGYNWD8nNyzA1A+etXUGBuAAAAAAAAoSBY9RgdY7Xb1KlmCQAAAAAo4ib5pRAFAOAFBKseUzcpSRJSUswSQPljjFX8P3vvASBZVab9P7dy7K4OkzMzwOAwzICkIQtIRkBRZEGFVTEtK6j/NbAK+n2gu58Kyuq6uizoigooQaJIlCxpCMPk2JM7d+V4/+c5996ZmpoKPT2pe+b9Dafr3nNPrjqHuk+99z2CIAiCIAiCIAiCMBREWB1mdN1/P5Zfey36nnnGjhEEYXeSmD9fB0EQBEEQBEEQBEEQhB1BhNVhBgUeblwlwqog7Bmm33yzDoIgCIIgCIIgCIIgCDuCCKvDjMjcuWi/8EJxBSAIe4iJ11yjgyAIgiAIgiAIgiAIwo4gwuowg6LqrPvuk82rBGEPsfGOO3QQBEEQBEEQBEEQBEHYEURYHWZwd8vMqlWyy6Ug7CEWX3mlDoIgCIIgCIIgCIIgCDuCCKvDDPpXfWXaNP0qCMLuh+43GARBEARBEARBEARBEHYEEVYFQdivef+bb+ogCIIgCIIgCIIgCIKwI4iwOsyYesMNOL63VzbTEQRBEARBEARBEARBEIRhjAirwxBPLKaDIAi7nxdaWnQQBEEQBEEQBEEQBEHYEURYHWZ03X8/Flx0kexSLgh7CG4UJ5vFCYIgCIIgCIIgCIKwo4iwOsxIzJ+vxdXMqlV2jCAIu5M5Tz+tgyAIgiAIgiAIgiAIwo4gwuowI3bKKZhy/fX6VRCE3Q/nmsw3QRAEYSRjGIaEfSQIwkii2md4XwoPXH+aDtWu7UtBEISdw9jQ0WGu7OjAvHnz7ChBEIT9h+XXXqtfp998s34VBEEQ9l8u/N5T+vX+75yqX0cKvDHeGBd/4SOdsdFemKZpnwm1eGXaNP103zErVyIwdaodK+wNZO0Z+ci6IzSCbvO4Jwn3AeJG68L2iMXqMENcAQjCnmXtLbfoIAiCIAiCIAiCIAiCsCOIsDrMkM2rBGHP0n7hhToIgiAIgiAIww9aS731gQ9sCc6mo7xncuL6nnlGxwmCIAjCnkaE1WEGzasZBEHYM8y67z4dBEEQBEEQhOGHc39E8ZTBEVb5pB/P+aRfZO5cHScIgiAIexoRVocZE6+5RvutmHrDDXaMIAi7E34ZF9cbgiAIgiAIw5d6vvAnfPnLYpgiCIIg7DVEWBUEYb+GGyAwCIIgCIIgCMMTblJVzXUT48decYV9JgiCIAh7HhFWhxncRIcij2ymIwiCIAiCIAiCYFHNalWsVQVBEIS9jQirwwz6DOJjyY7vIEEQdi90vcEgCIIgCIIgDF8qrVbFWlUQBEEYDoiwOszglwVupCNfEgRhz+BsiCAIgiAIgiAMb8qtVsVaVRAEQRgOiLA6zOCOlhRX+QusIAi7nwUXXaSDIAiCIAiCMLxxrFbFWlUQBEEYLoiwOszoe+YZ7V81MX++HSMIwu6k6/77dRAEQRAEQRCGP7RaFWtVQRAEYbggwuowg8Lq8muvFaFHEPYQU66/XgdBEARBEARhePL7Nzbjkl+/hyk3vIS2n3XgiOUH6eNLfr1AXxMEQRCEvYUIq8MMPtYSO+UUcQUgCHuIqTfcoIMgCIIg7Ku8+FzBPqpNozS//FnWPqpOf7+Jd98u2mfVefShvH1UnUZtGEwdjcq4686cfVSdXVHHruiHYPFaRxzH/Ph1fPfBZViydBMONNP4QKSAo9uD+njJ0k59jWmYVhheDGa+dawp2WfV2RPrRqM27OyawPJ3to5GY8n8jepo1E5BEIaGCKvDDPoKmvP00+IzSBD2EOIKQBAEQdjX+dXPM3XFCV5jmno89nCu7k07b9jrlUFx40ffT9tn1WEd9W78B1PHlz+ftM+q89hDO9cP8p1vpOyj6tx1Z7auCDKYOgTge4+twnE/eRO+VBozjQym+ExE3IDHMHXg8RRfSV/zqjRMe/0jK+3cwnCg0Xy767e5hnP6O1+vP9+Yf2fmG+s4/bgB+6w6V16aqNuPRm149ME8fnhT7fWPbfjIOfV/GGg0lrzOemoxmHVeEIShIcLqMKPQ16eDIAh7Btm8ShAEQdgXqHfDTUsm3nTXYsHbhbqCJvMvUKHeTfvdd2ZVHbWvs3yWU68elk9RshaDqYPjUGssKF5QXLjrt0Ovg31gqCeiLHiH/axdxkvP5+vWIQA3/XUN/t9THTg5UkBbqfZYO7SrNEz7o2fW6rzCnqPefOOcrCco8scUiqu14LUBVU6t+cY6WH+9+cY5XW/dceqv1Q/Od7ah1vrHNlh9rd0G9rPenG/UBsZz7ao/lvWFU647HAe2VRCEXYsIq8MMblz1QksLVsmjyYKwR+DGB7L5gSAIgjDSqSUW8mb87PO9dYUF3pCfdZ63pnjBvJdc7qt50+4ICyyjljBAYfeSy3w1hVO287gTPTXbOZg6KKDoOmqMBcWLq77kV/2ofp3ls+xG/fjaNwM1RRSnHxRXa8E+1uvrvkoyM7j+8pH+Gx5bhWNDeYR34G6VaeepPDf8ZZW4BdhJBvtekVpiH+fbZ7/or/k55xxrbjbqzjeKgbf8IlxzvrHss8711pxvzrrBNZBzsxoUPb/6zWDNdYNz/nv/FtLpqsE2HHqYu2YbKMw6/aw1Fk4baq2xFHWtdad6ftYxabJLt6PaWHIMmJfruPyoIwi7HhFWBUHYrzm+t1cHQRAEQRiOvLuqF0/N32Cf1aaWWEhhYt4J3ppCnnND/tkv1hYLedPP65OmuKretDvCwlnn1RY1KTpQnKglDNBqlvnZzqHU4YiirKPeWLAftYQc1sH6WUctgYMi9Ge/FKgporAfznhXE3IYx/H+2GX+uta5+yIU6y783lO47fGldkx1rvr9Ihzd4tohUdWBeY5sMlQZi+0YYSis3BjHZf/+Nzz4cocdU5tac4Hz7ZLL/TXXHoqFs2Zbc7qaOLtFFFXztVYd/DHFWTeq/TBEcfe4E9ScPpeC4vbXuQ5Q9LR+OKo+H50fnjpWV7eG121Q5ddqA/vONUGvXVXmfHkbalnFcg12xrJaG9g3rimso1oZztpG8bbaOAiCsHOIsDrMmHjNNXj/m2+Kj1VBEIR9gHVXh5F+8177TBAEYcehGPXTPy/UIkc9gXUwYmG1m3rnhryWxRWFDT4GSzHwEpWu2k17ubBQTZxwrDgpHtQUBh620rCd1UTNRnU4/axlAeeINOzHcSd6q/ajXIR2Hs0txxGhnX5UE1GcfrCcagIGhVeON9NUE5v2ByjW1RJYucN/IpnTj/YPldHIqzKyuixh6HDt4XvUSAyv9lnm/HPmW821xxYLawmnjihKqs03Z05z7eIPJtXmW7m4W60OR9yttW6Ur11f+1Zwu3XDaQPT1BIt2QZazNbqZ3kbWE5lGxyLV2csq4nQW9dPb1Vf1s76yXJYHtssCMKuQ4TVYQYfSY7MnYvA1Kl2jCAIu5NXpk3TQRB2F31/uBrJ535lnwmCIAyNRgJrNbGQYke5EFhNyHNuyAlfK8ULiht81Jbwpv3u320rkPAGnUIA87KeauKEY8VJqgkD2wmWFaLmYOqgcODUUWssHJGmlvig09SpwxGhCUWKSgvf8n5QRKk33k5fK+vYn6gmsP7h9Y2IlrYXvXeUJjOvytpknwk7Sz0xnHOhUjjl/HPmWzVBsVwsJJwLlfPFEV5JNYv68jnNcirnkiN6ltdRub6V11HtRx3nxxZSbf0rbwP7U9kG9pOUt6HSkr2yDZXrI+tw2lBrLJ11h6Fy7SpfPwndpYg7AEHYtRgbOjrMlR0dmDdvnh0l7E24O3n3Aw+g7YIL0H7hhXasIAi7i2cNQ7+ebMovt8Kuhxar7R/7Ifqe+ClCR1+K6Lnfsa8IgjAcoXBAPn3Ggfp1OLByc6KqkBoOeHQ7T507Dob6f9nitTEcPatfvzrwBn3WYR59M064Yz6trpybfN6QU2C8/fcRfc4bfoqHP/lFWJ8T7obNx+udPEepOu59NLrlnHkoPnzvByF9TuGiv88EfZk6nH78AP70SFTf9PMmf0fbOdg6nnihSR9Xq4P9oDjjiAuV/WIdFGedsWjUD+fcqZNU6wetfJ06q413eb8+9+M/6te9CT9Xu5N6vjvPP3YSbnihEwcjo3f83xkSRXVfZXjx4wtn2DEW9ep3SGQai06p3PaWh9UYTH17Mg1JDqJ/LKteeXyvuP5w7dkYb9luLnz4nLheR5z59Z1vpLQ46MwNzpWmZteW+VW59lAI/M7XU1vmCqmso3IOs45Zsz1aOCS//FkWA/0lvZYQlsl6nToq5yPXjdOPG8CrC5r1OWlUZ+W6UtmGyjWBIilF6FptIFxjy9swmLEsr6Ny7apcZ9jPa9Ta5NQ5NtoLU+6DhDpwc3XuA0QjQHGhVx0RVocZ3LRq9Xe/iynXX4+psoGVIOx2+GMGkR8yhN0BhdWxX7gHxWQv+p68Ff5px6D5kp/YVwVBGG44wupIYnQsgDu+fiY6Or3b3YBXCoG8wab1qCM0VN6Qk3IhgTfgFA7uVWU4UKwgzk175U1+pahZTTioFE4r20lhoGN1cUs7G9VRKRyQ8rGo1o9K8aFyLAbTD7bLcU9Aqo13uVjUqI7hIKzuTQ6d2oL/XjCAI0MleIydE3oKprHTZQjVofjOH3TOP3oSxrQEtbBaLihWmyuVgmKlQEnK155KUZSU11FtTjOuXDBsVAfnY7m4S8rXpmrrSvm6MZg2cB3isbMmkMG0wflBZjBjWbnuVK4rbGO5+EvKfyATYVVohAirjRFhdZjR98wz2mK1+eSTRegRBEEY4TjCqqZUQN+T/wEjMgqtn77TihMEYVjBDVueenujfTY82NyXwSuLOu2zrVDcOO/oiTht7vgt4kb5TX+1G/LKG+5KIZaUi4WVAiepFA4qb+pJuaBRTbwtL3cw7RxqHc5YVBNIWEe5dVq1OspFlmp1lAuntfqxI3UMB4FjsFaPQ2FzXxrX/vJV+2wrFFQ/fcYMTBsbRej/+xtOixbhxs4Lq5uLBi4/cowdYzEYi9yQr7G5bCSw9XNQj8HUtyfTkPAg2s618Pt3v2OfWbB8R1DlDzrEsVjl55+P0XMuVP744sDPPwXFanOFlP+AUm1tKl97qgmvxJlj9AvNuVUuepJycbaa8Fo+p6sJkuVzmmnLf6hyoGj5xItNerOrWv0cbBuqrTvEGctqaxtx1pUmtdawjspxKH+PRFgVGiHCamNEWBUEYb9m7S236FduHCcIu5pthFWb/r/9N0r5NNquugeGf9sv24IgCJVQVC0XOChuOIJqpbjBm/6PnBPXN9y1bsidG26KjtVu+iuFBW7Iws1hynGEA1pOlT8+71AualYTE9lORzht1E6KMOVWnw6DqcMZi2oCCXGstmqNRXkd1YQe4jy2O9TxLheZ93WBgz8SXPXTF+2zbQVVh0nXv4SDkd4lrgAWI4CO7x5nxwg7wrurevGvv3lTH1cTVB2ctYc4Yl+tueIIivy8V5srjjjLuVptrhBnrlNgrRQkiVMH51s14dURZ7muNaqjmiBJnDlNVwXV1keuBZOmuHe6n4MZywXvWP5XK+tw1hXWX0385Tg4/RNhVWiECKuN2XaGCnudzKpVSMyfrz+8giDsfpZfe60OgrCnaD7pM/A0jUbXzaeh2L3KjhUEQagPxY1LTpqKm686GpeecsB2AgehsEhBgb4EyzelKsfZ2IliJW/IK+ENOgVT3ngzVIoGhDtcc/MTZ4f7Srh5CoUDCggUBMoFT8JzZ4OVF58vVK3DaSfr4IYulQymDo4Fr5dv3FIOxQZnLBr1g+VVihuE5dYbb7b9rt9mG9Sx7YY4+zoUVG++6ij8308evo2oSg6fEEF3cdv3cih0F126LGHocM05ZuYo/T7Rn2q1Naccfv4p+NWaKx/7B79aW/J6zlebK1wHuDFTrbWJMB83t+Ocq1aHszN/+YZQ5bBter7WmI+EdVDwrLYuEa5NnNO11sfPfilgt6H6mlDez3ptoEDbcCzVWl0pqhJnXeEPX2xvJXocVpf0WAiCsPOIsDrM2HjHHXj98MO3WNEJgrB7iZ1yig6CsCeJHnUJ/FOPROctZyC3+nU7VhAEYXsGI6iWQysobTFVRWwkvOGmaEBxgwJpNRzxwtntupKt4kV14UCLAVNcWpSsLRx46woHTjt3pg6OBYWeavkJ66AA06gO7gReS+ihcEoLtlrjzXIpcNQSkJmH7dgfBA5+lmsJqg6XHjkG/Ub1sd4R+g2PKmusfSbsKKNjQf0+ffNjs2u+V5VwvnHdqDUf+fmnEMjPfLW5QjhfaG1Za23i2sM5fda51a+z3Ho/hBCnjprrwrk+/ag8X6vB+frD72dqro9OG2qtCWQwbeC60Wgsa+VnvWwnxd9aaZwflgRB2HlEWBUEYb9mztNP6yAIe5rwYecicsRF6L71HGTe+4sdKwiCsC207huMoOpwyeU+fcNe64acN9y80a4nPFAstATJ+uJFPeGAm6/QqrWmcKBu+tnOataohOXOOsy9U3VwLCg+1BJFWS6tthrVQZGlngjNOuqNN/vK11rj7Vjn7utQWG0k0l16xGhEI35sKFV/TwfDuqIbTaoMliUMDa43gxVUHfj55g8R1X5AcOA8qjVXCMVZ5q81V5x5VM0a1YFl1JrzxHl8v9ac55ymFWitdYX56B+1URsG0896beBY1moD4VjWEn8J19Za4i/R61Ksev2CIOwYIqwOM6becANONk39KgjC7oduN8T1hrC3CB58MppP/RJ6fvlxJF/+rR0rCIIwdHijzhv2euIGb7hnza59nTfzzN/opr6ecMCbduavJRwwvp54QShq7mwd7Ee1R2UdKJA0qoP56wk9LKPeeFM4rSUgExE4tuW2f5iJN+NAcghGvMzzVsLAf6syhD0PN1GqNVcIBcV6c555KXzWgz5J69XBH1Rq/RBCOGcb1UH/rfVgPxq1YWf7ybGstbaRRmPJa/XEX2ftEgRh55HNqwRB2K951rC+sPAHDUHY1VTbvKoa+U2L0ffETxH5wNWInP4VO1YQBGFwlG8gQ2hNWk/o4+Oh3DW7njDQqAw+us4dp+vd+A+mjHptYDvJztTBMurlH8xY0LK2ngAxmH40qoPIJjJb+b+Pr8b3VTguUkC4/rBtgaLqi0kPvnH6FHz7zCl2rLA7qVx7hJGHrDtCI2TzqsYM8n9Twp6CvlXpY5W+VgVBEIT9A++YgxE75zqkXvo1Bu7/lh0rCIIwNOoJjYRCYyORr1EZzF9PsCSDKaMeLH9n62iUfzBj0ciqazD9aJRG2JZ/PWMKvnzyBDwb9+hH+xuxNu/Saf/5pAkiqgqCIAh7FPk//DCDvwYk5s9HZpXsFC0Ie4L3v/mmDoKwt/G2jEfLudcht/Rv6Lvz83asIAiCIOyf3HT+dLx4zeEwo2G8lfNiVdaFRFHdL5mGDjxelTX0NTRHdNrvqzyCIAiCsCcRYXWY0X7hhZh+882yS7kg7CEic+fqIAjDAVcggpZzv4Vibwe6/+tiQB7NEgRBEPZjjpwUxWtfOxI3ffhgTJ3WisUI4Mm4WwceT53Wrq8xDdMKgiAIwp5GfKwKgrBfs/jKK/Xrwbffrl8FYVcyWB+r1Yi/+Bvk45vR+tm74Y6027GCIAjbI34O9w3E16Ew0pC1Z+Qj647QCPGx2hixWB1m0A0A/avyVRCE3Q/nm/g0FoYj0eM+CW/7NHTf8kEUNi2xYwVBEARBEARBEIThggirw4yu++/XFnR8FQRh9zPxmmt0EIThSPT9H0Hw4JPQ9ZMzkFv+oh0rCIKwY7z4XME+qk2jNL/8WdY+qg53vucO/fV49KG8fVSdRm0YTB2NyuAO//XYFXXsin4Iwr7AYOZCx5qSfVadPbFuNGrDzq4Jg+lno+uN1i62sVEZjdopCMLQEGF1mBGYOlX7e6SZtSAIux/6NGYQhOFKaNaZiB5zGTp/ejbS8+VHN0EQdpxf/TxTV5zgNaapx2MP5+retPOGvV4ZFBZ+9P20fVYd1lHvxn8wdXz580n7rDqPPbRz/SDf+UbKPqrOXXdm64ogg6lDEPYF+DmvN9/u+m1Ohdo/2nBOf+fr9ecb69jZdePDZ8fts+pceWlCp6sF1656a+xg+tmoDY3WLl5/9MHabaDwyrVJEIRdjwirw4yxV1yhdygXCzpB2DPQ7Ya43hCGO4EDj0fLOd9A72+vQuqF2+xYQRCErdS74eYNNW+6a7Hg7UJdYYL5F6hQ76b9bnXD/lgdYYHls5x69bD8ejf+g6mD41BrLCheUPyoJ3A0qoN9sASKOuP5DvtZu4yXns/XrUMQRhL15hvnZL35xrnwq5/Xvs78A6oczrlaWPNx59YN1lGrHyyf1+qVwbWr3ho72H42Gst6azDXHdZTC7ZP1h1B2D2IsCoIwn7N64cfroMgDHf8k+ag7fzvIP6Xf8PAo9+3YwVBECxqiRcUEs8+36tvymvx2MN5nHWet6ZYyLyXXO6rafXFm34GllFLGOBN/SWX+WoKIGzncSd6arZzMHVQQNF11BgLWo1d9SV/TYGD5bPsRv342jcDqp3VBQqnHxQ5asE+1uurIIwkaol9W9eN2vNtUHO6ztrTaH3bkTpq9YNz/ie/COvXalB4PfQw925tgzOWtaz+HeGZr6yrGs46zzETBGHXIsLqMGPVDTfoHdfW3nKLHSMIgiAIFp5RB6Dl3OuQefNP6P/jV+1YQRAEPg5bXbygBdO8E7w1hTzeiE+a7MJnv1hbLORjrrw+aYqrqjDAciksnHVebVGTQuP3/i1UU3yg1Szzs51DqYNiAvOxjnpjwX7UEjhYB+tnHbWEHIoTn/1SoKZwyn44411NwGAcx/tjl/nrWtkJwkihlthHsfCsc33159sJHjVXvFUFRUeQ/Oo3gzXXjfL1bajrhlPH3b+rvXbVKp9QcOV8rteGwfaz1rrDsfzYP/jrjiXHgT8sVbNKddZ5jkMtgVgQhKEjwuowpNDXp4MgCLufk01TB0EYjvTFk3j21Xfw6/ufwKp1m3ScOzoKLed+E7k1b6Dv11fqOEEQhMGIhdWEPEcUoPhQTSzkDT8fUeVN+SUqXTVhwBFQWE81UdOx4mxuNmqKDxQsnXZWExca1eH0k3VUGwtHvGA/agkc5SINrVsrccQJpx/VLHydfrCcagIGhVdHhGGbBWGkw89yrfnGa7XmG+c0r3G+VpvznB9cl+qtG+XrW7UyOAedOV1r3XDq6O/b/lF8xxLUWjfqz/m6a5e6xn5WE6HL21DtxytnLJmm1ljyxy9a7vJHn2rrjrPOn63aUM+aXhCEoSHC6jCDPlbnPP20fhUEQRD2bz593c047Ypv6NcZZ/yjFlmJyxtE61n/H8xUD7p/9iGY+eoWDoIg7D9Uu+HmDXu5EMjzShxRgFQTDigwnnWuVx9TGKi06uINP4UA5mU91URNx4qTUGD44U3bigvbCZYVouZg6qB44dRRayxoNUbqCRz16nDECUKRt9LCt7wfFDDqjbfT18o6BGGkwTldbb5RCCScS5WCoyMWOnOhmqBYPqdZR6XFaaN1g1BE5FysNaedH2wIrdmr9cNpAy1G6815tqEyv9PPegJxeRuq/XjVaCxZnvPjF+vgOess58XnrbWN8LXa2iQIwtARYXWYEZg6FbFTTtGvgiDsfip9rG684w4dHKvxxPz5+pyvhPFOGoeu++/fJk1m1Sp9zngHJ09luX3PPKPPiZPGgdd4XllueRqnbl4j9cqVPu35Pk38jxQ8s84dcnjgyZfw5U9egD/99F8Ri4bxm/uf0HU4NJ38ObiDzei65XQU+9bZsYIg7I9UEwspAjhCYLWb+nJRgFBAqBQOaMV5yeVby6i06uINunPDTqqJmpXibeWjquWCJeuoFEAGUwfTU0Ah1caC4gXzkWpj4Tyi74zFYPpRaflV3g/CNOUCRuV417JwE4SRBOdb5Q8u5aIoP++Vwmm5WEiqCYrlc5pzqdLitNG64VjKO1TO6XJxl9CHKde7chxLUML21pvz1daVyn5WitCVbWAfWGc55cJrtbFkec6PX4Rlla+xXHeYj4HwfdmX3ZCsuzqM9Jv32meCsGcQYXWYwRv85ddeu81NvCAIu4/E/PlbRDSy+MordWA82fTrX+vzdT/5iT5nWieNk4/XeO4IdMzLc85lh9Xf/a6Oc+Y20/Kc8aS8XEdQdOrmK2G8k8ahstxqdTt5pE97r087w7e/eBkuOG0ePnXR6VpopXuAcqLHXgrf+Fno+skZyK+zLFoFQdj/4E3zLHUDX37DXS4Ekkqrr0ohsPIxUeemn2KgQ6VVl/P4vEOlqFkpJvK1UgCpbCcFkPJ2NqqjUkCpHItK8YJUChzOI/oOjfpBWF65cFp1vMsEjGrCazUrO0EYSXBONKlQPqd57IiipFI4LRcLSaWgWG1OD2bdqJzT9daNStGTdXCec60grMuxBHVgfWybQ7klKKlcY6v1s1yErtaGjtWlumtX5ViW//hFuEZzrXHgMdvlwLLK1619kb4/XI3kc7+yzwRh9yPC6jAjoW70uXGVc1MvCMLu5eDbb0f7hRfaZ5Y7DgZPLKbPw3Pm6HO+EsY7aRzaLrhAn0fmztXntDjneXm5Yz71KR3nlMu0PG8++WR9TnjO4MBrPHfqdsotT+PU7Vi5O+XS8t3BySN92vN9WvtPIRQWPDykcNuNluDbN5DQr6vWbcaUCWO05WolkcMvQOjQs9B1yxnILpb/fwjC/kr5DXctIbDc6ouiQPlNPSm/6abVk/P4vEOlVRfTlt/0sz6eO8JApZhIyoXTau2k+LBNOxvUUSmgkPKxqBQviFXHVmvRSpGGdZQLOdX6UW75NRThtbIOQRiplM+3SlGU8HNeLpzyM185F8oFxWpzulw4rbVulAun1eZb+Q8ulT/YkPLNn1hXuSUoYXpHtGQbWGZ5G1ifs3ZREK3Wz3Kr/1ptaLR2OWuwI7yWi788dsonleIv28Dz8jT7Gq3n/SsST/wY8Ye/Z8cIwu7F2NDRYa7s6MC8efPsKGFvQusoWj05AoAgCIIwcuHjSGO/cI99tmPQMrX92I/p4zkzD8Bbi1bgO1/8B3znS5fpuGpkVryMvsdvRusnf4XgkR+3YwVB2NcxDAMb4y36Bvsj58TxxAtN2o/prMM821iNkS9/PomvfSuob+4pQtz++4h9xYKiCIWDn/wijCsvTeidqitv7I+a1Y8nXmzSN/20yKosg35aWf5VX/Lj9OMH8KdHotuID2zn0aqMxWtjDdtJ8cJpTzmDqcMZC/aDVlzl4gJhP+59NFpzLMrr+LAqi20oFzAIy3h1QfOQx5t1dKwu6jRjo70wZUNNYQRRvvZcoz7r/HzXmgvOfOOcpjD4vR+E7CsWzNfU7Nol68Zg5jTr4PpQTnk/uG58799C2815J98vf5ZFc8zQQmg55WtXtX4yHxlMG77zjZQWXuuNJUVo1lcO882a7VHBXXOdd/Lta+uO8927mOxF35O3wj/tGDRfYj3RJgwNPrH3QkuLNjw5vrfXjhXKEYvVYQYtp2bdd5+IqoIgCPs5tEx98o4faFF19bpN2h3AP39yq3VtNQIHHIvWD30Hffd8DYln/sOOFQRhf6HcGqrSWsvBsfqiWFlpKUV4A0/BlDf3DJWiKqHPQVp18ca80oqT0KKK1qC86a+0KiM8dyw1q1nNEqedrKP8MVaHwdTBseB11lNtLCgqOGPRqB8sr1JgIY7lV63xdh4Nrl/HVutcQRiJcH44c63WXHAsMatZaRLuaM9r9ea0Y3FaaYXpUL6+1ZpvtJytZlVLWAfLd9a/anOeYiXXSJbTqA3V+ulY/Q+mDayn3lhqVwPV1sdzrc31mJ/trYRl7uvrjjvcgrbzvoXCxoXoua22UYIg7ApEWB1m8NcA+udzfAIKgiAI+y8nHzUbr//pVnS9fPeWDawaQX+rred/G8ln/xPxhyzfsIIg7D/QMpPWXNWECUJhgRZMFCacTVkq4U03fX9WugFwoBUrRYNaAgrr5QYrFCWriRuE4gPbqdNWES+cdu5MHRwLWm5Vy08ckaVRHfSJWE0gIRQ1aKFWa7xZLgWMWgIy87AdFFIEYSRDsa/e2uMIp7XEQuahqMr5VmtO0+WAs25Uq4NziWtCvTlNlwO1RE/CfLQYrbX+cc5zXWFZtdYutoH+qistTYnTz1oCM2EbvvN1Wp26q/aTY8m1q9aPX8zP+llHuf9VB5a5X6w7Lg9iH7wGyKfRdevZMLOWey1B2NWIsDrMoH/VV6ZN06+CIAiCMBQ8rZPQeu51yCx4DL1/uNqOFQRhf4DWUHzctZYwwRtq3nTXEgWIIxxQ/KyGIwzUElAIBRDto7WKuEF4U892VrO2IiyX1mk7UwfHgiJOLfGC5VJkaVQHH92tJ0Kzjnrjzb7ytdZ4OxZugjCSodjXaO1ptG5wnukfdXZi3eCPIfXqsDZ3qr1u8DqtSWvVwXxcN6pZghKnDbXKJxSh6/WTP1416ifbUEv8Jbqda6z1rRr707rTfNJn4Gkaja6bT0Ox29p8VhB2JSKsCoIgCMI+iCsUQ8u530Rp8xL0/PeldqwgCPs6vOGmBVM1KyYH3qzXEgUIb8iZv54wQAGkloBCKIAwfy1xg/G05qpXB0XNna2D/ahmNeZAgaNRHcxfS5xgHSyj3nhTwKglkBAtvMaq90EQRgrOfNuZdYOCYqM5zbm4M+sGf3CpVwfL55yvNaeZj3O2miWoA9tQ6wcdQhF6MGtXo7Gs9eMXYf2Vm2+Vs7+tO9GjLoF/6pHovOUM5Fa/bscKwq5BNq8ahjhuAJxdqQVBEISRyc5sXrUr6X/uNpTSCbR97h4YwW03SRAEYeTjbCDjQKuwekIfHx8dqOE/0KFRGbSEalI3/7WEATKYMuq1ge0kO1MHy6iXfzBjQcsxiqe1GEw/GtVBZPMqYaRRufbUeszfYVfM6T21vtXLvyfWrkZjORjYjnptIPvq5lW1SC9+FvEX7kDLP/4GgfedaccK9ZDNqxojwqogCIIg7CaGi7BK4q/ehey699B21V3wtB9gxwqCsC9QKW4IIxMRVoWRhqw9I5/9TVgl2VWvoe/xm9H88VsRPvZyO1aohQirjan/s6mwx+m6/34suOgibLzjDjtGEARBEHYePgIVmn4sum45E7lVr9qxgiAIgiAIgrD/QJcArRd8B/EHv43EEz+2YwVh6IiwOsxIzJ+vxdXMKnGqLAiCIOxaQrPPRuT9H0bXrecg8+5jdqwgCIIgCIIg7D94xxyM2DnXIfXSrzFw/7fsWEEYGiKsDjNip5yCKddfr18FQRAEYVcTPPhkxE7/Z/TcdhlSL//GjhUEQRAEQRCE/Qdvy3i0nHsdckv/hr47P2/HCsKOI8LqMIOC6tQbbhBhVRAEQdht+Ke8H60f+jb6H7wBib/+yI4VBEEQBEEQhP0HVyCClnO/hWJvB7r/62JA/FwLQ0CE1WGGuAIQBEEQ9gTeMQeh9bzrkHzlf+URKEEQBEEQBGG/JXbql+B2e9B5y2koJrrsWEEYHCKsDjNk8ypBEARhT+FpHofWc69Ddtnz6PvtVXasIAiCIAw/Fr38Mu7+/k34zhmn4dqj3o/PHDRDv/Kc8bwuCIIwVKLHfRLe9mnovuWDKGxaYscKQmNEWB1meGIxHQRBEARhT+Dyh9F6zjdQ6l+Prl98GGapaF8RBEEQhL1PorcX//O1r+J//vmfsOnRR3CY4cIZY0fjslnvw5nqleeM/5+r/wn/fe01Or0wPHjxuYJ9VJ3+fhMda0r2WXUefShvH1VnMHW8+3b97zaN2tAo/67oZ6Prd92Zs4+qwzY2KqNROwUg+v6PIHjwSej6yRnILX/RjhWE+oiwOsyYeM01OF59GaCfVUEQBEHYUzR/4Itwe3z6V/pSotOOFQRhX2AwN9ON0vzyZ1n7qDoUDhqVsScEkkZ1NBInBlNHo3YOph+N0ggWz9/1B1x32geQXvAuzpkwHoe1tWJUKIiQx6tvZIPqleeMP2fieGTeW4BvnXoKnlP5hL3Pr36eqSv23fXbnAq11xbOle98PWWfVYd11JtPvMY0tWAdHz47bp9V58pLEzpdLR57OFd37RlMPxu14bGHcnXHktcffbB2G7iu3XVn/XVcsAjNOhPRYy5D50/PRnr+/XasINRGhFVBEARBEDRNfARq9HR03nwaChsX2bGCIIx0KCrUExQpCNQTHsjdv8vWvamneFHvpn2wAkm9dg5GIGlUB9vYqB8N6/hGqq7IMph+iMDRmJfuvw8P33IzThjVjkMDfhh2fC14fXYoiBNHj8Kjt9yi8wu7n1rziXNEf9brCIovPc+1p/Z15h9Q5dT7saORYHi3uvZYHdHTqaNWP1g+r9Urg4Imhc1aDLafjcaynnC64J2irqcWbF+9PgjbEjjweLSc8w30/vYqpF64zY4VhOqIsDrMWKu+BLwybZp+FQRBEIQ9TeSIixCaeSo6bzlD+17dUbgJoyAIe55awgNvyHmz/uJztW+oKTxQXK0lFmphYXWprkDCMnjjXwtHOKglOA62nYOpo9ZYMH6BCj+8KW3HbM9g6mA5tQQKpx/1RJZGdQhAZ0cH/vfb/4ojmqIYHQrZsYOD6Q9viuj8LEfYvdQS+/gZv+RyX01BkXOF4azzvDUFRc4Vq4zqP3Zw3Tr7fG/N+bQjddTqB+fyT34RrjmnuR4ceph7t7bBGcsffb/62uWseXxlXdV47OG8bkMjq/5K9ufvdf5Jc9B2/ncQ/8u/YeDR79uxgrA9IqwOMwp9fcisWqVfBUEQBGFvEJp1BqLzLkf3recg89bgHoHqe+YZvfkigyAIe55awgNvyM8616utmarBm3De7H/tmwH9uGo1KCh8799CDQUSChy1hFMKByyjlnDKdh53oqduOwdTx1e/Gaw5FuwHrzcSQBrVcfvvIzVFFme8OaYsq5LB1FGL/Ung+PXXvoq5kybtsKjqwHxzJkzAr7/6FTumOhxT2TR456gl9nGunHWur6agqOf8CR41771VBUVnrtSbs7TQnHeCV68dteqg6HnWeb6qPwyV10Gr/GpwTapVPuFa8LHL/HXbMNh+1lq7OJYf+wd/3bHkOFxymU+1Z/s6KLhOmuzS41Br7aqE3+ve+sAH9vvvdZ5RB6Dl3OuQefNP6P/jV+1YQdgWEVaHGe0XXohZ992HsVdcYccIgiAIwtD46f8+gFnnfQ6tx3wU533+eixauda+0pjggSeg5Zxvofe3n0fyuV/asdvjCKr88t11v/ihEoS9RS1BkjfkvKHnTX81IU/f9Ktrn/1SQPsJrIZj6VTrpp6CLIUD3vhXE04d4YA3/fXaSRGmVjsHW8dVX/LXrIPxFDQZqlltOQJIvTrY/7PVOFCoqDYW5eNdTeBw6vjsF9V474DAsT/9cPXmE39Fcv06HOz32TFDY2YwgOS6tbq8ShLz52P5tdfq/3et+8lP7FhhKFQTFJ35yGu1BEVnrnBdqSYocq5QFG1uNqrWQZz1i4JhtTI4xxzhtdoPQ+V19PdZ87scxxKUoiTLqLY2cX2s1wa9tqlr7Gc1Ebq8DZOmuGqOJdPUGkuu3VzX9DpeZV1xxF+uXbXWRwdHUGXg8f7GM39/Gz/8nz/hzgefRi5vCfru6Ci0nPtN5Na8gb5fX6njBKEcEVaHGZG5c7W4Gpg61Y4RBEEQhB2HoupXfvBLLF+zAbNmTMFjz72Gi7703S1fEgeDb9JhaD3/24j/9UeIP3KTHWshgqogDC94Y18pFpaLG7SGqnbD7QiavKnn4/7VhAWKCrxOcaDaY/S0Grvkcr++8a920+6IiaSaOFHezlqCY6M6HOGVVBuLcoGE/a01Fg37ocombGelwDHY8aY4wnZwrJm+FvvrOvv3e+/FeL/fPts5xvt9eFWV58AnAx1Bla7X+JSgPCm4c3BdqJwLjlhIqgmn5XOllqDIuUJRlLCOSovT8rWJ5VSzuNc/pqj6rfVr+x+GnPWPVJvT7IfThmo/uFS2odaawLFw0tRrwyWX+Xd4LFkeXaA47ai2rrz4/Na1i68ss5L9XVAlX/23X+L0K7+Jb/zof/Cpb/wQcy74whajBJc3iNaz/j+YqR50/+xDMPPbi+jC/ouxoaPDXNnRgXnz5tlRwt6Ei1hi/nzETjlFi6yCIAjCyGXd1WGM/cI99tmehZaqFFVXPnkHxo1q1b++84vizvDFc4/AjV/7kbbuqXeT74nF7KO9w96uf2cZqe0fyeM+0j8zx6r5+JtHouAGLvQF6EBxccHbBXztW0F9fvrxA/iTSsebb8Kbb+52fa+KIxQ8abVFq08HCqmzDvNocYLpTz9uAK8uaLavWjf1LOOJF5r0OTd2mjXbo61THXidbgB4488yrvl8Uj9O79ConYOp48PnxHXfWQdFg8qxKO8HYR1OeaRyLGr1g+ILhYnK9KSyH0fN6se9j0Z1m0ijOsZGe2Gapr4fqLbO0vDimJUr7bN9l2+efAKODYcR2wXial82i5eTSVz/69/qMeVj/5VCqjOuzjXnPoz3ZHwveJ2GL2TVDTfoVz5dyHhed9IwjvlXf/e7Os2U66/XawsF3Ozq1Wi74AJdNst1rGQPvv12/Uqxl3knfPnLum6+95t+/WuE58zBVLtOx2J5+s036/rYlv5nn8WYT31K181yWQ6Z8/TT+pV5KCazLewD+8i6WYdT9+uHH67r5lOTjGe5rJvpWRfzMw3hOLFPLJf9vkXlu3FtDB9R8698PvFz7lhIkvL5SThX+GPJ935guXqotvaUz1HOnaPVfFqs6nKonNNfVusK595g66icjzyvXJsq2125buyONlSuKzx31h1S2aZf/iyLgf7SlnWncl2h+EtLWadfbBN/9HHWx7PUunOH+lzy/ayGMz/42eJn2XnClp8L7g1Dju/t3fK54GeXnzd+bnnMOF5jGsI8zMsyWBbLZNmcG/zc8rP4QkuLTvv+N9/Un8nFV16pP7sTr7lGfyb5WXc+kyerNZPwnPHtJ/ow8xc7vnEdLVUpqrY0RXDnD7+Otxev1N+bzzrxSDz0C2tOO8Rf/j3y3SvR+tm74I5NsGP3XZz3pPx9FLZFhNVhBv9Hxv8ZO4uRIAiCMHLZm8IqH/+npepzd/5Qnz/+whs456pv6+Oh8vmTJ+Hrh03HpgdX6S/FgiAMD85TYVm8Zbubft6QO4ImqbzhrhQCeVNfKZBUipwUDmhV5dzkV97UUwTlzvzOTXw1oYCC4xMvNm0pk9dp4elYZVW2s1odFDQcYaBSeCWVY1HZj8HUMZh+lAunlePNMokjolQb73IhhwLHby+8cFj/cFXO7mrLHQEfPnLgDHjd1udhZ8gXi3j3tTdwVDqjxYFqOMKRI8xQuKGAQyGHgg6FHQo8hOICy6EARCFopIhNu6tP9E77A7X2NBIgKwVFzpVysbByflUKkqRStKyc05V1VIqerKNcnK1WR/naVG1d4bpBC9ZywZjz12lD5Ryv1s/yNbZWG5x1pdq6U20sy9edyrWL4zBpinvLWscyy38gu1itOz9Uc6DW97qRJqz+hzr+o44ZGheePg9//Mm/6uMTL/saFixbjZ5Xtv8un3jzAWSWPY/Wz/wB3gmz7dh9E+c9KX8fhW0RYXWYwQWDvxA6vzwKgiAII5e9KazSpyof///BV/8Rhx08DZd97d/QO6BuEG7/Pk45+jA71eAxC1n0P/kfcLVMRMsVv9ZflvlFuPKLOL+AOzdrewt+ARzJjNT2j+RxH+mfmVHqxnVjvGUbsbDaDTnjyoW8ypt+QrGC13mTzpt+PqbqpCeMK7d2qhRUSLngUSlYkh1tZ606HHGiUR2VFluEcXyk1ulHpThByvtRKZiQcoGjVj/K46rVUS7kNBI49hf+cPCBuHjmQfAYW8dpqFBYvXflKnzr05/VlprVxtYRjijwVFqW8r7M3dy8xeCl0mKV/y9kOoo/FIm4lvB+jlAEIrQEdNKwXLaBcRQpmIewHObldZbLNMzDNIzjNceikOeMZxoGHrNsUp6G8DrzMg3LJSyXOHmYhjANA9MzECePc87rDoxraWnRP+qUz4VqYmHlXCifvw7lgmKlKErK66g2p1lHuXBaPn8dyteSSpGUlIuQ1daV8vWvURt4XPlDFSnvZ602NDW76o6lsz5WjqtD+fhWWz/LRWrHUr7e9zrOj5GCYWx9v4fCCe+fhWd+8+/Y0NmDaaddgemTx2HBQ/9lX92W1MInkXj5d2j9zO/hP9iac/sinOsirNZHhFVBEARB2E3sTWGVPqHoU3Xp6vV2jPoi/ckL8KOvX2WfDZ5Cogt9T9yKwIEnovmjP7ZjLSq/iI+0L+CCsC/AG0kKq+WWnNVEAeLc1Dc1G1VvyMvFi2riBnFu2qsJC6Rc1KwmJpYLA/XaScGRfl8b1VFNOCgfi0b9qGaZRir7UW5VS9gPx/JrqONdLqIMRuDY2z9cleOIbbua6y+7FMc3N6N5JzevIo4rgO8/+7w+p3haKbDK/7eGjrP2lM/pWvPNmQtcNyrFQlI+F6qJoqzDES1r1eEIhlzDqq0b5etbufjoUN6PamsXcfJxzjfHDL0+lOO0oVY/mY8Mpg3VhFdSPpaVP/gQZ+2aNdtddRzKfyhy1h2Hkf69bqjfvbkHAX2q8nvzvLmH4NV3lqBQLOLH37gK//yJC+xU25NZ8TL6Hr8ZrZ/8FYJHftyO3bcQYbUxO/8zoLBL4Yd2d31JEQRBEPYfZk6biLce+E/8+gdf01artFQdkqjatRK9D92I4NyLthNVCa1t+IWbj3Txy7cgCHsPCgDOpkvcJZobMVVCC1VujkI/pM5mT+WUb47i7HZdCeMcyy1nY5dynI2bKFgyVAoTFEsoCPAahYdq7aRYwV31G9XBclheZR3bjkXtflBo4XicdW7tOih0MJSLqoT1Mq5ePyhcsPzyzbXK0bt4q/eqnHrrqmNVOBwC27Y7wgGHzcGm3PY7uA+FTek0pr5vln1mWZFybOlflHUJu4byOV1rvnFOcy5wrlSb05wLvMZyOH/LRVXC81n2fCvfjKkcZ9d8zlv6eK2E6xvnG9evavlZB8t35nzlukIoVnL9ZDmN2lCtn5dc7tP9HEwb9DpdZyz1xlfnbf8DBDfD4kZbzM/2VsIyf/Xz6nNsf/1e5/N6cN/Prtc+Vfn4Py1VG4mqJHDAsWj90HfQd8/XkHiGjgiE/RERVocZ/BWVvwY4j5kIgiAIwlDhl8TLzv8AvvaPHxnS4/+5jrfR8+D/QfSDX0XTOd+yY6vjfBF3NsMQBGHvwBtmWnM5u0RXwpt6CgK8Ia8mBPKmnmIhrapYVqW4QZxd9fWO2+dvLxwwD8UR7uJdTbAkFHh5nQJCtXZqwXEQddz9u607h1fC9tNyq5pIQ9gG1lFLhHbq+NXPMlVFUUJRg0J0rX5QyGEbawmvrINWucxfyf4qcBz9kY9gfXb7Hd6HwoZcXpV3sX22FT7KLwLrroViH9eemvPNFk5riYXlc7qaKEro35l1MG21OpwfhmqJu858qyV6EuajxWi9Oc91hWXVmvNsg1671HElTj9rCcxEr11fT2lRtFo/nR9kqv3gQ5if9ddbd9jOauuOw/74vY5GCdyoij5V+fh/I1HVwTd+FlrP/zaSz/4n4g9tu9GVsH8gwqogCIIgCNuRXvo8eh+5CS2X/yfCJw7e0tXx6yYIwt6BVpY//H6mpqDpCAu1rLGIIxzUu+mnOEJqlUGRhRZR1W7qCQXeRu2k+EDq1UGrrWrCK+FYUCCuJdKwXNbBuobaD4oTrKNeP+gGoJbwShyr1lrsbwLH4ad/EKFx47EoY1lOD5VFqTRC4yfo8mpBgZUb5gg7D8U+WoDXmm/OnOY84HE1OJe1dXcVUZRwvrGOalaaRM/lKa66dVg/qFQXXom26lfXa9XBfFxDq1mCEqcNtconXFfq9ZNrV6N+sg21xF+i21ln3XEsaxsh3+sGh6d1ElrPvQ6ZBY+h9w9X27HC/kL1WSbsNfh4Cn0nycZVgiAIwt4iteBxxF/6LdqufgSBORfZsYIgjAT0Tb26ka4lBBIKefVuyClesIxq1lYOFEBqCSiEIgtv7Gvd1A+2nY3qoLVWvTp4vZ7AwX7UEpAJ63CEmmqwDook9fpBK7tawivheNNXYyP2J4Hjih/9GG+tXYfNqZQds2Mw39vr1+OKH99sxwi7m8HOt3pzmoIi87OsajjrRr06ON/q1cEfderVwfK59lWzBCXMxznbaM43Wlca9XMwY0lxtBasf1esO8LgcYViaDn3myhtXoKe/77UjhX2B2TzKkEQBEHYTezNzauGSuKN+5BZ+QraPns3PGNn2rGCIAxnnA1kHGo9autAa9VargIcaFlWS1ggtISiJWYtYYDQ6queOFvPmoqwnaReHYNpZ6M6Go0FLccontZiV9RBKjeR2d956f778MD3b8L7Y80YHdp2A6B6UFR9va8fF3zzW5h3ofw4uDsZytpDdmZON7q+q9a3evn3xNrVaCwHA9tRrw1kX1t3hst37/7nbkMpnUDb5+6BEdx2g7KRhmxe1RgRVocZ3IWv+4EH0HbBBfqxH0EQBGHkMtKE1YEXf4PiwGa0XnUXXJFRdqwgCMOdSnFDGJmIsLo9f/vdnfjj//t3HDh6FGaHQqgnEXHk3k2msKSzExd/7f/DSZddbl0Qdhuy9ox8RFjdfcRfvQvZde+hTX2v9rQfYMeOPERYbUz9n02FPU5i/nxsvOMO/SoIgiAIe4r+p3+OYiGHtmv+KqKqIAiCMCw46R8uw01PPYPg+w7FI+vW4+3uHnSm0kgX8uC2Oyn1ynPGP7J2PYKHztbpRVQVBGFvEz3qEoSmH4uuW85EbtWrdqywLyLC6jCDvpPoZzUyd64dIwiCIAi7j1I2iZ5HfgBX83i0f/5eGK7aj6UJgiAIwp4m0tKCz9x8C/7xp/+BMWefg7fNEl5fuhx/e+VVPL5xsz5n/D/e+h/49I9v1ukFQRCGA6HZZyPy/g+j69ZzkHn3MTtW2NcQVwCCIAiCsJsY7q4ACv0b0PfkrQgeeg6aLrzJjhUEYaQhj+PuG4grgMHz1gc+oB9P5aa/wt5D1p6Rj7gC2DNkV7+OvsdvQeySmxE69pN27MhAXAE0RixWhxmZVau0GwB+eAVBEARhd5HftAQ9D92I8DGfEFFVEARBGDH0PfOMDrxn4v4UgiAIwx3/lPej9UPfRv+DNyDx1x/ZscK+ggirwwz6V3398MOx9pZb7BhBEARB2LXwV/OeP/8fNJ9/AyIf/KodKwiCIAjDn9Xf/a59tO2xIAjCcMY75iC0nncdkq/8Lwbu/5YdK+wLiLAqCIIgCPsR6cXPou+Jn6L103eOuEeRBEEQhP0bx1rVQaxWBUEYSXiax6H13OuQXfY8+n57lR0rjHREWB1mTL3hBpxsmvpVEARBEHYlqXceReL1P6H96kcQOPQsO1YQBEEQRgbVLFTFalUQhJGEyx9G6znfQKl/Pbp+8WGYpaJ9RRipiLAqCIIgCPsB8VfvQnrFK2i/5nH4ph5lxwqCIAjCyKDSWtVBrFYFQRiJNH/gi3B7fOi+5YMoJTrtWGEkIsLqMIO+Veljlb5WBUEQBGFX0P/cbSj2bcKoa56Ap/0AO1YQBEEQRg71LFPFalUQhJFI03GfhHf0dHTefBoKGxfZscJIQ4TVYUahr0//6ppZtcqOEQRBEIShYZYK6HviJ4Dbh7YvPwYj2GRfEQRBEISRg3N/FJg6VQcH55z3UNWsWQVBEIY7kSMuQmjmqei85Qzte1UYeRgbOjrMlR0dmDdvnh0l7E34pYFfCiJz5yJ2yil2rCAIgjASWXd1GGO/cI99NjT64km8tWgFVq3bhJOPmo2pE8bYV+pTSqmbzCdvhWfKkWj5+K12rCAI+yKGYdhHwkjHNE37SKjHs/ZnnntTCHsPWXv2DfaldWdXfPfeW6SXPo+Bp/4Drf/4GwTmXGjHNoYaEvWj3QV/uHqhpQWeWAzH9/basUI5IqwKgiAIwm5iV3y5+8g//1888ORL9hnw5B0/0AJrPQo9Heh78qcIHfExRM+/3o4VBEEQhH0D5+m+cutVQRCE4SCspjM5BAM++2zHyHW8jb4nbkHTh76H8IlX2bHVoUHeup/8RAurx6xcacfuekRYbYwIq8MMTgrnFwcGfoidLw7OrxC8TpxzXmc6ftCdLxe10vA609UrtzLNYMqtl6ZWuaQyzY6U66QZTLmVaQZTbr00tcollWl2pFwnzWDKrUwzmHLrpalVLqlMsyPlOmkGU25lmsGUWy9NrXJJZZodKddJM5hyK9MMptx6aWqVSyrT7Ei5TprBlFuZZjDl1ktTq1xSmWZHynXSDKbcyjSDKbdeGue8/7bjMfE/Uvp4Z/jyJy/ASUfOxqevuxkXnDYPt914rX1le3LrF6Dvrz9B9OxvIHLKP9mxgiAIgiAIgrBvszeF1Y1dvbj+1v/FHff9FYdMn4wrLzpDf4ffUQpdK9H7xE8RPvZTiJ7zLTt2K46g6mzax/sREVb3MhRWX3zxRVMYHqy8/nrzGcBcds01+rz36af1+fOxmD4nr82dq+M23H67Pu+4+WZ9zngHnjPE33xTn7M8ni+64gp9zngnTb63V8fNP+UUfc7ySOd99+nzl6dO1eeE7WAcrxGnve9eeKE+Z1k8Z0ivXKnjeI3n0ifpE5E+SZ94zrCv94n51v5TiM9W7XToevlus7DgYVN9OTNj0fCW88qQePDb5tqrI2bq1d/rdgiCIAjCvgj/3+78/10QBMGB372rfUfeE+HTF59pGoZhXn35h8xTj52rv8MvevRXVdM2CplXfm1uvPFIs+/ua+2eWfdIzn1TeSi/b9kdOPc65fdmwrbI5lXDjLFXXKF9q7qbm/U5fxWgBZRjBUUqz/kLBc8dyylSmcY/ZYo+Z3mkXrmVaeqVO5i6w3Pm6HPpk/SJSJ+kT5Vp9tU+OdfVl6MhB8cytW8goV9XrduMKRPGIBYN6/NyUgufxMCzv0L7lx5E8MiP27GCIAiCsO+x8Y47dBAE8vzzz4P+XmuFnb1eLfz7v/+7DtWulYcZM2bg4x//uK5D2Hfh4/+0VP2ny87Hzd/8HB6/7UYE/D7MPPuz8Mw6d4dD4JhPYex1r+EbP/sjOn98PhZcdBHe+sAHtliplsOn6WhRurvCK9Om2TUJtRBXAIIgCIKwm9jZx5G4cVX7sR/Tx3NmHqA3sfrOF/8B3/nSZTrOIfHmA8gsfR6tn/0DvBPq+18VBEHYE5x11ln4y1/+Yp9ty7/927/hX/7lX+wzCwoUX//61+2zwWGaphYuGnHmmWfi1FNPxac//Wm0tbXZscJIZvm11g+P02++Wb8KArnuuutw00032Wfqc7J8OQ444AD7DPjDH/6ASy+91D4Dfv/732vRk3At+cUvfoGLL75YrxOVaxLXG0KB9Fr1+fvoRz+6ZR076qij8Nprr+ljrjePPfaYPmZ9X/ziF9FrPz5dXp+we9ibrgDmXvQljG6N4U8//Vfc/ejf8Lnrf2pfGTqfP3kSvnvOLKz8z0VbXJrtLWgEePDtt9tnQjlisSoIgiAIwxRapnKzKoqqq9dt0v5V//mT2+4SGn/5d9qvavs1j4uoKgjCsIHCQldXl31m8YUvfEGLE5WiqsORRx6J+fPn6zSOiOFAMZZxLJPHDox7+OGH7TOL5557TsdTVLnkkku0wEuBhGJvd3e3nUoYyVBQFVFVqKTZfvrJoVxUJRMnTrSPLMrPuT597nOfa/jjywknnICbKz57tfJQRP35z39un0GLrLIG7bvQp+pTL8/HmOM/rkXVpkgIiTfuq/pUWqOQeeXX2HjjkfjBly7G6H95VPtQnXXffds8MefAOPo+3d1BRNXaiLAqCIIgCMOYk4+ajdf/dCu6Xr5b/wJe7gag/9n/QjE9gPZrnoA7NsGOFQRBGB5Uig1Tq9wQlnPXXXdhzpw59ll1WCaFWVqFOTQ1NdlH20JRhRZj06dP1+e0KLvtttv0sTCy4eYtDIKwqygXQBtBcbXWD0SVlIu3tFxduHChfSbsa3Cjqr/f/RN8+uIzceu/fhHr/3andgewo+jNqx66EcG5F6H5oz+2Y4H2Cy+sKbDSndnuDkJtRFgVBEEQhBFGKZ9Gz2P/D65QK9q+9GcY3oB9RRAEYWRCkaLSuqwezqO2g4E+Dh2eeuop+0gYydDXIIMgjDSi0ah9JOyLHDFrhhZVv3DpuUMSVXMdb6Pnwf+D6Ae/iqZzvmXHbks9gVXYO4iwKgiCIAgjiGK8E70PfR++yUcg9il5JEcQBGFHiInVjSAIe5C1a9faR5b/1UZW+cL+S3rp8+h95Ca0XP6fCJ94lR1bG0dglUf09z4irAqCIAjCCKHQuQK9D9+IwBEfQfPFP7JjBUEQhHosW7bMPgL+6Z/+yT4SRjInm6YOglCP8t35GU488UT7yu6HvlSdzasIfbjeeeed+lgQKkkteBzxl36LtqsfQWDORXbs4Iidcop9JOwthp2wyv89amf1KvBfSZ87F6wTJ15fs18ZrBPL2b2d1PlTESx4VFJ/Syjqv4IgCIIwXMl2vIXuB7+H6Jn/gqazv2nHCoIgCLV466239OYx3MSKfla5yRV9IwqCsH+gdYGywI3tdjfcLI8ibnt7Oy699FLtV5Wb8tGHa6ONsYT9E25wlVr0FEZd8zj8M+T/USOR4Wuxapa4EvIAMKwoLYOq+FKJMqiJggrd+SIW9aexNJFBf0ldM5w0TGELplxIVW7+dRZVnqlU+p8+t6sSBEEQhOFGZukL6H3kB2i5/JcIHf8ZO1YQBEGoBq3SKGzMnTtXb4h1ySWXaKvVc845x04hjHReP/xwHQRhuMHH/fljTktLix0DfOYz8t1NqM7Ai79BfvNyjLr2SXjGzrRjhZHGsBNWDVsMLRpsmgGXaemqpvpjqiNqnyV1KaEOFicLeGxVL+55Zz3ue289/rahD6tzJWTh1umLFFkNlZCZdAFWmYxgHSzNreLc8MBgoYIgCIIwzEgt+Avir9yJUf/8KIJzL7RjBUEQhFrQKu1b39q66QfF1UceecQ+E/YFEvPn6yAIwxFuxPfb3/7WPgNee+01XHfddfaZIFj0P/1zFAs5tF3zV7gio+xYYSQy7NREiqclw7AEUfucUqu2VjXUDabhwuqsicfXZ3D7u914byCHww8ah5lj2vDumj488N56vNQTx6YCpVOPyqfKozrLkhyBVXXbUP9cJXVVW7OaKKo0JbtOQRAEQRgOxF//E9KL/4b2Lz8O3/Tj7FhBEIR9D8cP4a7ixhtvxJFHHmmfAZdffjlWrFhhnwkjHW7WIhu2CMMZWsiX/8Bz0003yQ88gqaUTaLnkR/A1Twe7Z+/F4bLbV8RRirDTlilHalRMuApqSMzD9MsqLgSMjCxoVDCC105/G5BAvcsyWJhxg9/LIqZLT6cP6UZH507GS0BL15YvBaPLdmE9/py6C+6kDfduoySUUDRKKKgQlHVZRoUWS0R1zRUXTpWEARBEPY+cT4a1LVS/4rtGXOQHSsIgrDv4WzysquhparzOC79HNIlgLBvMPaKK3QQhOGM/MAjVFLo34Ceh2/UvlRjl//SjhVGOsNMWDVVgwrqtaT+8nF+ugQoordg4p3ePO5ZMoD/XRjHC3EXeoIR5D1RvLcujzc2JJFWuWaEPPjYIeNxwSHTUMyU8MiCDfjLij6sTOSQNFWphirfLMAoFVAyi6oOAwVax9J6Vf+j0CoIgiAIe5e+p36GYrGAUdc8CXek3Y4VBEHYN7nttttw9NFH22e7Dnkcd99l4x136CAI5fT399tHFpUi5tq1a+0ji8rzclatWmUfWfAHoFqUX6tMV+0HHm6sJ+x/5DctQc9DNyJ8zCfQdOFNdqywLzDMhFXrMX1CdwADph8LUy48vroPjy7ajPldeXR6QsiF/IC7BJfLhXTRh66UgWTRRMk04S+ZmN0awIePmIgjD2jDqr5u/HnRajy/fgDrMgZyhg9uww2PSucyi9palfW6Sh4YpphgC4IgCHuPUiaB3odvgrtlEto+90f1vyf5wU8QhJFLpcDwxhtv2EcWFD3+/d//HV//+tftmK1UCiKVgkk5AwMD9pFF+Xm1x3FZpzCyWXzllToIAnn++ef1hnWc3+VMnz5dxzvXuUt/OTx3rjtwfWDcf/7nf9oxFtzl/6yzzrLPLJy0/NHGgceMc9aZaj/wcGO98jqFfZ/s6tfR8+f/g+bzb0Dkg1+1Y4V9BWNDR4e5sqMD8+bNs6P2LJasuRXu9J9RkZ2ZAhZsTuG9DQPwevM4eFI7liXdeGpdEQkjBI9Le0ZFKJ/G+RO8+Mi0MJpdJRRNywKV//F1Y76I1zu6sHBjH4KhIGZPaMXBLSGMdpfgM0souSw/rC7TbolaBC0qWyYIgiAIO8a6q8MY+4V77LP65HvXY+CpWxGYfa78ii0IwoiHAsRf/vIX+6wxX/jCF/Dzn/9cH1OUqAU3pjrhhBPss/ppTf393uKoo47aRvzgzt2PPfaYfSaMNF4//HD9+v4339SvgiAIZEe+e+8p0oufxcDz/4PWK/8XgUO3FeeFfYNdL6za31/0JvzWEQzQXyq/9NhffIySTlCCS6dx8br6l1Ghs1jE0t4k3l3bh2yqgEPGNWPW+AhChqni8/jL2hzeS3qQd/tVMRkcEMziogOiOLYtCF9JlavQm/+z3pKqz+XS5a5J5vDK6o1Y2Z/G2JZmHK7KnR7xolldt2xki6qNqiWGW31BU8f0t6otWN2qJNVKVT/buKUPgiAIgtCAwX65y29ajL4nforIB65G5PSv2LGCIAiCIAiCIAyW4Saspt55FEkVWj/ze/imHmXHCvsau94VgH60niIlxUnTElQtpVNB4bMI/njMI74yeVGd9ZTyeHsgg4eXdOP5pZvREvLhQ3Mm4IRxfjSl4gjkizhiTATnT/PhpNYk5oT7cHwsgzNHGZjuyQO5jK7BNOgpVQUtkqqyVSVuFaaHfbjofZNx1sxJyKezeHzBejy6Jo6F2SIGVBuLWkC1G8W8pkfFUfJlrAosjME+FQRBEIRdQXbVa+h54HuInv9/RFQVBEEQBEEQhH2A+Kt3Ib3iFbRf87iIqvs4u9xiVVt30gJVC5C07mTgi4mSUYKp4rhFFY+p6xaKwJpMAa9u7MWKTQMYFQrjqKltmB5WBSTiupxIJAyvz4uCOu5PpZE1XMipvB51MVQqopRIwOfxIRRpgtvv1YIuoc9VQltTvd+/bpQLcVX1K+sG8GTHAAy/C8dNjOKIWBBjvS742VzTpdMbKr1Lt1P1ynSrnHZfSNmhIAiCIFSj0a/mfDQo/sIdaPnH3yDwvjPtWEEQBEEQ6vGs7QLiZPt+TxAEgQwXi9X+526DmUmi9aq7YQSb7FhhX2WXW6xaIqYbJRW0+KgCDVZN/T8/SpMGr6BolrAhk8Wz6+O4d8EmLO5KY+bkMTj7fWNxkCcNd38PwoEQYq3t8Hh8qgATmXwBpYKJVpcHowsFtKm4ADei8oVQNHwYSGVQKNLG1KqYOiotZtlJj4p1qXO2rFVFjIkFYQajeLvfh98vTuC3S3rxSm8OnUUDeZVdN9coqdSqTgrFFFi1MMt464UwxgmCIAiCMFiSbz+MxBv3oe3qR0RUFQRBEARBEIQRjlkqoO+JnwBuH9q+/JiIqvsJu1xYtSRNS2qkdWpJHXJDqaJ6NUzas5rYXCzh1e4cHlrYhTfWdGF0SwinHTIJs2IRIJFSeV1oam2DPxhQxbAsNtMFM5uF3zDg8XmRR1FbpPp9QXXNrT63LhjuEhKJBIqqMmajM3urPSq3+uNWR3QLkEQBCzozWDVQRDHYjE7vWPytx4tfL+zFfSv78HYii17TdgJgutXkcNP81WpLmahKnNOKaEEQBEGoCR8NoguAUXw0aMr77VhBEARBEAbDnKef1kEQBGG4UEr1offh78M1+iDtU1XYf9gNwio9k+bADaD0dk+mCXcpj5JZQm/Jhbf783hg6Wb8Zfl6BENufPjwyfjglBaMLRXgTWcRcBvwB4MwXT6VhxtcqQ+oy0CeYmmhAJ+7BI8q26XKo9Dp9qg6VCgVsgj7PSqugGQyri5RwqVTAhOlUglFlTefy6CQyyKVyiCRzqFouqCqg9ul0vhj2IBmvLQxgYcXd+Dpjl6sTJnImlqOVeXS1tVxMrAtIqoKgiAIg6X/b/+NwsBmtF/7JNxtU+1YQRAEQRAGS+yUU3QQBEHY1Tz76jv43s/uxK/vf8KOaUyhpwM9D9+IwPvOQsvHb7Vjhf0F99e+8pUb+gYGMGnSJDtq56HQSFEUhgumYSJl0o9qES9t6MeLa/rQVzQwc9IoHD2xBeO8Kl0ug2Ihh4DLQNTngc/n1u5ZuXUU5UyXKieTyaBUKiIQDMLlciNHtwBuN9xeH7zqNZfN65qDgSCSyRSyuaxOl83mkMmmVfosMuk88gUTBZVycyaHNakS0iU3PKYJV7GIFncBp04L4eCWIJati2NJZxpp1Y6g342AV9WlLWAtadWyhbVx1NayKEEQBEGIP3oTIkd9zDrRjwb9FEawGW2fvxcG3dwIgiAIgrDDLL/2WvT+5S9oPessfb7xjjvQ98wzcAUC8I0di8yqVTpu4OWX0XTssduk4XVPLKaPu+6/H7mNGxGaOVOnWXvLLToPz1mWk6ak7kUDU6fWLbey7tSiRYjMnavT1Cq3Wt2NypU+SZ+kT7X7VFx579bv3kPgI//8f/Gtm+/AW4tW4O5H/4bv/fx3mDPzAMw8oLZellu/AL2P/jsip1+L6Jn/YscK+xXcvOrFF180d5SSDvxbtM8siuqQoVQqmslSwVyaLZgPrOs3f/DaKvNf/77avHFJn3nj0pR53dtJ87tv95v/s6zffHZT3OxI58yMylgoFlReK5ilnCoxp0ovmn39fWb/wICuSV01e+Nxs3cgbuZyeTOXzZudnV3mmjVrzN6ebrNz82Zz2bLlZkfHOp0nlUmZ2XzWLBWLZjqTMzv7+81FPf3mrxYPmJ99rtv8xLNd5j8+t8H80bubzaWJrJlXdazIlMzfLVxv3vDce+bP3lprvtiZMjfnS6o17DF7bvVe/3GCIAiCIJSx9p9CZmHBw2b27781N33/GLPvD/9sXxEEQRAEYag8A+iQXrlSn7974YX6fNk11+jz3qef1ufPx2L6nLw2d66O23D77fq84+ab9TnjHXjOEH/zTX3O8ni+6Ior9DnjnTT53l4dN/+UU/Q5yyOd992nz1+eOlWfE7aDcbxGVl5/vT5nuwnL4jmD9En6RKRPQ+sTv3tDHe9MOPyQ6WbXy3ebr//pVnPqhDFmLBrW3+erhcSD3zbXXh0xU6/+XrdF2D8ZksUqXY1yQye9y7/6Z5RKepMoZ/f9ggH0F0t4pz+FZ1duxqquBMaOaUGwJYZFXXkVSujI+LEy7cbSngx6MlmMiXoxLuSFlyWo8ujbtGC4kVchkckhncvB7fbq/aMK+SwS8Tgy6RRMlbZQyMFwqbqLJlxuN1pamhEKR2CqvKbXj4zHj4TpQrZQQDqVRMDlxoRYBJNjAUwMG5gSKuLYcSEc2eJFq6rA7fEg4gHGhj0Y1RxGdzKHt9b2ojNThMvnRtALeFQfOQ5sq9V36A2vrI2zLDcF2q6VpzwkPBYEQRD2G2ixGjjgGPQ9+u8IHXUJmi680b4iCIIgCMJQoRsAb0uLtnKjtRyt1vzjxiE8Z84Wqzbut9F88slbXAbQSi58yCE6D63lmMcdDOo8Tppif7++znOWyzwsN3rMMbpc5uG+H04anpPgtGn6nOUyD8tlnvK6o4cfrstgGsL2s32M43WnXOmT9En6NPQ+mRsewY//zqeZh86Rsw/EFRd9EGPbW7RbgNXrNuFfPvNR++pWUgufRPyFX6Pt839C4LDz7Fhhf8SgxerKjg7MmzfPjhoEWjQs6Mf1TZN+TRlpoOgyMVAysTKRx1vre7CxdwCjAkHMmTgaaZXsoZVxLBjwo+iPwHS5UNJCZAmeTC+OjxVw6YwWTA+6UcwVUDDd6FPlrkwWsLanF1FXCQfHmtDiMeFR9eRyebhUGU1NTfB4VOGKdDql4tUED4dVnB+dmTxe25TC/L4ccqqm2U0eHDcmhLFBF8xSUftxhaHqU3nZhWwuh3gyg5LLq7tomEWEIgEkC0Us7VJt70wiqdp28JgYDh3TjHEBN0JqgaH/WCq+brgopepjXaJ2hyBqqiAIwv7KuqvDcIViaDrvBoRP/KwdKwiCIAiCIAjCrobfvcd+4R77bMfoiyfRfuzHMHXCGHzygtOwev1m7Wf1UxeejttuvNZOZZF48wFklj6P1s/+Ad4Js+1YYX9laBartlZomC6YcKHgMpBECR3pIv6+oR+vru5CNpvH0ZNH4fSDxmBUwINX1w7glR4g640ALpXPxZxE5Te8yOfzGO0pod1tIpdKI10q4O+b4rh3eQIv9QIbMiWMaw5gxqgmhP1+vSEVxU+fz6derQ2qXIaBdDqNoior4Pdgjcrz4NoUXugpYU3KQLGQx4xYAKODPuQp6pY88JhFuI08skWgP6Py593IFF3IlrgFlxvpTE5b0R6o6j1wbIvqr4GFG+JY1p1GTpXh87vh9RjwajGVPaKVarmYqo7LTwVBEIT9BlqstnziVwgdc7kdIwiCIAiCIAjC7mCb/Q12kIDfhwtOm4dNXb346f8+oOM+97Gz8Z0vXaavOcRf/h3ym5ei7Ut/hmf0DDtW2J8ZgsWqdgKAkukC/3EjqE35IhZ2JfDeul7kzSIOm9SGGWE/2j1AcyiInmIRdy8fwEMbPMh5w4DbhOky4CmoEkygYJqIlAZw/rgSzhrtR6yUw4DbjXs6Mnh0kxtZXwyBXBxnjcnhokkBxAygP57QYmwkEtFCJq1GXapNyWQauWwO0aYIFmeAe9bnsLAQUc124UBvHJcfFMYx7U263UaJj/QXkYeJ3pSBnrQBU5XlUnF0clAy3KpcEz7VyLagGzG/qkflW6vK/3tHN1ZuHkB7OIrZE1sxPeZDzO2Cl8arHCZ2zLFaFXFVEARBEARBEARBEARht7EzFquDof/Z/4JZMtF61d0wvJbrAkGwjEZ3CAM0FeWO/S51uCqZxUNLNuG1lZvRFvTi7EMm4sQxUYwppeEr5S0B0+XSPksDBh+6pwsAPn7vQlHVbhq0AS3Ab5QQdBvwqEINlxt+txttIS9ingIC+QG0efMYF/EhFgohFA5rQTUajerQ3NyMWHMMTbEmjBk3GrG2dphuP8aFAzgs6sZEVxpjjBQObfFicjSgO+2BCS+FT1V7RjUrUSih4Cqh5FYtduXgduVV/wpQTQLUNbongJpAPtXy6X4vLpwxDmcfOkWP4GOL1+KZNV3oLxRV21V6BX3P8p91xL+CIAiCIAiCIAiCIAjCSKKUT6Pnsf8HV6hVW6qKqCqUMwRhFdpC1MXH79Xx0u4BdKbyOPF9k3H++8ZinJlDumcA9Djq9/vhUemaVfpJYRdafFmYxQyNXnXevGEiR7G1mMVYfwnjte/TPFIlE/SaeniLBx8YBRwVjeOsScC8MSGE3G4qtXCrVwb6WWWwzl3wetV1Vw4D8W7EfCWcM7MNnzwoiEunenDmxAjG++k6gLXTmpQbT1EvNbTVKuPYL9N0oajaz1RsKa8WiwyqrbRAVRcCKhzWFMAls8fj0LERdPT0oTOR1jlUEdRgLViN/U8YOdC9BIMgCIIgCIIgCIIgCPsnxXgneh/6PnyTj0DsU7fbsYKwlSEJq7TypPRp2Z+aGB3yYnLAi5jLjaDHh0Qig2yhBMPrQ6mQR36gHxO9Jby/zY1xrgQC+SR8+Sy8xQwChRQmepI4epQLU0MGAoYBvyrDKBoIJxM4PmriUwe34INjQmhV9dFylIKX3iSqArNoIplIal+q0ZAPftW70R4XThoVwUltPoxGXiXiQ/4u0JVByaUSuAzdB4+pDimkMd7woWj4UaJdK3VU1a+MupZIZZBJZ7QLgmK+oPqWQ7Mqc0rIj5A3iLztfJZ/KclZwqzD9u0Vhj+irQqCIAiCIAiCIAjC/kehcwV6H74RgSM+guaLf2THCsK2DMHHKqEAaehd/f+6uhMr+ws4c/poTAu7kckV0N3VqWVEPpqfzWVhFExEmpvQa7jx7No+vLE5i96iV7sBoJXqYa0+HB5zY4zPQC6bh5kvIeDzIZNPw+Nxw+fz602k8kVTux8I+Lwo5IsomkWEwyH41fVCsYhUIqlfo81RLehmkmkEwhH4Az4kU2nkc/TJGoLH7dF+MaiDFs08EukMBjJAGj4U3G4VT72ZVrkluFR5HtXOoBcI07q2VEQun4fH5QZUWq/Pg7e6MninJ4Pjp8RwWCyoFdWSy4Sh/b5afltZl6uGuEqhmNawjoWkY4FbDq+VivRtu61c2xirTtUEy9JYtZvHW2Gd1ds1WLhxmLbmtTG0RbHq7bYVNaSynF2BY9VcD9aZU5+NbDarRfNcLodMJqviS/D7fQgGA3qTNJ/63NEKu/K9EQRBEARBEARBEARh77IrfaxmO95C319vRuyimxA6/jN2rCBszxCEVQpxlhhXUOGJNZuxoj+PM6aPwYyQB4lEUotTmUwaqVQKrW1tiDU3WyKbaaIrmUJXvoSU26MtOkeHfNqqNBcfQC5fRCQa1ZajnZ2d6jyHUe3tCIcjlvanqqU4ms0XkEqkkM2lEQoG4fUGkM5k4FHlxJqb4PGqsksm4qpM1tvU1KTFslQqjUDAj4DfjwKF12wW6WwO0CKpF/GCAVWKqoiuAOjMoAR3MY+w14WWSABhVT7hhlalUhFZisAuF17ZlMDC3gxOntqG2bGAymnCoOqs8psqC21iqwmYFBIp+vX29mLpshVIxJN6jCZMGI/pMw7QorIDhb7ly1dg0+bNtmipytRvhSqT/gzKUWUwxqX6rsdd/ceyQqEgmumLVo0HxUKKhlZ6FVQ6S9g1VZvYzlpipPOIvNUXlr9u3VqsWLFKjznF31hLDAceOF37waXbBd3WGvA6y6Pg29GxDqtWrkZRjQv98tIuWrdtCOgy3QamTZuGiRPHq3Mdq+PZdkP1sVgooKenB2vWWO1ft24d0hTgbXGVwirFVAr7waAfEyaOw4wZMzBlyhS0trboNlvl8X1k+fxjjYsgCIIgCIIgCIIgCHuOXSWsZpa+gL6n/gOtV/4awbkX2rGCUJ1dIqyuGsjjg9PHYnrAwObOLmSzOTQ1RbUlIIVDCmwej0cLrflsSluNur0hXVpJ/ytQCUMqzsfsCzpvOp3R6b1enxYFA4GALsNwW+JVvpBX6Sni5pDMpLW+2BSOwq/S06KQghjFMVohhkIhbXHY3z+gLVV9Xo8WfllWKByBz+9XZWQxkM7rx/kLWqx0aUtVVymPoNeF5nAAXgqwrJ1iZRkvbOzHW5sTOHFyK2bHgpTvGgqrliBnlbV48WL8/vd3Y8WqNTBU/KmnnoKPfvQjWgh16O3tw91334OXXn5ZW02yLBqvaj2voj120VvgZbfH0AJhOBxGW1sLZsyYjlmzZmHc+AlaaLbao9qt0lr927bMrVCctI6YjuN7//3344knnlTvtWVJPGbsaFxyyUcxZ85sK2EdLOHVqu+vf30S9/7pAf3eutV7rSuq7MwgKRYL+jNw8Ucv1uNJKGQTfibj8QTmvzUfL7/0Mlav6UAuk9cDpVvCDdTsY6sJliBLr8IU5inWnnDCcTj00EMRDKr3W/eBJVv9EARBEARBEARBEARhz7IrhNXUgr8g+eYDaP307+CbfpwdKwi10brcjmOJcA604KSVYW9/HxLJhBZS9U79sZgWpBKJhBZZaQloer0oen06vckn600DRdWMEtyIhqMI+Pzo7u5BJpNBS0uLCjFdRzwe10JrIZ+DoTK6Vb5sno/lmxjXPgYHTJyIpgjF2pIWTfv7++1HujMYGBjQFp+0WO0fiCNfKCLa1IyWWKt2OZDLZpFNpxH2uhE0ivDkk2jxuzA6GkR7JAhPMY+8uk7xzJLNKLJtK/rVMcosG6nq0KLTq8YlaFvTetyVj+szjQGvh4+iB+HzMfi1xSlFYYrGFKCdYFlZMqhjVS5FStNwI5PNoau7G4sWL8GDDz2K/77tDjzxxFPo7umx+qYrpeVqReXbYIuOdgNXrlyJJYuXwu3yqrYHtWuGvp5eLFZ1ZDM5nWawUDj3+dlH9svqg1cHnrNf7J8TpwKPy4L1uL4VPDqdJbITiqpsM0XV/r4B/OWxx3H33X/CkmXLdX99auz9QT+C6jMUaYpqcZ+WvZFIWIunFPb9voD63AILFy1Wef+I5557Xn++LItchnrjJgiCIAiCIAiCIAjCcCX++p+QXvw3tH/5cRFVhUHj/tpXvnJD38AAJk2aZEcNBkqFFEMNrOhPojOZx1i/F8F8SltEtrZymylbDPR6tVUjhVG3y4VgKAS32wNXiT5MaRVIRc+lSuM/QwtoyVRSi6iOpaljrZrNZLVYSvkqp4774wmEI2E0RSNa3HV7fNqak+lZL0mn09qtQDKZ0gIar0ebouq6R5dDwZfCr9/nhV/F5VR6n9uNSMiHoNcNr8cNs1jQlrEejxd8/JviLR+/t9puYG08g03JHCY3hTCGzlgVhhYn1TjxxfpjpbdxhEm+9vT04r2Fi7RFLWOnTp2CQw6ZuaUPJJ3OYtHCJVi/YT2rRDAYwvjx4zF69Gi0tMb0o+kUovna1taq3wMet6rjpuZmhNT7QjHW8jdqaMGRfmeXLV2GuKp37LhxWkx0cNpXSbmlLa2LX3zxJbzz7ntwqfeU0P0ALU75mP2ECRPQ1s7PAvNUL8/Qbgx4zcCqVau0SFssqfdStS+m+jN6zBjEYs1otgPdDFCwZ6Do7hxbgWI+QxOam5v0uBx88EEYp/pm1UUL2xyeePIJPPXUU+q9N9WYWJ+tUaPaVdqDcdjsQ3WgNe/MQw7GAdOnY9z4cepzG9S+dfn54+eXIv2GDRt0vZMmTdTlC4IgCIIgCIIgCIKwd4g/ehMiR33MPtsx4i/+Bvm+dWj70oPwtO6IPibs7wzNFQCfQYeBguHCE6s3Y2lPGqdOG43JnjxchluLq+VQWKUIxceox4wZrXLSryZlRhfVLhalSqW4ZupH9Sl00hKQ4hrFVQqlFMWo6ZuTmTQAAExdSURBVA3Ek+jq3azOS/AEoohGo4gF/XpTJ4P+UVUi+j+1/LxmtBBIMc2y5LTcAdBHKS0rKQJmVRqXx60tbFPJFLiJUcDerIjiKykVixgY6NfiIa0Y2X9rEymXFiqfX9+Lt7qSOGkyfazy8X3V0DJXAEyn/YXqPm6FbWO/li5dhnv++CesXt2h23/SSSfiwx++QFtKOvT19uPee+/H3197DfT9ecghh+AjF12ICRPGIV+obxlKK8tMOoPevj50qPf6vfcWahGT40O/qKbq31FHHYXzP3QuRo1qs3NVp1xYXbJkGf74x3uxavVqy6LTr96HUkmL2RyXM888E2effQY8arydvm6P9VlieObpZ/HAAw9q6+JAKIhzzz0XJ554nBa1We12uasVZzXPuqYyWe+jV9fPNi1Y8J52qbBx4yb9mfCrdh9++FyceNIJmDRxot4crRpZ9blYsmSpauMzWLJ0qa6H4v7sObPx0Y9+WH2ux9gpBUEQBEEQBEEQBEHY0wzVFUDfUz8DvEG0XaXyVtUtBKE2WvYbCvbD8BoKVxQO6R/US9+YFVDUokDKR6bTqSxKFEANN0wXc7EUyqpFFUrI5ij2lbQlIIXFZDKp/a3SjyXFUm6O1NzUinyoFe/EC5jfn8OmAlAy3SgWijrtwIDlNoCiGi0329raQD+tfMS8paVZC79+fwAJVfZAIq6tVukuIJlOo6DqtjaV2jqZXG7u/u/X9XOTLLoSoNCWzmSRUCGdK9jCX/UJuEumJQuxC+J4Uhz2B/nIu1uPU70QCQfR3t6CA2dMw6kfOAlXXPEJnH76qXqjL/2+qfflzTfn45VX/q6tUBtBgbSgxmDRwsXYsGGjKsPQAvdRRx2JyZMn6/GjZeeSJYuxft36LXkaovtopeMLN4yiJXFEvV/RSFg/lr9NUPHbhfJrkYj+DDiiKlm9eg26u3rgcXthqnbOnXMYLrjgPEyZZImqemMyHdQ4qz/sC/OzHbMPfR8+9KFzMXXKJPWZKWihvWNNBzo61uqyBUEQBEEQBEEQBEEYGZQyCfQ+fBPcLZPQ9rk/btEjBGFHGLKwSjHN+chRfMpkcjDVh5C+LcuhKEXLSAqrseZmLXymUxn7Iv9Q2mMzrJDPWsIeH89mHoqg6XRKW5omEgNg8Ua0CW+k3fjTeuA3S5J4aEUP1vQOYCA+gGzO2qyKYirzsn6Kqpwf2SwFWlWLFtlMLTqOHTcebe1tWiikGEghl4/mJxNJ1c6Utr5MqNciSkhns9i8ebN2a0ArWAqtdG/g83ng0n42dyeWAL01qL/sjMIS/7YNKpYpdDBN1fpS0bqm0kbCIZzxwdNxyiknqbEKaBGZfZ//5ltYsWIli6yJI5BSUFy0aLHKl2UNmDJ1Ck479QOYefBB+r3jGFNwXErrThu212lzNcov8Zj9ss+GFNhfWuQ6babgvmnTJv1es+ympghmzToE0UhEp2VgSloya8mf7h7svE5bpkyZgtmzZ+s+koT6nHRu7tLHgiAIgiAIgiAIgiAMf/K969H78I3wHXgSYpf9wo4VhB1nSGogZStLWqUIZT1m3t+fQCZbQK5YQl4FLUSZlqjK9B6vXz92HQz5kcqmkM6mVf4i5SsUVQLarBZo+Vkq2kKlyqTyBwN8HN5AZ2cXn8BHJBLFplQOL2/KYLkZxSpXDK90ZrG0s1+nD4eC2rqQJoe0PFR/QR01EAyqNvGxeNW+Qh6JgQF43R7dJvr09LgNxKJR7QOWj9on4wls3LgRGzZtQE93NzKpjHZlELA3jNKbczU3waPqGigZSJVcug/EGp/dixYo7YrYP1qdlgfrrWV71PtUfk0lZl6KqUcfdRRmzXqfGhc15qpfGzZuwOIlS/QYEUcI5b9yKFa+t3Ax1q5bp8o00KTG7X0zZ6KtrQUHHDAV48aO0e8FhWlulLVp82Y7J9tpjVEtrNqc+ramrSfI1oJ1lVen3UOoz6olRJv6feR7Sqw6rTpYFfNSLHfay1enDRMnTtA+ibVfV26upq4VCuWWvlvLEgRBEARBEARBEARh+JDftBh9j9yI0LxPoenCm+xYQRgaQxJWy9UqCnSFPB+LdsPj9iART2JgIKktWPlYeTaThtttwOP3omiWtDVp0B9EOplGLldQZXETLMu+ktamLNrv82tdihsgxeMDur72UaNB/60U60pFE0HDg3CpgGAhh9ZACBPGjtUWqAPxuLZspXjqSFv0h6p3x3e5VR059PX1I5vPaZExlU6jp6cP/b2WFSr3UuJj5KNGj8akyZMxfsJEtLa1IeALwqvaUVL54n10NZDR/VmWzOLVzhR68gYMNQa7X1Dj2NuCn974iWx9P6pDgXHbNBQJm5qbcOihs9DU0oyCGsu8Gm/6eaWIbaey69g279qOddoNQDqb0Y/MTz9gGg46aLq+xo2cDjxwhrbk5b9VK1di2fIV+hrZXiB1BGAbXV1ley1x2Eq3Y4HlOGXRZ69laUpXCh69odnmzk5dJT9blvjMtKYWVx0qx27K1Kn46EcvxhVXfgKfuuITeP/7j9AWultx6hcEQRAEQRAEQRAEYbiQXfUaeh74HqLn/x9ETv+KHSsIQ2cIwioFI5d+7J9iKDehoqDq9boRDnjQ3hxBwO9BKptBZ3cnUqkEPEYJrhJ9q5ZQVOkprtIvazyZRiZX0JsrZfMmMgUTpscHw+NFoVBCPJ7QO7FHomE0N0fhcruwYXMnPJl+HD/KjaPCBRzmSePYVgMToh69c3usqRkFVWBfPx/nH0A+V0QuW1R1JZEr5bC5tw8ru1NYXQxiUa6EDarOvNuLSNsoxNpaEY2G0ByLwuPzaBEuoNoTUe1tagoh2hRFU3OrtZlVTze6Eim8uSmBhT15FFz0M8sHyEmleLir2bnyy4XCiRMnYcK48do/Ld/b7u5u9Pb0WhfLKK9x0eLFWLN2jbbo5NjMnDkTo0eP1te4idUhh9B6tY3ypH78fvGiRer96LfrrRQcq/fFshq1T3YSR8yl79X2Ue2WT2BVdiKZwt///jreevsdLbA7LgLYzmp1sxxeD6s+T506GQcffCAOOnCG6nu7/oFBEARBEARBEARBEIThSXrxs+h/6mdoveoPCB97uR0rCDvHkCxWjZIKpvXgOzf44WP2wUgAmQI3dsogHPKjrTkKn5v+Vj3IZotI9A+gyMewtYBnIhiOoOjyoiueRncih854BvECkFXpk/kC+pIJmKqCaDSi8qjz3l79yHosFkXM78JR7V5ceWirCi04ssUDd44WsFn9SLbLo+p1G+jq7MS6teuRUHUUVJv7VHdXGhH83RyF+zt9+N2qHP64JoNXBoCkz6eF34IqgxsT0UkBH3nXfgrUK6U50+VCULUn1h7TIh39yQ7kTKRKPrgMrx6TcrY9G1444iofZ6d1rlv7RTWQVOPe39+vr+k0uhNbe7Ju3Xq899572vcs4ydPsQRG4giYU6dOxfQZ022B0sDy5SuwcuUqfW17wZJ5ykZKXXeETbpkIHyEfyiBm5I5QimhUD5t2lTEWmMo0v2B16fatRq/v/MPuPvuP+Lll/+ud/7fvLkTiURCu7Hg58kpg1apjmUq+2r5cKXvWn6mBUEQBEEQBEEQBEEYjiTffhiJN+5D29WPIPC+M+1YQdh53F/7yldu6BsY0D4jdwRKYQXDwIq+FOL5Eg4aE0MAJSRyJkxvAKlsHm4YaGlugS8Y1o/jU6ji4/bcIijPx/pLLqTyQLZoIgcXCipHrlhUabLwutzwe73IpFNaMKUlZDgcQYg+MU03MokMgvkUWnwuZNMZDPT1wWOa2oqSomgwFEBrSww+T1D7Zs16PZjfX8TjG4p4KxtCR8GLTaqtq5IlrOlPw+MxMSrshq+QV8duuGjVqP65VDBdKlDw4x9VmKlamkpmVR+L2JA3sSyRR3PAg7ltfoz20x2AndZQY8EX9c/Ced0KBbuenh68995CvUEX4QZJtPqkZaUDx27hwkVYt36DFnzHjBm9ZeMli+3LHgwUGzds3Ihly1ZoAZGWq5MmTdCP81tlWuKmI06+/sYbePXVV0E3D3xPTjh+HubOnaOvOWn8fp9+zyimZjJZpDNpRNV7N336dNUna9OnrfCTxHwGVq1ajcWLl+p2uNT7zx39E8mkdk/QsXat3gyrY439Wi2oNHRTwJ3/16tx8vv9aGqK6locotEmLZquVWkp3Hrc6rOkxpabcb3z7rt4990FWLhoIZYsXYrNmzajT32uEomULaRaYavAagnC7LbVd6v/giAIgiAIgiAIgiDsWeKP3oTIUR+zz7YSf/Uu5Na+jfYv/Rnece+zYwVh1zA0YVULhiUUaQHal0J/toRpbREYbi/e6MriVRU25kw0h7xoCdF3pWFtFBT0w6BVZLaAgXQB6ZJbW62WXG5VngrqGkVXa/f6Ik0WEfB5EQoFtS9PinS5TA65nGX1msskUSgWEI40wa/qptAXaYogqOqiP1VaYHJzKopn73X24y9r0liSjyIfCMPwuOBy+1D0BZEwXehPZtHmMzAp7EPA49HXWYdRMlBQ7aH1Y0G1O5NJIZOnC4MiSgV1XaXrzOagWo/D2kPbCKvaMQBFty2C2/bC294WVsm6dRv0Dv+01KUAPmP6dBx88EH6mqWVqn4aLmzcuAnPPPOstlqlsfMB06bhpJNORHNzk06jU7LLKpPfH9DiJkVbWjXzfZo4YTza29u1OMk0VlrmYyWWsMp2WNeBtWs78NZbb2HBggV49933VHh3i/i5fXhXp2OYr/IsXboMk/QmUxN1uwjLpVg7duw4bYna092lLVtZt8tNi12Xdj3R29uHDRs2YPmy5XjnnXe1he6CBQuxbNkydHf3aJcBbo9bvz/022pBK9uhvweCIAiCIAiCIAiCIAydasJq/9/+G8V0P9r/6SG4opYLQ0HYlQzJFQAVMe6BT0mJWhJFs/5cHm/3pvDI2jj+uCaN+1an8FZ/HimKdfks8pksCpk83IYbQX9AP65fNFwosQAGtkSdmy6Piveoc68Wu3KqXG4URb+mFLECoTDaYk0Y296MCWNHIxr0wY88/D4PsoWiFsaowlLiokhL68d8MYd4yY1+dxgljweqBrhVH0xVH8UwlzeAzQUflg8UkTbdemOjUjGPfC6rfW/GEwm9KVYql4HhcSMUjqCttQWeUhFjkMXclgBa3YYWEPnPERlHCvRdS1FRC4Oq6Rwzip4OjmC4ZOkSrFrFR/oNBIIBzJo1S4uXFlYaOyna29vwvvfNRCQSUWW7sXHDJu2blYLmVlHVSluOJUJbF5jPoz4nbjeDRwePx6fjtg++inRe9XFyRM+tUCBvbY3hggs+hI9+7GIcNudQjBrVrjc+c6s8zOv1+uDzBdSrX7chnkhiTUcH3nzzLTz40MO47X/uwG9+cydeevFlvRGabnW1zgiCIAiCIAiCIAiCsOcpFdD311sAbxDtVz8Kw+8YpQnCrmWHhVUtG5ZrSKoEw2UiVyhiXV8GG3IeZEMx9Jb8WN2TRWdfEslkEvF4CgPxNPr7E0imkqBvSlp0sigt0ppaBrXKd3lQKJooqkDLx2AgAJ/Pr60NoeqyNsFSKb0qLtSEdC6PfDGv49OZjCpAlasb6UYynUY+n0PB40XScCPncqNgqPLVMd2nuvjYuao7p5J3pjLoTeaRyeQxMDCg/Y0W1WRkG2LNMbQ2NyHkD6KYLSKTSsHn98Cv2hN1FRDxubUlaTWqxw4fKFprK2GOm2H5Et1WJ3Tp3fMXvPceEomkFkXHjxuHAw+cri2QKZbSipMbjlmvBZ1r2rRp2mKUZRfUe0D/pXwEn7B81rc9fNzecj/Q3NyM8ePHY5yqa9y4sXYYUyfw+jidZ8yYMfpzU85WS1l+rnx6N/9PfeoT+MdPX4kLL7xAW9/OmTMHkydPRmtrqxbQvbbAyuDx+uBxe7Xl9ML3FuHue/6I++//s7bk3Rb2a7i/64IgCIIgCIIgCIKw71FM9qL7oZvgGXsIWj99px0rCLsHY0NHh7myowPz5s2zoxpDq0wDRdCL6l87urGyK41jJrVjY6aAP3dksSlrYLQnh3Mmh/GBiVFEjJJ+pN4wXaAxaipfQFcqi0TRgGn44GI8ZVHDVMEFd7GEEAoIuUvwq0C/psUCNwoytbDKR/v5aDp1OVoUUojLZtLIqnJNjx/haBj+gF/7cy2kMmiO+PFaErh9WQ4r80EYXo+WvVRRKrA3BYQLSRzljeO8cSEc2BqF21eEV2/oRJ+glH6BQjaLZDKlex8KqfIzWQwUing9UcLqRBGnTIxhdsyvR8goUXG2hGJthVnFotER+vjY+j1//JP2JcpOUeD78Icv0FaUDvT1ee+99+Pvr76mxcvZsw/Fxz52McaNHaOuWrUMFkfQdKwsH3/8CTz40COq3LwWVc879xycfbblzJlJmeyll17B/fc9gHg8zkHHzJmH4Kgj3w+/36s3gtqK1Ra+L8VCAa+9/gYWLHhP1+nzeXH+eefgtNNO1ems/jMP/xh4+uln8MCfH9bvGy1iz/jg6Zh37NHIq3btCGyzSxUcDofVGG4rrjYiny8ioTfwGkB3V5cWlHt7etDT06sD+5/NZnTaQrGo+l7CB045Beeffw7CodB2YysIgiAIgiAIgiAIwu5n3dVhtH/sh+j7608QOu5TiJ71TfuKIOw+huRj1ZKMaDnqwvL+NAZyJRw2pgnTYwH4TROjPHkcM96PI0eH0Gr7KqV0SrGL1qOpVAq5Eu1L6ZmU1ylcUmQ1tOWql4/ql/LqmK4DTHi8HgQCIQR18MPr88Ljtnax11avqkFsEzfMWt6XwZt9OSweyKA3nkHE7UbU70JelbkxmUG3CqAwa7rgof9UlTGn6prkzuOEUW7MUW1uivi1eEu/ryXT0P5ds+m0tmDlZk/Rpmakcxlk8nkYoWasThbRl8nhgFgQYwLW5ky0mNVGszxm62oIbRTgdszH6notMFs+Vt+3Uz5WWTfLfXP+W1i5coUWBcOhIA4/fO6WzwObTVH3b88+hxXLV6mx92vXAYyz/Jm+jbfffndLeEuFd95+B2/Nfxvvquv0Scp6nLoo3E6ZPAmRSFTFWZapVtvpY3UVFi1agkK+oEXYI46Yq61iKVjuUAiHEFKv3CjLEm8tK1WOm2OZ6wigzivTELfqGy1dYzFay47DgTOmaxH7sMNmY+bMg9HW1qY/vwPxAZXHpa1zU6k0JkwYj9GjR6k4XcyW8gRBEARBEARBEARB2P3Qx2pm5d8RPftbiJz2ZTtWEHYvO+wKYCuUCw0ticKgeFVELJ/ESTHgEzPbccb4GMZ7DbiLBUtkVCmz+SwSiT54VPr2SBgRj1v7KaX1KzcxcpdK6ryAkMoX9ntUOgO0Vs1m04jH+5FIxvVmQ5SsKHBSAKNVYliVFWtpQdv4cVjrDuCRDQX8cVUWz27OocugR1UTY9wlHNfuwSGRHAKFpKonD6+ZR8DMYIzZj7nNRRzY5IYrn0E+m0MmV0IubyKbKyLeH0c6mdCbaEWbmlR7ssjkC8h7vEiVaO9qjQSHohy2c7jLaxR1N27YqN0umOp9ampq1o/Bl7N06QosW75SC6p8S0pFNTZ0v6DeC4qghZwKfFWhqI6LauwK+aIaRyuOG2IR+shdvXo1li9foc+3//hRgOUwWsInfb1W4oiitYKDc+wInCxr7dp1mD//bR244RX77viWrZa/PI4bo1E8PeWUk3DxxR/B1KnTtKhK8bu/vw+bNm3W6Yb/Oy4IgiAIgiAIgiAI+yaxj9+K8Imftc8EYfczJGGVUpNlgwp4SurIdCNbdCOdKyBomGh2A/5SgU/CqzQe9ceFTDaDRHxAWxFGmyIIqdfWoB/NATd8RgE+M4tAKYeo10BLyIu2aAjRcEhX5lL5g6GQtmLUj9/3x5EYiCOTTqOQz28R4FJFExsKHnR6m9EXGouOQhDdBROBYBgt4QiOGteCc6a24OjmIg7x9OMgbxzHRnL46AQ3jo+VEKAVaq6EZLaIZCKFns2dWL9uPQYScbj9HrCWgb4BHegRtuD2IENRzkW3BBRXa7BVrxsmUMC0WtvRsQ4bN22G22NZd1Kgbo4162tkQI3ze+++h96eXv14P62FW9tatXUmd/hn4OZPo/hqn7e1t22Jb29vRay5Sb/v3Ek/EU9oq1TuvG+JkGWjpg+3tq3auPFaveBQfkz4GXn99ddx++2343/+53/wm9/8r3ZRUE61/NbLtiLrlCmTtbUwxX3GcUOshPqM0EUDqaxbEARBEARBEARBEITdy4Rbkwge/mH7TBD2DEO0WKUFKu1MiQmXYSKVzaLg8sITCmvRsWTyUXqPfhw+m0whnUggEAgiEmkGH6E2TBNBL9DsB6LuIsLIIWimEXYVEeAu9apkbjDEDYy4U3sqmYRZKuld5puiUS1q8RFsPj6fTCT1Dv5eo4R2VWaLmUWkmEC7r4AWPwVgE9lsHvmBJGb4DJwzJYyPHhjBp2c147Pvi+H8qaPQptq9puDGQgSwwetH3udSdRTR3hbB6DGj4PUHkM3lVX39qsuq9y43svT7yk2wthhWbiuoWQK0Yi/rbI4oWC4OEoqbFBf5WL9b9YfC9bSpUzB61Gg7BbB8+XIsXbFMvY90/WBi5iGH4LJLL8UVn7wcn/zEP+CTn7wMnygLn/zU1sDzT33qk7j8sssw65CZVDf1WCxbvgwrVjhWq2Xopm1t49aW7jy0LI2qz42L7h3UBzcRT2Lzpk5teUvKhmULlkDqhK3jSGgt7fV4t7Z1VzZWEARBEARBEARBEARBGPbsuLBqgk/+642f9KkqIV/IoJjLwO8G6FKVYpOhXwtIJQeQTcURCYcQpj9Qww392LzLEquMUhF+PpIP9ari6FPVpPLFy6zL5UYkGkVzc1SLp3zsmpsG0YcmH1lvaorqcvrjcZSSSRwe8+OUVuCEUApnTwpgRpNXi2i0lPSqRuddHqzsziOVKWJiyMBYtwl6Ml2WAe5bV8R/vduP3767GcsSJTS3j0KsOYag14eQL6D9rtKac/TYUdqPp1eVpa1yVVuHu5GiIwBSLGRbc7kCXv3763hvwUItqnLjKu6kf+BBB2o/oySpxpPCa1dXtz6PxZrw/iPm4uCZMzB12hRMn36ACtPqhmkq3cyZB+HwIw633ivTQE9vHxYuWqw3ArM+glsHj83cavFpf8h2EfSbGggGrfJVWL1mNdatW6ev0V+vM0ZWvU7YFual9Wtff/+WTbXoTiASCcPjURNAEARBEARBEARBEARB2C+wFLQdgaqqfije2naKCmo8lUE6m4ZZzCGXzsAsFFHM5zEQjyOTyyHa3IRAKIAShStbqzJt4YrCKnfmd7tc8Pi8KBQLyOWyVhqDtqbqVV33eb1oiTXrXd65idRA3BK2PCq+qakJrS2tiASDmBgATmgBzh7nw/sCBZT6e8BNiygK0tp17UAazy7djHfW9mMgU1ClGxgoFbCwN4MlmTA2esZhadyDTWkTRbeP3l+11S2tY+lTNBC0Nm/iRlw+uOEtAm7dLas/e47yR9+3CpPVYDqKf4SPrdPK929/ew7PPPMsUhQ3VXZadM6dOwfTDzhApyPLl6/EkiXL9PixjAMPmoFpB0y1rzqwz/w8VA/cLIoccMA0HDBdlU1NU/1j2atWr9HXtowbr/E9d0639G+oWG1z2jBu3FiMHTtaf8ZobUp/ry+//Ire7Z9Y9ZW/h1vrLx/DpUuXYuHChdrHKj+bFIzp+oBsFWcFQRAEQRAEQRAEQRCEfZkdFlYpG/GxcAqsOnPJhVC4GbHWNsDjRzJXQG/fANZv2IxEKodAuAkGBUruyG7lZi4Nj7KmgSw8KLo9MPlotYs78VtpuKEVxS3tdoDimAFVVwSRlhbkVO098QRS6bQqm5tfGQj4/Ii6XWguJBHK9MOTy6uSvXCpuvP5AjL5HJo8JRw+JohD28KIebiDvypXtcHrMuB35+D2pOAPFLSbAm7MRWktk8nrEPAH4fP7YRZLMHM5lPJZuFS7nEHc2rNqVLm6RTgsu1a/ENUeCqrWbvSJhOp/KoV4PF430E9qT28v1qzpwN///ir+8Ie78dCDD1suADxu7WJh9uxZOOboo7QvVMId/BctXISuzi4tIjc3N+GQmTMRa6b/VTbSaSj7wBGoFSxL0FgspvIfgqao+jyo8jZv7sTSpcuQ135J7XGwi3SsR+l2obu7R28MtWnTJmzcWB42lx1vrAhW/IYNG7WITGGYjB8/ATNVG7gRFWMY/9JLL+P++/+MZcuWq/FMatcAhUIJRfUeU4QuqvZxrLPZnC7rjTfeUmP3CFavWq034zLNorbKnTRpoq6DaHFVBFZBEARBEARBEARBEIR9GmNDR4e5sqMD8+bNs6MaQ2+b3Om/aBj46+pOrIjnccb0sZgRdCGXz6N/YEBvKkUhEhRUTRNev1dbRXo8BrgZFQXUzkIR73WnkMmbmBIxMDHsA/JFGKUSmiIhbbFK0ZO6GMVcSlX9JWBNMoe+dAF+dT7KXUSrt4SA24NiAXpDK8MoobklBr8/oHenp1iWzmQQ7x+AmxanoQi31EJL0AufakeqCLy0eQAvbUqjp2hiesyDcye2YGrYi0yxiIGBFPxuN6JNIapxyCdVWemkKicEdziC59cOYFlvCqdMbsFhMdUH1VKjpPqohWGKs5bVqz7WBxSL9YuWFGkBec8992LN6rV6rE466UR8+MMXIBgKMJWmt68P9933AP7+99e0oEe3CmNGj1Jj7FPjY70fDpYcrMrmuKny6Bs1XyhoNwq0zqTIzDIYaIU58+ADcf5552DS5Ek6PYXP995biHvuvhfr168H3TYcccRcfOTDF2rLTN32waKHQJWpyqBAyn6+8+4CXQ+tWD/20Y+oV8sK9qmnn8EDf35QvWdsnwdR1cdQOGyVsbV7W1Hx1khWtojCLF1U5HHkUUfgtNNO1T5RyaZNnbj/gQfx1vy3tAsEPT7qPW5uacbUqVMxZcoUtLS06Mf6KShTfGV7urq6sHzFcqxevUZb+brVZ9lU+caOacOHLjwfc+YerltB21gX54ducLVGC4IgCIIgCIIgCIIgCPsCQxBWLYHTElaBx9dsxoqBHM44YCymeYropy9Tn0+LYhToSoUScvkcsrksigU+el9S193ocwfw5Losnl2X09aBx45y4UMzWtFsZrWQFYs2AW5VARUyCmWqrL6Sib9t6Mdf1yTQmTXQHvDgpLEhzPFn4Uv26jr9wbAWvUKhgCVtqewsgpabuWwOfn8QGdWWgWQ/ItEgmkJhZFMFxHMF5ClS+v1wFYsY41Nl+DzoTcS1+NYcDWsRMplIa5E2EvKpOsKqBjee29iLtzcncOKkdsxuoYBnVhFWLZFNR9kHtMylyLxkyVLcc/cftUUp0594MoXVixAKBvU5+09h9U9/uh+vvvoqfD4/6OeTflFL9Eera9C9VcH5a8G2O9CFAcVU57F2bgI2Z+4cnHLySRg1ul1brhoqnuP05wcewlNPPa3riURDuODCC3DiiSeo8igaqjJ1f7bWWR1LVCe0siVPPPEkHnr4YaTSGXg8Xpx37tk4++wzVSkGnnjqKVXvg9pilO2j4FnizmD22PGN3NobhYov05O3gfXms1mc+oGTcPHFH9E+eWldahhuPc4PPfQoFi1aZJWpCi2oz2ZR9ZViKut20V+qyxJWdTvUNdbuiNGUT8e0j8GZZ56OI48+Ah7byrak2sPx4ZZp9cdGEARBEARBEARBEARBGMlYatdOYGmHBjLZHBLJJAJ+P6KRqL5GMcpwG/AH/Fv8oDZFYvB4QljXn8Mb3TmsNaLY4I7gnVQJa9I5ld4S1NLqOJ8vIpPPIKsCha/VfUm8sCGFRbkIugOjsSgfwrPqvN/wYfLkCQgFA+jv7dOWqWZRNUy1i3JYItGPYjGL5tYoos1hjGqJIdYcRU6VuWFzJwYSAwiaRYxCAdNDPrQZQC6bRV8qBRRMNAeCcKuSEgP9up8h1ZcAhTrWodAPvG/R0KqLaVtlNubZ6veTaKGTflvVWHG8eO5g1UBswc9FYc/QFpV8pJ3iLkXDYCioLVytwOOg3qgpFA4h0hRBc3Ozfoyf1piTJ0/GccfNwz9c9g+46MMXaFHVEg4t6Pt0ydKlWnCk0Dpt2jQceOAM+yoj7deGQaHSsuSSGl9y0EEH6sfm3aqPxUIey5Yv1Y/vE4qrVv8oALu0QO9X76lf9ZPWx/pVB3/Fq3PshIAeG/rDpZW0A4VRtmPy5Em45JKP4PTTT0X7qFHaty/ropsHt8er+2wJ1xRUVT9UW91et7rOND71GYpi7uGzcMmlH8KRxxyuPwBF9dkxDO2RV/1znEgIgiAIgiAIgiAIgiAI+yrG+o4Oc9VQLFbVP0plf+3oxKLONE4Y34zDWv3wefmAvqGS0XqPaS2JiekpwlKLLBkG3u1N4q6Vcbyb8MNdMnFgNI+LDohgVqCEZM8AvN4QfEEfcmYKrgItKQN4oz+HP64HVqAZLo8L+byJiUYK/zjdg5PHRrSW193Zpf2Ocvd+iorZbFZbLoYjYXj9Pp2G1pOJVAb9ff3w+1Sbw0HE40k+O46IykODxHhyQFuUtre1w+/1oTcxgGIxj1ikCR51ToFO2yWqvry0sR9vdSZwwsQ2HNbC/m9vsUpxjnVTZuQBLS15xaX+dW7uwltvvat9nnK8Dph2AGYfNgs+n0dn4filVHvfeftdrF27VltN6qIULNt5Ryzs+nik2k/Bln0MBkOIRi2BtbWtBS0tTToNYT+dPGzmkiXL8S4f12dT1flBB8/ArFmHaGtOllku/A4WJx/dELz99jtYsXyljg+psT/88DkYP348lixdgQW2mwBuZrZ1szOnvm37ufW4OvSResD0qTh09vv05mdWeVY7GNi/9es3YfHixVizZg26uruRTCT15lZM6vzqQDcGHMOWlhjGT5iAgw6cgenTp8AXoHVyyRo/9V5bMj4leLfOu+OjJAiCIAiCIAiCIAiCIIwUdl5YXbMZS3uz+OD0cZgZ8ehHt3mNT34znanFJvW3aCKfK2i3APlCFvES8HbSjbd7SvAWi5jb6sX7xzehLehGOp6Cy+VFIOxXufNwqbyFogtv92Vw/8o03k57UfC64M6XcGjEhUsPDGBWs09bGOYyGT7Brd0OJPrj4KPfLW2tCIT8KPJRcNUml4rr74ujv6cXbe0t8DdFEI+n4TMMeD0mksk4NqzfAL8viDETxiOl2m0YHu0OwOdRHStRVDVAxwYUAF/c2I/5nUmcOKkVc2J+3d+awioVO3VI0ZnQn6y+7nLr8y2YLL0C1W6deadRg69VU4Zy7PdW12NtYmXB9JbLAQur7YNri5NWQTWZWbbpB6+rvtIy1MU6HTlzV6HK1W1nPWVtsRqi/rP6WSqqzyQ3Q0ul9MZdFGXZJgqwfr/6LIaC2hKbriasNnJMnPJYljWefLfpHsJxfSAIgiAIgiAIgiAIgiDsmwxdWDUtH6sUVpf35/DBA8bioDB3mGcaWlGWwMfduYlVNqcC/asabni5C30hj6ILKIabkIYbAdOEP5eGV+UJhyOID8RVOSYiTWHkCjlkUxkUVGX5YAjvxgt4fl0cXekCWtwGjp/cjHnjQ4ipvJlsEfFEBqGQX+/y39vVpeUufyQCXzAIn9cNj2Egm8lr8ZbuAYJNQf0YeLIvqTc4CkYC6E8MIJMuIpfMIJlJwB8KojXaAj9dAngM7U+T5remy0RKtf/Jjn4s60ngg1PaMTtGv6gldZmPg1tiniWs6kMrqGM9TOovUxXSCWR7NsLMpqDNZdV4aMmOeTTqjFaR6oj67Jay9CvT28fEOdZCri7Fto7lgfPHCU4m50gnUKgW2xXpWnU5qk0sqBzn1MlWjfI0LEed66K3KYuCJL2S8p+VxsEeiRo4hZHyfHrEVXVsszVu25XijI96I9hXuldwaf+zajy3lEOYk61Qn+hSSYWi/mzqcVcJ9RX1ufbH2lUYpeIo1LItVn8EQRAEQRAEQRAEQRCEfZMhCKvEkqkKMPDEms1Y3JXGB6ePxfua/dpilZsA5fM57ae0VCzA7fHBGwqh6HJhcyKLzqy67nYDPhfafG5M8HtgpBMwKKaGo0gmE8hmMtqvJcvz+vza16bH7UFa1btZ5e/PqNoLOYwJqDLCfmRNA13pPOKZHKIqX7BURFSVHfT7EE8kkc4XEPKH4FFtoFga8ge1xW28WNSP9hfTWRXnB3d0z+Vy8KnrA919gLuAllEtKGYLyKp4w3TD51b9CRooqvJf7crjviX9iKi+XDqzFbOi9OlpamHVGicGQ/1HCY6ndB/AWPVPtZE6as/aFVj14l+Q27RG+/is1C91RgqAPNCPnBMnEV8rM1Sia7OPnbR2u3RgmdZ1CpGMKk9FKDjqVNqK047UOCmqY/VlawYeGVrwZF0sV8Uw0i5mm7LV8dZ216I8QyXMy/aqtuv6nLEj1hH/6hrUH123CnoMVMSW94Gn+iLzO4k4HkxbRN7lw6T3n4QpKsBFlwN0XcFU/AwIgiAIgiAIgiAIgiAI+yJDFFYpSQJ5uPFURzfmb0jhmPExHN7qhVHMaWGSvkP9fp/eQMil0iULRSzrS+K1zgIWJYDufEE/Dj824MGx48OYHSmizWUi6AsiGY8jEeeu/VG0tLSCmwlpUUurYwb0s/6KVDaNXCYNVyiCd/pL+FtHDxL5PA5uCeH4CU2YFlJ1a+tCE6VCAQMDSfSqsoPNITQ3NWMgXUJvDqodblVkEV7Vr4BZQMBtiWdukxalBfgCftWPMErqeiFTRCaTV33PYVMJuL8jj2c3AYeO8uOKGUHMjvp0W3U7bYGOopzlFoCnWnGzj0vqUgmF5ACy3etgZhPq3LlYhl0MRVWNFgjL01nHlgipjvV/Zde3tKNS6FNpdJGW/ayFnVbHq3brLE59tYRCO48tPjp5iW6T05SyQwdaqloHzGRd3ZqcGXRhNuqKTmbH8bJ1VB19UWdQf3li/dVo1VQFZ7x1/UzBV5XO7osVy96rI36W2F59jXnVPKCvXcMLT+t4+FrGqWOPTmsVa5ctCIIgCIIgCIIgCIIg7HMMyRUAxSRKcQXDg6fX9eHR5UlMCntwSruJyTEvosEg/G6fFkQLpRLSqRTWZYp4YnMRf+sy0OcKwXR5Ve2qrEIO44wsTmotYl6rC2P8LnjchraMDKpyuLu7Fr22CGCWrEtdzK3KTqbiWJ0u4c9r83i2B8ireg/yZvGJgyI4dpS1cz+zcmOh/ngSqXQWbq8XedX2lOlF1qAQCrhUn+g11meWEFBpmyM+RAI+JAf6QHebkeYmuG2RkfIcW7OwP4PfLMvg1V5gbruBT80IYXbEr9tOQdJli6t0GUCqWTBqK0qnb/sEVl/Z212FUyIZriNF1xeWOGttaCYIgiAIgiAIgiAIgiDs22yv9DXEsK3yPDpzyWWg0yyhx+2CNxBEyBOCzx1QaVza52lfbwpplXZ1ycD8uIkeVxQFfxAFr4GSx0BRHa9HCK92F7A+byDa0oxYczO8Xi8KBe7Obqoqy4QqderWgYKkC0FvGLk8MFDIweVzw+XxIquuZQt5LcIZLkrAJhLxFMxCCaNGtcEfiSJTcqOoeuAyTB1oBWu63MiqMgteNwyPtZmU10+Zle4BMvqcW0oVqZ+pOsaG/Jja5EXYyMIwWR8vKLY01zrX7dgauQ2OCJdIJjEwEEdJW0Vui9brKuC4cId9btjFcWLedJqbLpV0nG6iHbhrPdOzHB5b+ayx1Qa9LNBOTb+4Oqg+8zWdSWNz52b09/epcy1p69Rb0249d+L4KDxfWQc3gcpmc+jrG0Amk7VSqqRWUOm3lGeVUA7z9vR0Y/26tXozsQ0bNqK3r09vgKZ9nW6p26LynDAunkjoUPX6Ni2wYDIrKf+UBwern7TM7urqVG3qATdn46ZfIqoKgiAIgiAIgiAIgiDsH+ywsEp5qUBhSf3jNj1GyYC3ZGJU0I0xzWGU8kUkk2mkcnn0JxPwBTwIhoPoTBTQk3Xp3f49RVbs1sFtulBy+zHgDiDn9sCtH1c3UCpaguF26MslmAzqn9vrwahIGAeGfZhmpDChNICZTT6MC/pglpjGg1QqjWw+g1DUB7fbhUy+hLzhRsnlVjXR/lSVZKg61XnR40ZG5ctk8yioPnBTIwqraVVGQeWjz9eCWdRWrM0eF44eF8AhbWoMTIqr24pv7IfltqCx2LZxw0asXbsW+XxeC4oU7bg7vTMGFEuz2eyWOKZh+vXr16s4Cnxd2jft5s2bsHr1KmQz9G9rtZeiLjde4nhk0hmsWbMGnZ1dWgSkn1f6s02ns7oOq60qmCqXei82bdyMBe++p8rv1X2mSJpOp7U4y3QsN8fNyVS81VZDddml0pq6no0bNyIeH1DHq9DT06P7R/+7jlBZUOWwPPaN7SuHdaxYsQrvvbdI9XUd1nasxaKFi7BmdYctQBtb2sPxsgRRU7eH48RrjIv3D6C/r0+XyTZSgGZ9bIs1RkV9zDgGiraWPuq8d4bqjxo7PU7qc6DGiWPDMXznnXexYf0mXe9WeFx+LgiCIAiCIAiCIAiCIOxruL/6la/c0DcwgEmTJtlRg8EEn26nMLcplca6eArRoA/jWgLwukro7+9HIZtBS7Mf4ZAfhWIJ724u4L2kF0WPT2uNJW0QasBNLc0w4SlmcEgUODDmh+Wl0rKudLtccHOjKxtLvyqhxDJUOgqfnlIB4WJe5Q3imMnNOGZsEG3FlPa/WiyUkM+mEQwF9a7/qilI5V3IqNeSar8lrCooMqoj/qVbAHc+i3wyiVQqoQW1lOpjMZ8HN7fSwqdqSE+ugM2FAjqTOTSpfLNaw2j1edQRhVpqwLpkdtNmy8F29PX1q7+8bmD9+o1atKOYSCvPcDiMzZs7sXLlKv2qBcGSieXLV+jzUCikxkjL3Fi3bj3WrOlAIBDUAiJFxnA4pMvvV+8zX5cvX66vRaMRLXYuW7YCGzdu0HGsi9bCfG+Tqv/Lli3T8WPHjlVjkVJ5V2oxl8ehUBi9vf1a/Ozs7FTvhUuV2aTb0d3dg4ULF+v6fD6/LiMep/DbqfL06TZRrFy5ciVWrVqjhWHmj0Qium7CfvKzNGHCRMyadYh6Ha/bxvHxeDw6/9Kly/U4JRJJPQ4crxUrVupx6OvrU3Xbrh5cbp2HYu+qVat1uyj4UhBlHevWrVNt26yFYNbBdjhQSOV1jvf69Rv0mHK8Oc4DakzHjh2DWCym278ttd9vQRAEQRAEQRAEQRAEYWQzRGEVWljlhkzBkA8BvxfruuNY3tmPJEraSrPJ70MsGKJMiZzpwqpkCYsTJWRcHphul964iiKllqKKRcSQw6yIC+O8JRiFvBa8KGhSVKXQZVkEbhWqLNHS0AJrIZ9GMZNGS9SPWDRoPe7u8yGZyyPV3Y1IwItIU4tqsFu1B8gWisgUaM1Kq0y7VL6qfIZZQkC1rSUUQEtTRIuHUfpX9XjgUiEYjaDg9mNNysTza3vw+qpNaFXxJ0wZhSlNAXh1eaoPqiL6VLU2f7LrsP9Wo6enV79StKWI19bWipaWGHp7e7WQRwGUwl17e5seFwqGtL6kH9q2tjZ0q35yszAKzRQmx40bp4VFCrCxWLMWOilsRiJhna+1tUXVVdCiZEtLC5qamrFp0yadl+eEYmR//4Cug/XxOts0YcIELULSypVWsr29PRg/fpxuB8VLRxil6BiJRFVdrVp4ZTkTJ07Q7aI1KgVWCpwTJ05S59Zj9RR2mY5Q0GQa9ot9IBQ0E4m4tizlmPGR/IkTJ2phltakFIPj8TjGjBmj+86mWBa2dEfQp9s0ZcpkXQ7b5PX6dDytmqdNm6byp7RIy36yL2Tdug3o6FinBdTRo0frcWDb/D4/XOrzOW7cWC3qOv223ufa77UgCIIgCIIgCIIgCIIw8qk0sRsUfGCcWz3RfLTN5cIpY6K4eNZETIlGsHBtHK91F7EwaWDVQAG9yTzSqSQmN5Uwo6kIbyGjN3ei61ODj3MXs4iU4pgRKWJKzA2/G/qx7kQioa0iKZRRSKQYqP1q8nFxipamAT7Iz0fR4+p1oy+Ip7qK+K93unHrO124fUkCL/X7EA+2IGt4kOSj/EXrsfVSLgs/8vCaBccEVouqtFT1qeBV9aiCtdWp2+OG2+WBLxxGxuPFykwJz3dn8JdlXehOFnHCgZNx8ZzJmN0SgJYDKc5qXc0S1lg6LVgHCwVkCpETJozD6NGjtNDoUW2gRWShkNeWrHzEnZt6UfRtbm7SlqeE6Rjf3NyMpqYmLYw6Wh/HibA85mlujuq4nBoLS7QuIRj06zTWuVVeMMjyoqALhUDAj/Hjx6K9vVULs9lsRr8nFHEpVlIQdfJSaGRdTU0RXS7bOm7cGC0MUyTle8r3mLAdHjW2Pl9AHW87VrzmtJ2wTsePLC1aCV/9fr8W4B1htqurW4u+FJopSFPIpdUsx3bMmNFaCG5vb9fXWO+YMWO1MMxxpihaKGytM6U+v6FQAJMmTcTYsaMRU+NXVO+Fz+fRY8Px3yqqCoIgCIIgCIIgCIIgCPsDQxJWXdrWk2KkCy7ThL9UwvQAcO6B7Tj3sCkIe114Zc1GPLhsA94ayFBlw6HtUXxwXACzfGk0F/oRLGQQUCFWSmB2NI+TR7swNWAiHPBrYXDUqFEIqnwUzWhxSCtEbkCUTGeQz+VRKOZVK0rI5opYEy/i6Y0FPLy2hJf7Q3g7HcYL3Sb+vCaNFxJu9HgjGEglsXHTBgwMJBHx+9AaDsDvtqxmKQbSUpXbWYV8bu3WIJNLoy+ZQMEE6BF0wHDjvXgejyxch/mrNmJyewgXzpmIE8ZGMUrl9aqEHAtVmJZRy2W2baXC2jiipOX6wPLraYmCfPS/pK0waTHJsaAlK0VDPvrubAplYcVZvkJLWywwafFKwZq+QSky0kKTQmQoFNHCIIMlhlqWl05b9GZUqhyKqpZVaZcWRGk1Ggh4bStZ9Ymw028VGHlOq+OsrpfCrDMqFE99Kh9F0FAwhOamGCLhqH78nkJuOSyXAjsf16dVLh+/Z3v4+aCYagnFzbrtfn9Ajxnjpk6drPM6rhPYforOtMDlRl+04KU1LPvndluWy1Z9/Mtzqz+EY8K+08KVFq8J9bnwB1S/1ftEYblc+BUEQRAEQRAEQRAEQRD2D3bYFQD1J0siU0c80c+8qwOKi0YRo3weTB1FkSyEDYkcVvZnkSy5EPJ70O4zMDGqXgNAqyuPaYESjhntxwljwpiEHEKlEuiPEy63LpN72/t8XsSiTTqeG0wVCwVksymkcznk8iV0ZXJ4fmMKL/d40euJweTj2S4DpteNpGppVyKPZlcJE4MmzGIOfr8XTYGALtft52ZaJvyq3JDHQDjgQpNqZ1il8Xq8SGby6CuUsCZTxItrOrF4Qx9GR8P4wIHjcOyYCNrctNvlY/90TGA9gs+hsMRFfaBfiB6vOlD8Y5soAFJYpdBJwZKP64dCQW2xS2Evk0lrIZEWlyyegilfmYaCIoVOCpBsA4XKvr5eLSDy0Xdaa9JalWIrhcq2tnb9KPyGDfRH2qPFSQq3FFwJy6UIyzQUMsnGjZuxadNmbRU6adIU/bi816vGTNVdDuundSnrp+UsXRBY7aNAm0dI5Wd7aJG8YcMG3a+W1hbthmCrOEuR2PLNSjGXfeXYTJkyRbWzXaVzqbhu7UKAedhGtp3jZPlX9erH9NlWCrYUVim00pqV/ae1LttA4dQRadk2j4e+YqO6b4TxdNHAdrJsXps8eYquk4Ixx7ncD7AgCIIgCIIgCIIgCIKw72Os7+gwV3V0YN68eXbUTmDyv6L2n1qEB91F4K31fXhnQ48WRWeOasJhY6NoDvjAh7gpRVGOo6Xn+o2b4XaZGDe6TT/izwf903nu1J5ByOcHfXxysyltUmgW9CZU8YEk3u4ewJ97PFiQi8H0BnTOkotyZ1E/3m9kMjg5lsMnD4piQtCL1EAPipkcAtEYPEFubMTyaINL61Va4jKvC7QBXZ3I4M113Vij6mgOh3HE5FE4pDUIvT1TiRtoqUYYFFVVoIGjowcOAVpXllt9OsdsHwVES6i0rFApAlJ05TGFQF5zxFDmowhLGEf3ATSodDZxYrG00mQaioF83J8WpU65Vho1FrYVqtMM1sHhz+XU2BeLWsClgM2y2UYrWOkcmJ+Wnmy/075ymJb1WhatlkBbjtVWq1ynHbQuJTxnVfl8Ufdxq+Ws5T7AKtOt+u1RcVbb6UZh9eoO3WemoYA6depU7TaB507bWafV363vA485/jznOFltZ7pt0wuCIAiCIAiCIAiCIAj7B7tWWCVmSQXamtK/p4GcCquyBby2thcdmwfQHvLj0IktmBoLotVtwqtSZktubB7IIl0ooLXJi2avoeI9yOYKiKfTCAb82kVAqVTQG2ZRyiwVTWSyebyt8v1uvYEFqRACtjCXM9zaPyof70c2i2ObsvjU9CAOjPq1IpdIJZHK5FSZAYRUoA1uyVBtVnkKpgud+QLe60ni3Y092mJ0zrhROGJMDG2egiqTga1WdVFUs+vRkpoxJM8KW6BAR/GOUAgtF+qcY+txfktIdQS9Wmx/3VRjqHrr2hqnkqg09sl2UFS0LtJylWNBK1BaoFJUzOUyqr1We1gXhU1ah9LSs7Luam1t1H5S2T7mcWDddDXAx/E5HrRItTbPshMoyutgVrpFoLUq20pfr7T0rdZWsjXftueVNLouCIIgCIIgCIIgCIIg7HvssCuARtAylRafNGSkhakBE60eYEZrBK2xCNb1p/HO+l5szhTg9vsQ8HqRyZtI5lwoFF3IF62NjPwuNzwqf7GYBy1PfV66CGBptHA1kEomtauAjDeIBf0FrFf5+fh/idqWysc0tIQ1SnlMDpRwWLMbrW4KgIYWB91uE6lMGqVCER5adqr6aGG7qDeJF1dsxPLOfkwc3YJTD5yIObEgmgzatJagWo2iSktB1apHvahDHttHQ4aWoIsWLcLatWv1I/G0jKRm5/g55WPxvEYrVT6O7sBrFPf4Wihwk69trS9ZLgPho+u8zjIY54isTMe8TjonP32WEj5uv3HjRgQCQd0upl26dClWrlypH9PnI/J8rJ/uBChwMh/LckRHUlm+1TYrHXHawFcGtlPFbmmLdU5h2KX97q5YsUKNRwd6enr1Tv2M52ZZzvvg5OeLVS436Apo61aOJceCfWE6XuerZfW6tX6rbVZ5TtustvOc42pZBzOf005BEARBEATh/2/vzH4bubI7/GNVcWlSOyVKYsvuRUonSJxBMvEYM0AeggxmnvOeP3Ke8pDHIC9OAjiBHRh2DzojtZqSWiu1keJWlfudIt20ppexp+NG2ueTuNStuufeW9LTh1PnOo7jOI7jvP+89YxV9GP+gwQtiEfkYyxUOBpFkdrh25fHHX22faLuYKRHd+f14eKMyuHaeDRSPEy1UI60VC2qGEsXF6FHGml2bkGjgrlVZb0bXV5fKrlT1U25pt88OdU/HQx1XlxQIUoUUfM0DBkNe1rILvXL9Vi/apS0EI7tsfRSpFKxoKGiEKenm0JRx4VEXxycqnVyoeb8jD6+t6rNmZLusJY0De8RNtCEMauhbAAvU8eIttCGVvtj1Br1P588eWLScHNz03baJysTaYm85HF5Nq5C4iHC8535e1ZPNN/Iinqk4b4kie1yTxvHZLkSk3qi7ITP9YhIJCF1SZeW6nYd4/NYPVK30VgJ/QZ2HRmhjI9o3NraskfnGQsJjER9+PChCUjmRwYpgpV5sZs+9WCp20obtVmJubS0YLvvkz16cXEe+hVUq82M+3QsW5c1MB79mc/l5YVdTz+yYvf29vT06VO7D2zq9exZy8ZDOHMNn9RkZZ4hjM2ff8Pl5bqN8fjx17ZG5s5mVmdnpzbm+vp6+EsUbJMqRCrrmZubtYxYNgzLr1mzDF7qzQ4G+XnmMCml4DiO4ziO4ziO4ziO47z/vPWM1fyx+Cx84YH9cdYothEvqZEqStWslvWwMW8Zfl/tn+p3Z9casMHRnZKWKAPQ74bLByqWirYrfzYKbXGiOIkIo+51xzJGKzM13YkiVRLprHOj9s3AJGyUFpSkqeaGV/rZ4ki/vDenD2fuaJgU9TzNdD0cajQY6mqQ6Eglffb8Qp9tH4X5SZ88XNff3l/Rh6VYRcbG0IZ55ht25TI1tpzcsK5winNvQ6ySucmO92yUhExkwyY+9/cPTCIi7xCLCEIkJps9scHVRJJ2wvqRp9VqTcfHpyZTyUolJvITOcq1SEUEKoIQWUi8bvcmjPPcsi+Rg2Sm0sbO+dfXVzYOm0SRoTkRiMwhz1LtmLhst89tHcxxe/t3yuu3JrZxFsKUmMwJKYpgpe3w8LkJTUQtbfRdXFywzbeOj4+/WStCuNXas8xUxOikDAI7/NPGXMmUXVxcsvtFbGQs4pVxWTPzQTQjXxG2CFfkK8KXa9iAiu9IVsZF1LJOxtre3gn9uiZs2fiKNbfbF3Zv2PgKCYsMvr2Bl+M4juM4juM4juM4jvP+8vZLAZhajCyzFLHKj+V4FjgTXmlqj8/PxAXdn6+ouTKn9s1Ijw/aOu8iu0qqlhMNB30lyFQV1e8NxGPbSZG6qz11en2VS5XwKoVYqRYqJdVrJZWzvkqDjuZ0o/XijX6+WtSv789rq0oc6Yujjv55+0S9pKS5uXl9ddbTv24f6LLf11/fW9ffba7q0UxJMynTzdjTKswYQRwIb9RhDQvIRbG95Vmsdjh+fV+QiUhQZCiZktQBJSJSEEm4ufnA5B/ylJ39OUc2K1IRqbdcXzahiHBEECJTySBFZG5tbY775rIUkfno0Z9YJuns7Lxt6nR+3rZ2ZCJxJnVIHzy4Z1mcUZRYtilysVJBrKbWj/GQvsCYkxqw9+/f08pK3eaCgCXDE/nJC6mJMCV7mKzcepg7sZrNZvg/3AjryePPzuabSrHmfN3zlnGLyKWdsgSIZGK3Ws9sfsTnHnI9cyGrl5qwzIM/GWvkXhAfiYsk7Xa6JmWpG0tc1o903dx8aPcaIY3QZU3MnfvOPcizYZlHZXxfqNfrOI7jOI7jOI7jOI7j/Bh4+xmrY8VYYMd8ZVZnFQFpOZ/hO5mmJl+zWEmWWobqg3pN9fkZHbav9d+tY7XTSElcsozRarmoESbNxGqsi86V0ijWbLVm2bEUHYjDdSuVRI/qVf3Z0h19tFTSzzeq+lmjrLViGDZl3Ei/Pe3o0/2OWn1p+7St/bNzba3O6VePmlZHdSHMmQxb5si0EcFUi83zUclZpY5mbGux3FVbW7gu9LFv4+PvCtmkT58+M0nJY+YIRcQjO+oj7hCVCD6yPxGjPL6P2EPEMuTa6qqJSbIt19ebGg2Zcz4Xsn7r9SWThZeX1yYU6YOMRUCSgYmEJLsUgUvWJXKVTMx+v2eZnkjGgwOyYrth7GU7j/zlUXtEJcJxZqZmfRCVnENaAnPON70qmxglFlmgEwHK5lHIX9bFHNhQijkhdrkX4ZZoZ2fHrr97txniFG3+rda+Dg8PtbbWCO3rti7kKfcGobq7+8zWzZj8+zBPxCuSF7ivcVxUlvK3LWi1sRr+54phHbN276jHSn/+DqyTtSFPJ+UFWAeClvns7++bhK7X6xbbcRzHcRzHcRzHcRzHef9562IVn5f7Rd5QVEjW8aEd5YIVNQkIrFL4XK0k2mrMqVYq6cnzth6f3Kg9GCquxOpEiU6HmXppatKwVi6rXIxVyEZikyrbsCrEu5NlWilFWg99lpKCKqENv8uD+1dhuKNw7eEg1fVNV/cXy/r1n67rF415rSBtEWxMKcrnaxsRTb04ttfYuoZ3wz7H13wXkKhATIQoNUQRh83mumWAsskSmZHlclE89r+zs2vlARCdZE8iCqn5iQjd+GBD152Orq4u1ev3TWSm6TAsJ9Pc7JzFGwwHJgTr9QWThTs72zo6OlSSUK/1Q2vjkXjEIWKULFVEJ4/EU2qA+SFBEauIUSDLFaF7cnJmj+AzP9aDtEU6IhuRnYuL82EdpRB/P8SnpMDA5s8tQ9IidMk6ZSzi8UJ8Nptrdg3jLy8vaW1t1e4bYxSLicnX3d2WiU1qxFKmYGOjGdov1G6fWfYrGa2t1q5lnXJPuF8rKw1dhGuurq+0OK5Te3B4ENraqtaqNlfGRfJOMoDpNykdwDnkLeUKkM/cD7J/WbPjOI7jOI7jOI7jOI7z4+Ctb171h4NYzGUm2Z8Yylw1SnujTJ/unenr1r4Ul3QTzavbT1UvDfWLu7P6ZHVW1QLykE6JhjG74qcqjfK6rrhPXmkUqZdl2r8Z6PPDc311eKFqMdHHG8v6qF7TfLgm/CpF1o2FnVmzH4BpsYqkQySS/YiM5BTnyehEbiI6Ly6uTELyuDm1QhGsk8fu6UfGJrVBqYOKAORu8h0ByYv4xCEesRGNfJKFmY+RmpxkzPn5WRuLeSBL2aAJ0ZskRRs3nyPz65v85TqOOYdk5DwxgXiUcaANEcqcySKdZMzymD7zQ5BSS5b19Xo3YjMrSgMcHuY1UtnMCxnLOoB4yGDuC8IYqTk7W7P5PnnyPzYPSiAgSRGn1EEFYrA2sm/zfrM2d0QsWbVz82GMEJcsYmKwNtY5WRtzBsYnJrGJSUat4ziO4ziO4ziO4ziO8+Ph3YnV3CuOP3n8nh33OaCOqdRTolZ3oE932/q35yPtDYsaDHv66Uqsf3y0oEfVWIOUR/PJfu2FnuEzLYVQIU4s9UOMs/5QX51d6b9aJ+r1R/qLjYZ+2lzQ3ShcPaJMAf1HoWcYuUAVVsTsDwMyb4IJ3QBtk3baJu3TcJ7m/DLO/36cvO1F3zwm8SbfX1w7GXN6vLyNa6Zjvug7YfrcNJN4Eyb9ptsQpNPHSE4yU5GvloW7cddqwpJFy+P9PGY/HXfy/fY4ZM4iPJGyZOoyDhJ0mum223ObPp4eDyZznm5zHMdxHMdxHMdxHMdxfpz84GJ17K2Mb/RUaCTDNP+aSz00J4/l9woFfXkx1L+0rvSfx10VleofHs7q7+/OqBQ62ab9Go5lV6JhOD7LUj25uNbnz451fN7VZmNRn9xr6INyrGKaKs2GGkXI1ERJFtlmWhk1A5Bm+cg/CNPibiL03kR+OW/5fbKjb+5d/vmH8G1pSMfp78TMj6dj3haN+fcX8/i+TOJOx77Nq+/PpD2fCxti5TAvzr06JjDmdOzpNU7PZ/qaSfvtOb1u/o7jOI7jOI7jOI7jOM77xbsTqwX7xX/lYEg5yNgqihfwbWiZpSdZrM+Puvps91Sj4UA/aS7oo8aMGsVYxdCXbNdO6PS0M9DnByfaOTxXo1bTJ/fXtDVfVpVYGXVHCxpZhddUkW1WhUiNTawyH6sB+46Ylnq/z/j+vJRJn1f1fRNviv26uK/rC2/q/8fwsrFvj/Xdxn7938BxHMdxHMdxHMdxHMdxct5JKQDk6rfcFW5sIlbzA3uNwvso/NCSKBbqk/qr/7F7rsd7Z5qpxPrJxqI2F6vqptIXR+f6eu9YlSjWxx+s6i8bM5oLoZCuhI+szEBoyNj4iq/U62QUxovzc47jOI7jOI7jOI7jOI7jOG/gHYhVNCnclpg8up1vTDQ5h/xEwlJ/FelJHusofPbCuaedof5950h7Z5eaq1XVH2W66Q/0581F/U1zQasJZQLSsTbNlGTkoo7raka5rOWxf/O5ogwAn7zdnpfjOI7jOI7jOI7jOI7jOM63eQelAHKxyuPW469TiaJZ+EGuRipQdJXz4aIMHxoOLYc1pT3SKC6Ifd5/277WlzsnqpSK+qv7y3pwp6hylpqkJQQ61WKFXxsoxMxswyqCxyEiJxiRIgGQvzuO4ziO4ziO4ziO4ziO47yKd1IK4PtjpvVFFqvYz18a2DfKBUhRlloWaiGaCNLwSbdX+NLXnHIcx3Ecx3Ecx3Ecx3Ecx3kp726npu8FWa55+mr+CD9Zppkq4VvJWnkL72PpOm755uNlvOaU4ziO4ziO4ziO4ziO4zjOS/l/JlbHznSMZaaGTwoI8M7j/FFo8V3dHcdxHMdxHMdxHMdxHMf5v0P6X8UIufTATV4kAAAAAElFTkSuQmCC", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -43,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -54,7 +76,7 @@ "\n", "# Import IDAES libraries\n", "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", - "from idaes.core.surrogate.alamopy import AlamoTrainer, AlamoSurrogate\n", + "from idaes.core.surrogate.alamopy import AlamoTrainer, AlamoSurrogate, alamo\n", "from idaes.core.surrogate.plotting.sm_plotter import (\n", " surrogate_scatter2D,\n", " surrogate_parity,\n", @@ -70,19 +92,19 @@ "\n", "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset to have cover different ranges of pressure and temperature. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", "\n", - "We rename the column headers because they contained \".\", we change \".\" to \"_\" as ALAMO accepts alphanumerical characters or underscores as the labels for input/output. Further, the input variables are ***CO2SM_Pressure***, ***CO2SM_Temperature***, while the output variables are ***CO2SM_CO2_Enthalpy***, ***CO2SM_CO2_Entropy***, hence we slice them and create the input and output data. " + "We rename the column headers because they contained \".\", we change \".\" to \"_\" as ALAMO accepts alphanumerical characters or underscores as the labels for input/output. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Import training data\n", "np.set_printoptions(precision=7, suppress=True)\n", "\n", - "csv_data = pd.read_csv('./500_Points_DataSet.csv') \n", + "csv_data = pd.read_csv(datafile_path('500_Points_DataSet.csv')) \n", "\n", "### ALAMO only accepts alphanumerical characters (A-Z, a-z, 0-9) or underscores as input/output labels\n", "cols=csv_data.columns\n", @@ -110,14 +132,14 @@ "source": [ "### 2.2 Training Surrogate with ALAMO\n", "\n", - "IDAES provides a Python wrapper for the ALAMO machine learning tool via an imported AlamoTrainer class. Regression settings can be directly set as config attributes, as shown below. In this example, allowed basis term forms include constant and linear functions, monomial power order 2 and 3, variable product power order 1 and 2, and variable ratio power order 1 and 2. ALAMO naturally seeks to minimize the number of basis terms; here, we restrict each surrogate expression to a maximum of 10 basis terms.\n", + "IDAES provides a Python wrapper for the ALAMO machine learning tool via an imported AlamoTrainer class. Regression settings can be directly set as config attributes, as shown below. In this example, allowed basis terms include constant and linear functions, monomial power order 2 and 3, variable product power order 1 and 2, and variable ratio power order 1 and 2. ALAMO seeks to minimize the number of basis terms; here, we restrict each surrogate expression to a maximum of 10 basis terms.\n", "\n", "Finally, after training the model we save the results and model expressions to a JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -219,12 +241,12 @@ " RIC: 606.\n", " MADp: 0.130E+04\n", " \n", - " Total execution time 0.20 s\n", + " Total execution time 0.38 s\n", " Times breakdown\n", - " OLR time: 0.11 s in 3863 ordinary linear regression problem(s)\n", + " OLR time: 0.30 s in 3863 ordinary linear regression problem(s)\n", " MINLP time: 0.0 s in 0 optimization problem(s)\n", " Simulation time: 0.0 s to simulate 0 point(s)\n", - " All other time: 0.94E-01 s in 1 iteration(s)\n", + " All other time: 0.78E-01 s in 1 iteration(s)\n", " \n", " Normal termination\n", " ***************************************************************************\n" @@ -233,34 +255,27 @@ ], "source": [ "# Create ALAMO trainer object\n", - "trainer = AlamoTrainer(\n", - " input_labels=input_labels,\n", - " output_labels=output_labels,\n", - " training_dataframe=data_training,\n", - ")\n", + "has_alamo=alamo.available()\n", + "if has_alamo:\n", + " trainer = AlamoTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + " )\n", "\n", - "# Set ALAMO options\n", - "trainer.config.constant = True\n", - "trainer.config.linfcns = True\n", - "trainer.config.multi2power = [1, 2]\n", - "trainer.config.monomialpower = [2, 3]\n", - "trainer.config.ratiopower = [1]\n", - "trainer.config.maxterms = [10] * len(output_labels) # max terms for each surrogate\n", - "trainer.config.filename = os.path.join(os.getcwd(), \"alamo_run.alm\")\n", - "trainer.config.overwrite_files = True\n", + " # Set ALAMO options\n", + " trainer.config.constant = True\n", + " trainer.config.linfcns = True\n", + " trainer.config.multi2power = [1, 2]\n", + " trainer.config.monomialpower = [2, 3]\n", + " trainer.config.ratiopower = [1]\n", + " trainer.config.maxterms = [10] * len(output_labels) # max terms for each surrogate\n", + " trainer.config.filename = os.path.join(os.getcwd(), \"alamo_run.alm\")\n", + " trainer.config.overwrite_files = True\n", "\n", - "# Train surrogate (calls ALAMO through IDAES ALAMOPy wrapper)\n", - "has_alamo = True\n", - "try:\n", + " # Train surrogate (calls ALAMO through IDAES ALAMOPy wrapper)\n", " success, alm_surr, msg = trainer.train_surrogate()\n", - "except FileNotFoundError as err:\n", - " if \"Could not find ALAMO\" in str(err):\n", - " print(\"ALAMO not found. You must install ALAMO to use this notebook\")\n", - " has_alamo = False\n", - " else:\n", - " raise\n", "\n", - "if has_alamo:\n", " # save model to JSON\n", " model = alm_surr.save_to_file(\"alamo_surrogate.json\", overwrite=True)\n", "\n", @@ -275,7 +290,9 @@ "\n", " alm_surr = AlamoSurrogate(\n", " surrogate_expressions, input_labels, output_labels, input_bounds\n", - " )\n" + " )\n", + "else:\n", + " print('Alamo not found.')" ] }, { @@ -289,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -410,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb index 68ed5a5e..07f04c18 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb @@ -1,19 +1,41 @@ { "cells": [ { - "attachments": { - "image.png": { - "image/png": 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" - } - }, "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - SCO2_flowsheet_optimization_alamo_surrogate (Part 3)\n", "\n", - "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. \n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", "\n", - "![image.png](attachment:image.png)" + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" ] }, { @@ -27,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -69,33 +91,33 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-08-08 10:29:36 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", - "2023-08-08 10:29:36 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:29:37 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:29:37 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-08 10:29:37 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:29:37 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-08 10:29:37 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:29:37 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:29:37 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", - "2023-08-08 10:29:37 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:29:37 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:29:38 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:29:38 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:29:38 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", - "2023-08-08 10:29:38 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:29:38 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-08 10:29:38 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:29:38 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:29:38 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-08 10:29:38 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", "--------------------------------------------------------------------\n", "The degrees of freedom for the flowsheet is 0\n", "--------------------------------------------------------------------\n", @@ -158,7 +180,7 @@ "Number of equality constraint Jacobian evaluations = 4\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 3\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.002\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", "Total CPU secs in NLP function evaluations = 0.001\n", "\n", "EXIT: Optimal Solution Found.\n", @@ -178,7 +200,7 @@ " Units Inlet Outlet \n", " flow_mol mole / second 1.2110e+05 1.2110e+05\n", " temperature kelvin 685.15 893.15\n", - " pressure pascal 34.510 34.300\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -199,7 +221,7 @@ " Units Inlet Outlet \n", " flow_mol mole / second 1.2110e+05 1.2110e+05\n", " temperature kelvin 893.15 692.18\n", - " pressure pascal 34.300 9.3207\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -217,7 +239,7 @@ " Units Inlet Outlet \n", " flow_mol mole / second 1.2110e+05 1.2110e+05\n", " temperature kelvin 692.18 489.15\n", - " pressure pascal 9.3207 9.2507\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -235,7 +257,7 @@ " Units Inlet Outlet \n", " flow_mol mole / second 1.2110e+05 1.2110e+05\n", " temperature kelvin 560.75 747.89\n", - " pressure pascal 34.560 34.490\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -253,7 +275,7 @@ " Units Inlet Outlet \n", " flow_mol mole / second 1.2110e+05 1.2110e+05\n", " temperature kelvin 489.15 354.15\n", - " pressure pascal 9.2507 9.1807\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -268,10 +290,10 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet Outlet\n", - " flow_mol mole / second 86647. 86647.\n", - " temperature kelvin 416.53 598.89\n", - " pressure pascal 34.620 34.620\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 416.53 598.89\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -287,10 +309,10 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet bypass to_cooler\n", - " flow_mol mole / second 1.2110e+05 30275. 90825. \n", - " temperature kelvin 354.15 354.15 354.15 \n", - " pressure pascal 9.1807 9.1807 9.1807 \n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -305,10 +327,10 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet Outlet\n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 354.15 308.15\n", - " pressure pascal 9.1807 9.1107\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -326,10 +348,10 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet Outlet\n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 308.15 416.53\n", - " pressure pascal 9.1107 34.620\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 416.53\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -347,10 +369,10 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet Outlet\n", - " flow_mol mole / second 30275. 30275.\n", - " temperature kelvin 354.15 473.64\n", - " pressure pascal 9.1807 34.886\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 473.64\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -366,10 +388,10 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet to_FG_cooler to_LTR\n", - " flow_mol mole / second 90825. 4177.9 86647.\n", - " temperature kelvin 416.53 416.53 416.53\n", - " pressure pascal 34.620 34.620 34.620\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 416.53 416.53 416.53\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -384,20 +406,20 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet Outlet\n", - " flow_mol mole / second 4177.9 4177.9\n", - " temperature kelvin 416.53 483.15\n", - " pressure pascal 34.620 34.560\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 416.53 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", "Unit : fs.mixer Time: 0.0\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units FG_out LTR_out bypass Outlet \n", - " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", - " temperature kelvin 483.15 598.89 473.64 560.75\n", - " pressure pascal 34.560 34.620 34.886 34.560\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 598.89 473.64 560.75\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", "====================================================================================\n", "659.042605510511 kW\n" ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc index 2b751874..4a3342a4 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc @@ -80,3 +80,9 @@ c:\Users\javal\Desktop\Internship\IDAES_core\workspace-idaes\CO2_example\ALAMO\a #filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model c:\Users\javal\Desktop\Internship\IDAES_core\workspace-idaes\CO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.46875000E-01, 1816, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.14062500, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 c:\Users\javal\Desktop\Internship\IDAES_core\workspace-idaes\CO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.62500000E-01, 2047, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.14062500, 0.0000000, 0.78125000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure^2 - 0.55969916790357242958320E-003 * CO2SM_Temperature^2 - 0.21077870265129327632947E-002 * CO2SM_Pressure^3 + 0.24061231665087056461711E-006 * CO2SM_Temperature^3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES-examples\idaes_examples\notebooks\docs\surrogates\SCO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.31250000E-01, 1816, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.17187500, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 +c:\Users\javal\Desktop\Internship\IDAES-examples\idaes_examples\notebooks\docs\surrogates\SCO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.93750000E-01, 2047, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.93750000E-01, 1, 0, 0.0000000, 0.0000000, 0.17187500, 0.0000000, 0.10937500, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure^2 - 0.55969916790357242958320E-003 * CO2SM_Temperature^2 - 0.21077870265129327632947E-002 * CO2SM_Pressure^3 + 0.24061231665087056461711E-006 * CO2SM_Temperature^3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES-examples\idaes_examples\notebooks\docs\surrogates\SCO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.15625000, 1816, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.31250000, 0.0000000, 0.17187500, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 +c:\Users\javal\Desktop\Internship\IDAES-examples\idaes_examples\notebooks\docs\surrogates\SCO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.14062500, 2047, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.31250000, 0.0000000, 0.14062500, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * 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z9p}Fm-rK5dkA5C?Y?6W8n1Gj0f>Va?SnuaQvXURAqo9AAg6mAqwbG z#Uh3BJ}T(cD3f&+#!!32L-RHlulPfKG`MzJ#%cV^O=80`!j5z->C)$)+efcGzSG4U z2(WEJvL(6;JTwGEms?sA^Lw*l;RTdQg3Efu-cFf9!8mutbcOMd7?awGa zX!^)PaqJ&2HVMsS*!?U?MYt$+)xw+JUv`z?KPv3q`t)CM)vm~FM<7=DBk!ik-PPR& z{e%+Z8`*j#b{*f$%bgN@25JJXIKgtsMON1LFU^rQXDMdbr>3Y%7q8" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", "\n", - "![image.png](attachment:image.png)" + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" ] }, { @@ -27,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -69,33 +91,33 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-08-08 10:28:35 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", - "2023-08-08 10:28:35 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:28:36 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:28:36 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-08 10:28:36 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:28:36 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-08 10:28:37 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:28:37 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:28:37 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", - "2023-08-08 10:28:37 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:28:38 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:28:39 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:28:39 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:28:39 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", - "2023-08-08 10:28:39 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:28:39 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-08 10:28:39 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:28:40 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:28:40 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-08 10:28:40 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:40 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 22:20:40 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:43 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", "--------------------------------------------------------------------\n", "The degrees of freedom for the flowsheet is 0\n", "--------------------------------------------------------------------\n", @@ -156,8 +178,8 @@ "Number of equality constraint Jacobian evaluations = 2\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 1\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.112\n", - "Total CPU secs in NLP function evaluations = 0.004\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.119\n", + "Total CPU secs in NLP function evaluations = 0.003\n", "\n", "EXIT: Optimal Solution Found.\n", "\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb index 37ed4efe..0a9eb9a4 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb @@ -1,11 +1,6 @@ { "cells": [ { - "attachments": { - "image.png": { - "image/png": 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" - } - }, "cell_type": "markdown", "metadata": {}, "source": [ @@ -25,13 +20,40 @@ "\n", "### 1.2 Supercritical CO2 cycle process\n", "\n", - "![image.png](attachment:image.png)\n", - "\n", - "The above flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature. Thus, surrogate should have pressure and temperature as the inputs.\n", + "The below flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", "\n", "In this example, we will train a tanh model from our data and then demonstrate that we can solve an optimization problem with that surrogate model. " ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -43,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -79,28 +101,29 @@ "source": [ "### 2.1 Importing Training and Validation Datasets\n", "\n", - "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using CoolProp package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", "\n", - "We rename the column headers because they contained \".\" and change them to the variable names to be used in the property package. Further, the input variables are ***pressure***, ***temperature***, while the output variables are ***enth_mol***, ***entr_mol***, hence we slice them and create the input and output data. " + "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsquent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Import training data\n", "np.set_printoptions(precision=6, suppress=True)\n", "\n", - "csv_data = pd.read_csv(\"./500_Points_DataSet.csv\")\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", "csv_data.columns.values[0:6] =[\"pressure\", \"temperature\",\"enth_mol\",\"entr_mol\",\"CO2_enthalpy\",\"CO2_entropy\"]\n", "data = csv_data.sample(n=500)\n", "\n", + "# Creating input_data and output_data from data\n", "input_data = data.iloc[:, :2]\n", - "output_data = pd.DataFrame(data.iloc[:,2:4])\n", + "output_data = data.iloc[:,2:4]\n", "\n", - "# # Define labels, and split training and validation data\n", + "# Define labels, and split training and validation data\n", "input_labels = input_data.columns\n", "output_labels = output_data.columns \n", "\n", @@ -119,10 +142,10 @@ "\n", "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", "\n", - "* Activation function: sigmoid, ***tanh***\n", - "* Optimizer: ***Adam***\n", - "* Number of hidden layers: 3, ***4***, 5, 6\n", - "* Number of neurons per layer: ***20***, 40, 60\n", + "* Activation function: sigmoid, **tanh**\n", + "* Optimizer: **Adam**\n", + "* Number of hidden layers: 3, **4**, 5, 6\n", + "* Number of neurons per layer: **20**, 40, 60\n", "\n", "Important thing to note here is that we do not use ReLU activation function for the training as the flowsheet we intend to solve with this surrogate model is a NLP problem and using ReLU activation function will make it an MINLP. Another thing to note here is the network is smaller (4,20) in order to avoid overfitting. \n", "\n", @@ -133,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -141,505 +164,505 @@ "output_type": "stream", "text": [ "Epoch 1/250\n", - "13/13 - 2s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 2s/epoch - 190ms/step\n", + "13/13 - 2s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 2s/epoch - 173ms/step\n", "Epoch 2/250\n", - "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 213ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 220ms/epoch - 17ms/step\n", "Epoch 3/250\n", - "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 237ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 228ms/epoch - 18ms/step\n", "Epoch 4/250\n", - "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 222ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 239ms/epoch - 18ms/step\n", "Epoch 5/250\n", - "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 259ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 229ms/epoch - 18ms/step\n", "Epoch 6/250\n", - "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 237ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 202ms/epoch - 16ms/step\n", "Epoch 7/250\n", - "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 246ms/epoch - 19ms/step\n", + "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 241ms/epoch - 19ms/step\n", "Epoch 8/250\n", - "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 258ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 233ms/epoch - 18ms/step\n", "Epoch 9/250\n", - "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 202ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 227ms/epoch - 17ms/step\n", "Epoch 10/250\n", - "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 259ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 240ms/epoch - 18ms/step\n", "Epoch 11/250\n", - "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 208ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 224ms/epoch - 17ms/step\n", "Epoch 12/250\n", - "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 300ms/epoch - 23ms/step\n", + "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 227ms/epoch - 17ms/step\n", "Epoch 13/250\n", - "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 287ms/epoch - 22ms/step\n", + "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 197ms/epoch - 15ms/step\n", "Epoch 14/250\n", - "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 269ms/epoch - 21ms/step\n", + "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 234ms/epoch - 18ms/step\n", "Epoch 15/250\n", - "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 257ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 207ms/epoch - 16ms/step\n", "Epoch 16/250\n", - "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 254ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 215ms/epoch - 17ms/step\n", "Epoch 17/250\n", - "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 277ms/epoch - 21ms/step\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 227ms/epoch - 17ms/step\n", "Epoch 18/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 259ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 234ms/epoch - 18ms/step\n", "Epoch 19/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 264ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 111ms/epoch - 9ms/step\n", "Epoch 20/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 257ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 246ms/epoch - 19ms/step\n", "Epoch 21/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 284ms/epoch - 22ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 172ms/epoch - 13ms/step\n", "Epoch 22/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 236ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 209ms/epoch - 16ms/step\n", "Epoch 23/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 150ms/epoch - 12ms/step\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", "Epoch 24/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 268ms/epoch - 21ms/step\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 219ms/epoch - 17ms/step\n", "Epoch 25/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 271ms/epoch - 21ms/step\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 212ms/epoch - 16ms/step\n", "Epoch 26/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 239ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 220ms/epoch - 17ms/step\n", "Epoch 27/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 255ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 224ms/epoch - 17ms/step\n", "Epoch 28/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 138ms/epoch - 11ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", "Epoch 29/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 134ms/epoch - 10ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 112ms/epoch - 9ms/step\n", "Epoch 30/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 272ms/epoch - 21ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 223ms/epoch - 17ms/step\n", "Epoch 31/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 273ms/epoch - 21ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 219ms/epoch - 17ms/step\n", "Epoch 32/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 258ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 228ms/epoch - 18ms/step\n", "Epoch 33/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 130ms/epoch - 10ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 112ms/epoch - 9ms/step\n", "Epoch 34/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 258ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 244ms/epoch - 19ms/step\n", "Epoch 35/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 210ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 202ms/epoch - 16ms/step\n", "Epoch 36/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 238ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 224ms/epoch - 17ms/step\n", "Epoch 37/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 139ms/epoch - 11ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 114ms/epoch - 9ms/step\n", "Epoch 38/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 228ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 231ms/epoch - 18ms/step\n", "Epoch 39/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 139ms/epoch - 11ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 107ms/epoch - 8ms/step\n", "Epoch 40/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 233ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 219ms/epoch - 17ms/step\n", "Epoch 41/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 123ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 167ms/epoch - 13ms/step\n", "Epoch 42/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 174ms/epoch - 13ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 100ms/epoch - 8ms/step\n", "Epoch 43/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0238 - mse: 0.0011 - val_loss: 9.4863e-04 - val_mae: 0.0232 - val_mse: 9.4863e-04 - 240ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0238 - mse: 0.0011 - val_loss: 9.4863e-04 - val_mae: 0.0232 - val_mse: 9.4863e-04 - 225ms/epoch - 17ms/step\n", "Epoch 44/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0236 - mse: 0.0011 - val_loss: 9.8018e-04 - val_mae: 0.0230 - val_mse: 9.8018e-04 - 134ms/epoch - 10ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0236 - mse: 0.0011 - val_loss: 9.8018e-04 - val_mae: 0.0230 - val_mse: 9.8018e-04 - 118ms/epoch - 9ms/step\n", "Epoch 45/250\n", - "13/13 - 1s - loss: 0.0011 - mae: 0.0239 - mse: 0.0011 - val_loss: 9.5093e-04 - val_mae: 0.0233 - val_mse: 9.5093e-04 - 511ms/epoch - 39ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0239 - mse: 0.0011 - val_loss: 9.5093e-04 - val_mae: 0.0233 - val_mse: 9.5093e-04 - 121ms/epoch - 9ms/step\n", "Epoch 46/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.4785e-04 - val_mae: 0.0223 - val_mse: 9.4785e-04 - 342ms/epoch - 26ms/step\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.4785e-04 - val_mae: 0.0223 - val_mse: 9.4785e-04 - 234ms/epoch - 18ms/step\n", "Epoch 47/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 114ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 108ms/epoch - 8ms/step\n", "Epoch 48/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 234ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 221ms/epoch - 17ms/step\n", "Epoch 49/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 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- val_loss: 6.8788e-04 - val_mae: 0.0198 - val_mse: 6.8788e-04 - 233ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 7.5092e-04 - mae: 0.0208 - mse: 7.5092e-04 - val_loss: 6.8788e-04 - val_mae: 0.0198 - val_mse: 6.8788e-04 - 218ms/epoch - 17ms/step\n", "Epoch 104/250\n", - "13/13 - 0s - loss: 7.0460e-04 - mae: 0.0200 - mse: 7.0460e-04 - val_loss: 7.2570e-04 - val_mae: 0.0200 - val_mse: 7.2570e-04 - 125ms/epoch - 10ms/step\n", + "13/13 - 0s - loss: 7.0460e-04 - mae: 0.0200 - mse: 7.0460e-04 - val_loss: 7.2570e-04 - val_mae: 0.0200 - val_mse: 7.2570e-04 - 115ms/epoch - 9ms/step\n", "Epoch 105/250\n", - "13/13 - 0s - loss: 6.9255e-04 - mae: 0.0202 - mse: 6.9255e-04 - val_loss: 6.7411e-04 - val_mae: 0.0199 - val_mse: 6.7411e-04 - 236ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 6.9255e-04 - mae: 0.0202 - mse: 6.9255e-04 - val_loss: 6.7411e-04 - val_mae: 0.0199 - val_mse: 6.7411e-04 - 193ms/epoch - 15ms/step\n", "Epoch 106/250\n", - "13/13 - 0s - loss: 6.8175e-04 - mae: 0.0196 - mse: 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"13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 155ms/epoch - 12ms/step\n", + "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 112ms/epoch - 9ms/step\n", "Epoch 246/250\n", - "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 113ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 107ms/epoch - 8ms/step\n", "Epoch 247/250\n", - "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 139ms/epoch - 11ms/step\n", + "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 106ms/epoch - 8ms/step\n", "Epoch 248/250\n", - "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 85ms/epoch - 7ms/step\n", + "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 118ms/epoch - 9ms/step\n", "Epoch 249/250\n", - "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 73ms/epoch - 6ms/step\n", + "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 106ms/epoch - 8ms/step\n", "Epoch 250/250\n", - "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 72ms/epoch - 6ms/step\n" + "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 109ms/epoch - 8ms/step\n" ] }, { @@ -675,7 +698,7 @@ "x = x.to_numpy()\n", "y = y.to_numpy()\n", "\n", - "# # Create Keras Sequential object and build neural network\n", + "# Create Keras Sequential object and build neural network\n", "model = tf.keras.Sequential()\n", "model.add(\n", " tf.keras.layers.Dense(\n", @@ -710,7 +733,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -722,6 +745,7 @@ } ], "source": [ + "# Adding input bounds and variables along with scalers and output variable to kerasSurrogate\n", "xmin, xmax = [7,306], [40,1000]\n", "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", "\n", @@ -747,14 +771,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "13/13 [==============================] - 0s 1ms/step\n" + "13/13 [==============================] - 0s 3ms/step\n" ] }, { @@ -801,7 +825,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "13/13 [==============================] - 0s 2ms/step\n" + "13/13 [==============================] - 0s 3ms/step\n" ] }, { @@ -828,7 +852,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "13/13 [==============================] - 0s 1ms/step\n" + "13/13 [==============================] - 0s 3ms/step\n" ] }, { @@ -891,14 +915,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "4/4 [==============================] - 0s 3ms/step\n" + "4/4 [==============================] - 0s 5ms/step\n" ] }, { @@ -945,7 +969,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "4/4 [==============================] - 0s 2ms/step\n" + "4/4 [==============================] - 0s 3ms/step\n" ] }, { @@ -972,7 +996,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "4/4 [==============================] - 0s 2ms/step\n" + "4/4 [==============================] - 0s 4ms/step\n" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/500_Points_DataSet.csv b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/500_Points_DataSet.csv deleted file mode 100644 index d963f97b..00000000 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/500_Points_DataSet.csv +++ /dev/null @@ -1,501 +0,0 @@ -CO2SM.Pressure,CO2SM.Temperature,CO2SM.CO2_Enthalpy,CO2SM.CO2_Entropy,CO2SM.Enthalpy,CO2SM.Entropy,CO2SM.status,graph.error -13.44352,853.1312,-368176.883626,8.552332,-87996.3871,2.044056,0,0 -31.863708,909.520515,-365486.064526,4.127018,-87353.2659,0.986381,0,0 -21.132666,713.351479,-376015.521854,-5.327858,-89869.8666,-1.273389,0,0 -21.981615,809.477728,-370818.552442,1.169161,-88627.7611,0.279436,0,0 -18.081246,960.589169,-362401.453621,12.388151,-86616.0262,2.960839,0,0 -16.411093,622.409936,-380610.848384,-10.110284,-90968.176,-2.416416,0,0 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b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb index 9ed41c53..ffe09b65 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb @@ -1,19 +1,41 @@ { "cells": [ { - "attachments": { - "image.png": { - "image/png": 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" - } - }, "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - SCO2_flowsheet_optimization_pysmo_surrogate (Part 3)\n", "\n", - "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. \n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", "\n", - "![image.png](attachment:image.png)" + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" ] }, { @@ -27,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -69,18 +91,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -91,7 +113,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -99,11 +121,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -114,7 +136,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -122,11 +144,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -137,7 +159,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -145,11 +167,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -160,7 +182,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -168,11 +190,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -183,7 +205,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -191,11 +213,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -206,7 +228,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -214,11 +236,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -229,7 +251,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -237,11 +259,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -252,7 +274,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -260,11 +282,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -275,7 +297,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -283,11 +305,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -298,7 +320,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -306,11 +328,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -321,7 +343,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -329,11 +351,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -344,7 +366,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -352,11 +374,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -367,7 +389,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -375,11 +397,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -390,7 +412,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -398,11 +420,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -413,7 +435,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -421,11 +443,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -436,7 +458,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -444,11 +466,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -459,7 +481,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -467,11 +489,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -482,7 +504,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -490,11 +512,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -505,7 +527,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -513,11 +535,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -528,7 +550,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -536,11 +558,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -551,7 +573,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -559,11 +581,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -574,7 +596,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -582,11 +604,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -597,7 +619,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -605,11 +627,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -620,7 +642,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -628,11 +650,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -643,7 +665,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -651,11 +673,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -666,7 +688,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -674,11 +696,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -689,7 +711,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -697,11 +719,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -712,7 +734,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -720,11 +742,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -735,7 +757,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -743,11 +765,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -758,7 +780,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -766,11 +788,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -781,7 +803,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -789,11 +811,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -804,7 +826,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -812,11 +834,11 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -827,7 +849,7 @@ "\n", "===========================Polynomial Regression===============================================\n", "\n", - "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-08-23_103459.pickle \".\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", "\n", "The number of cross-validation cases (3) is used.\n", "The default training/cross-validation split of 0.75 is used.\n", @@ -835,26 +857,26 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "2023-08-08 10:34:59 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", - "2023-08-08 10:34:59 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:35:00 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:35:00 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-08 10:35:00 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:35:00 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-08 10:35:00 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:35:00 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:35:00 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", - "2023-08-08 10:35:00 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:35:00 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:35:01 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:35:01 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:35:01 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", - "2023-08-08 10:35:01 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:35:01 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-08 10:35:01 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:35:01 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-08 10:35:01 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-08 10:35:01 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:45:31 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", "--------------------------------------------------------------------\n", "The degrees of freedom for the flowsheet is 0\n", "--------------------------------------------------------------------\n", @@ -918,7 +940,7 @@ "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 3\n", "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", - "Total CPU secs in NLP function evaluations = 0.000\n", + "Total CPU secs in NLP function evaluations = 0.002\n", "\n", "EXIT: Optimal Solution Found.\n", "\n", @@ -937,7 +959,7 @@ " Units Inlet Outlet \n", " flow_mol mole / second 1.2110e+05 1.2110e+05\n", " temperature kelvin 685.15 893.15\n", - " pressure pascal 34.510 34.300\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -958,7 +980,7 @@ " Units Inlet Outlet \n", " flow_mol mole / second 1.2110e+05 1.2110e+05\n", " temperature kelvin 893.15 729.38\n", - " pressure pascal 34.300 9.3207\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -976,7 +998,7 @@ " Units Inlet Outlet \n", " flow_mol mole / second 1.2110e+05 1.2110e+05\n", " temperature kelvin 729.38 489.15\n", - " pressure pascal 9.3207 9.2507\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -994,7 +1016,7 @@ " Units Inlet Outlet \n", " flow_mol mole / second 1.2110e+05 1.2110e+05\n", " temperature kelvin 535.47 736.02\n", - " pressure pascal 34.560 34.490\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -1012,7 +1034,7 @@ " Units Inlet Outlet \n", " flow_mol mole / second 1.2110e+05 1.2110e+05\n", " temperature kelvin 489.15 354.15\n", - " pressure pascal 9.2507 9.1807\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -1027,10 +1049,10 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet Outlet\n", - " flow_mol mole / second 86647. 86647.\n", - " temperature kelvin 378.99 566.32\n", - " pressure pascal 34.620 34.620\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 378.99 566.32\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -1046,10 +1068,10 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet bypass to_cooler\n", - " flow_mol mole / second 1.2110e+05 30275. 90825. \n", - " temperature kelvin 354.15 354.15 354.15 \n", - " pressure pascal 9.1807 9.1807 9.1807 \n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -1064,10 +1086,10 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet Outlet\n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 354.15 308.15\n", - " pressure pascal 9.1807 9.1107\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -1085,10 +1107,10 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet Outlet\n", - " flow_mol mole / second 90825. 90825.\n", - " temperature kelvin 308.15 378.99\n", - " pressure pascal 9.1107 34.620\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 378.99\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -1106,10 +1128,10 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet Outlet\n", - " flow_mol mole / second 30275. 30275.\n", - " temperature kelvin 354.15 460.04\n", - " pressure pascal 9.1807 34.886\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 460.04\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -1125,10 +1147,10 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet to_FG_cooler to_LTR\n", - " flow_mol mole / second 90825. 4177.9 86647.\n", - " temperature kelvin 378.99 378.99 378.99\n", - " pressure pascal 34.620 34.620 34.620\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 378.99 378.99 378.99\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", @@ -1143,20 +1165,20 @@ "\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units Inlet Outlet\n", - " flow_mol mole / second 4177.9 4177.9\n", - " temperature kelvin 378.99 483.15\n", - " pressure pascal 34.620 34.560\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 378.99 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", "====================================================================================\n", "\n", "====================================================================================\n", "Unit : fs.mixer Time: 0.0\n", "------------------------------------------------------------------------------------\n", " Stream Table\n", - " Units FG_out LTR_out bypass Outlet \n", - " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", - " temperature kelvin 483.15 566.32 460.04 535.47\n", - " pressure pascal 34.560 34.620 34.886 34.560\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 566.32 460.04 535.47\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", "====================================================================================\n", "667.9424945058901 kW\n" ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb index 5f7f7366..fe6bd96f 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb @@ -1,11 +1,6 @@ { "cells": [ { - "attachments": { - "image.png": { - "image/png": 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" - } - }, "cell_type": "markdown", "metadata": {}, "source": [ @@ -26,13 +21,40 @@ "\n", "### 1.2 Supercritical CO2 cycle process\n", "\n", - "![image.png](attachment:image.png)\n", - "\n", - "The above flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature. Thus, surrogate should have pressure and temperature as the inputs.\n", + "The below flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", "\n", "In this example, we will train the model using polynomial regression for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -44,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -69,22 +91,22 @@ "source": [ "### 2.1 Importing Training and Validation Datasets\n", "\n", - "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using CoolProp package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", "\n", - "We rename the column headers because they contained \".\" and change them to the variable names to be used in the property package. Further, the input variables are ***pressure***, ***temperature***, while the output variables are ***enth_mol***, ***entr_mol***, hence we slice them and create the input and output data. " + "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsquent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Import training data\n", "np.set_printoptions(precision=6, suppress=True)\n", "\n", - "csv_data = pd.read_csv(\"./500_Points_DataSet.csv\")\n", - "csv_data.columns.values[0:4]=[\"pressure\",\"temperature\",\"enth_mol\",\"entr_mol\"]\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "csv_data.columns.values[0:6] =[\"pressure\", \"temperature\",\"enth_mol\",\"entr_mol\",\"CO2_enthalpy\",\"CO2_entropy\"]\n", "data = csv_data.sample(n=500)\n", "\n", "input_data = data.iloc[:, :2]\n", @@ -113,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -181,37 +203,37 @@ " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", " Exceeded.\n", "\n", - "Best surrogate model is of order 5 with a cross-val S.S. Error of 26156.366230\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 20466.657669\n", "\n", "------------------------------------------------------------\n", "The final coefficients of the regression terms are: \n", "\n", - "k | -502464.012637\n", - "(x_ 1 )^ 1 | -4896.649136\n", - "(x_ 2 )^ 1 | 857.309074\n", - "(x_ 1 )^ 2 | 236.735794\n", - "(x_ 2 )^ 2 | -2.283672\n", - "(x_ 1 )^ 3 | -8.252162\n", - "(x_ 2 )^ 3 | 0.003144\n", - "(x_ 1 )^ 4 | 0.159508\n", - "(x_ 2 )^ 4 | -2e-06\n", - "(x_ 1 )^ 5 | -0.001228\n", + "k | -534397.59515\n", + "(x_ 1 )^ 1 | -2733.579691\n", + "(x_ 2 )^ 1 | 1036.106357\n", + "(x_ 1 )^ 2 | 32.409203\n", + "(x_ 2 )^ 2 | -2.852387\n", + "(x_ 1 )^ 3 | 0.893563\n", + "(x_ 2 )^ 3 | 0.004018\n", + "(x_ 1 )^ 4 | -0.045284\n", + "(x_ 2 )^ 4 | -3e-06\n", + "(x_ 1 )^ 5 | 0.000564\n", "(x_ 2 )^ 5 | 0.0\n", - "x_ 1 .x_ 2 | 4.603417\n", + "x_ 1 .x_ 2 | 4.372684\n", "\n", "The coefficients of the extra terms in additional_regression_features are:\n", "\n", - "Coeff. additional_regression_features[ 1 ]: -0.003097\n", - "Coeff. additional_regression_features[ 2 ]: 4.7e-05\n", - "Coeff. additional_regression_features[ 3 ]: -0.063913\n", - "Coeff. additional_regression_features[ 4 ]: 139048.007363\n", - "Coeff. additional_regression_features[ 5 ]: -71.706987\n", + "Coeff. additional_regression_features[ 1 ]: -0.002723\n", + "Coeff. additional_regression_features[ 2 ]: 3.6e-05\n", + "Coeff. additional_regression_features[ 3 ]: -0.050607\n", + "Coeff. additional_regression_features[ 4 ]: 169668.814595\n", + "Coeff. additional_regression_features[ 5 ]: -44.726026\n", "\n", "Regression model performance on training data:\n", - "Order: 5 / MAE: 111.978134 / MSE: 34702.874291 / R^2: 0.999740\n", + "Order: 5 / MAE: 134.972465 / MSE: 54613.278159 / R^2: 0.999601\n", "\n", "Results saved in solution.pickle\n", - "2023-08-08 10:16:16 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", + "2023-08-19 23:48:46 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", @@ -267,38 +289,43 @@ " - termination condition: maxIterations\n", " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", "\n", - "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.190163\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.156437\n", "\n", "------------------------------------------------------------\n", "The final coefficients of the regression terms are: \n", "\n", - "k | -408.470578\n", - "(x_ 1 )^ 1 | -13.06054\n", - "(x_ 2 )^ 1 | 2.970997\n", - "(x_ 1 )^ 2 | 0.656992\n", - "(x_ 2 )^ 2 | -0.008065\n", - "(x_ 1 )^ 3 | -0.0229\n", - "(x_ 2 )^ 3 | 1.1e-05\n", - "(x_ 1 )^ 4 | 0.000444\n", + "k | -519.862457\n", + "(x_ 1 )^ 1 | -8.820865\n", + "(x_ 2 )^ 1 | 3.676641\n", + "(x_ 1 )^ 2 | 0.18002\n", + "(x_ 2 )^ 2 | -0.010217\n", + "(x_ 1 )^ 3 | -0.000783\n", + "(x_ 2 )^ 3 | 1.4e-05\n", + "(x_ 1 )^ 4 | -6.9e-05\n", "(x_ 2 )^ 4 | -0.0\n", - "(x_ 1 )^ 5 | -3e-06\n", + "(x_ 1 )^ 5 | 1e-06\n", "(x_ 2 )^ 5 | 0.0\n", - "x_ 1 .x_ 2 | 0.010388\n", + "x_ 1 .x_ 2 | 0.010367\n", "\n", "The coefficients of the extra terms in additional_regression_features are:\n", "\n", "Coeff. additional_regression_features[ 1 ]: -7e-06\n", "Coeff. additional_regression_features[ 2 ]: 0.0\n", - "Coeff. additional_regression_features[ 3 ]: -0.000154\n", - "Coeff. additional_regression_features[ 4 ]: 274.423201\n", - "Coeff. additional_regression_features[ 5 ]: -0.164325\n", + "Coeff. additional_regression_features[ 3 ]: -0.000112\n", + "Coeff. additional_regression_features[ 4 ]: 484.312223\n", + "Coeff. additional_regression_features[ 5 ]: -0.1166\n", "\n", "Regression model performance on training data:\n", - "Order: 5 / MAE: 0.303688 / MSE: 0.282479 / R^2: 0.999317\n", + "Order: 5 / MAE: 0.398072 / MSE: 0.495330 / R^2: 0.998873\n", "\n", "Results saved in solution.pickle\n", - "2023-08-08 10:16:40 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" + "2023-08-19 23:49:20 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" ] } ], @@ -339,12 +366,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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", 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", 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11xAcHAybzQaLxSJljkQ33XST9PvevXvj5Zdfxvnnn4+TJ0+ic+fOLXIfRETUNnTtWgGLxaEIfiwWB7p2Pd6Ko2rUbjI+8+bNw/nnn4+IiAjExsbiyiuvxN69exXH/P7775g5cyaio6PRuXNnTJ48GUeOHGmlEbcPgiBIU1f5+fm4/PLLcdZZZyEiIgLXX389KioqUFNTY3iOLVu2YMKECejZsyciIiJwySWXAAB+/fXXZh8/ERG1LTZbNSZMWAuLxbl4xmJxYMKEtbDZqlt5ZE7tJvDZuHEjZs6ciW+//RZ5eXmoq6vD6NGjcerUKemYu+++G2vWrMG///1vbNy4EaWlpZg0aVIrjrrt+/HHH5GcnIwDBw7giiuuwKBBg7By5Ups2bIF//jHPwAYpyVPnTqFMWPGIDIyEh9++CG+++47fPTRR24/R0REHVdq6jbk5i7AtGmLkJu7AKmp21p7SJJ2M9X12WefKX5etGgRYmNjsWXLFowYMQJ2ux0LFy7EkiVLcNlllwEA3n33XZx77rn49ttvceGFF7bGsNu0L7/8Ej/88APuvvtubNmyBQ6HA88//7y0+ery5csVxwcHB7tsIfHTTz+hoqIC8+fPR2JiIgDg+++/b5kbICKiNstmq24zWR65dpPxUbPb7QCArl2dxVJbtmxBXV0dMjMzpWP69euHnj17oqCgQPc8Z86cQVVVleJXR3TmzBkcPnwYv/32G7Zu3YqnnnoKf/7zn3HFFVfghhtuQJ8+fVBXV4dXXnkFv/zyCz744AO88cYbinMkJSXh5MmT+OKLL1BeXo6amhr07NkTwcHB0udWr16Nxx9/vJXukoiIyFi7DHwcDgdyc3MxfPhwDBw4EABw+PBhBAcHuzTY6969Ow4fPqx7rnnz5sFms0m/xKxFR/PZZ58hLi4OSUlJGDt2LDZs2ICXX34Z//nPfxAYGIjBgwfjhRdewNNPP42BAwfiww8/xLx58xTnGDZsGG677TZkZ2ejW7dueOaZZ9CtWzcsWrQI//73v9G/f3/Mnz8fzz33XCvdJRERtYSKigps2XIEK1ZU4NNPj2HFigrs2lWpe7zdHoHi4iTY7REtN0gdFkEQhNYehKduv/12rFu3Dl9//TUSEhIAAEuWLMGNN96IM2fOKI694IILMHLkSDz99NOa5zpz5oziM1VVVUhMTITdbkdkZKTi2N9//x3FxcVITk5Gp06dPBpza/fxaW+a8qyJiKh5VFRU4OjRo5gzZx/WrLnij5VbAgCLVMSsrufZunWIdKzF4sCLL9bgrrt8v+K3qqoKNptN8/tbrt3U+IhmzZqFtWvX4r///a8U9ABAjx49UFtbi8rKSkXW58iRIy7Lr+VCQkIQEhLSnEMGAERHR2PWrFmt1rmZiIioKeQNCtesyZUtV3euDBaEAKxZcwVSUvbDZqvGqFGjcOpUF/z97/2kZoaCEIB77w3HRRcdQVJSUKt857WbwEcQBNxxxx346KOP8NVXXyE5OVnxflpaGqxWK7744gtMnjwZALB37178+uuvyMjIaI0hu2BQQ0RE7ZVRg0KR2KjQZqtGXl4eiouT4HCcqzimocGCV15Zh+Tkg60yy9FuAp+ZM2diyZIl+M9//oOIiAipbsdmsyE0NBQ2mw05OTm455570LVrV0RGRuKOO+5ARkYGV3QRERH5iFaDQpG6UaG7Zoat0fak3RQ3v/7667Db7bj00ksRFxcn/Vq2bJl0zIsvvogrrrgCkydPxogRI9CjRw9pOwYiIiJqOnWDQmeNj3ajwrbYzLDdZHzM1GB36tQJ//jHP6TGe0RERB2VuBO6nuasG01N3YaUlP04frwrrNZa1NUFo2vX45oBjfxYvWNaUrsJfIiIiMipLawU9qRBYVtqZthuprqIiIjISZ3p0euTw62DXDHjQ0RE1I6p++Ro9dKhRgx8iIiI2ilnT50rpFVT6l467nhSJ1RTU2M4juPHo9G1awWmTHGupM7Ly/PkVloMAx9qsq+++gojR47EiRMnXLYM0ZOUlITc3Fzk5uY269iIiDoyrZ468l46RjypEwKAxYsXS6/JA52ioj6KjFNR0do/ipmT0LVrheE4goOD3V7f1xj4+IHp06fjvffew6233uqy8ejMmTPx2muvYdq0aVi0aFHrDJCIqINqysoro8+Wl5cDcN8nx4hWnZAYzMiDFfVx8qk1wAFn5+bGzsyrV18BiwWaU2/Z2dmw2Wxu7705MfDxE4mJiVi6dClefPFFhIaGAnDuh7VkyRL07NmzlUdHRNS2eRPANGXlldnPin1y1DU+nq6gMqoTEoMswHVqTXuNVADEDjTqqTebzYa4uDiPxuZrDHz8RGpqKoqKirBq1Spcd911AIBVq1ahZ8+eiu0/zpw5g9mzZ2Pp0qWoqqrC0KFD8eKLL+L888+Xjvn000+Rm5uLkpISXHjhhZg2bZrL9b7++ms8+OCD+P777xETE4P/+7//w7x58xAeHt78N0tE5EPeBjBmV1RpHWc2GwM0vU+OuzoheSNgo+0q9JidemspXM7eSg4dAjZscP6zpdx000149913pZ/feecd3HjjjYpj7r//fqxcuRLvvfcetm7dij59+mDMmDE4ftyZNi0pKcGkSZMwYcIEbN++HTfffDPmzJmjOEdRURHGjh2LyZMnY+fOnVi2bBm+/vpraZ6YiKg9aUoAI6e35FytoqJCkWXZunUIFizIxXvvTcOCBbnYunWIy2dstmokJx90CS7M1NDo1QmVlCQoxmu3R+DUqTA4p7fkHLIuzg6InZxFZqfeWgozPq1g4UJgxgzA4QACAoC33gJycpr/ulOnTsWDDz6IgwcPAgC++eYbLF26FF999RUA4NSpU3j99dexaNEijBs3DgDwz3/+E3l5eVi4cCFmz56N119/HSkpKXj++ecBAOeccw5++OEHPP3009J15s2bh+uuu04qXO7bty9efvllXHLJJXj99dfRqVOn5r9ZIqI2xOySc3V2yV02ZtKkSYiJidG8ptkaGr06oRUrpgBwjnfQoJ3YuXPQH8cIcAY4jfcizzipi51be4sKNQY+LezQocagB3D+89ZbgTFjgISE5r12t27dMH78eCxatAiCIGD8+PGKf2GKiopQV1eH4cOHS69ZrVZccMEF+PHHHwEAP/74I9LT0xXnzcjIUPy8Y8cO7Ny5Ex9++KH0miAIcDgcKC4uxrnnKnfqJSJqT4ymnfSON7vkXJ01KilJNFy1FRMT43HNjFivJGaVtOqEnDU6jePdsWMwxAJmwAKLRcDkycuRmHhIugfxn21tiwo1Bj4tbN++xqBH1NAA7N/f/IEP4JzuEqecmmtPs5MnT+LWW2/FnXfe6fIeC6mJqD3zplmgt0vOxWupNWXqSK9eSR6snDoVjhUrrlJf1WX84eE1uuPX26JCPoXHVV1+om9f5/SWPPgJDAT69GmZ648dOxa1tbWwWCwYM2aM4r2UlBQEBwfjm2++Qa9evQAAdXV1+O6776Rpq3PPPRerV69WfO7bb79V/Jyamoo9e/agT0vdFBFRC/C2WaA3S85dV0+JtKeOzK46c1c0bbNVw26PcBmvc3qrMfjxNviSF0oDzbuXmB4GPi0sIcFZ03Prrc5MT2Ag8OabLZPtAYDAwEBp2iowMFDxXnh4OG6//XbMnj0bXbt2Rc+ePfHMM8+gpqYGOX8UId122214/vnnMXv2bNx8883YsmWLS/+fBx54ABdeeCFmzZqFm2++GeHh4dizZw/y8vJMrYwgImpN6iBCzFJ4m7nxZsm53uqpKVNWYODAH13G60kjQpFe9kprvPIaH2/qdsz2CGoJDHxaQU6Os6Zn/35npqelgh5RZGSk7nvz58+Hw+HA9ddfj+rqagwdOhSff/45unTpAsA5VbVy5UrcfffdeOWVV3DBBRfgqaeewk033SSdY9CgQdi4cSPmzp2Liy++GIIgICUlBdnZ2c1+b0RETWEURHiauZGvqDKqe9FaeaV3rcRE5VJgrW0kzAQZ7rJXWuO97LIvDet29K7b1vYSY+DTShISWi7gcdeR+eOPP5Z+36lTJ7z88st4+eWXdY+/4oorcMUVynln9bL4888/H+vXr9c9x4EDBwzHRETUGtxNBRllbtQBTHR0NGbNmuVV52ata2VkFLgcFxYWpvjZbJBhJnulrtPRq9sxum5T9xJrDgx8iIiINOh9maek7MfAgVdi8OBwxMefD+B83QCmKfUr4rUKC9OxeXMGNm8ejoKCDJeuylarFYBnNUjuslcjR46E1Wo1/B9YkdF1m7KXWHNh4ENERKTiLogYNy4UcXHdfX5drWmvgoIMyJeWe9JVWS/IcJe92rBhg+kxG123KXuJNRcGPkRERCqtlakQp8dKS0uxatUqj8bhaZDhTb8drToeo+v6ai8xX2LgQ0REpNKamQr5snO9cVitrnVDZoIMdUZJXrdjt0egpCQRABAVdQJ1dSGKAMeTVWDy67a1hoYMfLwgCIL7g6hJ+IyJqDW1lUyFehyAAEEIwMKFN2sWLpsJMrKzs1FXV4cTJ05IU1pbtw7B6tVXoHELT2ffHvmWFJ6uAlPfh9ZYzOwl5msMfDwg9r2pra1FaGhoK4+mYxP/b0fda4iIqKV4m6nQaiZYWVmJ+vp6AM6tgGw2m+J9oy7GqanbEBt7GG+/fTP0an3k9IKMmpoavPXWWy6v2+0RqqAHEJsViteZPHmlx6vARP/v//0/zT0ag4KCUFtbi4qKihZtYsjAxwNBQUEICwvDsWPHYLVaERDAze2bg8PhwLFjxxAWFoagIP4VJSLv6HUzFoMQrQDEbrcrftb7Mq+srERlZSUsFoviHHa7HcuWLfNqvPIuxupMSF1dCJSBiWvgkZ6eDpvNhi5dumgGVnpL9Z07rut/n4mZJnerwMR+b3L19fUu3f61tGQHZ36reMBisSAuLg7FxcXSDufUPAICAtCzZ09YLBb3BxORX9PLsCxfvtzrc44aNQoWi0V3ObfZc2sVA5tpMKgucjZTc1RYWCj9XiuQKCsrk36vrtcRd1vX4nzfgszMfOTnZzZpFVhb6ODMwMdDwcHB6Nu3b6u02fYnwcHBzKgRkVtmt2vQo/dFnJeX1+TzaBUDAzDdxVhe5Oxa6+PARRdtQklJIkpKgMTEEtOBhNZSfWdQJQ9+Gmt8BAFYseIqWCwOZGbmIz6+1Ksi5bbSwZmBjxcCAgI05yuJiKhlueu0bPS61hex2HRP/Xm9c9jtEVKDQSDAbTGwc92Gdp1OeXm5S62PetuL06c7SVmXTZtGoHHjUAcmTjQXSOgtkZ8ypTGLFRVVicrKKKxYMUUx3vz8TOTmLvBoywrxvbbSwZmBDxERtSqzO4u7o5dR0Atw1F/Eq1dfAYsFmhkJd9kbkbtiYDV5nY7YjFA+TRUdHY2srCwsX74cdnuEFPQ4yUsBnOM3E0gY7QMm/6y7uqKRI0cqVoUZZXPaUgdnBj5ERNRqPNlZ3Cj40csoxMYe1nxdKzABAiB20pBnJAC4zd7IGRUDqz+j1RtIHQRGRUUB0N+xXT5+M4GE2aX6XbtWQF37Ix+vWMxsJpvTljo4M/AhIqIm8zZrY7ZesrS0FLW1tS4rqcrLywHoZxRKSnrqZF5cAxM1MSMBWExlb0Ri9kRZkwMIAjB48E7s3DlIqtPR2njUbrcjLi7O5XWt4EGpMZCorKzUPIfIaKm+OGVVWhoHZVZJGSCdOHECgHE2R3y/a9eKNtEXCWDgQ0RETeRJ1gZQBjti4CLSqxOR70mlRX/65lfdaR11sbDzS96iOE4MJMxkb5wav9BTUvZD2Ys1ADt3DkJOztvYs2cACgq0Nx5dtmwZsrOzXYI7rWaG6hof8ZktX77cJUtm1LVZJJ+yUp4fsFggZcCAxpVces++tDQe779/gxToZGbmIzd3Qat3cGbgQ0RETWI2a3P06FHDZeBNWfWjN32TkFCmm2lQZz2KivroZiS0zgEoV2hlZBQgPb1Q+szx49HQqpGprIxCQUGG4dSQXi8g+Zit1lpUVkahpiYUYWG/IzGxRHGs+s9FXCJfW1sLu92Ouro6AJA6OKunrJTZHs82PJUvfRc/m5c3CgAwfLhrlqslOzgz8CEiohYhdi7WYlSjo94zSv4ZeXZIb/rGaFpHnvUwOk7vPaPOznqZEL2pMzGo0Mt6jRo1Cnl5eYoxHz3aA+vWjTcdLEZHR6OiokIzsHJfQ+RakyOONSVlvyKbo30uC/LyMjFw4C7YbNWYNGkSYmJiTBev+woDHyIi8imjZc16x+nViSxceLPpVVbihpla19R73exxWVlZiIqKcmmMqHe8eG9aTf8SE0s0anUElJbG48SJrrpZr+TkZMyaNQt79+5FXl6e10vE9TJ07mqIhg0rUJxX78/Abo/4oxu0cqrMqTHAi4mJMaxDai4MfIiIyGfMTlepj8vMzNcMBrS+1AHtVVbiF756+4Tq6mpFQ0K9wEzMQKjJMxJxcXHSdJFceXm5VIekdW/qpn+Zmfl/TP2IgYHljwAJMNqPKzo6GsnJyQDMLxFXF54b1VVNmLBWY98uZ6YqPb1Q8RmtPwN5nyFn3RSgVzfVWhj4EBGRT5jNQGgdl5+fqaoLcd1Cwd0qK/EL32j7BG/qiNQbaXq6rD4vLxN3361s+hcfXwatGho1+X2JAYu4n5i7JeLl5eVu9w7Teh53370AhYXpUh2S1gosvaArLy8TjX9u4p+jAHlzx9YqahYx8CEiIp8wm4HQOy4+vlSqE7Faa6VpLpG7VVbuMgnuAjN3K8fMbKSpXdsSgMLCdIwenS+9YrWe8bjPj3p87vrxuLsfveeRm7sAo0fnIz29UHe5u3Mqy7XHj9a9T5myHOHhNa26kkuOgQ8RkZ/xVadkNbNN6oyOk9fMGH2pm+kJo57SMhuYebKRpvgsi4uLpXvTylYVFGRIK760lozrrRTLzMz/Y3UYNIMGo4Jsd9w9jylTLnSZIpRngpxjd96r1kouQLsjdGtj4ENE5Ed81SlZTlyK7C4DYbVaDY8DgOLiJLertAD3X/h621S4C8w8mQrTepY2WzWGDSvA5s3DFa/LG/q5Lhl3ICfnbSQkOHdPF++rtDTepTBaayzuCqz1doZ3F6hGREQonotr7Y8FFouAyZOXS8FNaOjvppsUtuQSdjkGPkREfsRsz53du3crCoRFQUFBiI2NVQRF8v4wAPDww8dw4EAQkpLqER9/PoDzXb7ktHroLFiQq7lK68YbRyEoKAj19fWorq6GxWJB586dFeezWq2oq6vDqlWrDKdwjAIzT1dJ6W2Q2qvXAWzePAxaRb16U2F1dY3PR7yW2PxPayziKjO5ffv2Ge6dBciDLgdGjco3nTlbs8a14FkcV3h4jamWAPLi8ZZewi7HwIeIyI/pTesYFQgDrhkh+e/j4oC0NP3PiQGDuBLKXcBhtVoNC3RF2dnZAIyncIy+mJuykaZ2x2PlNJZ4DndZJ7s9Art3DzAcS1RUlMtS8MbiZ9fnKd+A1cnZUHDUqDzdbspihs6ov4/WVKZeBqq1lq+rMfAhIvJTTemUbDZzpKb1f/nuAg6xw7BIL1gTj3M3hXPjjaMUy9bFAMzbjTT1Ox67TmO5mw403jLC3FJwvayScvsM5/jy850NBZOTD7pkZMQ/Y73+Pp6u0mqtqS01Bj5ERH7IzLSO3R6BkpJEAEBiYkmzFai6CzjEzTABc8Gau+BCL/NgdtdyNeOOx8ppLEB/Okg7gNLOGhmxWs9AHTQ5f3YuK5eTB5jq51JWph2sAQ4MG6bcngOAYn8xtdac2lJj4ENE5EfEHjDusiyuxawCJk5cYzoj5Al3AYc47eYuWAsKavxKM5rSMso8eLNKyqjjsV6WRms6qKQkUeMcFowZ8xn6999jOvCsqwuBa8dk9c/G41NzV7sTHx/fZgIbdxj4EBH5CfkeTUZZFrs9QnMFj5ntELzlLuAwW/ei1VVZTivzYGbXcq3j5Mdr7ZouD+DcbePRGGgqWSwOzaBHayxiTY52IKa1fYRnU1VGtTvtJegBGPgQEfkNeUBglGUpLk6C3goeM4W+ZpkNODype/HmC1i9Kk1vrEbnVu+aXlcXLAVw7qbn9FZNqTNf7jb1FKeZtKamtP48p0xZgYEDf1Tco/qezWgrtTtmMfAhIvJTKSn7MXnySgCCosmcXhM+9XYIIm/rN8SA4+jRo4qNP+U8qXvxdkzeNnQ0E7iZqaXSqxGaPFkZmIgZndraWqn+Rq6mpkb6vToQ0+qCnZh4CID+VJUvAsK2iIEPEZEfMspC2GzVmDhRuVmlu+0QPGl4KBcdHa3bD0dv13a9uhdvxtSUho7ywKCyshL19fXSeydOnMCGDRtMLZHXm3YUAxORmSX9U6dORVhYGADlxqnuir31nlN7C2rMYOBDRORnzGQhxIxBSUkCAEgZIU+2c/CG2V3bHY4AzW7Eno7JKOiSn6u0tBSAayAg37VdK3NkZom8N6vJ9MYZFhYmrcySZ6S8LfbuiBj4EBH5GbON+pxTN41TLU3p+2OG3q7tF120CZs2jYC8P47Yf6aoqI/PxqS+v4yMxiXbYuZEnlGRq6ys1JyuMxvUmFlNJgY7paVxpray6KhTVU3FwIeIyM/oZSGs1lpprywAioyCp9s5eEMvIAsNPQ31iiRBCEBJSYLPxqR1f5s3D8fmzRmYOLExsFi8eLHp84nPz8yKNfHY5OSDmufTK/B2d8/+FtSYwcCHiMjPaGUhBg3aKSuAbWx2J2YUunQ54fV2DnrEqSGxKFkvIOvZ81fN1wGLz8ak34RQP7DQm27Sy4zZbNUYOXIkAOjuqZWZmY/4+DKXjUW1O0M37Z79FQMfIqIW4O3KIV/Sq/lwXfVjgTqjkJPztlfbOehRFxWLQURmZr7LNE5CQpnmdFFiYonPxmTUhFArsNALbtxlxuR7oGkdm5c3CvIVa6mp29x0hm7an4M/YuBDRNTMmrJyyN15PQmm1DUf4qqf4uIkwy9WQXBuu+DNdg565OPWznqUKqaF9KaLfDUm1943jbQ2EdULbozqpwDl9KHeijX1OY0aEjb1z8EfMfAhImpmZlcOebIyyttgSiuwMsp2ODlw6lQ4UlL26+7k7S29gubc3AUu59fqk6PXi8gd9TQb0BhcFRamY/PmDMin+szs4l5SkoDExEOaWajS0ni8//4NigAtJWW/4XMXA6bk5IMuAZ5WcEjmMPAhImpBvloZ5ctgSqvmx7mTt9j114IVK64yHK+3S6LNrjDT4u5Z6o1Jb5pNfHajR+cjPb1QN8DTCxRXrpyCCRPWagYp4vSdeH9r1lyB3NwFmltdiOSZJrN7iPnb0nRvMPAhImohzbUyyttgyqjPCwCUlCRg5copEATtFUTutlAww0yfG5E8QAFg+CyzsrJ0x2Q0zSYvRNb7MxEDRfV+ZvKARp4ZMwru5M+9tDTepb7Jtb2A65jEXdH9cWm6Nxj4EBG1kKZkN/Q0JZhS1/yI3Ycbuw6fNhyvGPTobaEAuC/aNtvnRrmc24EBA/YYji0qKsrlWurpraY8u9TUbQgOPoMVK67SHENy8kHFOYyCOzGgSU4+iIEDd+nugB4TE+MyDgY7nmPgQ0SkoTlWYXmS3TDLXTAlr2HRGrf892LH37KyMmzYsMHteO12u6ltFMSMhJxWbY2zS7QFiYklf5zfmeGxWs+oio4DsHv3QLjbsFROa3rL3W7vI0eOdFmFVVKSCABITCxxu6pM/LwnnZn1sjpae2mRdxj4EBGpNNcqLG+2JnDHXXCi3r/Kk3G7G29dXZ3ieL06IzE40nsfgEsH5kGDdmLnzkGymiOtAmBzG5YCQElJifR7s7u9d+nSRfEZ5dSWgIkT1xg+H/nnjWp0srKyNDNUImZ1fIuBDxGRSnOswhKZLVI1y5tgypNxG433xIkT0u/d1RkZ9b0pKUlUBBWCEIAdOwZDvrRbHaA0ct2w1G6PwPPPb9UMsDzZ7V3+GXU9D2DRrOeRf1bcTV2kl82JjY1lYNOCGPgQERnwxSos9UobvS9Ab1fk6AUnRhkWX4x3w4YNUuBiVCujV0tz+nQnxWonJXWQY4FzhZlrjx150OPuz6ukJFGzd47Wbu+i48ejXa4r3odWPY+oW7du3CurDWLgQ0QkU1FR4ZPiV7nm2CzSXXDSlIDNaLxi00P1NdTktTJ6dUh5eZnQCij+OALqKaicnLexZ88AFBRkaGa33P15NU5XuV4rMfFX3T9T5yoy7aBLnBZTFx8zoGm7GPgQEf1BXdvjy1VYvv4S1ApOxKDEFwGbu/G6ThkpyYMCvToko8+qa3zErSsSEsp0e+wYNRYEDmHNGvV0lXRFVFZGISFBuTJNnKqy2aoxcaJy+bo66IqJiZGKw6lt65CBzz/+8Q88++yzOHz4MAYPHoxXXnkFF1xwQWsPi4jaOHWGw2o9A/X/6belfZH0gpPmWDZv5hoidVBgs1UjMzNfyvBoNfUTPzd58gqpA/Nll32pGeDoTb0ZNRbMyCgw3JZjxYopqK1tzIqpV6IpV57Boy7R1LZ0uMBn2bJluOeee/DGG28gPT0dCxYswJgxY7B3717Exsa29vCIqJUZLVOXrwQSp3GcQY/n+yK15qakzbFs3sw1AAemTFnhEhRs3ToE+fnKoGf48AKEhv7uMh03cOCP0ueMmghqMWosWFCQAa3pqkbKrJjYEFB9fpvtR81Ps2Ny+9HhAp8XXngBt9xyC2688UYAwBtvvIFPPvkE77zzDubMmdPKoyOi1mR2mbrWyh+xxkQ9HdKU64jLmH0dBDXHsnmz15AHLoD+XlwDB+7yeoXb6NGjIQgC8vLyXN5LSdmPESP+i//+91LF64IQgGHDvlHUB7nLionTiceOHXNZui8KCgriqqx2pkMFPrW1tdiyZQsefPBB6bWAgABkZmaioKBA8zNnzpzBmTNnpJ+rqqqafZxE1DrMLlPXmyqqq1P+X73W/+VXVFSgtLTU1HiWL18u/d7TnkBajLagkN+fr7ITRtcYOHAgdu3apfssCwvTMXp0vm5WZ+TIkejSpQuCgoKkHjeVlZVYvnw51q9fD8B1Cwv55qJaxdHp6YVSfZDVWouFC292mxWLjo5mUNPBdKjAp7y8HA0NDejevbvi9e7du+Onn37S/My8efPw2GOPtcTwiKgNMVr15G6qaNKkSZqddPUyPeovaF/1BFJrjtVj7qgDF/Fe7faDsNmA0tI4aPXg2bw5A+nphbpZngEDBhiOU72FhfP88mvo9+cR/9ncWTFqmzpU4OONBx98EPfcc4/0c1VVFRITE1txRETU3NytenI3VRQTE6P5payVUVJmIRq/oJuyM7uR5s5O6GWLxHuVTyWJBczajQcbp5U8XQruOhWpV7dj3J/H180kqX3oUIFPTEwMAgMDceTIEcXrR44cQY8ePTQ/ExISgpCQkJYYHhG1EWZWPXnypaje/BLQ63HjupO3mSXm3hZKN0eBtZhVKi0tVfTz0Soo1m9OqMygeboU3GhFmfoaekGPyNMCamr/OlTgExwcjLS0NHzxxRe48sorAQAOhwNffPEFZs2a1bqDI6Im89UXudlVT2Y6LGtNb7nrcSMys8Tcm33DKioqcPToUUUNkZnPmRUdHS39OYj3qtfZWHslVdOmlfSWrfvyGlyl1XF1qMAHAO655x5MmzYNQ4cOxQUXXIAFCxbg1KlT0iovImqftHbX1qqVMfNF7m4qSz31IqcOrrQCMU8yEu6WmJstyD569Ciio6N1n5PVegZ1dSEunystLdUNJs0Eku76+aSmbsGWLWkQl7JnZBQY1vaYof7zAxywWCD9WWpdQ/wzFQMabiPhvzpc4JOdnY1jx47h4YcfxuHDh/GnP/0Jn332mUvBMxG1L/IvKqPCZL0vNPlWFIDxVFZTuvDa7RE4dSoMepkO+Re0OiOhldHSmz5T3/fy5ctdCpu1diFXf06crvI2kNTPvggQBGDLlvP/CEa+aXLAI6f+8wNgOC3Jzsok6nCBD+D8F5VTW0QdkzfbMehNF/l6s1DXQMMZ/MizEID2F/SOHTtQWFioe24z9y1fRq+9C7n257wJJEVa2Ze0tMYsj3jNgoIM6f7VvH3e6j8/o6CKU1ck6pCBDxF1XN5sx2B2ukhvmboZ2k0PBUyevNylk7HWONVBj3qM7vrhAI3ZG8B4Ckr+vNwFVGLGyWj6R519OX48Glu2nK97TZGnz9ts8DJq1ChERETAarVKHZg5dUUiBj5E1K40dTsGo+yG1jJ1dwXVlZWVAPQDsvDwGs1Ax6j2RmuMKSn7NaeUxH444hjE8xgVAMufl9649+zpj/799yiCKaNpL3n2ZdeugdBqIKj+M7JaraitrUVZWZmp4KQ1+hRRx8PAh4jalaZsx+DpNJnZFVWA+4Bs5MiR2LBhAwDj2puUlP2aY8zNXYCMjAJs3jxcdeUAl/45YjCnnIISKZ+XdoAk4PPPx2L9+tGKwFBeCG232zWfg90eodG7R0BmZr7LM162bJniZzOF6QxqqKkY+BBRu+Oux446SyNO13g6TeZJN2V3AVmXLl0AuK+9mTx5pe6UVnp6oawZosghBT3y86Sk7EdKyn4IgnKcFotzPyu9ccszNerA0LUQOsLluWlPsVkQH6+sP2qu7tVE7jDwIaJ2Sa8w2W63u2QSRE2dJtP7whYZBWRWqxWA+9obQDCc0po4URlcaWWBxGDOGcDoB3ri/aSk7EdOztt4++2b3R6v1Z05Pr7MzRSbA1arM6gxmmokagkMfIioXTBb2CqoUhzqYMXbaTKjL+zs7GzYbDbDsYvZDHe1N4mJh3SntI4f76q5jFue8RHPI76nF+ip7ycjowDaWz80Hq/VnTkvbxTEqToxCBK3qmjMIAVg4cKbVa971r2ayFcY+BBRu2C2sNVMvx9P92dyv7eXzW2PmLKyMgD6U0vyIExrSksezKizXfKAQh3MqQO9jIwCVFd3drmfgoIMuPYeEjBqlHPFmF53Zvm0mDwIuuiiTfj664sV19DawsJM92oiX2LgQ0QtRl57U1oagOLiICQn1yM+3gFAe0WOp9tUiAGGmY1I1WpqajSv4c0SeiNi8FVSkoCamjCEhdUolrzbbNUuU1ryYEZdKN0YUDizLvKpI/Fa4mapmzcP16gTct7PsGHfuExjDRy4C7t3DzDViVoeBG3adLHmNdTBlSdTjUS+wMCHiFqEfIWU0bSRes8pT/epcre83ChYWbx4sebKoqbWBgGuU3VFRX0Ma13MZKa0dinPz8/EwIG7XI53ZnTku5m7LjdPTy9EenqhdM2ioj5YsCDXpei5kdZrogDNZ2aUnSJqCQx8iKhFqDe11MvEyLM7ZhsP1tbWumzM6W2wopVdakptkCg6OhrZ2dlYtmyZ5jNYvfoKBAefQWJiiSLzo3UNMdtjNrjTW2klPh/1/Wg1NnQGOGKg48CwYQUIDz+lquVRBlJaQU5q6jYMHLjLo6lGIl9i4ENELcrbaSOjLFFlZaXLTuQ2W7VhdsHdCi1AmaUxysCYLbwWC6C1A5EArFhxle5KJ63xmu0dpHdcTs7bqKsL1gxA9IKlMWM+Q//+e6TjxSCmtDTedJDj661CiDzBwIeIWpS7L+vy8nKXuh13WaJjx465XMeo9sXskuqmdAo22nDUaGWXJ3tpuctEic9FPK5xVZbzuISEMt370vtzkgc94rlttmokJx/0KMgRd0t39xyJfI2BDxG1KK1NLZ1LqZ3EJnnyjYbdZYnEqR/RoUNxqqXXjbUvADzq3uzJl7EY7Bj1EgK0noGSIASgpCQBx4+fhtV6xnC8WpmoQ4fi8OuvvXDo0EEkJDSe17kzvPOf5eXRsNtdGxDqjdHM9J5ekKPF2z3RiJqKgQ8RtTj5SqOCAudKo4KCDN1dwfWyD1ZrLYqLkzT3udJrxAdYDIMova0YREYZHr1CbK1pKvnKrpUrp7jc24oVUyBmZ4yaCgLKgOOjj/6MHTsGQ6zJGTx4By677EuX4Elc3TVxYuMznzRpEqxWqxS0GU3vjRw5El26dMHp06cRGhqqGN/vv/+OTp06AQCCgoIQFRVl6hkStQQGPkTUavS2WlBnDbSyD4MG7cTChTcrshHqfa7kzDT1A1z3j9KitfJLrxC7tDROs/ZFvC+b7UfU1irvzdmD0XgFllaR9qFDcbKgBwAs2LFjMM4665DOcnTlM5dPPYn0sjh9+/Z127uIqC1i4ENErcLTImd59sFqrZWCHvFzevtcORk39dObwvF2TymtTUjl41QHd/J7O3UqHCtWXKU6o/4KrJEjR6K6uhrff/89fv21F1yXl1tw8mS4YU2R/JmbLTBmITK1Vwx8iKhFqL8ozSw3V39GzD4UFydpBk1a+1yJq5fkhbx6UzjyQMddnx01cYpMbxNS+Ti1gjvx3uz2CM29rrRWYNntEXjnneI/AjOgZ8+DcO2tI+Dss/chKqpaMxumfuZNKegmag8Y+BBRixC/UEtLS7Fq1SpTxbPqL+Hy8nKsWrVKN2hKTDykeU716qWsrCxERUVJ5wPUWRoHnMGDcaZGVFFRIU2RGW1CKo7TqJeQ6wosALDg6NEeisBLb6XX4ME7XGp8EhLKkJBQpujgLDYY1Mp2MaihjoyBDxG1mOjoaEUmwah4Vlz6HRwc7FJLYhQ0GZ0zOzsb3bp1c/li1+qArCbP1IhjU48VcGaytPa8Uu/HJY5H7O0jD8JSUvZLK7CcLIrAy2h5///9339w/vn/Q0lJTyQm/qoI+my2aowena/ozswGguRvGPgQUavSK54VgwBAu5jYKMC58cZRLoW6RtMz7rI0gDJTIx+bWlFRHyinmhwYNSof8fGlLuMUNzetqKhAUFDjf47d1T+5e1/M8OjxZNk5UUfDwIeImsTTTUSNimLdFRPr1fyoWa1Wj+pQtBsKOv7IumhPCWmNVczEKFdgQXPvLPF+tJbBu6t/8sXeYVpYsEz+gIEPEXnNm01E5XU7ejU2esXE4mfle3JpEetttDJFWvSmzvQySnpjNbNSTawvqqmpkZ6BnBhQGW234a4+SuyxI1dfX48ePXroPgMWLJO/YOBDRF7zZBNROXc1NkbFxOo6IU+ua0Rv6kx9faOxusvEZGVl4dxzz0VFRQXeeustlzGoA6rMTO0pMqPxAuyxQ2SEgQ8R+YTZ/a+0uMuUaO3f5Yvrmp06GzduHEJDQ7Fq1SrDsSYnHzTMxIgdjLUCt5KSRMVKLkFwbrORm7vAcFsJ1uoQeYaBDxE1mScZGy3uMiXy/bs82bzUnejoaEydOhWLFy82PG7dunXIysoyNVajTIwW5TJ6pcZtNuB2J3k51uoQ6WPgQ0RN5mkXZjWzG2KqMyVNvS4AhIWFmTquvr7e9Fj1MjFik0Oxrsd1Gb2SxeJAaWk83n//Bs2MlnqHc4C1OkTuMPAhoibzxSoj9ZYUdXUhLruHiwGD+M/mWN2kVy+kN1Z1VkcMRiorK10KsNX7gBktoxdrfMQCZ8A1oxUTE8NaHiIPMfAhoiZzlwVRr1wCnJkJrRobo60i1P1zzFzXkwyIUb2Q1Wp1ubbeUnotWgGV3jL6ESM24uyz96GuLqTJGS0iUmLgQ0ReE6duAOMsiF7Dv1mzZim2sTBTs6MOIMxcV94hWQyExP5DetNO6mvbbDZpGb5WNkektbu71mqt4cMLXAI3cauM//53JDZtugSZmfnN0q+HyJ8x8CEir8j3pxLp17boLzePi4uTanfc1ezoZWTkG3wWFye5XEc9Tq2CZjP1QnqZI6PpMa2AKi9vFABg+PACKXArKUnAypVTIAiN+4Pl52ca9vMhIs8x8CEir5jtpWM0fdSYbXFmjoxqdtxlZDxZ1l5TU+My9lOnwqDeY0ueXZFP18l/7+662nU8FuTlZUodnZ3bUJzWDLzi40uRm7uAe2sR+QgDHyJqMr0vf3fBiic1O8XFSboZGQCa14mNPYy6uhCXYOzEiROaY3duJuoMftTZFa3pOjNTc9p1PACgzCYZBX16mTQuWyfyHAMfog7i0CFg3z6gb18gIaHlrmv05a83fbR7d38MGLBH88tcr2bHKDDQu87bb98MeRAjZmI2bNigOXbnDuoCJk9ejsTEQ26zK2amx2y2amRm5v8xvSXfw0tZq+OuUFu9dJ3L1om8w8CHqB0TC3SXLAnF/ffb4HBYEBAg4Jln7Lj22tMt8uVo9OWvne0QsH79WOTljdadjtLLcGRkFGDz5gxoZWS0riPvgrx69RUIDj6DxMQS6TN6Yw8Pr4HNVo1JkyYBUGZ75FN6ZpfTDx9eAADIy8vUHLvIqFCbS9eJfIOBD1E7JW4QardHYMGCXKko1uGwYPbsSPz22zuw2apNb9TpLXdTNMpVSwLErIdel2WtWiH1VFpGxjdITy803LRTa2ppxYqrFNkfd4GL1WpFXV2d9J7WlJ6ZxouAM/gZOHCX21odTmsRNS8GPkTtlNmVUJ5s1OkNd1M0YhZj9+7+WL9+rO447fYIFBamo6AgQxbgFKB//90uU2kFBRlITy9UnEvdAHHhwpt1t4EQAy7AmUWSX1M+dvlqML0pvdzcBVLxsV7jRfmz0np96tSphh2kOa1F5DsMfIjaueboXuwpd/tT2WzVGDBgD/LyRmuOc+vWIYoNOgFnYLF583BpaktOr4mfPLBQZprg8nl5kAU4MGyYMoukVlKSaLg5qVHjRa2tJUQMaohaFgMfonbO7D5XTSHWEslVVla6jEPrmqNGjUJeXp7uOAHniix1cNNIOUUGKAO7UaNGoWvXrrDZbIrGgq79cZQBlzKg0s4iicQpLjWzS+1Zn0PUdjDwIeoAjDIuWttFAOYzDWItkTvy7sjya8gDJq1xai1Td2WRslrqwC4vL0+6flRUFLKyslBfX48TJ05gw4YNsNl+RG3tWlWNUAE2bx6uuII8izRp0iQEBQVh+fLlBhuJmltqz747RG0LAx+iDkIv4yJuBaHVXNBM4bPZRoU2m00zq1FRUWE4Tv0+N40sFgdyct5GXV2wbh2N1lYRInXABUA2zdV4DfE9+bSU3kaiU6aswMCBP+reA7eWIGqbGPgQtVPqqSY9Rp2FPS189qQ7sig6Olra40quvLwcq1at0tyvKiXlFxQV9YZ86XdCQpmp6+sHZsqAy+z0oF5Qk5h4SHHu5p5uJCLfYOBD1AbJa2pKSwNQXByE5OR6xMc7ADi3XJBvkqn3ZW+ms7BZTTmXu6yS1hSY856UP7u7vieBmdH0oHyKzmxQ4+58RNQ2MPAhamPkNTVmvsiNjnG31L28vByVlZWor68H4Oxbo67TETNLZroUe3KP7oqj1T+7u77dHqFYGWYmMLvxxlEuq63E2qeysjLpNaOgJisrC1FRUbr3ylVbRG0LAx+iNkbMNJjJcLg7xl3tidb+U3rM1LForf6SEzMfZoql09PTER0djU6dOmHVqlVur19YmA69Ze8AdHdP1wtM1FkavRqq2NhYBjZE7QgDH6I2ykyGxd0xRtM0WtNjelNmgPspH/lSciNZWVmKn/WuWVhYqDje3b0UFGRoXM2B0tJ4vP/+DZoZMTHw0yry1qtNkmM2h6j9YeBD1EZpr3Zy4NSpcGlFk5ksjNY0jdb0GAC302pGUz7idJlIL6CRH+e6FUWBSxNB+fF619dbeZWWtgX5+Zlu65L0ghsGNUQdDwMfojZKa7UTYHHZb8pM4a18mkZvekwQADP1MXpTPidPnpR+b3b1lXocYqfmiRMbj7darW6vrxcAJicfwJYt5yuOZX8dIv/GwIeomR06BOzbB/TtCyQkePZZ1+7Dyg0+Y2MPu90uQk1vekxNHSAYTYMBwPr166XjzKz+0svSAMrjbTaby5STuBRepDcNlphYopk1s1qbd/8yImq7GPgQNaOFC4EZMwCHAwgIAN56C8jJ8ewcNls1jh8/rRmsvP32zVJ2RCsYke8RJQYLetkRecZHfM1qrUVxcRJKS+OkKSN5BkcrGDK7+suocaH6eHdTTnZ7BLp0OSE1OZQHgK67wwdg4cKbTfUgIqKOh4EPUTOoqKjAgQP1mDEjFg6HM0vjcAC33irgT386iqSkII/qR/SDBOMl2/Hx8S7X0ZpCy8goQHj4KUVwM2jQTtkO5417ZYkZnNOnO2kGQ2a7GLuOA4bHy8mXwmtNqyUnH5TeT03dhhMnbNi0aYTLPXjTz4iI2jcGPkQ+JvbhKS5OgsMxTfFeQ4MFr7yyDsnJBzVXEmn1twGMgwT1HlNihsdoxZE4PSbuUL5583BYLA5kZuYjPr4UVmutLOgB5BuEitfMy8uEXk2QUd3RiRMnNMchbhrqrutxRUWFtHrM7JL/r7++WPMeWOtD5H8Y+BD5mFiL4i7zoV5JpN4MVD2NlJq6DbGxh/H22zdDPSUl32NKbxdwre7B8v2qBCEA+fmZyM1dYFB/IxKg1zNHHKte3dGGDRsUn7PZqjF6dD7S0wtN1SnJn5u3S/6duJcWkT9i4EPUTDzdv0n+ha63KiohoQwTJ+qfU28ndsAZ+MyaNQulpaVYtWqVYdCgPbUmTnc1TnvJqaen9FZ/6dE7Xp0Bk9+jmWk1vXsZNSpfuh63lCDyH6YDn6qqKtMnjYyM9GowRB2NpyuuAPfTN0bndNeJedasWdJUmFHQoBW0ZWbmw2JxYP36sRpnVgZgI0eOBKDM7rhbFSZu/WC32xU7rRs1RbTZqpGZme9Sa6Te+kLrXoYPLwAATJ06lf16iPyI6cAnKioKFovr/+XJCYIAi8WChoaGJg+MqKOQT7nIf7bb7ZrTUnqZmJKSBBw/floKHIyCKL0gQ55VcpeR0ts4NC9vtEuwlJPzNhISGve2Uk9nmenrExUVpTtNp2fr1iGyBoXOgEZ+3qlTpyIsLAwA8PDDx3DgQBCSkuoRH38egPPYeZnID5kOfNT/ISMic/S+9JctW6ZZ4KyXiVmxYgrkxb96S7HdBRnyaR13GSmtACsjo0CqDRLPLw961LQyWKtXO3sQyT+nFwia3XkecNYoDRy4CzZbNbKzs5GSkiIdHxcHpKXpDpOI/ITpwOeSSy5pznEQdUjupq20tkrQysQYdVUeOXIkunTpglWrVrm9Xnl5OeLj4w33oKqsrITFYoHNZlM0CpQHVIADw4Z947K9hNm+Plq9dMRAUK4pO8+rd5knIgKaUNxcWVmJhQsX4scffwQADBgwADfddBP/Y0Mk4+7Luby8XHO6RZ6JOXUqHCtWXKV7ji5dukh1O+6uJ9+UU29aSS/ros6uFBRkID29UDpGL0jR60GktfRcHow1ded5IiItRutVdX3//fdISUnBiy++iOPHj+P48eN44YUXkJKSgq1bt/p6jETtinwqSfxylhM7IgPOYuRXX30VFRUVLuex2aqRnHwQUVEn4FxJJSdobrugdT2tLRqMdhzXYhRQAfpBiriZqnMTVPW4lOfw9JriecX7VdcocaUWEWnxKvC5++67MXHiRBw4cACrVq3CqlWrUFxcjCuuuAK5ubk+HiJR+xIdHY2srCwArl/OgABBcE7zbN06RPpMbW2t7hd1XV0IXJePW1BX53q81vXEaSX59TylF8CJ2RV3QUpq6jbcfPPbGkGZgNLSeOkn+dJ1d9ecNGkS3njjfHz33TGsWFGB7747hjfeOB8zZszQrJ0iIgKakPF54IEHEBTUOFMWFBSE+++/H99//73PBkfUXkVFRUm/T03dhpyctyHurg4oMyKi6OhozJo1CzNmzMCMGTOkJeHuAgA18XrOz+hfzxPusitW6xmoMzrqMSYklCEzMx/K7JUF+fmZ0riWL18uBT/urik2a0xL647Jk6ORltYdcXFxiIuLY9BDRLq8qvGJjIzEr7/+in79+ileLykpQUSEd/9hJeooKioqXBoJOrM2xrU+csHBwejWrRsA98vOg4KCXLJFdXUhhrU+6hVUFRUVmtNf8nHprQATa3uc9+dsbqjXrDE+vgzuto6Qt80wWnXGqSwi8oZXgU92djZycnLw3HPPYdiwYQCAb775BrNnz8Y111zj0wECwIEDB/D444/jyy+/xOHDhxEfH4+pU6di7ty5iv/47dy5EzNnzsR3332Hbt264Y477sD999/v8/EQ6VFvOyFyV4ir1XhQnC4DjAOAqKgoKVu0e/dubNiwwe316urq3I5Zi3p5u2vRszPoufrqfyE4uF6q8TH7HJzXsBmuOgOM9yEjIjLiVeDz3HPPwWKx4IYbbkB9fT0AwGq14vbbb8f8+fN9OkAA+Omnn+BwOPDmm2+iT58+2LVrF2655RacOnUKzz33HABnZ+nRo0cjMzMTb7zxBn744QfcdNNNiIqKwowZM3w+Jmp5hw4B+/YBffsCCQlt8zrqL2v58m69rI1enxp1w1C9poVi8B8dHY0uXbpIxw4atBM7dgyGuM3EoEE7FRuFlpU5e+ios0164xE3QK2srMSxY8ewYcMG3dqepUuv0VyCrtdFWX1fDGqIqLl4FfgEBwfjpZdewrx581BUVAQASElJkTqk+trYsWMxdmxjm/zevXtj7969eP3116XA58MPP0RtbS3eeecdBAcHY8CAAdi+fTteeOEFBj4dwMKFwIwZgMMBBAQAb70F5OT47vziVM+SJaG4/34bHA4LAgIE3HXXYQwYcBq9ezegX7/OAMxnG7SWdzs3AG3M2hj1qWlK5sNuj8DOnYPQOK1kwc6dg3DZZV/CZqvGhg0bNJuSGo0nJiYGwcHBii0k9PbBUjcrDA4+g8TEEmnLjdOnOyEvL1PaGDU09HfdhoxERL7UpE1Kw8LCcN555/lqLB6x2+3o2rVxGWxBQQFGjBihmPoaM2YMnn76aZw4cUL6P2Fqfw4dagx6AOc/b70VGDPGN5kfcarHbo/AggW5EATLH9ex4MUXe0BesyJ+ObtbNaS3vDs3dwGSkw8aHiPva+MuwKqoqJAyNwBQXa3fNFBdSyOOQczuAHDb/FBNncFxFji7NitcseIq6RmmpOxHfn4m9BoyEhE1J68Cn99//x2vvPIKNmzYgKNHj8LhUK7maO5ePvv378crr7wiZXsA4PDhw0hOTlYc1717d+k9vcDnzJkzOHPmjPSzJ5uxUsvYt68x6BE1NAD79/sm8BEzKtodhpWroow6LsuZCTzMBid6jGpzzNTSqLM7GRkFppofytntEejS5QRyct5GXV0wrNZaLFx4s8ZzbHyGkyevbNJ9ExE1hVeBT05ODtavX48pU6bgggsucLt5qZ45c+bg6aefNjzmxx9/VKwe++233zB27FhcddVVuOWWW7y6rty8efPw2GOPNfk81DwqKioQGVmPgIBYOByNf88CAwVERBxFRUWQYVbEk3odvQ7DIk++nM0EHt50HpavvnJXm2O0Ekwr21RQkAF1xsZoPHrTYsraIiXn9QR2XCaiVuNV4LN27Vp8+umnGD58eJMufu+992L69OmGx/Tu3Vv6fWlpKUaOHIlhw4bhrbfeUhzXo0cPHDlyRPGa+HOPHj10z//ggw/innvukX6uqqpCYmKi2VugZiRmNLZuHQKH4wqIX6QWiwPjx6/F2rXa00569TrPPGPHtdee1q2LcZ22ESD/8pZ/OWstP5ef013gYeYY8RriuY0yPHpBiN5KML1s07Bh37hsQKpVgK03TRcbe1hVW6RksTiQmHjI8L65TJ2ImpNXgc9ZZ53lk3493bp1k3qVuPPbb79h5MiRSEtLw7vvvouAAOV/tDMyMjB37lzU1dXBarUCAPLy8nDOOecY1veEhIQgJCTE+5ugZnP06FEcOhSH1avFHjFOggCkpOyXfpZPOxnV68yeHYnffnsHNlu1bo2OPFgoLY1Hfn6mZhDw/PNbXVY9qTfYdLfzubtj5FNLU6dOdVk8IAYjVusZw9ocrevqZZv699+N9PRCtwXYXbqc0AycSkp66mbM5M8wNXUbZs3qi6qqWCQl1SM+/nwA53OZOhE1O68Cn+effx4PPPAA3njjDfTq1cvXY3Lx22+/4dJLL0WvXr3w3HPP4dixY9J7Yjbn2muvxWOPPYacnBw88MAD2LVrF1566SW8+OKLzT4+8r2KigrMmbMPa9bcDK1iWb0pJ6N6HflUlVGNjhgsJCcfxMCBu0yvwtLadkIv8NA7Rm8p+eLFi5GdnS39rN4p3ag5opze8nr5Vhrye9LL7IidoZXP2KEzXejAlCkrkJh4SDGe1NRYBjlE1OK8CnyGDh2K33//Hb1790ZYWJiUYREdP+7bufq8vDzs378f+/fvR4KqUEMQnO3vbTYb1q9fj5kzZyItLQ0xMTF4+OGHuZS9HaqoqMDWrUdVjfEamakH8dXO3eqgxMwqLDNL0AFnoFReXi5ldoyCKsCZAdMah7xjstG9ap0/J+dtvP12Y3Apn7KqqwvBqVNhmgFkXV2wxrSgs3/PoEE7sXPnIMV1Bg78EUBjLyBmdoiotXgV+FxzzTX47bff8NRTT6F79+5eFzebNX36dLe1QAAwaNAgbNq0qVnHQs1LnKoqLk6CIJyrcYT2VghqZmpsPGV2FZY3X+haQdXq1cqgSuy7o7f6TAz0tO5VL2ibPHkltLJFjcGQA+qgStztPTV1G2JjDytWcQlCAHbuHCSt8lJP38XHxzPgIaJW5VXgs3nzZhQUFGDw4MG+Hg/5OTFTopexycl5GwkJZXofVzBTYwOYL6b1VRZJi3YwE4DCwvQ/am4ap7+Mno1WsKF3fr0VVmL2RhwD4IDFIigyO+KUmF6tT11dMJKTD0oZHoDbTBBR2+BV4NOvXz+cPn3a12MhkuhlbMwGPfLzuMvyqKen7Ha7Yi+rEydOYMOGDc2SRRI5Gwi61ups3pzhssoqNXWbx89GL1jSWmGlFYCNG7cG69aNN1XrIw8GxR3UiYjaCq8Cn/nz5+Pee+/Fk08+ifPOO8+lxicyMtIngyP/ZjZj4wvyTIT6i7qsrEyaZmquMdls1Rg2rACbN6tbRATgjzI2RU2R0TgmTZqEoKAgxdYSRkGb/FxaDQgtFgfCwn43VevDpelE1NZ5FfiI+2ZdfvnlitcFQYDFYkFDQ0PTR0YEcxmblv5yNTMmb6SnF0rZHT3ymiK9cYSFhWkufZd3WFYHS/JzaQUyiYklupmd5OSDXJpORO2GV4GP1uaGRL5gt9tlv9de2i2XnZ2t+HI1GwR5Eiw1xzm1PqO975UF7lZrqS1evBhZWVnSz1qrucT9wuTE552Sst9lM1VAOyAS3+PSdCJqLyyCuB68GfzlL3/B3//+d6m4sT2oqqqCzWaD3W7nlF0Lk3cmdre0W6TViFC+rYMWbzIRZs/pybXFYw8dOoRPP/1UOsYZgDiDjqKiPqaeg9qkSZOwatUqWSNHZaYmN3eBIpg0et6jRo1CREQEgoKCUFPTFQcOBP2R2XG43BMRUWsx+/3dpN3Z3Vm8eDHuu+++dhX4UPNxFxRUVlYCMNcvZ9KkSbpLo5vjS9jMOY22lJATOzzrHSufdnJXU+QuK6a3mmvPnv7o33+P4fYT4vNOTk5W1D2lpbm9RSKiNqtZA59mTCZRO6MOCoy+sM30y2mL/WDUQZ3ePWoFf0bPQ6+Wx0xWTLuTsoDPPx+L9etHGy5J527pRNQRNWvgQ/5HL6sj39TT3Re2u345WVlZbS7oUTM7VefuWLEPTmVlJerr66Wl9WayYoDxxqtml6QTEXUkDHzIZ8xkdcx8YbvrlxMVFdWyN+Yhs0GJmWPF7R3kS9MB91mx3377TXpdnC7bs6c/Pv98rMtn3C1JJyLqSBj4kM/IMz16WQyz2z60ZA8fXzN7j2aP1ZpCO3UqDOqGh/IsTWFhoeIzNls1+vffg88/Hw1lk8TGJel6z5u9eIioI2HgQz5nlMXwZNsHX/bL0ZuCE7s0BwUFaWaSvFmx5Mk9eroNhnJXdgFi8GM+S6PcV0++zd6VVw5F3759Fe9zxRYRdTQeBz719fV46qmncNNNN7nslK42depULgn3Q0ZZjOTkgz6bVvFkibmZ1VZ6tJbMG3E3VSevd/JkGwzXXdktsFgEjBu3BmFhvyMxscRt0bg68JFnl7p168btJYiow/M48AkKCsKzzz6LG264we2xr7/+uleDovbNXRajqdNYwcHBHi0dNwqO5PSChtLSUtTW1nqU/TC6x1WrVpk+Vk4voPz00/Fo3End2fDQm6Lxtl47RUTkC15NdV122WXYuHEjkpKSfDwc6gjMZDH0prGys7Nhs9l0zy0GH2Vl5jYrNbt03GhllTxQMcr+qGthjKbq1GPQOzY4ONhwx3rXndT/eFUIwOrVnhWNExH5A68Cn3HjxmHOnDn44YcfkJaWhvDwcMX7EydO9MngqP3yNqvTrVs3r2pKzGxvAWgHOCkp+02vwlIHUurptnHjxmHdunWGYzWzfB1wDfK0Ahejfb2AABQWpmP06HzpFaM/FxYxE5E/8Crw+ctf/gIAeOGFF1ze4yalJNLLYsi/3OW8LaQ12zNHr+h68uSVbrsbazEz3aYOyMwuX6+trUVtbS3KysoUNUHudlJXKyjIQHp6IWy2at3nDrCImYj8h1eBj8Ph8PU4qAMwmzHwZddlT3rm6NXIAILb7sZagZS7Ts1aAZm7LsmVlZUuPXvU9HZSVy9vV587JiaGxctE5Pe8Cnzef/99ZGdnIyQkRPF6bW0tli5daqrwmTqe6Ohot8XEvs4seNIzR6+4NzHxkNvuxlqBlJw6yMnMzEd+fqZLQOauS3J9fb3ivO6m8NxlgNiBmYhIyavA58Ybb8TYsWMRGxureL26uho33ngjAx8/ZhTUiPUweoXJzd0zx6i41113Y6N9q7SyTvKgR34eT7okG03hZWVlSauwiouLkZeXBwAsXiYicsOrwEcQBFgsFpfXDx06ZLgih/yXJ8vPfdkzB1BOwRkV94rdjdevH+1R1kR/Ck27s7JRl+QTJ04AcD+FFxUVJU1bBQcHS4EPi5eJiIx5FPgMGTIEFosFFosFl19+OYKCGj/e0NCA4uJijB071uAM5K/M9tIxe5zZYEbMImVlZUm1M+L7zoZ+cAl+zDcUtAPQzzrJp7vU59Er/N6wYYM0NrNTeK0xxUhE1F55FPhceeWVAIDt27djzJgx6Ny5s/RecHAwkpKSMHnyZJ8OkEiLp1/28uZ87laBmcmaVFRUYNmyZQD0g6XU1G0YOHCX5nnkK6zKy8sVvYLEvbg82cqCQQ0RkTkeBT6PPPIIACApKQnZ2dno1KlTswyKOj6zfXeMeNvvx2gKyeySb3XAlZKyH5MnrwQgIDHxkNvMjt7KNuVeXA4p+GG9DhGRb3hV4zNt2jQAzv/4Hz161GV5e8+ePZs+MmqzzO6Rpcds353m4G4KyZsl3+7uR96N2m63QxAERZG32KfHdS+uAAiCA1OmLFcEU0RE5D2vAp99+/bhpptuwubNmxWvi0XPbGDYcTW1SNmTvjvNwdPd0N1xdz9ZWVno168fAOX0mBatoAwIQHh4jcuzYaEyEZF3vAp8pk+fjqCgIKxduxZxcXGaK7yoY2pqkbInRbvNwdf7Vbm7H3ltkbuGh+6CMnEajoXKRETe8yrw2b59O7Zs2SL9nyyRWe6+3OXbM8h5+mWvno4TV2ABTVvyLZ5XHKe3GSS96TGjoIydl4mIms6rwKd///66X1DUcWjV8nj75y4GFO4yLvLVTWpi0z53QZCZ6TibrRpTplyIiIgIBAUFeX1ebzJIRtNj3m7uSkRE5ngV+Dz99NO4//778dRTT+G8886D1WpVvB8ZGemTwVHrMVvLY5Z6+XlubhHee+8bj77c5XtYGTU6NDsdJzb9c3c+vfOKU1UpKfuRm7vAdLDibnpMbyUY63qIiJrOq8AnMzMTAHDZZZcp6ntY3NxxmA0ePFmWrgwsypCcfLDZxwcoxwhAc7yenA8wvzKtsrISgDJoMVvLI8e6HiIi3/Aq8BG7y5J/a65l6b7o8aM1RufmowIA9+M1muZzt5JLPn55lio7OxuA++kx1vIQETUfrwKfSy65BJs2bcKbb76JoqIirFixAmeddRY++OADJCcn+3qM1AY117J0b4IpdZCi3xfHAjO7rrub5jOaqioq6qM7/rq6Oul41vIQEbUOrwKflStX4vrrr8d1112Hbdu24cyZMwCcK2eeeuopfPrppz4dJLU97upUPK1HsdsjUFKSiNWrr4C4sacgBGD16isQHHwGiYklmsGBUZCi3RdHe7xy3i47t1prDYPB06dPK87LWh4iopbnVeDzxBNP4I033sANN9yApUuXSq8PHz4cTzzxhM8GR22X1pd/YKCAO+4Yh6SkII/qUZTTUWoBWLHiKt3sj1FtjtYY5fSW0ct/r5WBSknZj4yMAmzenAH5tFldXYhhMBgaGsrNRImIWplXgc/evXsxYsQIl9dtNptUzEkdm1in8sknE9DQYEFgIPDmmxakpXX36Dyu01HazE6lqbMz6loaQQDkwYp6GX3j5yMAQDODIz9HRsY3SE8vlGp7nDVEjQX/6p4+DGqIiFqXV4FPjx49sH//fiQlJSle//rrr9G7d29fjIvaGK2C49TUbXj44XRUV3dHnz5AQoLn53U3HSXnrsOzXn2QWEtjtdaisrIL1BuJ6n0+I6NAM4Mj/31BQQbS0wvlR0Ae+DiDJCIiaiu8CnxuueUW3HXXXXjnnXdgsVhQWlqKgoIC3HfffXjooYd8PUZqBfI6E6OCY+e0lvfn16uXGTfuE6xbN153ybe6DsZdsbVR0bHe5zdvzjCcKhOPE4Ox48ejIdYnNWq57TiIiMg9rwKfOXPmwOFw4PLLL0dNTQ1GjBiBkJAQ3Hfffbjjjjt8PUZqBWLDwQMH6vH3v8dCEBpXQ33yyQQ8/HC6x7U8Wuevra3FWWdV4YEHbH9MmQl49NEj+POfk5CRcRiPPhonvf7001W49tprNOtg9IqtS0oSABxyuwJNb4PQjIxvUFCQYapOyNcboBIRke95FfhYLBbMnTsXs2fPxv79+3Hy5En0798fnTt39vX4qBVFR0dj507A4VC+3tBgQXV1d68yPerzA8C99wLZ2cD+/UCfPhYkJMQBiMN55wHTp8tfjwIQpXkuvULmlSun6E5ZFRamY/TofABAaWkctOpz0tMLceZMMLZsOd/lmuo6IXf9edQdzomIqOV5FfiIgoOD0b9/f1+Nhdqgvn2BgABl8BMYCPTp49vrJCRo1wglJAChoc4+PWVlru+LK7DEoEO+HB5orMMBHFBPQ23e3Fifk5+fCXnQAwjIzHQGRVu3pmmM2IGcnLeRkKAclFF/HpvNpnP3RETUUpoU+FDHl5AAvPUWcOutQEMD/li95V0hszc82TMsNXUbgoPPYMWKqxSvC0IABgz4Abt3n6f6hLP+BrBoTGVZEB9fqlt8PWxYgUvQI2J/HiKitouBD7mVkwOMGSNOObVc0AOY30Nr5MiR2LBhAxITSzTrbDIyCrB79wDIsz5i08HS0nioM0Ly2hyt8ylXcjllZ2frZnXYn4eIqG1g4NPBaO0xJeftF7DeVFRTuRuv3W5X/ay9j1eXLl0A6NfZJCSUYeJE5euDBu3E22/fjMaAx1njo67NMarbAYCsrCzExsYysCEiagcY+HQgZqeFZs2a1Sa+pD2ZxgLM7+OlV2ej7umjDHoAMehR1+4Y1e1kZWXh3HPP9fjeiYiodTDw6UDMTguZPa65eTIOd3161Cum5HU26iyRzVaN4uIkuPbccZ63rs61FkevbicqKsr0PRARUetj4EPtgrtNUW02G2bNmoWjR4/i2LFj2LBhAwD9LFHXrhXQWunlad8dFiwTEbUvDHzII81VQwTo1+8A5psDLl++XHE+oyzRxInK5e/q+p3Ro0cjKipKN6vDgmUiovaHgQ+Z1pw1RO7qd9w1BwRcp85KShINs0Ri7Y6zuzNc9u9KSkpCXFycR/dBRERtGwOfDkDMwojN/JpLc9UQucvMiIyKjGtqalBTUyP9vHXrkD+yOUrqLJHzHIf+2GdLqbKykoEPEVEHw8CnnfN0ZVRb5K5+Jysry7CIuKamBosXL5Z+PnQoDmvWKDs4A65TWXZ7BAoL07F5cwYA10yTxWIBERF1LAx82rm2skKrKdzV70RFRRlmXspke1k0ZnpcV2xNnrwCAwf+KB0nzzIBrpkmbjFBRNTxaG85TR1aW1mJJI5DrN+xWJwbgqkzM2bHK06Zaf21tlgcSEw8pDhOaysKMdNEREQdEzM+fmLSpEmIiYlpUyuRoqOjMWvWLClr9fDDx3DgQBCSkuoRH38+gPOl8RqtJhNrm/T21VIHUnrHicd6spydiIjaFwY+fiImJqZNFurKg7C4OCBNYyN0s3VMWlNmWruoax/nPFa9UoyIiDoWTnWR1+z2CBQXJ8Fuj2jW66gzPXrX1Zoymzhxrcsu6lrHDRv2De6+e4HmFhhERNRxMOPTgckbAvqCvNbGqO9Oc9YQKYuSHbj44k3o3fuA1PTQaMm7uIM7YLw0noiIOi4GPh2UOjA566wq3Htv084p1uQcOFCPv/89FoLgXO4tCAH45JMJePjhdCQlBTVbDZFrUXIANm0agU2bLlEEX3r7aok7uIv0jhO1lSJwIiLyHU51tXNaX85aDQEfeMCGQ4eafr3o6GhUVXWHw6HscdPQYEF1dfdmLZzWLkpuDL7WrLnCcNotLCzM1HWys7PbzA72RETkW8z4tHPqlVEA8M03wXjxRWWA0NBgwf79QEJC06/Zty8QEAA4HI2vBQYCffo0/dxG9DYWFcmbHoqr2ETi6jD1s1JrS6veiIjI9xj4dADqL+oLL9QKTARERBxFWZkDWjz5wk9IAN56C7j1VqChwRn0vPmmb4IqIzZbNUaNykde3iiImR45+VJ0vVVsDGqIiPwbA58OyDUwETB+/BqsXWu8YsmT6Z2cHGDMGGD/fmemp7mDHtHw4QUAgPz8zD+mvQQAFs1NS4mIiNQY+HRQ8sAkIuKo26AH8Hz7i4SElgl41HVMw4cXYODAXTh+vCus1lrU1QVzZRYREZnS7oqbz5w5gz/96U+wWCzYvn274r2dO3fi4osvRqdOnZCYmIhnnnmmdQbZig4dAjZscP4zIQG49FIgPl57equ9iI6ORlZWluI1m60ayckHkZBQhuTkgwx6iIjIlHaX8bn//vsRHx+PHTt2KF6vqqrC6NGjkZmZiTfeeAM//PADbrrpJkRFRWHGjBmtNNqWtXAhMGOGs7YnIMA53ZWT07pjMtpqAjBfWxQbG+vLYRERkZ9qV4HPunXrsH79eqxcuRLr1q1TvPfhhx+itrYW77zzDoKDgzFgwABs374dL7zwgl8EPocONQY9gPOft97qnO4KDDR3jsrKSsP3PV3xZHariaysLERFRRleS1yRdfToUSxfvtztOdmDh4iItLSbwOfIkSO45ZZb8PHHH2v2YykoKMCIESMUX3hjxozB008/jRMnTrg0rxOdOXMGZ86ckX6uqqry/eBbwL59ylVcgLOwef9+4JxzzJ3DTEDhSQG02Zqhf/5zndRhWj5lNXXqVOnPurQ0AMXFQUhO7ors7GzU1dUhKCjIJWACuCSdiIj0tYvARxAETJ8+HbfddhuGDh2KAwcOuBxz+PBhJCcnK17r3r279J5e4DNv3jw89thjPh9zS2up3jrHjh3zOqiQb6EhBjhGW18sXrxY55hC6Rg2GiQiIk+0anHznDlzYLFYDH/99NNPeOWVV1BdXY0HH3zQ52N48MEHYbfbpV8lJSU+v0ZLEJewi9NazdVbZ9myZaio8Hzvr61bh2DBgly89940LFiQi61bh2h2mFZ3X3Z3jKcr0YiIyL+1asbn3nvvxfTp0w2P6d27N7788ksUFBQgJCRE8d7QoUNx3XXX4b333kOPHj1w5MgRxfvizz169NA9f0hIiMt52yu93jq+rnfxNNjQC14mT17psgWFIASgpCQBx4+fRteuFZrbVMg7NBMREXmiVQOfbt26oVu3bm6Pe/nll/HEE09IP5eWlmLMmDFYtmwZ0tPTAQAZGRmYO3cu6urqYLVaAQB5eXk455xzdKe5OiKt3jryrRrKy8uxatWqFh2TXvBSU9MJFotD8Z7F4sCKFVMAOKe1MjPzNY8ROzQTERF5ol308enZsycGDhwo/Tr77LMBACkpKUj441v+2muvRXBwMHJycrB7924sW7YML730Eu65557WHHqbER0djbi4OMTExMBuj0BxcZLhhp6+1LVrBSwW115Cn346Xtrh3UmAIADiX0tBCEB+fqYU/ABgh2YiImqSdlHcbIbNZsP69esxc+ZMpKWlISYmBg8//LBfLGX3xJIloViwIFezmFhOqxDZWzZbNSZMWKuY7nJSx92Cy2uCEID4+FLk5i7A8eNd2aGZiIiapF0GPklJSRCcqQGFQYMGYdOmTa0wovbh0CHg/vttUpZFrLVJSdmvCCaMVlp5Ql5blJq6DcHBZ7BixVUGnwiAevd1cVrLZqtmwENERE3WLqa6yDecvX6Uu5qLhcIirULk1auVK63cNToUibVFkyZNAgAkJpZoTnmJLBYHRo3itBYRETWfdpnxIe84e/0IiuBHXSisVYgMBKCwMB2jR+cDcDY6NNs/Jzo6WloF5jrl5YDFApfMkrgBqZlpLXZoJiIiTzDw8SMJCcAzz9gxe3akFGxkZubj+HFnAGOzVaNr1wqop5sAoKAgA+nphVIg4m5Ju3yPrvLycun11NRtSEnZLwU2AFyCHL1prUmTJiEmJkb6mR2aiYjIUwx8/Mz06Q347TdnoXBpaTzy8zMVGZeUlP0YMGAPdu8eqPicJ71z1Ht0OQulk6RCaXVgY3YqKyYmBnFxcSbvlIiIyBUDHz8THR2NuXOn4cCBelxwQaxLoXPjcnIBgP6UmJpehsdXhdJERES+wMDHD0VHR2PnTtdNTZW1PRbIgx9BAIqK+mgGLXoZHqv1jGbHZvUqMrNYz0NERE3FwMdPxcScgMVi0yhklpOvANMPWo4dOyb9Xp7h0aoVEoQA7NnTH/3778GVVw5Fly5dYLVaIQgC6uvrAQBWqxU2m03xOdbzEBGRLzDw8VNBQYcxYcImxTSUvGuyFq06n4qKCixbtgyAM9OzevUVsnO4TpkBAj7/fCzWrx+NoqK1SE3dAIC7rBMRUctg4OOHKioqsHz5cqSmQrHCqqioj2EgpFXnI1/dVViYDtfAySLba0s+dabMIHGXdSIiagkMfPyQPMgQV1jZ7RHo0uUEcnLeRl1dsGYgJG8mqK63sdsjUFCQoXE1B3Jy3kZJSU98/vlYxTvcZZ2IiFoaAx/SXHmVnHwQqanbMGtWX1RVxSIpqR7x8ecDOF+z3ka78SEwbFgBEhLKEBFxEuvXj+Yu60RE1KoY+Pg5rS0q5FNQAwdGIS7Ofe2NuAO7OrBJTy8E4Nq1mdtREBFRa2Dg4+cKC9NdMjXeTEGZCWzUXZsZ9BARUUtj4OPH7PYIbN7sWpfj7RSUmcCGu6wTEVFrYuDjx5x7dLnW5WRkFOgWMbvDwIaIiNoyo+511MGJdTlKjXU52dnZbnvr+KqbMrsyExFRS2DGxw+JQYa7upxu3boBUO7DpWXq1KkICwvTfb+mpsbwfXZlJiKilmIRBGebOnKqqqqCzWaD3W5HZGRkaw+n2ciDmdLSABw4EPTHknVnBkgMRrT34YqWdloXZWdn49SpLiguDkJysut5iIiImpPZ729mfPyUPBiJiwPS0rSPk2d6jHZaf+CBn3Xf43YURETUVrDGh0zR6/djt0cYvgeA21EQEVGbwYwPaRKnwsrLy2G3R2D37gG6/X4Ai096ARERETU3Bj7kQl7X45zeynXZZBRQ9vvR6trM7SiIiKit4VQXuRCnptRTWM6gx1kLr14BlpFRAMCh+R4REVFbwYxPB+Fuybk3q6u0Nx61YMyYz9C//x7YbNUuBc8ZGd8gPb2QQQ8REbVJDHw6APWScz2erq7S23hUDHq0ipoLCjKkBohERERtDQOfdkxegCyn12untrbWVGZI5K7BoVZGiEXNRETUljHwaaf0GguWlsYhPz9Ts5+O3W7HsmXLXD6jDpCysrKk3xttPKqXEVIXNXM7CiIiaisY+LRTeo0F5SuvxH46KSn7YbNVo66uTvMz6gCpvr5ecS29jUfdZYQmTZqE+Ph4Ni8kIqI2g4FPO6e98qqR1tSTXsNBMUDyhFFGKCYmhkEPERG1KQx82jntlVeN5FNPJ0+e1P2MPECyWq2659OaHtPLCBEREbU1DHzauEOHgH37gL59gYQE1/e16mwap7sEDBq0UwpKxKkud7U5NpsNs2bNcimCXrIkFH//uw0Oh8VleoyIiKg9YAPDNmzhQqBXL+Cyy5z/XLjQ9RixzsZicfzxiry7sgU7dw6S9szS+4xWw8Ho6GjExcUhLi4OwcHBKC0NwP33O4MewHU/LiIiovaAGZ826tAhYMYMwPFHPONwALfeCowZ45r5Eetsdu/uj/Xrxyrek09hBQUFuXxGqzZHTlw9VlycBIdjmu65tXA1FxERtTUMfNqgiooKfPst4HAoC4MbGoDCwgqEhrp+xmarxoABe5CXN1p3CisiwjXz4642R5zucjc9NmnSJMTExEjvedMpmoiIqLlxqquNETMsmze/J5u+crJYHPjmm/fw6quvoqamxuWz7qawTp8+bWoMWpkad+eOiYmRpsbi4uIY9BARUZvEjE8bI2ZY3PXICQsLkwqQy8vLsWrVKgDGU1jr1q2Tfq/XvHDq1Km6QYvZ6TEiIqK2ioFPG+Yu0NALUNxNYRk1L6ypqcGWLUdQXByE5OR6BAcrt8Pg0nUiImrPGPi0cVqBht0egW++CcaFFzoLnT0pInbXvPBvfyvGmjUDZEFRIVJTfXpLRERErYaBTzsjZmtefDEAAQHAW28BOTnRmn135MR9uoyaFwLwWUdnIiKitoiBTzuiztYol7ibKyY2Wp3F3daJiKij46qudkQrMBGXuFdUVJg6h9HqLDEoktPabV0Le/YQEVF7wIxPO6KXrfnmm/ewa1c1Zs2aZWoZuV7RtJnd1uW9ekTs2UNERO0FA582xihz4i4w0avxqaioQHm5udVZ7nZbj4uL8+a2iIiI2gQGPm1MdLRrofKuXZX48MP/oWvXCo976YgNEUV6/XvkuGSdiIg6KgY+bYxzN/ZoaTf2hQuBGTN6wOE4V9Fzx2xgIg+g9Pr3mAmGiIiIOgIGPm2IM8hxrtYKCADmzwfmzIHLjuieLC+32+1//FO7f8/p052Qn5+p2cxQjQXMRETU3jHwaSO0dmN3Bj3K4zxZXl5RUYFly5YB0F4RJggBUtAj/iwPrLKyshAVFQWABcxERNQxMPBpI/btcw1yxMyP/HWzy8sB5TRXaWkcAAGARX4Fw749UVFRLGYmIqIOhX182oi+fZ1BjlxgIPD000BgoADAdUd0s+z2COTnZ0IZ9Ai4+OJNXvftISIiao+Y8WkjEhKc20/cequzKWFgoICnn7bj2mtPY+jQU1i06Gu3q7j0anC0prkAC3r3LkaXLnbd5fFEREQdDQOfNiQnx7n9xJYtdmzcuBAnT1bjrbec7yUnux5vtgbHaJuK5OSDHi2PJyIias841dXGJCQAF1xQYyoAqanpip9+ikNDQ5xh4bHRNhXi+8nJBxn0EBFRh8eMTzu1desQ/P3vsX8UQAt45hnntJhe5sfTxodEREQdEQOfdqixJ4+zWNnhsGD27Ej89ts7sNn09+xiR2YiIvJ3DHzagIqKCsXSc/W+Wmp6PXnEZejiuZracJANC4mIqKNh4NPK1HtpyeltJWFUrCynte9X47ntqKurQ1BQkFQgLceGhURE1BEx8Gllejuq6+2rBbjfpV1OL3hhY0IiIvJHDHzaIL19teR7dLFYmYiIyHMMfNogdzU8IhYrExEReYZ9fNogsYZHLiBAwBVX9GulEREREXUMDHzaIK2Gg1dcsQa7d3/eyiMjIiJq3zjV1UY1pYaHy9CJiIi0MfBpw/RqeCZNmoSYmBjNz3AZOhERkb52NdX1ySefID09HaGhoejSpQuuvPJKxfu//vorxo8fj7CwMMTGxmL27Nmor69vncGa5E12JiYmBnFxcZq/GPQQERHpazcZn5UrV+KWW27BU089hcsuuwz19fXYtWuX9H5DQwPGjx+PHj16YPPmzSgrK8MNN9wAq9WKp556qhVHbkzeZLC8vByrVq0CoN+8kIiIiLzXLgKf+vp63HXXXXj22WeRk5Mjvd6/f3/p9+vXr8eePXuQn5+P7t27409/+hMef/xxPPDAA3j00UfbdN2LOktj1LyQiIiIvNcuprq2bt2K3377DQEBARgyZAji4uIwbtw4RcanoKAA5513Hrp37y69NmbMGFRVVWH37t265z5z5gyqqqoUv1qL3W7XbV5ot0dIx5WXl6OsrAxlZWWoqKhoreESERG1O+0i4/PLL78AAB599FG88MILSEpKwvPPP49LL70UP//8M7p27YrDhw8rgh4A0s+HDx/WPfe8efPw2GOPNd/gTaqoqMCyZctw/HiS2+aF4nSYSG83diIiIlJq1YzPnDlzYLFYDH/99NNPcDic/Wzmzp2LyZMnIy0tDe+++y4sFgv+/e9/N2kMDz744B+ZFuevkpISX9yax8Q9u7SaF4obkNrtESguTlJkf+SfJSIiImOtmvG59957MX36dMNjevfujbKyMgDKmp6QkBD07t0bv/76KwCgR48e+N///qf47JEjR6T39ISEhCAkJMSb4TcLvQ1Ii4r6sO6HiIioiVo18OnWrRu6devm9ri0tDSEhIRg7969uOiiiwAAdXV1OHDgAHr16gUAyMjIwJNPPomjR48iNjYWAJCXl4fIyEhFwNTaDh0C9u0D+vYFEhK0j1E3LwSABQtydTctLS8vlz7LPj5ERET62kWNT2RkJG677TY88sgjSExMRK9evfDss88CAK666ioAwOjRo9G/f39cf/31eOaZZ3D48GH87W9/w8yZM9tMRmfhQmDGDMDhAAICgLfeAmSL1BTkzQuLi43rfljzQ0REZE67CHwA4Nlnn0VQUBCuv/56nD59Gunp6fjyyy/RpUsXAEBgYCDWrl2L22+/HRkZGQgPD8e0adPw97//vZVH7nToUGPQAzj/eeutwJgxzsxPZWWl7mfFuh958CPW/WhhzQ8REZG2dhP4WK1WPPfcc3juued0j+nVqxc+/fTTFhyVefv2NQY9ooYGYP9+IDS0AsuXL9f9rF7dD+DMBrHJIRERkTntJvBp7/r2dU5vyYOfwECgTx/XDI1W12Z13U9RUR+p7ofFzkREROYw8GkhCQnOmp5bb3VmegIDgTffdL7+x6I1AMZdm8W6H70mh2KxMxEREWlrF52bO4qcHODAAWDDBuc/xcJmu93+xz+NuzYPHjwYAHD8eLRusTMRERHpY8anhSUkuC5jr6urA2Ac0Nhs1eja1RnYeFrsTERERE4MfFpARUWFoo6nsrIS9fX1AIBffqnF9u2nYLdHmA5o9IqdOc1FRERkjIFPM6uoqMCrr76q+Z6ynuciTJiwFhMmrMXq1VfAOQupDGiCghr/uNTFzvKgpy3vRE9ERNSaGPg0M72eOnr1PJmZ+bBYAEEALBblZyIiIjBr1izDPj3s3ExERKSPgU8r0avnyc/P1F2tFRQUxKCGiIioCbiqq5Vo7cIOOAxXa0VFRbXM4IiIiDooBj6tRCxQFoMfi8WBiy/e5BIMcbUWERGR73Cqq4W468ZcWhovm+YSAFhcVmuxaJmIiKhpGPg0o4qKCpSXl7vtxgwA779/g2yaywLAgZyct5GQUIZJkyYhPj6e9T1ERERNxMCnmYjL2J2rt3INt5fQKnQGAlBX58zwxMTEMOghIiLyAdb4NBNxybmZ7SW0Cp3ltT2c4iIiIvINBj7NzF1QA2gXOou1PVlZWcz2EBER+QinupqZ2e0l9Doxcwk7ERGR7zDwaQFaQY3WKi+brZr7bRERETUjBj4tRB7UGK3yIiIioubDGp8WprdHl90e0cojIyIi6vgY+LQwvVVeJSUJKC5OcgmAuKKLiIjIdzjV1Uz0AhZxlZcy+HFg5cop0tTXI4+UYsaMQO60TkRE5GMWQRCE1h5EW1JVVQWbzQa73Y7IyMgmnauiokLq5wMABw4cwPr16xU1PoADzk7NFum4wEABBw5YkJDQpMsTERH5DbPf38z4NCO9bI18ldepU+FYseIqxfsNDRbs3w8GPkRERD7GwKeViKu87PYIl6mvwEABffpYDD5NRERE3mBxcwvSqvvR6tr8/POnmO0hIiJqBqzxUfFljY8Wed1PaWkAiouDkJxcDwA4cCAIZ58dgPPO6+Lz6xIREXVkrPFpo8S6n4ULgRkzAIcDCAgA3noLyMlp5cERERF1cJzqagWHDjUGPYDzn7fe6nydiIiImg8Dn1awb19j0CNqaAD272+d8RAREfkLBj6toG9f5/SWXGAg0KdP64yHiIjIXzDwaQUJCc6ansBA58+BgcCbb7JvDxERUXNjcXMryckBxoxxTm/16cOgh4iIqCUw8GlFCQkMeIiIiFoSp7qIiIjIbzDwISIiIr/BwIeIiIj8BgMfIiIi8hsMfIiIiMhvMPAhIiIiv8HAh4iIiPwGAx8iIiLyGwx8iIiIyG8w8CEiIiK/wcCHiIiI/Ab36lIRBAEAUFVV1cojISIiIrPE723xe1wPAx+V6upqAEBiYmIrj4SIiIg8VV1dDZvNpvu+RXAXGvkZh8OB0tJSREREwGKxeH2eqqoqJCYmoqSkBJGRkT4cYfvBZ8BnAPAZAHwGAJ8BwGcANO8zEAQB1dXViI+PR0CAfiUPMz4qAQEBSEhI8Nn5IiMj/fYvuIjPgM8A4DMA+AwAPgOAzwBovmdglOkRsbiZiIiI/AYDHyIiIvIbDHyaSUhICB555BGEhIS09lBaDZ8BnwHAZwDwGQB8BgCfAdA2ngGLm4mIiMhvMONDREREfoOBDxEREfkNBj5ERETkNxj4EBERkd9g4OOB119/HYMGDZIaL2VkZGDdunXS+7///jtmzpyJ6OhodO7cGZMnT8aRI0cU5/j1118xfvx4hIWFITY2FrNnz0Z9fX1L34rPzJ8/HxaLBbm5udJrHf05PProo7BYLIpf/fr1k97v6Pcv+u233zB16lRER0cjNDQU5513Hr7//nvpfUEQ8PDDDyMuLg6hoaHIzMzEvn37FOc4fvw4rrvuOkRGRiIqKgo5OTk4efJkS9+KV5KSklz+HlgsFsycOROAf/w9aGhowEMPPYTk5GSEhoYiJSUFjz/+uGKvpI7+9wBwbpGQm5uLXr16ITQ0FMOGDcN3330nvd/RnsF///tfTJgwAfHx8bBYLPj4448V7/vqfnfu3ImLL74YnTp1QmJiIp555hnf3IBApq1evVr45JNPhJ9//lnYu3ev8Ne//lWwWq3Crl27BEEQhNtuu01ITEwUvvjiC+H7778XLrzwQmHYsGHS5+vr64WBAwcKmZmZwrZt24RPP/1UiImJER588MHWuqUm+d///ickJSUJgwYNEu666y7p9Y7+HB555BFhwIABQllZmfTr2LFj0vsd/f4FQRCOHz8u9OrVS5g+fbpQWFgo/PLLL8Lnn38u7N+/Xzpm/vz5gs1mEz7++GNhx44dwsSJE4Xk5GTh9OnT0jFjx44VBg8eLHz77bfCpk2bhD59+gjXXHNNa9ySx44ePar4O5CXlycAEDZs2CAIgn/8PXjyySeF6OhoYe3atUJxcbHw73//W+jcubPw0ksvScd09L8HgiAIWVlZQv/+/YWNGzcK+/btEx555BEhMjJSOHTokCAIHe8ZfPrpp8LcuXOFVatWCQCEjz76SPG+L+7XbrcL3bt3F6677jph165dwr/+9S8hNDRUePPNN5s8fgY+TdSlSxfh7bffFiorKwWr1Sr8+9//lt778ccfBQBCQUGBIAjOvywBAQHC4cOHpWNef/11ITIyUjhz5kyLj70pqqurhb59+wp5eXnCJZdcIgU+/vAcHnnkEWHw4MGa7/nD/QuCIDzwwAPCRRddpPu+w+EQevToITz77LPSa5WVlUJISIjwr3/9SxAEQdizZ48AQPjuu++kY9atWydYLBbht99+a77BN5O77rpLSElJERwOh9/8PRg/frxw0003KV6bNGmScN111wmC4B9/D2pqaoTAwEBh7dq1itdTU1OFuXPndvhnoA58fHW/r732mtClSxfFvwsPPPCAcM455zR5zJzq8lJDQwOWLl2KU6dOISMjA1u2bEFdXR0yMzOlY/r164eePXuioKAAAFBQUIDzzjsP3bt3l44ZM2YMqqqqsHv37ha/h6aYOXMmxo8fr7hfAH7zHPbt24f4+Hj07t0b1113HX799VcA/nP/q1evxtChQ3HVVVchNjYWQ4YMwT//+U/p/eLiYhw+fFjxHGw2G9LT0xXPISoqCkOHDpWOyczMREBAAAoLC1vuZnygtrYWixcvxk033QSLxeI3fw+GDRuGL774Aj///DMAYMeOHfj6668xbtw4AP7x96C+vh4NDQ3o1KmT4vXQ0FB8/fXXfvEM5Hx1vwUFBRgxYgSCg4OlY8aMGYO9e/fixIkTTRojNyn10A8//ICMjAz8/vvv6Ny5Mz766CP0798f27dvR3BwMKKiohTHd+/eHYcPHwYAHD58WPEfOfF98b32YunSpdi6datiDlt0+PDhDv8c0tPTsWjRIpxzzjkoKyvDY489hosvvhi7du3yi/sHgF9++QWvv/467rnnHvz1r3/Fd999hzvvvBPBwcGYNm2adB9a9yl/DrGxsYr3g4KC0LVr13bzHEQff/wxKisrMX36dAD+8e8BAMyZMwdVVVXo168fAgMD0dDQgCeffBLXXXcdAPjF34OIiAhkZGTg8ccfx7nnnovu3bvjX//6FwoKCtCnTx+/eAZyvrrfw4cPIzk52eUc4ntdunTxeowMfDx0zjnnYPv27bDb7VixYgWmTZuGjRs3tvawWkxJSQnuuusu5OXlufwfjr8Q/28WAAYNGoT09HT06tULy5cvR2hoaCuOrOU4HA4MHToUTz31FABgyJAh2LVrF9544w1MmzatlUfX8hYuXIhx48YhPj6+tYfSopYvX44PP/wQS5YswYABA7B9+3bk5uYiPj7er/4efPDBB7jppptw1llnITAwEKmpqbjmmmuwZcuW1h4aaeBUl4eCg4PRp08fpKWlYd68eRg8eDBeeukl9OjRA7W1taisrFQcf+TIEfTo0QMA0KNHD5dVHeLP4jFt3ZYtW3D06FGkpqYiKCgIQUFB2LhxI15++WUEBQWhe/fufvEc5KKionD22Wdj//79fvP3IC4uDv3791e8du6550pTfuJ9aN2n/DkcPXpU8X59fT2OHz/ebp4DABw8eBD5+fm4+eabpdf85e/B7NmzMWfOHFx99dU477zzcP311+Puu+/GvHnzAPjP34OUlBRs3LgRJ0+eRElJCf73v/+hrq4OvXv39ptnIPLV/Tbnvx8MfJrI4XDgzJkzSEtLg9VqxRdffCG9t3fvXvz666/IyMgAAGRkZOCHH35Q/IHn5eUhMjLS5Uukrbr88svxww8/YPv27dKvoUOH4rrrrpN+7w/PQe7kyZMoKipCXFyc3/w9GD58OPbu3at47eeff0avXr0AAMnJyejRo4fiOVRVVaGwsFDxHCorKxX/V/zll1/C4XAgPT29Be7CN959913ExsZi/Pjx0mv+8vegpqYGAQHKr5HAwEA4HA4A/vX3AADCw8MRFxeHEydO4PPPP8ef//xnv3sGvrrfjIwM/Pe//0VdXZ10TF5eHs4555wmTXMB4HJ2T8yZM0fYuHGjUFxcLOzcuVOYM2eOYLFYhPXr1wuC4Fy+2rNnT+HLL78Uvv/+eyEjI0PIyMiQPi8uXx09erSwfft24bPPPhO6devWrpavapGv6hKEjv8c7r33XuGrr74SiouLhW+++UbIzMwUYmJihKNHjwqC0PHvXxCcrQyCgoKEJ598Uti3b5/w4YcfCmFhYcLixYulY+bPny9ERUUJ//nPf4SdO3cKf/7znzWXtA4ZMkQoLCwUvv76a6Fv375tdgmvloaGBqFnz57CAw884PKeP/w9mDZtmnDWWWdJy9lXrVolxMTECPfff790jD/8Pfjss8+EdevWCb/88ouwfv16YfDgwUJ6erpQW1srCELHewbV1dXCtm3bhG3btgkAhBdeeEHYtm2bcPDgQUEQfHO/lZWVQvfu3YXrr79e2LVrl7B06VIhLCyMy9lb2k033ST06tVLCA4OFrp16yZcfvnlUtAjCIJw+vRp4S9/+YvQpUsXISwsTPi///s/oaysTHGOAwcOCOPGjRNCQ0OFmJgY4d577xXq6upa+lZ8Sh34dPTnkJ2dLcTFxQnBwcHCWWedJWRnZyv613T0+xetWbNGGDhwoBASEiL069dPeOuttxTvOxwO4aGHHhK6d+8uhISECJdffrmwd+9exTEVFRXCNddcI3Tu3FmIjIwUbrzxRqG6urolb6NJPv/8cwGAy30Jgn/8PaiqqhLuuusuoWfPnkKnTp2E3r17C3PnzlUsQfaHvwfLli0TevfuLQQHBws9evQQZs6cKVRWVkrvd7RnsGHDBgGAy69p06YJguC7+92xY4dw0UUXCSEhIcJZZ50lzJ8/3yfjtwiCrMUmERERUQfGGh8iIiLyGwx8iIiIyG8w8CEiIiK/wcCHiIiI/AYDHyIiIvIbDHyIiIjIbzDwISIiIr/BwIeIiIj8BgMfImqySy+9FLm5ua09jGb36KOP4k9/+lNrD4OImoCBDxH5vdra2ha9niAIqK+vb9FrEpETAx8iapLp06dj48aNeOmll2CxWGCxWHDgwAHs2rUL48aNQ+fOndG9e3dcf/31KC8vlz536aWX4o477kBubi66dOmC7t2745///CdOnTqFG2+8EREREejTpw/WrVsnfearr76CxWLBJ598gkGDBqFTp0648MILsWvXLsWYvv76a1x88cUIDQ1FYmIi7rzzTpw6dUp6PykpCY8//jhuuOEGREZGYsaMGQCABx54AGeffTbCwsLQu3dvPPTQQ9Lu0IsWLcJjjz2GHTt2SPe5aNEiHDhwABaLBdu3b5fOX1lZCYvFgq+++kox7nXr1iEtLQ0hISH4+uuv4XA4MG/ePCQnJyM0NBSDBw/GihUrfP1HREQyDHyIqEleeuklZGRk4JZbbkFZWRnKysoQERGByy67DEOGDMH333+Pzz77DEeOHEFWVpbis++99x5iYmLwv//9D3fccQduv/12XHXVVRg2bBi2bt2K0aNH4/rrr0dNTY3ic7Nnz8bzzz+P7777Dt26dcOECROkAKWoqAhjx47F5MmTsXPnTixbtgxff/01Zs2apTjHc889h8GDB2Pbtm146KGHAAARERFYtGgR9uzZg5deegn//Oc/8eKLLwIAsrOzce+992LAgAHSfWZnZ3v0rObMmYP58+fjxx9/xKBBgzBv3jy8//77eOONN7B7927cfffdmDp1KjZu3OjReYnIAz7Z6pSI/Noll1wi3HXXXdLPjz/+uDB69GjFMSUlJYqdzC+55BLhoosukt6vr68XwsPDheuvv156raysTAAgFBQUCILQuCv00qVLpWMqKiqE0NBQYdmyZYIgCEJOTo4wY8YMxbU3bdokBAQECKdPnxYEQRB69eolXHnllW7v69lnnxXS0tKknx955BFh8ODBimOKi4sFAMK2bduk106cOCEAEDZs2KAY98cffywd8/vvvwthYWHC5s2bFefLyckRrrnmGrdjIyLvBLVm0EVEHdOOHTuwYcMGdO7c2eW9oqIinH322QCAQYMGSa8HBgYiOjoa5513nvRa9+7dAQBHjx5VnCMjI0P6fdeuXXHOOefgxx9/lK69c+dOfPjhh9IxgiDA4XCguLgY5557LgBg6NChLmNbtmwZXn75ZRQVFeHkyZOor69HZGSkx/evR37N/fv3o6amBqNGjVIcU1tbiyFDhvjsmkSkxMCHiHzu5MmTmDBhAp5++mmX9+Li4qTfW61WxXsWi0XxmsViAQA4HA6Prn3rrbfizjvvdHmvZ8+e0u/Dw8MV7xUUFOC6667DY489hjFjxsBms2Hp0qV4/vnnDa8XEOCsGBAEQXpNnHZTk1/z5MmTAIBPPvkEZ511luK4kJAQw2sSkfcY+BBRkwUHB6OhoUH6OTU1FStXrkRSUhKCgnz/n5lvv/1WCmJOnDiBn3/+WcrkpKamYs+ePejTp49H59y8eTN69eqFuXPnSq8dPHhQcYz6PgGgW7duAICysjIpUyMvdNbTv39/hISE4Ndff8Ull1zi0ViJyHssbiaiJktKSkJhYSEOHDiA8vJyzJw5E8ePH8c111yD7777DkVFRfj8889x4403ugQO3vj73/+OL774Art27cL06dMRExODK6+8EoBzZdbmzZsxa9YsbN++Hfv27cN//vMfl+Jmtb59++LXX3/F0qVLUVRUhJdffhkfffSRy30WFxdj+/btKC8vx5kzZxAaGooLL7xQKlreuHEj/va3v7m9h4iICNx33324++678d5776GoqAhbt27FK6+8gvfee8/rZ0NExhj4EFGT3XfffQgMDET//v3RrVs31NbW4ptvvkFDQwNGjx6N8847D7m5uYiKipKmhppi/vz5uOuuu5CWlobDhw9jzZo1CA4OBuCsG9q4cSN+/vlnXHzxxRgyZAgefvhhxMfHG55z4sSJuPvuuzFr1iz86U9/wubNm6XVXqLJkydj7NixGDlyJLp164Z//etfAIB33nkH9fX1SEtLQ25uLp544glT9/H444/joYcewrx583Duuedi7Nix+OSTT5CcnOzFUyEiMyyCfGKaiKgN++qrrzBy5EicOHECUVFRrT0cImqHmPEhIiIiv8HAh4iIiPwGp7qIiIjIbzDjQ0RERH6DgQ8RERH5DQY+RERE5DcY+BAREZHfYOBDREREfoOBDxEREfkNBj5ERETkNxj4EBERkd9g4ENERER+4/8D5l0bd4+4jFcAAAAASUVORK5CYII=", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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7aAu5hs1SFHRSopsiRIBVgBMqCEiO5pgWRORbgWwCr62txbx58/Dvf/8bGRkZAIAOHTqgoKAAS5YswSOPPAIA+MMf/oDMzEwAQG5uLsaMGYMrV64gIiICzZo1Q6NGjRAfH2/3+ePHj8ekSZPcKtMrr7yCwYMHAwAmT56MvLw8FBcXo0OHDgCABx54AFu3bsXs2bNRVVWFRYsWYfny5cjKygIALF26FJs3b8bf/vY3PPvss55tGA+w5oaCToIhAvljeyL0xngIoYKAeWN7IMEQoXLJiEhPAt0Efvz4cVRXV+OOO+5As2bNzH8ffPABiouLzeulpaWZ/52QkACgfiJqZ/r16+d2mSy/Ky4uDpGRkebARlomfXdxcTGuXbtmDoYAoHHjxhgwYACOHj3q9nd7gzU3FJRy+idhaOcYnKioRnJ0JAMbIvI5R03g/rjmXL58GQCwYcMGtG3b1uq18PBwc4BjmQgsDXpnMpmcfn7Tpk3dLpPtd9kmIQuC4NJ3BxqDGwpaCYYIBjVE5DeBbgLv3r07wsPDUVpaam52smRZe6MkLCzM66kLPJWamoqwsDB88803aN++PYD6Xll79uzBzJkzA1oWBjdEREQypCbw59ccRp0o+r0JvHnz5njmmWfw9NNPw2QyYciQITAajfjmm28QFRVlDhgcSU5ORklJCQoLC9GuXTs0b94c4eHhfimvraZNm2LatGl49tln0apVKyQlJeHVV19FdXU1Jk+eHJAySBjcEBERKQh0E/jLL7+MmJgY5Ofn44cffkCLFi3Qp08fPP/88y41/9x///1Ys2YNhg8fjosXL+K9997DQw895NcyW5o/fz5MJhN+/etf49KlS+jXrx+++OILtGzZMmBlAABBFEXR+Wr6UVlZCYPBAKPRiKioKLWL45YyYw1KKqqQEt2UzTFERAquXLmCkpISpKSkoEmTJmoXh9zgaN+5c/9mzU2Q4Ii8RERErmFX8CDAEXmJiMiXHnvsMavu5pZ/jz32mNrF8xprboJAoLsjEhGRvs2dOxfPPPOM7GvBlrIhh8FNEOCIvERE5EuxsbGIjY1Vuxh+w2apIMAReYmIiFzHmpsgwRF5iYjco8WRc8kxX3XgZnATRDgiLxGRc2FhYQgJCcGZM2cQExODsLAw8zQFpF2iKOL8+fOy0zy4i8ENERHpSkhICFJSUlBWVoYzZ86oXRxygyAIaNeuHUJDQ736HAY3RESkO2FhYUhKSsL169dVm2uJ3Ne4cWOvAxuAwQ0REemU1LzhbRMHBR/2liIiIiJdYXBDREREusLghoiIiHSFwQ0RERHpCoMbIiIi0hUGN0RERKQrDG6IiIhIVxjcEBERka4wuCEiIiJdYXBDREREusLghoiIiHSFwQ0RERHpiqrBzaJFi5CWloaoqChERUUhIyMDmzZtcviejz/+GF27dkWTJk3Qs2dPbNy4MUClJSIiomCganDTrl07zJ8/H/v27cPevXtx++23495778WRI0dk19+5cyfGjRuHyZMn48CBA8jOzkZ2djYOHz4c4JITERGRVgmiKIpqF8JSq1at8Nprr2Hy5Ml2r+Xk5KCqqgrr1683Lxs4cCB69eqFxYsXu/T5lZWVMBgMMBqNiIqK8lm5iYiIyH/cuX9rJuemrq4OH330EaqqqpCRkSG7zq5duzBy5EirZaNGjcKuXbsUP7e2thaVlZVWf0RERKRfqgc3hw4dQrNmzRAeHo7HHnsMa9euRffu3WXXLS8vR1xcnNWyuLg4lJeXK35+fn4+DAaD+S8xMdGn5SciIiJtUT246dKlCwoLC7F7925MmzYNEydOxH//+1+ffX5eXh6MRqP579SpUz77bCIiItKeRmoXICwsDB07dgQA9O3bF3v27MEbb7yBJUuW2K0bHx+Ps2fPWi07e/Ys4uPjFT8/PDwc4eHhvi10AJQZa1BSUYWU6KZIMESoXRwiIqKgoXrNjS2TyYTa2lrZ1zIyMvDVV19ZLdu8ebNijk6wWrWnFIPnb8H4pbsxeP4WrNpTqnaRiIiIgoaqNTd5eXnIyspCUlISLl26hBUrVmDbtm344osvAAATJkxA27ZtkZ+fDwB46qmnkJmZiddffx1jxozBRx99hL179+Kvf/2rmj/Dp8qMNchbcwimG33YTCLw/JrDGNo5hjU4RERELlA1uDl37hwmTJiAsrIyGAwGpKWl4YsvvsAdd9wBACgtLUVIyM3KpUGDBmHFihX43e9+h+effx6dOnXCunXr0KNHD7V+gs+VVFSZAxtJnSjiREU1gxsiIiIXaG6cG3/T+jg3ZcYaDJ6/xSrACRUEFOQOZ3BDREQNVlCOc0P1EgwRyB/bE6GCAKA+sJk3tgcDGyIiIhep3luK7OX0T8LQzjE4UVGN5OhIBjZERERuYHCjUQmGCAY1REREHmCzFBEREekKgxsiIiLSFQY3REREpCsMboiIiEhXGNwQERGRrjC4ISIiIl1hcENERES6wuCGiIiIdIXBDREREekKgxsiIiLSFQY3REREpCsMboiIiEhXGNwQERGRrjC4ISIiIl1hcENERES6wuCGiIiIdIXBDREREekKgxsiIiLSFQY3REREpCsMboiIiEhXGNwQERGRrjC4IfKBMmMNdhZXoMxYo3ZRiIgavEZqF4Ao2K3aU4q8NYdgEoEQAcgf2xM5/ZPULhYRUYPFmhsiL5QZa8yBDQCYROD5NYdZg0NEpCIGN0ReKKmoMgc2kjpRxImKanUKREREDG6IvJES3RQhgvWyUEFAcnSkOgUiIiIGN0TeSDBEIH9sT4QK9RFOqCBg3tgeSDBEqFwyIqKGiwnFRF7K6Z+EoZ1jcKKiGsnRkQxsiIhUxuCGyAcSDBEMaoiINILNUkRERKQrDG6IiIhIVxjcEBERka4wuCEiIiJdYXBDREREusLghoiIiHSFwQ0RERHpiqrBTX5+Pvr374/mzZsjNjYW2dnZKCoqcvie5cuXQxAEq78mTZoEqMRERESkdaoGN9u3b8f06dPx7bffYvPmzbh27RruvPNOVFVVOXxfVFQUysrKzH8nT54MUImJiIhI61Qdofjzzz+3+v/y5csRGxuLffv2YejQoYrvEwQB8fHx/i4eERERBSFN5dwYjUYAQKtWrRyud/nyZbRv3x6JiYm49957ceTIEcV1a2trUVlZafVHRERE+qWZ4MZkMmHmzJkYPHgwevToobhely5d8O677+LTTz/Fhx9+CJPJhEGDBuH06dOy6+fn58NgMJj/EhMT/fUTiIiISAMEURRFtQsBANOmTcOmTZtQUFCAdu3aufy+a9euoVu3bhg3bhxefvllu9dra2tRW1tr/n9lZSUSExNhNBoRFRXlk7ITERGRf1VWVsJgMLh0/9bErOAzZszA+vXrsWPHDrcCGwBo3LgxevfujePHj8u+Hh4ejvDwcF8Uk4iIiIKAqs1SoihixowZWLt2LbZs2YKUlBS3P6Ourg6HDh1CQkKCH0pIREREwUbVmpvp06djxYoV+PTTT9G8eXOUl5cDAAwGAyIiIgAAEyZMQNu2bZGfnw8AmDt3LgYOHIiOHTvi4sWLeO2113Dy5ElMmTJFtd9BRERE2qFqcLNo0SIAwLBhw6yWv/fee3jooYcAAKWlpQgJuVnBdOHCBUydOhXl5eVo2bIl+vbti507d6J79+6BKjYRERFpmGYSigPFnYQkIiIi0gZ37t+a6QpORERE5AsMboiIiEhXGNwQERGRrjC4ISIiIl1hcENERES6wuCGiIiIdIXBDREREekKgxsiIiLSFQY3REREpCsMboiIiEhXGNwQERGRrjC4ISIiIl1hcENERES6wuCGiIiIdIXBDREREekKgxsiIiLSFQY3REREpCsMbkgzyow12FlcgTJjjdpFISKiINZI7QIQAcCqPaXIW3MIJhEIEYD8sT2R0z9J7WIREVEQYs0Nqa7MWGMObADAJALPrznMGhwiIvIIgxtSXUlFlTmwkdSJIk5UVKtTICIiCmoMbkh1KdFNESJYLwsVBCRHR6pTICIiCmoMbkh1CYYI5I/tiVChPsIJFQTMG9sDCYYIlUtGRETBiAnFpAk5/ZMwtHMMTlRUIzk6koENERF5jMENaUaCIYJBDREReY3NUkRERKQrDG6IiIhIVxjcEHmAoykT6RfP7+DHnBsiN3E0ZSL94vmtD6y5IXIDR1Mm0i+e3/rB4IbIDRxNmUi/eH7rB4MbIjdwNGUi/eL5rR8MbojcwNGUifSL57d+CKIois5X04/KykoYDAYYjUZERUWpXRwKUmXGGo6mTKRTPL+1yZ37N3tLEXmAoykT6RfP7+DHZikiIiLSFQY3REREpCsMboiIiEhXGNwQERGRrqga3OTn56N///5o3rw5YmNjkZ2djaKiIqfv+/jjj9G1a1c0adIEPXv2xMaNGwNQWiIiIgoGqgY327dvx/Tp0/Htt99i8+bNuHbtGu68805UVVUpvmfnzp0YN24cJk+ejAMHDiA7OxvZ2dk4fPhwAEtOREREWqWpcW7Onz+P2NhYbN++HUOHDpVdJycnB1VVVVi/fr152cCBA9GrVy8sXrzY6XdwnBsiIqLg4879W1M5N0ajEQDQqlUrxXV27dqFkSNHWi0bNWoUdu3a5deyERERUXDQzCB+JpMJM2fOxODBg9GjRw/F9crLyxEXF2e1LC4uDuXl5bLr19bWora21vz/yspK3xSYiIiINEkzNTfTp0/H4cOH8dFHH/n0c/Pz82EwGMx/iYmJPv18IiIi0hZNBDczZszA+vXrsXXrVrRr187huvHx8Th79qzVsrNnzyI+Pl52/by8PBiNRvPfqVOnfFZuIiIi0h5VgxtRFDFjxgysXbsWW7ZsQUpKitP3ZGRk4KuvvrJatnnzZmRkZMiuHx4ejqioKKs/IiIi0i9Vc26mT5+OFStW4NNPP0Xz5s3NeTMGgwEREfWTlk2YMAFt27ZFfn4+AOCpp55CZmYmXn/9dYwZMwYfffQR9u7di7/+9a+q/Q4iIiLSDlVrbhYtWgSj0Yhhw4YhISHB/Ldq1SrzOqWlpSgrKzP/f9CgQVixYgX++te/Ij09HatXr8a6descJiETERFRw+HyODfu9DLSctMPx7khIiIKPu7cv11ulmrRogUEQXC4jiiKEAQBdXV1rn4sERERkU+5HNxs3brVn+UgIiIi8gmXg5vMzEx/loOIiIjIJzzuLXXx4kX87W9/w9GjRwEAt9xyCx5++GEYDAafFY6IiIjIXR71ltq7dy9SU1Px5z//GT///DN+/vln/OlPf0Jqair279/v6zISERERucyjWcFvu+02dOzYEUuXLkWjRvWVP9evX8eUKVPwww8/YMeOHT4vqK+wtxQREVHwcef+7VFwExERgQMHDqBr165Wy//73/+iX79+qK6udvcjA4bBDRERUfBx5/7tUbNUVFQUSktL7ZafOnUKzZs39+QjiYiIiHzCo+AmJycHkydPxqpVq3Dq1CmcOnUKH330EaZMmYJx48b5uoxERERELvOot9Qf//hHCIKACRMm4Pr16wCAxo0bY9q0aZg/f75PC0hERETkDo9ybiTV1dUoLi4GAKSmpiIyMtJnBfMX5twQEREFH79MvyAnMjISPXv29OYjiIiIiHzKo+DmypUrePPNN7F161acO3cOJpPJ6nWOdUNERERq8Si4mTx5Mr788ks88MADGDBggNMJNYmIiIgCxaPgZv369di4cSMGDx7s6/IQERERecWjruBt27bleDZERESkSR4FN6+//jpmz56NkydP+ro8ROShMmMNdhZXoMxYo3ZRiIhU5VGzVL9+/XDlyhV06NABkZGRaNy4sdXrP//8s08KR0SuWbWnFHlrDsEkAiECkD+2J3L6J6ldLCIiVXgU3IwbNw4//vgj5s2bh7i4OCYUE6mozFhjDmwAwCQCz685jKGdY5BgiFC3cEREKvAouNm5cyd27dqF9PR0X5eHiNxUUlFlDmwkdaKIExXVDG6IqEHyKOema9euqKlhuz6RFqREN0WITeVpqCAgOVr7I4YTEfmDR8HN/Pnz8Zvf/Abbtm3DTz/9hMrKSqs/IgqcBEME8sf2ROiN5uFQQcC8sT1Ya0PkBJPw9cujuaVCQupjIttcG1EUIQgC6urqfFM6P+DcUqRXZcYanKioRnJ0JAMbIieYhB98/D631NatWz0qGBH5T4IhgkENkQuYhK9/HgU3mZmZLq33+OOPY+7cuYiOjvbka4iIiHyOSfj651HOjas+/PBD5uAQEZGmMAlf//wa3HiQzkNERORXTMLXP4+apYiIiIJZTv8kDO0cwyR8nWJwQ0REDRKT8PXLr81SRMGC410QEekHa26oweN4F0RE+uJ2zc3169cxd+5cnD592um6v/rVrzhQHmma0ngXrMEhIgpebgc3jRo1wmuvvYbr1687XXfRokUc44Y0zdF4F0REFJw8yrm5/fbbsX37dl+XhSjgON4FEZH+eJRzk5WVhdzcXBw6dAh9+/ZF06ZNrV6/5557fFI4In+Txrt4fs1h1Ikix7sgItIBrybOlP1ATpxJQYiTThIRaZvfJ840mUweFYxIqzjeBRGRfniUc/PBBx+gtrbWbvnVq1fxwQcfeF0oIiIiIk951CwVGhqKsrIyxMbGWi3/6aefEBsby2YpCiplxhqUVFQhJbopa2+IiDTK781SoihCEAS75adPn4bBYPDkI4lUwQH8iIj0x61mqd69e6NPnz4QBAEjRoxAnz59zH/p6em47bbbMHLkSJc/b8eOHbj77rvRpk0bCIKAdevWOVx/27ZtEATB7q+8vNydn0Eq0tI0BxzAj4hIn9yqucnOzgYAFBYWYtSoUWjWrJn5tbCwMCQnJ+P+++93+fOqqqqQnp6Ohx9+GGPHjnX5fUVFRVZVUrbNY6RNWqslcTSAH5uniIiCl1vBzYsvvggASE5ORk5ODpo0aeLVl2dlZSErK8vt98XGxqJFixZefTcFllItydDOMaoFEtIAfpYBDgfwIyIKfh71lpo4cSKaNGmCq1ev4vTp0ygtLbX687devXohISEBd9xxB7755huH69bW1qKystLqjwJPi9McSAP4hd7IH+MAfkRE+uBRQvH333+Phx9+GDt37rRaLiUa+6u3VEJCAhYvXox+/fqhtrYWy5Ytw7Bhw7B792706dNH9j35+fmYM2eOX8pDrtNqLUlO/yQM7RzDAfyIiHTEo67ggwcPRqNGjZCbm4uEhAS7nlPp6enuF0QQsHbtWnNej6syMzORlJSEv//977Kv19bWWo3JU1lZicTERHYFV8GqPaV20xywZxIREbnC713BCwsLsW/fPnTt2tWjAvrSgAEDUFBQoPh6eHg4wsPDA1giUsJaEiIiCgSPgpvu3bujoqLC12XxSGFhIRISEtQuBrmI0xwQEZG/eRTcLFiwAM899xzmzZuHnj17onHjxlavu9rcc/nyZRw/ftz8/5KSEhQWFqJVq1ZISkpCXl4efvzxR/OUDgsXLkRKSgpuueUWXLlyBcuWLcOWLVvw5ZdfevIz/C6QI99ylF0iIqJ6HgU30kB9t99+u1W+jbsJxXv37sXw4cPN/581axaA+t5Yy5cvR1lZmVXvq6tXr+I3v/kNfvzxR0RGRiItLQ3//ve/rT5DKwI5povWxo9pqBhgEhFpg0cJxdu3b3f4emZmpscF8rdAzC1VZqzB4Plb7HoGFeQO9/lNL5DfRcoYYBIR+Zc792+PxrnJzMxESEgIli5ditzcXHTs2BGZmZkoLS1FaGioR4XWk0CO6aLF8WMaGk7jQESkLR4FN5988glGjRqFiIgIHDhwwNzV2mg0Yt68eT4tYDCSxnSx5K8xXQL5XSSPASYRkbZ4FNy88sorWLx4MZYuXWqVTDx48GDs37/fZ4ULVoEc+Zaj7KqPASYRkbZ4lFBcVFSEoUOH2i03GAy4ePGit2XShUCO6cLxY9QlBZiWAxQ+l9UFJRVV5teJiChwPApu4uPjcfz4cSQnJ1stLygoQIcOHXxRLl0I5JguHD9GXZYB5sHTF7Fg0zEmFxMRqcSjZqmpU6fiqaeewu7duyEIAs6cOYN//OMfeOaZZzBt2jRfl5EoKCQYIpAcHYkFnx9jcjERkYo8qrnJzc2FyWTCiBEjUF1djaFDhyI8PBzPPPMMnnjiCV+XkShoOEouZs0aEVFgeBTcCIKA3/72t3j22Wdx/PhxXL58Gd27d0ezZs18XT6ioKLV2c+JiBoSj5qlJGFhYejevTsGDBjAwIYI7L1GRKQFHtXcEJEy9l4jIlIXgxsiP2DvNSIi9XjVLEVERESkNQxuiIiISFcY3BAREZGuMLghIiIiXWFwQ0RERLrC4IaIiIh0hcENERER6QqDmwArM9ZgZ3EFJ1IkIiLyEw7iF0Cr9pQib80hmEQgRADyx/ZETv8ktYtFRESkK6y5CZAyY405sAHqJ1Z8fs1h1uAQERH5GIObACmpqLKaKRoA6kQRJyqq1SkQERGRTjG4CZCU6KYIEayXhQoCkqMj1SkQERGRTjG4CZAEQwTyx/ZEqFAf4YQKAuaN7cHJFYmIiHyMCcUBlNM/CUM7x+BERTWSoyMZ2BAREfkBg5sASzBEMKghIiLyIzZLERERka4wuCEiIiJdYXBDREREusLghkiDOE0HEZHnmFBMpAFlxhqUVFQhJbopdvzvPKfpICLyAoMbIpVZzjkmjfMoDWYtTdMxtHMMe9kREbmIzVJEKrKdc0zEzcBGwmk6iIjcw+CGSEVyc47Z4jQdRETuYXCjIiaNktycYwJgXsZpOoiI3MecG5VY5ln4M2nUMlGVN0jtkeYce37NYdSJojmY8dU0Hdz/RNQQCaIoOqkU15fKykoYDAYYjUZERUWpUoYyYw0Gz99i1RwRKggoyB3u0xtQoAIo8l6Zscbnc45x/xORnrhz/2azlArk8ix8nTRqm6gq9bphE5g2JRgikJHa2meBDfc/ETVkDG5UIJdn4euk0UAEUKRd3P9E1JCpGtzs2LEDd999N9q0aQNBELBu3Tqn79m2bRv69OmD8PBwdOzYEcuXL/d7OX1NyrMIFeojHH8kjQYigCLt4v7XHnYgIAocVYObqqoqpKen4+2333Zp/ZKSEowZMwbDhw9HYWEhZs6ciSlTpuCLL77wc0l9L6d/Egpyh2Pl1IEoyB3u81yIQARQpF1a2v+8qdfnPw2evwXjl+7G4PlbsGpPqdpFItI1zSQUC4KAtWvXIjs7W3Gd2bNnY8OGDTh8+LB52YMPPoiLFy/i888/d+l7tJBQHEj+SFSl4KH2/mdSc+A6EBDpnW4Tinft2oWRI0daLRs1ahR27dql+J7a2lpUVlZa/TUkcomqfJJuOHydqOwOJjXXY/4TUeAFVXBTXl6OuLg4q2VxcXGorKxETY38BTM/Px8Gg8H8l5iYGIiialagq8cZSDVcvKnXY/4TUeAFVXDjiby8PBiNRvPfqVOn1C6SagL9JM08g4aNN/V6Wsp/ImoogmqE4vj4eJw9e9Zq2dmzZxEVFYWICPkLRXh4OMLDwwNRPM1z9CTt6wutUiDF2a0bDqXRlxvi/s/pn+SzUaeJyLmgCm4yMjKwceNGq2WbN29GRkaGSiUKLtKTtG1ioz+epAMZSJF28aZ+U4IhokH/fqJAUrVZ6vLlyygsLERhYSGA+q7ehYWFKC2tb77Iy8vDhAkTzOs/9thj+OGHH/Dcc8/h2LFjeOedd/DPf/4TTz/9tBrFDzqBrB5nkwRJ1ExqJqKGSdWam71792L48OHm/8+aNQsAMHHiRCxfvhxlZWXmQAcAUlJSsGHDBjz99NN444030K5dOyxbtgyjRo0KeNmDVaCepNkkQUREatHMODeB0tDGuVGb2uOsEBGRPrhz/w6qnBsKPswzICIKDmXGGpRUVCElumnQX7cZ3BARETVwvhhNXEvBEYMbIiKiBswXQ3dobaoV3Q/iF0w4mi8REQWat6OJa3GqFdbcaITWol4iImoYvB0DTYvjmrHmRgO0GPUSEVHD4O0YaFoc14w1NxqgxaiXiIgaDm/GQNPiuGYMbjQgENMiaCmLXW3cFkRE9rwZukNrU60wuNEAf0e9zOe5iduCiMg/tDSuGUco1hB/jOZbZqzB4Plb7GqFCnKHa+YgDBRuCyKi4MURioOUP6Je5vPcxG1BRNQwsLeUzh360Wi3TO0sdrVoMaOfiIh8j8GNjpUZa7Bg0zG75c+N7tIgayq87e5IRETBgc1SOibXDAMAae1aBLwsWqG1jH7yHfaCIyIJgxsdC0QX82CklNvEm2PwYi84Cja83vgXgxsd0+LASlrFm2Pw8sWkf0SBxOuN/zG40Tk2wzjHm2NwYy84Cia83gQGE4obgARDBDJSW/PEUeDtjLikLvaCo2Dir+tNmbEGO4srOCfhDQxuqMHjzTG4sRccBRN/XG9W7SnF4PlbMH7pbgyevwWr9pR6Wcrgx+CGFDWUJwHeHINfTv8kFOQOx8qpA1GQO1xX+QsN5TxsKHx9vVFq5mroxwtzbhowR9n6DS3hjblJwU9L89r4SkM7DxsKX15vmHMmj8FNA+XootlQE970eHOk4NVQz8OGwlfXGw75IY/NUg2Qs2pMJth6j00J5C2eh8EvENcBNqvLY81NA+SsGpNPAt5hUwL5As/D4BbI6wCb1e2x5qYBcpatzycBz2khuY+1RvrA8zB4qXEdCOSQH8FwjWHNTQNkO3JxCOwn0+STgGfUTu5jrZG+8DwMTmpfB/wpWK4xrLlpoHL6J+G5rC4QBMAEYMHnx+zGRuDgf+5Tc8wcXzwtBsMTWUPD8zD46HXsLC3UTLuKwY0PBdONocxYgwWbjkEMgoM0mKjZlOBtAioHAiPyDb02KQZTkjubpXwkWKrqJJ5Um3IWW9eo1ZTgTQIqux0T+Zan1wEtX2eDKcmdNTc+EExVdRJ3q035VO8eNZoSvHlaDKYnMlIWTLXHDUGCIQLJ0ZEoqahyaZ9o/TobTDVSrLnxgWBMHrNNKnZ0kPKpPnh4+rQYTE9kJC/Yao8bAnf2SbBcZ4MlyZ3BjQ8E643B1YNUKXjbcLAMY9ISNHtwN1SejHzqSrCr5eryhi5YbowNibv7JJgekoNhNHcGNz7gTi2I1rhykMoFbwDwyoajmLfxKJ8QdcJRsMtaAW0LphtjQ+HuPgnWh2StYs6Nj+h5VmLbdlZLwZBfRK6TyxUKxpyyhkavXY+Dmbv7JJjyWYIBa258KBiq6jwlPdVvOFiGVzYctXqNT4ja5KtmJNYKaF8w1x7rlSf7JFjyWYIBgxtyWYIhAmPSEjBv41FWnWqcL5uRfFldzrwd/+GNUXs82Sd6fkgOJDZLkVtYdap9vm5G8tU+13o3Vz3gaMbaw32iDtbckNv4hKht/mhG8nafszcPEQUSgxvyiG3VKZsbtMNfvS68qS5n3g4RBZImmqXefvttJCcno0mTJrj11lvxn//8R3Hd5cuXQxAEq78mTZoEsLTaoZXRSNncoC1abDpkbx4iCiTVa25WrVqFWbNmYfHixbj11luxcOFCjBo1CkVFRYiNjZV9T1RUFIqKisz/F2S6KOudVsYdYXODNmmt6ZC9eYgokFQPbv70pz9h6tSpmDRpEgBg8eLF2LBhA959913k5ubKvkcQBMTHxweymJqipYCCzQ3apbVeF1oLuIhIv1Rtlrp69Sr27duHkSNHmpeFhIRg5MiR2LVrl+L7Ll++jPbt2yMxMRH33nsvjhw5EojiaoaWJjl0p7lBK81o3tDDb1ATe44QUSCoWnNTUVGBuro6xMXFWS2Pi4vDsWPHZN/TpUsXvPvuu0hLS4PRaMQf//hHDBo0CEeOHEG7du3s1q+trUVtba35/5WVlb79ET7gbjKulobpdrW5QSvNaN5Q8zcwYZuItEDuWqTF65PqzVLuysjIQEZGhvn/gwYNQrdu3bBkyRK8/PLLduvn5+djzpw5gSyiWzy5YWotf8FZc4OWmtE8peZv0ENgSETBxzZokbsWAdDk9UnV4CY6OhqhoaE4e/as1fKzZ8+6nFPTuHFj9O7dG8ePH5d9PS8vD7NmzTL/v7KyEomJiZ4X2oe8uWFqLX/BUX6HFvJyvH2yUOs36CEwJP/S4lMzBT/bQGZ2Vlcs2HTM6lqU98khwKIVQUvXJ1WDm7CwMPTt2xdfffUVsrOzAQAmkwlfffUVZsyY4dJn1NXV4dChQ7jrrrtkXw8PD0d4eLiviuxTrtwwHV24tJYwqkTtZjRf1Hyo9Ru0EBiSdrFWj/xB7qHKMrCRmABAo9cn1ce5mTVrFpYuXYr3338fR48exbRp01BVVWXuPTVhwgTk5eWZ1587dy6+/PJL/PDDD9i/fz9+9atf4eTJk5gyZYpaP8FjzpJx9TJ+jJrjrvhqKgK1fgPHhyElnK2d/EXuocokAraDrgiwX6aV65PqOTc5OTk4f/48fv/736O8vBy9evXC559/bk4yLi0tRUjIzRjswoULmDp1KsrLy9GyZUv07dsXO3fuRPfu3dX6CR5zlDujt+YItZrRfFnzocZv0Fp+FWkHa/XIX5qGhUIQANGmpvqxzA5YtK0YJtwMaiwPQS1dnwRRFEXnq+lHZWUlDAYDjEYjoqKi1C4OgPonMNsb5s7iCoxfuttu3ZVTByIjtXWgixi0yow1GDx/i11zUkHucE2cgK6SO0aoYdPLsU3aYtnUKQkVBGT3boO1B360qsGxDB5CAKydPgjpiS39VjZ37t+qN0uR/NgfemmOUHtcGC1OReAJjg9DtvRybJN22LYYAPVBwl8n9DEHNkB9UGNbK2ICcPqCdsYBU71ZiuTpoTlCK8mOWutZRuQrwXpss4eXNsnm2gAoqai2W25LEIAZKw5AhDaS2xncaFiwXrgA7XVhDpaeZUTu8vbYDnSgoZWHHrKn1Cu0f3JLu+WCAAhiffATciM/R3pZ7es9wOBG84L1puws2ZFPbkTqC3SgobWHHrK243/nrZKIBQGYN7YH0hNbyrYkSA/fFZev4ImVhVafpXZyO4Mb8gtH48IE4oLK4InIMW8CDU/PL3/38OJ5L0/aLk3DQlF1tU52+0jHg+XuEURgaOcYAMotCdLDqlamBJIwuAmwhnLyKeUMAfD7kxurvYmc8zTQ8Ob8cmUwTE+vkTzv5cn1fpLbPkr5NpbHg1JLghZzRBnc+Jnlibrjf+cb1MknF+nvLK7w+5Mbq72JblIKFjwZddvZ+aX0XZbLHd0EPQ1QeN7Lk+v9BNycOqFpeCP0bd8SCYYIr0dh11qOKIMbP7I9UbWWcBUItpG+v6cx4MBmRDc5ChaU8iscnSeOzi+lhze5MhTkDre7CcoFKHmfHELX+OZOx05pyOe9o5ouue0iMaG+d5PlvvK29kVLOaIc58ZP5E5U22NMOvkaEn+PzaGX8YHIe2qPseQKf5bR0fQMzvIrlCidX5FhIbLf9d2pC7LLAdiN26TULJL9zk6nU8/IlQsADp6+6PB9wc7ZFD1K28WS5XGR0z8JBbnDsXLqQBTkDg/qlgUGN37iKGKWNNSbrj9PIA5sRkBwzMvm7zI6qs1wlF/hiNL5VXW1Tva79py4oFgGW0o3YtGFObMSDBGYndXVbvmrnxdpOrj1hitzi9nuLyWW+8TVAUO1/vDAZik/kWt+seRKFbCe+bP6UmttvxRYgc6/8CQBVqkJRsqBAKD4ma5+n7MmYE+bh+XOL6XeMnLjoyh9j3QjzvvkUP1s0xYcNTFJ26NtC/vX9Nw05WpTnOX+igwLwamfa/DkRwe8Sg0IhuRtBjd+Ij1J5G88Jvu6bRVwQ+lFFShaavsl33HlPAlk/oWnF3mlmpMZKw5Yzdtj+5mW3ycIQG5WVzw6NFXxeyYPScHfCkpgEu1rMb3Jr7A9v5R6yyiNj6L0PTn9k9A1vjmy39lpN2mj3M3Xanug/s92Ike91o67k79oub/SE1ui6up1j/d9sCRvM7jxo55tDYqvWXaxC4YoWCsYBDZcrp4n7lz0vTmevLnIO6rZtVxk+ZmA9TAKooj6hycReDTTOsCxvek/MjQFkwanKD7Ry9VwurttlD7P0ffIfUd6YkvMdyEgst3+Iup/q7Rdg71J2tn296b7tTe128GSvM3gxo8cXcCki22wRMFaoIUgkMGVOtw5T+Qu+s9ldUFJRZX5dUD5eHK2j6XXf6666vFF3raMjkifKUKUvZYs2HQM9/Rqo9jrSATwt69PYNLgFLvfplTD6em55mgcFNvljr7DlZuv3E1WBPDmg73Rulm4x03SWjjHXd3+3gQpntZu+3OsIl9icONHShcwywjb3+O+6IVSjkIgg0AtBFcNlbtPi5YX/YOnL2LBpmNW+21o5xjZYOlizTW7dS33sS+bQaQy7jtxwS4HwpLlZwo3hpSwZDvQmtK2eu+bEiz7usTp8fvdqQvIXXPI/D3+eOByJVh1dvNVusn2TW7p8H2Obry2+3fqbSmYNCRF8fP8cRN394HXlSDFl+V0VmOkleskgxs/s03mqr5qsoqwHUXBWoh+tUIpR+GtLccxJi3B79uINWz+48px7sn4SNJn/b9l39rttzfG9ZINAOZvPKY4FpU/mkESDBH4RXqEVQ6EgPoPFmU+M1cmj892O8htqxABWLqjxOq32Q7iBtTfmHI/OaQ4bIWvjnVvmjZcHRBQjlzO0j3pbcxTE9ju379+XYJlBSWyN2i5m/jQzjFOE8GbhoWi9OdqCIJgte19sW2c/WZfBRtKNUZauk4yuAkAR5G1UhTc0EYzdiYluqndUzIA/GN3Kf6xu9Tv2yhY2pmDjatNQ57mFyjtN4j2vYUA+bGo9p+8gDFpEV41gzgL4GxvFgBkmxoeHZoKiPVNUSbIB1Ry2+rhIclY+nWJ1XfaDuIm1WbJVSD5OjHX08E8XR0QUI5tjZSUsyQFi3K1YoD8DVruJp77ySEIN35TiFCfzP3wjVofuSkQgPrgeP791tctpeA0Msz9kVv8GWzI3de0dJ1kcKMBche2wfO3aCL61Yod/zsve9GV+Hsb+Xtk5YZI6cJ7sfoaFnxu3zTkSX6Bo6YLywDA0bANM1YcwOXa6xjaOcajZhBXn5zleiDJeTQzFff0auNwO8hdU6ReU7ak7b7wwXTZ1wUBeC6ri0/PK0+CVaXjpSB3ODJSWzv8PqUaKUuOUp9sb9BKga5lU97Sr0uw9OsSTB+WikXbixWTx/PWHLJrjrNNZzCJwH3v7HQ5L0wS6GBDS9dJDuKnEQmGmwMnOTogvaXFgZeclUm6qDnjzxGfpQsOBwf0HaXjfP6NnBfAfmAyy/PEEemYAqC43ywHk3ywf6LiZ4m4Oaquu8eAKwOtecKV7WC5ju1UC7bqAzxBcRC9BZuO+WyQQWnfDO0c49Zgnu5cFy2vKXKjMTsiN9ydUtOfK97eJh/YSEyi/eCJOf2TsObxDKuySMfOku3FioM/2l5LXR2x3Vf3BS1dJ1lzo0H+in61kujlSpksn0xcGe0Z8P8Tgt4GB/S2G7S3+WBK1e/ePmm603Qh/fujPaccfqZUBneOgTJjDdYfPOPzJ2d3t70rN/dQQUCf9i1lO0AAvqsZ9feM4nLfMWVIikvXD+nz1jyegQ0Hy7Gs4AfFXCp3ers5EyJA9rpVdbVOtpl0/ib5vDDLVAbLMZAc1ZCVGWvwXkF9DZMI39wXtHKdZHCjQZ7mFziipUQvZ2Wy7bEyO6ur/U0QwLRhqVi8/QefbSNXyuvtqLFa4c1Nxt33Km0bueP8uawu5n0vcSdoddR0kRwdadcdHHB/qhRXeqco5VgA9TUDngbhnuw3Z78vxGK0dOnGtOFgGV7ZcNRqPcv8I094eg1yJ4FY7juWfV2i2Owol7ydntgS6YktMWlIsrkjSNXVOnOthlQW84CDb+90qVZIrgzCjX0o9/vdCf73nbCew8t2DCS5YEPuGPXVfcHTbua+xOBGo9wd+MoZX7a9+upG7qhZwrLt+tVNRZg9uite/bzI6qKW0z8Jv8poj30nLgAC0Ld9S78FGY5uKlqsEXNE6SbTNb45qq7WOdx27t6gHG2bMmMNEltFYs3jGTh9oQYmUUS/5FZoEdHY48BesRt0wQnzk7htOeRuIlI3b6WkXUdst5EdF5sznH2uqzciZ1PB/OXB3vhFehvz/xMMERiTloB5G4/avUfKP/Lk+FbaN/tPXoCICxBv7H/L3+JuArFSr8pHhnTA3wpKrILotLYtHCZvS815ll3DgZsjR0sPXa4ENlKNUPVVk3kKBEEA+sj0lrL8fleDfyjsX8sxkBwFgZYsm/qC6YHNFoMbDZOLfj29kfqqqcuXN3LZJxPIP5mktWshe1FzdPFx1jXTVY5uKgA0VyPmjNJNRhry3tF+dSdIdhREbThYZq4Kl9tvBbnDrYJWVykdU1JgY1kOaR8p1ZT6cgRXS6IIjx4qPH1AkX6f3JxNUkK04ntsboBS/pEnx7dsECkA01ccuPl/3Ow95EkCsdJ1btKQZHNNjNz+lPstcl3/JSapZsQFIYC5Rkhi+W9H5B5y5YL/vu1bujQGksTRMRoqCDj440XzEArB8MAmh8FNEPFmIDtfNHW58uQoV3PiqFlidlZX85NIqCDgzlvisOlwudX3SkGYs6cP24uPbddMT4MdRzcVuVFjPRmvI5CBUNOwUNkLoSuDtrkzOulPl2vlgyibanzb/fb8msNWT6jujpBre5xPHpKMv9p0g7bdR0o1pZ7sF2c1JSHwrFuvNw8o0u+zrMFyZZ6nyLBQPLGy0Gq5pzW+tud7iNwxiJvXNGfBnO35I/1frpbXk/3paq6fIwKAtdMHuRzMyLG97sn1hCupqMLjw1Lx9tZiq/cqHR+OjtE7b4mzqh0Khgc2OQxugohSlesr64/id7/o5vTA8zbRy9nFRq5WB4DD5hzpJBIAPJbZAYu2F8NWTv92LpfHkgjrG3bumkOAKD8hoSOObirnKq/YBQqejtcRiCcj6XstyxsCuDwLs7Mg2fZ3yY1N5Ox+Idc06c7FVe7iv8ymG7TcPvJVnoDtNrLM6wDqt7Vlt15PP9eTyS6fH9PNYQ2GrX7JrWTzPiouX0GZscat7WV7vj/YPxEr/mOfyG0CzLV2tsePtN9sj7P7erfF2gM/WuXpSc1O7j7ASQGTsyDV1i96JmDDoTJzeaVaKGeBjeV3Aq41BUnHqu12uKtHPD4/XO60OdVRbd4XN95vyTbfKhhyDAVR9DLVO8hUVlbCYDDAaDQiKipKlTJ4emCUGWswKH+L4s1h/K2JyOjQ2q7d2lfKjDVW4+8A9SdQQe5wALB7LQSwawt2tr7tSWV+TSYAkCuPO6SyuLKtVu0ptbupALCrtrfMB1LiaDs6ynXxRfOa3DZfOrEvpn6wz+4G9saDvRSPpTJjjezopLafLwiAINbvV0f715LSeiunDnQ6nokSuf3n72DSchudq7xiN9N1iAB8k3u7Rzlv3vZEUTqe5JZbbjvhRrTh7gOC0rEnytTeADcTZy2bLC2bC52d9+6c2xLFhzOZAMCuvAC+ybsdAOpziEQ4Hf/I9jvlmmc9uY5IuT2uHB/rD57BDItmQYlcza4UrAHyD6yBCHjcuX+z5ibAvHliTzBEYOptKXZV7JIVu09hxe5TEADk3tUVPdsafHagOavylZsjywTYPaY7as5xdAGRe3p39IRseaFQ4k71urOBFoH6C9yaxzMcPql50j3Y21oeR81EJgCRYY2tt+ONbfjEykLF75Or5ZAd2EwE3hrfG62ahiMyLAT3vbNT9qYkfWeoIOC50V3Mg/hJvO3mr0b3VMttVFJRZZ8PIQLvfVOC5+/q7vHnekLpeFqyo9hcYyY3eOJXR8/id+uOWJXf1Ro1R4m+Swt+kN02wM0b/dx7bkFaOwOqrtZh74mfnT7QuNt05ii/Z+30QXaBqa3ZWV3N3+VqbzJnzerOtq1STboU2Mj1DLTVt31L2VppucRlqcnQ8oFVKqfSwJtqYnATQL7ojj1pSIpdFbstETeT3XxxoNleDOWqfJUSOeVqbqSblDtVvoD8Bcv2pvVZ4RnzOBACbt40pfZ9uSpuV1neVJSCueqryiGao+7BSmXx5pixHcNCkOmlI31vRmprDO0cg/0nL2DGigOK8ys5onQMtGsZYQ74bEcFnjKkAyYNSQZg3WOlRaTnPaaU+KrZyRNKTRzLdpRg0mDliRl9Tel4Kv2pGm9vu9kkbLvfd/zvPF749Ijd51n2rNl3Ur7HE+A40XdMWrzD7tQmEXjh0yPmJiql5k5L7p7bjprcM1JbY75N8G9ZezX7xngy7nLWrO4sQFPapgdPu54MrNTUmdM/CW0MTezyrZQeWJXG3lGzyYrBTQD5oju2Ui8GJbYHmqcDgFleDF/dVGRX5at0kgBQvElZ3ejgvMlC6YIl3bTKjDVY8PnNk0wEEHKj5qBP+5bY8b/zPrthupvc6ajrpaOyKB0zGw6WoX9yS1RdrUPTsFC7LtxygZTtk6dcsmXLplUeT5poewwA9vkljmpQHAWtriZny20LLUgwRGDykBTZ+Z0COe+O0vFkGdhYLpcCF9s8LYnUs2b80m/tck1smyqU8oVKKuyPOTmWN09BqD+3pdyS7N5tsO7AGafnttL1T+l8jgwLsRpN2dncX+5wmnguWCeey+XmzM7qilc33axJt61xcSXQUDrXZPOtALsHVl8MvOkPDG4CyFfdsc09H74psZrpV4l0oNlOxmk5sZsSVwIy6aSzvQBIryvdpKRBsPacuICU6Ei7vA+p5sWVnh1KZTUBaNU0HAmGCKc3THcCP3eTOx09pT03uovik5XSBdB2gDXAukeYs+A3RJBvQpOboFSA9aBzSr1UrAY2s6jGt73AulqD4up6coGcVqrGLT08JAXLvi5xWnvo69wFZ0mySjdYaeRcpWM3BPXH7gKLp3bgZvOF7WCctmPUAPU1oE3DQt2uxbVs7pTO5WdGdXEYcDhq3pU7n7N7tzE3o8odT76oSbRtDpZqhADr+aQAKObmzB7dFWntWijuK1eHC3C18wBg/cDq7cCb/sLgJoC87e1g+1nP39Udkwan4L2CE1j69Q+KQY70BGJbA7P06xIs+7rEblZaS84CMkfTJ+wsrrCa1dmWXI8H26cvd57eXQkeXS2LKzdGd2oXHD2lvfp5kXmgLVtytSFKpCDijXG9nN4oTKLjJjRLIuqbHPq2h12APLhjNL45XmG13RJbRdo95fvrSU6pRsxXVeNlxhqHzS226zoKShIMEZh/v+Pz39e96OQ+z5WB4QDrPBK5gGjt44NQdbVO9lgzwXocGMsclozU1g7P/RABmJDRHu/vPOnwmmY7AJ6jYNiV5l3L89k2P8xfTS2215BzlVesmuhMon2ei+U2MYn11w/LmnRfTt2jdI1zZewdtWtOGdwEmK8TGxMMN7t3Sln6P16ssUv6VboIiXB80iYY7MeikQ5cd2d1tiT33nUHzihm+ruSHGcbCIQIwMM38jkc8SavxZXaBXMytsV2tORoEDzLGjG5IfFt1YkiLlRddTknwbY556fLtbLvm7HigF3ekkkEvv6+wryOtN3WPJ4hW50tXWBdrZlwZT1HNWLeBlS2M0lbNrfIrWsbRMiNqeRs5HG5cay6xjf3aJwUR0mytjWsljenENQHNvektzE/oMg9lKUn1o8I7mqti20zl+25/1hmB7xzY5LJD3adxNg+NwMey84Crt48nc1PJ3d8SOezXE6dvwJ0y2uIXBOdXJ6LUrl8+QAtVz6lZVqZT8oSgxsVWB7Ilv/39jMts/Tv6dXG6kBzdBFydNIu2V5slaBr2YTicPqEG/9XChTkejxImf6W3X3deZK1HM5/w6EyLN1Rn0z7t4ISxfc56r301dGz6BDTzKvmAcsbpADg8eGpeGdrsdOmCbnfrTQkviUBwIufHbH6/BABGNIxGgXfV1iNf2FZC2O5rlJg5OoNrPqqCflje1oFBiLqa30A5XGPnP1+ufUc1Yh588QqN9mk1NxiO2jl3hM/292s5QaQtGwCAezPf6Vm1ex3dmK+g+NXaYwUZ0myjm5OO/533twb0NG0B9LDz/yNx5w2j0v7Q6lc72wrtrpu2D7sAK7nuch1gnB18Eml5jtfNLU4atJNMES43DHDkm251Ao0XG1GDhQGNyoIxAButgeaFNHLVeErnbRLdhQjf9PNqmUR1k0oSnPy2J6DtsGT9PttySXQ2T3Jrql/ko2NamJ1UbDdprY1DHIBlqPeSwDM3V7dHc/D8mZje4N/Z2sxHh+eikU3nlBdnfxPeuJ21EQVcuM75JoPpKdsR13Zpe+yTNZ0l3QsJUdHWo2VIeJmUqptlbttzYSj/W673rsKPQe9fWJVqhGSEoABWPVEsyVCecRnpfO/aVio/KCHLhy/tnkYUs2ROzdo6ZrhzrQH0sB8zgIbaQoCR81crjzsuLI/lTpBOBq52JXmO29rQJwNPCgdB87yXCxzc5TKpbVAQw0MbgJMzdm5LRORl+0ocTiKZZmxBvM32c+dIlcFmmvRk0LuImd5QXXUa8gygS6nf5L8k6wI3Pv2TqtuobNHd7UaF8WV2imnkxvafKcr+8j24nVPeoLs6LzSEOm2NWESR0/ctnkB0kR81VdN+Kmq1m5ALsvcGssLnlxtlbmMInBvehus++6Mw+1iK0S4eQP713c/yu47W3I1E0r7PfvtneZmIdsmI0tvjevt0gBqjijVCIUAVvPuuMpRk4xlU67SRzo7fi3fpxQMu3qDdrUTgW2NlRIpwI6NaqLYzOXLpFSl8qe1a4E1j2dgz4kL6J/c0hwou9N85ym57/hk/4/m1y2vMa7kuQDe99bSOwY3AebL2bk9YZmI7GjG8Z8u18p2/bTtOTO0c4zDwa1sL6jOxnawPMkdNTlYPv0v2HTM7W7k7s4bI7ePbGtp7PIICsscfqZtTZjEWZW45RN2SUUVYqOaKDY9KjV55X5iX3MmCRHgVmAjAJg6NMU8XoujmjnbsYYA+5oJpf0uon696GZh9QG1gzL5IpfNNnAXAMW8KUtSDZrlKs6aZOY7qf1w9/iVC4ZdvRG604lAjmXtk3CjRuJY+SW7nkeO8n5sc/vc6T2m1COs4PvzWLS92K6mxJ3mO0+5cr2pE0XsO3EBv0iX71EoVxvvCWdNY3rB4MYPHB0snrTl+uPgkzt5XJkXCKjPnbB8ylbywphuuCstwemN25blhWX26K5OL/wm2A8Xbjnsv9wTq7tt27b7yLZJ4MEBiW4FS7a/Va4J0Z1eNbOz6kekth33QqnJS6moIQKQ0z8RK2Xm/FHyxO2pGHdre9kmDfPnwvFw9nI1gnKfUyeKmPz+PoflkRus0JYr55QUGFgOp+/oJhUC4M3xvdGuZQTWHyzD3240mdnuB1e7Ykvnn6vHryW5YNhVjo4/ZzWe0vD/p36ugSAAfW7M6G7ZBKrUzCUXiHnShG9bfuk7lQYp9Fd+jSVX56l64qMDqLp63SdpCkpTabjSNKYHDG58zNnJ6G42e6AmWJSrNpUCBMvzUXp6tnzKlguCQgTYBTaAfI8mpZGDV+0pdVhVb7n+c1ldzDf1ENQ3VdkmVTsqh1zbtvm3oL75yFGTgDvBgG3Z5S6i7vaqsRyR2nLcC9vf7UrN2UcKv2ViRnt88O1Ju5q6v2wpxltbi83dwOU+/83xvTEmrQ0A2I2DI7cdzOPlOBi5Vo7lDVWJo+ELbG8Gton6gHwwEgIg//6euFx73VxDIQB4xKJGS/o8uSYZuYRcQQDefFC+iS3BEFHfHHuj1tKT3kQSud+tdPw5On4se1FZ5ka50/PIMhDzpgk/p38SSn+utpslW64MGamtFXuE+orcfs/u3QZr9/9oFeiLYv0Ev96mKSj13nO1aUwPNTgMbnzI1ZPR1apiX+XnOHpKdTTvkCgCkwYn471vTlgtt33Knn+/dc8YqSra2ROxZc8MVyallMhdyId2jsHJiiqs/M8pmAAs+PwYWkQ2lh1zR/rdUs8q267nUtkKjp83d021/DylC7yz7te2nF1ElZ64Hd1gTKL9uBeWXHmCtLvJAsjqEY+/ywQ2lt+r1A1cGpNEkp7Y0mo4e6XtkJ7YEvPvvzlzsbPtG+LkuAPq97vlsWrOeZEZcE7uIUIuOLecQsKyhkIE8LevT2DS4BSrz5A7/3+6VGs3Z5xJBFo3C5f9PVLgL22XcbcmIadfO5cnTLT8HEcD27lS8yzVWNmOO+PoPa7UjHjThF9mrME7MqMuW7J6iLKYrdw2D85XNedy+71XYgu7aS1EEVYzcLtL6b6x8MF0l5rG1B5Z2FcY3PiQOyejK1XFvsjPcXTxcqUZavnOE3bLlboe7jtxwfzk7Kx8lr9f7qSXe9oDbjZ1AbAKjmxnS7dN1JRqonKzuqJFRGO7bSLXI+P/LSuW7fGiVFslwrUAx9nNwJYr3UUtOTpG7G7OcN4r6uXsW/D7T49YfZ9SrzipG7iz3Am3ckGkL5OpSZRY9gpz5N0C+95N5pwXFx4iHAXFntZQAPJzxrk635gIYMXuUqzcXYr59/d0eeZ0Tx6elGo8pVo5d97j7Nj3prlIbqJSS0oPUSKs8+Bsm59zs7ri0Uz355GS2O73FpGNZdezLLuj7v5ylO4bIYLg9MHGtsdqMNNEcPP222/jtddeQ3l5OdLT0/Hmm29iwIABiut//PHHeOGFF3DixAl06tQJCxYswF133RXAEsvzdT6Nt23Bji5egH0CrGwzlGg/j4tS18NfpPtuoCil327Z1GWZAyB3vtomaoo3mm8sb8xKF3RnSYa5WV2tuslL5XtudBdzd1Ppqb518zC7PBi5m4ErbeRy3UVtOTtGHI3EKvdZhojGssm9tgFOiABUXL4iOw2HoyDbMm/LNtHR6sajcIwCro24XGaswd8KSmRfczaisu0kpHJBsTfnq7MAwNmAdIB1kzHg+U3Q2cOTJ0nKQzvH4I1xvQARLvdk8zQoApRrKKcPT8WQjjEOH6KUeraJQP05L8CjiTLl9EtuJTvdSd/k+iDdWXd/V8d+kmpP5eY3s2QSrXusAv7J+QwE1YObVatWYdasWVi8eDFuvfVWLFy4EKNGjUJRURFiY2Pt1t+5cyfGjRuH/Px8/OIXv8CKFSuQnZ2N/fv3o0ePHir8gpt8nU/jzckNOL54iRBlm6GeGtERb3x13G657Twu/ubqb3eY4Omkp5VE7oKu1ONCeqp5NDMVEGDXTp/TP0k21+eedOX8H8D1NnLb7qL7T15AwfEKrNpzyq18Actg0mp+G8CuyU9uAj3bQE5K6H5iZaHdsaw4krVFU5DchVsuf0cUgVeyb8ELnx5xmLMjx50ecs56B9kGxdINYHSPeGw8VG5eL7u3/LQacpSCBrvk8dH2A9JJ6kSxfqiHr0ucNrF5G4z5ovbYGU8HpJNrPpSbvdvRNlA6XhZsOoZ70l3fr87Kadn0KuVuyQb3Fu9zVMvmKJ8wPbGFXRlsgyvLz7adbkVptHktBj+CKDqqvPO/W2+9Ff3798dbb70FADCZTEhMTMQTTzyB3Nxcu/VzcnJQVVWF9evXm5cNHDgQvXr1wuLFi51+X2VlJQwGA4xGI6Kionz3QyxYDpbmKJ/GdhC1UEGQzZVw5fPc/Q7AfhA3qaeD7ZO8UrkCwdlvl/uNwM0eRJbNDY7k3WV/4Vu1p9Q+uVjmxu3teBNK+ymnfzuskEnuXTl1oN38PAKsu2N7UgZHY2hYbgvLQK7MWIP9Jy9gxooDdk2X0jGzs7gC45futvtO2x5ulpwdi3J5Ws5umErHihzpeHD2npVTB6L05yqHM757c+4oHRtK80EpJegrlUFpv/qKO9c5f3Dl/HR0bNs2d0ukc9Cf5VQ6b1wth+VnWgYplg8SoYKAh4cky9bmvDWuN5786IDDfReoDi8Sd+7fqtbcXL16Ffv27UNeXp55WUhICEaOHIldu3bJvmfXrl2YNWuW1bJRo0Zh3bp1suvX1taitrbW/P/KykrvC+6Er/Np3HlKsn2fo9oPpflifD0ypzec/Xa5HJIpNjd5ywn8lLy6qcjuaUyux47tE5On+8aS0rEgF9iECDDPCWX7VCeXvOoq299h+5uUnqATDBFo2dR+PhzLY1kpCdVRkOEsf8eTJ3rpWHFl4Lm0ti0AOO8dZDshrdzv8CZBU3FAurYt8E3u7XYDcsrdqByVwd9D9WthXC9n3+Po2FZqfvb1jNdy5XSnu7+jz5S7VoRY9MQDYB62wPKz5YbFsNx3ag5I6wpVg5uKigrU1dUhLi7OanlcXByOHZO/IZWXl8uuX15eLrt+fn4+5syZ45sC+1AgxlYAHF+8lF7T4iRojjgq76NDUwHx5kB/oYKAyUOS7XqmKF1wq67WudSM5Q2lJjC5i9qUIR2c5gr4a38p3ShcGXTQrvvz6C5Wo0rbkt6fkdpacd96EljKjdKt9N1Kvw24OZ2A0oS0cp/lCUfbNsFgPyAnIH+jcuUm6A+Bus55S2kbKDU/q9E072l3f6URvy174sk9RPRt39LhvlM7cHVG9Zwbf8vLy7Oq6amsrERiYqKKJarnbT6Nu9+l9LlKr/nzgucPjsr7aGaqVR4MAJd7pgTi4ix785dpdggBzF2OtXTTcOVYlgtAW0TeHJFWuNHwL1WVW77f18eibVBw8MeLigMfyuVuSF2/padXRxN3+mJGZmfb1nb7aK3mVUvl8cSjQ1Od5sz5i+15A7g/7YIr1wqlB0RH+05L1yA5qubcXL16FZGRkVi9ejWys7PNyydOnIiLFy/i008/tXtPUlISZs2ahZkzZ5qXvfjii1i3bh2+++47p98ZiJwbd/giZ4Pc506ugb/zEiS2x4Kz7w1UuTwtv7vvAdSbL8eV3C6l1233w3OjuygOouiv8nm7vr9prTwNjTfXCneOfX9fg9y5f2sioXjAgAF48803AdQnFCclJWHGjBmKCcXV1dX417/+ZV42aNAgpKWlaSahmIKDOxdctS7O3tx0KXC4H0jr/HWMBvLYD6rgZtWqVZg4cSKWLFmCAQMGYOHChfjnP/+JY8eOIS4uDhMmTEDbtm2Rn58PoL4reGZmJubPn48xY8bgo48+wrx581zuCs7ghoiIKPgETW8poL4m5vz58/j973+P8vJy9OrVC59//rk5abi0tBQhITdHTBw0aBBWrFiB3/3ud3j++efRqVMnrFu3TvUxboiIiEgbVK+5CTTW3BAREQUfd+7f+phEgoiIiOgGBjdERESkKwxuiIiISFcY3BAREZGuMLghIiIiXWFwQ0RERLrC4IaIiIh0hcENERER6QqDGyIiItIV1adfCDRpQObKykqVS0JERESuku7brkys0OCCm0uXLgEAEhMTVS4JERERuevSpUswGAwO12lwc0uZTCacOXMGzZs3hyAIahcnoCorK5GYmIhTp05xXi0vcDv6Brej73Bb+ga3o2/4azuKoohLly6hTZs2VhNqy2lwNTchISFo166d2sVQVVRUFE9cH+B29A1uR9/htvQNbkff8Md2dFZjI2FCMREREekKgxsiIiLSFQY3DUh4eDhefPFFhIeHq12UoMbt6Bvcjr7Dbekb3I6+oYXt2OASiomIiEjfWHNDREREusLghoiIiHSFwQ0RERHpCoMbIiIi0hUGNzq0Y8cO3H333WjTpg0EQcC6deusXhdFEb///e+RkJCAiIgIjBw5Et9//706hdUwZ9vxoYcegiAIVn+jR49Wp7Aalp+fj/79+6N58+aIjY1FdnY2ioqKrNa5cuUKpk+fjtatW6NZs2a4//77cfbsWZVKrE2ubMdhw4bZHZOPPfaYSiXWpkWLFiEtLc08wFxGRgY2bdpkfp3HomucbUe1j0UGNzpUVVWF9PR0vP3227Kvv/rqq/jLX/6CxYsXY/fu3WjatClGjRqFK1euBLik2uZsOwLA6NGjUVZWZv5buXJlAEsYHLZv347p06fj22+/xebNm3Ht2jXceeedqKqqMq/z9NNP41//+hc+/vhjbN++HWfOnMHYsWNVLLX2uLIdAWDq1KlWx+Srr76qUom1qV27dpg/fz727duHvXv34vbbb8e9996LI0eOAOCx6Cpn2xFQ+VgUSdcAiGvXrjX/32QyifHx8eJrr71mXnbx4kUxPDxcXLlypQolDA6221EURXHixInivffeq0p5gtm5c+dEAOL27dtFUaw//ho3bix+/PHH5nWOHj0qAhB37dqlVjE1z3Y7iqIoZmZmik899ZR6hQpSLVu2FJctW8Zj0UvSdhRF9Y9F1tw0MCUlJSgvL8fIkSPNywwGA2699Vbs2rVLxZIFp23btiE2NhZdunTBtGnT8NNPP6ldJM0zGo0AgFatWgEA9u3bh2vXrlkdk127dkVSUhKPSQdst6PkH//4B6Kjo9GjRw/k5eWhurpajeIFhbq6Onz00UeoqqpCRkYGj0UP2W5HiZrHYoObOLOhKy8vBwDExcVZLY+LizO/Rq4ZPXo0xo4di5SUFBQXF+P5559HVlYWdu3ahdDQULWLp0kmkwkzZ87E4MGD0aNHDwD1x2RYWBhatGhhtS6PSWVy2xEAxo8fj/bt26NNmzY4ePAgZs+ejaKiIqxZs0bF0mrPoUOHkJGRgStXrqBZs2ZYu3YtunfvjsLCQh6LblDajoD6xyKDGyIPPfjgg+Z/9+zZE2lpaUhNTcW2bdswYsQIFUumXdOnT8fhw4dRUFCgdlGCmtJ2fOSRR8z/7tmzJxISEjBixAgUFxcjNTU10MXUrC5duqCwsBBGoxGrV6/GxIkTsX37drWLFXSUtmP37t1VPxbZLNXAxMfHA4Bd9v/Zs2fNr5FnOnTogOjoaBw/flztomjSjBkzsH79emzduhXt2rUzL4+Pj8fVq1dx8eJFq/V5TMpT2o5ybr31VgDgMWkjLCwMHTt2RN++fZGfn4/09HS88cYbPBbdpLQd5QT6WGRw08CkpKQgPj4eX331lXlZZWUldu/ebdVWSu47ffo0fvrpJyQkJKhdFE0RRREzZszA2rVrsWXLFqSkpFi93rdvXzRu3NjqmCwqKkJpaSmPSQvOtqOcwsJCAOAx6YTJZEJtbS2PRS9J21FOoI9FNkvp0OXLl62i45KSEhQWFqJVq1ZISkrCzJkz8corr6BTp05ISUnBCy+8gDZt2iA7O1u9QmuQo+3YqlUrzJkzB/fffz/i4+NRXFyM5557Dh07dsSoUaNULLX2TJ8+HStWrMCnn36K5s2bm3MXDAYDIiIiYDAYMHnyZMyaNQutWrVCVFQUnnjiCWRkZGDgwIEql147nG3H4uJirFixAnfddRdat26NgwcP4umnn8bQoUORlpamcum1Iy8vD1lZWUhKSsKlS5ewYsUKbNu2DV988QWPRTc42o6aOBZV66dFfrN161YRgN3fxIkTRVGs7w7+wgsviHFxcWJ4eLg4YsQIsaioSN1Ca5Cj7VhdXS3eeeedYkxMjNi4cWOxffv24tSpU8Xy8nK1i605ctsQgPjee++Z16mpqREff/xxsWXLlmJkZKR43333iWVlZeoVWoOcbcfS0lJx6NChYqtWrcTw8HCxY8eO4rPPPisajUZ1C64xDz/8sNi+fXsxLCxMjImJEUeMGCF++eWX5td5LLrG0XbUwrEoiKIoBiaMIiIiIvI/5twQERGRrjC4ISIiIl1hcENERES6wuCGiIiIdIXBDREREekKgxsiIiLSFQY3REREpCsMboiIiEhXGNwQERGRrjC4ISJNuXr1qtpFsKPFMhGRMgY3RORXw4YNw4wZMzBjxgwYDAZER0fjhRdegDTzS3JyMl5++WVMmDABUVFReOSRRwAABQUFuO222xAREYHExEQ8+eSTqKqqMn/uO++8g06dOqFJkyaIi4vDAw88YH5t9erV6NmzJyIiItC6dWuMHDnS/N5hw4Zh5syZVmXMzs7GQw89ZP6/p2UiIm1gcENEfvf++++jUaNG+M9//oM33ngDf/rTn7Bs2TLz63/84x+Rnp6OAwcO4IUXXkBxcTFGjx6N+++/HwcPHsSqVatQUFCAGTNmAAD27t2LJ598EnPnzkVRURE+//xzDB06FABQVlaGcePG4eGHH8bRo0exbds2jB07Fu5Oo+dumYhIOzhxJhH51bBhw3Du3DkcOXIEgiAAAHJzc/HZZ5/hv//9L5KTk9G7d2+sXbvW/J4pU6YgNDQUS5YsMS8rKChAZmYmqqqqsHHjRkyaNAmnT59G8+bNrb5v//796Nu3L06cOIH27dvLlqdXr15YuHCheVl2djZatGiB5cuXA4BHZWrSpIlX24mIfIc1N0TkdwMHDjQHNgCQkZGB77//HnV1dQCAfv36Wa3/3XffYfny5WjWrJn5b9SoUTCZTCgpKcEdd9yB9u3bo0OHDvj1r3+Nf/zjH6iurgYApKenY8SIEejZsyf+7//+D0uXLsWFCxfcLrO7ZSIi7WBwQ0Sqa9q0qdX/L1++jEcffRSFhYXmv++++w7ff/89UlNT0bx5c+zfvx8rV65EQkICfv/73yM9PR0XL15EaGgoNm/ejE2bNqF79+5488030aVLF3MAEhISYtdEde3aNa/LRETaweCGiPxu9+7dVv//9ttv0alTJ4SGhsqu36dPH/z3v/9Fx44d7f7CwsIAAI0aNcLIkSPx6quv4uDBgzhx4gS2bNkCABAEAYMHD8acOXNw4MABhIWFmZuYYmJiUFZWZv6uuro6HD582OlvcKVMRKQNDG6IyO9KS0sxa9YsFBUVYeXKlXjzzTfx1FNPKa4/e/Zs7Ny5EzNmzEBhYSG+//57fPrpp+bk3fXr1+Mvf/kLCgsLcfLkSXzwwQcwmUzo0qULdu/ejXnz5mHv3r0oLS3FmjVrcP78eXTr1g0AcPvtt2PDhg3YsGEDjh07hmnTpuHixYtOf4OzMhGRdjRSuwBEpH8TJkxATU0NBgwYgNDQUDz11FPm7tVy0tLSsH37dvz2t7/FbbfdBlEUkZqaipycHABAixYtsGbNGrz00ku4cuUKOnXqhJUrV+KWW27B0aNHsWPHDixcuBCVlZVo3749Xn/9dWRlZQEAHn74YXz33XeYMGECGjVqhKeffhrDhw93+huclYmItIO9pYjIr+R6JxER+RObpYiIiEhXGNwQERGRrrBZioiIiHSFNTdERESkKwxuiIiISFcY3BAREZGuMLghIiIiXWFwQ0RERLrC4IaIiIh0hcENERER6QqDGyIiItIVBjdERESkK/8f6oV/FtX2wFMAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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" ] @@ -461,12 +488,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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v3LhxA8XFxZW2yczMFB/DyckJXl5eVbap7DEMx0xZsmQJdDqd+MVCrURERMpl1yBs9uzZ0Gg0VX6dPXtWbD9jxgycPHkS+/btg1arxciRIyEIAoDSLX4KCwvxwQcfoFu3bujZsyfee+89HDp0CGlpafZ6irUyZ84c6PV68euPP/6wd5eIiIjISuxarHX69OkYPXp0lW2aNm0q/r9hw4Zo2LAhmjdvjlatWiE4OBjff/89IiMjERgYiHr16qF58+Zi+1atWgEoXanYokULBAQEVFjFeO3aNXh6esLV1RVarRZarbbSNgEBAQCAgIAA3LlzB7m5uUajYeXblF9RaXhMQ5vKODs7w9nZucrzQURERMpg1yDM19cXvr6+Zt23pKQEQGkeFQA8+OCDuHv3LtLT0xEWFgYA+P333wEATZo0AQBERkZiz549Ro+zf/9+REZGAgCcnJwQERGBAwcOYMCAAeLPOXDgABISEgAAERERcHR0xIEDBzBo0CAAQFpaGi5duiQ+TmRkJF5++WVkZWXBz89P/Dmenp5o3bq1Wc+XiEgNsrOzcefOHZPHnZyc4OPjY8MeEVmPRjDM50nY0aNHcezYMTz00ENo0KAB0tPTMXfuXFy7dg2//vornJ2dUVJSggceeAD169fH8uXLUVJSgkmTJsHT0xP79u0DUFqi4r777sOkSZMwZswYHDx4EFOmTMHu3bsRHR0NoLRExahRo/D222+jU6dOWL58ObZs2YKzZ8+KeV0TJ07Enj17kJycDE9PT0yePBkAcOTIEQClJSratWuHoKAgJCUlITMzEyNGjMDYsWOxePHiGj/vvLw86HQ66PV6bltERIqXnZ2N1atXV9suISGBgRhJWk0/v2Wxd6SbmxtSUlIwf/585OfnIzAwEDExMXjhhRfE6TsHBwfs3LkTkydPRvfu3eHu7o5HH30Ur7/+uvg4oaGh2L17N6ZOnYoVK1agUaNGePfdd8UADACGDBmC69evY968ecjMzES7du2wd+9eo0T7ZcuWwcHBAYMGDUJhYSGio6OxZs0a8bhWq8WuXbswceJEREZGwt3dHaNGjcKiRYtscLaIiOSpqhEwc9oRSZ0sRsLUiiNhRKQmV69exbp168Tv9XoP5OT4wNs7GzrdTfH2+Ph4BAYG2qOLRDWiqJEwIiJSlxMn2mPnzr4QBAdoNCXo128XOnQ4ae9uEVmUrOqEERGR8un1HmIABgCC4ICdO/tCr/ewc8+ILItBGBERSUpOjg/Kb+giCA7IyfG2U4+IrINBGBERSYq3dzY0mhKj2zSaEnh755i4B5E8MQgjIiJJ0eluol+/XWIgZsgJK5ucT6QETMwnIiJJcHJyEv/focNJhIWdR06ON7y9c4wCsLLtiOSMQRgREUmCj48PEhISWDGfVINBGBERSQYDLFITBmFkVdwHjoiIqHIMwshquA8ckTLx4orIMhiEkdWUf5M2tQUJ94Ejkg9eXBFZDoMwsgluQUKkDNxkm8hyWCeMrI5bkBApl17vgYyMEP49E5mBI2FkdVVtQcLii3Un5/wcOfedOMJNVFcMwsjqDFuQlA3EuAWJZcg5P0fOfSfTI9xhYedVeXHFCwoyB4MwsjrDFiTlr5jV+EZtaXJe/CDnvhNHuMviBQWZi0EY2URVW5CQZch5akjOfVcrjnD/g4sVyFwMwshqyu/vptPdrDT44j5wdSfnqSE5913NOMJtmqlR3fI4hUkMwshquA+c7ch5akjOfVcjpW2ybelAqKajupzCJIBBGFkZ3zxsQ85TQ3Luuxop6eLK0oFQbUZ11ZgTyZG/ihiEESmAnKeG5Nx3tVLKB2VNA5wrV66YbFt2xM/cUV015ERy5K9yDMKIFELOix/k3HdSDlOjUSkpKVXeb8iQIQDMG9VVS06kGkf+aoJBGJGMyXnxg5z7TspTl9GooqIiAOaN6qoxJ1INI381xSCMSMbknJ8j576TstR0NMrU6E29ev98lNZ2sYLaciLVMvJXUwzCiGROzkGKnPtOylGT0aiqRm+8vLzMvqBQW06kGkf+qsIgjIiIVK260aiajN7U5YJCTTmRahv5q45D9U2IiIiUyzAapdGUAECF0aiqRm/MVVlOZGjoxQoBmNJyIqs712rDkTAiIlKlmhaetcbojdxzIutS80tNI3/VYRBGRESqVF0gdOPGDaSkpFgtb0uqAVZ1zKn5xdXQlWMQRkSkcKxUblpVz1tpWzRZijk1v+Q+8mctDMKIiBSMlcrNx8CherWp+aXm82QKgzAiMokjKPLHSuV1w9e3aaz5VXcMwoioUhxBUR5WKidLYs2vumOJCiKqVGUjKBkZIdDrPapsR9JkatSi/O+TqKYMq0bLUnPNL3NwJIyIqsURFPnjqAVZmtqq/VsDgzAiqhLzPpSBlcrJGljzq24YhFGtMVlbXTiCogwctSBLYc0vy2EQRrXCZG314QiKcnDUgiyBpTssh0EY1UpNk7CZrK0cHEGRN45akDUwwLIMBmFUJ6ZqDpGycARFvjhqQSRdDMLIbFwxp2wcQVEOBlhE0sQgjMzCFXPKxxEUIiLrYhBGZuGKOXVggEVEZD0Mwsgsllgxx1IXRESkZgzCyCzVrZi7ceMGANOBFEtdqAMDbSLL4N+SMjEIo1opm4Rd1Yq5lJQU8f+VBVIsdaF8DLSJLKP835KpVen8W5IfBmFUK2WTtW/cuIGUlJRqc8BqEkix1IXyVLYBeGW/YwbaRFUr+zdS1ap0/i3JD4MwqjVTV1rmBlIsdaF8/B0T1R1XpSsPgzCyCHM/ZPmmonz8HRNZBlelKw+DMKqzunzI8k1F+dTwO2bSNNkC93FVHgZhVGd1+ZDlm4ryKf13zAUIZCvcx1V5GIRRndXlQ5ZvKsqn9N8xFyCQLXEfV2VhEEZ1Zs6HbE1LXXBfQmVQywcHFyCQLZjax5Xkh0EYma0ugRT3JVQ+tW0AzgUIZC01/RtRyt+SmjAII7PVNZBigGUdUkkSt1agLZXnV54aFiCQffCiVbkYhFGd8I9eWqSWJG7pnyG151eW0hcgkH3xvVaZHKpvQkRyofTtoCpLgs/ICIFe71FlO1sw5EZqNCUAoLgFCERkeRwJI1IwJW8HJcUkeLUsQCDLkuoUO1kfgzAihZJikGIpUkqCV9sCBLIsbs6tbgzCiBRISkGKNUgpCZ5J01QX3Jxb3RiEESmQlIIUa5BaErwlAyxOTamT0i+cqHIMwogUSGpBiqUptQo/p6bUS+kXTlQ5BmFECqTUIKUsJSbBc2pKvZR+4USVYxBGssapG2NK3w5KLUnwnJpSHzVcOFFFDMJItqRcuNNelJ4krvTnZ8CpKXVS4uguVY1BGMmW0guTmkvuAUh1lP78AE5NqRk351YX2VTM79+/Pxo3bgwXFxcEBgZixIgRuHLlinh8wYIF0Gg0Fb7c3d2NHmfr1q1o2bIlXFxc0KZNG+zZs8fouCAImDdvHgIDA+Hq6oqoqCicO3fOqE1OTg6GDx8OT09PeHl5IS4uDrdu3TJq8/PPP6Nbt25wcXFBcHAwkpKSLHxGqDxT1dOJ5IbV99WDm3Orm2xGwnr16oXnnnsOgYGB+PPPP/Hss89i8ODBOHLkCADg2WefxYQJE4zu07t3bzzwwAPi90eOHMGwYcOwZMkS9O3bF5s2bcKAAQNw4sQJ3HfffQCApKQkrFy5Ehs2bEBoaCjmzp2L6Oho/Pbbb3BxcQEADB8+HFevXsX+/ftRVFSEp59+GvHx8di0aRMAIC8vD3369EFUVBTWrl2LX375BWPGjIGXlxfi4+NtcbpUR8mFSUmdODWlDmqZYqfKaQRBEOzdCXPs2LEDAwYMQGFhIRwdHSsc/+mnn9CuXTt8/fXX6NatGwBgyJAhyM/Px65du8R2Xbp0Qbt27bB27VoIgoCgoCBMnz4dzz77LABAr9fD398fycnJGDp0KM6cOYPWrVvj2LFj6NixIwBg7969+Ne//oXLly8jKCgIb731Fp5//nlkZmaKVy+zZ8/G9u3bcfbs2Ro/x7y8POh0Ouj1enh6epp9rqTIEgn1V69exbp166DXe2D58sQKUzeJicuh091EfHw8AgMDLdZ3ImsxvKarw9c0kbTV9PNbNiNhZeXk5GDjxo3o2rVrpQEYALz77rto3ry5GIABQGpqKqZNm2bULjo6Gtu3bwcAZGRkIDMzE1FRUeJxnU6Hzp07IzU1FUOHDkVqaiq8vLzEAAwAoqKi4ODggKNHj+Lf//43UlNT0b17d6Ph4+joaCxduhR//fUXGjRoYInTIFuWTqhnEjMpBaemiNRFVkHYrFmzsHr1ahQUFKBLly5GI1pl3b59Gxs3bsTs2bONbs/MzIS/v7/Rbf7+/sjMzBSPG26rqo2fn5/R8Xr16sHb29uoTWhoaIXHMBwzFYQVFhaisLBQ/D4vL6/SdnJn6YR6JjGTUnBqitSC5YVK2TUImz17NpYuXVplmzNnzqBly5YAgBkzZiAuLg4XL17EwoULMXLkSOzatQsajcboPp9++ilu3ryJUaNGWa3v1rBkyRIsXLjQ3t2wOVNVwWuK9XVISdTwwUPqxp0h/mHXIGz69OkYPXp0lW2aNm0q/r9hw4Zo2LAhmjdvjlatWiE4OBjff/89IiMjje7z7rvvom/fvhVGtAICAnDt2jWj265du4aAgADxuOG2svkW165dQ7t27cQ2WVlZRo9x9+5d5OTkGD1OZT+n7M+ozJw5c4ymS/Py8hAcHGyyvRLUJaFe6YVJiYiUiDtD/MOuQZivry98fX3Num9JSenS7bLTd0BpXtehQ4ewY8eOCveJjIzEgQMHkJiYKN62f/9+MYgLDQ1FQEAADhw4IAZdeXl5OHr0KCZOnCg+Rm5uLo4fP46IiAgAwMGDB1FSUoLOnTuLbZ5//nkUFRWJOWv79+9HixYtqswHc3Z2hrOzsxlnQ57qWhWcUzdERPLFnSFkkhN29OhRHDt2DA899BAaNGiA9PR0zJ07F2FhYRVGwd5//30EBgbi0UcfrfA4zzzzDHr06IHXX38dsbGx+Pjjj/Hjjz+Kq5E0Gg0SExPx0ksvITw8XCxRERQUhAEDBgAAWrVqhZiYGIwbNw5r165FUVEREhISMHToUAQFBQEAnnzySSxcuBBxcXGYNWsWTp8+jRUrVmDZsmXWPVEyY4mEegZYRNLFvB+qChdVySQIc3NzQ0pKCubPn4/8/HwEBgYiJiYGL7zwgtHIUUlJCZKTkzF69GhotdoKj9O1a1ds2rQJL7zwAp577jmEh4dj+/btYo0wAJg5cyby8/MRHx+P3NxcPPTQQ9i7d69YIwwANm7ciISEBPTu3RsODg4YNGgQVq5cKR7X6XTYt28fJk2ahIiICDRs2BDz5s1jjbBymFBPpFzM+6Hq8DNAJkFYmzZtcPDgwWrbOTg44I8//qiyzeOPP47HH3/c5HGNRoNFixZh0aJFJtt4e3uLhVlNuf/++/HNN99U3WGVY0I9kXLJPe+Ho3jWx88AmQRhpCxMqCdSDznm/XAUz3bUvjMEgzCyOSbUE6mHHPN+5D6KJzdq3rScQRjZBQMsInWQc96PHEfx5IA7Q/yDQRgREVmNnPN+5DiKJwecDfkHgzAiIrIqueb9yHkUT+rUEGDVhEP1TYiIiOpGp7uJ0NCLsgnAgH9G8TSa0uLgchrFI3ngSBgRkQTJvUSCUvJ+5DqKR/LAIIyISGLKl0gwxVolEiwRACop70fNq/fIuhiEERFJTPnAxVSdKmuUSLBkACiHAKsyShnFI+ljEEZEJGFV1amyBnsGgFKhpFE8kjYGYUREEmXvOlW2DgClhAEW2QJXRxIRSVRVdaqszVQAqNd7WP1nE6kFR8KIiCTKnnWqWKhUXeS+GleuGIQREUmUPavNs1CpenDDcvthEEZEJGH2qlMl5+2GqHa4Ybn9MAgjIpKY8qUPTNWpsnaJBBYqVRd7LwRRIwZhREQSY88SCVIJAMn2mAdoewzCiIgkyF65N6yRJR22TpZnHqDtMQgjWeJKHiLr4d+O/dkjWZ55gLbHIIxkx9776hERWZu9kuWZB2hbLNZKslPTNx2u5CEiubNH0Vyd7iZCQy8yALMBjoSR7JkapicikjtbJMtLZcNyNaaZMAgjWVPz3nZEpHy2SJaXwmIMtaaZMAgj2WJNG5IyNV7Vk+XZKlne3q/F8n8rpmY4lJZmwiCMZIs1bUiq1HpVT9ahtmR5Nc1wMDGfZMswTF8Wa9qQFFR2VZ+REVIhmVppV/VkPWpJlrfHQgR74kgYyZa1huk5jUSWpKarerIcqSTL25raZjgYhJHslH3TqWqY3pw3J04jqYu1A27mLdqfXC+qpJAsbw9qq9rPIIxkx5pvTqxBph62CLjVdlUvNXK/qJJin6xNbVX7GYSRLNnqzYk1yJTLFqux1HZVLzVqXXEnd2paiMAgjMgE5vKoh7V+12q7qpcy/j1LW/n0EZ3uZqV/J3q9vtL7ynXUkEEYUSWYy6Me1v5dq+mqXqr49yx9ptJMcnNzsWXLFvH7zZs3V3p/qU4pV4dBGFElmMujHtb4Xdf0ql5pK9ukin/P8lCTIEppU8oMwogqwVwe9bDG71qtK9ukin/PyqDEKWUWayWqhCGXx1AMlrk8ylXd7/rGjRvIzs6u9eP6+PggMDDQ5BcDMNvh37P8KbWIK0fCiMqwZg0ykq6qftcpKSkA5JtzQqWYmydvSp1SZhBGVAankdTDVN6WYYshpeScqBlz85RDqVPKDMKIymGApQ6GgPvKlSviaJcSc07UjBdVyqHUci8MwkhW5LoFCUmTj4+P+HpiGQNl4vuBcihxSplBGMmG3LcgIWlTas4JkZwpfUqZQRjJBrcgIWtSas4JkZwpfUqZQZhKKG0aj7k7ZGlKzTkhkjs5fTbVFoMwFVDaNB5zd8halJhzQkTSxSBMBZQ2jcfcHbIkpeecEJF0MQhTGSVM4zF3Rz1sMY2u9JwTIpIuBmEqUt00Xm5ubpX3l8oHEXN31MGW0+hSeF0TkfowCFOR6qbxtmzZUu1jSCVvjLk7yqe0aXQiovIYhKlIbabxpPiBx9wd9VLCNDoRUXkMwlSkptN4Uv3AY+6OOnE1LAHKK7NDBDAIU53qpvGk/oHHN1n14WpYUlqZHSIDBmEqUNNpPIAfeCQ9XA1LSswP5MgeAQzCVKEm03h6vR6bN2/mBx5Jhl6vB8DVsGRMqukStcGRPTJgEKYSNf1D5gceWUpdrvSzs7OxefNm8XuuhiVA+ukSNaXEkT0yD4MwqoAfeFRXdb3Sr+zDh6thSYnpEkoY2SPzMQgjACz/QJZl6St9U/d/4oknOF2jIkpLl1DKyJ6cSC0Xj0EYAWD5B7Keul7pV3V/Ly8vK/WapEhp6RJKHNmTMinm4jEIIxEDLLK0ul7pc6TA8qQ2ElBbSkqXUNrIntTVdOTdlrl4DMKIyGrqeqXPkQLLkuJIQE0oNV1CaSN7cmMqzcGWGIQRkdXU9UqfIwWWJddVeUpOl1DSyJ6cSGVBBIMwIrKaul7pc6TAeqTyIVRTcgywTFHqyJ5cSCnNgUEYEVmVOVf6ZT98qro/P6TMI6UPITVS8sieHEgpzYFBGBFZXF2v9PkhZV1S+hBSK752S9ljoYiU0hxqHITl5eXV+EE9PT3N6gwRKYMlgih+SFmPlD6ESL3stVBESmkONQ7CvLy8oNFoqmwjCAI0Gg2Ki4vr3DEikjcGUdIlpQ8hUi9bl4yQYppDjYOwQ4cOWbMfRERkQ1yVR1Jj7ZIRUkxzqHEQ1qNHD2v2g4iIrIyr8kiqbLVaV2oj9GYn5ufm5uK9997DmTNnAAD33nsvxowZA51OZ7HOERGR5UhxJIBIzat1HapvUtGPP/6IsLAwLFu2DDk5OcjJycEbb7yBsLAwnDhxwtJ9BAD0798fjRs3houLCwIDAzFixAhcuXLFqM0XX3yBLl26wMPDA76+vhg0aBAuXLhg1Obw4cPo0KEDnJ2d0axZMyQnJ1f4WW+++SZCQkLg4uKCzp0744cffjA6fvv2bUyaNAk+Pj6oX78+Bg0ahGvXrhm1uXTpEmJjY+Hm5gY/Pz/MmDEDd+/etci5ICIyl4+PDwIDA01+MQAjW6tqta7SmRWETZ06Ff3798eFCxeQkpKClJQUZGRkoG/fvkhMTLRwF0v16tULW7ZsQVpaGrZt24b09HQMHjxYPJ6RkYHHHnsMDz/8ME6dOoUvvvgCN27cwMCBA43axMbGolevXjh16hQSExMxduxYfPHFF2KbzZs3Y9q0aZg/fz5OnDiBtm3bIjo6GllZWUbPf+fOndi6dSu++uorXLlyxejnFBcXIzY2Fnfu3MGRI0ewYcMGJCcnY968eVY5N0RERHJlWK1bllpW62oEQRBqeydXV1ecPHkSLVu2NLr9t99+Q8eOHVFQUGCxDpqyY8cODBgwAIWFhXB0dMQnn3yCYcOGobCwEA4OpbHlzp078dhjj4ltZs2ahd27d+P06dPi4wwdOhS5ubnYu3cvAKBz58544IEHxGWzJSUlCA4OxuTJkzF79mzo9Xr4+vpi06ZNYhB49uxZtGrVCqmpqejSpQs+//xz9O3bF1euXIG/vz8AYO3atZg1axauX79e43yLvLw86HQ66PV6lv0gUjG5b7pNVJmrV69i3bp1AKrOCYuPj0dgYKA9u1prNf38NisnzNPTE5cuXaoQhP3xxx/w8PAw5yFrJScnBxs3bkTXrl3h6OgIAIiIiICDgwPWr1+P0aNH49atW/jwww8RFRUltklNTUVUVJTRY0VHR4ujd3fu3MHx48cxZ84c8biDgwOioqKQmpoKADh+/DiKioqMHqdly5Zo3LixGISlpqaiTZs2YgBm+DkTJ07Er7/+ivbt21f6vAoLC1FYWCh+X5vabESkTHLddJuoOlIsGWFrZgVhQ4YMQVxcHF577TV07doVAPDdd99hxowZGDZsmEU7WNasWbOwevVqFBQUoEuXLti1a5d4LDQ0FPv27cMTTzyB8ePHo7i4GJGRkdizZ4/YJjMz0ygwAgB/f3/k5eXh77//xl9//YXi4uJK25w9e1Z8DCcnJ3h5eVVok5mZWeXPMRwzZcmSJVi4cGENzwaRNHCUxrrkuuk2UXW4UMTMIOy1116DRqPByJEjxWRzR0dHTJw4Ea+88kqNH2f27NlYunRplW3OnDkjjrjNmDEDcXFxuHjxIhYuXIiRI0di165d0Gg0yMzMxLhx4zBq1CgMGzYMN2/exLx58zB48GDs37+/2kKzUjBnzhxMmzZN/D4vLw/BwcF27BFR1ThKY1ty23SbqDpqf18wKwhzcnLCihUrsGTJEqSnpwMAwsLC4ObmVqvHmT59OkaPHl1lm6ZNm4r/b9iwIRo2bIjmzZujVatWCA4Oxvfff4/IyEi8+eab0Ol0SEpKEtv/97//RXBwMI4ePYouXbogICCgwirGa9euwdPTE66urtBqtdBqtZW2CQgIAAAEBATgzp07yM3NNRoNK9+m/IpKw2Ma2lTG2dkZzs7OVZ4PIinhKI3tqHkZP5FS1WkDbzc3N7Rp08bs+/v6+sLX19es+5aUlK6kMORQFRQUiAn5Blqt1qht+elJANi/fz8iIyMBlAaXEREROHDgAAYMGCDe98CBA0hISABQmnvm6OiIAwcOYNCgQQCAtLQ0XLp0SXycyMhIvPzyy8jKyoKfn5/4czw9PdG6dWuzni+R1HGUxrq46TaR8pgVhN2+fRurVq3CoUOHkJWVJQY5BpauFXb06FEcO3YMDz30EBo0aID09HTMnTsXYWFhYuATGxuLZcuWYdGiReJ05HPPPYcmTZqIifATJkzA6tWrMXPmTIwZMwYHDx7Eli1bsHv3bvFnTZs2DaNGjULHjh3RqVMnLF++HPn5+Xj66acBADqdDnFxcZg2bRq8vb3h6emJyZMnIzIyEl26dAEA9OnTB61bt8aIESOQlJSEzMxMvPDCC5g0aRJHukiROEpjfdx0W96YO0mVMSsIi4uLw759+zB48GB06tTJ6vlWbm5uSElJwfz585Gfn4/AwEDExMTghRdeEIOahx9+GJs2bUJSUhKSkpLg5uaGyMhI7N27F66urgBKk/d3796NqVOnYsWKFWjUqBHeffddREdHiz9ryJAhuH79OubNm4fMzEy0a9cOe/fuNUq0X7ZsGRwcHDBo0CAUFhYiOjoaa9asEY9rtVrs2rULEydORGRkJNzd3TFq1CgsWrTIqueJyF44SmN93HRbvpg7SaaYFYTt2rULe/bswYMPPmjp/lSqTZs2OHjwYLXthg4diqFDh1bZpmfPnjh5suopkoSEBHH6sTIuLi5488038eabb5ps06RJkwpTn2RfvBK1Ho7S2AY33ZanmuZEMndSfcwKwu655x6b1AMjspTyV6KmEsh5JWoejtJYDzfdVh5T7z+kPmYFYa+//jpmzZqFtWvXokmTJpbuE5HFlb3CrCqBnFei5uMojXWwlpKycAELlWVWENaxY0fcvn0bTZs2hZubm1iR3iAnh1MQJE1MILcsjtLYBgMsZeD7D5VnVhA2bNgw/Pnnn1i8eDH8/f1lUQiVCGACuaVxlIao5vj+Q+WZFYQdOXIEqampaNu2raX7Q2RVTCC3PAZYRDXD9x8qz6H6JhW1bNkSf//9t6X7QmR1hgRyjaa0th0TyInIVvj+Q+WZNRL2yiuvYPr06Xj55ZfRpk2bCjlhnp6eFukckTUwgZyIbKlsTmRV7z/MnVQfs4KwmJgYAEDv3r2NbhcEARqNBsXFxXXvGZEVmUogJyKyNOZOkilmBWGHDh2ydD+IrKqmV5i8EiUia2CARZXRCIIgWOvB//Of/2DRokVo2LChtX6EouXl5UGn00Gv13OK1wJYMZ+IiGyhpp/fVg3CPD09cerUKTRt2tRaP0LRGIQRERHJT00/v81aHVlTVozviIiIiGTNqkEYEREREVWOQRgRERGRHTAIIyIiIrIDs0pUEKkBV1NSXfE1RERVsWoQ9tRTT3FVH8lSdnY2Vq9eLX6v13sgJ8cH3t7ZRkVeExIS+CFKlSr/GjKFryEi9TI7CMvNzcUPP/yArKwslJSUGB0bOXIkAOCtt96qW++I7KTs6MWJE+2xc2dfCIKDuNdbhw4nK7QjKqv8a8NUIM/XEJF6mRWE7dy5E8OHD8etW7fg6ekJjUYjHtNoNGIQRiR3er2HGIABgCA4YOfOvggLO89tj6jGqgrkiUi9zArCpk+fjjFjxmDx4sVwc3OzdJ+IJCMnx0cMwAwEwQE5Od4MwqhGGMiXYn4cUUVmBWF//vknpkyZwgCMFM/bOxsaTYlRIKbRlMDbO8eOvSI5YSDP/DgiU8wqUREdHY0ff/zR0n0hkhyd7ib69dsFjaY079EwlaSWD0+qO0MgX5baAvnK8uMyMkKg13tU2Y5I6Wo8ErZjxw7x/7GxsZgxYwZ+++03tGnTBo6OjkZt+/fvb7keEtlZhw4nERZ2Hjk53vD2zmEARrViCOTL54Sp9XXE/Diif9Q4CBswYECF2xYtWlThNo1Gg+Li4jp1ikhqdLqbqv3QpLpjIF+K+XFExmochJUvQ0GkZE5OThZtR+pT/rVhKpBX02uI+XHqwYUYNWNWYv4HH3yAIUOGwNnZ2ej2O3fu4OOPP2aJCpI9Hx8fJCQk8E2EzMbXUEVc6KIOXIhRc2YFYU8//TRiYmLg5+dndPvNmzfx9NNPMwgjRVD7mwPVHV9Dxpgfpw4sVFxzZgVhgiAYFWg1uHz5MnQ6XZ07RUREysT8OHXhQoyq1SoIa9++PTQaDTQaDXr37o169f65e3FxMTIyMhATE2PxThIRkXwxP06duBCjerUKwgwrJE+dOoXo6GjUr19fPObk5ISQkBAMGjTIoh0kIiJ5Y36cOnEhRvVqFYTNnz8fABASEoIhQ4bAxcXFKp0iIiJlYYClPlyIUT2zcsJGjRoFoDSpLisrq0L5isaNG9e9Z0RERCRbXIhRPbOCsHPnzmHMmDE4cuSI0e2GhH0WayUiIpI+a9fz4kKMqpkVhI0ePRr16tXDrl27EBgYWOlKSSIiIpIua9Xz4kKMmjMrCDt16hSOHz+Oli1bWro/REREZAPWqufFhRg1Z1YQ1rp1a9y4ccPSfSE7unwZOHcOCA8HGjWyd2/IHNwmhIjMZel6XnyvqRmzgrClS5di5syZWLx4Mdq0aQNHR0ej456enhbpHNnGe+8B8fFASQng4ACsWwfExVnmsRnc2Qa3CSEic7Gel/2YFYRFRUUBAB5++GGjfDAm5svL5cvAkSP/BGBA6b/jxwPR0XUPmqwZ3JExbhNCROZiPS/7MSsIO3TokKX7QTZWNkAqr7gYOH++bkHY5cvWC+6oatwmhIhqg/W87Meh+iYV9ejRAw4ODnjnnXcwe/ZsNGvWDD169MClS5eg1Wot3UeysPIBUnlarQAPj2vIzs426/Gzs7Px/ffZFR6/uBg4ejTb7Mel6pmaVtDrPezcMyKSKkM9L42m9E2b9bxsx6yRsG3btmHEiBEYPnw4Tp48icLCQgCAXq/H4sWLsWfPHot2kizr3DnTAZhGU4LY2F3Ytat05KS2OUSG3CS93gMaTWKFK6vvvtuA06dvMjfJSjitQETmYD0v+zBrJOyll17C2rVr8c477xgl5T/44IM4ceKExTpH1hEeDjg4COVuLcHgwVuQmLjcaOqqtjlEhvbVXVnVNTfp8mXg0KHSf+kfhmmFsjitQESVqayeV2joxQoBGOt5WY9ZI2FpaWno3r17hdt1Oh1yc3Pr2ieyskaNgKQkPWbM8DTKG7rvvjMW/TmWvrIylGDYtMkVM2fqUFKigYODgKQkPZ588m+WYAC3CbEHlgYhuWI9L/szKwgLCAjA+fPnERISYnT7t99+i6ZNm1qiX2Qlhg+MPn1u4M8/91t96NlUpeTaKjvNuXx5IgShdFVuSYkGM2Z44s8/34dOx2lOgNMKtsTSICR3fF3al1lB2Lhx4/DMM8/g/fffh0ajwZUrV5Camopnn30Wc+fOtXQfyULKf2DodJDNB7ThSq26nCe1lmDgNiH2wdIgRFQXZgVhs2fPRklJCXr37o2CggJ0794dzs7OePbZZzF58mRL95EsRAkfBFxKXTlOK9gfS4MQUW2ZFYRpNBo8//zzmDFjBs6fP49bt26hdevWqF+/vqX7R2SEOU+mMcCyn7pWHGdeGZE6mRWEGTg5OaF169aW6gtRjTDniaSmLqVBmFdGpF51CsJI+WqbQ1TT9nXNTbJUwj+RJdRlmpx5ZUTqxSCMKhg4cCAaNmxo1hQIc5NIjSw1Tc68MiJ1YRBGFTRs2BCBgYFm358BFqlRXafJ65pXRqRkSs2bZBBGsmCraU6i2rBkaRBuOUVUOSXnTTIIUxE5BzKc5iQpsuTrkuVXyFbkNqqk5LxJBmEqIvdARqr9InWz1OuS5VfIFuQ+qqS0vEkGYSojxT8qIirF8itkbXIeVVJi3iSDMCIiO+KWU2QvchtVUmLeJIMwIiI7knuaAMmTHEeVlJg3ySCMiFRHaonJDLDI1uQ4qqTEvEkGYUSkKnJPTCayBLmOKiktb9Kh+iZERMpR04RjKSYmE1mKYVRJoykBAEmPKlWWNxkaerFCX+WYN8mRMCJSNVOrw4jkoC5T63IZVVJy3iSDMCJSLbmtDiMqy5ypdbmuxpVjgFUTDMKISJXkuDqMqCxzan4peVRJjhiEEZEqyXF1GJEptRnVZYAlHUzMJyJVMqwOK0sOq8OIyjM1qqvXe9i5Z1QdBmFEpEpyWh1GVJWqRnVJ2mQThPXv3x+NGzeGi4sLAgMDMWLECFy5csWozZYtW9CuXTu4ubmhSZMmePXVVys8zuHDh9GhQwc4OzujWbNmSE5OrtDmzTffREhICFxcXNC5c2f88MMPRsdv376NSZMmwcfHB/Xr18egQYNw7do1ozaXLl1CbGws3Nzc4OfnhxkzZuDu3bt1PxFEVCdlE447dDiJxMTlGDUqGYmJy42mb6SWmExkCkd15Us2OWG9evXCc889h8DAQPz555949tlnMXjwYBw5cgQA8Pnnn2P48OFYtWoV+vTpgzNnzmDcuHFwdXVFQkICACAjIwOxsbGYMGECNm7ciAMHDmDs2LEIDAxEdHQ0AGDz5s2YNm0a1q5di86dO2P58uWIjo5GWloa/Pz8AABTp07F7t27sXXrVuh0OiQkJGDgwIH47rvvAADFxcWIjY1FQEAAjhw5gqtXr2LkyJFwdHTE4sWL7XD2iMiAicmkNEqsJK8WGkEQBHt3whw7duzAgAEDUFhYCEdHRzz55JMoKirC1q1bxTarVq1CUlISLl26BI1Gg1mzZmH37t04ffq02Gbo0KHIzc3F3r17AQCdO3fGAw88IC77LSkpQXBwMCZPnozZs2dDr9fD19cXmzZtwuDBgwEAZ8+eRatWrZCamoouXbrg888/R9++fXHlyhX4+/sDANauXYtZs2bh+vXrNb7CzsvLg06ng16vh6enp0XOGxERKcPVq1exbt068fvS1ZEVa37Fx8cjMDDQHl1UrZp+fstmOrKsnJwcbNy4EV27doWjoyMAoLCwEC4uLkbtXF1dcfnyZVy8eBEAkJqaiqioKKM20dHRSE1NBVC6jPf48eNGbRwcHBAVFSW2OX78OIqKiozatGzZEo0bNxbbpKamok2bNmIAZvg5eXl5+PXXX00+r8LCQuTl5Rl9EUlRdnY2rl69avIrOzvb3l0kUjwlV5JXC9lMRwLArFmzsHr1ahQUFKBLly7YtWuXeCw6OhpTp07F6NGj0atXL5w/fx6vv/46gNKrhZCQEGRmZhoFRgDg7++PvLw8/P333/jrr79QXFxcaZuzZ88CADIzM+Hk5AQvL68KbTIzM8U2lT2G4ZgpS5YswcKFC2txRohsj3svEkkDp9blz64jYbNnz4ZGo6nyyxD8AMCMGTNw8uRJ7Nu3D1qtFiNHjoRhNnXcuHFISEhA37594eTkhC5dumDo0KEASkez5GDOnDnQ6/Xi1x9//GHvLhFVwL0XiaTDx8cHgYGBJr8YgEmbXUfCpk+fjtGjR1fZpmnTpuL/GzZsiIYNG6J58+Zo1aoVgoOD8f333yMyMhIajQZLly7F4sWLkZmZCV9fXxw4cMDoMQICAiqsYrx27Ro8PT3h6uoKrVYLrVZbaZuAgADxMe7cuYPc3Fyj0bDybcqvqDQ8pqFNZZydneHs7Fzl+SAiIiJlsOsQka+vL1q2bFnll6m57JKS0uW4hYWFRrdrtVrcc889cHJywkcffYTIyEj4+voCACIjI8XAzGD//v2IjIwEUDpsGxERYdSmpKQEBw4cENtERETA0dHRqE1aWhouXboktomMjMQvv/yCrKwso5/j6emJ1q1bm3WuiKRKr/dARkYIC0MSEdWSLHLCjh49imPHjuGhhx5CgwYNkJ6ejrlz5yIsLEwMfG7cuIFPPvkEPXv2xO3bt7F+/Xps3boVX331lfg4EyZMwOrVqzFz5kyMGTMGBw8exJYtW7B7926xzbRp0zBq1Ch07NgRnTp1wvLly5Gfn4+nn34aAKDT6RAXF4dp06bB29sbnp6emDx5MiIjI9GlSxcAQJ8+fdC6dWuMGDECSUlJyMzMxAsvvIBJkyZxpIsUhRtgExGZTxZBmJubG1JSUjB//nzk5+cjMDAQMTExeOGFF4yCmg0bNuDZZ5+FIAiIjIzE4cOH0alTJ/F4aGgodu/ejalTp2LFihVo1KgR3n33XbFGGAAMGTIE169fx7x585CZmYl27dph7969Ron2y5Ytg4ODAwYNGoTCwkJER0djzZo14nGtVotdu3Zh4sSJiIyMhLu7O0aNGoVFixZZ+UwR2Q43wCYiqhvZ1glTA9YJIyky1CbKyAjBhg2jKhwfNSoZoaEXWZuIiFRL0XXCiMj+uFUKEVHdMAgjoloxLJapbgNsFogkIqoapyMljNORJFXZ2dliHbArVxxw4UI9hITcRVBQaUDGApFEpGY1/fyWRWI+WVbZD9DK8AOUqlP29REYCERE2LEzREQyxSBMZcpvOVO64asPvL2zjVa0ccsZIiIi62IQpjJlR8CqqvGkhC1nOOJHRERSxiBMpZRe44mbTBPZDy+AiGqGQZhK5eT4iAGYgSA4ICfHu05BmFTefLnJNJF9SPkCSCrvT0QGDMJUylDjqWwgVtcaT1J+8yUi2ygf5JjKO7X1BRDfn0iKGISplKHGU/mcsLqMgnH0iYjKktLeolINDuWCo4jWwSBMxTp0OImwsPPIyfGGt3eOInLBTDH1hktE1iHlvFMpBYdywFX11sMgTOV0upt2f0O0tqrecPV6fZX35dUdkXmslXdaV1IODqVKTavqbY1BmMrUdCsZS2w5I4XRp+recDdv3lztY/Dqjqj2rJF3aglSDQ7lgAGs5TEIUxkfHx8kJCRYfW5fKsP9lnjD5dWdMeaGUE1YI+/UEqQaHMoBA1jLYxCmQtb+gJTC1ZJhJI9vuJbFFWZUG1LMO5VqcCgHfD+1PAZhZHFSuFoqO+J3zz15mDVLh+JiDbRaAUuX5uHJJ4chNzcXW7ZssUl/lIIrYKk65VMZTOWdWiLlwVxSDA7lgAGs5TEII4up6eiTrd58DSMx06cDQ4YA588DzZpp0KiRFwAvm/SBSG1slfJQW3IIDuWAAaxlMQgji6nJ6JO98oUaNSr9IlI6KeTs1fbxbdFnqQaHcqSGVfW2wiCMLErOo09SWM1JVBdyzNmzZZ+l8pzlxpar6tWGQRhZjZxGn6SymlNuGLhKixxz9ljJXvo4img9DMJItQxXbdWt5uTVXeUYuEqf3IJkvqakiwGWdTAII9UyXN0dOgQsW1ZxNeeDD45Cz55886mMFMqQUNXkFtDwNUVq5FB9EyLl8vHxQZcuPnAo95eg1QKdO/swACvHMCpYVRmSsu3IPkwFNHq9BwDgxo0buHr1Kq5evYrs7Gx7dlVU3WuKSIk4Ekaq16gRsG4dMH48UFxcGoC9/bZ88tlsyTB6eOHCXXz4oYCSEo14TKsVMHnyowgJqcfg1c6qq9WXkpJidEwKifosBEpqxJEwIgBxccCFC8ChQ6X/xsXZu0fS5ePjg4gIf6xbp4FWW3pbaeCqQUSEv90/zOmfgKasqgIaKSS9GwqBGvrNQqCkBhwJI/ofOa3mlIK4OCA62lCGhOdOSkxVNgeAjIwQySbqsxAoqQ2DMCIyGwNXaSmbi1c+oElPb4blyxMll6jPSvakZgzCiIgUonw9pxs3biAlJUXSKw9Zg4rUjEEYEZGCVBasVJeoXxvW2GKIARapFYMwIiKFs9TKQzlui0QkZVwdSUSkcJZaeVjZFkMZGSFi/TFT7YiochwJIyJSqKoS9csGYOYkvcutIj+RFDEII7uyRn4JEZWyVtK7lBP9ieSEQRjZDfNLiKzPGn87lkz0J1IzBmFkNzXNG5F6fglH80htuMUQkWUwCCOqA47mkRqZqsjPUTCi2mEQRlQHShnNI6otqW4xxJFpkhMGYUREVCNS32Ko/Mi0Xu+BnByfCntlcmSapIJBGBER1YjUtxgq26+qSmhwZJqkgkEYERHVmBxGkFhCg+SCFfOJiEhRqiqhQSQlDMLIbmqaN2Kv/BIikidDCY2yWEKDpIjTkWQ3Us8vMYepRGAish2W0CC5YBBGdlU2wLp8GTh3DggPBxo1smOnaqHsKF1VicAczSOyLamW0CAqi0EYScJ77wHx8UBJCeDgAKxbB8TF2btX1TOM5l24cBeLFvlBEDQASvNPdu/uh3nzOiMkpJ6sRvOIlMJUCQ0iqWBOGNnd5cv/BGBA6b/jx5feLgc+Pj7Iy/NHSYnG6PbiYg1u3vRnAEZkI8wzJbnhSBjZ3blz/wRgBsXFwPnzlpuWtHYV7fDw0hG8ss9DqwWaNTP7IUnFWPXdPErMMyVlYxBGdmftAMYW+zs2alQ6hTp+fGkAqdUCb78tn9w2kg5Wfa8bnhOSEwZhZHfWDmBstb9jXBwQHV06gtesGQMwMg+rvhOpB4MwkgSlBDCNGsm37yQtrPpOpHwMwkgyGMAQ/aOqqu8MwoiUgUEYEdUJk8itw1D1vWwgxqrvRMrCIIyIzGaLRQ9qxarvRMrHIIxUh1sLWU75ETBT55ZJ5OZh1XciZWMQRqpS1WozqhueW+tg1Xci5WLFfFI8Q3VsU6vN9HoPo3ZUe9WdW6o5Vn0nUg+OhJHiGapoHzoELFtWcbXZgw+OQs+eLPJYF1zJZzms+k6kHgzCSBV8fHzQpUvllfk7d/YBP8/qhiv5LIsBFpE6cDqSVMNQmV+rLf2eWwtZjmEln0ZTGuFyJR8RUfU4EkaqopTK/FLElXxERLXDIIxUh5X5Lad8criplXxMIiciqohBGBGZjUnkZC1ld2K4csUBGRn1EBp6F0FBpVPefF2REjAII6I64QchWVrZnRiqqj/HnRhI7hiEERGRWaw1WmV4TFP158LCzkOnu8mdGEj2GIQREVGt2WK0ivXnSOlYooKIiGqtutEqw24JdRmtMtSfK4v150hJZBeEFRYWol27dtBoNDh16pTRsZ9//hndunWDi4sLgoODkZSUVOH+W7duRcuWLeHi4oI2bdpgz549RscFQcC8efMQGBgIV1dXREVF4dy5c0ZtcnJyMHz4cHh6esLLywtxcXG4detWrftCRCR3VY1W1RXrz5HSyS4ImzlzJoKCgircnpeXhz59+qBJkyY4fvw4Xn31VSxYsADr1q0T2xw5cgTDhg1DXFwcTp48iQEDBmDAgAE4ffq02CYpKQkrV67E2rVrcfToUbi7uyM6Ohq3b98W2wwfPhy//vor9u/fj127duHrr79GfHx8rfqiJJcvA4cOlf5LROpi7dGqDh1OIjFxOUaNSkZi4nJuCk+KIqucsM8//xz79u3Dtm3b8Pnnnxsd27hxI+7cuYP3338fTk5OuPfee3Hq1Cm88cYbYoC0YsUKxMTEYMaMGQCAF198Efv378fq1auxdu1aCIKA5cuX44UXXsBjjz0GAPjggw/g7++P7du3Y+jQoThz5gz27t2LY8eOoWPHjgCAVatW4V//+hdee+01BAUF1agvSvHee0B8fOlWQA4OpRXp4+Ls3Ssi5ZNKCQfDaFX5nDBLjlaZqj9HphUXF6OoqMje3VAsR0dHaA3br9SBbIKwa9euYdy4cdi+fTvc3NwqHE9NTUX37t2NikJGR0dj6dKl+Ouvv9CgQQOkpqZi2rRpRveLjo7G9u3bAQAZGRnIzMxEVFSUeFyn06Fz585ITU3F0KFDkZqaCi8vLzEAA4CoqCg4ODjg6NGj+Pe//12jvlSmsLAQhYWF4vd5eXm1O0k2dvnyPwEYUPrv+PGlFelZDJXIeqRWwoG7JUiHIAjIzMxEbm6uvbuieF5eXggICIBGozH7MWQRhAmCgNGjR2PChAno2LEjLly4UKFNZmYmQkNDjW7z9/cXjzVo0ACZmZnibWXbZGZmiu3K3s9UGz8/P6Pj9erVg7e3t1Gb6vpSmSVLlmDhwoWVnwQJOnfOeDNsACguLt0SiEGY8l2+XPoaCA/n79vWpFjCwZKjVTXdYYE7MVRkCMD8/Pzg5uZWpwCBKicIAgoKCpCVlQUACAwMNPux7BqEzZ49G0uXLq2yzZkzZ7Bv3z7cvHkTc+bMsVHP7GPOnDlGI3V5eXkIDg62Y4+qFh5eOgVZNhDTakv3ZCRl4zS0NFiqhINUpjYNuBODeYqLi8UAjOfGulxdXQEAWVlZ8PPzM3tq0q5B2PTp0zF69Ogq2zRt2hQHDx5EamoqnJ2djY517NgRw4cPx4YNGxAQEIBr164ZHTd8HxAQIP5bWZuyxw23lY1sr127hnbt2oltDNGvwd27d5GTk1Ptzyn7Myrj7Oxc4TlKWaNGpR++48eXjoBptcDbb3NURMmys7Nx4cJdxMf7oaSk9Aq7dBpaQLt2WQgJqcc3fxsyJMWXDcRqmxRv7tSmtUer+DqqPUMOWGUpO2R5hvNcVFQkzyDM19cXvr6+1bZbuXIlXnrpJfH7K1euIDo6Gps3b0bnzp0BAJGRkXj++edRVFQER0dHAMD+/fvRokULcfovMjISBw4cQGJiovhY+/fvR2RkJAAgNDQUAQEBOHDggBh05eXl4ejRo5g4caL4GLm5uTh+/DgiIiIAAAcPHkRJSUmt+qIUcXGlOWDnz5eOgDEAUy7Dh3VGRghKSkYZHSsu1mDVqs8RGnqRW8nYkCWS4s2d2uRolXRxCtI2LHGeZZET1rhxY6Pv69evDwAICwtDo/996j/55JNYuHAh4uLiMGvWLJw+fRorVqzAsmXLxPs988wz6NGjB15//XXExsbi448/xo8//iiWjtBoNEhMTMRLL72E8PBwhIaGYu7cuQgKCsKAAQMAAK1atUJMTAzGjRuHtWvXoqioCAkJCRg6dKhYOqMmfVGSRo0YfKmB4cO2utEXbiVjW5ZKijdnapMBFlHdyK5OmCk6nQ779u1DRkYGIiIiMH36dMybN8+oJETXrl2xadMmrFu3Dm3btsUnn3yC7du347777hPbzJw5E5MnT0Z8fDweeOAB3Lp1C3v37oWLi4vYZuPGjWjZsiV69+6Nf/3rX3jooYeMaoDVpC9EcsUCmtKj091EaOjFOv0OWJ2e7Gn06NHQaDTQaDRwdHSEv78/HnnkEbz//vsoKb8CrArJycnw8vKyXkctTBYjYeWFhIRAEIQKt99///345ptvqrzv448/jscff9zkcY1Gg0WLFmHRokUm23h7e2PTpk1V/pya9IVIrliSQHlsUe+LqCoxMTFYv349iouLce3aNezduxfPPPMMPvnkE+zYsQP16skyZKmS8p4REdkEC2jaj7WS4hlcq1vZVbKVsXaOn7Ozs7h47Z577kGHDh3QpUsX9O7dG8nJyRg7dizeeOMNrF+/Hv/3f/8Hb29v9OvXD0lJSahfvz4OHz6Mp59+GsA/+Vrz58/HggUL8OGHH2LFihVIS0uDu7s7Hn74YSxfvrxCySlbYxBGRCQz1kyKZ3CtTmVXyVbF1gtvHn74YbRt2xYpKSkYO3YsHBwcsHLlSoSGhuL//u//8J///AczZ87EmjVr0LVrVyxfvhzz5s1DWloagH9yyIuKivDiiy+iRYsWyMrKwrRp0zB69OgK+0fbGoMwIiIZYlK8fNl7xKkyNV1QY4+FNy1btsTPP/8MAEbVDUJCQvDSSy9hwoQJWLNmDZycnKDT6aDRaCqUgxozZoz4/6ZNm2LlypVi3rchULMHBmFERCrG6vS2JdURJykTBEGcXvzyyy+xZMkSnD17Fnl5ebh79y5u376NgoKCKuujHT9+HAsWLMBPP/2Ev/76S0z2v3TpElq3bm2T51EZBmFEVCP8sFYm1vuyLSmPOEnVmTNnEBoaigsXLqBv376YOHEiXn75ZXh7e+Pbb79FXFwc7ty5YzIIy8/PR3R0NKKjo7Fx40b4+vri0qVLiI6Otvt5ZhBGRDXCD2vl4u+MpOrgwYP45ZdfMHXqVBw/fhwlJSV4/fXX4eBQWmFry5YtRu2dnJxQXFxsdNvZs2eRnZ2NV155RdwK8Mcff7TNE6gGgzAiqjF+WBNZll7vgZwcH3h7Z6t+QURhYSEyMzONSlQsWbIEffv2xciRI3H69GkUFRVh1apV6NevH7777jusXbvW6DFCQkJw69YtHDhwAG3btoWbmxsaN24MJycnrFq1ChMmTMDp06fx4osv2ulZGlNMsVYiIiI5OXGiPZYvT8SGDaOwfHkiTpxob+8u2dXevXsRGBiIkJAQxMTE4NChQ1i5ciU+++wzaLVatG3bFm+88QaWLl2K++67Dxs3bsSSJUuMHqNr166YMGEChgwZAl9fXyQlJcHX1xfJycnYunUrWrdujVdeeQWvvfaanZ6lMY1QWdVTkoS8vDzodDro9Xp4enrauztGpLi6R8kuXwbOnQPCw7lFFJGcXb16FevWrYNe74HlyxMrbP+VmLgcOt1NxMfHIzAwsFaPffv2bWRkZCA0NNRol5fa9Ks65vRLqao63zX9/OZ0JNUaV/fY1nvvAfHxQEkJ4OAArFtXunE6EcmXOXt1WhMX3tgHgzCqNa7usZ3Ll/8JwIDSf8ePB6KjOSJGJGeGvTrLj4TZa69OLryxDwZhRBJ27tw/AZhBcTFw/jyDMCI5MowkVbdXpz1GnBhg2R6DMCIJCw8vnYIsG4hptUCzZvbrE5mHeZQEVBxxmjfvOi5cqIeQkLsICnoAwAN8LagIgzAiCWvUqDQHbPz40hEwrRZ4+22OgskN8yiprLK/48BAICLCjp0hu2IQRiRRhpGTf/0LOHrUoczVcgmuXuXIiZyUHwEzVRuKeZRE6sIgjEiCTI2cnD5t/D1HTuTnxIn2FfKAOnQ4ae9uEZEdsFgrkQRxBaoy6fUeYgAGlJYk2LmzL/R6Dzv3jIjsgSNhVGusJ0NkHqnVhlIzLpQgKWAQRrXGejJE5pFabSi14kIJ9Tl8+DB69eqFv/76C15eXjW6T0hICBITE5GYmGi1fnE6kszi4+ODwMBAk1984yKqyFAbSqMprTlSvjYU2Qan+6Vn9OjR0Gg0mDBhQoVjkyZNgkajwejRo23fMSvjSBgRkQ116HASYWHnkZPjDW/vHAZgRP8THByMjz/+GMuWLYOrqyuA0v0ZN23ahMaNG9u5d9bBkTAiIisrnx+p091EaOjFCgEY8yhJzTp06IDg4GCkpKSIt6WkpKBx48Zo3769eFthYSGmTJkCPz8/uLi44KGHHsKxY8eMHmvPnj1o3rw5XF1d0atXL1y4cKHCz/v222/RrVs3uLq6Ijg4GFOmTEF+fr7Vnl9lOBJGRGRlzKMkqpkxY8Zg/fr1GD58OADg/fffx9NPP43Dhw+LbWbOnIlt27Zhw4YNaNKkCZKSkhAdHY3z58/D29sbf/zxBwYOHIhJkyYhPj4eP/74I6ZPn270c9LT0xETE4OXXnoJ77//Pq5fv46EhAQkJCRg/fr1Nnu+HAkjkiCuQFUe5lGSnFy+DBw6VPqvLT311FP49ttvcfHiRVy8eBHfffcdnnrqKfF4fn4+3nrrLbz66qt49NFH0bp1a7zzzjtwdXXFe++9BwB46623EBYWhtdffx0tWrTA8OHDK+STLVmyBMOHD0diYiLCw8PRtWtXrFy5Eh988AFu375ts+fLkTAiCeLICRHZy3vvAfHxpXvWOjiUbp0WF2ebn+3r64vY2FgkJydDEATExsaiYcOG4vH09HQUFRXhwQcfFG9zdHREp06dcObMGQDAmTNn0LlzZ6PHjYyMNPr+p59+ws8//4yNGzeKtwmCgJKSEmRkZKBVq1bWeHoVMAgjkigGWES2YWobKTW6fPmfAAwo/Xf8eCA62nZ71o4ZMwYJCQkAgDfffNMqP+PWrVsYP348pkyZUuGYLRcBMAgjIiJVKTuNX9U2Umqc7j937p8AzKC4GDh/3nZBWExMDO7cuQONRoPo6GijY2FhYXBycsJ3332HJk2aAACKiopw7NgxsZ5Xq1atsGPHDqP7ff/990bfd+jQAb/99huaNWtmvSdSAwzCiIhIVQzT/Rcu3MWiRX4QBA2A0t0Ldu/uh3nzOiMkpJ4qR6PDw0unIMsGYlotYMtYRavVilOLWq3W6Ji7uzsmTpyIGTNmwNvbG40bN0ZSUhIKCgoQ97850wkTJuD111/HjBkzMHbsWBw/fhzJyclGjzNr1ix06dIFCQkJGDt2LNzd3fHbb79h//79NSrkaylMzCciItXx8fFBXp4/Sko0RrcXF2tw86a/KgMwoHS0a9260sALKP337bdtNwpm4OnpCU9Pz0qPvfLKKxg0aBBGjBiBDh064Pz58/jiiy/QoEEDAKXTidu2bcP27dvRtm1brF27FosXLzZ6jPvvvx9fffUVfv/9d3Tr1g3t27fHvHnzEBQUZPXnVpZGEATBpj+RaiwvLw86nQ56vd7ki5GIiMxz+TLQpEnFUZ8LF2wfdFjC7du3kZGRgdDQULi4uNTpsS5fLp2CbNZMnufCFqo63zX9/OZIGBERqZJURn2kqFEjoGdPngtrY04YERGpVlxc6co/jvqQPTAIIyIiVWvUiMEX2QenI4mIiIjsgEEYERERkR0wCCMiIlIQFj2wDUucZwZhRERECuDo6AgAKCgosHNP1MFwng3n3RxMzCciIlIArVYLLy8vZGVlAQDc3Nyg0WiquRfVliAIKCgoQFZWFry8vCpU9a8NBmFEREQKERAQAABiIEbW4+XlJZ5vczEIIyIiUgiNRoPAwED4+fmhqKjI3t1RLEdHxzqNgBkwCCMiIlIYrVZrkSCBrIuJ+URERER2wCCMiIiIyA4YhBERERHZAXPCJMxQCC4vL8/OPSEiIqKaMnxuV1fQlUGYhN28eRMAEBwcbOeeEBERUW3dvHkTOp3O5HGNwP0NJKukpARXrlyBh4eHagvu5eXlITg4GH/88Qc8PT3t3R3Z4/m0LJ5Py+L5tCyeT8uqzfkUBAE3b95EUFAQHBxMZ35xJEzCHBwc0KhRI3t3QxI8PT35JmJBPJ+WxfNpWTyflsXzaVk1PZ9VjYAZMDGfiIiIyA4YhBERERHZAYMwkjRnZ2fMnz8fzs7O9u6KIvB8WhbPp2XxfFoWz6dlWeN8MjGfiIiIyA44EkZERERkBwzCiIiIiOyAQRgRERGRHTAIIyIiIrIDBmEkCV9//TX69euHoKAgaDQabN++3ei4IAiYN28eAgMD4erqiqioKJw7d84+nZWB6s7n6NGjodFojL5iYmLs01mJW7JkCR544AF4eHjAz88PAwYMQFpamlGb27dvY9KkSfDx8UH9+vUxaNAgXLt2zU49lraanM+ePXtWeH1OmDDBTj2Wtrfeegv333+/WEA0MjISn3/+uXicr83aqe58Wvq1ySCMJCE/Px9t27bFm2++WenxpKQkrFy5EmvXrsXRo0fh7u6O6Oho3L5928Y9lYfqzicAxMTE4OrVq+LXRx99ZMMeysdXX32FSZMm4fvvv8f+/ftRVFSEPn36ID8/X2wzdepU7Ny5E1u3bsVXX32FK1euYODAgXbstXTV5HwCwLhx44xen0lJSXbqsbQ1atQIr7zyCo4fP44ff/wRDz/8MB577DH8+uuvAPjarK3qzidg4demQCQxAIRPP/1U/L6kpEQICAgQXn31VfG23NxcwdnZWfjoo4/s0EN5KX8+BUEQRo0aJTz22GN26Y/cZWVlCQCEr776ShCE0teio6OjsHXrVrHNmTNnBABCamqqvbopG+XPpyAIQo8ePYRnnnnGfp2SuQYNGgjvvvsuX5sWYjifgmD51yZHwkjyMjIykJmZiaioKPE2nU6Hzp07IzU11Y49k7fDhw/Dz88PLVq0wMSJE5GdnW3vLsmCXq8HAHh7ewMAjh8/jqKiIqPXZ8uWLdG4cWO+Pmug/Pk02LhxIxo2bIj77rsPc+bMQUFBgT26JyvFxcX4+OOPkZ+fj8jISL4266j8+TSw5GuTG3iT5GVmZgIA/P39jW739/cXj1HtxMTEYODAgQgNDUV6ejqee+45PProo0hNTYVWq7V39ySrpKQEiYmJePDBB3HfffcBKH19Ojk5wcvLy6gtX5/Vq+x8AsCTTz6JJk2aICgoCD///DNmzZqFtLQ0pKSk2LG30vXLL78gMjISt2/fRv369fHpp5+idevWOHXqFF+bZjB1PgHLvzYZhBGp0NChQ8X/t2nTBvfffz/CwsJw+PBh9O7d2449k7ZJkybh9OnT+Pbbb+3dFUUwdT7j4+PF/7dp0waBgYHo3bs30tPTERYWZutuSl6LFi1w6tQp6PV6fPLJJxg1ahS++uore3dLtkydz9atW1v8tcnpSJK8gIAAAKiwoufatWviMaqbpk2bomHDhjh//ry9uyJZCQkJ2LVrFw4dOoRGjRqJtwcEBODOnTvIzc01as/XZ9VMnc/KdO7cGQD4+jTByckJzZo1Q0REBJYsWYK2bdtixYoVfG2aydT5rExdX5sMwkjyQkNDERAQgAMHDoi35eXl4ejRo0bz9GS+y5cvIzs7G4GBgfbuiuQIgoCEhAR8+umnOHjwIEJDQ42OR0REwNHR0ej1mZaWhkuXLvH1WYnqzmdlTp06BQB8fdZQSUkJCgsL+dq0EMP5rExdX5ucjiRJuHXrltGVREZGBk6dOgVvb280btwYiYmJeOmllxAeHo7Q0FDMnTsXQUFBGDBggP06LWFVnU9vb28sXLgQgwYNQkBAANLT0zFz5kw0a9YM0dHRduy1NE2aNAmbNm3CZ599Bg8PDzGXRqfTwdXVFTqdDnFxcZg2bRq8vb3h6emJyZMnIzIyEl26dLFz76WnuvOZnp6OTZs24V//+hd8fHzw888/Y+rUqejevTvuv/9+O/deeubMmYNHH30UjRs3xs2bN7Fp0yYcPnwYX3zxBV+bZqjqfFrltWmxdZZEdXDo0CEBQIWvUaNGCYJQWqZi7ty5gr+/v+Ds7Cz07t1bSEtLs2+nJayq81lQUCD06dNH8PX1FRwdHYUmTZoI48aNEzIzM+3dbUmq7DwCENavXy+2+fvvv4X//Oc/QoMGDQQ3Nzfh3//+t3D16lX7dVrCqjufly5dErp37y54e3sLzs7OQrNmzYQZM2YIer3evh2XqDFjxghNmjQRnJycBF9fX6F3797Cvn37xON8bdZOVefTGq9NjSAIgrkRIxERERGZhzlhRERERHbAIIyIiIjIDhiEEREREdkBgzAiIiIiO2AQRkRERGQHDMKIiIiI7IBBGBEREZEdMAgjIiIisgMGYURERER2wCCMiMgMd+7csXcXKpBin4jINAZhREQAevbsiYSEBCQkJECn06Fhw4aYO3cuDDu7hYSE4MUXX8TIkSPh6emJ+Ph4AMC3336Lbt26wdXVFcHBwZgyZQry8/PFx12zZg3Cw8Ph4uICf39/DB48WDz2ySefoE2bNnB1dYWPjw+ioqLE+/bs2ROJiYlGfRwwYABGjx4tfm9un4hIGhiEERH9z4YNG1CvXj388MMPWLFiBd544w28++674vHXXnsNbdu2xcmTJzF37lykp6cjJiYGgwYNws8//4zNmzfj22+/RUJCAgDgxx9/xJQpU7Bo0SKkpaVh79696N69OwDg6tWrGDZsGMaMGYMzZ87g8OHDGDhwIGq7nW9t+0RE0sENvImIUDrylJWVhV9//RUajQYAMHv2bOzYsQO//fYbQkJC0L59e3z66afifcaOHQutVou3335bvO3bb79Fjx49kJ+fjz179uDpp5/G5cuX4eHhYfTzTpw4gYiICFy4cAFNmjSptD/t2rXD8uXLxdsGDBgALy8vJCcnA4BZfXJxcanTeSIiy+FIGBHR/3Tp0kUMwAAgMjIS586dQ3FxMQCgY8eORu1/+uknJCcno379+uJXdHQ0SkpKkJGRgUceeQRNmjRB06ZNMWLECGzcuBEFBQUAgLZt26J3795o06YNHn/8cbzzzjv466+/at3n2vaJiKSDQRgRUQ25u7sbfX/r1i2MHz8ep06dEr9++uknnDt3DmFhYfDw8MCJEyfw0UcfITAwEPPmzUPbtm2Rm5sLrVaL/fv34/PPP0fr1q2xatUqtGjRQgyUHBwcKkxNFhUV1blPRCQdDMKIiP7n6NGjRt9///33CA8Ph1arrbR9hw4d8Ntvv6FZs2YVvpycnAAA9erVQ1RUFJKSkvDzzz/jwoULOHjwIABAo9HgwQcfxMKFC3Hy5Ek4OTmJU4u+vr64evWq+LOKi4tx+vTpap9DTfpERNLAIIyI6H8uXbqEadOmIS0tDR999BFWrVqFZ555xmT7WbNm4ciRI0hISMCpU6dw7tw5fPbZZ2IS/K5du7By5UqcOnUKFy9exAcffICSkhK0aNECR48exeLFi/Hjjz/i0qVLSElJwfXr19GqVSsAwMMPP4zdu3dj9+7dOHv2LCZOnIjc3Nxqn0N1fSIi6ahn7w4QEUnFyJEj8ffff6NTp07QarV45plnxLIPlbn//vvx1Vdf4fnnn0e3bt0gCALCwsIwZMgQAICXlxdSUlKwYMEC3L59G+Hh4fjoo49w77334syZM/j666+xfPly5OXloUmTJnj99dfx6KOPAgDGjBmDn376CSNHjkS9evUwdepU9OrVq9rnUF2fiEg6uDqSiAiVr0YkIrImTkcSERER2QGDMCIiIiI74HQkERERkR1wJIyIiIjIDhiEEREREdkBgzAiIiIiO2AQRkRERGQHDMKIiIiI7IBBGBEREZEdMAgjIiIisgMGYURERER2wCCMiIiIyA7+HzacbTcR2AnFAAAAAElFTkSuQmCC", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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", 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", 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pKVOmmPQpAQBmycnJ0YwZM2zaHn74YTVs2NCkihzjqi4vsXr1atWvX19NmjRRr169tH79es2cOVMffvihfH191a5dO02fPl0vvvii2rZtq8WLF9uNzXbp0kXDhw/Xvffeq/DwcE2dOlXh4eFauHCh3n33XbVu3VopKSl66aWXTPqUAAAzbNmyxSb0+Pn5aeLEiVUu9EiSxXDGddEeJCcnR6GhocrOzra5b9f58+d14MABRUdHV4kxSrgO5x4AHLs4RSI3N9fa9uc//1ldunRxeS0lfX9fiqEuAABQZllZWXr99ddt2h5//PEqf2sjgg8AACiTVatWacuWLdbnV111lYYMGWJdHqUqI/gAAIBSycvLU0pKik1bVVibpywIPgAA4Ir27t2rJUuW2LRVlbV5yoLgAwAASmQYhp599lmbtqq2Nk9ZEHwAAIBDP//8s958802btmuueVh33FH1LlMvLYIPAACwM3v2bP3yyy82bc8/P0EdO/ppwACTinICgg8AALAqLCzU888/b9NWs2ZNNW8+Vh07SvHxJhXmJAQfAAAgSfr444+Vnp5u0xYXF6fGjRtLkvr2NaMq5+KWFXCauLg49evXz/r81ltv1ZgxYyp0TGccAwBwZUlJSXahZ9KkSdbQ4yno8fECcXFxWrRokaQL909p1KiRBg0apAkTJqhatcr7I/DBBx/Iz8+vVPtu2LBB3bp108mTJ21W/SzLMQAAZXf06FG98cYbNm2//NJUs2YNNKmiykXw8RK9evXSggULlJeXp48++kgjR46Un5+fEhISbPbLz8+Xv7+/U96zdu3aVeIYAADHkpKS7NpWrRquCRPqmVCNazDU5SUCAgIUGRmpxo0ba8SIEerevbtSU1Otw1PPP/+8GjRooBYtWkiSDh06pL/97W8KCwtT7dq1deeddyozM9N6vKKiIo0dO1ZhYWGqU6eOnn76aV16v9tLh6ny8vI0fvx4RUVFKSAgQM2bN9ebb76pzMxMdevWTZJUq1YtWSwWxcXFOTzGyZMnNWjQINWqVUvBwcHq3bu3fvjhB+v2hQsXKiwsTGlpaWrVqpVq1KihXr166ejRo9Z9NmzYoE6dOql69eoKCwvTjTfeqJ9++slJv9MAUPUZhuEw9EyenCgfn3oeMZenJAQfLxUUFKT8/HxJ0tq1a7Vnzx6tWbNGK1asUEFBgXr27KmaNWvqs88+06ZNm6wB4uJrXn75ZS1cuFDz58/X559/rt9++03//e9/L/uegwYN0pIlSzRz5kzt3r1br7/+umrUqKGoqCi9//77kqQ9e/bo6NGjeuWVVxweIy4uTl9//bVSU1OVnp4uwzD0l7/8RQUFBdZ9cnNz9dJLL+mtt97Sp59+qoMHD+rJJ5+UdOFqhX79+umWW27Rt99+q/T0dD3yyCNucX8ZAHCGjz/+2G5BwtOnIxQTk6jYWPe/autKGOryMoZhaO3atUpLS9Pf//53nThxQtWrV9e8efOsQ1xvv/22iouLNW/ePGsgWLBggcLCwrRhwwb16NFD//znP5WQkKC77rpLkjR37lylpaWV+L579+7Vf/7zH61Zs0bdu3eXJDVt2tS6/eKQVkRERIl39v3hhx+UmpqqTZs2qUuXLpKkxYsXKyoqSsuXL9c999wjSSooKNDcuXPVrFkzSdKoUaOsf8lzcnKUnZ2t22+/3brdne4xAwAV4aiXZ/36cRo7tob69vWMq7auhB4fk6SmSl26XPjpCitWrFCNGjUUGBio3r17695779XkyZMlSddee63NvJ4dO3Zo3759qlmzpmrUqKEaNWqodu3aOn/+vPbv36/s7GwdPXpUnTt3tr6mWrVq6tixY4nv/80338jX11e33HJLuT/D7t27Va1aNZv3rVOnjlq0aKHdu3db24KDg62hRpLq16+v48ePS7oQsOLi4tSzZ0/dcccdeuWVV2yGwQDAE509e9Zh6ElMTNSGDTW8IvBcRI+PSVJSpPT0Cz9d8QeuW7dumjNnjvz9/dWgQQObq7mqV69us++ZM2fUoUMHLV682O444eHh5Xr/oKCgcr2uPC69CsxisdjMP1qwYIFGjx6t1atXa9myZZo4caLWrFmjG264wWU1AoCrOAo8hw9fpzfeuNOEasxHj49J4uPl0rHU6tWrq3nz5mrUqNEVL2Fv3769fvjhB0VERKh58+Y2j9DQUIWGhqp+/fr68ssvra8pLCzU1q1bSzzmtddeq+LiYm3cuNHh9os9TkVFRSUeo1WrViosLLR5319//VV79uxR69atL/uZLhUTE6OEhARt3rxZbdu21TvvvFOm1wOAO3AUetLSJun2270z9EgEH9P07Stt3lw1x1MfeOAB1a1bV3feeac+++wzHThwQBs2bNDo0aP1888/S5Ief/xxpaSkaPny5crIyNBjjz2mU6dOlXjMJk2aaPDgwRoyZIiWL19uPeZ//vMfSVLjxo1lsVi0YsUKnThxQmfOnLE7xtVXX60777xTw4YN0+eff64dO3bowQcfVMOGDXXnnaX7S3zgwAElJCQoPT1dP/30kz7++GP98MMPzPMB4FG+/fbbEoe2Nm+2VMnvHlch+MBOcHCwPv30UzVq1Eh33XWXWrVqpaFDh+r8+fMKCQmRJI0bN04DBw7U4MGDFRsbq5o1a+qvf/3rZY87Z84c3X333XrsscfUsmVLDRs2TGfPnpUkNWzYUElJSYqPj1e9evU0atQoh8dYsGCBOnTooNtvv12xsbEyDEMfffRRqRc5DA4OVkZGhvr3769rrrlGjzzyiEaOHKlHH320DL9DAFB1JSUl2V1l279/fyUmJppUUdViMS5dfMXL5eTkKDQ0VNnZ2dYveUk6f/68Dhw4oOjoaAUGBppYIVyNcw/AHRiGYXeZuiSlpSVq82YTCnKxkr6/L8XkZgAA3Nw///lPZWdn27WnpSV6/Lo8ZUXwAQDAjTmay/P4448rLCxMjG7ZY44PAABu6OTJkyVctZVY4kKwoMcHAAC34yjw+PgEaNWqeIa2roDgU0bMBfc+nHMAVUlJa/Ns3mzRP/5hQkFuhuBTShcvl87NzXXpKsQwX25uriT7FaEBwJU+//xzrV271q6dCcxlQ/ApJV9fX4WFhVnv+RQcHMwdvT2cYRjKzc3V8ePHFRYWJl9fX7NLAuClHPXydO3aVd26dWMCcxkRfMogMjJSkqzhB94hLCzMeu4BwJUutzYPgad8CD5lYLFYVL9+fUVERKigoMDscuACfn5+9PQAMIWjXh5JWro0US++6OJiPAjBpxx8fX35MgQAVBpHoWfEiBGKiIigp6eCCD4AAFQRJ06c0GuvvWbXzn22nIfgAwBAFVDS0Bahx7kIPgAAmMxR6HnppUlavJirh52N4AMAgElmzZql3377za49KSlRy5dLffu6viZPR/ABAMAEjnp5Tp5spDfffEgJCYSeykLwAQDAhUpam2fy5ETVqCGdPm1CUV6E4AMAgIuUNIE5P/9C6Bk92sUFeSGCDwAALuAo9AwaNEjR0dGSpOefd3VF3ongAwBAJdq3b58WL15s185l6uYg+AAAUElYm6fq8TG7AAAAPJGj0LN06STFxBB6zESPDwAATnS5Xh46esxH8AEAwEkchR4/Pz9NmDDBhGrgCMEHAIAKKmltnrS0RG3ebEJBKBHBBwCACihpaCstLVHx8S4uBldE8AEAoJwchZ77779fV199NfN5qiiCDwAAZbRr1y69++67du1cpl71EXwAACgD1uZxb6zjAwBAKTkKPUuWsDaPO6HHBwCAK2BtHs9Bjw8AAJdRUuihl8c90eMDAIADxcXFmjJlil07c3ncG8EHAIBLMIHZczHUBQDAHzgKPYsXD1BaGqHHE9DjAwCApEWLdioz83279piYRKWliVWYPQTBBwDg9a40tNW3ryurQWXyyKGu2bNnq0mTJgoMDFTnzp21ZcsWs0sCAFRRjkLPpEmTmM/joTwu+Cxbtkxjx45VYmKitm3bpnbt2qlnz546fvy42aUBAKqQpKQkh6EnLS1RFovFhIrgCh4XfKZPn65hw4bpoYceUuvWrTV37lwFBwdr/vz5ZpcGAKgiShraWrKEO6p7Oo+a45Ofn6+tW7cqISHB2ubj46Pu3bsrPT3d4Wvy8vKUl5dnfZ6Tk1PpdQIAzHGltXkY3fJ8HhV8fvnlFxUVFalevXo27fXq1VNGRobD1yQnJ5eY/AEAniE1Vdq+nbV54IFDXWWVkJCg7Oxs6+PQoUNmlwQAcDJHoWfx4vtZm8cLeVSPT926deXr66tjx47ZtB87dkyRkZEOXxMQEKCAgABXlAcAcLGtW7dqxYoVdu2szeO9PCr4+Pv7q0OHDlq7dq369esn6cJ47tq1azVq1ChziwMAuExphrZYm8c7eVTwkaSxY8dq8ODB6tixozp16qR//vOfOnv2rB566CGzSwMAuIij0DNp0iQuU4fnBZ97771XJ06c0KRJk5SVlaXrrrtOq1evtpvwDADwPNxcFFdiMQzDMLuIqiQnJ0ehoaHKzs5WSEiI2eUAAEqJ0OPdSvv97XE9PgAA73KltXmAPyL4AADcFr08KCuCDwDALTkKPffff7+uvvpqE6qBuyD4AADcyvz5W3XokP3aPPTyoDQIPgAAt8HQFiqK4AMAcAuOQg9r86CsCD4AgCqNXh44k9ffpBQAUHUReuBs9PgAAKqcoqIiPffcc3btBB5UFMEHAFCl0MuDykTwAQBUCSXdUX3gwIFq2rSpCRXBExF8AACm27Jli7ZvX2XXHhOTKDIPnIngAwAwFUNbcCWCDwDANKzNA1cj+AAAXI5eHpiFdXwAAC5F6IGZCD4AAJcoLCx0GHrS0hIVE0PogWsw1AUAqHSX6+WhoweuRPABAFQqR6FnwIABuuaaa0yoBt6O4AMAqBSffvqp1q9fb9fOXB6YieADAHC6koa2mMsDsxF8AABO5Sj0XHfdJN15J2vzwHwEHwCAU3CZOtwBl7MDACqM0AN3QY8PAKDcCgsL9fzzz9u1E3hQVRF8AADlQi8P3BHBBwBQZo5Cz8aNA7RuHWvzoGoj+AAASm3Dhg3auHGjXXtaWqLi400oCCgjgg8AoFS47QQ8AcEHAHBFjkIPc3ngjgg+AIASsQIzPA3BBwDgEFdtwRMRfAAANgoKCvTCCy/YtRN44AkIPgAAq5J6eZYuZQIzPAPBBwAgyXHoadbsAb32WnO9+KIJBQGVgOADAF5uzZo12rx5s117WlqiNm+WHnzQhKKASkLwAQAvVtLQFgsSwlMRfADAS11ubR7m88BTEXwAwMtwmTq8mY/ZBQAAXIfQA29Hjw8AeIH8/HwlJyfbtRN44G0IPgDg4bjtBPA7gg8AeDDHa/M8qAcfbGZCNYD5CD4A4GZSU6WUFCk+Xurb1/E+n3zyiTZt2mTXztAWvB3BBwDcTEqKlJ5+4aej4MMEZqBkXNUFAG4gNVXq0uXCz/h4KTZWDhcYdBR6Jk9OVFoaoQeQytHj4+vrq6NHjyoiIsKm/ddff1VERISKioqcVhwA4II/9vJs3mzf03O5CcwlhSTAG5U5+BiG4bA9Ly9P/v7+FS4IAGAvPv73eT2XutLQVknzgABvVOrgM3PmTEmSxWLRvHnzVKNGDeu2oqIiffrpp2rZsqXzKwQAqG9f+wBT0to8F28uCsBeqYPPjBkzJF3o8Zk7d658fX2t2/z9/dWkSRPNnTvX+RUCgJe63NVb3FwUKB+LUdLYVQm6deumDz74QLVq1aqsmkyVk5Oj0NBQZWdnKyQkxOxyAHixLl0uzOuJjZVND46j0DN48GA1adLEdcUBVUxpv7/LPMdn/fr1FSoMAFA6l87rYW0eoOLKHHyGDBly2e3z588vdzEAgN/9cV4Pa/MAzlHm4HPy5Emb5wUFBdq5c6dOnTql2267zWmFAQAucBR6CDxA+ZQ5+Pz3v/+1aysuLtaIESPUrBn3fgEAZ6GXB3A+p6zc7OPjo7Fjx1qv/AIAVAx3VAcqh9Pu1bV//34VFhY663AA4JXy8vKUkpJi104vD+AcZQ4+Y8eOtXluGIaOHj2qlStXavDgwU4rDAC8DUNbQOUrc/DZvn27zXMfHx+Fh4fr5ZdfvuIVXwCACy5dnNBR6BkyZIiioqJMqA7wXKzjAwAmuHjT0fffX6vt2z+32x4TkygyD+B85Z7jc/z4ce3Zs0eS1KJFC7u7tQMAShYfL23fztAW4GplDj45OTkaOXKklixZouLiYkmSr6+v7r33Xs2ePVuhoaFOLxIAPI2j0EPgASpfmS9nHzZsmL788kutXLlSp06d0qlTp7RixQp9/fXXevTRRyujRgDwGElJSSxICJiozDcprV69utLS0nTTTTfZtH/22Wfq1auXzp4969QCXY2blAKoLI4CT2BgoMaPH29CNYBnqbSblNapU8fhcFZoaKjH3rEdACqCtXmAqqPMwWfixIkaO3as3nrrLUVGRkqSsrKy9NRTT+kf//iH0wsEAHfG2jxA1VLmOT5z5szRF198oUaNGql58+Zq3ry5GjVqpM2bN+v1119X+/btrQ9nyczM1NChQxUdHa2goCA1a9ZMiYmJys/Pt9nv22+/1c0336zAwEBFRUVp6tSpTqsBAMrKUeh5+OGHCT2Aicrc43PnnXfKYrFURi0lysjIUHFxsV5//XU1b95cO3fu1LBhw3T27Fm99NJLki6M7fXo0UPdu3fX3Llz9d1332nIkCEKCwvTI4884tJ6AXi3NWvWaPPmzXbtMTGJatjQhIIAWJV5cnNVMW3aNM2ZM0c//vijpAs9Uc8884yysrLk7+8vSYqPj9fy5cuVkZFR6uMyuRlARVzu5qJ9+7q4GMCLVNrk5qZNm+qrr75SnTp1bNpPnTql9u3bW4NIZcvOzlbt2rWtz9PT09W1a1dr6JGknj176sUXX9TJkydLnHidl5envLw86/OcnJzKKxqAR+MydaDqK3PwyczMVFFRkV17Xl6efv75Z6cUdSX79u3TrFmzrMNc0oUJ1tHR0Tb71atXz7qtpOCTnJxc4v/QAKA0mMAMuI9SB5/U1FTrr9PS0mwuaS8qKtLatWvtgseVxMfH68UXX7zsPrt371bLli2tzw8fPqxevXrpnnvu0bBhw8r0fo4kJCTY3HE+JyeHmwICKDVHoad69ep68sknTagGwJWUOvj069dPkmSxWDR48GCbbX5+fmrSpIlefvnlMr35uHHjFBcXd9l9mjZtav31kSNH1K1bN3Xp0kVvvPGGzX6RkZE6duyYTdvF5xcvu3ckICBAAQEBZaobAM6fP+/wP2708gBVW6mDz8X7ckVHR+urr75S3bp1K/zm4eHhCg8PL9W+hw8fVrdu3dShQwctWLBAPj62V+LHxsbqmWeeUUFBgfz8/CRduLKiRYsWLKwIwKkY2gLcl1tc1XX48GHdeuutaty4sRYtWiRfX1/rtou9OdnZ2WrRooV69Oih8ePHa+fOnRoyZIhmzJhRpsvZuaoLwOU4Cj3Dhg1TgwYNTKgGwEWVdlXXs88+e9ntkyZNKushr2jNmjXat2+f9u3bp6uuuspm28XcFhoaqo8//lgjR45Uhw4dVLduXU2aNIk1fAA4xerVq/Xll1/atdPLA7iXMvf4xMTE2DwvKCjQgQMHVK1aNTVr1kzbtm1zaoGuRo8PgEsxtAVUfZXW47N9+3aHbxYXF6e//vWvZT0cAFRprM0DeBanzfH57rvvdMcddygzM9MZhzMNPT4AJHp5AHdTaT0+JcnOzlZ2drazDgcApnEUekJCQvTEE0+YUA0AZypz8Jk5c6bNc8MwdPToUb311lvq3bu30woDAFcraW2etLREObjnKAA3VObgM2PGDJvnPj4+Cg8P1+DBg5WQkOC0wgDAlUoa2kpLS1R8vIuLAVBp3GIdH1dijg/gfRyFnuHDh1vv9weg6qvUOT6nTp3Svn37JEnNmzdXWFhYuYoEADOlpaXpiy++sGuPiUkUmQfwTGUKPpmZmRo5cqTS0tKsCwdaLBb16tVLr776qpo0aVIZNQKA03HVFuCdSh18Dh06pBtuuEF+fn6aMmWKWrVqJUnatWuX5syZo9jYWH311Vd2KysDQFXD2jyA9yr1HJ+hQ4dq3759SktLU2BgoM22c+fOqVevXrr66qs1b968SinUVZjjA3iuy01g5qotwL05fY7P6tWrtWzZMrvQI0lBQUGaMmWK7rvvvvJVCwCVzFHoCQyspw8/HM5VW4AXKXXw+eWXXy47h6dp06b67bffnFETADjNuXPnNHXqVLv2i0Nb48e7uiIAZip18Klfv7527dpV4hyenTt3KjIy0mmFAUBFlTS0FRPDfB7AW5U6+PTr109PPvmk1q5dq/DwcJttx48f1/jx49WvXz9n1wcA5cLaPAAcKfXk5pMnT6pz587KysrSgw8+qJYtW8owDO3evVvvvPOOIiMj9cUXX6h27dqVXXOlYnIz4N5WrFihrVu32rVz1Rbg2Zw+ublWrVr68ssvNWHCBC1dulSnTp2SJIWFhen+++/XCy+84PahB4B7Y20eAFdSrltWGIahEydOSJLCw8NlsVicXphZ6PEB3BNr8wDerVJvWWGxWBQREVHu4gDAWejlAVAW5Qo+AFAVOAo9derU0ahRo0yoBoA7IPgAcDu5ubmaNm2aXTu9PACuhOADwK2wNg+AiiD4AHAbjkJPy5aP6d57wx3sDQD2ShV8Zs6cWeoDjh49utzFAIAjK1eu1Ndff23XPnlyomJjpXvvNaEoAG6pVMFnxowZpTqYxWIh+ABwqssNbcXGihuMAiiTUgWfAwcOVHYdAGDnSmvz9O3rymoAeIJyz/HJz8/XgQMH1KxZM1WrxlQhAM7D2jwAKotPWV+Qm5uroUOHKjg4WG3atNHBgwclSX//+9+VkpLi9AIBeBdHoadBgwaEHgBOUebgk5CQoB07dmjDhg0KDAy0tnfv3l3Lli1zanEAvEdubm6JQ1vDhg0zoSIAnqjMY1TLly/XsmXLdMMNN9jco6tNmzbav3+/U4sD4B1YmweAq5S5x+fEiRMO79N19uxZj7pZKQDXcBR65s37u/LzE5m8DMDpyhx8OnbsqJUrV1qfXww78+bNU2xsrPMqA+DR1qxZ4zD0pKUl6uefa2v9ehOKAuDxyjzU9cILL6h3797atWuXCgsL9corr2jXrl3avHmzNm7cWBk1AvAwl7tqKyZGSklhfR4AlcNiGIZR1hft379fKSkp2rFjh86cOaP27dtr/PjxuvbaayujRpfKyclRaGiosrOzFRISYnY5gMe50to8AFAepf3+Llfw8WQEH6BysDYPgMpU2u/vUg115eTklPqNCQsALuUo9Fx77bW66667lJr6+9AWk5kBVLZSBZ+wsLBSX7FVVFRUoYIAeI5z585p6tSpdu1/7OVJSZHS0y/8JPgAqGylCj7r/3B5RWZmpuLj4xUXF2e9iis9PV2LFi1ScnJy5VQJwO1cbmjrj7088fFMZgbgOmWe4/OnP/1JDz/8sAYMGGDT/s477+iNN97Qhg0bnFmfyzHHB6g4R6FnzJgxCg0NlSR16XKhlyc2Vtq82dXVAfBEpf3+LvM6Punp6erYsaNde8eOHbVly5ayHg6AB9m4cWOJV21dDD3Shd6d2Fh6eQC4XpmDT1RUlP71r3/Ztc+bN09RUVFOKQqA+0lKSnLY4+voqq2+fS/09DCnB4CrlXkBwxkzZqh///5atWqVOnfuLEnasmWLfvjhB73//vtOLxBA1cfaPADcRbnW8fn555/12muvKSMjQ5LUqlUrDR8+3CN6fJjjA5Qea/MAqCpYwLCcCD5A6TgKPR07dlSfPn1MqAaAt3PqAoaXOnXqlN58803t3r1bktSmTRsNGTLEZvIiAM9UmrV5AKCqKnOPz9dff62ePXsqKChInTp1kiR99dVXOnfunD7++GO1b9++Ugp1FXp8gJIxtAWgqqq0oa6bb75ZzZs317/+9S9Vq3ahw6iwsFAPP/ywfvzxR3366acVq9xkBB/AMUeh54knnuDvCYAqodKCT1BQkLZv366WLVvatO/atUsdO3ZUbm5u+SquIgg+gK3169c7/A8NvTwAqpJKW8AwJCREBw8etGs/dOiQatasWdbDAajCkpKSrhh6UlMvrMScmurKygCgfMo8ufnee+/V0KFD9dJLL6lLly6SpE2bNumpp56yu40FAPdV2rV5uMkoAHdS5uDz0ksvyWKxaNCgQSosLJQk+fn5acSIEUpJSXF6gQBcq6wTmLnJKAB3Uu51fHJzc7V//35JUrNmzRQcHOzUwszCHB94M0eh5/rrr9df/vIXE6oBgNKr1HV8JCk4OFjXXntteV8OoArJzc3VtGnT7NqZwAzA05Q6+AwZMqRU+82fP7/cxQBwPdbmAeBNSh18Fi5cqMaNGysmJkbc5QLwDI5Cz9ixY7lCE4DHKnXwGTFihJYsWaIDBw7ooYce0oMPPqjatWtXZm0AKsmmTZv0ySef2LXTywPA05V6HZ/Zs2fr6NGjevrpp/W///1PUVFR+tvf/qa0tDR6gAA3kpSU5DD0xMQQegB4vjItYBgQEKABAwZozZo12rVrl9q0aaPHHntMTZo00ZkzZyqrRgBOUtLaPBd7eliIEICnK/dVXT4+PrJYLDIMQ0VFRc6sCYCTlWYCMwsRAvAGZerxycvL05IlS/TnP/9Z11xzjb777ju9+uqrOnjwoGrUqFFZNQKoAEehJzLyFrv5PPHxUmwsCxEC8Gyl7vF57LHHtHTpUkVFRWnIkCFasmSJ6tatW5m1AaiAc+fOaerUqXbtJU1g7tuXnh4Anq/UKzf7+PioUaNGiomJkcViKXG/Dz74wGnFmYGVm+EJWJsHgLdx+srNgwYNumzgAWC+1FRp+3b70PPUU095zG1lAKAiyrSAIYCq68svv9T27avt2unlAYDflfuqLgBVB0NbAFA6BB/AzZW0Ng8AwB7BB3BTzz33nMM1tAg9AFCyMq3jUxXk5eXpuuuuk8Vi0TfffGOz7dtvv9XNN9+swMBARUVFObyUF/AESUlJdqFn167ehB4AuAK3Cz5PP/20GjRoYNeek5OjHj16qHHjxtq6daumTZumyZMn64033jChSqBynD9/3uHQVlpaoh54oJMJFQGAe3Groa5Vq1bp448/1vvvv69Vq1bZbFu8eLHy8/M1f/58+fv7q02bNvrmm280ffp0PfLIIyZVDDhPSROYY2ISRUcPAJSO2wSfY8eOadiwYVq+fLnD9UjS09PVtWtX+fv7W9t69uypF198USdPnlStWrUcHjcvL095eXnW5zk5Oc4vHqggR6Fn/PjxCgwMNKEaAHBfbjHUZRiG4uLiNHz4cHXs2NHhPllZWapXr55N28XnWVlZJR47OTlZoaGh1kdUVJTzCgcqaNu2bSVetUXoAYCyMzX4xMfHy2KxXPaRkZGhWbNm6fTp00pISHB6DQkJCcrOzrY+Dh065PT3AMojKSlJ//vf/2zafH19mcAMABVg6lDXuHHjFBcXd9l9mjZtqnXr1ik9PV0BAQE22zp27KgHHnhAixYtUmRkpI4dO2az/eLzyMjIEo8fEBBgd1zAbCVNYN682YRiAMCDmBp8wsPDFR4efsX9Zs6cqeeee876/MiRI+rZs6eWLVumzp07S5JiY2P1zDPPqKCgQH5+fpKkNWvWqEWLFiXO7wGqmmeffVaO7huclpao+HgTCgIAD1Pqu7NXJZmZmYqOjtb27dt13XXXSZKys7PVokUL9ejRQ+PHj9fOnTs1ZMgQzZgxo0xXdXF3dpjFUS/PnXfeaf0zDgAomdPvzl7VhYaG6uOPP9bIkSPVoUMH1a1bV5MmTeJSdlR5586dc7jYJnN5AMD53LLHpzLR4wNX4uaiAOAcXtfjA7gb1uYBANcj+AAutnXrVq1YscKunV4eAKh8brGAIeApkpKS7EKPv79/mUNPaqrUpcuFnwCA0qPHB3CRklZgLo+UFCk9/cLPvn0rWhkAeA+CD1DJpk6dqnPnztm1x8SUf2grPv5C6GFtHwAoG4IPUIkc9fLcfffdatOmTYWO27cvPT0AUB7M8QEqwblz50oc2iop9DBvBwAqHz0+gJOVd20e5u0AQOUj+ABO5Cj0xMfHl+pGuMzbAYDKR/ABnOCbb77Rhx9+aNdelqu2mLcDAJWP4ANUkKNenlq1amn06NEmVAMAuByCD1ABzlybBwBQ+Qg+QDnMmjVLv/32m107oQcAqjaCD1BGjnp57rnnHrVu3dqEagAAZUHwAUrp/PnzevHFF+3a6eUBAPdB8AFKobxr8wAAqhaCD3AFjkLPhAkT5OfnZ0I1AICKIPgAJcjIyNCyZcvs2unlAQD3RfABHHDUy9OiRQvdd999JlQDAHAWblIKXKKktXkuDT3cVBQA3A89PsD/mTdvng4fPmzXXtLQFjcVBQD3Q/AB5LiXZ9CgQYqOji7xNdxUFADcD8EHXq0ia/NwU1EAcD8EH3gt1uYBAO9D8IFXYm0eAPBOBB94lT179mjp0qV27TExiSLzAIDnI/jAazjq5WnZsqXuvfdeE6oBAJiB4AOv4Cj0pKUliuk8AOBdCD7waEuWLNHevXvt2tPSErkMHQC8EMEHHstRL8+QIUMUFRVFTw8AeCmCDzxORdbmAQB4NoIPPApr8wAALofgA4/hKPQ888wzqlaNP+YAgAv4RoDb27t3r5YsWWLXTi8PAOBSBB+4NUe9PB06dNDtt99uQjUAgKqO4AO35Sj00MsDALgcgg/czttvv639+/fbtRN6AABXQvCBW3HUyzN06FBdddVVJlQDAHA3PmYXAHOlpkpdulz4WZWdP3++xKEtQg8AoLTo8fFyKSlSevqFn337ml2NY6zNAwBwFoKPl4uPvxB6qup9q1ibBwDgTHx7eLm+fatmT8+ePXu0dOlSu3Z6eQAAFUHwQZXjqJfn+uuv11/+8hcTqgEAeBKCD6oU1uYBAFQmgg+qhH//+986cOCAXTuhBwDgTAQfmCo1Vdq+3b6X5+GHH1bDhg1NqAgA4MlYxwemSE2VunY95zD0JCYmEnoAAJWCHh+YYvv2JP3pT/btDG0BACoTwQcux9o8AACzMNQFp7nS7S8yMjJKvGqL0AMAcAW+beA0l7v9haPA06lTJ/Xu3dtF1QEAQPCBE/3x9hepqb//uqQJzAAAuBrBB07zx9tfdOkiXXPNIm3fnmm3H6EHAGAWgg8qRc+e9r08w4YNU4MGDUyoBgCACwg+cKrz58/rxRdftGunlwcAUBUQfOA0jiYwS4QeAEDVQfCBUzgKPRMnTpSvr68J1QAA4BjBB+WWmirNnfuTOndeaLeNXh4AQFVE8EG5bd+epM6dbdt69eqlzpc2AgBQRRB8UC4lrcAMAEBVRvBBmXz++edau3atXTuhBwDgDgg+KDVHvTyPP/64wsLCXF8MAADlQPDBFeXn5ys5OdmunV4eAIC7IfjgsmbOnKmTJ0/atLVs2VL33nuvSRUBAFB+BB+UyNHQ1j/+8Q/5+PiYUA0AABVH8IGdw4cPa968eXbtDG0BANydW/3XfeXKlercubOCgoJUq1Yt9evXz2b7wYMH1adPHwUHBysiIkJPPfWUCgsLzSnWTSUlJdmFnv79+xN6AAAewW16fN5//30NGzZML7zwgm677TYVFhZq586d1u1FRUXq06ePIiMjtXnzZh09elSDBg2Sn5+fXnjhBRMrdx+szQMA8HQWwzAMs4u4ksLCQjVp0kRJSUkaOnSow31WrVql22+/XUeOHFG9evUkSXPnztX48eN14sQJ+fv7l+q9cnJyFBoaquzsbIWEhDjtM1Rln332mdatW2fXTugBALiL0n5/u8VQ17Zt23T48GH5+PgoJiZG9evXV+/evW16fNLT03XttddaQ48k9ezZUzk5Ofr+++9LPHZeXp5ycnJsHt4kKSnJLvQ8/vjjhB4AgEdyi+Dz448/SpImT56siRMnasWKFapVq5ZuvfVW/fbbb5KkrKwsm9Ajyfo8KyurxGMnJycrNDTU+oiKiqqkT+EcqalSly4XflZEQUFBiUNbLEgIAPBUpgaf+Ph4WSyWyz4yMjJUXFwsSXrmmWfUv39/dejQQQsWLJDFYtG7775boRoSEhKUnZ1tfRw6dMgZH63SpKRI6ekXfpbXkiVL7OY9tWrVil4eAIDHM3Vy87hx4xQXF3fZfZo2baqjR49Kklq3bm1tDwgIUNOmTXXw4EFJUmRkpLZs2WLz2mPHjlm3lSQgIEABAQHlKd8U8fEXQk98fPlez9o8AABvZmrwCQ8PV3h4+BX369ChgwICArRnzx7ddNNNki4M1WRmZqpx48aSpNjYWD3//PM6fvy4IiIiJElr1qxRSEiITWByd337XniU1fHjxzVnzhy7dnp5AADexC0uZw8JCdHw4cOVmJioqKgoNW7cWNOmTZMk3XPPPZKkHj16qHXr1ho4cKCmTp2qrKwsTZw4USNHjnSrHp3K4KiX58EHH1SzZs1MqAYAAPO4RfCRpGnTpqlatWoaOHCgzp07p86dO2vdunWqVauWJMnX11crVqzQiBEjFBsbq+rVq2vw4MF69tlnTa7cXKzNAwDA79xiHR9X8pR1fPbu3aslS5bYtNWsWVNjx441qSIAACpPab+/3abHB6XnqJenbdtx6t+/hgnVAABQdXApjwcpLCx0GHomT07Uyy8TegAAoMfHQ3zyySfatGmTTVufPn105EhHxcaW//J3AAA8CcHHAzjq5Zk0aZIsFouk8l3+DgCAJyL4uLGTJ09q5syZdu1ctQUAgGMEHzc1e/Zs/fLLLzZtw4YNU4MGDUyqCACAqo/g44ZYmwcAgPIh+LiRI0eO6F//+pdNW5s2bXT33XebVBEAAO6F4OMmXn31f/r11202bU8//bSCgoJMqggAAPdD8KniiouLNWPGDJ05c8ba5ufnpwkTJphYFQAA7ongU4UdO3ZMc+fOtWlr3nygHnigqUkVAQDg3gg+VVRaWpq++OIL6/P69etr2LBh1rV5AABA2RF8qpj8/HwlJyfbtN19991q06aNSRUBAOA5CD5VyL59+7R48WKbNiYwAwDgPASfKuLtt9/W/v37rc/btWunfv36mVcQAAAeiOBjstOnT2v69Ok2bUOGDFFUVJRJFQEA4LkIPib6+uuvtXLlSutzHx8fTZgwQb6+viZWBQCA5yL4mMDR2jzdu3fXjTfeaGJVAAB4PoKPizlam2f06NGqVauWSRUBAOA9CD4udOnaPA0aNNDDDz/M2jwAALgIwcdFFi5cqJ9++sn6/J577lHr1q1NrAgAAO9D8HGRkydPWn/N2jwAAJiD4OMigwcP1tmzZ7lMHQAAExF8XKR27dqqXbu22WUAAODVfMwuAAAAwFUIPgAAwGsQfAAAgNcg+AAAAK9B8HGR1FSpS5cLPwEAgDkIPi6SkiKlp1/4CQAAzEHwcZH4eCk29sJPAABgDtbxcZG+fS88AACAeejxAQAAXoPgAwAAvAbBBwAAeA2CDwAA8BoEHwAA4DUIPgAAwGsQfAAAgNcg+AAAAK9B8AEAAF6D4AMAALwGwQcAAHgNgg8AAPAaBB8AAOA1uDv7JQzDkCTl5OSYXAkAACiti9/bF7/HS0LwucTp06clSVFRUSZXAgAAyur06dMKDQ0tcbvFuFI08jLFxcU6cuSIatasKYvFYnY5lS4nJ0dRUVE6dOiQQkJCzC4HJeA8uQfOk3vgPLmHsp4nwzB0+vRpNWjQQD4+Jc/kocfnEj4+PrrqqqvMLsPlQkJC+AfADXCe3APnyT1wntxDWc7T5Xp6LmJyMwAA8BoEHwAA4DUIPl4uICBAiYmJCggIMLsUXAbnyT1wntwD58k9VNZ5YnIzAADwGvT4AAAAr0HwAQAAXoPgAwAAvAbBBwAAeA2Cj5dbuXKlOnfurKCgINWqVUv9+vWz2X7w4EH16dNHwcHBioiI0FNPPaXCwkJzivVyeXl5uu6662SxWPTNN9/YbPv222918803KzAwUFFRUZo6dao5RXqpzMxMDR06VNHR0QoKClKzZs2UmJio/Px8m/04T1XD7Nmz1aRJEwUGBqpz587asmWL2SV5reTkZF1//fWqWbOmIiIi1K9fP+3Zs8dmn/Pnz2vkyJGqU6eOatSoof79++vYsWPlfk+Cjxd7//33NXDgQD300EPasWOHNm3apPvvv9+6vaioSH369FF+fr42b96sRYsWaeHChZo0aZKJVXuvp59+Wg0aNLBrz8nJUY8ePdS4cWNt3bpV06ZN0+TJk/XGG2+YUKV3ysjIUHFxsV5//XV9//33mjFjhubOnasJEyZY9+E8VQ3Lli3T2LFjlZiYqG3btqldu3bq2bOnjh8/bnZpXmnjxo0aOXKkvvjiC61Zs0YFBQXq0aOHzp49a93niSee0P/+9z+9++672rhxo44cOaK77rqr/G9qwCsVFBQYDRs2NObNm1fiPh999JHh4+NjZGVlWdvmzJljhISEGHl5ea4oE//no48+Mlq2bGl8//33hiRj+/bt1m2vvfaaUatWLZtzMn78eKNFixYmVIqLpk6dakRHR1ufc56qhk6dOhkjR460Pi8qKjIaNGhgJCcnm1gVLjp+/Lghydi4caNhGIZx6tQpw8/Pz3j33Xet++zevduQZKSnp5frPejx8VLbtm3T4cOH5ePjo5iYGNWvX1+9e/fWzp07rfukp6fr2muvVb169axtPXv2VE5Ojr7//nszyvZKx44d07Bhw/TWW28pODjYbnt6erq6du0qf39/a1vPnj21Z88enTx50pWl4g+ys7NVu3Zt63POk/ny8/O1detWde/e3drm4+Oj7t27Kz093cTKcFF2drYkWf/ubN26VQUFBTbnrGXLlmrUqFG5zxnBx0v9+OOPkqTJkydr4sSJWrFihWrVqqVbb71Vv/32myQpKyvLJvRIsj7PyspybcFeyjAMxcXFafjw4erYsaPDfThPVc++ffs0a9YsPfroo9Y2zpP5fvnlFxUVFTk8D5wD8xUXF2vMmDG68cYb1bZtW0kX/m74+/srLCzMZt+KnDOCj4eJj4+XxWK57OPifARJeuaZZ9S/f3916NBBCxYskMVi0bvvvmvyp/B8pT1Ps2bN0unTp5WQkGB2yV6ptOfpjw4fPqxevXrpnnvu0bBhw0yqHHA/I0eO1M6dO7V06dJKfZ9qlXp0uNy4ceMUFxd32X2aNm2qo0ePSpJat25tbQ8ICFDTpk118OBBSVJkZKTd1Q4XZ9JHRkY6sWrvU9rztG7dOqWnp9vdq6Zjx4564IEHtGjRIkVGRtpd4cB5co7SnqeLjhw5om7duqlLly52k5Y5T+arW7eufH19HZ4HzoG5Ro0apRUrVujTTz/VVVddZW2PjIxUfn6+Tp06ZdPrU6Fz5ozJSHA/2dnZRkBAgM3k5vz8fCMiIsJ4/fXXDcP4fXLzsWPHrPu8/vrrRkhIiHH+/HmX1+yNfvrpJ+O7776zPtLS0gxJxnvvvWccOnTIMIzfJ83m5+dbX5eQkMCkWRf7+eefjauvvtq47777jMLCQrvtnKeqoVOnTsaoUaOsz4uKioyGDRsyudkkxcXFxsiRI40GDRoYe/futdt+cXLze++9Z23LyMio0ORmgo8Xe/zxx42GDRsaaWlpRkZGhjF06FAjIiLC+O233wzDMIzCwkKjbdu2Ro8ePYxvvvnGWL16tREeHm4kJCSYXLn3OnDggN1VXadOnTLq1atnDBw40Ni5c6exdOlSIzg42BpgUfl+/vlno3nz5saf/vQn4+effzaOHj1qfVzEeaoali5dagQEBBgLFy40du3aZTzyyCNGWFiYzdWrcJ0RI0YYoaGhxoYNG2z+3uTm5lr3GT58uNGoUSNj3bp1xtdff23ExsYasbGx5X5Pgo8Xy8/PN8aNG2dEREQYNWvWNLp3727s3LnTZp/MzEyjd+/eRlBQkFG3bl1j3LhxRkFBgUkVw1HwMQzD2LFjh3HTTTcZAQEBRsOGDY2UlBRzCvRSCxYsMCQ5fPwR56lqmDVrltGoUSPD39/f6NSpk/HFF1+YXZLXKunvzYIFC6z7nDt3znjssceMWrVqGcHBwcZf//pXm/9UlJXl/94YAADA43FVFwAA8BoEHwAA4DUIPgAAwGsQfAAAgNcg+AAAAK9B8AEAAF6D4AMAALwGwQcAKsmGDRtksVh06tQps0sB8H8IPgDc1uTJk3XdddeZXQYAN0LwAeDxCgoKzC4BQBVB8AFgmuLiYiUnJys6OlpBQUFq166d3nvvPUm/DxOtXbtWHTt2VHBwsLp06aI9e/ZIkhYuXKikpCTt2LFDFotFFotFCxculCRZLBbNmTNHffv2VfXq1fX8889fto6L75WWlqaYmBgFBQXptttu0/Hjx7Vq1Sq1atVKISEhuv/++5Wbm2t9XV5enkaPHq2IiAgFBgbqpptu0ldffVU5v1kAnKPCdxgDgHJ67rnnjJYtWxqrV6829u/fbyxYsMAICAgwNmzYYKxfv96QZHTu3NnYsGGD8f333xs333yz0aVLF8MwDCM3N9cYN26c0aZNG7s7OksyIiIijPnz5xv79+83fvrpp8vWcfG9brjhBuPzzz83tm3bZjRv3ty45ZZbjB49ehjbtm0zPv30U6NOnTo2NxYdPXq00aBBA+Ojjz4yvv/+e2Pw4MFGrVq1jF9//dXmuCdPnqyc30AAZUbwAWCK8+fPG8HBwcbmzZtt2ocOHWoMGDDAGho++eQT67aVK1cakoxz584ZhmEYiYmJRrt27eyOLckYM2ZMqWtx9F7JycmGJGP//v3WtkcffdTo2bOnYRiGcebMGcPPz89YvHixdXt+fr7RoEEDY+rUqTbHJfgAVUc1s3qaAHi3ffv2KTc3V3/+859t2vPz8xUTE2N9/v/+3/+z/rp+/fqSpOPHj6tRo0aXPX7Hjh3LXNMf36tevXoKDg5W06ZNbdq2bNkiSdq/f78KCgp04403Wrf7+fmpU6dO2r17d5nfG4BrEHwAmOLMmTOSpJUrV6phw4Y22wICArR//35JF8LERRaLRdKFuUFXUr169TLXdOl7/fH5xbbSvDeAqovJzQBM0bp1awUEBOjgwYNq3ry5zSMqKqpUx/D391dRUVElV+pYs2bN5O/vr02bNlnbCgoK9NVXX6l169am1ATgyujxAWCKmjVr6sknn9QTTzyh4uJi3XTTTcrOztamTZsUEhKixo0bX/EYTZo00YEDB/TNN9/oqquuUs2aNRUQEOCC6i/0KI0YMUJPPfWUateurUaNGmnq1KnKzc3V0KFDXVIDgLIj+AAwzZQpUxQeHq7k5GT9+OOPCgsLU/v27TVhwoRSDSn1799fH3zwgbp166ZTp05pwYIFiouLq/zC/09KSoqKi4s1cOBAnT59Wh07dlRaWppq1arlshoAlI3FMAzD7CIAAABcgTk+AADAaxB8AHi84cOHq0aNGg4fw4cPN7s8AC7EUBcAj3f8+HHl5OQ43BYSEqKIiAgXVwTALAQfAADgNRjqAgAAXoPgAwAAvAbBBwAAeA2CDwAA8BoEHwAA4DUIPgAAwGsQfAAAgNcg+AAAAK/x/wHMk0G0xPAGDAAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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ZGani4mJfFK9cmjRposjISK1evVqNGjWSdHrah6ysLI0dO7bSykHYAQCgDCXNEh5euFXFxlRKs4QaNWpo/PjxGjdunBwOh7p27Sq73a7Vq1crNjbWGRrOJyUlRbt27dLmzZt10UUXqUaNGoqKivJZmctSrVo13XvvvZowYYJq1aqlhg0b6umnn1ZhYaFGjBhRaeUg7AAAcB7+aJbw+OOPq27dusrIyNAPP/yg+Ph4tW/fXg8//LBbt4gGDBighQsX6qqrrlJ+fr5ee+01DRs2zOflLs3MmTPlcDg0ZMgQHT58WB07dtQXX3yhmjVrVloZbMYYc+HNrK2goEBxcXGy2+2KjY31d3EAABV0/Phx7dq1S6mpqapataq/i4MKON9r6e71m95YAADA0gg7AABA99xzj0tX9zP/7rnnHn8Xr0JoswMAAPTYY49p/Pjxpa4L9iYehB0AAKCEhAQlJCT4uxg+wW0sAABgaYQdAIBlBdpIvig/b7yG3MYCAFhOZGSkwsLCtHfvXtWtW1eRkZHOKRUQHIwxKioq0oEDBxQWFqbIyEiP90XYAQBYTlhYmFJTU5Wbm6u9e/f6uziogJiYGDVs2FBhYZ7fjCLsAAAsKTIyUg0bNtSpU6cCYp4olF94eLgiIiIqXCtH2AEAWFbJrNxnz8yN0EIDZQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGl+DTvFxcWaMmWKUlNTFR0drSZNmujxxx+XMca5jTFGjz76qJKTkxUdHa1evXppx44dLvs5dOiQBg8erNjYWMXHx2vEiBE6cuRIZR8OAAAIQH4NO0899ZRmz56tF198Ud99952eeuopPf3003rhhRec2zz99NOaNWuW5syZo7Vr16patWrq06ePjh8/7txm8ODB2rZtm5YuXarFixfrq6++0t133+2PQwIAAAHGZs6sRqlk1157rRITE/XKK684lw0YMEDR0dF64403ZIxRvXr19OCDD2r8+PGSJLvdrsTERM2bN0+33HKLvvvuO6WlpSkrK0sdO3aUJH3++ef67W9/q59++kn16tW7YDkKCgoUFxcnu92u2NhY3xwsAADwKnev336t2encubOWLVum77//XpK0ZcsWrVq1Sn379pUk7dq1S3l5eerVq5fzMXFxcerUqZMyMzMlSZmZmYqPj3cGHUnq1auXwsLCtHbt2lKf98SJEyooKHD5AwAA1hThzyefNGmSCgoK1KJFC4WHh6u4uFgzZszQ4MGDJUl5eXmSpMTERJfHJSYmOtfl5eUpISHBZX1ERIRq1arl3OZsGRkZmj59urcPBwAABCC/1uz885//1Jtvvqm33npLGzdu1Pz58/XnP/9Z8+fP9+nzTp48WXa73fm3Z88enz4fAADwH7/W7EyYMEGTJk3SLbfcIklq3bq1fvzxR2VkZGjo0KFKSkqSJO3bt0/JycnOx+3bt0+XXnqpJCkpKUn79+932e+pU6d06NAh5+PPFhUVpaioKB8cEQAACDR+rdkpLCxUWJhrEcLDw+VwOCRJqampSkpK0rJly5zrCwoKtHbtWqWnp0uS0tPTlZ+frw0bNji3Wb58uRwOhzp16lQJRwEAAAKZX2t2rrvuOs2YMUMNGzbUJZdcok2bNukvf/mLhg8fLkmy2WwaO3asnnjiCTVr1kypqamaMmWK6tWrp/79+0uSWrZsqWuuuUZ33XWX5syZo5MnT2rUqFG65ZZb3OqJBQAArM2vYeeFF17QlClTdN9992n//v2qV6+e/vCHP+jRRx91bvPQQw/p6NGjuvvuu5Wfn6+uXbvq888/V9WqVZ3bvPnmmxo1apR69uypsLAwDRgwQLNmzfLHIQEAgADj13F2AgXj7AAAEHyCYpwdAAAAXyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAAS/N72Pn555912223qXbt2oqOjlbr1q21fv1653pjjB599FElJycrOjpavXr10o4dO1z2cejQIQ0ePFixsbGKj4/XiBEjdOTIkco+FAAAEID8GnZ+/fVXdenSRVWqVNFnn32mb7/9Vs8884xq1qzp3Obpp5/WrFmzNGfOHK1du1bVqlVTnz59dPz4cec2gwcP1rZt27R06VItXrxYX331le6++25/HBIAAAgwNmOM8deTT5o0SatXr9a///3vUtcbY1SvXj09+OCDGj9+vCTJbrcrMTFR8+bN0y233KLvvvtOaWlpysrKUseOHSVJn3/+uX7729/qp59+Ur169S5YjoKCAsXFxclutys2NtZ7BwgAAHzG3eu3X2t2Fi1apI4dO+r3v/+9EhIS1K5dO82dO9e5fteuXcrLy1OvXr2cy+Li4tSpUydlZmZKkjIzMxUfH+8MOpLUq1cvhYWFae3ataU+74kTJ1RQUODyBwAArMmvYeeHH37Q7Nmz1axZM33xxRe69957NXr0aM2fP1+SlJeXJ0lKTEx0eVxiYqJzXV5enhISElzWR0REqFatWs5tzpaRkaG4uDjnX4MGDbx9aAAAIED4New4HA61b99eTz75pNq1a6e7775bd911l+bMmePT5508ebLsdrvzb8+ePT59PgAA4D9+DTvJyclKS0tzWdayZUvl5ORIkpKSkiRJ+/btc9lm3759znVJSUnav3+/y/pTp07p0KFDzm3OFhUVpdjYWJc/AABgTX4NO126dNH27dtdln3//fdq1KiRJCk1NVVJSUlatmyZc31BQYHWrl2r9PR0SVJ6erry8/O1YcMG5zbLly+Xw+FQp06dKuEoAABAIIvw55OPGzdOnTt31pNPPqmbb75Z69at09/+9jf97W9/kyTZbDaNHTtWTzzxhJo1a6bU1FRNmTJF9erVU//+/SWdrgm65pprnLe/Tp48qVGjRumWW25xqycWAACwNr92PZekxYsXa/LkydqxY4dSU1P1wAMP6K677nKuN8Zo6tSp+tvf/qb8/Hx17dpVL730kpo3b+7c5tChQxo1apQ+/vhjhYWFacCAAZo1a5aqV6/uVhnoeg4AQPBx9/rt97ATCAg7AAAEn6AYZwcAAMDXCDuQJOXaj2lN9kHl2o/5uygAAHiVXxsoIzAsyMrR5IXfyGGkMJuUcWNrDbysob+LBQCAV1CzE+Jy7cecQUeSHEZ6eOFWangAAJZB2Alxuw4edQadEsXGaPfBQv8UCAAALyPshLjUOtUUZnNdFm6zKaVOjH8KBACAlxF2QlxyXLQybmytcNvpxBNus+nJG1spOS7azyUDAMA7aKAMDbysobo1r6vdBwuVUieGoAMAsBS3w05BQYHbO2VgvuCTHBdNyAEAWJLbYSc+Pl42m+282xhjZLPZVFxcXOGCAQAAeIPbYWfFihW+LAd0uhv4roNHlVqnGrUsAAB4idthp3v37r4sR8hjYD8AAHzD4wbK+fn5euWVV/Tdd99Jki655BINHz5ccXFxXitcqChrYL9uzetSwwMAQAV51PV8/fr1atKkiZ599lkdOnRIhw4d0l/+8hc1adJEGzdu9HYZLY+B/QAA8B2PanbGjRun66+/XnPnzlVExOldnDp1SnfeeafGjh2rr776yquFtLqSgf3ODDwM7AcAgHd4XLMzceJEZ9CRpIiICD300ENav3691woXKhjYDwAA3/GoZic2NlY5OTlq0aKFy/I9e/aoRo0aXilYqGFgPwAAfMOjsDNw4ECNGDFCf/7zn9W5c2dJ0urVqzVhwgQNGjTIqwUMJQzsBwCA93kUdv785z/LZrPp9ttv16lTpyRJVapU0b333quZM2d6tYAAAAAVYTPGmAtvVrrCwkJlZ2dLkpo0aaKYmOBsUFtQUKC4uDjZ7XamugAAIEi4e/2u0ESgMTExat26dUV2AR9jVGYAQKjzKOwcP35cL7zwglasWKH9+/fL4XC4rGesncDAqMwAAHgYdkaMGKElS5bopptu0uWXX37BCUJR+RiVGQCA0zwKO4sXL9ann36qLl26eLs88JLzjcpM2AEAhBKPBhWsX78+4+kEuJJRmc/kzqjMufZjWpN9ULn2Yz4sHQAAlcejsPPMM89o4sSJ+vHHH71dHniJJ6MyL8jKUZeZy3Xr3LXqMnO5FmTlVFZxAQDwGY9uY3Xs2FHHjx9X48aNFRMToypVqrisP3TokFcKh4opz6jMtPEBAFiVR2Fn0KBB+vnnn/Xkk08qMTGRBsoBzN1RmWnjAwCwKo/Czpo1a5SZmam2bdt6uzzwE2ZeBwBYlUdtdlq0aKFjx2jAaiXMvA4AsCqPpotYsmSJpk+frhkzZqh169bntNkJtikXmC7i/+XajzHzOgAgKLh7/fYo7ISFna4QOrutjjFGNptNxcXF5d2lXxF2AAAIPj6dG2vFihUeFwwAAKAyeRR2unfv7tZ29913nx577DHVqVPHk6cBAACoMI8aKLvrjTfeUEFBgS+fAgAA4Lx8GnY8aA4EAADgVT4NOwAAAP5G2AEAAJZG2AEAAJZG2AEAAJbm07Bz2223MUgfAADwK4/G2ZGk/Px8rVu3Tvv375fD4XBZd/vtt0uSZs+eXbHSAQAAVJBHYefjjz/W4MGDdeTIEcXGxrpMG2Gz2ZxhBwAAwN88uo314IMPavjw4Tpy5Ijy8/P166+/Ov8OHTrk7TICAAB4zKOw8/PPP2v06NGKiYnxdnkAAAC8yqOw06dPH61fv97bZQEAAPA6t9vsLFq0yPnvfv36acKECfr222/VunVrValSxWXb66+/3nslBAAAqACbcXMCq7Aw9yqBbDabiouLK1SoylZQUKC4uDjZ7Xa6ygMAECTcvX67XbNzdvdyAACAYOBRm53XX39dJ06cOGd5UVGRXn/99QoXCgAAwFvcvo11pvDwcOXm5iohIcFl+S+//KKEhARuYwEAAJ9z9/rtUc2OMcZlIMESP/30k+Li4jzZJQAAgE+UawTldu3ayWazyWazqWfPnoqI+P+HFxcXa9euXbrmmmu8XkgAAABPlSvs9O/fX5K0efNm9enTR9WrV3eui4yMVEpKigYMGODVAqLy5dqPadfBo0qtU03JcdH+Lg4AABVSrrAzdepUSVJKSooGDhyoqlWr+qRQ8J8FWTmavPAbOYwUZpMybmytgZc19HexAADwmEcNlEsUFRWVOut5w4bBdXGkgfJpufZj6jJzuRxnvCPCbTatmnQVNTwAgIDj9XF2zrRjxw4NHz5ca9ascVle0nA52Hpj4bRdB4+6BB1JKjZGuw8WEnYAAEHLo7AzbNgwRUREaPHixUpOTi61ZxaCT2qdagqz6ZyanZQ6TPgKAAheHoWdzZs3a8OGDWrRooW3ywM/So6LVsaNrfXwwq0qNkbhNpuevLEVtToAgKDmUdhJS0vTwYMHvV0WBICBlzVUt+Z1tftgoVLqxBB0AABBz6NBBZ966ik99NBD+vLLL/XLL7+ooKDA5c9TM2fOlM1m09ixY53Ljh8/rpEjR6p27dqqXr26BgwYoH379rk8LicnR/369VNMTIwSEhI0YcIEnTp1yuNyhLrkuGilN6lN0AEAWIJHNTu9evWSJF199dUu7XUq0kA5KytLL7/8stq0aeOyfNy4cfrkk0/07rvvKi4uTqNGjdKNN96o1atXSzo9mGG/fv2UlJSkNWvWKDc3V7fffruqVKmiJ5980pPDAwDAibHHgp9HYWfFihVeLcSRI0c0ePBgzZ07V0888YRzud1u1yuvvKK33npLV199tSTptddeU8uWLfWf//xHV1xxhZYsWaJvv/1W//rXv5SYmKhLL71Ujz/+uCZOnKhp06YpMjLSq2UFAIQOxh6zBo9uY3Xv3l1hYWGaO3euJk2apKZNm6p79+7KyclReHh4ufc3cuRI9evXz1ljVGLDhg06efKky/IWLVqoYcOGyszMlCRlZmaqdevWSkxMdG7Tp08fFRQUaNu2baU+34kTJ7x26w0AYE259mPOoCOd7qn68MKtyrUf82/BUG4ehZ33339fffr0UXR0tDZt2qQTJ05IOl0TU95bR++88442btyojIyMc9bl5eUpMjJS8fHxLssTExOVl5fn3ObMoFOyvmRdaTIyMhQXF+f8a9CgQbnKHGpy7ce0JvsgH3AAIeV8Y48huHgUdp544gnNmTNHc+fOVZUqVZzLu3Tpoo0bN7q9nz179mjMmDF68803K3XqicmTJ8tutzv/9uzZU2nPHWwWZOWoy8zlunXuWnWZuVwLsnL8XSQAqBQlY4+dibHHgpNHYWf79u3q1q3bOcvj4uKUn5/v9n42bNig/fv3q3379oqIiFBERIRWrlypWbNmKSIiQomJiSoqKjpnn/v27VNSUpIkKSkp6ZzeWSX/L9nmbFFRUYqNjXX5w7mowgUQykrGHgv/X0ccxh4LXh41UE5KStLOnTuVkpLisnzVqlVq3Lix2/vp2bOnvvnmG5dld9xxh1q0aKGJEyeqQYMGqlKlipYtW+acTX379u3KyclRenq6JCk9PV0zZszQ/v37lZCQIElaunSpYmNjlZaW5snh4X+YPgJAqGPsMWvwKOzcddddGjNmjF599VXZbDbt3btXmZmZGj9+vKZMmeL2fmrUqKFWrVq5LKtWrZpq167tXD5ixAg98MADqlWrlmJjY3X//fcrPT1dV1xxhSSpd+/eSktL05AhQ/T0008rLy9Pf/zjHzVy5EhFRUV5cnj4H6aPAIDTNTyEnODmUdiZNGmSHA6HevbsqcLCQnXr1k1RUVEaP3687r//fq8W8Nlnn1VYWJgGDBigEydOqE+fPnrppZec68PDw7V48WLde++9Sk9PV7Vq1TR06FA99thjXi1HKGL6CACAFdiMMebCm5WuqKhIO3fu1JEjR5SWlqbq1at7s2yVxt0p4kNVrv0YVbgAgIDj7vXbo5qdEpGRkbSLCQFU4QIAgplHvbEAAACCBWEHAABYGmEHAABYGmEHAYWpKQAA3lahBsqANzG7MADAF6jZQUBgagoAgK8QdhAQmF0YAOArhB0EBGYXBgD4CmEHAYHZhQEAvkIDZQQMZhcGAPgCYQcBhakpAADexm0sAABgaYQdeITB/wAAwYLbWCg3Bv8DAAQTanZQLgz+BwAINoQdlAuD/wEAgg1hB+XC4H8AgGBD2EG5MPgfACDY0EAZ5cbgfwCAYELYgUcY/A8AECy4jQUAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsBNEmHwTAIDyo+t5kGDyTQAAPEPNThBg8k0AADxH2AkCgTT5JrfSAADBhttYQaBk8s0zA48/Jt/kVhoAIBhRsxMEAmHyTW6lAQCCFTU7QcLfk2+e71Yac2QBAAIZYSeI+HPyzUC5leZNufZj2nXwqFLrVCOwAYCFcRsLbgmEW2netCArR11mLtetc9eqy8zlWpCV4+8iAQB8xGaMMRfezNoKCgoUFxcnu92u2NhYfxcnoOXaj/ntVpq35NqPqcvM5efUUq2adFXQHhMAhCJ3r9/cxkK5+PNWmrfQ/ggAQgu3sRByStofnSnY2x8BAMpG2EHIsVr7IwDA+XEbCyHJ3135AQCVh7CDkGWF9kcAgAvjNhYAALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wk6QyLUf05rsg8q1H/N3UQAACCqMsxMEFmTlaPLCb+QwUphNyrixtQZe1tDfxQIAIChQsxPgcu3HnEFHkhxGenjhVmp4AABwE2EnwJ1vhm4AAHBhhJ0AxwzdAABUDGEnwDFDNwAAFUMD5SDADN0AAHiOsBMkmKEbAADPcBsLAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYml/DTkZGhi677DLVqFFDCQkJ6t+/v7Zv3+6yzfHjxzVy5EjVrl1b1atX14ABA7Rv3z6XbXJyctSvXz/FxMQoISFBEyZM0KlTpyrzUACfYzJYAPCMX8POypUrNXLkSP3nP//R0qVLdfLkSfXu3VtHjx51bjNu3Dh9/PHHevfdd7Vy5Urt3btXN954o3N9cXGx+vXrp6KiIq1Zs0bz58/XvHnz9Oijj/rjkACfWJCVoy4zl+vWuWvVZeZyLcjK8XeRACBo2Iwx5sKbVY4DBw4oISFBK1euVLdu3WS321W3bl299dZbuummmyRJ//3vf9WyZUtlZmbqiiuu0GeffaZrr71We/fuVWJioiRpzpw5mjhxog4cOKDIyMgLPm9BQYHi4uJkt9sVGxvr02MEyivXfkxdZi53mSMt3GbTqklXMfYSgJDm7vU7oNrs2O12SVKtWrUkSRs2bNDJkyfVq1cv5zYtWrRQw4YNlZmZKUnKzMxU69atnUFHkvr06aOCggJt27at1Oc5ceKECgoKXP6AQMVksABQMQETdhwOh8aOHasuXbqoVatWkqS8vDxFRkYqPj7eZdvExETl5eU5tzkz6JSsL1lXmoyMDMXFxTn/GjRo4OWjAbyHyWABoGICJuyMHDlSW7du1TvvvOPz55o8ebLsdrvzb8+ePT5/zlBGw9qKYTJYAKiYgJgba9SoUVq8eLG++uorXXTRRc7lSUlJKioqUn5+vkvtzr59+5SUlOTcZt26dS77K+mtVbLN2aKiohQVFeXlo0BpFmTlaPLCb+QwUphNyrixtQZe1tDfxQo6TAYLAJ7za82OMUajRo3SBx98oOXLlys1NdVlfYcOHVSlShUtW7bMuWz79u3KyclRenq6JCk9PV3ffPON9u/f79xm6dKlio2NVVpaWuUcCEqVaz/mDDqS5DDSwwu3+q2GJ9hrmJLjopXepDZBBwDKya81OyNHjtRbb72ljz76SDVq1HC2sYmLi1N0dLTi4uI0YsQIPfDAA6pVq5ZiY2N1//33Kz09XVdccYUkqXfv3kpLS9OQIUP09NNPKy8vT3/84x81cuRIam/87HwNayv7gk0NEwCELr/W7MyePVt2u109evRQcnKy82/BggXObZ599llde+21GjBggLp166akpCQtXLjQuT48PFyLFy9WeHi40tPTddttt+n222/XY4895o9DCniVWbsRKA1rA62GCQBQuQJqnB1/CZVxdvxRu7EgK0cPL9yqYmOcDWsru0ZlTfZB3Tp37TnL377rCqU3qV2pZQEAeI+71++AaKAM3yurdqNb87o+vaUUCA1rS2qYzh6Uj67bABAaAqbrOXzLnwPT+bthLV23ASC0UbMTIkK9diMQapiCTa79mHYdPKrUOtU4XwCCGmEnRJTUbpzdfiaULmLJcdEhdbwVQe81AFZCA2WFTgNl6fSvdWo3cD5MPAogWNBAGaWidgNnKu1WVSCNjwQA3kDYAUJUabequjWvq1+OnAjp9l2oGNp6IRARdoAQVNpQBJPe/0a2/4UcmySbTTKG3mtwH229EKgIO0AIKu1WldHpcFPy7zAjvXhrO7VvVJOggwvy11hegDsYZwcIQaVN5XE2h6Ra1aK4UMEtG3781W9jeQEXQtgBQtDZAy2G6fStqzPRTgfuWpCVo/vf2nTOct5DCBTcxgIsorwNQ88eaPGr7w+E9DhM8EzJ7auzxzAJs4n3EAIGYQewAE8bhp45FAGjTMMTpbX/kqRZt7TTtW3rVX6BgFJwGwvwo1z7Ma3JPqhc+7EK7aO0hqGe7NPf85gh+JTW/ivcZlOHlJr+KRBQCsIO4CcLsnLUZeZy3Tp3rbrMXK4FWTke7cefk7wCTLSLYMBtLMAPvNlNN9QneYX/cQsUgY6aHQQVb9z2CQTerI3hlzVKU9mfFW6BIpBRs4OgYaXRWb1dG1Pyy3rjj7/KYYw6ptTyUkkRjKz0WQG8gZodBAVvNsINBL6ojfnq+wO6/+1Nuv/tzRVqA4TgFmyfFavU1iKwUbODoGDFmbi92c6BofpRIpg+K9RAobJQs4OgUFb31mBvhOutdg70yEKJYPmsBFsNFIIbYQdBgUa45xcsFzj4XrB8VgjoqEzcxkLQoHtr2UoucEz3ACk4PiulNdIPkxQTyW9wbynvFDJWZjPGlDLQd2gpKChQXFyc7Ha7YmNj/V0cwGO59mMBfYELRcF6wTm73L44jgVZOc6AXoK2O97hi/ZQgfhedvf6TdgRYQeAbwTrBefsct/Qrr4+2PSzTxoSb9nzq/q/tEbmrGEYVk26KmAuqMEm135MXWYuP2doi4qc00BtTO7u9Zv6QgQkuqMi2PmiAa63phg5n9LK/f7Gn33SkDjXfkzrdh/S2T+5y2q7w/eCe7zdHsoKjclps4NKd6FfpoH6CwIoD293Aa+s4QXKmsX8TN7oyn7m5/xspTWu53vBfd4etDSYhjMoCzU7qFQX+mVqhV8QgOT9HnKV1XuptHKfraI9/c7+nJ+977Mb1/O9UD7e7pFnhd6ehB1UGne+sNz9Qqc6G4EuWC84pZV7QPv6Xu3KXlbt0ZR+LbVq0lXn1NjQTb38Bl7WUKsmXaW377qi1HNaHme/J8Ikjeia4p2CVhJuY6HSuFMV6k71K9XZCBbe7AJe1vACkrQm+6BXGyyXVu7xfS72Wk+/sj7nv22TXOq+PbktE4g9h3yhrOMsbXnJsmqR4TpaVFyuc1Pynnht1W79fdUP+tu/d+nvq3YFzfcvYQeVxp0vrAuNF8O0CAg2yXHRPgshX31/wNnrxtvB/+xye/M4yjsuVHm3D+QfRN4MYWUdZ2nLJZ1z69CTc/P3VT8E5fcvXc9F1/PKdOa4GiVfWKV90MoaL2ZN9kHdOnftOdu/fdcVSm9S26dlh/eFyq9vX/BF9+LKVt5xodzZPpDPizdDWFnHufC+dN3w0ppzBmvUWT80z3yMu+cmEL9/3b1+U7ODSuVutX5ZvyK93csA/hPIv76DgRV6yJS3tsid7QP1vHi7Vrqs48za/es5yx2SVEa1hrvnJtd+TL8cORG03780UEalq8jkl8Ey708wq4zG3/SuqTh3GyyHWmN+d86LP86JtxtZl3Wcl6XUPGd5mFRmDzt3wkpJL9r7394sY6T/ff0G1fcvNTsIOsEw708wKG06gNdW7dLcf++SUflqW8p7OypQf30HE3fasfiz9sxftygvdF78dU6qRYaXutyducBKO5dlHWfbBjXLbMh+9tQc7oSVs3+YGElhRnrx1nZq36hm0HxeCTsISt5sLBmKzjcdQAl3q9k9uXhwO7J05Q0I5wv+/mzM7+9blGWdF3+ek6NFxaUuLyxynPdx5zuXZR1nWctLlsVEhqmwyOHWj8XSfpg4JNWqFhVU38GEHSDElDUdQGkuVNvi6cWDWdrP5WlAKCv4+6v2LFB6TJZ2XvxZo+hp9/kLncuyXv/Slp+9rOR23vnCtVV+mBB2gBDjznQAJS70pVaRiwe3I/+fLwKCvy5SgXyL0p8Xbk8Cvi/Ppbvh2io/TAg7QIgp7Qu/NGHSBb/UKnrx4Hbkab64qPnrIhXINQH+vnCXN+D76lyWN1xb4YcJYQcIMaV94fdvV08fbtqrYmMUZpPu7NpYd3RNueCXmr8vHlbhq4uaPy5Sgf6eGHhZQ7VIqqGs3b/qspSaatugZqU+f3kCvq/OpSfhOth/mDCooBhU0JsYJC54nD1AW3kHeDvfvlB+7g64GSwC9T3hrcbTlfld5+1zGcgDL5aXu9dvwo4IO97i7x4YQLC70EWNHxMV462LfGV81/n6tbZKuGYEZVSqQOmB4SkuIggE57tVwI+JivNG26jK+K6rjNfaCu1wyoOwA68I5B4YF8JFBIEu2H9MBApvtI3y9XddZb7Wwd4OpzyYLgJe4e7Q9YHGX9MWhNoQ/qgYb081EKq8Md2Mr7/reK19g5odeEWg98Aoiz9qpMqqSeJWWvCo7NcqkLtz+4Ivz29Fb9/46ruu5JirRYaH1GtdWQg78Bpf3AP29UWlsi8iZdUk5R87qac++y+30oKAP257JsdFa2LfFs73SLD8mPBEZZzfit6+8fZ3XWnTt5QMBWHl17oy0RtL9MYKVJV1UanMXglrsg/q1rlrz1lus0nmrMAVjN1Arc5fXXbP/CzYJE3q20J/6N7EZ8/nL1bqEu2uso554X3pbs9fFcrojYWgVpmN9CqzV0JpNUlhOnc042Bp3B1q/HHbs7RZp5/+fLuuv7Se5d4fwdzRwVNlHXNhkUPpTWr7p1AWRANlBKTKbqSXHBet9Ca1ff6FWloDyYl9W3jc4JGGzpXLW41Ty/O6hVKD1UDr6FAZn69AO2aromYHAcnKDTJLq0mKj6lS7gaPdJl3daH2Xd5o/+WNxqnlfd2s/Fk4WyB1dKisz1cgHbOV0WZHtNkJVFYZ4dNd5RkSPhTbNpzPhS5M3r5weTp8v6evG5+F8j++IsHWH5+vQJ1ew13+6k1Kmx0EvVAb4bM8PURCsW1DWS7Uvsub7b/O/EL3pD2Fp68bnwX3eSPY+uPzFcwD/AVDLTNhBwEtmL8AfMmftzYCbTygC12YvHXh8sYXekVeNz4LF+atYBtKtw4rKlhG96aBMhCEvDESrCcWZOWoy8zlunXuWnWZuVwLsnJ8+nzuuFADT280APXWSNv+et1Chbcac/M6uS9YGtBTswPIs9oKf9dwVPatjUD9BXehBp7eaADqzdsavnzdyvOe9Pf71xe8WSMTarcOPRUstWCEHYQ8T25PBMo96sq8tRHI7YQudGGq6IWrPF/o7oQIX7xu5XlPBsr719u83bOJW4fuGdE1Va+s2lXm6N6BEKzpjSV6Y4UyT3pdhGpPqFA97hLu9IjyV4goz2tT3tcxEC5U5RXsPZuCxZnvd0m6tk2S7rqysdo2qFnqNr74TNAbC3CDJ7UVgVzD4UuhPh7IhWqH/HmbrzzvSXe2LQk43/xsD8o523xdIxOMAdDbzn6/S9Lir/P06Td5LpMbB8qtb8IOQpon95uD5R61LwRrOwZvXZzOdxH1Zwguz3vyQtue/Wu9RKC00fI3q94CLK/S3u+S6/skkH4YWqY31l//+lelpKSoatWq6tSpk9atW+fvIiEIeNLrItR7alTW1BreUhk9yHLtx/TLkRN+G/a/PO/J821b2q/1M/mql02wTHtS3l55wXJcniitl2OJkvdJIE2FYYmanQULFuiBBx7QnDlz1KlTJz333HPq06ePtm/froSEBH8XDwHufLUVZdUIBGsNR6ipjGr0s2ckL5nBvrJDcHnek2VtW9av9RK+uFAFU01JeWoqgum4PFESmie//40cZ60reZ8E0q1vSzRQ7tSpky677DK9+OKLkiSHw6EGDRro/vvv16RJky74eBooozRW/7IKBWuyD+rWuWvPWf72XVd4ZUbp0hr7hkl64dZ2at+oZtCF4NKOp4QvpqkItkbv7pY32I6rInLtx/Taqt36+6ofXHpjnfk+8WWD8ZBpoFxUVKQNGzZo8uTJzmVhYWHq1auXMjMz/VgyBLNAalgHz/m6fVVpv/QdkmpViwrK90lpv8QfuuZitbko3icXqkBq0+EOd2sqgu24KiI5LloP92upO7qmlBloAqELf9CHnYMHD6q4uFiJiYkuyxMTE/Xf//631MecOHFCJ06ccP6/oKDAp2VE8AmlLysr83U1uhUbq1fmLdpgPH/unJ9gPK6KCoRAcz6WaaBcHhkZGYqLi3P+NWjQwN9FQoAJpIZ1qJiBlzXUqklX6e27rtCqSVd59TaMVRurV1Yj9GA9fxc6P8F6XFYW9G12ioqKFBMTo/fee0/9+/d3Lh86dKjy8/P10UcfnfOY0mp2GjRoQJsduHBnEDlAYhC7irLq+bPqcQWSkGmzExkZqQ4dOmjZsmXOsONwOLRs2TKNGjWq1MdERUUpKiqqEkuJYESPK7gr0KvwA51Vz59VjysYBX3YkaQHHnhAQ4cOVceOHXX55Zfrueee09GjR3XHHXf4u2gIcnxZAUDws0TYGThwoA4cOKBHH31UeXl5uvTSS/X555+f02gZAACEnqBvs+MNjLMDAEDwcff6HZK9sQAAQOgg7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEsj7AAAAEuzxHQRFVUyiHRBQYGfSwIAANxVct2+0GQQhB1Jhw8fliQ1aNDAzyUBAADldfjwYcXFxZW5nrmxJDkcDu3du1c1atSQzWZz6zEFBQVq0KCB9uzZE9LzaXEeTuM8cA5KcB5O4zycxnnw7Tkwxujw4cOqV6+ewsLKbplDzY6ksLAwXXTRRR49NjY2NmTfwGfiPJzGeeAclOA8nMZ5OI3z4LtzcL4anRI0UAYAAJZG2AEAAJZG2PFQVFSUpk6dqqioKH8Xxa84D6dxHjgHJTgPp3EeTuM8BMY5oIEyAACwNGp2AACApRF2AACApRF2AACApRF2AACApRF2zjB79my1adPGOfBRenq6PvvsM+f648ePa+TIkapdu7aqV6+uAQMGaN++fS77yMnJUb9+/RQTE6OEhARNmDBBp06dquxD8aqZM2fKZrNp7NixzmWhcC6mTZsmm83m8teiRQvn+lA4B5L0888/67bbblPt2rUVHR2t1q1ba/369c71xhg9+uijSk5OVnR0tHr16qUdO3a47OPQoUMaPHiwYmNjFR8frxEjRujIkSOVfSgeS0lJOee9YLPZNHLkSEmh814oLi7WlClTlJqaqujoaDVp0kSPP/64y7xEofB+OHz4sMaOHatGjRopOjpanTt3VlZWlnO9Fc/BV199peuuu0716tWTzWbThx9+6LLeW8f89ddf68orr1TVqlXVoEEDPf300945AAOnRYsWmU8++cR8//33Zvv27ebhhx82VapUMVu3bjXGGHPPPfeYBg0amGXLlpn169ebK664wnTu3Nn5+FOnTplWrVqZXr16mU2bNplPP/3U1KlTx0yePNlfh1Rh69atMykpKaZNmzZmzJgxzuWhcC6mTp1qLrnkEpObm+v8O3DggHN9KJyDQ4cOmUaNGplhw4aZtWvXmh9++MF88cUXZufOnc5tZs6caeLi4syHH35otmzZYq6//nqTmppqjh075tzmmmuuMW3btjX/+c9/zL///W/TtGlTM2jQIH8ckkf279/v8j5YunSpkWRWrFhhjAmN94IxxsyYMcPUrl3bLF682Ozatcu8++67pnr16ub55593bhMK74ebb77ZpKWlmZUrV5odO3aYqVOnmtjYWPPTTz8ZY6x5Dj799FPzyCOPmIULFxpJ5oMPPnBZ741jttvtJjEx0QwePNhs3brVvP322yY6Otq8/PLLFS4/YecCatasaf7+97+b/Px8U6VKFfPuu+8613333XdGksnMzDTGnH4zhIWFmby8POc2s2fPNrGxsebEiROVXvaKOnz4sGnWrJlZunSp6d69uzPshMq5mDp1qmnbtm2p60LlHEycONF07dq1zPUOh8MkJSWZP/3pT85l+fn5Jioqyrz99tvGGGO+/fZbI8lkZWU5t/nss8+MzWYzP//8s+8K70NjxowxTZo0MQ6HI2TeC8YY069fPzN8+HCXZTfeeKMZPHiwMSY03g+FhYUmPDzcLF682GV5+/btzSOPPBIS5+DssOOtY37ppZdMzZo1XT4TEydONBdffHGFy8xtrDIUFxfrnXfe0dGjR5Wenq4NGzbo5MmT6tWrl3ObFi1aqGHDhsrMzJQkZWZmqnXr1kpMTHRu06dPHxUUFGjbtm2VfgwVNXLkSPXr18/lmCWF1LnYsWOH6tWrp8aNG2vw4MHKycmRFDrnYNGiRerYsaN+//vfKyEhQe3atdPcuXOd63ft2qW8vDyX8xAXF6dOnTq5nIf4+Hh17NjRuU2vXr0UFhamtWvXVt7BeElRUZHeeOMNDR8+XDabLWTeC5LUuXNnLVu2TN9//70kacuWLVq1apX69u0rKTTeD6dOnVJxcbGqVq3qsjw6OlqrVq0KiXNwNm8dc2Zmprp166bIyEjnNn369NH27dv166+/VqiMTAR6lm+++Ubp6ek6fvy4qlevrg8++EBpaWnavHmzIiMjFR8f77J9YmKi8vLyJEl5eXkuX2Yl60vWBZN33nlHGzdudLkPXSIvLy8kzkWnTp00b948XXzxxcrNzdX06dN15ZVXauvWrSFzDn744QfNnj1bDzzwgB5++GFlZWVp9OjRioyM1NChQ53HUdpxnnkeEhISXNZHRESoVq1aQXMezvThhx8qPz9fw4YNkxQ6nwdJmjRpkgoKCtSiRQuFh4eruLhYM2bM0ODBgyUpJN4PNWrUUHp6uh5//HG1bNlSiYmJevvtt5WZmammTZuGxDk4m7eOOS8vT6mpqefso2RdzZo1PS4jYecsF198sTZv3iy73a733ntPQ4cO1cqVK/1drEq1Z88ejRkzRkuXLj3n10soKfm1Kklt2rRRp06d1KhRI/3zn/9UdHS0H0tWeRwOhzp27Kgnn3xSktSuXTtt3bpVc+bM0dChQ/1cOv945ZVX1LdvX9WrV8/fRal0//znP/Xmm2/qrbfe0iWXXKLNmzdr7NixqlevXki9H/7xj39o+PDhql+/vsLDw9W+fXsNGjRIGzZs8HfRUAZuY50lMjJSTZs2VYcOHZSRkaG2bdvq+eefV1JSkoqKipSfn++y/b59+5SUlCRJSkpKOqcHRsn/S7YJBhs2bND+/fvVvn17RUREKCIiQitXrtSsWbMUERGhxMTEkDkXZ4qPj1fz5s21c+fOkHk/JCcnKy0tzWVZy5YtnbfzSo6jtOM88zzs37/fZf2pU6d06NChoDkPJX788Uf961//0p133ulcFirvBUmaMGGCJk2apFtuuUWtW7fWkCFDNG7cOGVkZEgKnfdDkyZNtHLlSh05ckR79uzRunXrdPLkSTVu3DhkzsGZvHXMvvycEHYuwOFw6MSJE+rQoYOqVKmiZcuWOddt375dOTk5Sk9PlySlp6frm2++cXlBly5dqtjY2HMuGIGsZ8+e+uabb7R582bnX8eOHTV48GDnv0PlXJzpyJEjys7OVnJycsi8H7p06aLt27e7LPv+++/VqFEjSVJqaqqSkpJczkNBQYHWrl3rch7y8/NdfvUuX75cDodDnTp1qoSj8J7XXntNCQkJ6tevn3NZqLwXJKmwsFBhYa6XjfDwcDkcDkmh936oVq2akpOT9euvv+qLL77Q7373u5A7B5L3Xvf09HR99dVXOnnypHObpUuX6uKLL67QLSxJdD0/06RJk8zKlSvNrl27zNdff20mTZpkbDabWbJkiTHmdPfShg0bmuXLl5v169eb9PR0k56e7nx8SffS3r17m82bN5vPP//c1K1bN+i6l5bmzN5YxoTGuXjwwQfNl19+aXbt2mVWr15tevXqZerUqWP2799vjAmNc7Bu3ToTERFhZsyYYXbs2GHefPNNExMTY9544w3nNjNnzjTx8fHmo48+Ml9//bX53e9+V2qX03bt2pm1a9eaVatWmWbNmgV0N9vSFBcXm4YNG5qJEyeesy4U3gvGGDN06FBTv359Z9fzhQsXmjp16piHHnrIuU0ovB8+//xz89lnn5kffvjBLFmyxLRt29Z06tTJFBUVGWOseQ4OHz5sNm3aZDZt2mQkmb/85S9m06ZN5scffzTGeOeY8/PzTWJiohkyZIjZunWreeedd0xMTAxdz71t+PDhplGjRiYyMtLUrVvX9OzZ0xl0jDHm2LFj5r777jM1a9Y0MTEx5oYbbjC5ubku+9i9e7fp27eviY6ONnXq1DEPPvigOXnyZGUfitedHXZC4VwMHDjQJCcnm8jISFO/fn0zcOBAl/FlQuEcGGPMxx9/bFq1amWioqJMixYtzN/+9jeX9Q6Hw0yZMsUkJiaaqKgo07NnT7N9+3aXbX755RczaNAgU716dRMbG2vuuOMOc/jw4co8jAr74osvjKRzjs2Y0HkvFBQUmDFjxpiGDRuaqlWrmsaNG5tHHnnEpatwKLwfFixYYBo3bmwiIyNNUlKSGTlypMnPz3eut+I5WLFihZF0zt/QoUONMd475i1btpiuXbuaqKgoU79+fTNz5kyvlN9mzBlDXwIAAFgMbXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAeKRHjx4aO3asv4vhc9OmTdOll17q72IAqADCDoCQVFRUVKnPZ4zRqVOnKvU5AZxG2AFQbsOGDdPKlSv1/PPPy2azyWazaffu3dq6dav69u2r6tWrKzExUUOGDNHBgwedj+vRo4fuv/9+jR07VjVr1lRiYqLmzp2ro0eP6o477lCNGjXUtGlTffbZZ87HfPnll7LZbPrkk0/Upk0bVa1aVVdccYW2bt3qUqZVq1bpyiuvVHR0tBo0aKDRo0fr6NGjzvUpKSl6/PHHdfvttys2NlZ33323JGnixIlq3ry5YmJi1LhxY02ZMsU56/K8efM0ffp0bdmyxXmc8+bN0+7du2Wz2bR582bn/vPz82Wz2fTll1+6lPuzzz5Thw4dFBUVpVWrVsnhcCgjI0OpqamKjo5W27Zt9d5773n7JQJwBsIOgHJ7/vnnlZ6errvuuku5ubnKzc1VjRo1dPXVV6tdu3Zav369Pv/8c+3bt08333yzy2Pnz5+vOnXqaN26dbr//vt177336ve//706d+6sjRs3qnfv3hoyZIgKCwtdHjdhwgQ988wzysrKUt26dXXdddc5Q0l2drauueYaDRgwQF9//bUWLFigVatWadSoUS77+POf/6y2bdtq06ZNmjJliiSpRo0amjdvnr799ls9//zzmjt3rp599llJ0sCBA/Xggw/qkksucR7nwIEDy3WuJk2apJkzZ+q7775TmzZtlJGRoddff11z5szRtm3bNG7cON12221auXJlufYLoBy8Mp0ogJDTvXt3M2bMGOf/H3/8cdO7d2+Xbfbs2eMyU3j37t1N165dnetPnTplqlWrZoYMGeJclpubaySZzMxMY8z/z7b8zjvvOLf55ZdfTHR0tFmwYIExxpgRI0aYu+++2+W5//3vf5uwsDBz7NgxY4wxjRo1Mv3797/gcf3pT38yHTp0cP5/6tSppm3bti7b7Nq1y0gymzZtci779ddfjSSzYsUKl3J/+OGHzm2OHz9uYmJizJo1a1z2N2LECDNo0KALlg2AZyL8GbQAWMeWLVu0YsUKVa9e/Zx12dnZat68uSSpTZs2zuXh4eGqXbu2Wrdu7VyWmJgoSdq/f7/LPtLT053/rlWrli6++GJ99913zuf++uuv9eabbzq3McbI4XBo165datmypSSpY8eO55RtwYIFmjVrlrKzs3XkyBGdOnVKsbGx5T7+spz5nDt37lRhYaF+85vfuGxTVFSkdu3aee05Abgi7ADwiiNHjui6667TU089dc665ORk57+rVKniss5ms7kss9lskiSHw1Gu5/7DH/6g0aNHn7OuYcOGzn9Xq1bNZV1mZqYGDx6s6dOnq0+fPoqLi9M777yjZ5555rzPFxZ2ugWAMca5rOSW2tnOfM4jR45Ikj755BPVr1/fZbuoqKjzPicAzxF2AHgkMjJSxcXFzv+3b99e77//vlJSUhQR4f2vlv/85z/O4PLrr7/q+++/d9bYtG/fXt9++62aNm1arn2uWbNGjRo10iOPPOJc9uOPP7psc/ZxSlLdunUlSbm5uc4amTMbK5clLS1NUVFRysnJUffu3ctVVgCeo4EyAI+kpKRo7dq12r17tw4ePKiRI0fq0KFDGjRokLKyspSdna0vvvhCd9xxxzlhwROPPfaYli1bpq1bt2rYsGGqU6eO+vfvL+l0j6o1a9Zo1KhR2rx5s3bs2KGPPvronAbKZ2vWrJlycnL0zjvvKDs7W7NmzdIHH3xwznHu2rVLmzdv1sGDB3XixAlFR0friiuucDY8Xrlypf74xz9e8Bhq1Kih8ePHa9y4cZo/f76ys7O1ceNGvfDCC5o/f77H5wbA+RF2AHhk/PjxCg8PV1pamurWrauioiKtXr1axcXF6t27t1q3bq2xY8cqPj7eedunImbOnKkxY8aoQ4cOysvL08cff6zIyEhJp9sBrVy5Ut9//72uvPJKtWvXTo8++qjq1at33n1ef/31GjdunEaNGqVLL71Ua9ascfbSKjFgwABdc801uuqqq1S3bl29/fbbkqRXX31Vp06dUocOHTR27Fg98cQTbh3H448/rilTpigjI0MtW7bUNddco08++USpqakenBUA7rCZM286A0CA+fLLL3XVVVfp119/VXx8vL+LAyAIUbMDAAAsjbADAAAsjdtYAADA0qjZAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlvZ/5oPfigwJYygAAAAASUVORK5CYII=", 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uzz//XOHh4Y4gUJrY2FilpKRo165duuiii1SvXj2FhIRUWpkLq1Onju655x498MADatiwoVq0aKE//elPysrK0vjx46ukDK4QWgAANZ4vbvs/9thjioqKUlJSkn744QfVr19fl156qR566KFy3Ya56aabtHr1al155ZXKyMjQ0qVLNWbMmEovd4H58+crPz9fo0aN0qlTpxQfH68PP/xQDRo0qLIyFGUzxpiyF6ueMjMzFRERIbvdrvDwcF8XBwDgZWfPnlVKSori4uJUu3ZtXxcHbijtvfP0+k3vIQAAYAmEFgAALOzuu+926lZd+O/uu+/2dfG8ijYtAABY2KOPPqrp06e7nFfTmk74NLTk5eVpzpw5Wr58uY4ePapmzZppzJgx+uMf/+jorw4AAEoWHR2t6OhoXxejSvg0tCxYsECLFi3Siy++qE6dOmnbtm0aO3asIiIiNHnyZF8WDQAAVDM+DS2bN2/W9ddfr6FDh0q60Cf9tdde01dffeXLYgEAqpnqPlIriquMzsk+DS29e/fW3//+d33//fdq27atdu/erU2bNunpp592uXxOTo5ycnIc/8/MzKyqogIAfCA4OFgBAQE6cuSIoqKiFBwcTPMBCzDG6Pjx4y4fF1ARPg0tiYmJyszMVPv27RUYGKi8vDzNmzdPI0eOdLl8UlKS5s6dW8WlBAD4SkBAgOLi4pSWlqYjR474ujhwg81m00UXXaTAwEDvrdOXg8utWLFCDzzwgJ588kl16tRJu3bt0tSpU/X0009r9OjRxZZ3VdMSExPD4HIAUMMZY5Sbm+uzZ/HAfbVq1SoxsHg6uJxPQ0tMTIwSExM1ceJEx7THH39cy5cv13fffVfm6xkRFwAA67HkiLhZWVkKCHAuQmBgIA2uAABAMT5t03Lttddq3rx5atGihTp16qSdO3fq6aef1rhx43xZLAAAUA359PbQqVOnNGvWLL355ps6duyYmjVrphEjRuiRRx5RcHBwma/n9hAAANZjyTYtFUVoAQDAeizZpgUAAKC8CC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASCC0AAMASfB5afv75Z91+++1q1KiRQkND1aVLF23bts3XxQIAANVMkC83fvLkSfXp00dXXnml1q5dq6ioKB04cEANGjTwZbEAAEA15NPQsmDBAsXExGjp0qWOaXFxcT4sEQAAqK58entozZo1io+P1y233KLo6Gh1795dS5Ys8WWRAABANeXT0PLDDz9o0aJFuvjii/Xhhx/qnnvu0eTJk/Xiiy+6XD4nJ0eZmZlOfwAAwD/YjDHGVxsPDg5WfHy8Nm/e7Jg2efJkbd26VVu2bCm2/Jw5czR37txi0+12u8LDwyu1rAAAwDsyMzMVERHh9vXbpzUtTZs2VceOHZ2mdejQQYcPH3a5/MyZM2W32x1/qampVVFMAABQDfi0IW6fPn20f/9+p2nff/+9WrZs6XL5kJAQhYSEVEXRAABANePTmpZp06bpiy++0BNPPKGDBw/q1Vdf1d///ndNnDjRl8UCAADVkE9Dy2WXXaY333xTr732mjp37qzHHntMCxcu1MiRI31ZLAAAUA35tCFuRXnakAcAAPiOJRviAgAAlBehBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWAKhBQAAWEJQeRfMzMws90rDw8M9KgwAAEBJyh1a6tevL5vNVuoyxhjZbDbl5eVVuGAAAACFlTu0rF+/vjLLAQAAUKpyh5b+/ftXZjkAAABKVe7QUlRGRob++c9/6ttvv5UkderUSePGjVNERITXCgcAAFDAo95D27ZtU+vWrfXMM8/oxIkTOnHihJ5++mm1bt1aO3bs8HYZAQAAZDPGGHdfdMUVV6hNmzZasmSJgoIuVNbk5uZqwoQJ+uGHH/Tpp596vaCuZGZmKiIiQna7nR5LAABYhKfXb49CS2hoqHbu3Kn27ds7Tf/mm28UHx+vrKwsd1fpEUILAADW4+n126PbQ+Hh4Tp8+HCx6ampqapXr54nqwQAACiVR6Fl+PDhGj9+vFauXKnU1FSlpqZqxYoVmjBhgkaMGOHtMgIAAHjWe+jPf/6zbDab7rjjDuXm5kqSatWqpXvuuUfz58/3agEBAAAkD9u0FMjKylJycrIkqXXr1goLC/NawcqDNi0AAFiPp9dvj8dpkaSwsDB16dKlIqsAAAAoF49Cy9mzZ/X8889r/fr1OnbsmPLz853mM1YLAADwNo9Cy/jx4/XRRx/p5ptvVs+ePct8kCIAAEBFeRRa3n33Xb3//vvq06ePt8sDAADgkkddnps3b854LAAAoEp5FFqeeuopzZgxQz/++KO3ywMAAOCSR7eH4uPjdfbsWbVq1UphYWGqVauW0/wTJ054pXAAAAAFPAotI0aM0M8//6wnnnhCjRs3piEuAACodB6Fls2bN2vLli3q1q2bt8sDAADgkkdtWtq3b6/s7GxvlwUAAKBEHoWW+fPn6/7779eGDRv066+/KjMz0+kPAADA2zx69lBAwIWsU7QtizFGNptNeXl53ildGXj2EAAA1lOlzx5av369Jy8DAADwmEehpX///uVa7t5779Wjjz6qyMhITzYDAADg4FGblvJavnw5bVwAAIBXVGpo8aC5DAAAgEuVGloAAAC8hdACAAAsgdACAAAsoVqFlvnz58tms2nq1Km+LgoAAKhm3A4tubm5evTRR/XTTz+Vueztt99e7kFjtm7dqhdeeEFdu3Z1t0gAAMAPuB1agoKC9OSTTyo3N7fMZRctWlSuMVpOnz6tkSNHasmSJWrQoIG7RQIAAH7Ao9tDV111lTZu3Oi1QkycOFFDhw7VoEGDvLZOAABQs3g0Iu6QIUOUmJiovXv3qkePHqpTp47T/Ouuu67c61qxYoV27NihrVu3lrlsTk6OcnJyHP9n4DoAAPyHR6Hl3nvvlSQ9/fTTxea588DE1NRUTZkyRevWrVPt2rXLXD4pKUlz5851r7AAAKBG8Ogpz97y1ltv6YYbblBgYKBjWl5enmw2mwICApSTk+M0z1VNS0xMDE95BgDAQjx9yrNHbVpeeuklp/BQ4Ny5c3rppZfKvZ6BAwdq79692rVrl+MvPj5eI0eO1K5du5wCiySFhIQoPDzc6Q8AAPgHj2paAgMDlZaWpujoaKfpv/76q6Kjo8t9e8iVAQMG6JJLLtHChQvLXNbTpAYAAHynSmtajDGy2WzFpv/000+KiIjwZJUAAAClcqshbvfu3WWz2WSz2TRw4EAFBf3v5Xl5eUpJSdE111xToQJt2LChQq8HAAA1k1uhZdiwYZKkXbt2afDgwapbt65jXnBwsGJjY3XTTTd5tYAAAACSm6Fl9uzZkqTY2FgNHz68XN2UAQAAvMGjcVpGjx4t6UJvoWPHjik/P99pfosWLSpeMgAAgEI8Ci0HDhzQuHHjtHnzZqfpBQ10K9J7CAAAwBWPQsuYMWMUFBSkd999V02bNnXZk6gmSLNnKyX9jOIi66hpRKiviwMAgF/zKLTs2rVL27dvV/v27b1dnmpj5dbDmrl6r/KNFGCTkm7souGXcdsLAABf8Wiclo4dOyo9Pd3bZak20uzZjsAiSflGemj1PqXZs31bMAAA/JhHoWXBggV68MEHtWHDBv3666/KzMx0+rO6lPQzjsBSIM8YHUrP8k2BAACAZ7eHBg0aJEm66qqrnNqz1JSGuHGRdRRgk1NwCbTZFBsZ5rtCAQDg5zwKLevXr/d2OaqVphGhSrqxix5avU95xijQZtMTN3amMS4AAD7kUWjp37+/PvvsM73wwgtKTk7WqlWr1Lx5c7388suKi4vzdhl9YvhlLdSvbZQOpWcpNjKMwAIAgI951KbljTfe0ODBgxUaGqqdO3cqJydHkmS32/XEE094tYC+1DQiVAmtGxFYAACoBjwKLY8//rgWL16sJUuWqFatWo7pffr00Y4dO7xWOAAAgAIehZb9+/erX79+xaZHREQoIyOjomUCAAAoxqPQ0qRJEx08eLDY9E2bNqlVq1YVLhQAAEBRHoWWO++8U1OmTNGXX34pm82mI0eO6JVXXtH06dN1zz33eLuMAAAAnvUeSkxMVH5+vgYOHKisrCz169dPISEhmj59uu677z5vlxEAAEA2Y4wpezHXzp07p4MHD+r06dPq2LGj6tat682ylSkzM1MRERGy2+0KDw+v0m0DAADPeHr99qimpUBwcLA6duxYkVUAAACUi0dtWgAAAKoaoQUAAFgCoaWKpdmztTk5XWn2bF8XBQAAS6lQmxa4Z+XWw5q5eq/yjRRgk5Ju7KLhl7XwdbEAALAEalqqSJo92xFYJCnfSA+t3keNCwAA5URoqSIp6WccgaVAnjE6lJ7lmwIBAGAxhJYqEhdZRwE252mBNptiI8N8UyAAACyG0FJFmkaEKunGLgq0XUgugTabnrixs5pGhPq4ZAAAWAMNcavQ8MtaqF/bKB1Kz1JsZBiBBQAANxBaqljTiFDCCgAAHuD2EAAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCCwAAsARCSzWVZs/W5uR0pdmzfV0UAACqhSBfFwDFrdx6WDNX71W+kQJsUtKNXTT8sha+LhYAAD5FTUs1k2bPdgQWSco30kOr91Hj4gFqqwCgZvFpaElKStJll12mevXqKTo6WsOGDdP+/ft9WSSfS0k/4wgsBfKM0aH0LN8UyKJWbj2sPvM/0W1LvlSf+Z9o5dbDvi4SAKCCfBpaNm7cqIkTJ+qLL77QunXrdP78eV199dU6c+aML4vlU3GRdRRgc54WaLMpNjLMNwWyIGqrAKBm8mmblg8++MDp/8uWLVN0dLS2b9+ufv36+ahUvtU0IlRJN3bRQ6v3Kc8YBdpseuLGzmoaEerrollGabVVTSNClWbPVkr6GcVF1uG4AoCFVKuGuHa7XZLUsGFDl/NzcnKUk5Pj+H9mZmaVlKuqL3LDL2uhfm2jdCg9S7GRYVxY3VRQW1U4uBTUVtHIGQCsq9o0xM3Pz9fUqVPVp08fde7c2eUySUlJioiIcPzFxMRUerl81TaiaUSoElo3IrB4oKC2KtB24T5bQW2VJG4bAYCF2YwxpuzFKt8999yjtWvXatOmTbroootcLuOqpiUmJkZ2u13h4eFeL1OaPVt95n9S7Bf7psQrCRMWkGbPdqqt2pycrtuWfFlsudfuvFwJrRv5oIQA4J8yMzMVERHh9vW7WtwemjRpkt599119+umnJQYWSQoJCVFISEiVlausthGo3ppGhDq9T6XdNgIAVH8+vT1kjNGkSZP05ptv6pNPPlFcXJwvi1MMPXlqlpJuGxFAAcAafFrTMnHiRL366qt6++23Va9ePR09elSSFBERodBQ319IKtKThx4q1RONnAHAunzapsVms7mcvnTpUo0ZM6bM13t6T8xdRdtGlIUeKgAAlMySbVqqSRvgMhVtG1GakgY269c2il/1AABUQLXp8lxTMAw/AACVg9DiZTTeBQCgchBavMxVD5UHh7RTSvoZBjEDAKACqsU4LTVN4R4qe37K0IK139EoFwCACqKmpZI0jQhVbGSYFnzwHcPGAwDgBYSWSkSjXAAAvIfQUomqW6PcNHu2NienU9MDALAkQkslqk7DxvvqadUAAHhLtXnKsyeqakTcinJ3RN3K2D5PqwYAVBeWHBHXX7gzom5l4GnVAICagNtDfqC6ta0BAMAThBY/UJ3a1gAA4CluD/mJwgPe+aptDQAAFUFo8SO+blsDAEBFcHsIfo2xawDAOqhpgd9aufWwZq7ey3OhAMAiqGmBX0qzZzsCi8RzoQDACggt8Es8FwoArIfQAr/E2DUAYD2EFvglxq4BAOuhIS78FmPXAIC1EFrg1xi7BgCsg9tDAADAEggtNRgDpwEAahJuD9VQDJwGAKhpqGmpgRg4DQBQExFaaiAGTgMA1ETcHqqBCgZOKxxcyho4Lc2erZT0M6oTHKgz5/IUF1mHXjUAgGqF0FIDFQyc9tDqfcozpsyB0wq3fylAOxgAQHVjM8aYshernjIzMxURESG73a7w8HBfF6faSbNnlzlwWpo9W33mf1LsdpJ0oXZmU+KV1LgAALzK0+s3NS01WHkGTnPV/qVAQTsYQgsAoDqgIa6fc/XgwAI8QBAAUJ0QWvxc0QcHFuABggCA6obbQ3B6cGBYcICyzuXzAEEAQLVDaIEk/3twYEEXb7p2A4B1EFrgd3jEAQBYE21a4Fd4xAEAWBehBX6FRxwAgHURWuBXXHXxpms3AFgDoQV+pWgXb7p2A4B10BAXfqdwF2+6dgOAdRBa4Jf8rYs3ANQE3B4CAACWQGjxoTR7tjYnp9PdFgCAcuD2kI8wwBkAAO6hpsUHGOAMAAD3EVp8gAHOAABwH6HFBxjgDAAA9xFafIABzgAAcB8NcX2EAc4AAHAPocUH0uzZSkk/o7jIOkpo3ajEeQQZAAD+h9BSyYqGkNK6OvtjN2hCGgCgvAgtlahoCJkxpL0WrP2uWFfnfm2jJKlYN+iZb+xVnZAg9WjZoEZe0P0xpAEAPEdD3EriaiyWwoGlQEFXZ1fdoPMlTXp1p/rM/0Qrtx6uknJXFcaqAQC4i9BSSVyGECMV6ens6Orsqht04dfVtAv60k0pjFUDAHALoaWSlDQWS+KQ9i67OhftBl1UTbqgp9mzteSzlGLTA2xirBoAQIlo01JJCkLIQ6v3Kc8YR0AZflkLXXdJM5ddnQu6QW8/dFKTV+x0qomoSYPPpaSfkXExfULfVjWy7Q4AeJM/d2AgtFSiksZiKahZcaVpRKh+2y1UZ87lFgs8np6c1e0EL6iFKhzKAiSN7RvrqyLBT1S3zwJQkpLO1bI6MNT0c9xmjHH1o9cSMjMzFRERIbvdrvDwcF8Xx+vS7NkVHnyupBPc1yf2yq2HXdZCAZWF3mrFpdmzte3QCdlsNo96Kfr6e6Qy+WLfCra59ye7FnzwnVPP0y7NI1QnOFA3/G1zsVr4TYlXljmkRnXj6fWb0FKDpdmz1Wf+J8VO8Aevaef0gfDVie2NUAaUR0mfhYIve3+0cuthJb6x13Gr1iZp/k3l/y544dNkzV/7nUwZ3yO+DjaebN8XF//C2yyJzSa5umK/duflio0Ms9Q57un1m9tDNVhJT5Oev/Y7xxdV4bFiqvrELu02GUrm64tAdVOe41Hak9X96RgWHKs6wYFOgUWSjC6MFVWe74IXNiYrae13jv+X9D3i61/+nmy/pOEYKvM7sug2S+IqsBS0d/SXc5zQUoO5bDtS5P9SzTyxayp/v59dVHkvSq4+CzWpcXuB0t7/wsfKJrlsDJ9vVOZ3QZo9W/MLBZYCRb9HKnLx98Z57On2fXHxd7XN0gTowjheRds7+sM5TpdnN6XZs7U5Od0SY6a4epr0jCHtXXbFLu+JbaX9r2nKGpBv5dbD6jP/E9225MtqNSBhZZ0z7gxQWN2frF74GHl6vEp7/4seq5Kuj+UZdqCk3n9FX1vaxd/T/XCHp9svabiKyrz4lzZOV1GBNpvenNhbr915uTYlXukI6ZV1jle373xqWtzg66pOT7jqwVQ/tFa5eia589wkVL6yvoSrukq7LGn2bP1rU4r++d+BBL19zrj7i7i6Plm9aA2IdCFUuHO8yqpVKOmXfOEaF9t/t1fWcXFVayVdaCxa+LWe1G6Vp3akvLUwntaulTRcRWWeL662+eCQduravL72/JShP32w36ks3WIaOL2+4Jj0axulTYlXeu0cr47f+YSWcvLFfc7ylKk8H96ibUfK8+XtznOTrNjjoDqUwV2lfQlXt/vZRRt5St7/zHhyUfJlOypX51xpNSDuPH+srPe/pGO1+t4EpZ7Ils0mXVrO3kNFL7ABuvD9cF23ZtqcnO7YP08u/mXthzsX0WLltEnjyjmsQkUDriffLyVtM6F1oxLH9pIqL1hUx2ueRGgpt+p4UajIiVral7e7z01yZ/+rQ3KvDmUoj6JffGVdBCrjfrYnX74F54+rWwje/MyUdDwkOV08SytnWfvmrXBb7EfANe3V5aIInThzrtS2DAXPHyt6nhYtV1kBrqRj1S2mQbFf7eVR9AL76ffHHT1XCpfV3Yt/afvhyUW0YPtLP0/Rkk9TtOSzCzV/5fnMexpwK/L9Unibrj7/RadLlVfDWt2ueQUILeVUnRryVXYCLu25SYUnu7v/VZncS7rYVEYZKqPWpqQvvtIGLCz66/fBa9p59UJb3i/f0hoVevszU9bFc3zfOI3rG+fRr1NvhVtX51xBzxubSm4UW1jh8/TT74+7LFdZtRrevj1WcCEt6zNVeLmywmRpwXxzcnq5LqKuPo//+CylUnpMFt1WSceifZN6OnMur9zfESWde0WnT+gbV2nBojpd8wojtJSTL+5zlqSyE3BJJ+uD17Qrdm/Vne1VVXIv7WLjaRk8HZ3SE+W9CBQ1/LIWysg+r/n/rRWbv/Y7/Xo6R2NdXLQrWobSlNTmIcAmxznjzaBX2sVzyWcp+sdnKU7jj5S33YS7+1/SPpUW4owuhJaC42X7b4IpqZZq+6GTJZarPKGkMm6Plecz5c7npKT9iIusUyzg2eTc+NfVdmIahlXK94472xr2t81ljmdToLTgU3T6Pz5LqbRgUZ2ueYURWtxQXRryVXYCLulkdfXcJHcuPlWR3Mu62HhShtJGFa6MmqOKBKsF/x3sS7rw5f73z1L0j3JWh3ujDJLrWp8J/eI0tk9cpTboLikcGJXdKDXPGO348aQa1LlwLru7/6XtU0khrnD5nv9ddzWqG+I4D0t6/pjKGLLAF212yvpMefI5Kfd+FOpxU9J2Vt+b4PXvHXe2Jf1vfJXy7HtJ597WQyeL14BLuqtvK/1zU0qlBIvqcs0rrFp0ef7rX/+q2NhY1a5dW7169dJXX33l6yKVqGlEqBJaN6q0N6883csKLgqV2X1z+GUttCnxSpfd6gr235OuieP7xjm69lVGucvqYePusSutW+32H4t/iXjjadyedrks6aJduMyVXYYChc+fz2depYf+r2Op1efe6E5ZJzhQJTwk3RFKpJK7l058dafjXN77k73c+1/WPhU954oKkBTT8H+fq6YRofptt2Yuz9MeLRtUeXfcspT1mfK063FRrrpaGyPHekraTta5/FLL50mX3vJuy9UFtqx9L+mzd1ms6/d+bN9Yl9/V3lLZ1zx3+bymZeXKlfrDH/6gxYsXq1evXlq4cKEGDx6s/fv3Kzo62tfFq1LeqEKtCFcNv9y9mJf0C6Jot867Cv3y9qby1KS4c+xK+nKa9+43enfv0WLLe+MC4mm1bGm/6N2tDvdG1bCr86eybhEWnF+lPZRk0qs7dTonV8Mva6EZQ9or6f3iA6RJF47fnz7YrxlD2utPa8u+HVqefSp8zu35OcOxXunCr+Ub/ra52Oe9pPO0OlbZl/aZ8lYNa53gwFLb1ZW2nYTWjdSvbZS2Hzop2aQeLS80Pva01q882zqUnqWw4ACXzwoqq4dbSY2mb+jeXG/s+Nmx7LDuzZzatfkDnz97qFevXrrsssv0l7/8RZKUn5+vmJgY3XfffUpMTCz1tTXp2UO+fjaKux/ezcnpum3Jl8Wmv3bn5Upo3chpWlXvmzcfxlh0uPLSeLsnkifPZlq59bBmvrFX+UWme3q8vf18qMo4F1ytU3LdwLVgWynpZ1yev4UVPM+lrP33ZJ92p550tHMo72uKbrM6VdmXpaKfSVfP5XG1ntK246oHV8Ez2Aqvs7zvQXn3ydN9L/oe+/oa4W2WfPbQuXPntH37ds2cOdMxLSAgQIMGDdKWLVuKLZ+Tk6OcnBzH/zMzM6uknFXBl93LPLnn7M6vp6reN2/VQqXZs7Xgg/IFFkl67nfd9dtuzTzaliuetE9wdPHcdEj/2PSD8k3FbsN5u41EZTTuK+m22H1XtdFznxx0mlZw3rlq1FlYwblcnv33ZJ/OnMsrVivkzmfCF21XKqIin0lXz+UJsEmr700o1l27pO2UOIxDkW258x6Ud5883fei73F17YJc1XwaWtLT05WXl6fGjRs7TW/cuLG++674xSIpKUlz586tquJVKV92L/Pkw+DOF7Uv9s0bX+ruPA8k0GZTj1j3x7uoDE0jQvXQ0A4a2ze2Wv4a9/atzZLOr4EdovWX9QddnndNI0KVOKS9y1q0AMntIOXN8UhqKk8/kyUNwZB1rmjkKHk7Lteh4k9Ndvc9KO8+eeP7yB/PGVeqRUPc8po5c6bsdrvjLzU11ddF8pqqaFxbEk8bXZbUWLcoX+5bRZT3eSCeXOSqQnVrQFeYN8tW0vnVLaZBqefd7/u31sz/+9+zuAJs0l1XtNLnM6/y6BafO/tk1c+EL3jjWUAlrSNxSHvLvAecMxf4tE3LuXPnFBYWplWrVmnYsGGO6aNHj1ZGRobefvvtUl9fk9q0FPDVvWpvtgMpidXuw0vFj8uw7s301s4jjmHBJ/RtpbF9Yy2zPzVZSedXWeedL89LK34mfMEb308lrcNq74HVylsST6/f1aIhbs+ePfX8889LutAQt0WLFpo0aZJfNcStDmrKh8HbXDWI4zgBVcsbnzs+u9WHJRviStIf/vAHjR49WvHx8erZs6cWLlyoM2fOaOzYsb4umt+xWuO+qlL0uHCcgKrnjc8dn13r83loGT58uI4fP65HHnlER48e1SWXXKIPPvigWONcAADg33x+e6giuD0EAID1eHr9tlTvIQAA4L8ILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBJ8Pox/RRQM5puZmenjkgAAgPIquG67Oyi/pUPLqVOnJEkxMTE+LgkAAHDXqVOnFBERUe7lLf3sofz8fB05ckT16tWTzWbzeD2ZmZmKiYlRamqq3z/DiGNxAcfhAo7DBRyHCzgOF3Ac/sfTY2GM0alTp9SsWTMFBJS/pYqla1oCAgJ00UUXeW194eHhfn8CFuBYXMBxuIDjcAHH4QKOwwUch//x5Fi4U8NSgIa4AADAEggtAADAEggtkkJCQjR79myFhIT4uig+x7G4gONwAcfhAo7DBRyHCzgO/1PVx8LSDXEBAID/oKYFAABYAqEFAABYAqEFAABYAqEFAABYQo0NLYsWLVLXrl0dA94kJCRo7dq1jvlnz57VxIkT1ahRI9WtW1c33XSTfvnlF6d1HD58WEOHDlVYWJiio6P1wAMPKDc3t6p3xavmz58vm82mqVOnOqb5y7GYM2eObDab01/79u0d8/3lOEjSzz//rNtvv12NGjVSaGiounTpom3btjnmG2P0yCOPqGnTpgoNDdWgQYN04MABp3WcOHFCI0eOVHh4uOrXr6/x48fr9OnTVb0rHouNjS12PthsNk2cOFGS/5wPeXl5mjVrluLi4hQaGqrWrVvrsccec3omjD+cD9KFIeWnTp2qli1bKjQ0VL1799bWrVsd82vqcfj000917bXXqlmzZrLZbHrrrbec5ntrv/fs2aMrrrhCtWvXVkxMjP70pz+5X1hTQ61Zs8a899575vvvvzf79+83Dz30kKlVq5bZt2+fMcaYu+++28TExJiPP/7YbNu2zVx++eWmd+/ejtfn5uaazp07m0GDBpmdO3ea999/30RGRpqZM2f6apcq7KuvvjKxsbGma9euZsqUKY7p/nIsZs+ebTp16mTS0tIcf8ePH3fM95fjcOLECdOyZUszZswY8+WXX5offvjBfPjhh+bgwYOOZebPn28iIiLMW2+9ZXbv3m2uu+46ExcXZ7Kzsx3LXHPNNaZbt27miy++MJ999plp06aNGTFihC92ySPHjh1zOhfWrVtnJJn169cbY/znfJg3b55p1KiReffdd01KSop5/fXXTd26dc2zzz7rWMYfzgdjjLn11ltNx44dzcaNG82BAwfM7NmzTXh4uPnpp5+MMTX3OLz//vvm4YcfNqtXrzaSzJtvvuk03xv7bbfbTePGjc3IkSPNvn37zGuvvWZCQ0PNCy+84FZZa2xocaVBgwbmH//4h8nIyDC1atUyr7/+umPet99+aySZLVu2GGMuvIkBAQHm6NGjjmUWLVpkwsPDTU5OTpWXvaJOnTplLr74YrNu3TrTv39/R2jxp2Mxe/Zs061bN5fz/Ok4zJgxw/Tt27fE+fn5+aZJkybmySefdEzLyMgwISEh5rXXXjPGGPPNN98YSWbr1q2OZdauXWtsNpv5+eefK6/wlWjKlCmmdevWJj8/36/Oh6FDh5px48Y5TbvxxhvNyJEjjTH+cz5kZWWZwMBA8+677zpNv/TSS83DDz/sN8ehaGjx1n7/7W9/Mw0aNHD6bMyYMcO0a9fOrfLV2NtDheXl5WnFihU6c+aMEhIStH37dp0/f16DBg1yLNO+fXu1aNFCW7ZskSRt2bJFXbp0UePGjR3LDB48WJmZmfr666+rfB8qauLEiRo6dKjTPkvyu2Nx4MABNWvWTK1atdLIkSN1+PBhSf51HNasWaP4+Hjdcsstio6OVvfu3bVkyRLH/JSUFB09etTpWERERKhXr15Ox6J+/fqKj493LDNo0CAFBAToyy+/rLqd8ZJz585p+fLlGjdunGw2m1+dD71799bHH3+s77//XpK0e/dubdq0SUOGDJHkP+dDbm6u8vLyVLt2bafpoaGh2rRpk98ch6K8td9btmxRv379FBwc7Fhm8ODB2r9/v06ePFnu8lj6gYll2bt3rxISEnT27FnVrVtXb775pjp27Khdu3YpODhY9evXd1q+cePGOnr0qCTp6NGjTl9GBfML5lnJihUrtGPHDqd7swWOHj3qN8eiV69eWrZsmdq1a6e0tDTNnTtXV1xxhfbt2+dXx+GHH37QokWL9Ic//EEPPfSQtm7dqsmTJys4OFijR4927IurfS18LKKjo53mBwUFqWHDhpY6FgXeeustZWRkaMyYMZL863ORmJiozMxMtW/fXoGBgcrLy9O8efM0cuRISfKb86FevXpKSEjQY489pg4dOqhx48Z67bXXtGXLFrVp08ZvjkNR3trvo0ePKi4urtg6CuY1aNCgXOWp0aGlXbt22rVrl+x2u1atWqXRo0dr48aNvi5WlUpNTdWUKVO0bt26Yr8g/E3BL0dJ6tq1q3r16qWWLVvq3//+t0JDQ31YsqqVn5+v+Ph4PfHEE5Kk7t27a9++fVq8eLFGjx7t49L5xj//+U8NGTJEzZo183VRqty///1vvfLKK3r11VfVqVMn7dq1S1OnTlWzZs387nx4+eWXNW7cODVv3lyBgYG69NJLNWLECG3fvt3XRcN/1ejbQ8HBwWrTpo169OihpKQkdevWTc8++6yaNGmic+fOKSMjw2n5X375RU2aNJEkNWnSpFhPgYL/FyxjBdu3b9exY8d06aWXKigoSEFBQdq4caOee+45BQUFqXHjxn5zLIqqX7++2rZtq4MHD/rVOdG0aVN17NjRaVqHDh0ct8oK9sXVvhY+FseOHXOan5ubqxMnTljqWEjSjz/+qP/85z+aMGGCY5o/nQ8PPPCAEhMT9bvf/U5dunTRqFGjNG3aNCUlJUnyr/OhdevW2rhxo06fPq3U1FR99dVXOn/+vFq1auVXx6Ewb+23tz4vNTq0FJWfn6+cnBz16NFDtWrV0scff+yYt3//fh0+fFgJCQmSpISEBO3du9fpjVi3bp3Cw8OLfeFXZwMHDtTevXu1a9cux198fLxGjhzp+Le/HIuiTp8+reTkZDVt2tSvzok+ffpo//79TtO+//57tWzZUpIUFxenJk2aOB2LzMxMffnll07HIiMjw+kX6CeffKL8/Hz16tWrCvbCe5YuXaro6GgNHTrUMc2fzoesrCwFBDhfCgIDA5Wfny/J/84HSapTp46aNm2qkydP6sMPP9T111/vl8dB8t77n5CQoE8//VTnz593LLNu3Tq1a9eu3LeGJNXcLs+JiYlm48aNJiUlxezZs8ckJiYam81mPvroI2PMhe6MLVq0MJ988onZtm2bSUhIMAkJCY7XF3RnvPrqq82uXbvMBx98YKKioizXndGVwr2HjPGfY3H//febDRs2mJSUFPP555+bQYMGmcjISHPs2DFjjP8ch6+++soEBQWZefPmmQMHDphXXnnFhIWFmeXLlzuWmT9/vqlfv755++23zZ49e8z111/vsotj9+7dzZdffmk2bdpkLr744mrftbOovLw806JFCzNjxoxi8/zlfBg9erRp3ry5o8vz6tWrTWRkpHnwwQcdy/jL+fDBBx+YtWvXmh9++MF89NFHplu3bqZXr17m3LlzxpiaexxOnTpldu7caXbu3Gkkmaefftrs3LnT/Pjjj8YY7+x3RkaGady4sRk1apTZt2+fWbFihQkLC6PLc4Fx48aZli1bmuDgYBMVFWUGDhzoCCzGGJOdnW3uvfde06BBAxMWFmZuuOEGk5aW5rSOQ4cOmSFDhpjQ0FATGRlp7r//fnP+/Pmq3hWvKxpa/OVYDB8+3DRt2tQEBweb5s2bm+HDhzuNTeIvx8EYY9555x3TuXNnExISYtq3b2/+/ve/O83Pz883s2bNMo0bNzYhISFm4MCBZv/+/U7L/Prrr2bEiBGmbt26Jjw83IwdO9acOnWqKnejwj788EMjqdi+GeM/50NmZqaZMmWKadGihaldu7Zp1aqVefjhh526pvrL+bBy5UrTqlUrExwcbJo0aWImTpxoMjIyHPNr6nFYv369kVTsb/To0cYY7+337t27Td++fU1ISIhp3ry5mT9/vttltRlTaNhDAACAasqv2rQAAADrIrQAAABLILQAAABLILQAAABLILQAAABLILQAAABLILQAAABLILQAAABLILQAfmjAgAGaOnWqr4tR6ebMmaNLLrnE18UA4CWEFgCWc+7cuSrdnjFGubm5VbpNAMURWgA/M2bMGG3cuFHPPvusbDabbDabDh06pH379mnIkCGqW7euGjdurFGjRik9Pd3xugEDBui+++7T1KlT1aBBAzVu3FhLlizRmTNnNHbsWNWrV09t2rTR2rVrHa/ZsGGDbDab3nvvPXXt2lW1a9fW5Zdfrn379jmVadOmTbriiisUGhqqmJgYTZ48WWfOnHHMj42N1WOPPaY77rhD4eHhuuuuuyRJM2bMUNu2bRUWFqZWrVpp1qxZjqfILlu2THPnztXu3bsd+7ls2TIdOnRINptNu3btcqw/IyNDNptNGzZscCr32rVr1aNHD4WEhGjTpk3Kz89XUlKS4uLiFBoaqm7dumnVqlXefosAlIDQAviZZ599VgkJCbrzzjuVlpamtLQ01atXT1dddZW6d++ubdu26YMPPtAvv/yiW2+91em1L774oiIjI/XVV1/pvvvu0z333KNbbrlFvXv31o4dO3T11Vdr1KhRysrKcnrdAw88oKeeekpbt25VVFSUrr32Wke4SE5O1jXXXKObbrpJe/bs0cqVK7Vp0yZNmjTJaR1//vOf1a1bN+3cuVOzZs2SJNWrV0/Lli3TN998o2effVZLlizRM888I0kaPny47r//fnXq1Mmxn8OHD3frWCUmJmr+/Pn69ttv1bVrVyUlJemll17S4sWL9fXXX2vatGm6/fbbtXHjRrfWC8BDnj0TEoCVFX3S92OPPWauvvpqp2VSU1OdnoDcv39/07dvX8f83NxcU6dOHTNq1CjHtLS0NCPJbNmyxRjzv6fHrlixwrHMr7/+akJDQ83KlSuNMcaMHz/e3HXXXU7b/uyzz0xAQIDJzs42xhjTsmVLM2zYsDL368knnzQ9evRw/H/27NmmW7duTsukpKQYSWbnzp2OaSdPnjSSzPr1653K/dZbbzmWOXv2rAkLCzObN292Wt/48ePNiBEjyiwbgIoL8mVgAlA97N69W+vXr1fdunWLzUtOTlbbtm0lSV27dnVMDwwMVKNGjdSlSxfHtMaNG0uSjh075rSOhIQEx78bNmyodu3a6dtvv3Vse8+ePXrllVccyxhjlJ+fr5SUFHXo0EGSFB8fX6xsK1eu1HPPPafk5GSdPn1aubm5Cg8Pd3v/S1J4mwcPHlRWVpZ+85vfOC1z7tw5de/e3WvbBFAyQgsAnT59Wtdee60WLFhQbF7Tpk0d/65Vq5bTPJvN5jTNZrNJkvLz893a9u9//3tNnjy52LwWLVo4/l2nTh2neVu2bNHIkSM1d+5cDR48WBEREVqxYoWeeuqpUrcXEHDhrrgxxjGt4FZVUYW3efr0aUnSe++9p+bNmzstFxISUuo2AXgHoQXwQ8HBwcrLy3P8/9JLL9Ubb7yh2NhYBQV5/2vhiy++cASQkydP6vvvv3fUoFx66aX65ptv1KZNG7fWuXnzZrVs2VIPP/ywY9qPP/7otEzR/ZSkqKgoSVJaWpqjhqRwo9ySdOzYUSEhITp8+LD69+/vVlkBeAcNcQE/FBsbqy+//FKHDh1Senq6Jk6cqBMnTmjEiBHaunWrkpOT9eGHH2rs2LHFLvqeePTRR/Xxxx9r3759GjNmjCIjIzVs2DBJF3oAbd68WZMmTdKuXbt04MABvf3228Ua4hZ18cUX6/Dhw1qxYoWSk5P13HPP6c033yy2nykpKdq1a5fS09OVk5Oj0NBQXX755Y4Gths3btQf//jHMvehXr16mj59uqZNm6YXX3xRycnJ2rFjh55//nm9+OKLHh8bAOVHaAH80PTp0xUYGKiOHTsqKipK586d0+eff668vDxdffXV6tKli6ZOnar69es7bqdUxPz58zVlyhT16NFDR48e1TvvvKPg4GBJF9rJbNy4Ud9//72uuOIKde/eXY888oiaNWtW6jqvu+46TZs2TZMmTdIll1yizZs3O3oVFbjpppt0zTXX6Morr1RUVJRee+01SdK//vUv5ebmqkePHpo6daoef/zxcu3HY489plmzZikpKUkdOnTQNddco/fee09xcXEeHBUA7rKZwjd2AcCLNmzYoCuvvFInT55U/fr1fV0cABZHTQsAALAEQgsAALAEbg8BAABLoKYFAABYAqEFAABYAqEFAABYAqEFAABYAqEFAABYAqEFAABYAqEFAABYAqEFAABYAqEFAABYwv8Dy9AaUBUCbhEAAAAASUVORK5CYII=", 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", 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" ] From 1bd8a63b211d209dd15dbd5d2de576f829a09c3a Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Tue, 22 Aug 2023 10:32:30 -0400 Subject: [PATCH 04/75] Cleaned up pdf plots and filenames --- .../ALAMO/SCO2_alamo_surrogate.ipynb | 24 ++++++++---------- .../SCO2_example/ALAMO/alamo_run.trc | 3 +++ .../SCO2_example/ALAMO/alamo_train_parity.pdf | Bin 29828 -> 0 bytes .../ALAMO/alamo_train_residual.pdf | Bin 47095 -> 0 bytes .../ALAMO/alamo_train_scatter2D.pdf | Bin 67174 -> 0 bytes .../SCO2_example/ALAMO/alamo_val_parity.pdf | Bin 22863 -> 0 bytes .../SCO2_example/ALAMO/alamo_val_residual.pdf | Bin 27075 -> 0 bytes .../ALAMO/alamo_val_scatter2D.pdf | Bin 33248 -> 0 bytes .../OMLT/SCO2_keras_surrogate.ipynb | 16 +++++------- 9 files changed, 20 insertions(+), 23 deletions(-) delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_train_parity.pdf delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_train_residual.pdf delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_train_scatter2D.pdf delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_val_parity.pdf delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_val_residual.pdf delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_val_scatter2D.pdf diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb index 187b9b05..57ceee63 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb @@ -165,7 +165,7 @@ " User provided an initial data set of 400 data points\n", " We will sample no more data points at this stage\n", " ***************************************************************************\n", - " Iteration 1 (Approx. elapsed time 0.47E-01 s)\n", + " Iteration 1 (Approx. elapsed time 0.62E-01 s)\n", " \n", " Step 1: Model building using BIC\n", " \n", @@ -241,12 +241,12 @@ " RIC: 606.\n", " MADp: 0.130E+04\n", " \n", - " Total execution time 0.38 s\n", + " Total execution time 0.52 s\n", " Times breakdown\n", " OLR time: 0.30 s in 3863 ordinary linear regression problem(s)\n", " MINLP time: 0.0 s in 0 optimization problem(s)\n", " Simulation time: 0.0 s to simulate 0 point(s)\n", - " All other time: 0.78E-01 s in 1 iteration(s)\n", + " All other time: 0.22 s in 1 iteration(s)\n", " \n", " Normal termination\n", " ***************************************************************************\n" @@ -411,11 +411,10 @@ } ], "source": [ - "if has_alamo:\n", - " # visualize with IDAES surrogate plotting tools\n", - " surrogate_scatter2D(alm_surr, data_training, filename=\"alamo_train_scatter2D.pdf\")\n", - " surrogate_parity(alm_surr, data_training, filename=\"alamo_train_parity.pdf\")\n", - " surrogate_residual(alm_surr, data_training, filename=\"alamo_train_residual.pdf\")" + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(alm_surr, data_training)\n", + "surrogate_parity(alm_surr, data_training)\n", + "surrogate_residual(alm_surr, data_training)" ] }, { @@ -532,11 +531,10 @@ } ], "source": [ - "if has_alamo:\n", - " # visualize with IDAES surrogate plotting tools\n", - " surrogate_scatter2D(alm_surr, data_validation, filename=\"alamo_val_scatter2D.pdf\")\n", - " surrogate_parity(alm_surr, data_validation, filename=\"alamo_val_parity.pdf\")\n", - " surrogate_residual(alm_surr, data_validation, filename=\"alamo_val_residual.pdf\")" + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(alm_surr, data_validation)\n", + "surrogate_parity(alm_surr, data_validation)\n", + "surrogate_residual(alm_surr, data_validation)" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc index 4a3342a4..4e4089e4 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc @@ -86,3 +86,6 @@ c:\Users\javal\Desktop\Internship\IDAES-examples\idaes_examples\notebooks\docs\s #filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model c:\Users\javal\Desktop\Internship\IDAES-examples\idaes_examples\notebooks\docs\surrogates\SCO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.15625000, 1816, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.31250000, 0.0000000, 0.17187500, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 c:\Users\javal\Desktop\Internship\IDAES-examples\idaes_examples\notebooks\docs\surrogates\SCO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.14062500, 2047, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.78125000E-01, 1, 0, 0.0000000, 0.0000000, 0.31250000, 0.0000000, 0.14062500, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure^2 - 0.55969916790357242958320E-003 * CO2SM_Temperature^2 - 0.21077870265129327632947E-002 * CO2SM_Pressure^3 + 0.24061231665087056461711E-006 * CO2SM_Temperature^3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +c:\Users\javal\Desktop\Internship\IDAES-examples\idaes_examples\notebooks\docs\surrogates\SCO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.10937500, 1816, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.21875000, 1, 0, 0.0000000, 0.0000000, 0.42187500, 0.0000000, 0.20312500, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 +c:\Users\javal\Desktop\Internship\IDAES-examples\idaes_examples\notebooks\docs\surrogates\SCO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.18750000, 2047, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.21875000, 1, 0, 0.0000000, 0.0000000, 0.42187500, 0.0000000, 0.21875000, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure^2 - 0.55969916790357242958320E-003 * CO2SM_Temperature^2 - 0.21077870265129327632947E-002 * CO2SM_Pressure^3 + 0.24061231665087056461711E-006 * CO2SM_Temperature^3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045 diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_train_parity.pdf b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_train_parity.pdf deleted file mode 100644 index 362b0d71f4d59db49b2ce4f85b44dfccddf638d4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 29828 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fd6f6ce2..0e763b7f 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb @@ -164,505 +164,505 @@ "output_type": "stream", "text": [ "Epoch 1/250\n", - "13/13 - 2s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 2s/epoch - 173ms/step\n", + "13/13 - 5s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 5s/epoch - 368ms/step\n", "Epoch 2/250\n", - "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 220ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 475ms/epoch - 37ms/step\n", "Epoch 3/250\n", - "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 228ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 307ms/epoch - 24ms/step\n", "Epoch 4/250\n", - "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 239ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 392ms/epoch - 30ms/step\n", "Epoch 5/250\n", - "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 229ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 329ms/epoch - 25ms/step\n", "Epoch 6/250\n", - "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 202ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 246ms/epoch - 19ms/step\n", "Epoch 7/250\n", - "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 241ms/epoch - 19ms/step\n", + "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 476ms/epoch - 37ms/step\n", "Epoch 8/250\n", - "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 233ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 272ms/epoch - 21ms/step\n", "Epoch 9/250\n", - "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 227ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 248ms/epoch - 19ms/step\n", "Epoch 10/250\n", - "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 240ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 443ms/epoch - 34ms/step\n", "Epoch 11/250\n", - "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 224ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 382ms/epoch - 29ms/step\n", "Epoch 12/250\n", - "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 227ms/epoch - 17ms/step\n", + "13/13 - 1s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 596ms/epoch - 46ms/step\n", "Epoch 13/250\n", - "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 197ms/epoch - 15ms/step\n", + "13/13 - 1s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 566ms/epoch - 44ms/step\n", "Epoch 14/250\n", - "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 234ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 289ms/epoch - 22ms/step\n", "Epoch 15/250\n", - "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 207ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 348ms/epoch - 27ms/step\n", "Epoch 16/250\n", - "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 215ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 307ms/epoch - 24ms/step\n", "Epoch 17/250\n", - "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 227ms/epoch - 17ms/step\n", + "13/13 - 1s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 515ms/epoch - 40ms/step\n", "Epoch 18/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 234ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 396ms/epoch - 30ms/step\n", "Epoch 19/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 111ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 404ms/epoch - 31ms/step\n", "Epoch 20/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 246ms/epoch - 19ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 300ms/epoch - 23ms/step\n", "Epoch 21/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 172ms/epoch - 13ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 315ms/epoch - 24ms/step\n", "Epoch 22/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 209ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 240ms/epoch - 18ms/step\n", "Epoch 23/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 135ms/epoch - 10ms/step\n", "Epoch 24/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 219ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 260ms/epoch - 20ms/step\n", "Epoch 25/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 212ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 223ms/epoch - 17ms/step\n", "Epoch 26/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 220ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 244ms/epoch - 19ms/step\n", "Epoch 27/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 224ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 250ms/epoch - 19ms/step\n", "Epoch 28/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 111ms/epoch - 9ms/step\n", "Epoch 29/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 112ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 106ms/epoch - 8ms/step\n", "Epoch 30/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 223ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 229ms/epoch - 18ms/step\n", "Epoch 31/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 219ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 208ms/epoch - 16ms/step\n", "Epoch 32/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 228ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 231ms/epoch - 18ms/step\n", "Epoch 33/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 112ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 120ms/epoch - 9ms/step\n", "Epoch 34/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 244ms/epoch - 19ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 363ms/epoch - 28ms/step\n", "Epoch 35/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 202ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 226ms/epoch - 17ms/step\n", "Epoch 36/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 224ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 206ms/epoch - 16ms/step\n", "Epoch 37/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 114ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 119ms/epoch - 9ms/step\n", "Epoch 38/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 231ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 256ms/epoch - 20ms/step\n", "Epoch 39/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 107ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 146ms/epoch - 11ms/step\n", "Epoch 40/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 219ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 250ms/epoch - 19ms/step\n", "Epoch 41/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 167ms/epoch - 13ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 161ms/epoch - 12ms/step\n", "Epoch 42/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 100ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 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8.2851e-04 - val_mae: 0.0214 - val_mse: 8.2851e-04 - 224ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 8.9349e-04 - mae: 0.0220 - mse: 8.9349e-04 - val_loss: 8.2851e-04 - val_mae: 0.0214 - val_mse: 8.2851e-04 - 358ms/epoch - 28ms/step\n", "Epoch 65/250\n", - "13/13 - 0s - loss: 8.7848e-04 - mae: 0.0216 - mse: 8.7848e-04 - val_loss: 8.5189e-04 - val_mae: 0.0218 - val_mse: 8.5189e-04 - 112ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 8.7848e-04 - mae: 0.0216 - mse: 8.7848e-04 - val_loss: 8.5189e-04 - val_mae: 0.0218 - val_mse: 8.5189e-04 - 157ms/epoch - 12ms/step\n", "Epoch 66/250\n", - "13/13 - 0s - loss: 8.9773e-04 - mae: 0.0219 - mse: 8.9773e-04 - val_loss: 8.5650e-04 - val_mae: 0.0211 - val_mse: 8.5650e-04 - 111ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 8.9773e-04 - mae: 0.0219 - mse: 8.9773e-04 - val_loss: 8.5650e-04 - val_mae: 0.0211 - val_mse: 8.5650e-04 - 131ms/epoch - 10ms/step\n", "Epoch 67/250\n", - "13/13 - 0s - loss: 8.7443e-04 - mae: 0.0217 - mse: 8.7443e-04 - val_loss: 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- val_loss: 6.8788e-04 - val_mae: 0.0198 - val_mse: 6.8788e-04 - 218ms/epoch - 17ms/step\n", + "13/13 - 1s - loss: 7.5092e-04 - mae: 0.0208 - mse: 7.5092e-04 - val_loss: 6.8788e-04 - val_mae: 0.0198 - val_mse: 6.8788e-04 - 522ms/epoch - 40ms/step\n", "Epoch 104/250\n", - "13/13 - 0s - loss: 7.0460e-04 - mae: 0.0200 - mse: 7.0460e-04 - val_loss: 7.2570e-04 - val_mae: 0.0200 - val_mse: 7.2570e-04 - 115ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 7.0460e-04 - mae: 0.0200 - mse: 7.0460e-04 - val_loss: 7.2570e-04 - val_mae: 0.0200 - val_mse: 7.2570e-04 - 154ms/epoch - 12ms/step\n", "Epoch 105/250\n", - "13/13 - 0s - loss: 6.9255e-04 - mae: 0.0202 - mse: 6.9255e-04 - val_loss: 6.7411e-04 - val_mae: 0.0199 - val_mse: 6.7411e-04 - 193ms/epoch - 15ms/step\n", + "13/13 - 0s - loss: 6.9255e-04 - mae: 0.0202 - mse: 6.9255e-04 - val_loss: 6.7411e-04 - val_mae: 0.0199 - val_mse: 6.7411e-04 - 259ms/epoch - 20ms/step\n", "Epoch 106/250\n", - "13/13 - 0s - loss: 6.8175e-04 - mae: 0.0196 - mse: 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- 222ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 5.2334e-04 - mae: 0.0179 - mse: 5.2334e-04 - val_loss: 5.2811e-04 - val_mae: 0.0178 - val_mse: 5.2811e-04 - 183ms/epoch - 14ms/step\n", "Epoch 152/250\n", - "13/13 - 0s - loss: 5.2768e-04 - mae: 0.0178 - mse: 5.2768e-04 - val_loss: 5.5139e-04 - val_mae: 0.0184 - val_mse: 5.5139e-04 - 118ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 5.2768e-04 - mae: 0.0178 - mse: 5.2768e-04 - val_loss: 5.5139e-04 - val_mae: 0.0184 - val_mse: 5.5139e-04 - 111ms/epoch - 9ms/step\n", "Epoch 153/250\n", - "13/13 - 0s - loss: 5.2962e-04 - mae: 0.0179 - mse: 5.2962e-04 - val_loss: 5.7462e-04 - val_mae: 0.0178 - val_mse: 5.7462e-04 - 99ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 5.2962e-04 - mae: 0.0179 - mse: 5.2962e-04 - val_loss: 5.7462e-04 - val_mae: 0.0178 - val_mse: 5.7462e-04 - 117ms/epoch - 9ms/step\n", "Epoch 154/250\n", - "13/13 - 0s - loss: 5.0260e-04 - mae: 0.0173 - mse: 5.0260e-04 - val_loss: 5.3387e-04 - val_mae: 0.0181 - val_mse: 5.3387e-04 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- loss: 3.2338e-04 - mae: 0.0137 - mse: 3.2338e-04 - val_loss: 3.4566e-04 - val_mae: 0.0133 - val_mse: 3.4566e-04 - 108ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.2338e-04 - mae: 0.0137 - mse: 3.2338e-04 - val_loss: 3.4566e-04 - val_mae: 0.0133 - val_mse: 3.4566e-04 - 96ms/epoch - 7ms/step\n", "Epoch 236/250\n", - "13/13 - 0s - loss: 3.1652e-04 - mae: 0.0134 - mse: 3.1652e-04 - val_loss: 3.9728e-04 - val_mae: 0.0136 - val_mse: 3.9728e-04 - 111ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 3.1652e-04 - mae: 0.0134 - mse: 3.1652e-04 - val_loss: 3.9728e-04 - val_mae: 0.0136 - val_mse: 3.9728e-04 - 98ms/epoch - 8ms/step\n", "Epoch 237/250\n", - "13/13 - 0s - loss: 3.2047e-04 - mae: 0.0136 - mse: 3.2047e-04 - val_loss: 3.3756e-04 - val_mae: 0.0130 - val_mse: 3.3756e-04 - 225ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 3.2047e-04 - mae: 0.0136 - mse: 3.2047e-04 - val_loss: 3.3756e-04 - val_mae: 0.0130 - val_mse: 3.3756e-04 - 161ms/epoch - 12ms/step\n", "Epoch 238/250\n", - "13/13 - 0s - loss: 3.3167e-04 - mae: 0.0138 - mse: 3.3167e-04 - val_loss: 3.3191e-04 - val_mae: 0.0126 - val_mse: 3.3191e-04 - 228ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 3.3167e-04 - mae: 0.0138 - mse: 3.3167e-04 - val_loss: 3.3191e-04 - val_mae: 0.0126 - val_mse: 3.3191e-04 - 186ms/epoch - 14ms/step\n", "Epoch 239/250\n", - "13/13 - 0s - loss: 3.2033e-04 - mae: 0.0134 - mse: 3.2033e-04 - val_loss: 3.2969e-04 - val_mae: 0.0128 - val_mse: 3.2969e-04 - 215ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 3.2033e-04 - mae: 0.0134 - mse: 3.2033e-04 - val_loss: 3.2969e-04 - val_mae: 0.0128 - val_mse: 3.2969e-04 - 190ms/epoch - 15ms/step\n", "Epoch 240/250\n", - "13/13 - 0s - loss: 3.5224e-04 - mae: 0.0141 - mse: 3.5224e-04 - val_loss: 3.9061e-04 - val_mae: 0.0148 - val_mse: 3.9061e-04 - 110ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.5224e-04 - mae: 0.0141 - mse: 3.5224e-04 - val_loss: 3.9061e-04 - val_mae: 0.0148 - val_mse: 3.9061e-04 - 103ms/epoch - 8ms/step\n", "Epoch 241/250\n", - "13/13 - 0s - loss: 3.9777e-04 - mae: 0.0153 - mse: 3.9777e-04 - val_loss: 3.7065e-04 - val_mae: 0.0137 - val_mse: 3.7065e-04 - 107ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.9777e-04 - mae: 0.0153 - mse: 3.9777e-04 - val_loss: 3.7065e-04 - val_mae: 0.0137 - val_mse: 3.7065e-04 - 99ms/epoch - 8ms/step\n", "Epoch 242/250\n", - "13/13 - 0s - loss: 3.2502e-04 - mae: 0.0138 - mse: 3.2502e-04 - val_loss: 3.3236e-04 - val_mae: 0.0124 - val_mse: 3.3236e-04 - 109ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.2502e-04 - mae: 0.0138 - mse: 3.2502e-04 - val_loss: 3.3236e-04 - val_mae: 0.0124 - val_mse: 3.3236e-04 - 101ms/epoch - 8ms/step\n", "Epoch 243/250\n", - "13/13 - 0s - loss: 3.0734e-04 - mae: 0.0133 - mse: 3.0734e-04 - val_loss: 3.2635e-04 - val_mae: 0.0126 - val_mse: 3.2635e-04 - 227ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 3.0734e-04 - mae: 0.0133 - mse: 3.0734e-04 - val_loss: 3.2635e-04 - val_mae: 0.0126 - val_mse: 3.2635e-04 - 198ms/epoch - 15ms/step\n", "Epoch 244/250\n", - "13/13 - 0s - loss: 3.2928e-04 - mae: 0.0137 - mse: 3.2928e-04 - val_loss: 3.2871e-04 - val_mae: 0.0125 - val_mse: 3.2871e-04 - 104ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.2928e-04 - mae: 0.0137 - mse: 3.2928e-04 - val_loss: 3.2871e-04 - val_mae: 0.0125 - val_mse: 3.2871e-04 - 100ms/epoch - 8ms/step\n", "Epoch 245/250\n", - "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 112ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 92ms/epoch - 7ms/step\n", "Epoch 246/250\n", - "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 107ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 95ms/epoch - 7ms/step\n", "Epoch 247/250\n", - "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 106ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 95ms/epoch - 7ms/step\n", "Epoch 248/250\n", - "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 118ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 110ms/epoch - 8ms/step\n", "Epoch 249/250\n", - "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 106ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 105ms/epoch - 8ms/step\n", "Epoch 250/250\n", - "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 109ms/epoch - 8ms/step\n" + "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 248ms/epoch - 19ms/step\n" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb index fe6bd96f..aca9aa02 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb @@ -151,7 +151,13 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "No iterations will be run.\n", + "No iterations will be run.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", @@ -203,37 +209,37 @@ " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", " Exceeded.\n", "\n", - "Best surrogate model is of order 5 with a cross-val S.S. Error of 20466.657669\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 22655.436683\n", "\n", "------------------------------------------------------------\n", "The final coefficients of the regression terms are: \n", "\n", - "k | -534397.59515\n", - "(x_ 1 )^ 1 | -2733.579691\n", - "(x_ 2 )^ 1 | 1036.106357\n", - "(x_ 1 )^ 2 | 32.409203\n", - "(x_ 2 )^ 2 | -2.852387\n", - "(x_ 1 )^ 3 | 0.893563\n", - "(x_ 2 )^ 3 | 0.004018\n", - "(x_ 1 )^ 4 | -0.045284\n", - "(x_ 2 )^ 4 | -3e-06\n", - "(x_ 1 )^ 5 | 0.000564\n", + "k | -509611.829792\n", + "(x_ 1 )^ 1 | -3514.24009\n", + "(x_ 2 )^ 1 | 881.280222\n", + "(x_ 1 )^ 2 | 115.258156\n", + "(x_ 2 )^ 2 | -2.391388\n", + "(x_ 1 )^ 3 | -2.712279\n", + "(x_ 2 )^ 3 | 0.003345\n", + "(x_ 1 )^ 4 | 0.037981\n", + "(x_ 2 )^ 4 | -2e-06\n", + "(x_ 1 )^ 5 | -0.000196\n", "(x_ 2 )^ 5 | 0.0\n", - "x_ 1 .x_ 2 | 4.372684\n", + "x_ 1 .x_ 2 | 4.574188\n", "\n", "The coefficients of the extra terms in additional_regression_features are:\n", "\n", - "Coeff. additional_regression_features[ 1 ]: -0.002723\n", - "Coeff. additional_regression_features[ 2 ]: 3.6e-05\n", - "Coeff. additional_regression_features[ 3 ]: -0.050607\n", - "Coeff. additional_regression_features[ 4 ]: 169668.814595\n", - "Coeff. additional_regression_features[ 5 ]: -44.726026\n", + "Coeff. additional_regression_features[ 1 ]: -0.003097\n", + "Coeff. additional_regression_features[ 2 ]: 4.9e-05\n", + "Coeff. additional_regression_features[ 3 ]: -0.066624\n", + "Coeff. additional_regression_features[ 4 ]: 117026.221822\n", + "Coeff. additional_regression_features[ 5 ]: -62.034801\n", "\n", "Regression model performance on training data:\n", - "Order: 5 / MAE: 134.972465 / MSE: 54613.278159 / R^2: 0.999601\n", + "Order: 5 / MAE: 124.816299 / MSE: 43122.530042 / R^2: 0.999692\n", "\n", "Results saved in solution.pickle\n", - "2023-08-19 23:48:46 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", + "2023-08-22 10:19:11 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", @@ -295,37 +301,37 @@ " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", " Exceeded.\n", "\n", - "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.156437\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.176582\n", "\n", "------------------------------------------------------------\n", "The final coefficients of the regression terms are: \n", "\n", - "k | -519.862457\n", - "(x_ 1 )^ 1 | -8.820865\n", - "(x_ 2 )^ 1 | 3.676641\n", - "(x_ 1 )^ 2 | 0.18002\n", - "(x_ 2 )^ 2 | -0.010217\n", - "(x_ 1 )^ 3 | -0.000783\n", - "(x_ 2 )^ 3 | 1.4e-05\n", - "(x_ 1 )^ 4 | -6.9e-05\n", + "k | -431.52518\n", + "(x_ 1 )^ 1 | -11.451609\n", + "(x_ 2 )^ 1 | 3.128102\n", + "(x_ 1 )^ 2 | 0.469184\n", + "(x_ 2 )^ 2 | -0.008586\n", + "(x_ 1 )^ 3 | -0.013135\n", + "(x_ 2 )^ 3 | 1.2e-05\n", + "(x_ 1 )^ 4 | 0.000209\n", "(x_ 2 )^ 4 | -0.0\n", - "(x_ 1 )^ 5 | 1e-06\n", + "(x_ 1 )^ 5 | -1e-06\n", "(x_ 2 )^ 5 | 0.0\n", - "x_ 1 .x_ 2 | 0.010367\n", + "x_ 1 .x_ 2 | 0.010646\n", "\n", "The coefficients of the extra terms in additional_regression_features are:\n", "\n", - "Coeff. additional_regression_features[ 1 ]: -7e-06\n", + "Coeff. additional_regression_features[ 1 ]: -8e-06\n", "Coeff. additional_regression_features[ 2 ]: 0.0\n", - "Coeff. additional_regression_features[ 3 ]: -0.000112\n", - "Coeff. additional_regression_features[ 4 ]: 484.312223\n", - "Coeff. additional_regression_features[ 5 ]: -0.1166\n", + "Coeff. additional_regression_features[ 3 ]: -0.000162\n", + "Coeff. additional_regression_features[ 4 ]: 277.590963\n", + "Coeff. additional_regression_features[ 5 ]: -0.183622\n", "\n", "Regression model performance on training data:\n", - "Order: 5 / MAE: 0.398072 / MSE: 0.495330 / R^2: 0.998873\n", + "Order: 5 / MAE: 0.357715 / MSE: 0.361988 / R^2: 0.999176\n", "\n", "Results saved in solution.pickle\n", - "2023-08-19 23:49:20 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" + "2023-08-22 10:20:14 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" ] } ], @@ -371,7 +377,7 @@ "outputs": [ { "data": { - "image/png": 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", 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7R54/fx7r1q1Dz549ERwcDAD47LPP0KJFC2zZsgV9+vQBx3FITU3FwoULERUVBQDIy8tDamqqw7nS0tKQlZUFAKisrMTevXsxc+ZM4fOAgACkpqYiLy8PALB3715UVVU5nKdt27Zo1qyZIMLy8vLQsWNHQYDxvzN+/Hj88ssv6NKliyr1ogX2nVthYQAKCoKQnHwVCQk1AKhzI4wHDdjmoqSkRJhEeyIzM5PuK6E7hhJh06dPx/Lly1FRUYEePXpgy5Ytwmf/93//h+PHj+Ojjz7Cu+++i+rqakyaNAmDBg3Czp07AQDFxcUOwggAmjRpgrKyMvz111+4cOECqqurXR7z22+/CecICQlBZGRknWOKi4s9/g7/mTuuXLmCK1euCK/LysrEVItm2Hdu+/Z1wWef9QPHBcBiqcH9929B164/AqDOTW9IVIiHBmzz4antyzmOINREVxE2Y8YMLFiwwOMxhw4dQtu2bQEAU6dOxahRo3D8+HHMmzcPQ4cOxZYtW2CxWFBTU4MrV67g3XffxfXXXw8AWLVqFbp164bDhw+7dVGyxPz58zFv3jy9i+EWvtOy2RoKAgwAOC4An33WDy1b/gGr9SJ1bjriLCpstoY4fz4aUVElsFovCu+TqKjFua26qy9q08bF3T3VExYnSiyWyR/QVYRNmTIFw4cP93hMixYthL9jYmIQExOD66+/Hu3atUNiYiK+++47pKSkID4+HkFBQYIAA4B27doBqF2p2KZNG8TFxdVZxXj69GlEREQgLCwMgYGBCAwMdHlMXFwcACAuLg6VlZUoLS11sIY5H+O8opI/J3+MK2bOnInJkycLr8vKypCYmOixfvTg/PloQYDxcFwAzp+PYqaT81fsO1FP1koSFXXxVF+EMWHxnrJofWWxTP6CriIsNjYWsbGxsr5bU1Mbg8S772699VZcvXoVR48eRcuWLQEAv//+OwCgefPmAICUlBR8/vnnDufJyclBSkoKgFql361bN+zYsQP9+/cXfmfHjh3IzMwEAHTr1g3BwcHYsWMHBg4cCAA4fPgwTpw4IZwnJSUFL774Is6cOYPGjRsLvxMREYH27du7vabQ0FCEhobKqg8tiYoqgcVS4yDELJYaREWdV/y3/CkGTcmZqDdrpZmRU4/+XF9mhdV7yqK7lGWLsNktdIaICcvPz8eePXtw2223oVGjRjh69Chmz56Nli1bCsInNTUVXbt2xciRI7FkyRLU1NRgwoQJuOeeewTr2Lhx47B8+XJMmzYNI0eOxM6dO7Fx40Zs3bpV+K3Jkydj2LBhuOmmm3DLLbdgyZIlKC8vx4gRIwAAVqsVo0aNwuTJkxEVFYWIiAhMnDgRKSkp6NGjBwDg3nvvRfv27fHYY49h4cKFKC4uxqxZszBhwgRDiCxvWK0Xcf/9W+rMMJXu2MTGoD366KOC8DYqSrsR/dVaKXdG76/1xRJKD7ZGuaesuUtZsh76Q3iFIURYeHg4srOz8eyzz6K8vBzx8fHo06cPZs2aJYiagIAAfPbZZ5g4cSJ69eqF+vXr47777sOiRYuE8yQnJ2Pr1q2YNGkSli5diqZNm+Ltt99GWlqacExGRgbOnj2LOXPmoLi4GDfeeCO2bdvmEGj/2muvISAgAAMHDsSVK1eQlpaGN954Q/g8MDAQW7Zswfjx45GSkoL69etj2LBheO655zSoLW3o2vVHtGz5B86fj0JU1HlVOg9PMWibN1+b0b7//vuGfggB5d2IWlorWUKulcFf64sV1HCHGeGesiR4APash/4QXmEIEdaxY0dhhaMnEhIS8PHHH3s85s4778SPP3pu5JmZmYL70RX16tXDihUrsGLFCrfHNG/evI7r02xYrRc1eTBdzWiBAOTnd8e99+YCMOZDaD/zP3fuHADlOkGtrJWsI9bKQPWlL2q46Fi9pzab7X//syV4AHathyzWlVIYQoQR/k1UVAmAGjhv8JCXl4Lu3fMN+RC6NrMnobw8XLFOUAtrJctItTL4e32xhC8uOvtk2J7uqdik2UpSUlKCDRs2AGBT8LBqPWSxrpSCRBghGrGdltKdm9V6ET175mH37lsd3jfyQ+jOzF4rNjkAFuFzsZ1gSUmJYFHj0cpayRpiZs4lJSUoLS11+J67+tJjwPZXfHXRRUdHIzMzk8lgbvsysSh4fLEeqhlAz2JdKQWJMEI0enZu3bvnIy8vxXQPobNYqLX21cBi4SR1gmIDWHnMLiq8zZxtNptgkfBERkYGYmNjDR1vaCSUcjsZ4X5ZrReRmpqLnJxUAOy4S+VYhNVOccGqa1kJSIQRktCrczPrQ+gu3m3gwI2oX7+iTid47tw5l0JXbADrgAEDkJCQYIhByhe8zZyrqqocjncnWq1Wq6Z1Zfbl+N4ws9vJmX37uiA395oAS03NdbD4aTlRcv4tqRZhLVJcmDVcgEQYYRjM+BC6EwuJiadcXl92djYA9zNKb5aEmJgYUw/iPFJEOysr1FhOmKmmOLR3o5vZ7WSPq+c0NzcVHTochNV6Eenp6ZreYyW9HGo+T2YMryARRjCLq1mXHg+hmgOQN7EgdUbpT5YEV4gNyr548Vr9srLqisUknoC64tD53Ga1eDvj7Tl13ptYC5QQfUo/T3rFIWsJiTCCWaKjo/Hoo4/i/fff93qsWg+hFtYJd2JBzozSXywJztgL5fT0dFy9ehUAcOnSJWzfvr3O8Tk5OQDYFq0sJPEsKSlBYWGhqGPliENX32FtRaMamPU5Vfp5YnmRhVKQCCOYpmXLlro+hFpt5+Fs4ZM7ozSbJUGMFRKAKKEM1L1/rA6GLLhI3U1AtBCHzs+D2WIZzfac8qjxPJnlnruDRBihGkq58Vh5CJUcGL3N6MXOKPk6tk9NYZbYObFWyPT0dFHnc3f/WBsMWXGRunp21RaH7gSeWWIZWc5hpgRmFZdqQiKMUAWWg4zloPTA6M7Mfu7cOWRnZ4uaUXqqYzMEsIq1QvKuR0/Hebp/rIlWVl2kp07FqyoOlRJ4LK8w9Qf3GmvPE+uQCCNUQSs3nlaoMTB66mjFzCjF1rE9Rp1hix2g3R3n7f6xlKSVRRfpvn1dsHlzPzjvWqGUOFRqkmOEyZ+RBZY7fE1x4c+QCCNUh4X4Fl/RY2CUMqP0lhssJibGsDNssQO0p+Ok3j89Y5BYc+nw9eoswADlngGlJjmsrjA1O/5g4VMLEmGEqrAS3+IrWg2MYmeUFRUVqKioACAuN1h8fLyi5dQSsQO0p+OSk4+7vH8AUFCQxEQMEqvxQq4TCkPRZ0CtSY5aiwj0dnnq/fuuIIElDxJhhKqwGt8iBy1iHcTMKCsqKhzSdhipjuUMHmIHaG/HOd+/o0dbYcmSLGYstKxaE1zVK1CDUaPeRtOmRQ5lkwr/HW+THDnnVssCr7fLU+/fdwWLotAokAgjVIXF+BYp6BHr4K2zKioqcnhtlDqWO3iItUKKOY6/f6xaaFkcqNzVKy/AfHHdOgvPOXPO4tixICQlXUVCws0AbpY1gKt5f/V2ebIWb8uiKDQSJMIIVWEtvkUqWlgnlJhFpqTkCRucs1rHvgwenqyQwcHBoo7jk7hmZ2cbynqoF2Ldo77Gztl/Nz4e6NZN9qkEtLy/nlyealuIWIi31VuUGh0SYYTqGH3JstqxHb7MIu07YaAGPXt+i+7d85mvYzGDh1grZGxsrGihzFsRjWI91BNW3aNi0Or+emrHJ0+exBdffOH1HHItRKxac1nY6cFIkAgjVIGWLIvDF+uQcycMBCAvLwXdu+c7HMdaHYsdPNQUAUa30GoFiwJLDFrcX2/tWIwAA+RbiFi05rJgmTMaJMIIVTDyLFovpHZg3jphVrd6kTJ4KF12VlYgUiCzOmh5f6WKIKUtRKxZc1m1zLEOiTBCNWgQEY+cDsxbJ8zqVi/eym2/BZPSYoSFyQEFMquHlvdXigj69tsU5OamKmohYsWaW1paCoBNy5wRIBFGEAwgpwNjpRN2hStLDy+uvJU7Ozvb4XtKixG9hQ0FMquLVvdX7PP37bcpyMm5B4AFgLIWIr3jbUtKSrBx40YA7FnmjAKJMIJgACkdGJ+kFWArqSePGEuPlMHD7GKEApmNhViXJ1B7b3NyUsELMB5fLEQsxdvaP5tW60WkpubWsfhRm/YMiTCCYACxs+qSkhKHRK38d52Pe/TRR3Wz+IgVTc7l9kcxQoHMxkOMy9Nms2HDhg04fz4aSm/3xIJL3RX79nURBBhQg9TUXIe2rJYoNHp8JYkwwpAY/cFzhRjrkNjVlOHh4aqXVyzuynjXXXdh165dAPxTjFAgs3ER27e43m2AQ2pqrk/3mLW+zdVK7dzcVHTocBBW60VkZGT4ze4BUiERRhgOMzx4PL64FowgXDyVsVGjRgD8V4xQILP5cbZwAzW4555c3HprnnAMaylk5OCtLVutVlV+V+yktLCw0OWknYXJOokwwnCwtm2HL8h1LRhBuIgto7+KEb0Dmc1oTWYFsXFjeoYNKInebRnwPOFzXuxjj96TdRJhhKExgjXIG3I6ACMIF7Fl1KsD11uE6Lm61UzWZBZhNW5LLfReqS1mwsfqZJ1EGGFYjGANUgsWZp7eEFtGPTpwPUUICwlj1bQm6y1uWcGo1yj3/umZLsPbhI/lyTqJMMKwGMEapBZ6zzzF4K2MQUHXuh+txYieubrUtJLIGUCVHKD0Erck/JRB6v1jJV2Gpwkf65N1EmGEYTGCNUhNWEjU6HpPS5vwt6cyNm7cWLQYUXuQ1To9ht4rxXiUHqD0iNck16pySL1/Sk0ofH2+PU34CgqSmJ6skwgjRMPabNMI1iBfca5zfosQHqVnnmLvsdiBLz09HZGRkW7PI7ZMag6yLLsqpCDHuqemNVmreqUdCNTB0/2z31oM8K3vV+r5djfhY32yTiKMEAWrs029rUFqIrbOMzIyHJaA++LKEnuPxQ5okZGRiI+Pl1wWe9QcZH21BLE2MbFHjHVPrQFKTxeQPyb9VRpv98/VakO5fb8v1lMx7lDWJ+skwghRsDTbZCUOQW3EZ563+ix0pPyeaxekdgOfkr/liyWI1YkJIN4KpdYApVe8plmsmnrj6f7xn6vhYpZ6/zy5Q8+dOyeIRZYn6yTCCFnoOds06vJvX60mStS5pzI4uxjE/p6WA5/Sv+WLJYjVfHVSrVBqDFB6uIBYD8A2Eu7uX2FhAt59d6gqz7rc++euzzTKZJ1EGCEZFmabrAksb/hqNVGizsWWQcrvaTnwqfFbSlmCWHgmeMRYoZQYoFwJel7I6+EC8ufV0krj6v7Zb84NKP+sK33/jDJZJxFGSIJmm/Lw1dWnRJ2LtdxI+T0tBz5vv8ULADEdq5K5ulh7JsRYoXwdoMQIeq1dQKwHYBsN5/un9rOuxv3TW2CJgUQYIQmabSqDFNeiGnXuyXIj5ffEdpxKBLB7+y37YGFvcVhKzpJZeybEWqF8GaDECnotXUCsB2AbAW8WUjVFrr/ePxJhhCRotukesfFWUl1XSte5N8uNlN8T03EqFcDuzkVy/ny08DmPGMujUrNkb/VVWlqqyMIJb+iVid9Tex4wYABiYmLqlFNJC4XeOxCwvEJWKq4mJ3yAuxYiieUAerUgEUZIwl9nK94QKzTkuK6UrnNvlhsxvydl4PM1gN3dbxUWJggxKmrFYYkR1t7qa+PGjZqskNQjBsZbe46JiVFdgOoZ+8PyClm5yMnF5QtGCaBXCxJhfobcWZves03WESs0pLiu1KpzMZYub78nZeArKioS3pMTwG7/W/bLzvlVWoA6cVhSFjLw9XXyZFMAFiQmnnT4XKsVkloP9Ky4YvUSOKyukFUStUWSUQLo1YJEmB/hy6zN3x8UKXgSGlJcfWrVuTfLjSsXkqvfk/q7vgSwO/+WFoO/2AGW5+jRVsyskNQKCk+4BksrZJVEi77fn8cNEmF+hK8JV/35QRGLO6EREnIFiYknJbsW1apzT5YutVxISgonrQd/bwMsaysktYLCE2rR8/5rEZNGfb96kAgjdMFMwaz2uBMamzY95DB46+HO1Tv2QknhpOXgL2aAZcUtpwf+GEztjF7334wxaf4GiTBCc8zccbgSGjzOg/eIEfc4uP3UFp5auBW0TOCp1eAvZoD1N7ec3oKeNXy9/3Inpf4Qk2Z2SIT5MWpvgwO47jxY2odSaZyFhjP2g7cWK8ecUVPkaZHAU4/BX8wA629uOYoRdcSX+6/UpNSsMWlmh0SYn6LlNjjeOg8996FUA/uVcps2DQLgH9YRLRJ46jH4ix1g/c0t5y8CSyxy778S1iytY9LMGk6iByTC/BC1tsGRc5yWszc1Ow7XFppDqKz0H+uIPWom8NSjcxc7wPq7W87fUNoyK7c/1DImzczhJHpAIswPUeuBlWrR0nL2pnbH4S6Xlb9ZRwA2EngqgdgB9tFHH0V4eLjH85hhMCLrR12UtMz60h9qGZNIcWjKQiLMj+AHFW8PrJxZu5wZnFJiUMzgoEXHwXe0/h60bJaVgq4GWJvNhqqqKuF1UFCQgwDTQ4hoIY6cJzHunh9/tH4odb2+PDd6xSRSHJrvkAjzI+wHleuuK8P06VZUV1sQGMhhwYIyPPzwEFkdttwZnBKzN7EWrvT0dOFvtTsOfw9aNtNKQft7VFJSgg0bNnj9jpZCRCvXkH1b9vT8kPVDPr4+N1pb3f01N57SkAjzM/iOeMoUICMD+OMPoFUrC5o2jQQQKeuccmdwSszexFq4rl69KnyuRcdhVoElBrOuFGTRDaN1mcwy8LLoWpXz3OhpdTeLxVtvSIT5MU2b1v7zFakzOLX2RBRj4dK742Cx81cDs8fCseiG0aJMej8/SsByYLnU50ZPq7uZLN56QiKM8Bk5W/F46zgqKipQWVnpsPmzPc4di9gZup4dB8udvxL4Sywci9YgrcpkhoGXNYumr8+NXn2FlhZvM09eSYQRsvHFouXpgSkpKcE///lPr7+fmZkp/C12hq6nq0ytzp+VDkqNWTkr12YPi9YgrcpkNlczCxZNKc8Na8+DFhZvs09eSYQRslHLFC5HrEiZobPgKlOq82etg1LyN1i7Nh4WrUFalomF50cJWLJoimm/Yleoqp0yRWuLN2uWS6UhEUb4hNqDn1ix4m2GHhwcXOd4vVxlSnb+Zu6gWL02Fq1BWpfJ3fNjJLSyHiplvRK7QvX999/3ei5fJi56xqGxYLlUGhJhBLNIFSueZuhWq5WZtBFqdf5m7KB4WLs2Fq1BapZJ7OTESPF+WlgP1bDmSukX1Zq46OH2Y8lyqSQkwghmESNWpJjGWYkXUKPzN2sHBbBzbSwuPNCqTGbMfaeF9VANa67YSRxrExdfYTEWUwlIhBHMIkasGHFwUKPzN2sHBbBzbSy2NS3LxNIzpBRaWjSVEkVi+kVvE5dz5865PT9r/SUPi7GYSkAijGAWMWKFtdVCYlG68zdrBwV4vzZ3A4oa957FtsRimVhGD4umktZcMf2it4kLv7etkbafYjEWUwlIhBFM40mslJaWYuPGjV7PwUqHombnb9YOCvB+bfyA4gpW7j3BDnpYNJW25nqbxImZlKm1/ZSaE2MWYzF9hUQYwRxixYrFYnF4zcrqOXeo3fmbsYPi8XZtrN97JTCq1ZdFtK4nNSzVnlaoepu4KGWZc26TzhNjJSxtLMZiKgmJMII5xIoVsUu2WULpzt8IHZRc8SD22oxy731BbI4osvyxiRKWaqnPsKeJixKWOW8rP5WytLEYi6kkJMIIJhHzQPFbGrGyek4P1NgCSkl8WaLv6drOnTuH7Oxsv7n3Yiccci1/ZGVTH18t1WKfdfs8Ye4mLkrEWXpa+QlA0efSzG2PRBhheFhZPacXSm0BpUZH5+sSfW9l8rd770102mw2xMfHSzonWdnUQ2lLtZj69yTU+MmLmDhLKe3AeWKQkpLnV8+lLxhGhD3wwAPYv38/zpw5g0aNGiE1NRULFixAQkICAGDu3LmYN29ene+Fh4ejvLxceP3RRx9h9uzZOHbsGFq3bo0FCxbgb3/7m/A5x3F49tln8a9//QulpaW49dZb8eabb6J169bCMefPn8fEiRPx2WefISAgAAMHDsTSpUvRoEED4ZgDBw5gwoQJ2LNnD2JjYzFx4kRMmzZNjaphDq1n1WZeGegrLGWdV8Nt6G/33pvo3LBhg2SxpLaVzZ/Rw5Um9lyeLHNS2oGriUFeXgqAGgD+8Vz6gmFE2F133YWnn34a8fHx+PPPP/HUU09h0KBB2L17NwDgqaeewrhx4xy+07t3b9x8883C6927d2PIkCGYP38++vXrh/Xr16N///7Yt28fOnToAABYuHAhli1bhnfeeQfJycmYPXs20tLS8Ouvv6JevXoAgEceeQRFRUXIyclBVVUVRowYgbFjx2L9+vUAgLKyMtx7771ITU3FypUr8fPPP2PkyJGIjIzE2LFjtagu3dBjvz8zrwxUEk8dq9ppHtRyG/rbvXclOgEOhYUJSE4+DkC+WPIX167WsGw9dGWZk9oO3E0Mevb8Fnl5KX7xXPqCYUTYpEmThL+bN2+OGTNmoH///qiqqkJwcDAaNGjgYIn66aef8Ouvv2LlypXCe0uXLkWfPn0wdepUAMDzzz+PnJwcLF++HCtXrgTHcViyZAlmzZqFBx98EADw7rvvokmTJvj0008xePBgHDp0CNu2bcOePXtw0003AQBef/11/O1vf8Orr76KhIQErFu3DpWVlVi9ejVCQkJwww03YP/+/Vi8eLHpRZhYy0thYaHbwULOwG/mlYFK4K1jVTvNg5puQ3+691brRaSm5iIn5x4A/OpgC3JzU9Ghw0FYrRdRWloq2SUJ+J9r1x8R4/aU2g7cWaO7d89H9+75fvFc+oJhRJg958+fx7p169CzZ886GzPzvP3227j++utx++23C+/l5eVh8uTJDselpaXh008/BQAUFBSguLgYqampwudWqxXdu3dHXl4eBg8ejLy8PERGRgoCDABSU1MREBCA/Px8/P3vf0deXh569erl0ODT0tKwYMECXLhwAY0aNXJZ5itXruDKlSvC67KyMvGVwiCeLC+eBn1A3MBvhJWBrCCmY1XTVSnFbSjGne3P9z4hoQjXBFgt9vdy48aNsoSzv7l2/RExi12ktgNv1mgSX54xlAibPn06li9fjoqKCvTo0QNbtmxxedzly5exbt06zJgxw+H94uJiNGnSxOG9Jk2aoLi4WPicf8/TMY0bN3b4PCgoCFFRUQ7HJCcn1zkH/5k7ETZ//nyXcW1GxFfXhpiB3+xLl5XEW8eqdpoHsW5DKe5ss917T+LT3l2slkvS31y7/oq3Z0JOO5BqjTbj5EguuoqwGTNmYMGCBR6POXToENq2bQsAmDp1KkaNGoXjx49j3rx5GDp0KLZs2VInaecnn3yCixcvYtiwYaqVXQ1mzpzpYKkrKytDYmKijiWSj1iTtjvri1iMNMjqiaeOVatYIDEdtZSFBHJcbqwidoUiIM4lKRd/cu0S7hHTDsRaozMyMmC1Wh2+R/32NXQVYVOmTMHw4cM9HtOiRQvh75iYGMTExOD6669Hu3btkJiYiO+++w4pKSkO33n77bfRr1+/OhatuLg4nD592uG906dPIy4uTvicf8++gz99+jRuvPFG4ZgzZ844nOPq1as4f/68w3lc/Y79b7giNDQUoaGhbj83Eu4sL8HBlSgoSEJUVAmOHm1l+iSbLOGuY1UzFsgXt6E/JGHlkZp42JtL0hc8ZWIn/Adv7YA8EcqgqwiLjY1FbGysrO/W1NQAgEMMFVAb17Vr1y5s3ry5zndSUlKwY8cOZGVlCe/l5OQIIi45ORlxcXHYsWOHILrKysqQn5+P8ePHC+coLS3F3r170a1bNwDAzp07UVNTg+7duwvHPPPMM8KiAf532rRp49YVaTZcWV46dTqAVatG/2/Ar0HtIFI7kNBKLHUQI4LUjAWS21H760o9T9cNQLCOKXnPxLqG3B1HiV7NgZx2QPfVdwwRE5afn489e/bgtttuQ6NGjXD06FHMnj0bLVu2rGMFW716NeLj43HffffVOc+TTz6JO+64A4sWLULfvn3x4Ycf4ocffhCSWVosFmRlZeGFF15A69athRQVCQkJ6N+/PwCgXbt26NOnD8aMGYOVK1eiqqoKmZmZGDx4sJCz7OGHH8a8efMwatQoTJ8+HQcPHsTSpUvx2muvqVtRjGFveQkOrrQTYIB9/hgeWomlPGICcdWOBZLTUfvrSj13152f373Ocn+l7pkvFg09UtIQ6kCWLX0whAgLDw9HdnY2nn32WZSXlyM+Ph59+vTBrFmzHNx3NTU1WLt2LYYPH47AwMA65+nZsyfWr1+PWbNm4emnn0br1q3x6aefCjnCAGDatGkoLy/H2LFjUVpaittuuw3btm0TcoQBwLp165CZmYnevXsLyVqXLVsmfG61WrF9+3ZMmDAB3bp1Q0xMDObMmWOI9BS+zmrdWV4KCpLqDC7O0EosdRDTabIWC8TSSj0tLT3urnv37hTwExfeOpaVtQRZWUsUuWdyy89SMmCpkAWvLv52vSxgCBHWsWNH7Ny50+txAQEBOHnypMdjHnroITz00ENuP7dYLHjuuefw3HPPuT0mKipKSMzqjk6dOuG///2v5wIzhhKzWufZlKdlz0ANLBbQSiydYDnNAysr9bTe0sfVdaek5GH37lsdjuOtgsnJx73WiVZiQ0oyYL0FDm3VRLCCIUQYoQ1iZ6vejnPVabkbVN1ZX2gJs/qw7n5gwTqnx5Y+ztcNQHBF8nizCvLPj1Ziw1ss26JF++r85qOPPorw8HCP16BW26OtmghWIBFGqIq9mHI3qFqtF5Geno7IyEjhOzT71AbW6plV65zWCwWcr9uTVXDAgAGIiYkRjrV/frQSG1Ji2fjffP/994Vj9bJE+esCEIIdSIQRbvE1hxfAvrWFYAtP7cVms6GqqgpBQUGorKxEUVGRw+dqtiO1Fwp4E5WerIIxMTFec6apLTakxLI5/6aelihv99Vms3n8PvVdhK+QCCNcomSOJuqkCCm4W4W3YcMGr99Vy3Ki9kIBV+KTj6fk8SV/l9Iiko8z42O9pMay2W+Vpaclytt9tW9zLMSN0WIC80EijKiD3h0jC1BnxxZyVuEpeQ+1WCjgXBZf83fZo6SIdBdn1rLlHw6rNQHvsWx6pyIRe19ZiBujxQTmhEQYUQe9O0a9odxHbCPGSqvGPdR6oYCSrnwlRaSU7P7efpOFVCTe7isrk1JaTGBOSIQRdVCrYzSKdUmpVaKE8ogdENXKX6XGlj5aPRdKi0hv98Jma4hGjS5g1Ki3UVUV4vI3WUlF4um+sjYpZUUUysEoY4CWkAgjBHi3hreOUaybxP6Bs9lsusb0+IISCxSkQp2Va+QMiL7ENyrpEnSFHnnIXNWTcx4vHk/tzNO9cLUvbHLycZfn0SMViZT7xYK1zh4tRKEa/Q+5U11DIowQcHZ/zJlzFseOBSEp6SoSEm4GcLPoh0+sO8gZLa1L7joa+wFJj02kyR3qHqkDoq9WA7VX96rtYhIrNrKzsyUPiu7uRXBwpeQ613rTcDH3tbS0FBs3bmTGWsejtihUSyyRO9U1JMIIB+wfqvh44H97lEvGmztID+uSPWKEjl5mfyNvBaM2UgdEJawGWghdtdqamL1DAXmDort7UVUV6rHOBwwYgPDwcIc8Ye5QMx+clPuqpbXO2+RQbVGotlgysjtVDUiEEarj/CB36nQABw500sy65KpTc3a/uBI6LMSC6GGJYx0pAyJrriR3qNnWvIkNXwZFV/fCZmvosc75vGZGyx+ohbVOrBVKC1GollhioV9lCRJhhANKxwK4epB/+qkzAIvwWs1ZkBiLlzuho/cATjPGa8jNpM+aK8kderY1XwdF53shts5ZEliuUDse0BXerFC1wuuaKFOzHasllvTuV1mDRBghoEYskqsHmRdgPPYPdmlpqdfs31IQ4xb1JHT0HMBpxngNX2KzWNiD0ht6tjWpg6IY0WGEOveGnrt9uOuXOA4APFvF5Sycsof3EqgllvTuV1mDRBghoEZqBlcPMsDBXojZP9gbN25ULeDc1cyyUaMLHoWOp8FE7f0LacboiJQ2weoelJ7QS7hIHRTdiRMlMvyztipYL2uduwmY/d/2k0V+/1AlF06pKZbMINKVgkQY4RYlguddPcjOMWGpqbmqB5y7m1mOGvW2R6HjvDkyjxaDAc0Y5WPUPUuVcDHJETJSB0VX9ear+05sPFRGRgasVqvbc7N2T+XgevLqiP1kUcz+ofaIXfyjpljSekUsq5AII1yiZEC4qwe5ceMzyMlJBccFIDc3FWFhl1UNOHc3s6yqCvEodKR2bkpDM0b5GGEwVjruSEpIgTO+Doq+Cl+xq/L4fINmzjPlagLGuyJ5lLKKe+vrlRJLesTYGQESYUQdlAgI9+QOstkaIjc3FXyHokXAuSfXXnLycaaEjhFdaYQ8lLbYSUlvosagqIT4EdP/mCHPlLdV284TMFcJcNXe9cATUvsfo1qn1YZEGFEHpXIrOT9wfMyIL+eXGzPizbXHktChzsq/UOs+erNwsNrOvPUPZlg17Nn1eu04+35JDau4t7pWOhyD+qy6kAgj6qBUQLi7B07u+X1dvempE3PV2WgxALEWiExcw8j3RqxQYbH87vqH8vL6glgx+qphKZug26N0HJW3vljvcAx/gEQYUQe1A8Llnl/O6k2xrr2EhATNByTanohdjH5vjCxU3MVDbdr0kLCQxyyrhr2J5XvuuQc5OTlezyPXYk+Lf/SHRBghYP8gq52aQQnTurt4F+ds+BkZGaiqqkJQUBAiIyPrnEcviwZtT8QmJSUlKCwsdHiPxXvjKabI6OlN+P7h5Mmm2LRpEOzjR3NzU5Gamovc3FSXwsH++WfZWgl4F8vJycmiXcZyLbdaLP4xslVZbUiEEQJqx4goGXDuyYRvn6vIGVYtF7Q9ERu4itXJz++OvLwUpu6Nt5giM1g4rNaLOH/+L9ivCARqRUpCQiGyspbU2S7p/PlorFmTY5jVkmLEslJ5v4BrdaHl4h+jW5XVhkQY4YCaD4FSIk9MvAuLlgt3mCHQ2Cw4x+ps3twP9iLA3b3ReqYvJqbIaOlNXA34nkSKvXAw6mpJpcSyVKu6losyyOLvGRJhhKYo8VB7M+Ebzapk5Pgds8ILY2crDOB4b86dOwebzSbkrvKEGjN9bwKepVW/3rAXBvxKajEixeiTGKXFstj+Tw+rk9H6Zi0gEUYYDk+zYyN2yEaP3zEjrvc8rcX+3rhyfWs505eTYoDl+Bs5MUtGmMQ4W0qd41aVWvXIcv/Hctn0hEQYYTg8zY4LCpI0z0HmK2aI3zEb7raNcXdveOFVWBhfJ2BczZm+UikGWA+c9iRSWJ/EuI/fa6h4QlSWBSnLZdMTEmGEYRCzelOvHGS+YrT4HR61Bm+9RYGzMAZq0LNnHrp3z69zb+xdLPab02sx01dCwIvds1HLwGkp4sNbHdhsNl1zXYnNCeZstZTTxlkWpCyXTU9IhBGGwVMwqZQYElfoETxq9O2J1BKuegtiHjHC2NnFwgswHi1m+r4KeLEiQcvAaTGB46Wlpdi4cSMAz3WwYcMGJlbeeXPHKZEYlWWrOstl0xMSYYShENOR+jooaRU8yuq2MWJRS7iytJrKnTC+6667sGvXLo+xY4B2M30lYopYi9mR2u75Mp4/H+3wGmBj5Z1W7jiWreosl00vSIQRpkApq5LWAxGrAksqaglXrVdTibU6Nm7cGIC72LFal6SaM301Nt82WsyO87WxvvJOTXccy1Z1sWWz2Wx1vmeW/tETJMIIU6CUVcloAxELeBOuzivB7PF0T6ScV6kOW2w74j935WJJTc1FQkKhqjN9Nayo3kTCuXPnmBoYo6OjkZ6ejo0bNzJnxXOFmu44lq3qrspm70rmcZXmhQU3stqQCCNMgxIPKwWPSsebcPW0gwHgvqOVel6lOmwx5ygqKhL+FutiUdoKofTg5GoxQkpKnvA5X98sDYz8NmRGmTyp6Y5j5Z64Qm7ZWHAjqw2JMMIleq9O0wsKHpWOWOEqNbbL3XmDgytRUJCka8ZtsS6W9PR0REZGGuZ54UUCv1XT7t23Ii8vxcG1d/bsWeauRc7kSc+UNNSfOOKub/AHSIQRdWBldZpeUPCouAGKR4xwlROv4+q8nTodwKpVo32K+1Fi8GXZ/aME/F6ZQF3XHiurDe2ROnnSso9TI37PTHjqG1yFMhj5uXIFiTCiDmItCmYyFYu1bJSWltb5npk6BED8AJWRkSH87Um4+hKvY3/e4OBKQYBJPY/UaxMz+EpNu8GqYHNu+2Jceyw++1ImT1quwDW7YPcFb32Du1AG1iYBvkAijPCKP5iKXXWUrvYEdA4mBYzfIXjbUsUdHMc5vHYnXKXG67gTxHJ2Q/B2bVqkv2Ddssy3/cLCQmRnZ7td8VlYmIDk5OOal88TSqwK1GJVpZj7yrJQVwtvfYM/bPZNIozwCOvLvuUgt7MzY4cgRiC4u+7IyEiPM3w+ga7UeB1nQSz3PN6uTau2zVLeM3dER0c7rPhMTc1FTs49uJZ81oLc3FR06HCQqYmYr1YmVlZVsi7U1cLTM+3L82kkQUsijHALKx2UkkhxtVVVVQmvzShGAe8Cwdt1i+nI5Cx2cHVeqefxdG0AdGnbRmlHCQlF0CP7vxx8GUxZWVVpBKGuBu6eaUD+82k0QUsijHALKx2Ukojt7OzdkGYUo65wFgipqbnCZtSAb9et1GIHuedxvraUlDzN27aR2pG/pGph8TqlBqoDbFl2xOBtH2A5oQc8RotpJhFGuIXFDkpJ3HV2zsLMjGLUGVcCwV6A8Ui5bqWyePt6HlfXtnt3iuZtm/V2JHXFqxlg7TrFBKqzsMG6r7hzI8sNPfAE6zHNJML8BDk+ctY6KCVx19n99Vc9QXzw19uy5R+mFqOAe4EA1ACQd91KrQrz9Tyu93cMQErKt0IqBjXaNv/M8dYL1ic19hnoAf9J1cLSdXoT6qxssK4Enp57pcYeI7j/SYT5AVJ95N5MxTxGzmvjrrPLyUkFLzp4YZaVtcS0YpTHnUCwd0nKuW6lZua+nMfdtXXvno/u3fMVH3xLSkpw9uzZOitrjTCp4ffE5GFpD0IlYXWvRU9C3UjubCXwVRwbpb5IhPkBUn3k/pDXxl1n524WytJsWQ3cCYSuXX9Ehw4HDS3CvYkfJQdfdxMe3iXSsuUfyMpawmw78odnH2D3Oj21VW9xUnL3aPWGlisNlRTHrLv/eUiE+SHufOTOGyLHx8frUTxNcLfxsnMclL27iJXZslq4E5pW60WMGHEPYmJihGPVGqDU6vA9iegBAwYodm2uFn7wWwB5c4ko2Y58qUejCyyxsHqd7tqqN3e23D1aPaH1SkMlxTHr7n8eEmF+hicfufNDzO97B5hj9uuMq84uLOyyW4uJ82ANGL9exM48ExISVL9OpTt8va9t374u2Ly5H+xj6pxdInybUrIdGW2JPiGurYp1ZyuZ3kKP1BlKtUkjuP8BEmF+hVQfuXN2+IyMDFit1jrHGUmIeOvsPFlMYmJiTGcdZMkto3SHr+e18c+avQDjsXeJqNGm/DXnlJHx1Fb5FYOA9zgpNQPRjRDkDnhPf+HqOD0hEeZHuPORnzzZFOfP/+V1Ca9zoLE9RplVu+rsSktLHQSn2d2OzrB435Tq8PW6NtcrMmvR0iVilIGTEN9W3fVPagaiGyXIHWBrYikGEmF+hDsf+aZNgwA4pmRwl1fFDLNq54cvPj7eUA+t2TFSh+8O1/svwieXiNQ4LyPWo5G2m9EKsZM/NQPRjRLkzmOkNkIizI9w5SOv3YP5Wie9eXM/WCxwOXM286zaSA+t2TFah+8K52cNqEHPnnno3j1ftgATG+fFY7R6dL5GMyQlVQJvlh01Epw6Y5QgdyNCIswPcOcjLy+vj02bHnI6OuB/wsxx5gzos9ce4X+YpcMXk9ZErJVDTpyX0erRvuxmSkqqBGrt0SoWowS5GxESYX6A80yKnznZbA1dukzs4WfOgMVQs2qCbZzdTqWlpTh79iwAY3f4Yldkpqeno3HjxrIsOmIt0katRyO6UVlBzXyGZs+VqBckwvwEV529K5cJYPnfv1rsZ85GmlUT+iAmpgeAV9eaUTt8tYOCpQoUI9aj0dyoWuHu2bLZbA6vlVxYxOrOAmaCRJgf4mkJ79GjrdzOnPWYVVOgrnEQG7d0zz33OLx2dq3Zv05OPl7n+6x3+Gq2RzECxegDp9HcqFog9tmyz+1oj9x+0mgrDY0IiTA/xPnBstlsQvoJTzNnrWfVZk06aVZhKTZuKScnR/jb2bXWqdMBHDjQyaWrbcCAAZokjWUZMQLF6AOnUd2oaiL22YqMjDRdLkMxGLlPJRHmp9g3SE8pGvTMoWXGpJNmFZbOOIur1NRcJCQUOdw7V661n37qDN4d7uxqi4mJMXSdKIE3geJu/0CWByFXGNGNqhVarlI3Qn9lhDJ6gkQYAcC9C4WVHFpmSY9hRmHpjCtxlZNzDwCLw71zndDU4vCKYoHq4kmgeNo/kNVByB3uJnz+jNaLFsT2Q3r2V0YooydIhBFe0bvjFtvxGM0kbRZh6YwncWV/71wnNOXgbmGIPyM2zgtgQ9jLeRbFWtNZjWXTAr0XLbhrWyxhhDLaI1qElZWViT5pRESErMIQhCvEdDxaJnpUQuyZeRm+u2zxPPy9S04+Xse15iomzOj1oQRi9xZkQdjLdQ8ZPZZNC/RctMBC2/KGEcrojGgRFhkZCYvF4vEYjuNgsVhQXV3tc8EIgkdMx6NVokexA4z9ZueuBg69Z7RqUjf1iXvrlivX2t1376RYIBcYRdj74nJXWmAZzTruDb0WLbDStjxhhDK6QrQI27Vrl5rlIAi3SOl41H4QxQ4wzpudO8/6zb4M315cFRYmIDc31e29c3atGS2lAiuwKOz1tEwYPWDbHXosWmCxbTljhDK6QrQIu+OOO9QsB6EzrM8YxXY8Wj6IrgYYd5ufO9etGZfhu4tbSk4+jg4dDrq8dwMGDEBMTAxKS0tx9epV4f3g4GDBksif20gDpR6wJuz1tkyYaRGM3rnf5LQtrccU1tq/WGQH5peWlmLVqlU4dOgQAOCGG27AyJEjHTpOwhiwunGulI6Hf9i1ehDdDTD8huhiZv1mW4ZvH9MjNrUJn/fLH3MbKQ1rwp4ly4QRY4Xs0TteTmrb0sMKyVr7F4ssEfbDDz8gLS0NYWFhuOWWWwAAixcvxosvvojt27eja9euihaSUBdWN86V0vEUFRUBUOdBtJ/R8XmY3A0w9n+7mvXrPaOVitTZLP83K6lNjIpcKwJLwp4Vy4TeFjml0ONZ8bS7iqd+TUsrpNwysoIsETZp0iQ88MAD+Ne//oWgoNpTXL16FaNHj0ZWVhb+85//KFpIQhtY7KxcdTz2A1RlZSWKioocklQqORC5m9F5WwEIuJ716z2jlYKvs1kWrsEX9HLRS613VoU9K5YJlixyRkOJ/kpJK6S7ZzI9PR1Xr16tE8Ygtox6ItsSZi/AACAoKAjTpk3DTTfdpFjhCG0xQmcldoBSKtGjpxmd8wDDuyJ53M36We0MnDF6EkRf0DOoW6oVgWVhz4JljhWLnNqoNWnwpd0oObGXszKdp7KyEiUlJUz2vbJEWEREBE6cOIG2bds6vH/y5Ek0bNhQkYI588ADD2D//v04c+YMGjVqhNTUVCxYsAAJCQnCMV9++SWeffZZ/PLLL6hXrx569eqFRYsWISkpSTjmq6++wuTJk/HLL78gMTERs2bNwvDhwx1+a8WKFXjllVdQXFyMzp074/XXXxfcrgBw+fJlTJkyBR9++CGuXLmCtLQ0vPHGG2jSpIlwzIkTJzB+/Hjs2rULDRo0wLBhwzB//nwH4coaRuisxA5Q7vDFGuBqRpeVtUTU5udmwGhJEH2BlaBusVYElgYX1ixzrFjk1ITVlaBKTuzFPmvOK9PtYXElrCxFkJGRgVGjRuHVV19Fz549AQDffvstpk6diiFDhihaQJ677roLTz/9NOLj4/Hnn3/iqaeewqBBg7B7924AQEFBAR588EFMnjwZ69atg81mw6RJkzBgwADs27dPOKZv374YN24c1q1bhx07dmD06NGIj49HWloagNobOHnyZKxcuRLdu3fHkiVLkJaWhsOHD6Nx48YAat2xW7duxUcffQSr1YrMzEwMGDAA3377LQCguroaffv2RVxcHHbv3o2ioiIMHToUwcHBeOmll1SpHyUwWmflaYDiV93Z44s1wN2MLitrCZKTjwNgY9avFkYPbPYFva6dxfAAMbBomTPzswmwM2lwRs2JvZxJIYtWe1ki7NVXX4XFYsHQoUOFZeXBwcEYP348Xn75ZUULyDNp0iTh7+bNm2PGjBno378/qqqqEBwcjL1796K6uhovvPACAgJqb/hTTz2FBx98UDhm5cqVSE5OxqJFiwAA7dq1wzfffIPXXntNEGGLFy/GmDFjMGLECADAypUrsXXrVqxevRozZsyAzWbDqlWrsH79etx9990AgDVr1qBdu3b47rvv0KNHD2zfvh2//vorcnNz0aRJE9x44414/vnnMX36dMydO5fZAEHAOJ2VtwEqJiZG0RV3Ymd0es/61cCoYkAJ9Lx2I4QHuIMFa4PSFjnW0/jwsDRhUmti7+0ajWS1lyXCQkJCsHTpUsyfPx9Hjx4FALRs2RLh4eGKFs4d58+fx7p169CzZ08EBwcDALp164aAgACsWbMGw4cPx6VLl/Dee+8hNTVVOCYvLw+pqakO50pLS0NWVhaAWpW8d+9ezJw5U/g8ICAAqampyMvLAwDs3bsXVVVVDudp27YtmjVrhry8PPTo0QN5eXno2LGjg3syLS0N48ePxy+//IIuXbq4vK4rV67gypUrwmspW0UpiRE2ztV6gPI2o3NleeNhpXOWi5HFgBhcDa7eVsFqce1GCA9gGSUtcqy6+5xhccKk9MTe2zWyJELF4FOAUnh4ODp27KhUWbwyffp0LF++HBUVFejRowe2bNkifJacnIzt27cjPT0djz/+OKqrq5GSkoLPP/9cOKa4uNhBGAFAkyZNUFZWhr/++gsXLlxAdXW1y2N+++034RwhISGIjIysc0xxcbHH3+E/c8f8+fMxb948kbWhHL5unKv0DNHT+fjBUesBytuMTmnLm1IocW+81bX9ylSx52QFb4OrnkLIaOEBLKJUO2TV3ecMKxMmNeMCPV0jAOZEqDdkibDLly/j9ddfx65du3DmzBnU1NQ4fM7HYHljxowZWLBggcdjDh06JCwAmDp1KkaNGoXjx49j3rx5GDp0KLZs2QKLxYLi4mKMGTMGw4YNw5AhQ3Dx4kXMmTMHgwYNQk5Ojtd9L1lg5syZmDx5svC6rKwMiYmJqv+uLzNGpRO9ij2fHgOUUVy1PErN3r3VNb95tJRzsoK3QVNvIWS0NucPsGxpYcV6qmZcoKdrZEWESkGWCBs1ahS2b9+OQYMG4ZZbbpEtcKZMmVJnZaIzLVq0EP6OiYlBTEwMrr/+erRr1w6JiYn47rvvkJKSghUrVsBqtWLhwoXC8e+//z4SExORn5+PHj16IC4uDqdPn3Y4/+nTpxEREYGwsDAEBgYiMDDQ5TFxcXEAgLi4OCEjuL01zPmY77//vs45+M/cERoaitDQUI/1oRZyB0ylE72KPR+gzQDF2kovKfiaYkJMEkRWLQJycXU9WgshI7c5s8Oiu88evScN9uiVvZ8FESoFWSJsy5Yt+Pzzz3Hrrbf69OOxsbGIjY2V9V3e+sbHUFVUVAgB+TyBgYEOxzq7JwEgJycHKSkpAGo7tW7dumHHjh3o37+/8N0dO3YgMzMTQG3sWXBwMHbs2IGBAwcCAA4fPowTJ04I50lJScGLL76IM2fOCCsqc3JyEBERgfbt28u6XtZRunMSez61BygWV3rJRWqwqqtrP3funGD5YtUiINcF6+l6tBRCZmpzZsMIlhazWk/FTApZEqFikSXCrrvuOtXygbkiPz8fe/bswW233YZGjRrh6NGjmD17Nlq2bCkIn759++K1117Dc889J7gjn376aTRv3lwIhB83bhyWL1+OadOmYeTIkdi5cyc2btyIrVu3Cr81efJkDBs2DDfddBNuueUWLFmyBOXl5cJqSavVilGjRmHy5MmIiopCREQEJk6ciJSUFPTo0QMAcO+996J9+/Z47LHHsHDhQhQXF2PWrFmYMGGCbpYutVG6c/J2Pi0D4c0w2MkVTO6unVWLgFgXbHp6ujBBAqRfz4ABA4R9L9XADG3OjLDi7nPGH6yn0dHRyMjIEPKAubtGo21dJEuELVq0CNOnT8fKlSvRvHlzpctUh/DwcGRnZ+PZZ59FeXk54uPj0adPH8yaNUsQNXfffTfWr1+PhQsXYuHChQgPD0dKSgq2bduGsLAwALXB+1u3bsWkSZOwdOlSNG3aFG+//baQngKozYF29uxZzJkzB8XFxbjxxhuxbds2h0D71157DQEBARg4cKBDslaewMBAbNmyBePHj0dKSgrq16+PYcOG4bnnnlO9rvRC6c7J2/lYDYRnETUEE6sWAbEB1Pzm4unp6QDcX8/Jk01x/vxfdb4fExNDQskP8WZpcV6kwqO25dJfrKfOmfDdPd8jRtyjaJ5INZElwm666SZcvnwZLVq0QHh4uJACguf8eWVnBR07dsTOnTu9Hjd48GAMHjzY4zF33nknfvzRswUgMzNTcD+6ol69elixYgVWrFjh9pjmzZvXcX2aGaXNwEqezyj5fdRCqmBicXWqHOytf0ANevbMQ/fu+Q7XzOc5dHc9mzYNAsCWu5XQF0+WFleLVHjUXqxi5j7MFZ6s+0aapMsSYUOGDMGff/6Jl156CU2aNDHEykNCfZSORVDifEqv3tQbOYJSimCSsjcny7EXztY/IAC7d9+KvLwUl2LK1fXY7wXKirtVKfx9YiIVse4+wHyLVViE1XAIOcgSYbt370ZeXh46d+6sdHkIg6N0ole55+MHGXv3gBKrN/VEbsoJKYJJSj2wHADsyvoH1O2s7a349tdTXl4fmzY9VOe7ertblcAoiUdZwpO7zwiLVcwGq+EQcpAlwtq2bYu//vpL6bIQBsTXRK9qHOfK+nXyZCI2b+4HI1s2pCaMFLOayPk4Z7ytqGQ1ANiV9Y/HvrPm937l64wfUG22hsy7W+Xia+oSf8WbIDWTdYZ1jBAOIRZZIuzll1/GlClT8OKLL6Jjx451YsIiIiIUKRzBPkoHhCpxPne5xpwx6swJEDfj9rUupW6QLuacWsFb/+yFN49zZ+2qrKy7W5XESPvssYyZrDOsY6bnU5YI69OnDwCgd+/eDu9zHAeLxYLq6mrfS0YYBqUHXKXOVzcuyBGjzpykzLjl1qXWG6SrAW/9y8/vjry8FFGdtRLWQyNB7jPlMJN1hlXM+HzKEmG7du1SuhwEoTju4oIAGHrmpMWM26izelcB1Pfem4vu3fNFxa75y1J/gNxnSmMm6wyrmPH5lCXC7rjjDlHH/eMf/8Bzzz3n1m1BEGribmY6cOAmJCaeMmznqMWM26izer6TPnv2rJDUEZAWu2akDtwXjCq0vaH1ys/S0lLhb9YWq5hxFazRyusNWSJMLO+//z6eeuopEmGELribmXbocEjvovmEFjNuI8/qo6OjTTljVhqjCm1PaL3ys6SkREj8y8PKYhV/XAVrRNGpqgjjahPtEIRuiJ2ZGimGAFBvxm2mmAvWOlvWMLLQdofUFcRa/V5GRobm7dHfVsEaVXSqKsIIggXczUz5FX4szo5coeb+cPYzyIyMDFRVVTl8HhQUhMjISOH8RqgvwjVmEtqe0HrRgecN4K1evq0+Zl8Fa1TRSSKMMB1iBw81N2BWA7VcbEadQbKC0VwgRnTVSq1jrRcdsL7IwR9XwRpFdJIII0yHEQcZsahRZq1dOGZCrIDNyMhwaw3Roy0aqe1LmSTwaL3ogOVFDqwLRED5iYyRRCeJMMKUGGmQYQkjdV4sIFaY2q/UdAVZGd0jx82k9aIDlhc5sCwQAeUt8UYQnfa4TqKkEI8++ihlzycIg+Cu87LZGupcMuNgszVEQUGS5DojK6N4xNQxv+jAYqkBID4vYElJCYqKitz+KykpUfT3tIAXiPawIhAB5WO5PIlOFpFtCSstLcX333+PM2fOoKbG8QYPHToUAPDmm2/6VjqCIDSD9RmzK1iKxxJjRTRKnAqrSLHUSl1B7KtFhrUcYTxGWwXr6zPCslXSFbJE2GeffYZHHnkEly5dQkREBCwWi/CZxWIRRBhBGBGWBnYtMVrnxdKCAjEuEHL1+oaYOvZlBbGc2Eg1Vyz7ihFXwSrxjBhNdMoSYVOmTMHIkSPx0ksvITw8XOkyEYRuOA/s7jpiM8bwGK3zYmlBgTcrotHiVFhEjKVWqUU5YsUAy4uAWC6bK3x9RowoOgGZIuzPP//EE088QQKMMB32HZanjpjFGB4lLHisulS8obeVyZsV0YiuXtYQa6n1VVRIFQOsiBhXsFw2Z3x9RowmOnlkibC0tDT88MMPaNGihdLlIQgmMJrlwhfXHMsuFTGwcK+8WRGN5uqVg9pufK0stSSY9UGJZ4Q1gSUG0SJs8+bNwt99+/bF1KlT8euvv6Jjx44IDg52OPaBBx5QroQEoQNG64h9cc0ZdQbJo+e9EusCMZqrVypqxudp7WbyB8HMImZ/RtwhWoT179+/znvPPfdcnfcsFguqq6t9KhRB6I2RO2I5rjlWBZYY9LxX3gRsaWmpsMGzkeJUpOJLmgExFjQtJwlGEgNmWERk1FgupRAtwpzTUBCEmTFSR2wPC645rdH7Xnka5OLj4w1tZZSL2DQDUixo8fHxShbRI0aIjWRpdbAvGN0S7yuyYsLeffddZGRkIDQ01OH9yspKfPjhh5SigjAFRuiInTGaG1UpWL5XZh083CHFEsvSpstGi40UG4JQWFhY51jWRA1LZdEaWSJsxIgR6NOnDxo3buzw/sWLFzFixAgSYYRpcNcRs4qR3ahSMdqg6Q94s8SeO3dOONbVfdEzma2RLTKehG92drbL78i1kJnBBcoSskQYx3EOCVp5Tp065XaTWoIwAmIHbFYHdj1cc3p1ykYeNM2KN0ussyDIyMgQ/tY7zQggzSLDihiRG4Igx7poFhcoS0gSYV26dIHFYoHFYkHv3r0RFHTt69XV1SgoKECfPn0ULyRBaIUZBnYtXXN6d8r253Q1KFZWVqKoqAgA+/fNDLizxAYHV6KgIKmOhauqqgqA8WIZ9W739ohJFKyUdZEl97FZkCTC+BWS+/fvR1paGho0aCB8FhISgqSkJAwcOFDRAhKE1vjSaeo1O9bLNcdK1nqWBkUeViwlWuLKEtup0wGsWjXao4XLaLGMrLR7wHMIgtrWRSUEnj8+J/ZIEmHPPvssACApKQkZGRmoV6+eKoUiCLHYP8CFhQEoKAhCcvJVJCTUrubV8gHWc8sjFix4erqTWBoUATZFoZq4SzMQHFwpCDDAvYVLTiwjK4O33m5UdyEIAFS1Lipx3f72nLhCVkzYsGHDANR2aGfOnKmTvqJZs2a+l4wgvGD/AHvqELR6gPXe8kjPToold5LegyLAnihUG+dJwLlz55CdnY2CgiRRFi6psYysDN6stHtXIQhi614OSl03uTdlirAjR45g5MiR2L17t8P7fMA+JWsltIB/ML11CFo/wFp1zKxYAgB23EmsDIr2sCAKtcBVW/Nm4bKPK5aSqJMVkcvKbg1A3RAENVdKy71u5z7LfrUsoO/qWL2QJcKGDx+OoKAgbNmyBfHx8S5XShKEVrAiALQsj1hLQHp6OiIjI11+pqRIYyU1BmttQY4oZElc+4o3C1dkZKTPbnQ9Ra7S7V7KvXcXgsBbIdVcKS3Xfeypz/KXyYozskTY/v37sXfvXrRt21bp8hCEZFgRAFqWR6wlgN8yxx1KuWv0zlrPw1pbkCoKWXGzKYm31bq+XIfelk8l272ce++q7rTYBkjOdXsSl0rfRyNNZGSJsPbt29cxIxKEXrAiAPQqj5gZpBbuGhay1rPWFqSKQilZ0AF2M41rtVqXBcunUu1eqfgoNRfpKCnw7Nu2kvfRaBMZWSJswYIFmDZtGl566SV07NgRwcHBDp9HREQoUjiCEAsLAkCP8oiZQapp5mcxaz1LbcEXUSgmCzorA4kzWq3W1cvyqUW79yU+Sq02odR9dW7bqam5it1HowX7yxJhqampAIC7777bIR6MAvMJPWFtiyEtyiMmUaOa7hoWUmPwv2EPC2KQR44oFHvfWBlIXKGFONTL8ql2u2c5PsrX++qqbefmpiI1NRe5uamK30fWg/1libBdu3YpXQ6CcMupU8CRI0Dr1kDTpnqXxj16bHnkzRKghbuGBUsMK2LQ/rfskSoK3d23kyebwmo9pFxBTYBSlk+pcURqtSW949zUxl3bTkgoRFbWEkXj11gWszyyRNgdd9yB//73v3jrrbdw9OhRbNq0Cddddx3ee+89JCcnK11GQgNYEzp8h7h+fRimTbOipsaCgAAOCxfa8PDDfyEkJIS5fR71EALOlgCg1rTPd2CsBaqrCQtikMfXtuDqvgHApk2DUFnJ3kCiNUpbPlmKI2Ihzk1NPPVJVutFDBrUw0FHyO0zpWwmb4/WQfuyRNjHH3+Mxx57DI888gh+/PFHXLlyBQBgs9nw0ksv4fPPP1e0kIS6rFoFjB0L1NQAAQHAP/8JjBqlzLnliDu+Q7TZGmLJkixwXK3Lu6bGgqlTI/Dnn6thtV5EZmam4qLH11U10dHRHs9RWVmJkpISRR/yrl1/xF9/1RNM+bm5qQgLu4yuXX9kLlDdyGhpKakrrnlcDyQsrfbSAqUnPCzFEZl94uStT8rJyVFE7ErdTN4eLWMtZYmwF154AStXrsTQoUPx4YcfCu/feuuteOGFFxQrHKE+p05dE2BA7f+PPw6kpcm3iImxYnlq4HxH5+0hqqysRHx8vLxCuim3r9sOaTWjtp/h22wNBQEG1J3xsRSorie+CGw9LCVdu/6IkJAr2LTpIYf33Q0krAbpq4XamfC1iiPi2yUvqM06cRK7shJQRuz6Ima1jLWUJcIOHz6MXr161XnfarWitLTU1zIRGvHzzxfw8ccBqKmxOrxfXQ3k55cgLEx6RyfFiuXt3FrPCJXYdkirTN7R0dFIT0/Hxo0bRbkvWApU1wNfRZRWlhLn+5GYeNLlM1BeXh82W0PDBOkbCS3jiNy1S7Xye+lJdHQ0MjIysGHDBgAQrun8+WiH10ohRsyyELQvS4TFxcXhjz/+QFJSksP733zzDVq0aKFEuQiVWbr0EiZNsv6vc+cAXFvlarHU4Ntv38HBg+LEkj1SrFje0GtGqFRgrJjO3BfrDJ8J35tYHTBgAGJiYiSd22woLY7V6rx5N1thYaHbrOccB2za9BCzgcZGRuugeE/tzX7ixD/DRn9mrdZrE361xK5YixsrQfuyRNiYMWPw5JNPYvXq1bBYLCgsLEReXh6eeuopzJ49W+kyEgpz6hQweXJ9wUpVK8BqhZiz0JE7u1bKiuXqIeIHwIMHS4XjlOyclAiMFdOZK+Xi8iZWY2JiFHXbGh1fO19P37fZbD7XtfO95p+BkyebYtOmQQDMuWqOBfQOincn7s32DKspdj3FC/JbOrG0AlWWCJsxYwZqamrQu3dvVFRUoFevXggNDcVTTz2FiRMnKl1GQkFKSkrw3XdATY3zoG5BWto2tG//qyKNUEkrlv2M0H4AfPddxwFQqZgYJQSkmM5cSesMxX2Jw9fO19v3N2zYoEg7tB9I+IHj/Pm/wAswHjOtmmMBPYPiWbHMaIHaYtfb86e32LZHlgizWCx45plnMHXqVPzxxx+4dOkS2rdvjwYNGihdPkJB7OO1LJasOh2NUgKMR2lh4G0AVComRgkBKbUzl9MBs5yglFV87XzliGu5OA8kZl81xwJGD4EwCnq3Zb1/3x5ZIownJCQE7du3V6oshMrwg4OWHY2SWeO1nL34KiCl1LHcDpi1BKVGwNfO13X+Lm06b7OummMBLTa99gRLlhkt0Lst6/379vgkwgjjwrL7yl1Hp/XsxVcBKbaOfemASWBJw9fOl//+5s39cM01aMHRo61EuY58zUPH8nNrZPSe0LBkmVETvcWu3r/vChJhfoySViolce4Q+ZgYtWcvSmTgl+Ii5K/PXzpgVpAjZOzva8uWf8BiATiOf8ciynIpdyEGuZ21Qc8JDUuWGTXRW+zq/fuuIBFGKIpSWwm5ewjUtAQo8YCKPQcA0ydnZAmxQsZms7k9x3333YcvvvhCtuVSbq4xFgcOX/HVImgWWLTMqI3e91Xv33eGRBjhEakPvxYDhlwLnlYdv7dzuLKIkJtJXby1y9LSUmzcuFFIJOkJpSyXUnKNsTZw+AJL+zTqjRkFNiENEmFEHXxNDKhnh2EvtAoLA1BQEITk5KsIDz+PjRs3CsfJ2ZJIKTylpkhOPl7neDPNgvVEqfuqhOXSn9IROKPVrhJGwZ8FFllESYQRLmAtMaBYEVJRUYF//vOfAFwNcvno2hVuPhO3JZEaeCrLgAEDkJCQYPpOiEW8Wal8cR2ZMR2B3MHUn8Wov0MW0VpIhPkRSsVraY1Ykz3/uadBDgAzA6C3wTgmJsbUnQ+reBIG6enpwnZRrhAzczdbOgK5g6kZxajeGMmypNV+rKxDIsyPMHL8gZgyFRUVAfA8yAEWZgZAsw3GZsCbMIiMjPTZSqz3alilB2q57kVq/8pidMsSC5tp6wGJMD+DxYdPabwNcqykg9B7MCbqooUw0HM1rNoDtRT3IrV/ZTFyrJ0/u6VJhBGmw9sgx0o6CEpNwR5qCgMW0hGo6QKS6l6k9q8eRhI1/u6WJhFGmBJPgxxL6SBYKguhrjBgMRxASReQHCuimPZvpDgnFjCaqPF3tzSJMEJ33KWVSEioASC/k/WUT0zP3QIoA7q2iBnEtbJSsSQWlLaWiLUiSmn/Ro9z0gOjiRp/d0uTCCN0xb6T9TQoaNXJaiF8WLSIKAGLFgspg7gZ74k71LCWiLUiSmn//GIbb7AY56QXRhM1/u6WJhFG6IqYtBJW60VRnaxYAZWRkQGr1ery+1oNsmYZzHlYtVhIiYFiKTee2qhlLRHrXpfbBvx1BZ0UWBM17iZnpaWlwt/+smWTK0iEEQKnTgFHjgCtWwNNm2r720oMCma1MBkBo+T8oUG8FiWtJVq5140UbK4Ucq3LrMSaip2cyZ0Ys2h9lwqJMD+Hb8Tr14dh2jQramosCAjgsHChDQ8//JdmjVipQYH1B04vtO6sWBQ7/jiIu0NJa4kWkx9Wgs21fI6kWpdZjDUVmzbDarVKtkSzan2XCokwP4ZvxDZbQyxZkgWOswAAamosmDo1An/+uRoA0LnzIHToEOpzoLwnWDOhmwmtOysWxQ4rg7jeqLUAQe1BjoVgc62fI6l5v1j3BCjdLxg5L5o9JML8GL5xuuvg8vO7Iy8vRXhoUlNzkZBQhKioEjzzzDDFH2ZWTOhmQ0tXoZJiR0mrAwuDOAuwPlC7g4Vgcz0HfbEChrX7xqP2JIjFiZ9YSIQRbju43btTAFx7aHJy7gFggcVSA6u1EGPHFineYeuZOsJfUNNVqJTYUdrqwMIgzgqsDtSeYM1SruWgbwYrrpqTIKPXD4kwwmUHl5KSh927b3U6stZdyXEBmDcvATbbElitF5n3uesJa4Gjag8eSokdpa13rA3ihDj03GXA+dk9d+4cAO0HfSUEjN79kJqTIKNbuQ0jwh544AHs378fZ86cQaNGjZCamooFCxYgISFBOGbjxo146aWX8PvvvyM2NhaZmZmYOnWqw3m++uorTJ48Gb/88gsSExMxa9YsDB8+3OGYFStW4JVXXkFxcTE6d+6M119/Hbfccovw+eXLlzFlyhR8+OGHuHLlCtLS0vDGG2+gSZMmwjEnTpzA+PHjsWvXLjRo0ADDhg3D/PnzERTEZpU7d3AABFekK+wbuaeH29vDX1FRIap8RlymzFrgqBaDh1piR671joWtggj56OU+9fTsaj3o+ypgWOiH1JwEGd3KzaYicMFdd92Fp59+GvHx8fjzzz/x1FNPYdCgQdi9ezcA4IsvvsAjjzyC119/Hffeey8OHTqEMWPGICwsDJmZmQCAgoIC9O3bF+PGjcO6deuwY8cOjB49GvHx8UhLSwMAbNiwAZMnT8bKlSvRvXt3LFmyBGlpaTh8+DAaN24MAJg0aRK2bt2Kjz76CFarFZmZmRgwYAC+/fZbAEB1dTX69u2LuLg47N69G0VFRRg6dCiCg4Px0ksv6VB74nB2Bdo/NAAH3hIGiGvkzg+/u4H00UcfRXh4uNvzsBijIgbW0jaoOXioKXZ8sd4ZNQaKuIYe98ZT/JfWg76vAkaNWDY5ljW1Yn6NbuU2jAibNGmS8Hfz5s0xY8YM9O/fH1VVVQgODsZ7772H/v37Y9y4cQCAFi1aYObMmViwYAEmTJgAi8WClStXIjk5GYsWLQIAtGvXDt988w1ee+01QYQtXrwYY8aMwYgRIwAAK1euxNatW7F69WrMmDEDNpsNq1atwvr163H33XcDANasWYN27drhu+++Q48ePbB9+3b8+uuvyM3NRZMmTXDjjTfi+eefx/Tp0zF37lzDzLrtH5rCwgTk5qZKauT2D6mngTQ8PNwvEmXqnbZBzcFDLbGjhPWONYGlt2vIn5FT9676Lq0HfaUEjBLhCFIsa1qlzTDyoi7DiDB7zp8/j3Xr1qFnz54IDg4GAFy5cqWONSUsLAynTp3C8ePHkZSUhLy8PKSmpjock5aWhqysLAC1omHv3r2YOXOm8HlAQABSU1ORl5cHANi7dy+qqqocztO2bVs0a9YMeXl56NGjB/Ly8tCxY0cH92RaWhrGjx+PX375BV26dHF5XVeuXMGVK1eE12VlZTJqR1n4hyY5+Tg6dDgoq5EbPXBSCVhYvePLjFEv4cBKvIdS18+Ca8hfkVL3PO76rqysJcjKWqLqoK+0gFGqH5ZiWYuPj1fNEs1iXjQ5GEqETZ8+HcuXL0dFRQV69OiBLVu2CJ+lpaVh0qRJGD58OO666y788ccfgsWrqKgISUlJKC4udhBGANCkSROUlZXhr7/+woULF1BdXe3ymN9++w0AUFxcjJCQEERGRtY5pri4WDjG1Tn4z9wxf/58zJs3T0KN+IbUxil35SIrA6le6C1CfXUV6ikcWIj3UPL6WXNR+xNy3HKe+q7k5OMun1+bzabIpERp67Ia/bCYyaVakwmzhBroKsJmzJiBBQsWeDzm0KFDaNu2LQBg6tSpGDVqFI4fP4558+Zh6NCh2LJlCywWC8aMGYOjR4+iX79+qKqqQkREBJ588knMnTsXAQGug8tZY+bMmZg8ebLwuqysDImJiar9nqtGXFpaio0bNyr6OywMpHqitwj1tbPSUziwEO+h5vXr7aL2V8RapuX0XRs2bACgzKRESQGhdD+s9+QSYC/UQA66irApU6bUWZnoTIsWLYS/Y2JiEBMTg+uvvx7t2rVDYmIivvvuO6SkpMBisWDBggV46aWXUFxcjNjYWOzYscPhHHFxcTh9+rTD+U+fPo2IiAiEhYUhMDAQgYGBLo+Ji4sTzlFZWYnS0lIHa5jzMd9//32dc/CfuSM0NBShoaEe60NpnBuxvfn43LlzyM7O9vk3WBhI9YQFEWq0zorlVY1KCScWXNT+iBTxIKbvMkqmdqX7Yb0nl2ZBVxEWGxuL2NhYWd+tqandQsc+hgoAAgMDcd111wEAPvjgA6SkpAi/kZKSgs8//9zh+JycHKSkpACo7dC7deuGHTt2oH///sLv7NixQ4gT6NatG4KDg7Fjxw4MHDgQAHD48GGcOHFCOE9KSgpefPFFnDlzRlhRmZOTg4iICLRv317W9WoJP2CLHeDEHGfkwElfMZsI1cJ6w6qrQSnhxIIVQStYW4ggVTx46ruUag9a1ZGS/TALk0szYIiYsPz8fOzZswe33XYbGjVqhKNHj2L27Nlo2bKlIHzOnTuHTZs24c4778Tly5exZs0afPTRR/j666+F84wbNw7Lly/HtGnTMHLkSOzcuRMbN27E1q1bhWMmT56MYcOG4aabbsItt9yCJUuWoLy8XFgtabVaMWrUKEyePBlRUVGIiIjAxIkTkZKSgh49egAA7r33XrRv3x6PPfYYFi5ciOLiYsyaNQsTJkzQ3NIlBk8dQHp6OiwWi8sd7gFpnYO/ZcNn2ZojFy2tN6xZ75QUTmZIwCkGFhciiBEPYoK+lWoPYlP5yK0jtQLYzTa51AtDiLDw8HBkZ2fj2WefRXl5OeLj49GnTx/MmjXLQdS88847eOqpp8BxHFJSUvDVV185JFlNTk7G1q1bMWnSJCxduhRNmzbF22+/LaSnAICMjAycPXsWc+bMQXFxMW688UZs27bNIdD+tddeQ0BAAAYOHOiQrJUnMDAQW7Zswfjx45GSkoL69etj2LBheO6551SuKemo2QEoaUkzIqxac+TiT9YbVyjpfjFDAk4xsLjJshjx4OnZ5cM0lGoPYlP5yK0jNfshf/ZwKIUhRFjHjh2xc+dOj8fExMQIaSQ8ceedd+LHHz3P3DMzMx2WKTtTr149rFixAitWrHB7TPPmzeu4PllEzQ7AbCJEDkpem9KWD6nn8/cYECXdL0on4PT1OC1gKQZOjHjw9iwZKdBdyX7IKKkhjGApBgwiwgj1UasDYKGRmwGlLR9ycib5ewyIEu4XtVzUrK+y1NuKWlJSApvN5vCer+LBXwPdWZlcexJZzqv8lXbxKgmJMAKAcToAf0Vpy4eU4/hBydugo/fMVy2UFE5qDGAsWZjcoWf/InbCkZ6ejsaNG0uqe38NdNdbuIgNowHUcfEqCYkwP8R+BnHu3DkAxuoACG1xFg5z5pzFsWNBSEq6ioSEmwHczIxpXw2UFk5K1pPeFiax6Nm/iI1Li4yMdHlvnC0upaWlDp9ToLv2iA2jMcLzQSLMz3A3K6QOwFgo7X7ydj77wSk+HujWzeefNBRihZPWcShGsWCz0r9ItRqKtaJlZGQ4rCCnQHdt8CayjPB8kAjzMzwNEGbpAIwSkCkXpd1PRnBnGQE9ViwayYKtd/8ixyoi1opmtVoRHx+vSDn9LZWPL3gTWUZ4PkiEEQ4YvQMwytJ9uShtXjeCud4o6LFikRULkztYWknnq1VEzGRFzgTQ31P5+II3kcX68wGQCPN7xLq1jNIBGHHpvhSUNq8bwVxP1IWFRMBiBQcLK+kA36yGYiYrcnMuslRHRkOMyNLbAusNEmF+jKeZ3YABAxATEwNAfAdgdjcgCyhtXjeCud6oqJk2Qu+Bm1WLs6s+iF985ItVRMxkxZeci9QvykeMyGLZw0MizE/xNrOLiYmRFOPAaqdsFpROE2HGtBMsTQK0iLOjTPiOeLZE1b4n1yoiZbJCLn7tkSuyWOjfSIT5KUq7ocR2toWFhS6PVWuAZD2JpViUThNhtrQTLE0C/G0QZmVhh1hLlJy4NClWNKO5+O0nL4WFASgoCEJy8lUkJNQAYNODIVY8Oa9adT4HC9dFIsxP0csNlZ2d7fYzpQdIVgYHpVA6TYSZ0k6wFAtotEHYF1gSnHxGfG9lsg+14LEfkF3lUQTEW9G89a3253RGa2FgP3nx1F+y5sHQ2x2vJCTC/AyW3VBKDpDeOmLnLUycMcoDTLCHP8XZsSI4S0pKsGHDBlFl8hRq4dqlmSRY0j1Z0fj+y1vfyk9EWdhKhy+zt/6SxYVMZumfSYT5GVq5ofR2A3rriPkO2xOszf4I8ejR/lie4KgFK4LTXiQUFsYD4ABYJJdJrEvT1cKloqIi4bverGasbaXDipj2R0iE+SFqu6H0dAPyA5wSgwOLsz89YCngXQx6tT+zxdmJgbU8TDZbQ+TmpsJegAEcUlNzJZVJiYVL7qxmLLlweVgR0/4IiTBCUfTuYOwHwuuuK8P06VZUV1sQGMhhwYIyPPzwEJSWlmLjxo2ql8UMsBTwLgYW2h+P0ePsxMJSHiZXFh3AgoSEQp/P480yJNbCyaLViTUx7U+QCCMURUwHo7ariB8Ip0wBMjKAP/4AWrWyoGnTSACRiv+emWEp4F0MLA5wZoSlTPj2KGXRkXMeb8Hi586dQ3Z2NrNWJ5bEtD9BIoxQBLFuQK1dRU2b1v4zOkZzCeoFqwOc2ZCzOk2LNizGoiNGGMq1DIkpP8tWJ5aTmpoVEmGEInhyA86eXQjgou6uInfovYjAG0ZzCeqBPwbF6y3MpZxbyzbsyaKTnp4u+vxqWoaManXSu82ZERJhhGK4cwOGhYVh+XLvriI9Bkgj5BJjySXIqmD1t6B4owlzrTPsu7PoREZGKnIeJWDB6iRlhw25e2MSniERRqiCoxuwdoA8duwq3nuPQ03NtZVLgYEcJk68D0lJQZo/uKxa5liFdcHqT0HxLG4bJBY12pHU7brUPo/W55aLFLeyfQoO1lJsGBkSYYQmREdHIzoa+Oc/gccfB6qrgcBA4K23LOjWrYmmZeE7ORYtc6xCgpVdWBfH9vjajjy5wzIyMsBxnFtrlxhLqJqZ2FnN8i7196gvUBYSYYRs5MQHjBoFpKXxrkp9gub5zpA1yxzL0KpDNjHagOhLO9LKHcZKHJ1WSN07kvoCZSERRsjCl5gUFlYssmSZk4qWcVliV72S1VAfpAyILARV+7J6VWw2e3KHiUfK3pE8tAJZWUiEEbJgKVjcF1iwzElBa9eTmOS3Zgp4NxpiB0RWAvmVSM+gt/VPquWIZeTsHclyig0jQiKM8HtYsMyJQa/Bh5LfsovYAZGlQH5f0zPo6Q6TYjkyihADpNepUVNssAiJMIJgHJYWEhhFsPoTUgdEPQL5lcywr6c7TI7lyAjIqVMWUmyYARJhBME4tJCAcEauqNHTmqrUykAW3GFmC05XaqcBKccRtZAIIwgDYOSFBITyyBU1eooHJScJervDzBic7q1OWU2xYXRIhBGEgTDaQgJCPeQMdlIC+VkfbPV0h7FgjVMDb3Wq9z03IyTCCEICLAxOSsVlsXAthLaIEQ+sbk+jpjtMzopHva1xhDkgEUbIwh/jA1hZ5q8Evl4LCTjj4k08sJqPSy13mJxcWTxGD073x36cNUiEEbJw7hCNni9HDGbJjQb4di1mEqP+gpRAfrErANVCa4Fv1hWPYqA4L/0hEUbIhn8wV60Cxo4FamqAgIDa4PFRo7x/n6wpxsRMYtRfkLNRsx5B/Hq6QsVcrxktR9TH6guJMMInTp26JsCA2v8ff7w2eNxT3BJZU6Sjt2jlf//cuXOq/QahHlLbhh4rAKW4QpXOXC/meslyRCgNiTDCJ44cuSbAeKqra1fveRJhZram8CJFyc5YbdHqbT9Ksb9PmAc9VwB6cw2WlpZi48aNAJTLXC/2eklgEUpCIozwidata12Q9kIsMLA2fYK/kp2dLfytlCVPTdEqJoO6p/NquaE4oS16rQD05hq8cOECAOXjuGjFI6E1JMIIn2ja1FUCUf/IXyVGfLBuyfM1+FqPLXAIbdFjBaA312BOTg4AdeLWjL7ikTAWJMIIn/HHBKJmER9yBzGbrSFOnkzE5s39AGi7eo5QFxaCz8W6BgsL4wFwAK5t5WX0zPWEf0EijFAEf9jYmR909Fq6ryT8tXizOLgaaO0FqDNiBJzeCwwIz7ASfO7NNWizNURubirsBRjAITU1V9JzyILoJPwXEmEEIRJ+cNq1C3jtNX037/U1Dst+oL3uujJMn25FdbUFgYEcFiwow8MPD3E50DoLUGe8CThaFWsMWKl7T65BV1ZcwIKEhEJJv8GK6CT8ExJhBCGB6Oho9OgBBARwqKnRxwWilCuUH1SmTAEyMnh3sgVNm0YCiHT5HdcDXy0WSw2efbYQY8e6FnCA+Bi5wsJChzIS/oEUa5OSKTSonRF6QSKM0AUjuwCaNgUWLrRh6tQIQQilpubi/PnajlxNa5harlCx7mR3A9/AgZuQmHgKY8cOQXx8vOjfdWfR41eYkkXMv3C2SpWWluLq1asAgOLiIJw4EYqYmAvYv3+LaTfRJrxz6lRteqTWrY0fBkMijNAFo2979PDDf+HPP1fj/PkoFBYmIDc3VdUgfV6MegukV0u08ud1N/B16HDI4Tgx+JoagzAn/DNfUlLiIRfYn+ja9UevcWMsTuIIefCxpOvXh2HaNCtqaiwICOCwcKENDz/8F9PjhSdIhBG64eu2R3oSEhIidPjvvjvUrWVKqUGAF63Hjl3Fe+85ukIDAzlMnHgfkpKCVOuEnEXznDlncexYEJKSriIh4WYAN0vqBF1Z9DZv7ofGjYvRtGmRKtdAGAuxezq6ixtLT0835KBM1IWPJbXZGmLJkixwXG3/V1NjwdSpEfjzz9WwWi8a0nJOIozQFbnbHumNtyD9W28dhjvvVDbWJDo6GtHRrvKyWdCtWxPFfsfT7/PExwPdusk/l+vYsgCsWjXasOk+CHWQm0alcePGaheN0AhekHtrC0a0nJMII3RF7rZHYlEzHcK1IP26OwZ0714rmNTADHnZXMWWAcZM9+ELlK7DO67bCofCwgQkJx8HUGv1ioyMFD6lejMneuxnqjYkwghdUXPbIy3SIei1Y4DR87LxsWX2yV55tE73oRfO7dPdIgUjuliUxGq9iNTUXOTk3INrOcEsyM1NRYcOB2G1XkRkZKSkBSGEMVFqMQZLgf0kwghdUVPEaLVJuBksU1phHyPXteuPaNy4GKtWjTbVzFYs9u3O0yIFI7pYlCYhoQiOSVn9R6wTjsjd35PVwH4SYYTumEHEGN0ypRV8LF1hYSGys7PRtGmR36cZMMMODGpjRjcUIR+p+3uyHNhPIoxgAhIx/oNzJ+dpZusPKQbU2ITabFBOMMIXWA7sJxFGEITm0FYxtYlIAbLyuMNZgMt1QxEED4vPmuv9RwjChNhsDVFQkASbraHeRSFQK8Ti4+Pd/jOzALNPRMoHnlsstatTyMpTS3R0NNLT0x3es1ovIjn5uN/Xjb+h1A4rvEWVpWeNLGGEX6DUfov+AqVOUBfnoHx+xwWgdgssapu12KedIPwXJS3nrFlUSYQRpocCn6WhRWoPohbntgkEOKRe8HeMvMcsoSxK9jVSA/vVhEQYYVr03m9RD5SwYDl/313+Kkqd4DtigvLN1D6lQrGDhNkhEUaYFr33W9QaNZJ/khtXXbwFCmdkZBi6fSoxKTDy9ROEN0iEEaZG7/0WtUTp5J9S3Lj2g21hYQAKCoKQnHwVCQm1AbBkrXCNt9QLVqtV5xLKh3YEIFiBZbc2iTDCLzBDQlixKBUDJzZ/lf1g60n80WDrGtYChZWCdgQgWIFltzaJMMJv8JeEsEol/xSbU4fv2LyJPxps3cNSoLDS0MIYQkuMZpUnEUYQJkOphIRSs5RT5nfxsOweURpqF4RWGNEqTyKMINxw6hRw5AjQurWxLGhKbvEixVXGYjZqVmHZPaI03trFuXPnABjDakGwjRGt8iTCCMIFq1YBY8cCNTVAQEBtYP+oUXqXSjy+xBk5W1/cucpcHUf7+4nHKOLCV/eOt3aRnZ1tGKuFr1ASZG0wkvWVRBhB2FFSUoJjx65i7NjGQkqLmhrg8cc53HjjGUOltJAbZ+SLlcasQeb+ilLuHU/twkhWC1+g1aLaYSSrPIkwgvgffCdZUJCEmpphDp9VV1vw+utfIDn5OLOdpNQ4I0/uVl+uz8xB5v6Gku4dd+3CSFYLX6DVotphJKs8iTCC+B985+dtFsVqJynGglVRUYHKykosWlSKadOsqKmxICCAw8KFNjz88F/kDiFcIkcoiZ0UGMlqoQS0WlQbjGKVD/B+CFtcuXIFN954IywWC/bv3+/w2YEDB3D77bejXr16SExMxMKFC+t8/6OPPkLbtm1Rr149dOzYEZ9//rnD5xzHYc6cOYiPj0dYWBhSU1Nx5MgRh2POnz+PRx55BBEREYiMjMSoUaNw6dIlyWUh2ISfRVkstTEvLM+inImOjkZ8fLzLfyEhIXj//ffxyisfYOrUCDt3qwVTp0bglVc+wPLly1FSUiLpN/1ppZ+/wgsle7wJJX5SMHbsWJf/BgwYAMDYz5scPAlaQlms1otITj7OdFsynCVs2rRpSEhIwE8//eTwfllZGe69916kpqZi5cqV+PnnnzFy5EhERkZi7NixAIDdu3djyJAhmD9/Pvr164f169ejf//+2LdvHzp06AAAWLhwIZYtW4Z33nkHycnJmD17NtLS0vDrr7+iXr16AIBHHnkERUVFyMnJQVVVFUaMGIGxY8di/fr1osuiFDU1NcxaZoxGVVUV6tevDwC4/fbf0aXLW7hwwYrg4NNo2LBM59L5Dt9OvFk1pLYnf1rp56/Ide+IvedGsVoogb9Z/gjPGEqEffHFF9i+fTs+/vhjfPHFFw6frVu3DpWVlVi9ejVCQkJwww03YP/+/Vi8eLEgfJYuXYo+ffpg6tSpAIDnn38eOTk5WL58OVauXAmO47BkyRLMmjULDz74IADg3XffRZMmTfDpp59i8ODBOHToELZt24Y9e/bgpptuAgC8/vrr+Nvf/oZXX30VCQkJosqiBJWVlSgoKEBNTY33gwmvVFdX49Zbb63z/l9/xWHPnj24fPmyDqXyHT72KyKittNXYxAggWV+1BZK/hJLaKR4JaNhRKu8YUTY6dOnMWbMGHz66acIDw+v83leXh569erlULlpaWlYsGABLly4gEaNGiEvLw+TJ092+F5aWho+/fRTAEBBQQGKi4uRmpoqfG61WtG9e3fk5eVh8ODByMvLQ2RkpCDAACA1NRUBAQHIz8/H3//+d1FlccWVK1dw5coV4XVZmXvrC8dxKCoqQmBgIBITExEQYDjPMnNUVlaitLTU4T2O43DhwgW0adOmjvXVCDim2miMfv26oGvXH2UNAkbNm0Yoh78IJbXxJ8uflhjRKm8IEcZxHIYPH45x48bhpptuwrFjx+ocU1xcjOTkZIf3mjRpInzWqFEjFBcXC+/ZH1NcXCwcZ/89d8c0btzY4fOgoCBERUU5HOOtLK6YP38+5s2b57oSnLh69SoqKiqQkJDgUpQS0gkICEBQUN1Hwmq1IjY2FiEhIT65frUWMadOXRNgQG3sFx8ALHUQMHreNII9jGi1UBIStOrAksASg64ibMaMGViwYIHHYw4dOoTt27fj4sWLmDlzpkYl04eZM2c6WOrKysqQmJjo8tjq6moA5u2gWCIgIAABAQEIDg6WLcL0EDFHjlwTYDz2sV9iBgEz5U3TG6PtaQeoK5SMaLXwBX8XnYRrdBVhU6ZMwfDhwz0e06JFC+zcuRN5eXkIDQ11+Oymm27CI488gnfeeQdxcXE4ffq0w+f867i4OOF/V8fYf86/Fx8f73DMjTfeKBxz5swZh3NcvXoV58+f9/o79r/hitDQ0DrX6A2LxSLpeMI97ly6znUstZOsa5ECHn8cSEtTzyJWUlKCiIirCAi4Jp4AabFfRs+bxhJG3NMOUF8osXStauNvopMQh64iLDY2FrGxsV6PW7ZsGV544QXhdWFhIdLS0rBhwwZ0794dAJCSkoJnnnkGVVVVCA4OBgDk5OSgTZs2gvsvJSUFO3bsQFZWlnCunJwcpKSkAACSk5MRFxeHHTt2CKKrrKwM+fn5GD9+vHCO0tJS7N27F926dQMA7Ny5EzU1NZLKQrBHUFAQGjduXGehw+XLl1FWVob09HTUr19fcifpyiJVXQ388Yc6Isx+wO/Xr+6AL9YFYvS8aSxhxD3teEgUKAfVJeGMIaK5mzVrhg4dOgj/rr/+egBAy5Yt0fR/o9jDDz+MkJAQjBo1Cr/88gs2bNiApUuXOrj3nnzySWzbtg2LFi3Cb7/9hrlz5+KHH35AZmYmgFqLR1ZWFl544QVs3rwZP//8M4YOHYqEhAT0798fANCuXTv06dMHY8aMwffff49vv/0WmZmZGDx4MBISEkSXxV8ZPnw4LBYLLBYLgoOD0aRJE9xzzz1YvXq1pFWea9euRWRkpOLlCwoKQkhISJ1/gYGBaNy4saxOtHXrWhekPYGBQKtWChXaCfuBvGvXH5GVtQTDhq1FVtYSweLiCXd7QvpLHic1oRxRBEHYY4jAfDFYrVZs374dEyZMQLdu3RATE4M5c+Y4pITo2bMn1q9fj1mzZuHpp59G69at8emnnwo5woDaPGTl5eUYO3YsSktLcdttt2Hbtm1CjjCgNh1GZmYmevfujYCAAAwcOBDLli2TVBZ/pk+fPlizZg2qq6tx+vRpbNu2DU8++SQ2bdqEzZs3uwyONzJNm9bGgD3+eK0FLDAQeOst7VYYuov9GjBgAGJiYhzeoz0h1YVyRBEEYY8hR7ukpCRwHFfn/U6dOuG///2vx+8+9NBDeOihh9x+brFY8Nxzz+G5555ze0xUVJSQmNUdYsqiJ/ZBwq5QMzYhNDRUiI277rrr0LVrV/To0QO9e/fG2rVrMXr0aCxevBhr1qzB//3f/yEqKgr3338/Fi5ciAYNGuCrr77CiBEjAFyL13r22Wcxd+5cvPfee1i6dCkOHz6M+vXr4+6778aSJUvqrGjVCr6e//Y3ID8/AMeOBSEpqTYYu6hI3xiQmJgYh9hHb9BqLt+hHFEEQdhjSBFG+IZ9zJAntAwSvvvuu9G5c2dkZ2dj9OjRCAgIwLJly5CcnIz/+7//wz/+8Q9MmzYNb7zxBnr27IklS5Zgzpw5OHz4MACgQYMGAGqz3j///PNo06YNzpw5g8mTJ2P48OF1tqfSAnf1fPCg42vWgrEJdSGrIkFoj56GB0+QCPNDxAb/ah0k3LZtWxw4cAAAHBZPJCUl4YUXXsC4cePwxhtvICQkBFarFRaLpc5q05EjRwp/t2jRAsuWLcPNN9+MS5cuCUJNK1itZ0J/yKpIENrBouGBh0QYwQwcxwnuxdzcXMyfPx+//fYbysrKcPXqVVy+fBkVFRUek9Pu3bsXc+fOxU8//YQLFy4Iwf4nTpxA+/btNbkOgiDqwqolQmn85TqNBMsTYhJhBDMcOnQIycnJOHbsGPr164fx48fjxRdfRFRUFL755huMGjUKlZWVbkVYeXk50tLSkJaWhnXr1iE2NhYnTpxAWloaWZtkQMkllcPf65JlS4SS+Mt1Gh2brSHOn49GVFSJ7hZpEmEEE+zcuRM///wzJk2ahL1796KmpgaLFi0SEqhu3LjR4fiQkBBh1wCe3377DSUlJXj55ZeFnQZ++OEHbS6AIZQa8P0puaTa2ez9qS5d4Xzd7gZBo0+WWLa4ELV4SpasByTCCM25cuUKiouLHVJUzJ8/H/369cPQoUNx8OBBVFVV4fXXX8f999+Pb7/9FitXrnQ4R1JSEi5duoQdO3agc+fOCA8PR7NmzRASEoLXX38d48aNw8GDB/H888/rdJX6oeSAb1ZRYI9W2ez9oS7FwNogqCYsWVwI78mS9cAQyVoJc7Ft2zbEx8cjKSkJffr0wa5du7Bs2TL8+9//RmBgIDp37ozFixdjwYIF6NChA9atW4f58+c7nKNnz54YN24cMjIyEBsbi4ULFyI2NhZr167FRx99hPbt2+Pll1/Gq6++qtNV6kt0dDTi4+Pd/iNBcA1v2exttoYOxxHy8VbHZmLfvi5YsiQL77wzDEuWZGHfvi56F8nvYTFZMlnCCE1Zu3Yt1q5d6/W4SZMmYdKkSQ7vPfbYYw6v33zzTbz55psO7w0ZMgRDhgxxeM9VTjkjcOpU7ZZHrVtrl9jVn/HUQRvFisF6ULgZ6lgMLFpcCDaTJZMI80P8PUhYK3yp51Wrrm36HRBQm3F/1CilS0jYw2IHLQUjBIV7q+PS0lJJCYRZxV/EptFgMVkyiTA/xN+DhLVCbj2fOnVNgAG1/z/+OJCWRhYxNWGxg5aCEYLfvdXxxo0bTbFy0OiC3mzYT3Q9JUvWw/BAIsxPMXonZxTk1PORI9cEGE91NfDHH8qJMHJ1usYs2ey1DH4X4wK1x1sdmyH2zuiC3mywbHggEUYQjNG6da0L0l6IBQYCrVopc35ydXpGiWz2esZmaRmPJNYF+uijjzq8NuuOASxbXPwdVg0PJMIIgjGaNq0VRo8/XmsBCwwE3npLGYsVuTrlc+7cOVHiSe/YLC3jkcRarcLDw5GRkYENGzYI75kxfQPLFheCTUiEEQRD8BaUv/0NyM8PwLFjQUhKqk0aWlTkeweuhavTaLizSjiLhOzsbADexZPesVmsxiNZrVbhbzPnCiOBRUiBRBhBMII7C8rBg46v5VpQSkpKEBFxFQEBjVFTYxHeDwzk0LDhGZSUBPnlAMJbLwoLCwWh5UkkSBFPeogNPeORxFi3KH0DQVyDRBhBMIKaW57YC7x+/RyFQd++W7BlizJZ4Y1KdHS016StUkWCnmJDzgIDX+PYxApOSt/gO6zngyPEQyKMIPwA+w7b0wBthpVpvqKUSNBabDi7Vd0Fv7tyv/oaxyZFcGrlLjWrUNE75pBQFhJhhKn46quvcNddd+HChQuIjIwU9Z2kpCRkZWUhKytL1bKxhFlXpymBUiJB69gsX4LCfbXCihGcvPjz5i5VYuWgmYUKbRJuLkiEEZoyfPhwvPPOO3j88cfrbMo9YcIEvPHGGxg2bJiorY0Ic2BvsSgsDEBBQRCSk2sXIwDaWyyUiqnSIzaL1Uz4QF2ROGfOWbuFJzcDuFmxe01ChTAKJMIIzUlMTMSHH36I1157DWFhYQCAy5cvY/369WjWrJnOpSO0xN5i4SmmSGuLhVJJW82S/NUdUq1b9vcwPh7o1k37MhMESwR4P4QglKVr165ITEwUVqIBQHZ2Npo1a4YuXboI7125cgVPPPEEGjdujHr16uG2227Dnj17HM71+eef4/rrr0dYWBjuuusuHDt2rM7vffPNN7j99tsRFhaGxMREPPHEEygvL1ft+gjxeAuGt9kaOhynJVbrRSQnH5csnFzFZrk6D8sJO222higoSBLq3x28dWvs2LFYufJm7NlzFps2lWDPnrNYufJmjB071pAuP4LQCrKEEbowcuRIrFmzBo888ggAYPXq1RgxYgS++uor4Zhp06bh448/xjvvvIPmzZtj4cKFSEtLwx9//IGoqCicPHkSAwYMwIQJEzB27Fj88MMPmDJlisPvHD16FH369MELL7yA1atX4+zZs8jMzERmZibWrFmj5SUTHmBhxZxSG9sbPWGn1LQaZN0iCPmQCCN02Ufw0UcfxcyZM3H8+HEAwLfffosPP/xQEGHl5eV48803sXbtWtx3330AgH/961/IycnBqlWrMHXqVLz55pto2bIlFi1aBABo06YNfv75ZyxYsED4nfnz5+ORRx4Rgu5bt26NZcuW4Y477sCbb76JevXqaXPBIlBKBBgRFhKMKimeWBVY3qAcXgShLSTC/By99hGMjY1F3759sXbtWnAch759+yImJkb4/OjRo6iqqsKtt94qvBccHIxbbrkFhw4dAgAcOnQI3bt3dzhvSkqKw+uffvoJBw4cwLp164T3OI5DTU0NCgoK0K5dOzUuTxZqWlBYF3isbHhsVPGkFCxYJAlpmHH7J3+CRJgfo/c+giNHjkRmZiYAYMWKFar8xqVLl/D444/jiSeeqPMZi4sA1BIBRnCRmT2InWV48e3NImlUK6yZhIr9PfDkOjbqvfI3SIT5MXrvI9inTx9UVlbCYrEgLS3N4bOWLVsiJCQE3377LZo3bw4AqKqqwp49ewTXYrt27bB582aH73333XcOr7t27Ypff/0VrVq1Uu9CDIIRrDyUv0wf7EX6ddeVYfp0K6qrLQgM5LBgQRkefniI7iJdCmYWKvy9OnbsKp57rjE4rnYLMo4LwNat92POnO5ISvLPLciMCIkwP6Z161oXpL0QCwwEtNIrgYGBgmsxMDDQ4bP69etj/PjxmDp1KqKiotCsWTMsXLgQFRUVGPU/f+m4ceOwaNEiTJ06FaNHj8bevXvr5BebPn06evTogczMTIwePRr169fHr7/+ipycHFHJHAnCX+AH7SlTgIyM2slYq1YWNG0aCSBSz6JJxuxCJTo6GgcOuJpEW3DxYhMY9LL8EkpR4cc0bVobA8brn8BA4K23tAvOB4CIiAhERES4/Ozll1/GwIED8dhjj6Fr1674448/8OWXX6JRo0YAat2JH3/8MT799FN07twZK1euxEsvveRwjk6dOuHrr7/G77//jttvvx1dunTBnDlzkJCQoPq1Ed5hPVbNX2naFLjzTm37AqWJjo5GWVkTh83qAXuhYmylwk+i7dFyEk0og4XjOE7vQhCuKSsrg9Vqhc1mqyNULl++jIKCAiQnJ/u8wu/UKX7Wa+xOVy2UrGuiLmbd44/Qn1OngObN61r7jx0zR1+3alVtHG919bVJtBYLqwjveBq/7SF3JIGmTc3RIRHGhAQWoRa8td9ZqJilvxs1qnYhFU2ijQuJMIIgCMK0mF2o0CTa2JAIIwiCIEwNCRWCVSgwnyAIgiAIQgdIhBEEQRAEQegAiTCDQ4tb1YfqmCAIglADEmEGhU9u6mlpP6EMfB07J5QlCIIgCF+gwHyDEhQUhPDwcJw9exbBwcEIcM7aRyhCTU0Nzp49i/DwcAQF0eNCEARBKAeNKgbFYrEgPj4eBQUFOH78uN7FMTUBAQFo1qwZLBaL94MJgiAIQiQkwgxMSEgIWrduTS5JlQkJCSFLI0EQBKE4JMIMTkBAAG2lQxAEQRAGhKb3BEEQBEEQOkAijCAIgiAIQgdIhBEEQRAEQegAxYQxDJ8ktKysTOeSEARBEAQhFn7c9pbsm0QYw1y8eBEAkJiYqHNJCIIgCIKQysWLF2G1Wt1+buFoTxZmqampQWFhIRo2bOiXOarKysqQmJiIkydPIiIiQu/iGBaqR2WgevQdqkNloHpUBjXrkeM4XLx4EQkJCR5THJEljGECAgLQtGlTvYuhOxEREdTRKADVozJQPfoO1aEyUD0qg1r16MkCxkOB+QRBEARBEDpAIowgCIIgCEIHSIQRzBIaGopnn30WoaGhehfF0FA9KgPVo+9QHSoD1aMysFCPFJhPEARBEAShA2QJIwiCIAiC0AESYQRBEARBEDpAIowgCIIgCEIHSIQRBEEQBEHoAIkwQnf+85//4P7770dCQgIsFgs+/fRTh885jsOcOXMQHx+PsLAwpKam4siRI/oUllG81eHw4cNhsVgc/vXp00efwjLM/PnzcfPNN6Nhw4Zo3Lgx+vfvj8OHDzscc/nyZUyYMAHR0dFo0KABBg4ciNOnT+tUYjYRU4933nlnnTY5btw4nUrMJm+++SY6deokJBNNSUnBF198IXxObVEc3upRz7ZIIozQnfLycnTu3BkrVqxw+fnChQuxbNkyrFy5Evn5+ahfvz7S0tJw+fJljUvKLt7qEAD69OmDoqIi4d8HH3ygYQmNwddff40JEybgu+++Q05ODqqqqnDvvfeivLxcOGbSpEn47LPP8NFHH+Hrr79GYWEhBgwYoGOp2UNMPQLAmDFjHNrkwoULdSoxmzRt2hQvv/wy9u7dix9++AF33303HnzwQfzyyy8AqC2KxVs9Ajq2RY4gGAIA98knnwiva2pquLi4OO6VV14R3istLeVCQ0O5Dz74QIcSso9zHXIcxw0bNox78MEHdSmPkTlz5gwHgPv66685jqtte8HBwdxHH30kHHPo0CEOAJeXl6dXMZnHuR45juPuuOMO7sknn9SvUAalUaNG3Ntvv01t0Uf4euQ4fdsiWcIIpikoKEBxcTFSU1OF96xWK7p37468vDwdS2Y8vvrqKzRu3Bht2rTB+PHjUVJSoneRmMdmswEAoqKiAAB79+5FVVWVQ3ts27YtmjVrRu3RA871yLNu3TrExMSgQ4cOmDlzJioqKvQoniGorq7Ghx9+iPLycqSkpFBblIlzPfLo1RZpA2+CaYqLiwEATZo0cXi/SZMmwmeEd/r06YMBAwYgOTkZR48exdNPP4377rsPeXl5CAwM1Lt4TFJTU4OsrCzceuut6NChA4Da9hgSEoLIyEiHY6k9usdVPQLAww8/jObNmyMhIQEHDhzA9OnTcfjwYWRnZ+tYWvb4+eefkZKSgsuXL6NBgwb45JNP0L59e+zfv5/aogTc1SOgb1skEUYQfsDgwYOFvzt27IhOnTqhZcuW+Oqrr9C7d28dS8YuEyZMwMGDB/HNN9/oXRRD464ex44dK/zdsWNHxMfHo3fv3jh69ChatmypdTGZpU2bNti/fz9sNhs2bdqEYcOG4euvv9a7WIbDXT22b99e17ZI7kiCaeLi4gCgzoqf06dPC58R0mnRogViYmLwxx9/6F0UJsnMzMSWLVuwa9cuNG3aVHg/Li4OlZWVKC0tdTie2qNr3NWjK7p37w4A1CadCAkJQatWrdCtWzfMnz8fnTt3xtKlS6ktSsRdPbpCy7ZIIoxgmuTkZMTFxWHHjh3Ce2VlZcjPz3fw5xPSOHXqFEpKShAfH693UZiC4zhkZmbik08+wc6dO5GcnOzwebdu3RAcHOzQHg8fPowTJ05Qe7TDWz26Yv/+/QBAbdILNTU1uHLlCrVFH+Hr0RVatkVyRxK6c+nSJYcZR0FBAfbv34+oqCg0a9YMWVlZeOGFF9C6dWskJydj9uzZSEhIQP/+/fUrNGN4qsOoqCjMmzcPAwcORFxcHI4ePYpp06ahVatWSEtL07HU7DFhwgSsX78e//73v9GwYUMhtsZqtSIsLAxWqxWjRo3C5MmTERUVhYiICEycOBEpKSno0aOHzqVnB2/1ePToUaxfvx5/+9vfEB0djQMHDmDSpEno1asXOnXqpHPp2WHmzJm477770KxZM1y8eBHr16/HV199hS+//JLaogQ81aPubVGXNZkEYceuXbs4AHX+DRs2jOO42jQVs2fP5po0acKFhoZyvXv35g4fPqxvoRnDUx1WVFRw9957LxcbG8sFBwdzzZs358aMGcMVFxfrXWzmcFWHALg1a9YIx/z111/cP/7xD65Ro0ZceHg49/e//50rKirSr9AM4q0eT5w4wfXq1YuLioriQkNDuVatWnFTp07lbDabvgVnjJEjR3LNmzfnQkJCuNjYWK53797c9u3bhc+pLYrDUz3q3RYtHMdx6ks9giAIgiAIwh6KCSMIgiAIgtABEmEEQRAEQRA6QCKMIAiCIAhCB0iEEQRBEARB6ACJMIIgCIIgCB0gEUYQBEEQBKEDJMIIgiAIgiB0gEQYQRAEQRCEDpAIIwiCIAiC0AESYQRBEDKorKzUuwh1YLFMBEG4h0QYQRAEgDvvvBOZmZnIzMyE1WpFTEwMZs+eDX5nt6SkJDz//PMYOnQoIiIiMHbsWADAN998g9tvvx1hYWFITEzEE088gfLycuG8b7zxBlq3bo169eqhSZMmGDRokPDZpk2b0LFjR4SFhSE6OhqpqanCd++8805kZWU5lLF///4YPny48FpumQiCYAMSYQRBEP/jnXfeQVBQEL7//nssXboUixcvxttvvy18/uqrr6Jz58748ccfMXv2bBw9ehR9+vTBwIEDceDAAWzYsAHffPMNMjMzAQA//PADnnjiCTz33HM4fPgwtm3bhl69egEAioqKMGTIEIwcORKHDh3CV199hQEDBkDqdr5Sy0QQBDvQBt4EQRCotTydOXMGv/zyCywWCwBgxowZ2Lx5M3799VckJSWhS5cu+OSTT4TvjB49GoGBgXjrrbeE97755hvccccdKC8vx+eff44RI0bg1KlTaNiwocPv7du3D926dcOxY8fQvHlzl+W58cYbsWTJEuG9/v37IzIyEmvXrgUAWWWqV6+eT/VEEIRykCWMIAjif/To0UMQYACQkpKCI0eOoLq6GgBw0003ORz/008/Ye3atWjQoIHwLy0tDTU1NSgoKMA999yD5s2bo0WLFnjsscewbt06VFRUAAA6d+6M3r17o2PHjnjooYfwr3/9CxcuXJBcZqllIgiCHUiEEQRBiKR+/foOry9duoTHH38c+/fvF/799NNPOHLkCFq2bImGDRti3759+OCDDxAfH485c+agc+fOKC0tRWBgIHJycvDFF1+gffv2eP3119GmTRtBKAUEBNRxTVZVVflcJoIg2IFEGEEQxP/Iz893eP3dd9+hdevWCAwMdHl8165d8euvv6JVq1Z1/oWEhAAAgoKCkJqaioULF+LAgQM4duwYdu7cCQCwWCy49dZbMW/ePPz4448ICQkRXIuxsbEoKioSfqu6uhoHDx70eg1iykQQBBuQCCMIgvgfJ06cwOTJk3H48GF88MEHeP311/Hkk0+6PX769OnYvXs3MjMzsX//fhw5cgT//ve/hSD4LVu2YNmyZdi/fz+OHz+Od999FzU1NWjTpg3y8/Px0ksv4YcffsCJEyeQnZ2Ns2fPol27dgCAu+++G1u3bsXWrVvx22+/Yfz48SgtLfV6Dd7KRBAEOwTpXQCCIAhWGDp0KP766y/ccsstCAwMxJNPPimkfXBFp06d8PXXX+OZZ57B7bffDo7j0LJlS2RkZAAAIiMjkZ2djblz5+Ly5cto3bo1PvjgA9xwww04dOgQ/vOf/2DJkiUoKytD8+bNsWjRItx3330AgJEjR+Knn37C0KFDERQUhEmTJuGuu+7yeg3eykQQBDvQ6kiCIAi4Xo1IEAShJuSOJAiCIAiC0AESYQRBEARBEDpA7kiCIAiCIAgdIEsYQRAEQRCEDpAIIwiCIAiC0AESYQRBEARBEDpAIowgCIIgCEIHSIQRBEEQBEHoAIkwgiAIgiAIHSARRhAEQRAEoQMkwgiCIAiCIHSARBhBEARBEIQO/D/ADpKDalS5HgAAAABJRU5ErkJggg==", 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gDAaD1bHr16+LI0eOiOvXr1f5fOYuXboksrKy7H5dunSpRuetzIwZM8SoUaNEdna2OHfunEhNTRUvvPCCaNOmjRg9erQoKSmp0nk+/PBDodPp6uQaLd3sZ01ERI2D/Lvv1VcvC43GKAAhNBqjePXVy7X6u6+i39/mmmSLiry8PHz66acYNGiQ0jJg69at6NSpE7Zt24ZRo0ZBCIGoqCgsW7YMnp6eAIDk5GRERUWpzhUTE4P4+HgApuanqampeOyxx5TjGo0GUVFRSE5OBgCkpqaipKREdZ7u3bujQ4cOSE5OxsCBA5GcnIzevXvDz89P9Tr3338/fv/9d/Tr169OPpeqys3NxcqVKysdN2/ePHh5edX66zs5OaFdu3YAgPbt2yM8PBwDBw7E7bffjtWrV2P27Nn4z3/+gw8//BB//PEHPD09MW7cOCxbtgxt2rTB999/j7vuugtAedH8008/jSVLluDjjz/GihUrcPz4cbi6uuK2227D8uXL4evrW+vvg4iIGr/c3FwUFxcjPz8f//vfN8jMDMLGjRMBmH5/GI0SFi50x59/fgCdrrDOfvfZ0mSWIwHg0UcfhaurK7y8vHD27Fl89dVXyrE//vgDZ86cwYYNG/DRRx9h9erVSE1NVS1Z5uTkqIIRAPj5+aGgoADXr1/HpUuXUFZWZnNMTk6Ocg5HR0erpTDLMbbOIR+zp6ioCAUFBaqvulDVbY7qczuk2267DX379sXmzZsBmMLvG2+8gd9//x1r1qzBd999h0WLFgEABg0ahOXLl8Pd3R3Z2dnIzs7GI488AgAoKSnBc889h19//RVffvklTp8+jZkzZ9bb+yAiosYjPT0dK1euxHvvvYfFi09i+fJ4bNz4T1jGHyE0yMszTdhcvHix3q6vQUPY4sWLlfoge1/Hjh1Txi9cuBAHDx7Ezp074eDggDvvvBNCCACmzuZFRUX46KOPMHToUNx66614//33sXv3bhw/fryh3mK1LF26FDqdTvkKCgpq6EuqV927d8fp06cBAPHx8RgxYgSCg4Nx22234fnnn8f69esBmPp26XQ6SJKEdu3aoV27dmjTpg0A4O6778bo0aPRqVMnDBw4EG+88Qa++eYbXLlypaHeFhERNYDc3Fx88sknAACDwQ1bt46FEPZijxGennkATP+Yry8Nuhz58MMPVzpL0alTJ+XP3t7e8Pb2RteuXdGjRw8EBQXhp59+gl6vh7+/P1q1aoWuXbsq43v06AHAdKdit27d0K5dO6u7GM+fPw93d3c4OzvDwcEBDg4ONsfIy2ft2rVTpjXNZ8Msx1jeUSmfUx5jy2OPPYYFCxYo3xcUFLSoICaEUJYXExMTsXTpUhw7dgwFBQUoLS3FjRs3cO3atQp3CEhNTcWSJUvw66+/4vLly0qx/9mzZxEWFlYv74OIiBqe+WpOSkpkBQEMaKh2kA06E+bj44Pu3btX+GWvI7z8y7WoqAgAMHjwYJSWliI9PV0Zc+LECQBAx44dAQB6vR67du1SnSchIQF6vR6AaYYlIiJCNcZoNGLXrl3KmIiICGi1WtWY48eP4+zZs8oYvV6P3377TXVHZUJCAtzd3SsMAk5OTnB3d1d9tSRHjx5FSEgITp8+jbFjx6JPnz7YtGkTUlNT8d///hdAxUukV69eRUxMDNzd3fHpp59i//79+OKLLyp9HhERNW25ubnIzs7GsWPH8Ntvv+G3337DyZMnAZhmwZKS9BU+33w5sj41icL8lJQU7N+/H0OGDEHbtm2Rnp6OJ598EqGhoUrwiYqKQnh4OO6++24sX74cRqMRc+fORXR0tDI7dt9992HlypVYtGgR7r77bnz33XdYv349tm/frrzWggULMGPGDPTv3x8DBgzA8uXLcfXqVaUQXKfTYdasWViwYAE8PT3h7u6OBx54AHq9HgMHDgQAjBw5EmFhYZg+fTqWLVuGnJwcPPHEE5g7dy6cnJzq+dNrGr777jv89ttvmD9/PlJTU2E0GvHaa68pG5PLS5EyR0dHq22Ejh07htzcXLz00kvKDOIvv/xSP2+AiIgaRHp6urLsCJhCV16eF7TaIpSUBOPqVRdUNuckSeXLkfWpSYQwFxcXbN68GU8//TSuXr0Kf39/jBo1Ck888YQSajQaDbZu3YoHHngAw4YNg6urK0aPHo3XXntNOU9ISAi2b9+O+fPnY8WKFQgMDMSqVasQExOjjImLi8PFixfx1FNPIScnB7fccgt27NihKrR//fXXodFoMHHiRBQVFSEmJgZvvfWWctzBwQHbtm3D/fffD71eD1dXV8yYMQPPPvtsPXxajV9RURFycnJQVlaG8+fPY8eOHVi6dCnGjh2LO++8E2lpaSgpKcGbb76JcePGYd++fXjnnXdU5wgODsaVK1ewa9cu9O3bFy4uLujQoQMcHR3x5ptv4r777kNaWhqee+65BnqXRERU18zrvgBg3z49EhKiYApdAoAESTICMMJ+EDNi3Lht0OkK6/x6LUlCrmynRqegoAA6nQ4Gg8FqafLGjRvIyMhASEgIWrduXa3zZmdn47333qt03Jw5c+Dv71+tc1dm5syZWLNmDQCgVatWaNu2Lfr27Yt//etfmDFjhjLz9frrr+OVV15Bfn4+hg0bhqlTp+LOO+/E5cuXlVq8+++/Hxs2bEBubq7SouLzzz/Hv//9b2RnZyM8PByPPfYYxo8fj4MHD+KWW26p0TXfzGdNRER1x/z3mSmARUNuPaEmRx3rY5MmrUevXkeV72NjY9G7d++buq6Kfn+bYwhrxOoqhDV0n7CmhiGMiKjxkPt+AcClS5ewefNmGAxuWL48vsLie9uMmD9/uWoWbPLkycqNfTVV1RDWJJYjqXZ5eXlh3rx5FRarOzo6MoAREVGjYm8SIS/PqwoBzLQ8af59dHSi1TJkfTb3ZghroRiwiIioqbGcPDAvwpckYyVBTFLGSJIRUVGJGDw4WTkaGxuLgICAev39yBBGREREjZL50qPBYFAaegPAgQP9zBqwGtGjx1EcO9ZD+d4061U+8yVJRsyatQolJY7w9MyzmgGr7wAGMIQRERFRI1RR/bJ1B3wNjh4Nw9ChP6JTpwx4euYhPb2zMkaSTHdABgZmAwBGjBiBtm3bAgC0Wi18fHwaZIWIIYyIiIgaDXn269KlS6rH5aVHT89cZGYG2Vh6lLB371D0758Kna4Q4eEHERp6Cnl5nlYzX126dKn1u/9rgiGMiIiIGgV7s1+WS4+221CUd76XA5dOV2iz/5e93XjqG0MYERERNQq2Cu8zM4OwZctYlDdbrWgPyPLO9/KSY6tWrVR7PTemu/8ZwoiIiKhByEuPBoMBJSUluHz5snJMPftVObnuS575aixLjhVhCCMiIqJ6V73C+8qY7nyUC++biuq2liVq1L7//ntIkoT8/PwqPyc4OBjLly+vs2siIiJrtpYeMzKCce6cP37/vWe1Atj48dusAlhjqfuqCGfCqF7Je0fee++9Vptyz507F2+99RZmzJiB1atXN8wFEhFRvVMvPcqd7S073FsS6N37V0RFfacqvm+Ipqs1xRBG9S4oKAhr167F66+/DmdnZwCm/Rk/++wzdOjQoYGvjoiIapt509X8/HyUlpbizz//BGBr6VEy+6+9IGZEdLS6472sqQQwgMuR1ADCw8MRFBSEzZs3K49t3rwZHTp0QL9+/ZTHioqK8OCDD8LX1xetW7fGkCFDsH//ftW5vv76a3Tt2hXOzs4YMWKEqpuybO/evRg6dCicnZ0RFBSEBx98EFevXq2z90dEROXk2q/33nsP7733HtavX4/NmzcjJSUFQGX7PspBTGZERMR+zJ+/3CqA/f3vf8e8efOaTAADGMIIwLlzwO7dpv/Wl7vvvhsffvih8v0HH3yAu+66SzVm0aJF2LRpE9asWYMDBw6gc+fOiImJQV6e6fbjzMxMxMbGYty4cTh06BBmz56NxYsXq86Rnp6OUaNGYeLEiTh8+DDWrVuHvXv3Yt68eXX/JomIyKr2y5KnZy4kyVjBCAnDhu3GpEnrMX/+cowb97Vq+XH06NGYN28e/va3vzWpAAYwhLV4778PdOwI3Hab6b/vv18/rztt2jTs3bsXZ86cwZkzZ7Bv3z5MmzZNOX716lW8/fbbeOWVVzB69GiEhYXhf//7H5ydnfH+Xxf59ttvIzQ0FK+99hq6deuGqVOnYubMmarXWbp0KaZOnYr4+Hh06dIFgwYNwhtvvIGPPvoIN27cqJ83S0TUwuTm5iI7OxvZ2dlIS0tTHZML8A0GN6UL/pAhe8yCmFCNlyQjIiIOolevo1aNV6dNm4YBAwY0ufAlY01YC3buHDBnDmD86+feaATuvReIiQECA+v2tX18fDBmzBisXr0aQgiMGTMG3t7eyvH09HSUlJRg8ODBymNarRYDBgzA0aNHAQBHjx5FZGSk6rx6vV71/a+//orDhw/j008/VR4TQsBoNCIjIwM9evSoi7dHRNTimPf8Wrdunc0x1gX4Aqb5ILn2y4guXU7i5MkuADRWvb/kBqwNud9jbWIIa8FOniwPYLKyMuDUqboPYYBpSVJeFvzvf/9bJ69x5coV3HvvvXjwwQetjvEmACKi2lFZz6+8PC9otUU2CvDNi/ABQINTp7pg9uxVKClxbLR7PtYWhrAWrEsXQKNRBzEHB6Bz5/p5/VGjRqG4uBiSJCEmJkZ1LDQ0FI6Ojti3bx86duwIACgpKcH+/fsRHx8PAOjRowe2bNmiet5PP/2k+j48PBxHjhxB5/p6U0RELZC9ui/rPR8rr4ISQoOSEkeEhJyxOtYUen9VB0NYCxYYCLz3nmkJsqzMFMDefbd+ZsEAwMHBQVladHBwUB1zdXXF/fffj4ULF8LT0xMdOnTAsmXLcO3aNcyaNQsAcN999+G1117DwoULMXv2bKSmplr1F3v00UcxcOBAzJs3D7Nnz4arqyuOHDmChIQEu/9qIyKiqsnNzcXFixdx7Ngx1ePyno/qmS/zZUf7zPd/jI2NVUpVGtOej7WFIayFmzXLVAN26pRpBqy+ApjM3d3d7rGXXnoJRqMR06dPR2FhIfr3749vv/0Wbdu2BWBaTty0aRPmz5+PN998EwMGDMCLL76Iu+++WzlHnz598MMPP+Dxxx/H0KFDIYRAaGgo4uLi6vy9ERE1Z+np6fjkk0+sHq94z0d7AcwUzixrwLy9vZvV8qMlSQghKh9GDaGgoAA6nQ4Gg8EqrNy4cQMZGRkICQlB69atG+gKWwZ+1kREJnLxfX5+PtavX291/Nw5f6xaNRvVab4gSUYMGbIHnTplWNWANbW+X7KKfn+b40wYERERVaqi4nsA2LdPj4SEaFS23FhO4O9/34Zu3U5a9f0KCgpqlsuPlhjCiIiIyC559uvSpUuqx+W7Hj09c/HLLxHYs2cYqhPAwsKOYMCAAwBMrSe6dOnSIoKXOYYwIiIissne7Jd1vy+g8gBW3gts6NA9uP3275Uj7du3b9a1X/YwhBEREZFNlq0nbN/1WPnslyQZERWViICALKu6r/HjxyM0NLQ2L7vJYAhr4nhfRd3jZ0xELY28BHn69GnlsYrverRHICLiFwwbtkcVvOLi4qDT6Vrc8qMlhrAmSu6rVVxcDGdn5wa+muZN/pegZS8zIqLmyNYSpMHgVoMAZkR0dCIGD05WHpk8eTJ8fX1bdPAyxxDWRLVq1QouLi64ePEitFotNBruxV4XjEYjLl68CBcXF7Rqxf+7EFHzlpubixMnTqgeMxjc8PvvPasRwAQmTdqAoKBzqtmvadOmtdhlR3v4W6WJkiQJ/v7+yMjIwJkz1ls7UO3RaDTo0KEDJKmqd/0QETU9tmbArAvwK/t70Ijx47ehVy/Tbih9+vRBjx49msVm23WBIawJc3R0RJcuXezu2UW1w9HRkTONRNRs5Obm4sKFCzh37pzq98fly5dV486d88eWLWNR3njVfgCTJCP0+mRERqaoZr8GDhzYIu96rCqGsCZOo9GwizsREVVJZQ1XZQcO9LMIYPYYMWnSRqulR1lz23C7tjGEERERtRBVWTkxGNyqHMDMlx7NxcbGIiAggEuQlWAIIyIiasbk5cfS0lKrJUe571dubluUlbVC164ncORIT1QWwCTJiFmzViEwMNvmcQawqmEIIyIiaqYqWn40LTmOg3mt148/Dq/0nJJkxLhx25QANmLECPj4+MDDwwMAWnzvr+pgCCMiImpG5EarBoNB1WxVZt713rrYvqK7H223nujZsydDVw0xhBERETUTlRXe79unR0JCFCqv91KTZ7/k+q/IyEj07duXs143iSGMiIiomaio8N4UwKJRtY22BUxBzYhBg6xbT3Ts2JGtJ2oBQxgREVETJi8/AkBGRobqmMHghrw8LxQXt6pyAOvb91fcdtt3yMvztNpsW+br61tLV9+yMYQRERE1UZUV3le12/0tt/wCd/dCdO16Uim4txW+uPdj7WIIIyIiaqLsLT9ab7hdcbf7ESN+hE5XiODgYLRp0wtt2rRB27ZtERQUpIxj/VftYwgjIiJqQqqy/JiWFlalDbflgnt51mvkyJGs9apHDGFERESNWG5uLi5evIiSkhIUFhYiISHB5jjr5ceK9ez5G0aOTLC57Ej1gyGMiIioEZI73a9fv97uGHnmS6stqvLyo4nRZgDjXo/1iyGMiIiokanKRtvVKbw3MY2xXIKMjo5GSEgIa74aAEMYERFRI5Kbm4usrCybx8r3evTA7t23o7zpqq0AJgczU6+vsLDfUVLiaNV2IiQkhHVgDYQhjIiIqJGofK/Hsahqt/uIiF/Qq9fvdnt9ybgE2XAYwoiIiBoJey0nzp3zr1YAA4wYNmyPzfAVFxcHnU4HgG0nGhpDGBERUSORn5+v+t5gcENKSiSSkgah8povmUB0dKKq7URwcDAAhq7GhiGMiIioEcjNzVXdCVnd5UcTgaFDf8TgwcnKI8HBwaz5aqQYwoiIiOqZecNV2aVLl5Q/GwxuVQxg5ZttS5IRUVGJqgAGsOarMWMIIyIiqgdy8MrPz6+w9xcA/PjjUFQWwIYN242IiIMAYHez7WnTpnH5sRFjCCMiIqpjFd31KDdc9fTMhU5XCIPBDampEZWc0YiIiINK6LK32XZoaOjNXjrVIYYwIiKiOmbvrkfzhqvycqIkmZYX7TNi/PhtlW435OvrW/MLpnrBEEZERFRH5CVI83ovwDT7dfx4V3z99d8hBy4hNEhIiIbpLkjLDvgCvXv/im7dTiAo6JwqgMXGxsLb21t1ft4F2TQwhBEREdUiOXgZDAasW7fO6njFdz1KZv8t73gfHW1dcC8LCAhg4GqiGMKIiIhqSWW1X5mZQdVoOyEhJmYHwsKOWC09yrNfnPFq2hjCiIiIaoll7ZdcdJ+V5Y/ExKi/NtuuGkky2gxgAGe/mguGMCIiohqy7Pd1+vRp5c/mRffWNV6VEYiKKu96P2LECLRt2xatWrWCr68vA1gzwRBGRERUA5UtPaqXHasTwKxrwLp06cKu980QQxgREVE15ebmIisry+7xlJRIVFz3JdCt2xGcONHjr5kyIyIiUhESkmF19yM1XwxhRERE1WBvBkwuvM/La/vXhtsVkTBw4H78/e/f2u12b45bDzVPDGFERESVyM3NxcWLF1FSUoI///xTdcxgcENKSiSSkvSo+mbbRiV4VRS+Ro8ejdDQUNaANVMMYURERBWoqPZLXXxfVQLR0YkVhq/JkyezAL8FqM5PTYMaP348OnTogNatW8Pf3x/Tp09XrccvWbIEkiRZfbm6uqrOs2HDBnTv3h2tW7dG79698fXXX6uOCyHw1FNPwd/fH87OzoiKisLJkydVY/Ly8jB16lS4u7vDw8MDs2bNwpUrV1RjDh8+jKFDh6J169YICgrCsmXLavkTISKi+mCr7URGRjDOnfPHli3jqh3Ahg79UVV0Hxsbizlz5ihf8+bNQ48ePRjAWoAmE8JGjBiB9evX4/jx49i0aRPS09MxadIk5fgjjzyC7Oxs1VdYWBj++c9/KmOSkpIwZcoUzJo1CwcPHsSECRMwYcIEpKWlKWOWLVuGN954A++88w5SUlLg6uqKmJgY3LhxQxkzdepU/P7770hISMC2bdvw448/Ys6cOcrxgoICjBw5Eh07dkRqaipeeeUVLFmyBO+9914df0pERFSXDhzoh+XL47FmzQysWnUPqn/XYwJuv/171aPe3t7w9/dXvhi+Wg5JCCEa+iJqYsuWLZgwYQKKioqg1Wqtjv/666+45ZZb8OOPP2Lo0KEAgLi4OFy9ehXbtm1Txg0cOBC33HIL3nnnHQghEBAQgIcffhiPPPIIAMBgMMDPzw+rV6/GHXfcgaNHjyIsLAz79+9H//79AQA7duzA3//+d5w7dw4BAQF4++238fjjjyMnJ0cpply8eDG+/PJLHDt2rMrvsaCgADqdDgaDAe7u7jX+rIiIqGJyv6/8/HyUlpaqjl2+fBm7d++GweCG5cvjqznzBQBGTJq00e5dj/PmzWPwamaq+vu7SdaE5eXl4dNPP8WgQYNsBjAAWLVqFbp27aoEMABITk7GggULVONiYmLw5ZdfAgAyMjKQk5ODqKgo5bhOp0NkZCSSk5Nxxx13IDk5GR4eHkoAA4CoqChoNBqkpKTg//2//4fk5GQMGzZMdTdLTEwMXn75ZVy+fBlt27atjY+BiIhuUm5uLi5cuID169dXOjYvz6sKAcyI7t2P4dix7gA0kCQjxo3bhl69jtocPW3aNAawFqxJhbBHH30UK1euxLVr1zBw4EDVjJa5Gzdu4NNPP8XixYtVj+fk5MDPz0/1mJ+fH3JycpTj8mMVjfH19VUdb9WqFTw9PVVjQkJCrM4hH7MXwoqKilBUVKR8X1BQYHMcERHdvIoK7oHyLYc8PXOh0xXC0zMXgBH2K3mE0mTV9NyKW09MmzYNoaGhN/0+qOlq0JqwxYsX2yymN/8yX75buHAhDh48iJ07d8LBwQF33nknbK2mfvHFFygsLMSMGTPq8+3ctKVLl0Kn0ylfQUFBDX1JRETNlmXBvcxgcMPOnVF4/XVT7dfy5fE4cKAfACAiIhWmLYgsqQvudbpChIScsbnxtlx8zwBGDToT9vDDD2PmzJkVjunUqZPyZ29vb3h7e6Nr167o0aMHgoKC8NNPP0Gv16ues2rVKowdO9ZqRqtdu3Y4f/686rHz58+jXbt2ynH5MfPtIc6fP49bbrlFGXPhwgXVOUpLS5GXl6c6j63XMX8NWx577DHVcmlBQQGDGBFRPbHX70sIDbZsGQtJgtLd3kQuyrfeZsgebrxN5ho0hPn4+MDHx6dGzzUaTf8nMF++A0x1Xbt378aWLVusnqPX67Fr1y7Ex8crjyUkJCghLiQkBO3atcOuXbuU0FVQUICUlBTcf//9yjny8/ORmpqKiIgIAMB3330Ho9GIyMhIZczjjz+OkpISpWYtISEB3bp1q7AezMnJCU5OTjX4NIiI6GZU3u9Lg/KFF1MQ+/vft8HF5brdgvvJkyfDw8ND+d7R0ZEBjFSaRE1YSkoK9u/fjyFDhqBt27ZIT0/Hk08+idDQUKtZsA8++AD+/v4YPXq01XkeeughDB8+HK+99hrGjBmDtWvX4pdfflFaR0iShPj4eDz//PPo0qULQkJC8OSTTyIgIAATJkwAAPTo0QOjRo3CPffcg3feeQclJSWYN28e7rjjDgQEBAAA/vWvf+GZZ57BrFmz8OijjyItLQ0rVqzA66+/XrcfFBERVZvB4FaDhqsa+PjkIiTkjNURdrmnqmoSIczFxQWbN2/G008/jatXr8Lf3x+jRo3CE088oZo5MhqNWL16NWbOnAkHBwer8wwaNAifffYZnnjiCfz73/9Gly5d8OWXX6JXr17KmEWLFuHq1auYM2cO8vPzMWTIEOzYsQOtW7dWxnz66aeYN28ebr/9dmg0GkycOBFvvPGGclyn02Hnzp2YO3cuIiIi4O3tjaeeekrVS4yIiOqO3HLCnmvXrillIlW761HAvCeYJJm2HQJMdV7e3t4AONtF1dNk+4S1BOwTRkRUfZXd9Wjp3Dl/rFo1G/bvVTPVfCUmRkGI8rYT4eEHAQBz5sxR1RETNes+YURERPZUNAMmMxjckJkZhIyMYKSmRqAqbSd69UqrtO0EUXUwhBERUYuyb58eCQlRqLxLk0B0dIKq7QTDF9UmhjAiImo2cnNzcenSJdVj5k1X09J6ISEhGpXv+WjE7NmrEBiYXelrmu+OQlQdDGFERNQs2KoFM289IUnGv9pMVB7Axo/fZhXAoqOjrXZDYSE+3QyGMCIiahYsa8HOnfPHli1jIS87Vq0FhbA7A9atWzcGLqpVDGFERNSkye0oMjIylMcOHOiHLVvGofJZL0BuPyHf9WgZwGJjY9npnuoEQxgRETVZlkuQ8l2PFQcwueeXERERqejX7yBKShzt3vXIAEZ1hSGMiIgardzcXFy8eBElJSVWx65cuQKDwaB8X/nWQzIJMTE7EBZ2pMK7HaOjo7kESXWKIYyIiBql9PR0fPLJJ1Uaa1n/VRFJMlYawADTfsIMYFSXGMKIiKjRyc3NtRnAzNtNyCFKngGrOICp676q0u+LrSeorjGEERFRo2Or6/2+fXqrrYNCQ0/V2hLk5MmT4eHhAYCtJ6h+MIQREVGDsrXZtmXD1V27bsWePcMgF9sLocHWrWPRq9dvVWo9YW8JUt58m6GLGgJDGBERNZiq1H3t26dXBTCZEBr89lufKryK/SVI3vlIDYkhjIiIGoS9ui9zBoPbX/s82mo3Iew8bgRgWrLU65MRGZmiCmBy53vOflFDYwgjIqIGYbkEKff4AoCgoEzodIXIy/OC7YJ7+wFs9uxVFfb9CgkJgb+//01fP9HNYggjIqIGZ93h3rR/4+XLOlQ241VOIDo6sdJNt3nXIzUWDGFERNSgDAa3v1pMmActzV99vyTYDmASJElACDmIGREdnYjBg5Ptvk5cXBx8fHy4BEmNBkMYERHVGVt3PsrkOyDz8rzs3OFY8V2PQmgwadJ6uLpes7n0OHLkSAQHBwNgywlqnBjCiIioTlju62iPp2cuJMlYpVYT5iTJiKCgc3b7fnXt2pXBixq16v3EExERVZG9GTBLOl0h+vQ5DFPtV0WEMqayzvfTpk1jAKNGjzNhRERUKyyXHi0brppvOQRA9efDh/vAdu2XzFTz1atXGvLyPK2WH+WmqwCXHqnpYAgjIqKbVtnSo7y/o2nJUZ7xkpReXhUvRQrMnr1KueuRTVepuWAIIyKim2ar55f5TJd6f8fyGS8hNEhKGgTrNhTqDbdttZ2Q93rkzBc1VQxhRERUq8xnvao202W5DCkwdOiP6NQpw27D1WnTpiE0NLRWr5uovjGEERHRTcvPzwdQ3vNLDl2mmS497DdctUVCp04ZCAk5Y3UkOjoa3bp148wXNQsMYUREVCUV9fw6c8YUmGz3/DI1UzWpPIhJkhGennk2jzGAUXPCEEZERJWqTs8v27NeckG++VZDtsZZt55g7Rc1VwxhRERUqar2/EpL61XBUdNWQ6NHb4WLy3UYDB5ITIxS1Y5FRqaoAlhcXBy6d+9+k1dP1DgxhBERUbXJdz9qtUXIygrAlStt0L79n0hIiEJFS45CaODjk6vUe9nr+yXT6XR19RaIGhxDGBERVYt1zy85dFVefG9Z76XTFdrteg+YGq8SNVcMYUREZFNubi4uXLiA0tJSXL58GYD13Y/q0CXBdr8vAUBT6VZD5uLi4uDj48MaMGrWGMKIiMiKvUJ823c/mpNQXnxf9a2GzLEAn1oKhjAiIrJiqwN+ZmYQrl1zhvoOR0sCU6Z8DkfHElXosjX75e3tDX9//9q9cKImhCGMiKiFk/t/5efno7S0FACU5UfAVAO2Zcs4qGu/bBHo2/dXdOt2qkqvy3ovaukYwoiIWhjzpqsGgwHr1q2zO1auAbOu/VLr0+cgBgzYb3OPR5nc7wvgkiMRwBBGRNSiVLXpqtyC4upVl0pqwEx3PN5++26rJcfo6GiEhIQAYOgisoUhjIioGbPcaujSpUuq43LY8vTMhU5XCIPBDSkpkX/t9yhvN2S/9URFdzxyiyGiijGEERE1U5XNepn3+5IkIzp0OIMzZ4KhDlwVz4IJAYSGlteAyXc8cuaLqHIMYUREzdSFCxfsHrPs9yWEBmfOhNTgVTTIy/NUZsJ4xyNR1TGEERE1M/IS5MWLF1WPnzvnj7NnO6JDhzMoKXGqtNbLNvXSpGUHfN7xSFR1DGFERE2UZb0XYP9uxy+++Ad+/bUv5K72YWFHIEnGagex3r1/RVpaH2UJ07weLC4ujkuQRNXAEEZE1ARV5y7H48e7mgUwAJBw5EgYhg79EXv3Dq1WEOvW7QSior6z2QGfm20TVQ9DGBFRE2Q5A2aLeqNtSxIkyYj4+OUWd0PaJ0lGBAWds7vpNpciiaqHIYyIqAmoSasJ+wHM5Mcfh8PDoxAjRyYiMjIFx493wddfj4E6jJlqwCyXHvv374/Q0FBl9ot3QxJVH0MYEVEjl56ejk8++cTucctWE+PGbUPbtpersMyowZYtY+Hrm4PAwGwMGHAArVoJ1bmiohIREJBltfTYoUMHdO/evZbeIVHLxBBGRNSIVRbAbLWa2Lp1LGbNWmWj8N7WxtsarFo1G+PHb0N4+EGEhx9EaOgpmzVf5lxcXG7ujRERQxgRUWOVm5trFcAMBjdkZgYBAIKCMpGX52U14yWEBiUljhg3bhu2bBmL8uAlwV4Q27JlLEJDTyn1XrbC14gRI9C2bVu4uLggNDS0Vt4jUUvGEEZE1EhZFt/v26dHQkI0yu9yNGLo0D1WM15y7y5PzzxIkqmr/V9HYKrxskXddNWWnj17su6LqBYxhBERNQHWAQwANNizZxjCwo7g6NEeVr27MjKCbdSFaWB7L8jypqvy1kPmWHhPVPsYwoiIGjmDwQ0JCVGwvYm2hKNHe+COOz5Hbq4XOnQ4Cze3K8jICIZWW2RzliwqKhGJiVFmjxsxfvw2bj1EVM8YwoiIGhm5HUVaWhoAIC/PCxX18BJCg88/n/LXGCNMYc1U/xUa+gfS0zsBKJ8lCw8/iF690pCZGQgASu8vIqpfDGFERI2IrU74np65lWwxJFAe0szHaJCe3hmSZIRevw+RkSlK2DIV3x+1eTY2XSWqHwxhREQNwNa+jwBw+vRpq8d0ukJERSX+tSSpnu2qyv6PQmiQnKxHZGSK6vHo6Gi4ubkp32u1Wvj4+LD2i6ieMIQREdWzqu77CJjqwUzbCg2CeU1YdHQCAgKyUFysNVuKtE+I8rsfY2NjERAQwLBF1MAYwoiI6pnlDJjllkMy0x2R8uyXOQ0SEqIQHW0qsLd/x2M5uW0FYCq8ZwAjangMYUREDUi9ybYR0dGJGDw42U5LCnMaizsc5R5gpiXKTp3S8ccfoVZtK4io8ahyCCsoKKjySd3d3Wt0MURELYn1JtsaJCRE48YNJ+zdOxQVzWwBtmrBJMTE7EBY2BFlE+/Kth8iooZT5RDm4eEBSaroLwRACAFJklBWVnbTF0ZE1BzYKsC/dOkSANjccgiQsGfPMFQWwGwfFwgKOmtxB6R1+OLdj0SNQ5VD2O7du+vyOoiImpXc3FxcuHAB69evtzvG0zMXtvdyLF9aVBMYMSIR339/O4Sw3bi1pMQUsPr3748OHTpAq9VCp9MpI9j5nqjxqHIIGz58eF1eBxFRs2Hv7kfLAnydrhDR0Yk2ar/sFdlLcHQss9uSwrz4vkOHDujdu/dNvxciqjs1LszPz8/H+++/j6NHTc3+evbsibvvvlv1Ly4ioubKXp8voHy5UVbeZkIPy871gwcnA4BZkb39uxwlyYigoLM2e4NZFt+3asX7rogaO0kIIar7pF9++QUxMTFwdnbGgAEDAAD79+/H9evXsXPnToSHh9f6hbZEBQUF0Ol0MBgMvNmBqBGxnOmy12ICsLz7sZwkGREfv1wZbzC44ciRMHz77Sibr2ke3CzvqBw0KFnVDR8A5s2bx2VHogZS1d/fNfqn0vz58zF+/Hj873//U/61VVpaitmzZyM+Ph4//vhjza6aiKgJMJ8BMw9E5kEJsHX3Yznz5qmAqYg+LOwIvv12JNQ1YgIREb9g2LA9ytjw8IMIDT1l887HyZMnw9fXlwGMqAmoUQj75ZdfVAEMME19L1q0CP3796+1iyMiamxyc3OV5UbLkCWEBlu3jkVo6CnodIV27n6UGXH1qisMBjclRKWnd4Z6KbK8b5gsOjoanp6eNks/WHRP1LTUKIS5u7vj7Nmz6N69u+rxzMxM1T5kRETNieUyZGZmkFXIEkKDlJRIjByZWMHG26YWExs3/lOZPQsNPYWtW8fCPIRJEtCrV5ryfVxcnNXfu0TUdFW82ZgdcXFxmDVrFtatW4fMzExkZmZi7dq1mD17NqZMmVLb1wgAGD9+PDp06IDWrVvD398f06dPR1ZWlmrMt99+i4EDB8LNzQ0+Pj6YOHGi1Wa433//PcLDw+Hk5ITOnTtj9erVVq/13//+F8HBwWjdujUiIyPx888/q47fuHEDc+fOhZeXF9q0aYOJEyfi/PnzqjFnz57FmDFj4OLiAl9fXyxcuBClpaW18lkQUcOwXIbctGmizXFJSXqkpYWhsLAN9PpkmIrtZeWbbwPls2f2Al1enqfyvY+PTy29EyJqDGoUwl599VXExsbizjvvRHBwMIKDgzFz5kxMmjQJL7/8cm1fIwBgxIgRWL9+PY4fP45NmzYhPT0dkyZNUo5nZGTgH//4B2677TYcOnQI3377LS5duoTY2FjVmDFjxmDEiBE4dOgQ4uPjMXv2bHz77bfKmHXr1mHBggV4+umnceDAAfTt2xcxMTG4cOGCMmb+/PnYunUrNmzYgB9++AFZWVmq1ykrK8OYMWNQXFyMpKQkrFmzBqtXr8ZTTz1VJ58NEdWvimq9TDTYuPGfWLXqHiQlDbY6Znn3o3xXpCQZVY+bt5yYPHkylxqJmhtxE65evSoOHz4sDh8+LK5evXozp6q2r776SkiSJIqLi4UQQmzYsEG0atVKlJWVKWO2bNmiGrNo0SLRs2dP1Xni4uJETEyM8v2AAQPE3Llzle/LyspEQECAWLp0qRBCiPz8fKHVasWGDRuUMUePHhUARHJyshBCiK+//lpoNBqRk5OjjHn77beFu7u7KCoqqvJ7NBgMAoAwGAxVfg4R1Z2srCyxZMkSMWPGagGIWvwyiujob8X48V8JSSoTgBCSVCbGj/9KLFmyRCxZskRkZWU19Nsnoiqq6u/vGs2EyVxcXNC7d2/07t0bLi4utRIKqyIvLw+ffvopBg0aBK1WCwCIiIiARqPBhx9+iLKyMhgMBnz88ceIiopSxiQnJyMqKkp1rpiYGCQnm4pei4uLkZqaqhqj0WgQFRWljElNTUVJSYlqTPfu3dGhQwdlTHJyMnr37g0/Pz/V6xQUFOD333+3+76KiopQUFCg+iKixiM/Px8AlFovtWp3+zEjITExCqGhpxAfvxwzZqxGfPxy5S5LgFsNETVHNSrMv3HjBt58803s3r0bFy5cgNGo/svowIEDtXJxlh599FGsXLkS165dw8CBA7Ft2zblWEhICHbu3InJkyfj3nvvRVlZGfR6Pb7++mtlTE5OjioYAYCfnx8KCgpw/fp1XL58GWVlZTbHHDt2TDmHo6MjPDw8rMbk5ORU+DryMXuWLl2KZ555poqfBhHdLLnhqsFgQElJCQDgypUrKCgoQGlpKRwcHFTjU1JSAJjaSYwbt03VmuJvf0tB69Y38OOPw2G70sN+E1agvP4rJOQM7rorGt7e3sox3vVI1DzVKITNmjULO3fuxKRJkzBgwIBKN/a2Z/HixZXWkB09elS5G2jhwoWYNWsWzpw5g2eeeQZ33nkntm3bBkmSkJOTg3vuuQczZszAlClTUFhYiKeeegqTJk1CQkJCja+xPj322GNYsGCB8n1BQQGCgoIa8IqImi97DVe12iKUlDjZbLxqLjz8IK5fb42EBFOn+59/HojyPR/l/SDNg5f0152SgK2QZl7/5e3tDX9//1p5n0TUeNUohG3btg1ff/01Bg+2LDitnocffhgzZ86scEynTp2UP3t7e8Pb2xtdu3ZFjx49EBQUhJ9++gl6vR7//e9/odPpsGzZMmX8J598gqCgIKSkpGDgwIFo166d1V2M58+fh7u7O5ydneHg4AAHBwebY9q1awcAaNeuHYqLi5Gfn6+aDbMcY3lHpXxOeYwtTk5OcHJyqvDzIKLaYa/hqhycJMmIqKhEBARkK4HMvDN+YWEbJCREoTxQmYctASGsN+YWQoNBg/YhOVmvKuq33HKIiFqGGoWw9u3b10o/MB8fnxrfci0vgRYVFQEArl27Bo1G/ReevJQgj7VcngSAhIQE6PV6AKYp/4iICOzatQsTJkxQnrtr1y7MmzcPgKn2TKvVYteuXZg40XR7+vHjx3H27FnlPHq9Hi+88AIuXLgAX19f5XXc3d0RFhZWo/dLRHXD+k7H8tYR8sbakmREjx5HcfRoD2WrIPM2E5Yq2mA7MjIFkZEpyMwMxLVrznBxuY6goHOqAMb6L6KWoUYh7LXXXsOjjz6Kd955Bx07dqzta7KSkpKC/fv3Y8iQIWjbti3S09Px5JNPIjQ0VAk+Y8aMweuvv45nn31WWY7897//jY4dO6Jfv34AgPvuuw8rV67EokWLcPfdd+O7777D+vXrsX37duW1FixYgBkzZqB///4YMGAAli9fjqtXr+Kuu+4CAOh0OsyaNQsLFiyAp6cn3N3d8cADD0Cv12PgwIEAgJEjRyIsLAzTp0/HsmXLkJOTgyeeeAJz587lTBdRI1NxV/vyQHbkSBjKQ1dl9zTZqv8SiIpKNNum6KjNZ06bNo31X0QtRI1CWP/+/XHjxg106tQJLi4uyt2Hsry8vFq5OJmLiws2b96Mp59+GlevXoW/vz9GjRqFJ554Qgk1t912Gz777DMsW7YMy5Ytg4uLC/R6PXbs2AFnZ2cApuL97du3Y/78+VixYgUCAwOxatUqxMTEKK8VFxeHixcv4qmnnkJOTg5uueUW7NixQ1Vo//rrr0Oj0WDixIkoKipCTEwM3nrrLeW4g4MDtm3bhvvvvx96vR6urq6YMWMGnn322Vr9XIioauQCfHPy1kP2u9pbqqyuVA5etgKY9fZDI0eORJs2bZTvtVotfHx8GMCIWhBJCFHt+6qjoqJw9uxZzJo1C35+flZF7zNmzKi1C2zJqroLOxHZZ1mAb4utmrDK7ma0JTj4D5w+3cnq8UmT1qNXr/KZr2nTpiE0NLRa5yaipqOqv79rNBOWlJSE5ORk9O3bt8YXSERUHyxnwMyL6+WlwfDwgwgNPYW8PE9otcU4cqQnkpL0sB/CbNWEGXHmTLDNsUFB5wAAsbGxCAgI4GwXEQGoYQjr3r07rl+/XtvXQkR00yyXHuVlR0A94yXfkSg3RNXpCpU7IJOT9ai47ksuzhcATOfS65NtbFEEDBqUrIQ9b29vBjAiUtQohL300kt4+OGH8cILL6B3795WNWFcOiOihmCv95enp+lubvO7IIXQYMuWsXB0LEJQUKYSlCou1DdnCmKTJq1XZrpstZ6IjExRvuddj0RkrkYhbNSoUQCA22+/XfW4EAKSJKGsrOzmr4yIqJrs9f6SZ6qsw5Vpo23zWbE//giGdT2YvfowDVxdrykBzrKLvnnvL27ATUSWahTCdu/eXdvXQURUayx7fwmh+avGy7qBqnx869axuHxZhz17hsEygA0d+iOuXXNBamp/1THzLveAurbM0zNP1ftL7hlIRCSrUQgbPnx4lcb93//9H5599lnVHmhERHXN9pKiBj17puH333vZfI4QGuzZMxTWM14S/PzOY9OmSbAMZ+Z9v2JjY+3+Xce9H4nIlqoUPtTYJ598goKCgrp8CSIiK3LvL3OmJckkq8fNj9v+K9F0J6R1qJMQEJClfBcQEAB/f3+bXwxgRGRLnYawGrQgIyK6aTpdIcaN26YELrk+KzAwW/W4qdYLyj6R1gFNIDo6EUFBmTZDnbwUyXovIqqJGi1HEhE1JFsd8AF1Owp79Vny46a9G10ACLi43EBQUCacnW+oCuujosq73FdUdO/h4VHn75mImh+GMCJqUqrSAV8m9/6ylJ7e2UaHfCMGDUrGrFmrUFLiaFVYX1HRPVtPEFFNMIQRUZNiawasOizvnDTflDspaTCSk/UYN24bQkLOAAAGDhwId3d3eHh42JzxYtE9EdUUQxgRNWm2tiGqSGXNWOV2FaGhp6DTFaJPnz7w9/evzUsmIgJQxyFs2rRp7J5PRHXGVkPWyMiUCsOYfOdkZUEsL8+zSqGOiKimahzC8vPz8fPPP+PChQswGtV3Dd15550AgLfffvvmro6IyA7bDVkHIylJj/HjTd3vbc2SyXdObtkyFvZuELdswkpEVBdqFMK2bt2KqVOn4sqVK3B3d4ckmXeQlpQQRkRUXeZ3PhoMBpSUlKiO5+TkAKhoWdG0nHj9emskJkapZsnCwn5HSYkTQkNPYf785UhJiTTb79FUoG955yMRUV2RRA2aeXXt2hV///vf8eKLL8LFxaUurosAFBQUQKfTwWAwcFmXmh3zsJWVpUFGRit4e1/GDz98WqXnGwxuWL48voJlRVv7PaqDVvlsmSe02mKbd0XOmzePhfdEVC1V/f1do5mwP//8Ew8++CADGBHViHmbCXVdV1uMG9cP4eEHAaiL7gGolhZ1ukIMGbLHxl6PMvuPWRbf3357N7i5uaFVq1Zwc3ODVquFTqfjnY9EVKdqFMJiYmLwyy+/oFOnTrV9PUTUApQvN1rXdcnhSN3Ly7R1kPksFgDs3Wtrr8eqMS++Dw8P5x2QRFTvqhzCtmzZovx5zJgxWLhwIY4cOYLevXtDq9Wqxo4fP772rpCImgXz5Ue5s72tui4hNMjMDLTo5aVRHTcdUz9eXSy+J6KGVuUQNmHCBKvHnn32WavHJElCWVnZTV0UETVt584BJ08CXboAgYHWXe5Ny4zB0GqLbLSLEDh8uE+lLSTsHIF6ZsxWXRgAqIvv2fGeiBpClUOYZRsKIiJz8kzXZ585Y9EiHYxGCRqNwLJlBowcmaWMs+zt1aPHURw5EobysCThxInusB+g8FdwA9QzYbbGS1aPS5IRs2atQmBgNqKjo9GtWzfWfRFRg6jRXP5HH32EoqIiq8eLi4vx0Ucf3fRFEVHTIs90vfLK51i40B1Goyn0GI0SFi50x4cfJgCwXQN29GgP2C+il2/eNpr9WaBPn8MYP34bJMn0j0PTf+3VhkmqcePGbUNgYDYAMIARUYOqUWH+XXfdhVGjRsHX11f1eGFhIe666y72CSNqYeRaL3s1XnIBvL3jppBl69+EEoYP340ffxwOIcpnyg4f7oPbbvsO8fHLlfYS778/2+YypTzzVVLiiKlTI9Gr198A/I13PhJRg6vRTJgQQtWgVXbu3DnodLqbvigiaprkLYHMmRfA2zseHZ0IUxCD1TEfn0sVBruQkDMIDMzGuHHbzM4tlOfLM18hIWfQq5cH/P394e/vzwBGRA2uWjNh/fr1gyRJkCQJt99+O1q1Kn96WVkZMjIyMGrUqFq/SCJqGuQtgcxrvswL4O0dDw8/iF690lQd7OVjQUGZVsX7tu5sDA8/iNDQUxU2XmUBPhE1JtUKYfIdkocOHUJMTAzatGmjHHN0dERwcDAmTpxYqxdIRI2TrZYTgDoMWYagio7rdIWIjExBQEAWAIGgoHPKsYqCXVxcHHQ6HfLz81FaWmp1nWy8SkSNVY22LVqzZg3i4uLQunXrurgm+gu3LaLGwjxwHTt2BWlpRcjI2AnAVAem1RahpMRJtVG2JVubaZszv2sSMGLQoGRERqZYvIb17NacOXPYaJWIGpU63bZoxowZAEzFuBcuXLBqX9GhQ4eanJaIGiF7WwwBPWGqvbLe/Fredkhm2ZbCcozlXZOABklJg5GUNEh5Dfl5ISFnVOfmEiMRNVU1CmEnT57E3XffjaSkJNXjcsE+m7USNR/2thiStxEq/7P1noy2nmdrjK27Ji1fw/J5sbGxCAgI4BIjETVZNQphM2fORKtWrbBt2zb4+/vbvFOSiJoX+0FJzfzORXvPE0KDH38cil69jsDTM1e5a7Ky85uf29vbmwGMiJq0GoWwQ4cOITU1Fd27d6/t6yGiRqqqQcnyzkV7z0tN7Y/U1L8py4zjxm3Dli1jUVHnHO73SETNSY36hIWFhanuhiKi5k9uL2HZi8v8z5Z3LsrP0+uTbZzReplx9uxVsO4XZv/cRERNWY1mwl5++WUsWrQIL774Inr37g2tVqs6zjv5iJqn8PCDuHxZhz17hsFyo+xJkzao2kqY3w0ZGZmCpCQ97P27TwgNMjMD4ep63cYYCTExOxAWdoQ9v4ioWalRCIuKigIA3Hbbbap6MBbmEzVvBoMb9u4dCut9GjVwdb2mhCRbd0OOH7/NrEDferPtTZsmISoq0WZjVjmAxcbGwtvbmz2/iKhZqFEI2717d21fBxHVg3PngJMngS5dgMBA9THzXmDmzEsP7BfnG6HVFiMjIxhabZHNuyHj45crez1mZQUgISEK5rNeQmiQmBiFqKhEJCZG2WzMyrshiag5qVEIGz58OPbs2YN3330X6enp2LhxI9q3b4+PP/4YISEhtX2NRHQT5HD12WfOWLRIB6NRgkYjsGyZAf/613VlWU/uBQbYb6xqu8heoEePo8oG2raK8OW7GkNCzkCnK4SnZx4SEqKtrlUIDQICshAfvxy9ek1A376uCAjghttE1DzVKIRt2rQJ06dPx9SpU3Hw4EEUFRUBAAwGA1588UV8/fXXtXqRRFQ581kuwPRnb+/L2Lx5JQwGNyxfHg8hTEuARqOEhQvd8eefH/y1XVCkcp6KGqva2vtxyJA92Lt3qGrmy3K50fKuxrw8L1gvaQKAUemIP3q0M/z9/WrzIyIialRqFMKef/55vPPOO7jzzjuxdu1a5fHBgwfj+eefr7WLI6Kqef99YM4cwGgE5DJNIQCNxgNjx/ZD27aXbc5Opab2Q0TEQaSkmLYHqkpjVcu9H/PyvLBnz3CLK5JgusvRekkRsD+jFh2dyLsfiajFqFGLiuPHj2PYsGFWj8ub6BJR/cjNzUVq6nnMmSMg7x4mhOkLMM14bd06FlptkVlriXI//jgCr78ejwMH+gEAMjOD7C4lmtPpCs2WFnNtnFtg6NA9mDRpPUaP3g5HxyIYDG6q55u3u5AkI6KjEzB4cHkrC979SETNXY1mwtq1a4dTp04hODhY9fjevXvRqVOn2rguIrJDXnaUlxozMoJhNM6wO14IDUpKHCtohqrBli1jcf16ayQmRtk4gxFXr7rCYHCzOUul0xUiKirxrxqv8m2M9uyR76KUlPOMH1++tGk5o2Z+7mnTprH+i4iavRqFsHvuuQcPPfQQPvjgA0iShKysLCQnJ+ORRx7Bk08+WdvXSER/MV92lJcaQ0NPVdjJXq7HCgk5A0fHImzc+E8bozRWdyuaGAFI2Ljxn3Y35waAgIBs2GpbYfn9li3qvR+9vb2tzsUCfCJqKWoUwhYvXgyj0Yjbb78d165dw7Bhw+Dk5IRHHnkEDzzwQG1fI1GLl5ubi9OnSzFnji+MxvLiern1g/Usl6kw3rweS14OtB3YjLBdnWB/A22ZweCGq1ddKjiHOfXej/7+/tX5GIiImpUahTBJkvD4449j4cKFOHXqFK5cuYKwsDC0adOmtq+PqMXLzc3FypW2lx3lei3TbFh5LZgcwGbNWoXAwGzVHY+msGR+96IR0dHlvbnKWYcqy8251ecVULNuyCrf/UhERDUMYTJHR0eEhYXV1rUQkQ1yA1VbdxTKS422mqjKtWCWdzyagpURf//7Nri4XFdtNWTeJNW8aarl6wHWd1JaBy4JlmFv/Hju/UhEJLupEEZE9cdWjy7z1g/VCWiABj4+uQgJOQPANKNVHrhMvb8CArIr7F5vv3t++evPmrUK+fkeAKAKe0RExBBG1KTYu6OwpgENsDWjpcGePcOwZ89wZUYsICDL6g5Ge72+zGvRAgOzERiYbfO9sAUFEbV0DGFETYxOV2hzRqmmAc32jFZ5MX5iYhTi45crxf3m2xlZntdeYIuLi4NOp1O+5x2QREQMYUSNWm5urrKBtr39HM1VJ6DJ55MbudpbWpSL8dPTO9vczsher6/JkyfDw8ODgYuIyA6GMKJGSr4rErC/n2NVgpnMPKBZnq9Tp3Skp4fCVLRvve+jVltc4XZGd90Vrer5xeBFRFQ5hjCiRkq+K9Lefo5yh3tbG21bMg9rAKzOl57eBYARgwbtg6vrVdV59fpk5Oe3tbudEXt+ERHVDEMYUSNnr/2EefsIe41UAetZL70+2c7SowbJyXrExy9Hr15pSEmJRFKSHklJg2HdW0xd3E9ERNVXow28iaj+2N4g27qGy3yj7REjRgCwPYuWlKS3uZm3fDwzMxCZmUFIStKj/K8I0zKl+Ybb5sX9RERUfZwJI2rk7N2FWFEjVZm9HmFCWM9syefYuHESbP/7TIOJE9fD1fWaVRE+200QEVUfQxhRE2DrLkRn5xt2207s3r0bgL1eXoDcNb9371/x2299AGj+GicfsyZJRqXhqvnm2yzCJyKqGYYwonpy7hxw8iTQpQsQGFj951u2n6ioPQRQXowfFZWIhIQoWIcrjRLAAFFp93vzkMdCfCKim8cQRlSHcnNzUVxcjM8+c8aiRToYjRI0GoFlywz417+u3/Qskr2+YJbF+EOH7sHevUNtdLe3t+/jX49KRkycuJFbDhER1QGGMKI6Ivf5MhjcsHx5PIQwBR2jUcLChe74888PoNMVYt68eUoQk0MbAJw7d65KrzN58mQAwPr16wHYLsbfu3eo1T6QFc18AeWzX716HbU6xhowIqKbxxBGVEfkMGWvxYTcY0seZ96ctaqmTZuG0NBQZGeX789o7/UCArIQH78ceXme0GqL8f77s+0EMSMmTbKe/ZLrwFgDRkRUOxjCiOqYreJ4W3cyymGsOlxcXKwey8ryh72eXubLl+Z3XFpuvG1r9isgIIDhi4ioFjGEEdWxyjbQlveGlP8rs+xyb297Isv9JRMTo6Cu8RKIikq0ep55Yb9WW4ySEkerAn/OfhER1R2GMKJqqsldjhXdybh582ar8eaF9aZu9RLMZ6rk7Yny8/OVWjDAXl8wCTpdvs3rslfYL+PsFxFR3WEII6qC2rjL0VbgsbUBt2VhvXlrCcvtiUpLS1Xn02qLYApt6iC2ceMkFBeXh7fY2Fi0atXK6vmmc2ih0+k4+0VEVMcYwogqkJubi4sXL2LdunU4d87/r2L2yu9yrArLNhLyDJft2axy5kX9ly9ftjqf3PdLvSSpDm/s80VE1PAYwojsML9b8cCBftiyRQ445cwDUVZWlqq43rLGy5ytNhJySLLf5d7EvKhf7oxvPXtm3ffL/FqJiKjhNZkNvMePH48OHTqgdevW8Pf3x/Tp05GVlaUas379etxyyy1wcXFBx44d8corr1id5/vvv0d4eDicnJzQuXNnrF692mrMf//7XwQHB6N169aIjIzEzz//rDp+48YNzJ07F15eXmjTpg0mTpyI8+fPq8acPXsWY8aMgYuLC3x9fbFw4UKbSz/UeF28eBFAecCx9X8X80C0efNmvPfee8qXrVovWWVtK8aN22a2ybaxwo2zDQY3/P57zyr1/bK8I5OIiBpOk5kJGzFiBP7973/D398ff/75Jx555BFMmjQJSUlJAIBvvvkGU6dOxZtvvomRI0fi6NGjuOeee+Ds7Ix58+YBADIyMjBmzBjcd999+PTTT7Fr1y7Mnj0b/v7+iImJAQCsW7cOCxYswDvvvIPIyEgsX74cMTExOH78OHx9fQEA8+fPx/bt27FhwwbodDrMmzcPsbGx2LdvHwCgrKwMY8aMQbt27ZCUlITs7Gzceeed0Gq1ePHFFxvg06Pqys3Nxbp16wDYK3a3HYgqEhsbC8AU1mzPdglkZQUgJOSMVSG/6Tqsi/rVBfzWbSnkvSCre61ERFT3JCFMf003NVu2bMGECRNQVFQErVaLf/3rXygpKcGGDRuUMW+++SaWLVuGs2fPQpIkPProo9i+fTvS0tKUMXfccQfy8/OxY8cOAEBkZCT+9re/KctQRqMRQUFBeOCBB7B48WIYDAb4+Pjgs88+w6RJkwAAx44dQ48ePZCcnIyBAwfim2++wdixY5GVlQU/Pz8AwDvvvINHH30UFy9erHK38YKCAuh0OhgMBri7u9fK50ZVk52djffeew8AsG+fHgkJ0VAv8Rkxe/YqBAZmWz3XVrE9AMyZMwcAKjyvJBkRH78cOl0hIiMjlT5g165dQ0pKiup1ymvULLciKr+L0t4dmXPmzGFNGBFRHanq7+8mMxNmLi8vD59++ikGDRoErVYLACgqKrJqXOns7Ixz587hzJkzCA4ORnJyMqKiolRjYmJiEB8fD8DULDM1NRWPPfaYclyj0SAqKgrJyckAgNTUVJSUlKjO0717d3To0EEJYcnJyejdu7cSwOTXuf/++/H777+jX79+Nt9XUVERioqKlO8LCgpq8OlQbbLXdys6OtFmALNXbA+YasSuXLmijA0IyIZl7Zb5kqRl6DJnCnC2NuWWEBOzA2FhR5TQxdkvIqLGqcnUhAHAo48+CldXV3h5eeHs2bP46quvlGMxMTHYvHkzdu3aBaPRiBMnTuC1114DAGVLl5ycHFUwAgA/Pz8UFBTg+vXruHTpEsrKymyOycnJUc7h6OgIDw+PCsfYOod8zJ6lS5dCp9MpX0FBQVX9aKiO2Ou7FRCQZTXWXrG9weAGwLQMuXPnTmW8vCSpOrNF3ZbB4IaMjGDlHID5DJrtJVLzAGYP934kImp4DToTtnjxYrz88ssVjjl69Ci6d+8OAFi4cCFmzZqFM2fO4JlnnsGdd96Jbdu2QZIk3HPPPUhPT8fYsWNRUlICd3d3PPTQQ1iyZAk0mqaRNR977DEsWLBA+b6goIBBrI6Yb5RtS35+PoCqbzkEVF5sb0mnK0RUVKIyo2VZt2VrVi009JSNmbny6zJ//ogRI9C2bVul75eM/b+IiBqHBg1hDz/8MGbOnFnhmE6dOil/9vb2hre3N7p27YoePXogKCgIP/30E/R6PSRJwssvv4wXX3wROTk58PHxwa5du1TnaNeundVdjOfPn4e7uzucnZ3h4OAABwcHm2PatWunnKO4uBj5+fmq2TDLMZZ3VMrnlMfY4uTkBCcnpwo/D7p5lhtl26vhAirfcshcVQOb/HpZWf5/BSrTeaOiEpWly3Pn/G3Oqk2cuMnuptuzZqlr1Lp06cK6LyKiRqxBQ5iPjw98fHxq9Fyj0bSMY15DBQAODg5o3749AODzzz+HXq9XXkOv1+Prr79WjU9ISIBerwdgmiGIiIjArl27MGHCBOV1du3apdxhGRERAa1Wi127dmHixIkAgOPHj+Ps2bPKefR6PV544QVcuHBBuaMyISEB7u7uCAsLq9H7pdpjPgNWUQ2XrKIthwAgOjoaCQkJVQps9u5mFEKDxMQo9OqVhvT0znZ7kgHC5l2VtmrUuORIRNS4NYnC/JSUFOzfvx9DhgxB27ZtkZ6ejieffBKhoaFK8Ll06RI2btyIW2+9FTdu3MCHH36IDRs24IcfflDOc99992HlypVYtGgR7r77bnz33XdYv349tm/froxZsGABZsyYgf79+2PAgAFYvnw5rl69irvuugsAoNPpMGvWLCxYsACenp5wd3fHAw88AL1ej4EDBwIARo4cibCwMEyfPh3Lli1DTk4OnnjiCcydO5czXfXM1rKj+WbX9hqm2poRszX7NXnyZPj6+iIhIQFAxYGtsoaqQmiQmRlYYU+yoKBzVkEvKioRgwcnK+NiY2O55yMRURPQJEKYi4sLNm/ejKeffhpXr16Fv78/Ro0ahSeeeEIVatasWYNHHnkEQgjo9Xp8//33GDBggHI8JCQE27dvx/z587FixQoEBgZi1apVSo8wAIiLi8PFixfx1FNPIScnB7fccgt27NihKrR//fXXodFoMHHiRBQVFSEmJgZvvfWWctzBwQHbtm3D/fffD71eD1dXV8yYMQPPPvtsHX9SZM5y2dFSdWu4bPH19YWXlxfmzZuHCxcuYP369XYDW2XbEUmSEbm5npX2JKtsZo4BjIioaWiyfcJaAvYJuznmvb4A69ovg8ENy5fHW9VwyX26AGDQoEFWs5darRZt2rSBVquFj4+PEnhq8nrljAgMPIdz54JgXXRvvyeZpWnTpiE0NLTScUREVHeadZ8wouqyV/tVWQ2XvCNDRWxt3K2u/TJi0KBkREamQK9PRlLSYBtnkewGsPHjt1kFsMmTJ1u1SeFdj0RETQtDGDV7FdV+yUt7mZmBACQEBWVW+/yWNWfWtV8aJCUNRnKyHlFRiQCMsNVk1ZZhw35Q3SjAei8iouaDIYyanMp6fFnOCFVW+5We3rnSOySBiltZmLNX+yXfAdm792H89ltfWHbhN1E/1rXrSdU5GMCIiJoPhjBqUiortpfJLUWAivt3VXaHpGVPL3vbEZn/1/bm3FDO/9tvt8A0GwbI+zzKG22bjUSPHkeUZcgRI0agZ8+eDGBERM0IQxg1KRXNgNkbV1H/royMYJuzZJmZgUhJaY/kZL3Nnl7mQW3z5s2q58uvZ6vXVzlTrdikSRsASNi48Z8WxyUMGLBf+a5t27YMYEREzQxDGLUI9to62Jsl27hxEtQByv5G2xW9XkpKJJKS9LAdxjRwdb0GT8+8SjvtW25OT0RETV/T2FSRqAYsO8brdIUICTmjCk7yrJW8kbbtpUFrkmSEVltstbm25etFRqZAsl1zrwQtW9dgfpfm6NGj2XaCiKgZ4kwYNWkVFcvLTVRtLWHm5+dj/fr1ANSzZFevutpYGpSZliQlyYg+fQ7j/fdn26wRi4yMREpKCoCKGrSqg1ZFDVi5iTsRUfPEEEZNVlX2fbRXR+Xv768KaJcuXcLmzZthMLjZLKqXtwcKCMiCVlusBDBAXSMGAGvXnoenpxt0ukK7y52Wm20D9rdGIiKi5okhjJqk6uz7aI95QDMYDACsi/jNG63K57VXzJ+SEqkU8lfWELYq3e9l3IibiKh5YgijJqk29n1UP7d8967K9ma03YLCaHYnpelatmwZC1/fnArPN3LkSHh4eFh1v5exCz4RUfPFEEZNijwrVFHvL8BU81WdAGM7BNmuqLc1W9az5xH8/nsvi5EavP/+bGVGzFY47Nq1K0MWEVELxRBGTYpcbH/x4kWkp9vf91Euure1r2NlqlJrZt6CIjlZ/1cAK+8lJrNcJo2NjYW3tzcAznIREbV0DGHU5Hh5eaG4uLjSZUMAuHDhQrW2OKqs1iwuLg4lJSVKg1bzJUh7M2fmy6Te3t7w9/ev4TsnIqLmhCGMmrTK7iiUZ8QqYr7FUWW1ZjqdrsKxtlg2XiUiIgLYrJVINVMm15qZsxeibI0tJ5Tnmi+TEhERyTgTRmSmon0mKxtr3sxV7ilmb5mUiIiIIYyaJLmvV/n39jvnVzbm0qVL0Gq1yvcV1Zrl5+ejtLTU5litthglJY4MXkREVCUMYdQklZSUKH+uyt2MFY2Ri+zNybVmkydPhiRJWLduHQBTjZkpzAUrYa46ne7ZeJWIiGQMYdSkVaVzflXH2Jols+wfVpXAZ2ny5Mnw8PBgSwoiIlJhCKMmqVUr049uVTrnVzamomB18uRJ5bUqC3MjRoxA27ZtAQBarRY6nY7Bi4iI7GIIozqTm5tbrR5d1SHPUFXWOb+yMbaClbzdUGBgNnbv3q0sP1696lJhmOvSpQt7gBERUZUxhFGdyM3NxcqVKysdV5OO9ubkOxS3bBkLU8cV67sZK7rj0dZm3ObbDQFQPQ8wwryzC3uAERFRTTGEUZ2oaAasJuMqI0mAEKb/mpPrsQwGA0JDlyt3PAJARkYwtNoiG5txl8+Imc5bPktmGmsKYuwBRkREN4MhjJq0yuq0PDw8lCVC+S5G8xowwIjQ0D+Qnt4J1r2LNRBC/YgQGkyatB6urtfYioKIiG4KQxjVKrkO7NKlS3X6OnKrh8qK7i1bQliGNkCD9PTOMDVaVS81AkbVTBhgWn4MCjrH8EVERDeNIYxqTVXrwGqDl5cX5s2bh9OnS/HxxwJGY/k6pIODwAMPjEZwcCurejP7+z1KkCRhtdQIoErd8wH2ACMiouphCKNaU1v1XVXl5eUFLy/gvfeAe+8FysoABwfg3XclBAe3QnFxMbKzswFAmZmzdaekzN5So73u+bGxsfD29gZwc3d6EhFRy8QQRk3erFlATAxw6hTQuTPg7KyekStvxOpm427KcvaWGu11xPf29mZLCiIiqjGGMGpQtmrH5GW96vQYCww0fQFAdnb58ywbsZo21s7G7NmrcORITyQn66u01GjvGoiIiGqKIYwalK19G6tq2rRpCA0NtXvc1p2TCQnRMNV/mQLZxImbAAirGbCRI0eiTZs2AEzd+S23L+LyIxER3SyGMLpp9XVHpKVPPvnEZrPX/Px8APaK8E0F/JaBzHIPyODgYC41EhFRnWIIo5tSn3dE2mK5ZJmbm4v169cDqLgI36Q8kFlu6M2lRiIiqmv2fjsRVUlV74g0GNyQkREMg8GtVl9fnvWSXbx4UflzWlovi2arFp1XzY/81VsMAOLi4rjUSEREdY4zYVTnLIvjLZf+KmMwuCEzMwgAEBSUqardWr9+vbIkmZubi3Xr1gEA9u3TK8uN5cRfX5q//lt+zHwPSJ1OV7M3SkREVA0MYVSnKttWqDIHDvSzaCchMH78VlWIKy4uRm5uLrKysgAA5875IyEhCuoABgDlfcCysgKQmBhV4zsjiYiIbhZDGNWpyrYVKu/hlWsVggwGNxv9vCSrEGcwGJQZMHnWzfZKe3kfsJCQM+jVK81mE1YiIqL6wBBGdcZgcMPVqy5WxfHy0l9ly5SmJUjbne1TUiIxcmQiAKCkpER5PfW+kKpnITo6URW27DVhJSIiqg8MYVQnzAOWaSNsoypsAahwmbJ8GdK2pCQ9IiNTVCHK/r6QRkRHJ2Lw4OTafItEREQ3hSGMap31jJQGQhgxadJ6ZTkwIyPY7jIlgAqWFGXlS5oyWy0pJMmIWbNWITAwu8rXz/YURERUHxjC6KbYCiy2Z6Q0cHW9poQme4HJ0zOvghktWI0FgMuXLwOAsi+k5RKnrQA2efJkqy748vthewoiIqoPDGF0U7y8vDBv3jxVv7CsLA0+/ljAaLTdAkIuxo+KSqzgDkUj1DNhRkgSrJY0Tb3HfoHcVSI09JTdrYhkkydPRo8ePWrxUyAiIqo+hjC6aZYzR/7+wHvvAffeC5SVAQ4OAmPGbFNqvaw31M5S7lA0GNyQkhIJdXsJI8aP34bQ0FPK3Yzp6Z2xfHm83TqzivqR+fr61uGnQUREVDWSEMJ+G3FqUAUFBdDpdDAYDHB3d2/oy6mSc+eAkyeBLl1M3586BXTuDDg75+L06VIMGOCrmiHTaAQeeuh1q4BmTpKMiI9froS0zMwgbNo00Wop0/STrLH5PACIjY1FQEAAlxuJiKhOVfX3N2fCqNa8/z4wZw5gNAIajWk2bNYs+agXDh82HTNnNEqqYnxbtWBywX56eucKx9h7nhzCGMCIiKgxYQijasvNzbXaMzIrS4M5c8pnuYxG03JkTAwQGGga06WLKZyZBzEHB1FpMb4kGaHVFlfQA8z+TJhch8b9IImIqLFhCKNqyc3NxcqVK60ez8gIhtE4Q/VYWZlpOVIOYYGBlrViwMsvG3DlimmmyvJuSRNTbVdJiVOFAcxeTZg8C8b9IImIqLFhCKNqsZwBk9lqOeHgYKoHMzdrlml2TK4Vc3C4jvfes91eQq9PVhqyGgxuNkKaEZMmbVTdBWlevM9u+ERE1JgxhFGtsAxRDg4C774rKbNg5gIDy2fHcnPL+4yFhx+0G6Ls9QDr1euo1XUwfBERUVPAEEa1xjxEPfDAaERE+FX6HFt9xsxdunQJmzdvtjo/Z7qIiKipYwijWiXPRAUEmKrvbRXxy/Lz8yFJEnQ6HbKyNMjIaIWQkFLluY6OjvD29rZ5fiIioqaOIYzqjGURv9wp39MzVxWkLBu4mjdZjYuLq5Vr4X6QRETU2DCE0U2xF6zy8/NRWlqqfG8vaFlu9i2EBlu3jkVo6CnodIUQQth9DXP29oIEuB8kERE1TgxhVGMVzWCtX79eGXfunL/doGWrP5h5k9X1692wYsV8GI0SNBqBp576E7Gxl6HVapW2EwxZRETUFDGEUbXIy3qVzWDJDhzohy1bxkK9GXd50LLV2kJusmowuGH58gAIITeAlfDMMwEwGNZDpyvEvHnzGL6IiKjJst39ksgO+W7GQYNm2J3BMhjckJERrMyA2foxk4OWTleIqKhEAEblcbnJakWzZID9nmVERERNAWfCqNq8vLwwcKD1FkSSZERWVgA++uhOZYnSVpd786B14EA/JCZGATCNj4pKVJY0K5olIyIiauo4E0Y1Im9B5OBg+l4OUImJUaolSkBYPNOIWbNW2S3KT0yMgsHgBqC8QaskWc+SERERNXWcCaMak7cgSknJxb59a+xswi2Z/VkgOjoRgYHZAGB3ufH338PQs+cR6HSFbNBKRETNFkMY3ZTAQMDBoRhpaRVtwi2TEBCQBcBU2H/1qouN8QI7d45CQsJI5W5LNmglIqLmiCGMao3l/o6mpcjymTC5nsu8tYWpIF8eVz7e3t2WREREzQVDGFXZuXPAyZNAly6wuTE3oN7fMSsrQKkRk+u5CgvbWLSsMIW1rl2P4cSJ7qpzmfcLIyIiam4YwqhC8t6Pn33mjEWLdErT1GXLDPjXv67b3A5IXj4MCTmDXr3SlHqu9PTOeP/92bC+H0TCiRNdq30nJLciIiKipowhjOyS9340NU2NVzVNXbjQHX/++QEAIDw8DgaDm80ZKzmQWd4JaU0DvX4fkpP1qpkz+ZyxsbGqzbzZJZ+IiJq6JteioqioCLfccgskScKhQ4dUxw4fPoyhQ4eidevWCAoKwrJly6yev2HDBnTv3h2tW7dG79698fXXX6uOCyHw1FNPwd/fH87OzoiKisLJkydVY/Ly8jB16lS4u7vDw8MDs2bNwpUrV6p9LY2d3AzV3l2MKSmRWL48HtOnt8eKFfMBzEJ0dLTNc9m+c7KcJBkRGZmC+PjlmDFjNeLjlyv9wgDA29sb/v7+yhcDGBERNXVNLoQtWrQIAQEBVo8XFBRg5MiR6NixI1JTU/HKK69gyZIleO+995QxSUlJmDJlCmbNmoWDBw9iwoQJmDBhAtLS0pQxy5YtwxtvvIF33nkHKSkpcHV1RUxMDG7cuKGMmTp1Kn7//XckJCRg27Zt+PHHHzFnzpxqXUtTIjdNNSdJRiQl6ZVgJW8ptHHjTwBMG2rPmTMHsbGxds9hTq9PVi1jsg6MiIiauyYVwr755hvs3LkTr776qtWxTz/9FMXFxfjggw/Qs2dP3HHHHXjwwQfxn//8RxmzYsUKjBo1CgsXLkSPHj3w3HPPITw8HCtXrgRgmgVbvnw5nnjiCfzjH/9Anz598NFHHyErKwtffvklAODo0aPYsWMHVq1ahcjISAwZMgRvvvkm1q5di6ysrCpfS1Niq2mqXp8Me/tBAoCHhwf8/f2VJUTLc6iZZsGIiIhakiYTws6fP4977rkHH3/8MVxcXKyOJycnY9iwYapi7ZiYGBw/fhyXL19WxkRFRameFxMTg+TkZABARkYGcnJyVGN0Oh0iIyOVMcnJyfDw8ED//v2VMVFRUdBoNEhJSanytdhSVFSEgoIC1VdjER5+ULVUGBmZYnN2rKJCevkcgwbtg/lekePHV94Fn0X4RETU3DSJwnwhBGbOnIn77rsP/fv3x+nTp63G5OTkICQkRPWYn5+fcqxt27bIyclRHjMfk5OTo4wzf569Mb6+vqrjrVq1gqenp2pMZddiy9KlS/HMM8/Y/hAaATko5eV5wdMzV9UTrKpbCul0hRg5MhGRkSl2u+BPnjwZHh4eyvcswiciouaoQUPY4sWL8fLLL1c45ujRo9i5cycKCwvx2GOP1dOVNYzHHnsMCxYsUL4vKChAUFBQA16RmnmTVTl0xccvR16eJ7TaYpSUOCl3SV66dKnC2St7XfDj4uLQvXt3G88gIiJqXho0hD388MOYOXNmhWM6deqE7777DsnJyXByclId69+/P6ZOnYo1a9agXbt2OH/+vOq4/H27du2U/9oaY35cfszf31815pZbblHGXLhwQXWO0tJS5OXlVfo65q9hi5OTk9V7bEhZWRpkZATD0zMXAKw22966dSzi45fj8mVPq3AGbAYATJs2rVqv6ePjU6vvgYiIqLFq0BDm4+NTpV+6b7zxBp5//nnl+6ysLMTExGDdunWIjIwEAOj1ejz++OMoKSmBVqsFACQkJKBbt27K8p9er8euXbsQHx+vnCshIQF6vR4AEBISgnbt2mHXrl1K6CooKEBKSgruv/9+5Rz5+flITU1FREQEAOC7776D0Wis1rU0tMq637//PjBnji+MxhlKIb6tNhWZmYE2w5m83ZCLiwvmzZuH4uJi5Ofno7S01Oq1tFotdDodlx2JiKhlEU1QRkaGACAOHjyoPJafny/8/PzE9OnTRVpamli7dq1wcXER7777rjJm3759olWrVuLVV18VR48eFU8//bTQarXit99+U8a89NJLwsPDQ3z11Vfi8OHD4h//+IcICQkR169fV8aMGjVK9OvXT6SkpIi9e/eKLl26iClTplTrWqrCYDAIAMJgMNTgU7Jv1SohNBohANN/V61SH8/MLD8uf0lSmQDKrB6bNGm96jH5a8aMD8WSJUtEVlZWrV47ERFRY1fV39/NJoQJIcSvv/4qhgwZIpycnET79u3FSy+9ZPXc9evXi65duwpHR0fRs2dPsX37dtVxo9EonnzySeHn5yecnJzE7bffLo4fP64ak5ubK6ZMmSLatGkj3N3dxV133SUKCwurfS2VqYsQZitgOTiYHpd99511qAKEGDRo719hzBTAxo//Ssyf/5rymHk4mz//NYYwIiJqkar6+1sSQoiGnIkj+woKCqDT6WAwGODu7l4r59y9G7jtNtuP33qr6c/nzgEdOwJGsw4UkmTExImb4OFxGSUljqq7Gm0V7Mvd7ufMmaOqryMiImruqvr7u0m0qKDa06ULoNGoA5aDA9C5s+nPubm5cHAoxrJlznj0UR3KyqS/NtYGNm78pxKyQkLOKM8PDz+I0NBTdltOEBERkTWGsBYmMBB47z3g3nuBsjJTAHv3XdPj8obdsgcfdENmZiA2bpwEua+vZeG9zF7LCSIiIrKtyXTMp9ozaxZw+rRpCfL0adP3QPmG3TKdrhCurtdR0fZElTEYDDd/wURERM0QZ8JaqMBA260pLMkbb5u3p6hseyJzLDkkIiKyjTNhVCFbm3ebmrECGRnBMBjcAAAGg5vqe5n59kNERERUjjNhVKnQ0FOYOHETAIGgoHNIT++M5cvjlbsh+/Q5jMOH+9i8O5KIiIhsYwijClm2n4iKSkRiYpSqQ/6vv/YFICnf2yrcJyIiIjWGsBYkNzfXqvjenGURvcHgZrUlkXkAKyepvpML9xnCiIiI7GMIayEs209URV6el839IgEjKionrE7hPhERUUvFwvwWwtYMmL1iepl8Z6Q5STIiIiLV7uvINWGcBSMiIqoYZ8JaKHtbDQ0cOBA//fQTACA9vTPMO0zI40JDTyE1NQLqDG/EpEkbERR0ThXAHB0d6+cNERERNTEMYS2QrVovuZi+tLRUNcY8aAkBpeB+/PhtViGuV6+jAIDJkyfDw8MDjo6O8PLyqvf3R0RE1BQwhLVA9mq98vI88csvv9gdA5QX3IeHH8T06b4oKPBFUFAR2rULgVbbFT4+PgxeREREVcAQ1gJ5eubCurheXUxflTGDB3eEv79/3V4sERFRM8UQ1mKp20pIElBY2AaZmUEAAA+PyzbHmGO9FxERUc0xhLVAeXlesNXba9Wqe8weN9ocIy9Hjhw5ksuOREREN4EtKloI81krW60nAAF16LL+0TDv/8U9IYmIiG4OQ1gL4eXlhZEjRwKwvSm35ayXJcv+X76+vnV6vURERM0dlyNbiNzcXJSUlCjfh4cfRGjoKeTleUKrLcb778+2cTdkuYkTN6JXr6OIjY1FQEAAlyKJiIhuEkNYC2BvyyKdrlCZ2Ro3bpuqd5g5STIiKOgcAMDb25sBjIiIqBYwhLUAllsWGQxuyMvzgqdnrhLC5JmxzMxAHD/eFWlpfVSNWOVxvCOSiIiodjCEtTC2tisyLUuaQllxcWslgAFGREUlIjz8IAAgLi6Os2BERES1hCGsBbG1XdGWLWMhSVBCmWmvSHlJUoPExCj06pUGna4QPj4+DXXpREREzQ5DWAtibysieZNuW/VgQmgwePAM3HorOAtGRERUixjCWhC5P1hFd0FacnAAIiO9wPxFRERUu9gnrAWx7A9m6oovVGNMx0zHHRyAd98FAgPr9TKJiIhaBM6EtTDm/cGysgKQkBBtdrS8UH/48FmIiNAxgBEREdURhrAWwLKthNxu4qOP7oR5p3yNRsJTT0UiOHgwvLx09XmJRERELQ5DWAvg5eWFefPmqfqF7dvniNdfV69GG40SCgv9WP9FRERUDxjCWgjLOxsHDgQ0GsBoto+3gwPQuXM9XxgREVELxcL8FiowEHjvPVPwAliET0REVN84E9aCzZoFxMQAp06ZZsAYwIiIiOoPQ1gLFxjI8EVERNQQuBxJRERE1AAYwoiIiIgaAEMYERERUQNgCCMiIiJqAAxhRERERA2AIYyIiIioATCEERERETUAhjAiIiKiBsAQRkRERNQAGMKIiIiIGgBDGBEREVED4N6RjZgQAgBQUFDQwFdCREREVSX/3pZ/j9vDENaIFRYWAgCCgoIa+EqIiIiougoLC6HT6ewel0RlMY0ajNFoRFZWFtzc3CBJUo3PU1BQgKCgIGRmZsLd3b0Wr7Dp4Gdgws+BnwHAzwDgZyDj51A3n4EQAoWFhQgICIBGY7/yizNhjZhGo0FgYGCtnc/d3b3F/p9Mxs/AhJ8DPwOAnwHAz0DGz6H2P4OKZsBkLMwnIiIiagAMYUREREQNgCGsBXBycsLTTz8NJyenhr6UBsPPwISfAz8DgJ8BwM9Axs+hYT8DFuYTERERNQDOhBERERE1AIYwIiIiogbAEEZERETUABjCiIiIiBoAQ1gT9fbbb6NPnz5Kczm9Xo9vvvlGOX7jxg3MnTsXXl5eaNOmDSZOnIjz58+rznH27FmMGTMGLi4u8PX1xcKFC1FaWlrfb6XWvPTSS5AkCfHx8cpjLeFzWLJkCSRJUn11795dOd4SPgMA+PPPPzFt2jR4eXnB2dkZvXv3xi+//KIcF0Lgqaeegr+/P5ydnREVFYWTJ0+qzpGXl4epU6fC3d0dHh4emDVrFq5cuVLfb6VGgoODrX4OJEnC3LlzAbSMn4OysjI8+eSTCAkJgbOzM0JDQ/Hcc8+p9u9r7j8HgGmrnPj4eHTs2BHOzs4YNGgQ9u/frxxvjp/Bjz/+iHHjxiEgIACSJOHLL79UHa+t93z48GEMHToUrVu3RlBQEJYtW3ZzFy6oSdqyZYvYvn27OHHihDh+/Lj497//LbRarUhLSxNCCHHfffeJoKAgsWvXLvHLL7+IgQMHikGDBinPLy0tFb169RJRUVHi4MGD4uuvvxbe3t7isccea6i3dFN+/vlnERwcLPr06SMeeugh5fGW8Dk8/fTTomfPniI7O1v5unjxonK8JXwGeXl5omPHjmLmzJkiJSVF/PHHH+Lbb78Vp06dUsa89NJLQqfTiS+//FL8+uuvYvz48SIkJERcv35dGTNq1CjRt29f8dNPP4k9e/aIzp07iylTpjTEW6q2CxcuqH4GEhISBACxe/duIUTL+Dl44YUXhJeXl9i2bZvIyMgQGzZsEG3atBErVqxQxjT3nwMhhJg8ebIICwsTP/zwgzh58qR4+umnhbu7uzh37pwQonl+Bl9//bV4/PHHxebNmwUA8cUXX6iO18Z7NhgMws/PT0ydOlWkpaWJzz//XDg7O4t33323xtfNENaMtG3bVqxatUrk5+cLrVYrNmzYoBw7evSoACCSk5OFEKYfWI1GI3JycpQxb7/9tnB3dxdFRUX1fu03o7CwUHTp0kUkJCSI4cOHKyGspXwOTz/9tOjbt6/NYy3lM3j00UfFkCFD7B43Go2iXbt24pVXXlEey8/PF05OTuLzzz8XQghx5MgRAUDs379fGfPNN98ISZLEn3/+WXcXX0ceeughERoaKoxGY4v5ORgzZoy4++67VY/FxsaKqVOnCiFaxs/BtWvXhIODg9i2bZvq8fDwcPH444+3iM/AMoTV1nt+6623RNu2bVX/f3j00UdFt27danytXI5sBsrKyrB27VpcvXoVer0eqampKCkpQVRUlDKme/fu6NChA5KTkwEAycnJ6N27N/z8/JQxMTExKCgowO+//17v7+FmzJ07F2PGjFG9XwAt6nM4efIkAgIC0KlTJ0ydOhVnz54F0HI+gy1btqB///745z//CV9fX/Tr1w//+9//lOMZGRnIyclRfQ46nQ6RkZGqz8HDwwP9+/dXxkRFRUGj0SAlJaX+3kwtKC4uxieffIK7774bkiS1mJ+DQYMGYdeuXThx4gQA4Ndff8XevXsxevRoAC3j56C0tBRlZWVo3bq16nFnZ2fs3bu3RXwGlmrrPScnJ2PYsGFwdHRUxsTExOD48eO4fPlyja6NG3g3Yb/99hv0ej1u3LiBNm3a4IsvvkBYWBgOHToER0dHeHh4qMb7+fkhJycHAJCTk6P6y1Y+Lh9rKtauXYsDBw6o6h1kOTk5LeJziIyMxOrVq9GtWzdkZ2fjmWeewdChQ5GWltZiPoM//vgDb7/9NhYsWIB///vf2L9/Px588EE4OjpixowZyvuw9T7NPwdfX1/V8VatWsHT07PJfA6yL7/8Evn5+Zg5cyaAlvP/hcWLF6OgoADdu3eHg4MDysrK8MILL2Dq1KkA0CJ+Dtzc3KDX6/Hcc8+hR48e8PPzw+eff47k5GR07ty5RXwGlmrrPefk5CAkJMTqHPKxtm3bVvvaGMKasG7duuHQoUMwGAzYuHEjZsyYgR9++KGhL6veZGZm4qGHHkJCQoLVv/paEvlf+QDQp08fREZGomPHjli/fj2cnZ0b8Mrqj9FoRP/+/fHiiy8CAPr164e0tDS88847mDFjRgNfXf17//33MXr0aAQEBDT0pdSr9evX49NPP8Vnn32Gnj174tChQ4iPj0dAQECL+jn4+OOPcffdd6N9+/ZwcHBAeHg4pkyZgtTU1Ia+NLLA5cgmzNHREZ07d0ZERASWLl2Kvn37YsWKFWjXrh2Ki4uRn5+vGn/+/Hm0a9cOANCuXTurO6Pk7+UxjV1qaiouXLiA8PBwtGrVCq1atcIPP/yAN954A61atYKfn1+L+BwseXh4oGvXrjh16lSL+Vnw9/dHWFiY6rEePXooy7Ly+7D1Ps0/hwsXLqiOl5aWIi8vr8l8DgBw5swZJCYmYvbs2cpjLeXnYOHChVi8eDHuuOMO9O7dG9OnT8f8+fOxdOlSAC3n5yA0NBQ//PADrly5gszMTPz8888oKSlBp06dWsxnYK623nNd/H+EIawZMRqNKCoqQkREBLRaLXbt2qUcO378OM6ePQu9Xg8A0Ov1+O2331Q/dAkJCXB3d7f6ZdZY3X777fjtt99w6NAh5at///6YOnWq8ueW8DlYunLlCtLT0+Hv799ifhYGDx6M48ePqx47ceIEOnbsCAAICQlBu3btVJ9DQUEBUlJSVJ9Dfn6+arbgu+++g9FoRGRkZD28i9rx4YcfwtfXF2PGjFEeayk/B9euXYNGo/615uDgAKPRCKBl/RwAgKurK/z9/XH58mV8++23+Mc//tHiPgOg9v531+v1+PHHH1FSUqKMSUhIQLdu3Wq0FAmALSqaqsWLF4sffvhBZGRkiMOHD4vFixcLSZLEzp07hRCm29E7dOggvvvuO/HLL78IvV4v9Hq98nz5dvSRI0eKQ4cOiR07dggfH58mdTu6LeZ3RwrRMj6Hhx9+WHz//fciIyND7Nu3T0RFRQlvb29x4cIFIUTL+Ax+/vln0apVK/HCCy+IkydPik8//VS4uLiITz75RBnz0ksvCQ8PD/HVV1+Jw4cPi3/84x82b1Hv16+fSElJEXv37hVdunRp1LflWyorKxMdOnQQjz76qNWxlvBzMGPGDNG+fXulRcXmzZuFt7e3WLRokTKmJfwc7NixQ3zzzTfijz/+EDt37hR9+/YVkZGRori4WAjRPD+DwsJCcfDgQXHw4EEBQPznP/8RBw8eFGfOnBFC1M57zs/PF35+fmL69OkiLS1NrF27Vri4uLBFRUt09913i44dOwpHR0fh4+Mjbr/9diWACSHE9evXxf/93/+Jtm3bChcXF/H//t//E9nZ2apznD59WowePVo4OzsLb29v8fDDD4uSkpL6fiu1yjKEtYTPIS4uTvj7+wtHR0fRvn17ERcXp+qP1RI+AyGE2Lp1q+jVq5dwcnIS3bt3F++9957quNFoFE8++aTw8/MTTk5O4vbbbxfHjx9XjcnNzRVTpkwRbdq0Ee7u7uKuu+4ShYWF9fk2bsq3334rAFi9LyFaxs9BQUGBeOihh0SHDh1E69atRadOncTjjz+uainQEn4O1q1bJzp16iQcHR1Fu3btxNy5c0V+fr5yvDl+Brt37xYArL5mzJghhKi99/zrr7+KIUOGCCcnJ9G+fXvx0ksv3dR1S0KYtRImIiIionrBmjAiIiKiBsAQRkRERNQAGMKIiIiIGgBDGBEREVEDYAgjIiIiagAMYUREREQNgCGMiIiIqAEwhBERERE1AIYwImpWbr31VsTHxzf0ZdS5JUuW4JZbbmnoyyCim8AQRkTUiBQXF9fr6wkhUFpaWq+vSUQmDGFE1GzMnDkTP/zwA1asWAFJkiBJEk6fPo20tDSMHj0abdq0gZ+fH6ZPn45Lly4pz7v11lvxwAMPID4+Hm3btoWfnx/+97//4erVq7jrrrvg5uaGzp0745tvvlGe8/3330OSJGzfvh19+vRB69atMXDgQKSlpamuae/evRg6dCicnZ0RFBSEBx98EFevXlWOBwcH47nnnsOdd94Jd3d3zJkzBwDw6KOPomvXrnBxcUGnTp3w5JNPoqSkBACwevVqPPPMM/j111+V97l69WqcPn0akiTh0KFDyvnz8/MhSRK+//571XV/8803iIiIgJOTE/bu3Quj0YilS5ciJCQEzs7O6Nu3LzZu3Fjb/xMRkRmGMCJqNlasWAG9Xo977rkH2dnZyM7OhpubG2677Tb069cPv/zyC3bs2IHz589j8uTJqueuWbMG3t7e+Pnnn/HAAw/g/vvvxz//+U8MGjQIBw4cwMiRIzF9+nRcu3ZN9byFCxfitddew/79++Hj44Nx48YpYSk9PR2jRo3CxIkTcfjwYaxbtw579+7FvHnzVOd49dVX0bdvXxw8eBBPPvkkAMDNzQ2rV6/GkSNHsGLFCvzvf//D66+/DgCIi4vDww8/jJ49eyrvMy4urlqf1eLFi/HSSy/h6NGj6NOnD5YuXYqPPvoI77zzDn7//XfMnz8f06ZNww8//FCt8xJRNdzU9t9ERI3M8OHDxUMPPaR8/9xzz4mRI0eqxmRmZgoA4vjx48pzhgwZohwvLS0Vrq6uYvr06cpj2dnZAoBITk4WQgixe/duAUCsXbtWGZObmyucnZ3FunXrhBBCzJo1S8yZM0f12nv27BEajUZcv35dCCFEx44dxYQJEyp9X6+88oqIiIhQvn/66adF3759VWMyMjIEAHHw4EHlscuXLwsAYvfu3arr/vLLL5UxN27cEC4uLiIpKUl1vlmzZokpU6ZUem1EVDOtGjIAEhHVtV9//RW7d+9GmzZtrI6lp6eja9euAIA+ffoojzs4OMDLywu9e/dWHvPz8wMAXLhwQXUOvV6v/NnT0xPdunXD0aNHldc+fPgwPv30U2WMEAJGoxEZGRno0aMHAKB///5W17Zu3Tq88cYbSE9Px5UrV1BaWgp3d/dqv397zF/z1KlTuHbtGqKjo1VjiouL0a9fv1p7TSJSYwgjombtypUrGDduHF5++WWrY/7+/sqftVqt6pgkSarHJEkCABiNxmq99r333osHH3zQ6liHDh2UP7u6uqqOJScnY+rUqXjmmWcQExMDnU6HtWvX4rXXXqvw9TQaU4WJEEJ5TF4atWT+mleuXAEAbN++He3bt1eNc3JyqvA1iajmGMKIqFlxdHREWVmZ8n14eDg2bdqE4OBgtGpV+3/l/fTTT0qgunz5Mk6cOKHMcIWHh+PIkSPo3Llztc6ZlJSEjh074vHHH1ceO3PmjGqM5fsEAB8fHwBAdna2MoNlXqRvT1hYGJycnHD27FkMHz68WtdKRDXHwnwialaCg4ORkpKC06dP49KlS5g7dy7y8vIwZcoU7N+/H+np6fj2229x1113WYWYmnj22Wexa9cupKWlYebMmfD29saECRMAmO5wTEpKwrx583Do0CGcPHkSX331lVVhvqUuXbrg7NmzWLt2LdLT0/HGG2/giy++sHqfGRkZOHToEC5duoSioiI4Oztj4MCBSsH9Dz/8gCeeeKLS9+Dm5oZHHnkE8+fPx5o1a5Ceno4DBw7gzTffxJo1a2r82RBRxRjCiKhZeeSRR+Dg4ICwsDD4+PiguLgY+/btQ1lZGUaOHInevXsjPj4eHh4eyvLdzXjppZfw0EMPISIiAjk5Odi6dSscHR0BmOrMfvjhB5w4cQJDhw5Fv3798NRTTyEgIKDCc44fPx7z58/HvHnzcMsttyApKUm5a1I2ceJEjBo1CiNGjICPjw8+//xzAMAHH3yA0tJSREREID4+Hs8//3yV3sdzzz2HJ598EkuXLkWPHj0watQobN++HSEhITX4VIioKiRhXjxARERV8v3332PEiBG4fPkyPDw8GvpyiKgJ4kwYERERUQNgCCMiIiJqAFyOJCIiImoAnAkjIiIiagAMYUREREQNgCGMiIiIqAEwhBERERE1AIYwIiIiogbAEEZERETUABjCiIiIiBoAQxgRERFRA2AIIyIiImoA/x8XV8X3Pvc5BQAAAABJRU5ErkJggg==", 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ioqKUROlNmzahdevW2Lx5MwYNGgQhBKKjo7Fw4UL4+voCAJKTkxEdHa26VmxsLBISEgCYSj6kpqbiqaeeUs5rNBpER0cjOTkZAJCamoqioiLVdTp06ICWLVsiOTkZvXv3RnJyMrp27YpmzZqpPufhhx/GH3/8gR49elTJc6kLJk6ciBUrVgAAnJ2d4evri27dumHMmDGYOHFimVd5Ll++HAkJCcjNza3C3hIRUV2l1+uVOppHj15GWloBMjK2Q6vNd/i+adOmoWnTptXRxbq1OvLJJ5/E0qVLcfXqVfTu3RubN29Wzv311184deoU1q5di08//RTFxcWYPn06Ro0ahe+//x4AkJ2drQqMAKBZs2bIy8vDP//8g0uXLqG4uNhmm6NHjyrXcHV1hbe3t1Wb7Oxsh58jn7OnoKAABQUFyuu8vLyyPJYKMf/htMXV1bXKfggHDRqETz75BMXFxTh37hy2bduGxx57DOvWrcPGjRvh7FynfiyJiKiWSU9Px+effw4A2L+/BzZtGgIhNJCkThg6dDPCww/YfW91FkCv0d92s2fPxquvvuqwzZEjR9ChQwcAwMyZMxEfH49Tp05h/vz5uO+++7B582ZIkgSj0YiCggJ8+umnaNeuHQDgo48+QkREBI4dO4b27dtX+f3crAULFmD+/PlV/jl6vR5Lly4ttV1V/WvAzc0NzZs3BwDccsstCA8PR+/evXHXXXdh+fLlmDx5Mt5880188skn+Ouvv+Dr64uhQ4di4cKFaNKkCX744Qfcf//9AEqS5ufOnYt58+bhs88+w+LFi3Hs2DE0btwYd955JxYtWoSAgIBKvw8iIqp99Hq9EoAZDJ5KAAYAQmiwadMQuLoWwNv7EoqK3ODrqy91dKyq1GgQ9vjjj2PixIkO27Ru3Vr53s/PD35+fmjXrh06duyI4OBg/Pzzz9DpdAgMDISzs7MSgAFAx44dAZhWKrZv3x7Nmze3WsV47tw5eHl5wd3dHU5OTnBycrLZRg4amjdvjsLCQuTm5qpGwyzbWK6olK8pt7HlqaeewowZM5TXeXl5CA4Odvh8KqKsUX51/mvgzjvvRPfu3bFhwwZMnjwZGo0GS5YsQWhoKP766y/897//xaxZs/Duu+8iKioKixYtwpw5c3Ds2DEAQJMmTQAARUVFeOGFF9C+fXucP38eM2bMwMSJE7F169ZquxciIqo55r+7UlIilQBMJoQG69b9G4AAIEGSjKWOjlWVGg3C/P394e/vX6H3ylv1yNN3ffr0wfXr15Geno6wsDAAwJ9//gkAaNWqFQBAp9NZ/TJOTEyETqcDYJqCi4iIwI4dOzB8+HDlc3bs2IFp06YBACIiIuDi4oIdO3Zg5MiRAIBjx47h9OnTynV0Oh1eeuklnD9/XhmBSUxMhJeXFzp16mT3ntzc3ODm5lah51EfdOjQAYcOHQIAJU8PAEJCQvDiiy/ioYcewrvvvgtXV1dotVpIkmQV1E6aNEn5vnXr1liyZAluu+02XL58WQnUiIio/tDr9bhw4QJycnKQlaXBH38UwmDwBADs3atz8E7TTIo8OhYWdqLaR8TqRPJNSkoK9u3bh759+8LHxwfp6el47rnnEBYWpgQ+0dHRCA8Px6RJk7Bo0SIYjUZMnToVMTExyujYQw89hKVLl2LWrFmYNGkSvv/+e6xZswZbtmxRPmvGjBmYMGECevbsiV69emHRokW4cuWKMv2l1WoRHx+PGTNmwNfXF15eXnjkkUeg0+nQu3dvAMDAgQPRqVMnjB8/HgsXLkR2djaeffZZTJ06tUEHWaURQijTi0lJSViwYAGOHj2KvLw8XL9+HdeuXcPVq1cd7hCQmpqKefPm4bfffsOlS5eUYP306dMOA2AiIqpb9Ho9zp8/jzVr1gBQ534BfRERkYqyVuISQoOcHN9qD8LqRJ0wDw8PbNiwAXfddRfat2+P+Ph4dOvWDbt27VKCGo1Gg02bNsHPzw/9+/fH4MGD0bFjR3z55ZfKdUJDQ7FlyxYkJiaie/fueOONN7Bs2TLExsYqbeLi4vD6669jzpw5uPXWW3Hw4EFs27ZNlWj/1ltvYciQIRg5ciT69++P5s2bY8OGDcp5JycnbN68GU5OTtDpdBg3bhzuu+8+PP/889XwtOquI0eOIDQ0FCdPnsSQIUPQrVs3rF+/HqmpqXjnnXcAOJ4ivXLlCmJjY+Hl5YWVK1di3759+Oqrr0p9HxER1S1ybrMcgJ05E4iNG4eYTT1qkJraE6Ypx9JJkhG+vjlV01kH6sRIWNeuXZUVjo4EBQVh/fr1DtvccccdOHDA8bzvtGnTlOlHWxo1aoR33nlHCQxsadWqFfOQyuH777/H77//junTpyM1NRVGoxFvvPGGUrJC/h9N5urqarWN0NGjR6HX6/HKK68ouXS//vpr9dwAERFVG/MC6vIImPW4Utl2O5FzwmoiOb9OBGFUvxQUFCA7O1tVomLBggUYMmQI7rvvPqSlpaGoqAhvv/02hg4dij179uD9999XXSMkJASXL1/Gjh070L17d3h4eKBly5ZwdXXF22+/jYceeghpaWl44YUXauguiYioKuj1euUf5parH8vHiFGj1iE4+IwqAKvOPZnrxHQk1S/btm1DYGAgQkJCMGjQIOzcuRNLlizBN998AycnJ3Tv3h1vvvkmXn31VXTp0gUrV67EggULVNeIiorCQw89hLi4OPj7+2PhwoXw9/fH8uXLsXbtWnTq1AmvvPIKXn/99Rq6SyIiqkx6vR5Hjx7Fvn37lGO2Vj+WjUBMTBK6dDmiBGCjR4+u1kKtACAJIco2YUrVLi8vD1qtFgaDAV5eXqpz165dQ0ZGBkJDQ9GoUaNyXbem64TVNTfzrImI6OZZ/t4yGDyRmRmMdetGomzjSeLGlwaAETExSejTJ1k5O3r0aKWsVWVw9PvbHKcjG6CmTZti2rRpNVYxn4iIqDTyzi65ubm4cOGCcly9CrJs7rlnM9q3P46cHF/4+uZY5X/VVEFvBmENFAMsIiKqrezN2FQkB0ySjGjf/ji02nwl+BowYAB8fHzg7OyMgICAGvudyCCMiIiIapz5nsZpaWmqcwaDJ3JymuLKFY9yB2C2Vj527ty5VgxGMAgjIiKiGuUoV1m9AbcRgBGl5YFJkhEjR1qvfBw9enSNjnxZYhBGRERENcoyR1ke+XJxKcDGjSU1wEpGwUz7Pqqp94Ls0uUIAGDEiBHw8/OrlbnODMKIiIioWslTjwaDAUVFRcjKylLOqRPv7Y16STAPxCTJiOjoJAQFZVkl3gcFBdW64EvGIIyIiIiqjaOpR+vEe0fTjhJuv30n/P0vWE07xsTEIDQ0tFaOfpljEEZERETVxtHU4+nTrcqReG9EePgBm9sNtW/fvlYHXzIGYURERFQj1FOP8vSirXwvS6aK93IA1rNnT/j4+MDHx6dWJd6XhtsWUb3yww8/QJIk5Obmlvk9ISEhWLRoUZX1iYioIdPr9Th79izOnj2Lo0eP4tChQwBsTT1KFn/aI9Cv34+qive9e/dGVFQUOnbsWGcCMIBBGFWziRMnQpIkPPTQQ1bnpk6dCkmSMHHixOrvGBERVTo5/+uDDz7ABx98gNWrV+Pnn38GAOTkNK3Avo9GxMQk4q67flCOjBs3rk4FXuY4HUnVLjg4GF9++SXeeustuLu7AzDtz7hq1Sq0bNmyhntHRESVxXy7IUu+vnqUpeaXiRFRUcmIjExRpiDvvvtuhIWF1dkADOBIGNWA8PBwBAcHY8OGDcqxDRs2oGXLlujRo4dyrKCgAI8++igCAgLQqFEj9O3bF/v27VNda+vWrWjXrh3c3d0xYMAAnDx50urzfvrpJ/Tr1w/u7u4IDg7Go48+iitXrlTZ/RERNWTy9OPRo0eRnp6uOnfmTCD27u2NM2cCkZbWBaVNPUZG7sWoUWswffoiDBxYkgM2btw49OrVq04HYABHwgjAmTPA8eNA27ZAixbV85mTJk3CJ598grFjxwIAPv74Y9x///344YcflDazZs3C+vXrsWLFCrRq1QoLFy5EbGwsTpw4AV9fX2RmZmLEiBGYOnUqpkyZgl9//RWPP/646nPS09MxaNAgvPjii/j4449x4cIFTJs2DdOmTcMnn3xSPTdLRNRAOCo/8dVX/8Jvv3VHSfI94CgIkyQjoqJ+Vq1+vOeee9C6des6H3zJOBLWwH30EdCqFXDnnaY/P/qoej533Lhx+Omnn3Dq1CmcOnUKe/bswbhx45TzV65cwXvvvYfXXnsNd999Nzp16oQPP/wQ7u7u+OhGJ9977z2EhYXhjTfeQPv27TF27FirfLIFCxZg7NixSEhIQNu2bREVFYUlS5bg008/xbVr16rnZomIGghb5ScyMkJw7FgbswAMN/60FYCZgjPLPR9HjBiBadOm4bbbbqs3ARjAkbAG7cwZYMoUwGg0vTYagQcfBGJjq35EzN/fH4MHD8by5cshhMDgwYPh5+ennE9PT0dRURH69OmjHHNxcUGvXr1w5IhpK4ojR44gMjJSdV2dTqd6/dtvv+HQoUNYuXKlckwIAaPRiIyMDHTs2LEqbo+IqMEw33j74sWLynHryvelrXo0YsyYL+DqWlSnqt7fDAZhDdjx4yUBmKy4GDhxonqmJSdNmoRp06YBAN55550q+YzLly/jwQcfxKOPPmp1josAiIgqTq/X48KFC1i9erXVufJVvgfkul/t258AAAwYMAD+/v7w9vau9VXvbwaDsAasbVtAo1EHYk5OQJs21fP5gwYNQmFhISRJQmxsrOpcWFgYXF1dsWfPHrRq1QoAUFRUhH379iEhIQEA0LFjR2zcuFH1Pnnpsyw8PByHDx9Gm+q6KSKiek6v1+P8+fNYs2aN3TblKz9hXferbdu2CAwMvMme1n7MCWvAWrQAPvjAFHgBpj//97/qS853cnLCkSNHcPjwYTjJnbihcePGePjhhzFz5kxs27YNhw8fxgMPPICrV68iPj4eAPDQQw/h+PHjmDlzJo4dO4ZVq1Zh+fLlqus8+eST2Lt3L6ZNm4aDBw/i+PHj+Oabb5QROCIiKrv09HQsXbrUbgAm54AVFjrDNAVpj1H507LuFwC4urpWQm9rP46ENXDx8aYcsBMnTCNg1RWAyby8vOyee+WVV2A0GjF+/Hjk5+ejZ8+e+O677+Dj4wPANJ24fv16TJ8+HW+//TZ69eqFl19+GZMmTVKu0a1bN+zatQvPPPMM+vXrByEEwsLCEBcXV+X3RkRUn+j1enz++ec2zxkMnkhJiURyss5iCyJrkmREfPwyFBW5WuV+1bfVj6WRhBCi9GZUE/Ly8qDVamEwGKyClWvXriEjIwOhoaFo1KhRDfWwYeCzJqKGyjLp3ry+o8HgiczMYGRkhCA1NQJlm1wTiIlJVKYe62vul6Pf3+Y4EkZERERWHNX82r+/BzZuHIrSVzyaMyImJkmV+9W5c+d6E3hVBIMwIiIismKr5ldOTlO4uBRg06YhKHsAZsSoUesQHHxGNfVYl/d8rCwMwoiIiEghT0FmZGQox8xrfkmSsQwrH005YXLR1S5dTPUdR4wYAT8/v3o19XgzGIQRERERANtTkJY1v0pLvO/c+XfodMk2E+/9/PwaROmJsmIQVsdxXUXV4zMmovpOrv116tQpq3O2a37ZX/k4cGCiKvAy11BKT5QVg7A6Sq6rVVhYCHd39xruTf0m50VY1jIjIqrL5Ir3ly5dwvbt2222OXMmEEePtoOjkS+Z5X6PMTExCA0NVc5zCtIag7A6ytnZGR4eHrhw4QJcXFyg0bDublUwGo24cOECPDw84OzM/12IqO6Sc73OnDkDvV6PlJQUh+2/+upfFptu22M78T40NJRTj6Xgb5U6SpIkBAYGIiMjw+bwMVUejUaDli1bQpLKsxSbiKj2cFRuwpLB4InffutWSgBmO/HeHKceS8cgrA5zdXVF27ZtrZYRU+VydXXlSCMR1Wml/Z6QC68eO9YWv/9e+uhXRMSv6NLlD6vEe65+LB8GYXWcRqNhFXciIrJJzvs6f/686rhc86uw0BmpqRH48892KOt20pJkRP/+u20m3wcFBTH4KgcGYURERPWQvSlI85pfZUm4N2eZfA8AcXFx0Gq1HP2qAAZhRERE9ZDl6BdgWu24ceMQlIx63VzV+9GjR6NDhw433deGikEYERFRPZOeno41a9aoju3Zo0NiYgzKt98jABgxbJjt5PuAgICKd5IYhBEREdV1cvkJAMjMzMS3336rOr9jxx3Yvbs/yjPy1bXrIbRv/6fV6NfAgQMREhLC6cdKwCCMiIioDiut/ITjAMw8J0yga9ffbAZe5tq1a8fgq5IwCCMiIqpDzEe9cnNzceHCBdV5eeWjr68ev/4aUcoImITu3Q/gllvOoH3743YDLybfVw0GYURERHVEaaNe1isfAUdTkJJkxJ137rQZfI0ePRre3t4MvKoQgzAiIqI6wlHR1TNnAs0CMKCs1e7lAEwutApwn8fqwiCMiIioljKfegSAixcv2my3f38Pi9IT9nXocBiRkfusqt37+flxr8dqxiCMiIioFirrfo8Ggyc2bSpbAAYYcffd39mcfuRej9WPQRgREVEto9frkZWVVWo7g8ETf/zR2WwK0hFTvS9Wu689GIQRERHVIvZGwORNtgEgODgT6eltSt1+SJKMCA9PRWhohlXZiXvuuYfV7msYgzAiIqJawt4ImCnnayhKAi3jjT8dJ+GPHLnOZqV7AGjduvVN9ZVuHoMwIiKiWsDeCJhpv0fzAAwoS/6XJBkRHHxGdWzgwIHw9vZGQEAApx9rAQZhRERENcR89ePJkydV5wwGT6SkRGLvXh3Ku9+jrfITQUFBDLxqGQZhRERENcDR6sfylJxQM2LUqHVW+V9+fn4MwGohBmFEREQ1wLLwqrzdkItLQYUCMHn0y1YOGMtP1E4MwoiIiKqRPAVpXnhVvd2QEY4DMPV5STJi5Ejr0S+5Aj7LT9ReDMKIiIiqmBx45ebmYs2aNapzcrHVklpf9gMwSTIiOjoJSUnREELjcPSLOWC1H4MwIiKiKpSeno7PP//c5rnyFFuVA67w8APo0iUNOTm+VlsPcfSrbmEQRkREVEX0er1VACbnfmVlBSojWvaKrZYwIj5+GVq0OAsA0GrzbW49xNGvuoVBGBERURWxTL7fs0dnJ/ByHIANG7ZZCcBk8qiXjKNfdQ+DMCIiomqwZ48OiYkxKFvgBQBG3HPPFrRvf5yjXvUUgzAiIqIqZjB4IjExGuUpuhoVlYxevfZbHR89ejQr3tcTDMKIiIiqgF6vV8pQ5OQ0RXnqfkmSEZGRKcrrAQMGICAgAP7+/gy+6hEGYURERBUgl50wGAwoKipSncvPz0diYqLy2sWlAKXX/zLliFluOQQAnTt3ZvBVDzEIIyIiKidHWw5Zkguxllb/6957v4Cra5FV2YnRo0czAKunGIQRERGVk6Mth4qK3ODrqwcAZGYGWxRitcU08tW+/QmbZwMCAiqr21TLMAgjIiIqA3n6EYCDLYfkshPixpfj0S+dLhmRkSk2Vz8yAb/+YxBGRERUCntV7623HDIvP2FvJaQRo0ZZ7/Uoi4uLYwJ+A8EgjIiIyAFHVe8vXGhapi2HZI72emTw1fCU/Senhg0bNgwtW7ZEo0aNEBgYiPHjxyMrK0s5P2/ePEiSZPXVuHFj1XXWrl2LDh06oFGjRujatSu2bt2qOi+EwJw5cxAYGAh3d3dER0fj+PHjqjY5OTkYO3YsvLy84O3tjfj4eFy+fFnV5tChQ+jXrx8aNWqE4OBgLFy4sJKfCBERVTa9Xo+zZ8+qvsx/1wCmoqtvvZWAFSsmYOvWwWW8shGjRq1BQsIihIcfUI6OGDECU6ZMwbRp09ChQwcGYA1MnRkJGzBgAJ5++mkEBgbi77//xhNPPIFRo0Zh7969AIAnnngCDz30kOo9d911F2677Tbl9d69ezFmzBgsWLAAQ4YMwapVqzB8+HDs378fXbp0AQAsXLgQS5YswYoVKxAaGornnnsOsbGxOHz4MBo1agQAGDt2LM6ePYvExEQUFRXh/vvvx5QpU7Bq1SoAQF5eHgYOHIjo6Gi8//77+P333zFp0iR4e3tjypQp1fG4iIionMqy4rH8Ve8BedshW6Nffn5+CAwMLHdfqX6QhBCipjtRERs3bsTw4cNRUFAAFxcXq/O//fYbbr31Vvz444/o168fANNQ75UrV7B582alXe/evXHrrbfi/fffhxACQUFBePzxx/HEE08AAAwGA5o1a4bly5fj3nvvxZEjR9CpUyfs27cPPXv2BABs27YN99xzD86cOYOgoCC89957eOaZZ5CdnQ1XV1cAwOzZs/H111/j6NGjZb7HvLw8aLVaGAwGeHl5VfhZERGRfXLC/cWLF7FhwwbluDzl6Ourh1abD4PBE2+9lYDyFl0133jb0rRp0zj6VQ+V9fd3nRkJM5eTk4OVK1ciKirKZgAGAMuWLUO7du2UAAwAkpOTMWPGDFW72NhYfP311wCAjIwMZGdnIzo6Wjmv1WoRGRmJ5ORk3HvvvUhOToa3t7cSgAFAdHQ0NBoNUlJS8H//939ITk5G//79lQBM/pxXX30Vly5dgo+Pj80+FxQUoKCgQHmdl5dX9odCRETlotfrceHCBaxevdrqnHrFoxH9+u2Gu/s1lC0AUxddtbfxNjfcpjoVhD355JNYunQprl69it69e6tGtMxdu3YNK1euxOzZs1XHs7Oz0axZM9WxZs2aITs7WzkvH3PUxrJmi7OzM3x9fVVtQkNDra4hn7MXhC1YsADz58+3ffNERFRpHE09Wq941GD37v4oKT1hawqyJPCKjk5CUFCWVdFVGTfeJlmNJubPnj3bZjK9+Zf59N3MmTNx4MABbN++HU5OTrjvvvtgazb1q6++Qn5+PiZMmFCdt3PTnnrqKRgMBuUrMzOzprtERFQvWRZblRkMnkhNDbex4tE8B8z8945AVNQeTJ78ISZMWI6EhEXo0ycZoaGnrAKwuLg4Tj+SSo2OhD3++OOYOHGiwzatW7dWvvfz84Ofnx/atWuHjh07Ijg4GD///DN0Op3qPcuWLcOQIUOsRrSaN2+Oc+fOqY6dO3cOzZs3V87Lx8wTJc+dO4dbb71VaXP+/HnVNa5fv46cnBzVdWx9jvln2OLm5gY3Nze754mIqGoYDJ5ISYnE3r1RKD3ZXsLtt++Ev/8Fu7W+ZDExMQgNDeXUI9lUo0GYv78//P39K/Reo9EIAKocKsCU17Vz505s3LjR6j06nQ47duxAQkKCciwxMVEJ4kJDQ9G8eXPs2LFDCbry8vKQkpKChx9+WLlGbm4uUlNTERERAQD4/vvvYTQaERkZqbR55plnUFRUpOSsJSYmon379nanIomIqPoYDAbl+/37e2DjRsd7O5qTJCPCww84DL5k7du3Z/BFdtWJnLCUlBTs27cPffv2hY+PD9LT0/Hcc88hLCzMahTs448/RmBgIO6++26r6zz22GO4/fbb8cYbb2Dw4MH48ssv8euvv+KDDz4AAEiShISEBLz44oto27atUqIiKCgIw4cPBwB07NgRgwYNwgMPPID3338fRUVFmDZtGu69914EBQUBAP7zn/9g/vz5iI+Px5NPPom0tDQsXrwYb731VtU+KCIiKpVer1eS8eX8r9IDMHWyvb0AbODAgfD29oa3tzdHv6hUdSII8/DwwIYNGzB37lxcuXIFgYGBGDRoEJ599lnV9J3RaMTy5csxceJEODk5WV0nKioKq1atwrPPPounn34abdu2xddff63UCAOAWbNm4cqVK5gyZQpyc3PRt29fbNu2TakRBgArV67EtGnTcNddd0Gj0WDkyJFYsmSJcl6r1WL79u2YOnUqIiIi4Ofnhzlz5rBGGBFRNTDf49GcwWBAUVGRsogKAHJyylLx3ogxY76Aq2uRVbK9vNIRAIMuKrc6WyesIWCdMCKi8ilLwVWgpAbYkSPt8csvkbCXByaPfJlXuTc3ZcoUFlslK/W6ThgREZEt9lY9mjNVvY+GaQrSXskJI+65Zwvatz/uMPfLvB4kUXkxCCMiogZjx447zGp+AfYCsGHDbI9+cfqRKhODMCIiqhf0ej0uXryoOma+9VBaWheLAMwWgcmT7W8zxEKrVJkYhBERUZ1nKxfMcushE8cBWExMIrcZomrDIIyIiOoMy5WP8orHS5cuqdqdORNoUfur9BWQMTFJ6NMn2eoMR7+oqjAIIyKiOqGsKx9NxVeHovTK9wBgRFRUMiIjU2xuM+Tv788AjKoMgzAiIqq1zEe+LPO9LBkMnsjMDC5jAGZEREQq+vffbXP1Y1xcHDp06FDBXhOVDYMwIiKqldLT0/H555/bPW+ZdJ+UFF2GwqsAYHSYfA+gwlvqEZUHgzAiIqp1SgvA1En3cs3xskw/CsTEJNkNwEaPHo2AgABOQVK1YBBGRES1il6vtwrA5KnGq1cbAZCwdes9KEm2Ly34Ktn3MTradvL93XffjbCwMAZfVK0YhBERUa0g539Z5n6pK9xXhITY2G3o1OmwVf7XiBEjuPqRagyDMCIiqjFy4JWbm4s1a9ZYnTcFYDEo21SjbZJktBmAASw/QTWLQRgREdUIR3lf8vSjaQSsIgFYyRTk0KGbbQZg48aNYwBGNYpBGBERVTtbeV8yddJ9+cm5X0FBWfD1zVEFYDExMQgNDWX1e6oVGIQREVG1M696D5gn3rtbJN2XnSQZMXLkOgQHn7E58gUAoaGhCAwMrEiXiSodgzAiIqpRZa9wL260EVZt5WnHLl2OOLyCq6vrTfSUqHIxCCMiohpjMHhi06YhKFvel2T2pxyI2d92SDZ69Gh4e3tzCpJqHQZhRERU6Sw32raUm5sLAMjJaVrB3C/7ZScA5n5R3cAgjIiIKlVZN9oGAF9fPSTJWO5AzFHZCQBo3749gy+q9RiEERFRpXI0AmZJq81Ht26H8Ntv3eF4SlLc+NI4LDvBbYeoLmEQRkREN818+tGy4r288hEAgoMzAUDZeBsADh3qBscBmBExMUno0iUNOTm+VmUnRowYAT8/P049Up3DIIyIiG6Ko+lH08rHISgpOVGy2bYkGREenlrKVKQRkycvUzbcZtV7qk8YhBER0U2xVfMrJ6cpXFwKLAIwwHzESwgNUlN7AjBatClZ+Ths2GYlADMXFxcHrVbL0S+q0xiEERFRpTGvdi9JlsGVLRLMS044qnYvGzduHMLCwiq/80TVjEEYERHdFIPBcONPT9V2Q6Y/rQur2mYKwOLjl9kc+QJMuV+ceqT6hEEYERGVylHdr1OnTgGwV/PLdoV7W4TQoKjIfkV7BmBU3zAIIyIih8pa98u02tHWFKQciAkb58xaSUb4+uaojrHaPdVnDMKIiMghe4n3vr56Vc5WWloX2B/xMgVi99yzCefONUdqagRMAVlJLph57S9OPVJDwCCMiIjKzDLxPjw8FaGhJ+HtfQmJidFwPO2ogb+/Hr167Uf//ruRk+MLF5dCFBW5WiXh+/n5MQCjeo9BGBERlYmtxPvU1NuQmnobbE9DqplPN2q1+Xa3HAIAV1f7uWFE9QWDMCIisqLX63HhwgUUFRXh0qVLAErbbNvWcXVhVvPpxpCQELi7u8PJyQmNGzeGh4cH3N3d0aJFC+Z/UYPBIIyIiFTsJeKXd7PtiIhflWlHy+nGgQMHIjAwsNL6TFQXMQgjImpgHJWbAErqfpW8Ltn7sWPHIzh8uBNKKzkhSUb077+71GlHooaMQRgRUQNS1nITMtt7P9oLwGyvdLSFOV9EDMKIiBqUCxculLmtweDpcO9Ha6b9Hm1VvZfrfQFgzhfRDQzCiIgaCL1ej9WrV6uO2ar5JR+7csUDpe/9aMm66j33eiSyjUEYEVE9J+eAXbx4UXV8zx7djdpeGmXj7CtXGmPvXh1MwZcRjqcfjTf+LAnUzMtQsOAqkWMMwoiI6im5zITl6BcgB2AxkAMsITSq1yal1f0CoqOTkJQUrRRvNc8FY8FVIscYhBER1UP2EvDllY62q9uXvsm2OSE0CArKQkLCIptlKJh8T+QYgzAionrI1n6PKSmRSE7WlbnOV2nkqUe5DEVcXBy0Wi0AJt8TlQWDMCKiOqg8tb6sy0xUTNu2R3HiRDubU4+jR49Ghw4dbur6RA0NgzAiojqmPLW+5P0ebzYAAwRuv/1HDBmy1ebUo1x+gojKrnLGpImIqNo4GgGz5Gi/R0kyIiJiH0pWOToioajIFVptPkJDT1kVYmX+F1H5cSSMiKiWs5x6tCw1YavWl8zXVw9TkGUZiAl063YIQ4duRf/+u3HsWFts3TrYRjsT89ITAwYMQNu2bZVzzP8iqhgGYUREtVh6ejo+//xzu+f37++BTZuGqPK0wsMPmL2/DWyvepTw22/dcNttv6BFi7Po1Ws/nJ2Fci3ACEmCzfwvf39/br5NVAkYhBER1VKlBWByvpc83SiEBps2DUFAQDaKitzg4lJwIx/MXukJDZYtm4xhw0yBW3j4AYSFnVByvgDYzP8KCAiorFskatAYhBER1UJ6vd5mAGY+9Wgr30sIU2BVUvG+tNRfDTZuHIKwsBNKqQnzgEv+fsSIEfDz8+PUI1ElYhBGRFQL2arz9eOP/ZCaGgHzbYYkyWgRiAmUBF5lXXulQU6Or1U+mTluP0RU+RiEERHVcrbqfAmhQVJStNW2QaUXYrW1F6R6v0c/Pz/VWY5+EVUNBmFERLWYweBpt9CqvG1QfPwynD7dErm5WvzyS2842nA7JqYkaJOPDRum3u+RSfdE1YNBGBFRLZSbmwvAVOfL/rSiEVlZQWZBleUol4A8PWm+crJLlzRkZrYAAAQHn3E4DUlEVYdBGBFRLZOeno41a9YAcFznq1+/3RajWtYbco8atRaNG19VrXA0Jd8fsfnZLLpKVH0YhBERVTN7+z7m5ubi0qVLSExMVI5ptfmIiUlCYmI0SgIxgZiYRAQFncXu3bfb/RxJMtod6YqJiYGnp6fy2sXFBf7+/sz9IqpGDMKIiKpRefZ9BIA9e3Q2A7A+fZJx5kygndWRklWB1ZiYGISGhgJgoj1RbcEgjIioGtkqPWFry6GSkhQ9oZ5mlJCUFA0AVrlgctmKoKAsqwKroaGhTLgnqmUYhBER1RDLLYeio5PQp0+yzZIU5oTQWIyOSQCMiI9fhhYtzlZT74noZjEIIyKqQvY237a15VBiYgyuXXPDTz/1g+NCq7YS9TUoKrKfVM+Ee6Lap8xBWF5eXpkv6uXlVaHOEBHVJ5Z7P5ZMPXra3HIIkLB7d2kBmLB5VJJKCq727NkTYWFh0Gq1AJgDRlRblTkI8/b2hiTZKwBoIoSAJEkoLi6+6Y4REdVVer0eFy5cwOrVq5VjtqYebVe4t7fno0CHDodx9GhHG+cAYRabtWzZEh06dKikuyGiqlLmIGznzp1V2Q8ionrB1upHW1OPSUnR6Nt3N3bv7g/rAqu2RsIktGqViaNHO9v55NL3fySi2qXMQdjtt9uvRUNE1NDYq/Ul53zJDAZPbN8+0GrESwgNWrfOQKNGBWZJ9rb2dTQx1fw6bXd/SPPpSBcXlwrdExFVrwon5ufm5uKjjz7CkSOmqsudO3fGpEmTlBwEIqL6ynK0y16ZCUerHOWgKTT0FAAgMTEGjgKwoUM3o0WLsxg6dLPZqJrtmmD8e5iobqhQEPbrr78iNjYW7u7u6NWrFwDgzTffxEsvvYTt27cjPDy8UjtJRFSbmI+AWeZ6yfszylOQ9pLsdbpkaLX5MBg8b9T9shWAGTFq1DpV1fvw8AMICzuBnBxfuLgUoqjI1aomGFdCEtUNFQrCpk+fjmHDhuHDDz+Es7PpEtevX8fkyZORkJCAH3/8sVI7SURUG8hTkI7KTGzaNORGkGRr9aPMiMjIFABw0M6IYcM2o0sX6z0eR43qrdpyCACcnZ3h7e3NlZBEdUiFR8LMAzDA9BfArFmz0LNnz0rrHBFRbWEr4d5WACWEKUHe11dvM3/LcurQ3gbdkgSEhZ1QXo8YMQJ+fn4MsojqEUfFaOzy8vLC6dOnrY5nZmZa/eussgwbNgwtW7ZEo0aNEBgYiPHjxyMrK0vV5rvvvkPv3qZ/Ifr7+2PkyJE4efKkqs0PP/yA8PBwuLm5oU2bNli+fLnVZ73zzjsICQlBo0aNEBkZiV9++UV1/tq1a5g6dSqaNm2KJk2aYOTIkTh37pyqzenTpzF48GB4eHggICAAM2fOxPXr1yvlWRBR9bO13dCVKx6QJKNFS4G0tM7Iz28CnS7Z7LyphoSwKPOl1eYjKirZ6vPkYE4WFBSEwMBABmBE9UiFgrC4uDjEx8dj9erVyMzMRGZmJr788ktMnjwZY8aMqew+AgAGDBiANWvW4NixY1i/fj3S09MxatQo5XxGRgb+9a9/4c4778TBgwfx3Xff4eLFixgxYoSqzeDBgzFgwAAcPHgQCQkJmDx5Mr777julzerVqzFjxgzMnTsX+/fvR/fu3REbG4vz588rbaZPn45NmzZh7dq12LVrF7KyslSfU1xcjMGDB6OwsBB79+7FihUrsHz5csyZM6dKng0RVa/9+3tg0aIErFv37xtBlXlkJSE19TYsW/YA9u7tc+O8ESU5Xxps3DgEx461QUZGCAwGT0RGplgFc+arHUePHs3gi6g+EhVQUFAgHn30UeHq6io0Go3QaDTCzc1NJCQkiGvXrlXkkuX2zTffCEmSRGFhoRBCiLVr1wpnZ2dRXFystNm4caOqzaxZs0Tnzp1V14mLixOxsbHK6169eompU6cqr4uLi0VQUJBYsGCBEEKI3Nxc4eLiItauXau0OXLkiAAgkpOThRBCbN26VWg0GpGdna20ee+994SXl5coKCgo8z0aDAYBQBgMhjK/h4iqRlZWlpg3b56YPv0NIUnFwjSmdTNfRgEIIUnFYtiwb8SwYd8o15WPzZs3T8ybN09kZWXV9O0TUTmU9fd3hUbCXF1dsXjxYly6dAkHDx7EwYMHkZOTg7feegtubm6VGiTakpOTg5UrVyIqKkqphxMREQGNRoNPPvkExcXFMBgM+OyzzxAdHa20SU5ORnR0tOpasbGxSE42TQUUFhYiNTVV1Uaj0SA6Olppk5qaiqKiIlWbDh06oGXLlkqb5ORkdO3aFc2aNVN9Tl5eHv744w+791VQUIC8vDzVFxHVDgaDAYCjRPryMo2MmSfzJyQswoQJy5GQsAjh4Qcq4TOIqDa7qQ28PTw80LVr18rqS6mefPJJLF26FFevXkXv3r2xefNm5VxoaCi2b9+O0aNH48EHH0RxcTF0Oh22bt2qtMnOzlYFRgDQrFkz5OXl4Z9//sGlS5dQXFxss83Ro0eVa7i6usLb29uqTXZ2tsPPkc/Zs2DBAsyfP7+MT4OIboa80jE3N9cqX/Py5csQFslbiYmJAGAn4d4ISYLd4MzUHrCXASLnf4WGnrJZ8Z4lJ4jqpwoFYdeuXcPbb7+NnTt34vz58zAa1bkM+/fvL9N1Zs+ejVdffdVhmyNHjih7oM2cORPx8fE4deoU5s+fj/vuuw+bN2+GJEnIzs7GAw88gAkTJmDMmDHIz8/HnDlzMGrUKCQmJpa672Vt8NRTT2HGjBnK67y8PAQHB9dgj4jqJ3vFVl1cClBU5GZVdNWcVpuvKpgqr3YMCzuBlJRI7N2rg7r6vUC3bodw222/YNmyB2CrHph5/pe8ClLG1ZBE9VeFgrD4+Hhs374do0aNQq9evSoc4Dz++OOYOHGiwzatW7dWvvfz84Ofnx/atWuHjh07Ijg4GD///DN0Oh3eeecdaLVaLFy4UGn/+eefIzg4GCkpKejduzeaN29utYrx3Llz8PLygru7O5ycnODk5GSzTfPmzQEAzZs3V/71bD4aZtnGckWlfE25jS1ubm7VMp1L1NDZK7ZqWYHefErQvCp+WNgJjBy5HoBQFVIdODAJnTr9gWXLJqNk1EvCoUPdcOed3yMmJtFGZXyB6Ogk5Rp+fn4IDAyswrsnotqiQkHY5s2bsXXrVvTp0+emPtzf3x/+/v4Veq88+lZQUAAAuHr1KjQa9VC/k5OTqq3l9CRgmmLQ6XQATP/ijIiIwI4dOzB8+HDlvTt27MC0adMAmHLPXFxcsGPHDowcORIAcOzYMZw+fVq5jk6nw0svvYTz588jICBA+RwvLy906tSpQvdLRJXPstiqrTwtABYjXPJKR9vBWlGRGyynHeXpxj59THmjJXtFGhETk6QcBzj1SNSQVCgIu+WWW6qsHpgtKSkp2LdvH/r27QsfHx+kp6fjueeeQ1hYmBL4DB48GG+99Raef/55ZTry6aefRqtWrdCjRw8AwEMPPYSlS5di1qxZmDRpEr7//nusWbMGW7ZsUT5rxowZmDBhAnr27IlevXph0aJFuHLlCu6//34Apj3Z4uPjMWPGDPj6+sLLywuPPPIIdDodevfuDQAYOHAgOnXqhPHjx2PhwoXIzs7Gs88+i6lTp3Kki6gWcZRkL4QGKSmRSE7WWbTRqNrIwZp58VXLnDHz6cY+fZLRpUvajYKu6u2Gxo0bx6lHogakQkHYG2+8gSeffBLvv/8+WrVqVdl9suLh4YENGzZg7ty5uHLlCgIDAzFo0CA8++yzSlBz5513YtWqVVi4cCEWLlwIDw8P6HQ6bNu2De7u7gBMyftbtmzB9OnTsXjxYrRo0QLLli1DbGys8llxcXG4cOEC5syZg+zsbNx6663Ytm2bKtH+rbfegkajwciRI1FQUIDY2Fi8++67ynknJyds3rwZDz/8MHQ6HRo3bowJEybg+eefr/JnRURlZ6+qvYnRRgBmTR7lkveBzMlpiujoJCQlRatyxuRga8CAAfDx8VFdw8XFBf7+/gzAiBoYSVguASqDCxcuYPTo0fjxxx/h4eGhlICQ5eTkVFoHG7K8vDxotVoYDAZ4eXnVdHeI6iR5FaQsNzcXFy5cwM6dOwHYzwkLDMxCVlaLUq8vSUYkJCxCenobVbJ+dHQSgoKyrEa7pkyZwpwvonqurL+/KzQSNmbMGPz99994+eWX0axZszqx8pCIGh57qyBNqx9Nx8LDD9zYcNsXLi6FOHy4M/bu1TkIwOR/t5bkhAGw2sg7KSkaCQmLrFZZMueLiGQVCsL27t2L5ORkdO/evbL7Q0RUYZajXhcvXlS+Nx/xskyo12rzlenE5GQ5Ad8e+R+dptGu8PADyMgIsbuRt1abz823icimCgVhHTp0wD///FPZfSEiqjD7o16mRUSWI1WbNg1BQEC2qi5Y+arhm0a7unRJKzUZn2UniMiWCgVhr7zyCh5//HG89NJL6Nq1q1VOGPOXiKi62av9JUlG6HTJNkeq5OKp8shYQEA2TCUoLAMxufAqrK4hV7q3VcDVXsFXIiKggkHYoEGDAAB33XWX6rgQApIkobi4+OZ7RkRUAZa1v4TQ3JhitBVcqeuClWwtVJKgr9Mlo1OnP5Cb643160fZHe0yzy2zTMZnHhgR2VKhIExeVUREVNvYmlIUQoPOnX/HH3/Y3+tW/R5TABYfvwwtWpwFALRocRaFhfZHuyy3G5IxD4yI7KlQEHb77beXqd1///tfPP/88zb/YiIiqgr28rN0umT88UdnOE66LyGEBkVF6hEsR6NdzPsiovIqawZqhXz++efIy8uryo8gIlKRN9iWJNN2ZfKIVYsWZzFsWMlxwKhqY5quLGE+1Wh5/dDQUyw9QUQ3rUIjYWVVgTqwREQ3zd6IlXw8M7MFAAkuLoXIyWmK4ODTOH++ud2pxri4OGjlwmI2cMqRiCqiSoMwIqLKZlkLTGZeEwwoqf1lybyyvXkCfnR0EhISFtmcauSWQkRUFRiEEVGdYVkLrLwsV06ar45MTIzBtWtuuOuuH5T2I0aMQFBQEAMwIqoSVZoTRkRUmSxHwAwGT2RkhMBg8CzT+x0XY5Wwe3d/7NmjU474+fkxACOiKsORMCKqkxxtQ2SPrZWTapJSBZ+FVomoqlXpSNi4ceNYPZ+IKp2tgqwbNw5BWlonZVTM1iiZ5crJks24S8hV8ImIqlqFR8Jyc3Pxyy+/4Pz58zAa1Uu777vvPgDAe++9d3O9I6IGzd6G3LanFTVYt+7fkCQjunU7hEOHulmNkhkMnvDxuYT4+GUoKnLFkSPt8csvvWG+JZG90hRERJWtQkHYpk2bMHbsWFy+fBleXl6QJPO/wCQlCCMissfeKkfZ1atX8fnnn9s852haUQgNfvutOyy3JPrnn0ZISopWBWb33LMdWm2+1XFORRJRdZBEBYp5tWvXDvfccw9efvlleHh4VEW/CEBeXh60Wi0MBgOndalesVzlaDB4IienKXx99XYDIMs2e/bokJgYA1sba9um3jtSkoxISFgErTb/xrWtS1NMmzaNiflEVG5l/f1doZGwv//+G48++igDMCKqEPMRsLIk2NtqExR0FmUNwGyNmsm5X1ptPu6/P8ZqezUWYCWiqlahxPzY2Fj8+uuvld0XImogcnNzAdhOsN+0aYgqmd5eGxeXArMEe/vkQqyWbc1zv+R9H82/GIARUVUr80jYxo0ble8HDx6MmTNn4vDhw+jatStcXFxUbYcNG1Z5PSSieuf69esAbCfYm49QOWpTVOSKoUM3mwVoRphGxsxHx4yIj1+GFi3Owt39mt1tiYiIakKZg7Dhw4dbHXv++eetjkmShOLi4pvqFBE1DL6+eshbB8ksVyfaSsKX24SGnlLtEWm+JZH5xt2A/f0kAW6+TUQ1o8xBmGUZCiKiyqEOwiyXCmm1+YiOTrK7gtF8j0hHgdaIESOs8r4A5n4RUc2pUE7Yp59+ioKCAqvjhYWF+PTTT2+6U0TUMOTkNIX1X0PqYqn79/dQAjDAiL59d8PH55LdrYq02nyEhp6ymmoMCgqyyvti7hcR1aQKrY68//77MWjQIAQEBKiO5+fn4/7772edMCIqE0dTjQBw5kwgNm4cgpJATYPdu/tj9+7brVZS2hvpAjjaRUS1U4WCMCGEqkCr7MyZM9BqtTfdKSKq20orxHrt2jUAJdsI2UqYl8tSWI+UqYuwhoWdgFabr6xwJCKqK8oVhPXo0QOSJEGSJNx1111wdi55e3FxMTIyMjBo0KBK7yQR1R2WhVhLI+dxZWa2ACAhODjTqiyFPZYrKYmI6pJyBWHyCsmDBw8iNjYWTZo0Uc65uroiJCQEI0eOrNQOElHt42ikS97fUVaWaviWqxo7djxsJwBzvJKSiKguKVcQNnfuXABASEgI4uLi0KhRoyrpFBHVHuYBV1aWBr//fg2HDq1XAipHQZZlpXudLhmRkSmqdraKsR4+3AWWAVcJ0/ZDrPVFRHVdhXLCJkyYAMC0GvL8+fNW5Statmx58z0johpnPrWoDqgSMHToZgCwu+WQreBq794+2LtXh2HDStrZKsZqYisAkzBq1Fo0bnzVqgQFEVFdU6Eg7Pjx45g0aRL27t2rOi4n7LNYK1H9II+A2ds6yFTTS31MTpS3H1xpsHHjEAQEZKOoyE3Zfqi0/C/ANP0YHHzGZvDFgqtEVNdUKAibOHEinJ2dsXnzZgQGBtpcKUlE9Ye9rYMsmSfK2yo/UUKDZcseACBBkozo1u0QDh3qVkogJhAdnaQEYOYlKViCgojqogoFYQcPHkRqaio6dOhQ2f0holrIXj0v85Ew+ZicKG9ZfsJaSamJQ4e6IT5+GQ4c6IHU1J6wnoo0IiYmCX36JCtHgoKCGHgRUZ1WoYr5nTp1sloBRUT1lxxQSZKc/yluBFYSTInyUCXKGwyeyMgIQVjYCcTHL4Mpyd4+eUPuLl0Ow1Yu2KhR61QB2Lhx4xiAEVGdV6GRsFdffRWzZs3Cyy+/jK5du8LFxUV13svLq1I6R0S1R3j4AQQEZGPZssko+febBEBg1Kg1Sq6W5YrIoUM3Y9iwTWYjYsYb7zMPtgSysoLQpUuazRG34OAzAExTkBwBI6L6okIjYdHR0fj5559x5513IiAgAD4+PvDx8YG3tzd8fHwqu49EVA30ej3Onj2r+rIc8c7N9YGtvR4BKCNgthL4w8JOICFhEaKi9tx4jyl4KyEhKSkaAFQjbpZlKPz8/BiAEVG9UaGRsJ07d1Z2P4ioGpw5Axw/DrRtC7RoUXK8vFXuLV296o6MjBBcueJhM4E/J8cXvr45SE7WQT2KZt1OrqAvv8d8JSRXQBJRfVKhIOz222/H7t278b///Q/p6elYt24dbrnlFnz22WcIDQ2t7D4SURk4CrAKCwuxapU7Zs3SwmiUoNEILFxowH/+8w9cXV2tqt/bK8AaHJwJ6yKqAlu3DoYpuDJanZeT9e2XrCi5jouLqR/Dh/dE27ZtVWe5ApKI6psKBWHr16/H+PHjMXbsWBw4cAAFBQUAAIPBgJdffhlbt26t1E4SkWMffQRMmQIYjYBGA7zyCtCzJ+DndwkbNiyFweCJRYsSIIQpODIaJcyc6YW///4YWm0+oqKilGvZyumSC6tqtfmq/C7rFZKmQEyShNWG3ABKqQcmoajINNLl4+PDzbiJqN6rUBD24osv4v3338d9992HL7/8Ujnep08fvPjii5XWOSIq3ZkzJQEYYPpz1izTaJRG440hQ3rAx+eS3WlCrTZfKbzsKKdLDqTMpwuvXGmMdev+bdEjDUaOXGNV1d66ZIX9fSCdnSv0VxMRUZ1Sob/pjh07hv79+1sd12q1yM3Nvdk+EZED5tOO7u56/PwzYDRaTtOVjHht2jQE8fHLbIxCCaSldVYFSvaKssrBmvk0ZWjoKRgMnjavazB4Izj4DDIzg5GZaZrG1GrzVQFcVlYQkpKibY6YeXt7V+5DIyKqhSoUhDVv3hwnTpxASEiI6vhPP/2E1q1bV0a/iMgG9bSjwJAhexAWdgKSlGB3mk+uwRUdnYTExBiUjD5JSE29DampEcpejrar3Bvh4lJod5rS1nUTE6ORmBiNkmlKgWHDNiE8/AC02nxotfkIDT2FLl3SmIBPRA1WhYKwBx54AI899hg+/vhjSJKErKwsJCcn44knnsBzzz1X2X0kItiadjSNciUkLCrjNJ9lbS6Zespx6NDN2LhxCMzzvEy1wUreL09TBgRkQ5IsE/UB6zIWkuozzLccssQEfCJqKCoUhM2ePRtGoxF33XUXrl69iv79+8PNzQ1PPPEEHnnkkcruI1GDp9fbnnY0L+vwzz+NzEafTIGR+TRffn4TmFYvOt7z0TSyhhsJ9zLb7/noo8k2Az9bzD/Dz8+PifdE1OBVKAiTJAnPPPMMZs6ciRMnTuDy5cvo1KkTmjRpUtn9I2rw5Bpepvwr9bSjPMplMHjeKHZqXoPLiPj4ZWjR4qwylWgeoJkzT4ovvZSETJi1k4uvytsYWY+6mX8GERFVMAiTubq6olOnTpXVFyKyQa7hZbm60HyUKyMjxEbgZMoFs1zxWBIwCQAl1wGAjIwQuLgU2CklYbwxQibXA7OecoyN3YZOnQ4jPb2NakrTMvGeiIhuMggjouplr5q8rYR6x0VSJYwatVYpI5Ge3uZGHTFTUNat2yEcOtTN7H1GDBu2WflsF5dCs6nIks/r1OmwahVkZqapaqy8r6SMifdERAzCiOoceXWh5TF7o2SAdZFUeVNse/s9HjrUDfHxy5Cb6w1AHUTJfzr6vAEDBljsI9sRgKn+V0BAABPviYjAIIyo3rA3SlZagGavNlhRkSu6dDlS7s8DgM6dOzPQIiIqBYMwolpMr9fj4sWLZW5va5QMsA6YAFP+l6+v3uFUJgBVOQl5GtFyr0lzLDFBRFQ2DMKIahl5w+3c3Fx8+OG3NyrUewKA1aba9jbatkUO0CyLrkZHJ0GnS8bevTqYJ+pb5nAxsCIiqlySEOpqQFR75OXlQavVwmAwwMvLq6a7Q9VALkcBqDfStrea0d5G25bkYM3FpcAqod68pphOl4zIyBSbAd20adMYiBERlUFZf39zJIyoFpGn+WyXlSipVr9x4xCzchHWG20PGDAAO3fuBACrkS9bKyXlayQn6xAZmeKwb0REVDnKUpGRiKpZ6QVTNXY32jZna+WjaeTLNiE0yMxsgYyMEBgMnhXtPhERlQFHwohqIdsbaZszqkbCAHUyvTwKZq9GWEmxVet9JtetGwXzaU97U5xERHRzOBJGVAvJZSUkybRbt+nPku+HDdtsdd5WRXo5mLMlImIf1KNi4sZ+keopTo6IERFVDY6EEdVStspKWNbksleny3zVpGnlYx+Lq2uQmhoB9b/DBCz/XWa+6TYREVUuBmFEVUguNwEAWVkaZGQ4IzT0OoKCTKNTpZV+sKz7ZatSvuUxWyUoLPd6tD3Vab0nJDfdJiKqOgzCiKqIvXITlrlWcumH8hZmlUVFRWHv3r0AbCfiJyVFIyYmCUlJ0arATH4tszzOTbeJiKoWgzCiKmKv3IRlOYnCwkJVwFZezZs3V763twVRUFAWEhIWqaYu3d2v2QwMu3RJsznFyU23iYgqF4Mwogo4cwY4fhxo2xZo0cJxW3uBkXmuVVlqcNmrjp+fX/J9VlYgbK14lAMq8/c52mvy/vtjlK2KAFbMJyKqCgzCiMpIzu9atcods2ZpYTRK0GgEFi404D//+cduoFLa3oy2piAtAy5H05mJiYnKe5KSomEegAEC4eGpdu/J3l6TQUFBDLqIiKoYgzCiMkhPT8fnn38Og8ETixYlQAhToGM0Spg50wt///0xtNp8m1v7yOUmLIMoOfjZsGGDqr2txHrz/C3L6UyZvZpgqam3Yf/+CFXgNmDAALRt29bmvXLUi4ioejAIIyqFXq9XArDt2wc6nFq0N61ob+rPkq38scTEaJRWOsJg8ERaWidYTkWatzcP3Hx8fBAYGFi+B0FERJWKQRiRA3q9HllZWRabaauVtYyDrak/y2lH26NZ1ns+mn+mo76Zk7ck0mqPlNpXIiKqegzCiOyQVyyaRqcS7AY5Ol2yElyZ53eVVm7CVp5XWNgJmwGXvdIR1ht9ExFRXVFn/uYeNmwYWrZsiUaNGiEwMBDjx49HVlaWqs2aNWtw6623wsPDA61atcJrr71mdZ0ffvgB4eHhcHNzQ5s2bbB8+XKrNu+88w5CQkLQqFEjREZG4pdfflGdv3btGqZOnYqmTZuiSZMmGDlyJM6dO6dqc/r0aQwePBgeHh4ICAjAzJkzcf369Zt/EFRtLly4AKC0zbSNiIxMUV5t2LABH3zwAT744AOrXC9z9spWALC5HVGfPslISFiECROWIyFhkZLb5bhvlht1GxEcfMbxTRMRUbWpMyNhAwYMwNNPP43AwED8/fffeOKJJzBq1CilSOW3336LsWPH4u2338bAgQNx5MgRPPDAA3B3d8e0adMAABkZGRg8eDAeeughrFy5Ejt27MDkyZMRGBiI2NhYAMDq1asxY8YMvP/++4iMjMSiRYsQGxuLY8eOISAgAAAwffp0bNmyBWvXroVWq8W0adMwYsQI7NmzBwBQXFyMwYMHo3nz5ti7dy/Onj2L++67Dy4uLnj55Zdr4OlReen1eqxevRqAaXWjZSV5E9MejmUtZjpixAgApkDNXtmKlJRIDByYZLd0BGAKvPLzm6CoyA0uLgV2qt8b0b37Ifz2WzfIlfDL01ciIqp6khDC8p/LdcLGjRsxfPhwFBQUwMXFBf/5z39QVFSEtWvXKm3efvttLFy4EKdPn4YkSXjyySexZcsWpKWlKW3uvfde5ObmYtu2bQCAyMhI3HbbbUrhTKPRiODgYDzyyCOYPXs2DAYD/P39sWrVKowaNQoAcPToUXTs2BHJycno3bs3vv32WwwZMgRZWVlo1qwZAOD999/Hk08+iQsXLpS56GVeXh60Wi0MBgO8vLwq5blR2Zw9exYffPABANO04caNQ1GS8G5EVFQyIiNTbAY19up5TZkyBQaDAatXrzZbZWkdPE2fvghabT4GDBgAANi5c6fSj5LRM1MCviQZ0a3bIRw61E2ZqtTpSvpm6ov1YoC4uDh06NChkp4WERGZK+vv7zozEmYuJycHK1euRFRUFFxcXAAABQUF8PDwULVzd3fHmTNncOrUKYSEhCA5ORnR0dGqNrGxsUhISABgKpiZmpqKp556Sjmv0WgQHR2N5ORkAEBqaiqKiopU1+nQoQNatmypBGHJycno2rWrEoDJn/Pwww/jjz/+QI8ePWzeV0FBAQoKCpTXeXl5FXg6VJnkaUN18VPYDcAc1fPKyMhQanpptfl2N9aWVz3KwZd5P0qCNlN/hNDg0KFuiI9fhqIiV6tgy14dMH9//wo8DSIiqkx1Kgh78sknsXTpUly9ehW9e/fG5s2blXOxsbGYPn06Jk6ciAEDBuDEiRN44403AJhGNUJCQpCdna0KjACgWbNmyMvLwz///INLly6huLjYZpujR48CALKzs+Hq6gpvb2+rNtnZ2UobW9eQz9mzYMECzJ8/vxxPhKpaWardy0rbnkgOwGSRkSnYu1cHextmm4+oOcr9EkKDoiJXhIaesjo3cOBA+Pj4QKvVKsdYB4yIqHao0SBs9uzZePXVVx22OXLkiDJtMnPmTMTHx+PUqVOYP38+7rvvPmzevBmSJOGBBx5Aeno6hgwZgqKiInh5eeGxxx7DvHnzoNHUjfUHTz31FGbMmKG8zsvLQ3BwcA32iEqrdm+uPAGbLCoqWQnEzFc92irYajsvDQBK+jNixAhluyEGW0REtVuNBmGPP/44Jk6c6LBN69atle/9/Pzg5+eHdu3aoWPHjggODsbPP/8MnU4HSZLw6quv4uWXX0Z2djb8/f2xY8cO1TWaN29utYrx3Llz8PLygru7O5ycnODk5GSzjbxJcvPmzVFYWIjc3FzVaJhlG8sVlfI1zTdbtuTm5gY3NzeHz4OqV2nV7gGgZ8+e+PXXX8sUsMmjW1lZgaqSEzrdHlUel+WIWlJSNCIiUpGaeptVH6OiSkpk+Pn5sQgrEVEdUaNBmL+/f4VzU4xG0xJ+8xwqAHBycsItt9wCAPjiiy+g0+mUz9DpdNi6dauqfWJiInQ6HQDTyEFERAR27NiB4cOHK5+zY8cOZYVlREQEXFxcsGPHDowcORIAcOzYMZw+fVq5jk6nw0svvYTz588rKyoTExPh5eWFTp06Veh+qeaUVu3ex8cHQEnAtnHjEMgrEs0DNluJ9YApyEpO1imlLlJSIm2OqIWGZmD//girIM+8RAYREdUddSInLCUlBfv27UPfvn3h4+OD9PR0PPfccwgLC1MCn4sXL2LdunW44447cO3aNXzyySdYu3Ytdu3apVznoYcewtKlSzFr1ixMmjQJ33//PdasWYMtW7YobWbMmIEJEyagZ8+e6NWrFxYtWoQrV67g/vvvBwBotVrEx8djxowZ8PX1hZeXFx555BHodDr07t0bgCkPp1OnThg/fjwWLlyI7OxsPPvss5g6dSpHuqqRvOG2PeWZrrOX4D569GirY5IECGH6U2YvsV4mT1vm5ze5MT1peU1Tja/SRuXKuvKWiIhqXp0Iwjw8PLBhwwbMnTsXV65cQWBgIAYNGoRnn31WFdSsWLECTzzxBIQQ0Ol0+OGHH9CrVy/lfGhoKLZs2YLp06dj8eLFaNGiBZYtW6bUCANMS/cvXLiAOXPmIDs7G7feeiu2bdumSrR/6623oNFoMHLkSBQUFCA2Nhbvvvuuct7JyQmbN2/Gww8/DJ1Oh8aNG2PChAl4/vnnq/hJkUyudi+zVzbC1obbQNmDmYCAACXQc5SY77ioqinIysoKQlKS9T6RQElVfkejcqNHj2YOGBFRHVJn64Q1BKwTVnGWdb7slY2YMmWKVQ6VPIJmMBhw6dIlFBUVqc47OzvD09MTzs7OShD2wQcfICMjBCtWTLDqy4QJy+Hrm2OjLpg8JSnQqdNhHDnS0U6gVlI7zBF7ASUREVWvel0njKisSisbYclyBK0s5ClJe4n5Li6FyMlpqtr/0TwnDJAcBmCWle5Hjx5tVSKFKyGJiOoeBmFUr5W3bISjHDJ7pBvJX7ZWUnbrdggffTRZVWpCkozYvn2QVZ/UgRkACEyevAwtWpxVjrDSPRFR/cEgjOqcsiTcy8pT56ui5P1D5T5Nm3YMK1emwMWlUAnAAFOglZgYjXvu2Wpjv0fLAMx0zNPzsuoIK90TEdUfDMKoTinrdGFcXByAstX5csQ8oR+AzeT+ixcvqgqkdunijdDQU8jICLExxajB1q1DABjNAjFbAZiprTxiN2DAAHTu3JlTjkRE9QiDMKpTyjpdeP78eeX70up82aOu62WEKVCSrJL7N2zYoHqfHADaGoUroYEQRvTv/wN+/PEOm59vPmLn4+PDAIyIqJ6pG/v5EJWT+ebXgGlELDT0VLlGwNR1vTQwL666adMQGAyeNt+bk5OjfObQoZthCuBs0SAg4DwkydZ59YidvFE9ERHVHwzCqE4zGDyRkRFiNyAqjb16YKXV9ZKT+20x36g7ICAbtqca1QVY5UBMkoyIitqD6dMXKSNtAFQbcBMRUf3A6UiqM/R6PS5evKi8dlT/qzQjRoxAUFCQ3Sk+x1OJJaUnMjJCrHLEzPtn2sLIVhBWMtJV0elSIiKq2xiEUZ1gqwJ+eep/WfLz87MKwPR6PXJzcwFYJ/SbEunhsPSEVmsAAAQHZwIANm2S95BUkyQj4uPVpSfsbYsk43ZERET1D4MwqhPME+2Bstf/srddkSVbqy4tR6hMn+trp/REDEpGvIyIiEi1OYomj9iZB2COxMXFwd/fn0n5RET1EIMwqvX0ej3WrFmjOlaW+l/lma60t+rScoRKq823U3rCfMpRg9TUnjauZj0CNmDAAPj7+1tVwAdYBZ+IqL5jEEa1nq0AqbT6Xzc7XelIafliJpZ5YKbthyxHwFj7i4io4WIQRnWSweAJH59LiI9fhqIiV6uE9vJuV2Tr+vamMa3zxUo3atQ6dOlyRHld2sIAIiKq/xiEUZ1ja5oxNPSUqk1p05VyAr6tKT9H05gxMTFITExEWNgJCFG2/sqlKMzZWhhAREQNC+uEUZ1ib5rRsk6YVpuP2bMzVPW3zKcr16xZgw8++ABLly6FXq8v8/U9PU1/5uQ0RVn+97G3TRJXOxIREUfCqE4pzzTjmDFXce3aolLrb5nnnJX1+qa9JI2wH4gZMWrUOgQHn1G9j9OQREQk40gY1SlZWYEwbXhdwnJVpCw/P7/c2xXJ05j2rn/58mUAppG2YcMstyQSSvthwzajS5cjVp/LaUgiIpJxJIzqDIPBE0lJ0VCvPBTo23f3jelBqIIe8+2DHLl48aKyN2Npqy63b9+uvE+uI5aZ2QIA4O2da3ORABERkS0MwqjWk/OnbO/nKGH37n7Yvfv2UmuB2VvxuGHDBlU7dXAlKRXw7V1Hqz2CsmIuGBERyRiEUa3XtGlTTJs2DSdPXsdnnwkYjeqRMHlW3VEtsNIKt1oGVunpbWy2r8h+laNHj4a3tzeLrxIRkQqDMKoTmjZtiqZNgQ8+AB58UKC4WLJZMNXe1kWOCrdaBlbR0UlISoq2ah8QkO3wOj179oSnpyecnZ3h4+PDwIuIiBxiEEZVRq/X290OCKjYtjzx8UBsrIQdO07hp5++U+3hCNhO0ne04hGAVWCVmBgNyzUrQmiQmdnSYdAXHh6OwMDAct0PERE1XAzCqErY2hDblmnTppU7EGvRAhg40BUnT561qFxvGsWynIp0VLjVdp6ZBqZpTvW059Wr7qXuV0lERFRWDMKoSjgaAatIO3vCww/gn38aKdOHSUnRcHe/puRpxcXFQavVwtc3G3PmNLe54tH2PpAS1IGYhJ9+6qeaqrRXiJWIiKgsGIRRnSaXrbCXp6XVahEYGIj4+LPIySkp3AoAGRkh8PXVY+jQzdi4cSisN91WvxZCg6CgLCQklF4AloiIqDQMwqhOclS2wjxPy7wkhCkos52Ibz39CKtj8tSjfB0iIqKbwSCM6iRHZSucnAQeeeRuhIQ4W+Wb2VopaSoAa715RFjYCfz1V1iZpx5ZA4yIiMqDQRjVWeqyFUBxMeDkBPzvfxIiIprZfI+9kTPrfSCNGDZs0433WE89jhgxAn5+fsprlqIgIqLyYhBGdZ6pbAVw4gTQpo1p9aRleYyLFy8CsL9S0lHCva3RLz8/P5ajICKim8IgjGqUHByZk6f1ylNjrEUL0xdgXR6jpBq+JwBAp0tGcrLOqup9ly5pTLgnIqJqwyCMqkRZ86Ms920sj3HjxiEsLMzquHnwZp6Eb0q0l7c5MiIqag8iI1NUI15lDb6Y/0VERDeLQRhVCTlxXg6IsrI0yMhwhlZ7Hnv3rqmUz/j8889tFns1GAw3/lQn4ZtWOsoJ/BokJ+sQGZlS6ucw/4uIiKoCgzC6aaVtT7RmjSdmzGgCoxHQaHwxZEiPUje9LivLz9Xr9Vi9ejUA20n45mztM2lLUFAQgy4iIqp0DMLoppS2PZHB4IlFixIghOm10SipiqlWtvPnzyvf//VXCGzX/zIx33LIcrRLxlEvIiKqKgzC6KaUtu1QacVUy6IksV7v8D16vR5r1pimOvfs0WH37v6w3P9RzgmzXAHJ1Y5ERFTdGIRRlXK0eXZZ2Kpu36dPslU7vV6PrKwsAKagLTExGra2IRo1ai0aN77KFZBERFTjGIRRldJq8zF06GZVIGU+AuVolMtWdfvExBgAUAVillOimZnBsFUBX5KMCA4+w+CLiIhqBQZhVOXCww8gLOyEVQ0uy1EuuV6XLDMz2EZivYTExGh06ZKmXMdWSQprAtHRSQzAiIio1rC/dIyoEmm1+QgNPaUaAbMc5dq0aQgMBlNB1f37e2D9+pF2rmbKKbNkXZJCZkRMTKLNaUwZ634REVF140gY1Qh7Cft//NEJLVuethNMmZjnlLm6uiojYfZKUowatQ5duhxRHTNfDckVkEREVBMYhFGNsJWwDwhs3z7IxvESkmTE3LlZmDJljBI8nT171u415TwwS6z9RURENY3TkXRTKjKNJyfjR0cnQZKMN46W1PMq2WKohCQZMWrUGiQkLMKIEZcAACdPXse6dXqkpeUCME15ml/TchGALC4ujgEYERHVOI6E0U2xtz1RaOh1BAWZgiGDwaBUsbdVckKSjNi+fZDFlSXIgZkcTMlTihs2bLC4jg+GDu0BAEhKir4RxJmubasyv7+/f1U8CiIionJhEEY3TR5V+ugjYMoU3NieCPjgAyA+vmS0zFYyflJSNOLjl9mZgpQAGBEfvwwtWpzFgAEDsHPnTpvX2bhxCCQJZtcwXdt8FeWIESM4DUlERLUGpyOpUpw5UxKAAaY/H3zQdFweLYuKmmAzGb+oyBVDh26G5RSkiem8zGDwxB9/dLYRsGnsVuaXMQAjIqLahCNhVG62Nuz++WdXGI3qAKe4GDhxAmjRwhSI9e5tGiGTAzUAcHIS8PXNubHa0dY+jyUrId94IxebNiWY5YxJqnbqkTD1KkrmgRERUW3DIIzKxd6G3QaDJyQpQRUEOTkBbdqUtGnRwjRF+eCDpgDNyQl49VUDLl/OR0ZGCGwNzEZEpEKrzbdRA6wkZwwwYtiwzQBgtzK/VqutlPsnIiKqLAzCqFzsbdhtuT2Rk5PA//4noUULdbv4eCA21jRC1qYN4OT0Dz74wF7JCiA1NQK33HIWPj6X7OSMAdKNATF7lfmJiIhqI+aEUaUJDz+AhIRFmDBhOVJSziM+3na7Fi2AO+6AKkCTg7iSkhUyUyV9F5cCG+dMzKvtW1bmJyIiqq0YhFGlkoMguTxFaczrjIWHH8DIkeut2pgn7zsKxGxtZURERFRbcTqSaoyc4B8XF4fz589j586dCA7OtFn13tc3B6GhpxAWdgKZmS2wfv0ou0n4REREdQGDMLopcvV7X199uaYALRP8TdcJga+vXpVbZp1gnw+t9ggKC+23sYUbdBMRUW3DIIwqzLL6/dChm5UK9RcvXrT7PvNNt+1dJyFhkcMEe0dJ+Oabc8ufx/IURERU2zAIowqxVbV+06YhCAs7Aa02Hxs2bHD4/ri4OIfXSUhYhNDQUw6vYRoVsw7Q/Pz8EBgYWJHbIiIiqjZMzKdykaf1cnKallqhHjAFWRkZITAYPFXHi4qKynUdIiKi+oYjYVQu8hZEJ09ex2efCRiNJVXr5eR4OU8sKytQ2VDbcrry8uXLAGzXBzNPsjefWszNzcWaNWuq61aJiIiqFIMwKremTZuiaVN19Xs5yEpPb2M2vViytZDldOX27dsBmKYUo6OTkJgYDcA6yd58arGsyfVMwiciorqAQRhVmFz9PiVFjz17VgAAFi1KsNhaqIQ8zWiex7V/fw8kJZUEYNHRScpomSV5FM5e1X6ASfhERFR3MAijm9KiBeDkVIi0NNP+j9ZbC5kTcHEpCaBsJeUnJkZDqzUgODgTWm0+Ll68qAqsGGAREVF9wSCMKo29/R9LSCgqKpkqtJWUD2iwbt2/lWlJwLTKctq0aQzAiIioXuHqSCqzM2eAnTtNf9pivf+jUJ03T7g3GDxx5YoHgNL3gwTsbxxORERUV3EkjMrko4+AKVMAoxHQaExJ+bY26DYvopqVFaRaHRkdnYScnKZIS+uiHDcFakbY+veArRwyIiKi+oJBGDmk1+tx8uR1TJkSoJSjMBqBBx8UuPXW8wgJsf4RkouohoaeQpcuaVYBmfmqSUCCJAncffcmfPvtYO4HSUREDQaDMLJL3t8xIyMERuME1bniYglvv/0tfH1zEB4eB4PB0+aIlXzs00/vc7hq0t/f8Z6RRERE9Q2DMLJLzsOyV1A1KytICa40mumYM+dvDBjwF3bu3Km6ju0EfHOmVZOO9oMkIiKqb+pcYn5BQQFuvfVWSJKEgwcPqs4dOnQI/fr1Q6NGjRAcHIyFCxdavX/t2rXo0KEDGjVqhK5du2Lr1q2q80IIzJkzB4GBgXB3d0d0dDSOHz+uapOTk4OxY8fCy8sL3t7eiI+PVyrAl6cvdYVlwr2c31UyvQgYjRLmzw/C11//avV+OYizr2TVpDyNyQCMiIjquzoXhM2aNQtBQUFWx/Py8jBw4EC0atUKqampeO211zBv3jx88MEHSpu9e/dizJgxiI+Px4EDBzB8+HAMHz4caWlpSpuFCxdiyZIleP/995GSkoLGjRsjNjYW165dU9qMHTsWf/zxBxITE7F582b8+OOPmDJlSrn6UteEhx9AQsIiTJiwHAkJixAUdNbhno8jRozAiBEjAJRv1SQREVFDUaemI7/99lts374d69evx7fffqs6t3LlShQWFuLjjz+Gq6srOnfujIMHD+LNN99UAqTFixdj0KBBmDlzJgDghRdeQGJiIpYuXYr3338fQggsWrQIzz77LP71r38BAD799FM0a9YMX3/9Ne69914cOXIE27Ztw759+9CzZ08AwNtvv4177rkHr7/+OoKCgsrUl7pIHp3KyWkKF5cCh3s+yvs9yhytmixL7he3IiIiovqmzgRh586dwwMPPICvv/4aHh4eVueTk5PRv39/1S/r2NhYvPrqq7h06RJ8fHyQnJyMGTNmqN4XGxuLr7/+GgCQkZGB7OxsREdHK+e1Wi0iIyORnJyMe++9F8nJyfD29lYCMACIjo6GRqNBSkoK/u///q9MfbGloKAABQUFyuu8vLzyPaQqtn9/D1XifMeOR3DkSMcyB1O2Vk1a5n4NGDAAbdu2Vb2PWxEREVF9VCeCMCEEJk6ciIceegg9e/bEyZMnrdpkZ2cjNDRUdaxZs2bKOR8fH2RnZyvHzNtkZ2cr7czfZ69NQECA6ryzszN8fX1VbUrriy0LFizA/PnzbT+EGpCVpUFGRgh8ffUAYLXF0OHDndCv349o3Tqj3In0ckBmKSAgQNmwm4iIqD6r0Zyw2bNnQ5Ikh19Hjx7F22+/jfz8fDz11FM12d0q99RTT8FgMChfmZmZNdaXjz4CevUKwIoVE7BoUQJSUiJtrHCUsHt3P2UKMiMjRKlwf/HiRVy9erXcn+vv73+zXSciIqoTanQk7PHHH8fEiRMdtmndujW+//57JCcnw83NTXWuZ8+eGDt2LFasWIHmzZvj3LlzqvPy6+bNmyt/2mpjfl4+Zj4ac+7cOdx6661Km/Pnz6uucf36deTk5JT6OeafYYubm5vVPdaEM2fk6vimel5CaJCcrIPtyvYapKREIjlZp5qWlPd8HDdunDJ9nJubi+vXr6ve7eLiAq1WC4DTjkRE1LDUaBDm7+9fppGPJUuW4MUXX1ReZ2VlITY2FqtXr0ZkZCQAQKfT4ZlnnkFRURFcXFwAAImJiWjfvr0y/afT6bBjxw4kJCQo10pMTIROpwMAhIaGonnz5tixY4cSdOXl5SElJQUPP/ywco3c3FykpqYiIiICAPD999/DaDSWqy+12fHjpqr45oTQICJiH1JTe0JdbNWoBGByu02bhiAs7AS02nx4eHgoAS2nGYmIiMyIOigjI0MAEAcOHFCO5ebmimbNmonx48eLtLQ08eWXXwoPDw/xv//9T2mzZ88e4ezsLF5//XVx5MgRMXfuXOHi4iJ+//13pc0rr7wivL29xTfffCMOHTok/vWvf4nQ0FDxzz//KG0GDRokevToIVJSUsRPP/0k2rZtK8aMGVOuvpSFwWAQAITBYKjAU6q4zEwhNBohgJIvSSoW06e/IWJivhNAsXIsKuonVTv5a8KET8S8efNEVlZWtfadiIioppX193edSMwvC61Wi+3bt2Pq1KmIiIiAn58f5syZoyoJERUVhVWrVuHZZ5/F008/jbZt2+Lrr79Gly5dlDazZs3ClStXMGXKFOTm5qJv377Ytm0bGjVqpLRZuXIlpk2bhrvuugsajQYjR47EkiVLytWX2qxFC9MG3Q8+CBQXQ7XysU+fZNXKRgCqkTCAdb+IiIjKQhJCiNKbUU3Iy8uDVquFwWCAl5dXtXymXq9XtivKytLgt9+uIC3ta4crHy1LVwwduhnh4QcAAFOmTOE0JBERNShl/f1db0bC6ObJG3ZbupE3bxf3fCQiIio/BmGkkEfAKsJe3a/c3FyOhBEREdlQ5/aOpLrFsiQFERERmTAIo0phMHiqirXK5BIdREREpMbpSCqVweCJnJym8PXVK1OO5sfS09vYTczXlpZQRkRE1EAxCCOHbK18BMz3kZSrutou1kpERES2MQgjuwwGT6tNu02vgZKZbOsZbSE0yMnxZRBGRETkAHPCGrAzZ4CdO01/2pKT09Rq027Ta8c/NizWSkREVDoGYQ3URx8BrVoBd95p+vOjj0wbaJvz9dVDktSbSJpeW2wsaXFerq5PRERE9jEIa4DOnAGmTCnZpNtoNG1R9M8/TXH33Xcr7bTafERHJymBmBxgxcQkAVBvtCBJRowatQYJCYuUpHzAOrAjIiIiE+aENUDHj5cEYLLiYiA11QAfn2vKsf37eyApKVpJwI+OTlIFWPI5OTjr0uUIAGDEiBHw8/ODq6srmjZtWh23REREVOcwCGuA2rYFJAkw3zVUkgR27fpIVYLCPCkf0CApKRpduqTZ3MjbfPoxKCiIwRcREVEpGIQRAMByH3d7Sfnmqx6HD+8JHx8f5byzszMCAgIYgBEREZUBg7AGRq/X4+efASEsAyUNMjNbIDNTAgB4e1+CKe9LUlpYrnrs3LkzAy4iIqIKYhDWgOj1eixduhQGgyckKUE10iVJRqxb92+UBF3WKyDNB8sGDhzIAIyIiOgmcHVkA3L+/HkAplWPQ4duVq16NAVYkllrjcVr07GcHF8AQJMmTaq6u0RERPUaR8IakOvXryvfh4cfQFjYCeTk+OLKlcY3RsEcM5+O5MbcREREN4dBWAOm1eZDq82/MT1ptErEV1MXYfX396+eThIREdVTDMIaCL1ej0uXLgEwlZ/IyWkKX1+9EogNHbrZoiRFCUkyIj5+GVq0OIsRI0awBAUREVElYBDWAMgJ+YCpAKscbMlFVsPDDyjTk5mZLZCREYr9+yNUbVq0OAuANcCIiIgqC4OwBqCwsBCAdQFWITTYtGkIAgKyUVTkBl9fPbp0OYIuXY6gf//dqkKso0ePZg0wIiKiSsQgrAGxV4B12bLJANQjY/I0JQCMGzcOYWFhNdBjIiKi+oslKhoQX1+9UpaihID8YyCPjBkMnsrZuLg4BmBERERVgEFYA2KrPphlLTB5a6KS92irs4tEREQNBqcjGxjz+mAuLoX46KPJFlOUAllZQQgNPQUAcHV1rZmOEhER1XMMwhogOd9r//4esNi3G4CEpKRoPPpoAHS6YCbiExERVREGYQ2UvFLS1oy0EBo0aXIrGH8RERFVHeaENQC2phRtrZSUOTkBbdpUda+IiIgaNo6ENQBNmzbFtGnTlHphAJCVpcFnnwkYjerEfI0G+N//gBYtqruXREREDQuDsAbCMrcrMBD44APgwQeB4mLT6Nf06cBjjzEAIyIiqg4Mwhqw+HggNhY4ccI0/cjgi4iIqPowCGvgWrRg8EVERFQTmJhPREREVAMYhBERERHVAAZhRERERDWAQRgRERFRDWAQRkRERFQDGIQRERER1QAGYUREREQ1gEEYERERUQ1gEEZERERUAxiEEREREdUABmFERERENYB7R9ZiQggAQF5eXg33hIiIiMpK/r0t/x63h0FYLZafnw8ACA4OruGeEBERUXnl5+dDq9XaPS+J0sI0qjFGoxFZWVnw9PSEJEkVvk5eXh6Cg4ORmZkJLy+vSuxh3cFnwGcA8BkAfAYAnwHAZwBU7TMQQiA/Px9BQUHQaOxnfnEkrBbTaDRo0aJFpV3Py8urwf7PJuMz4DMA+AwAPgOAzwDgMwCq7hk4GgGTMTGfiIiIqAYwCCMiIiKqAQzCGgA3NzfMnTsXbm5uNd2VGsNnwGcA8BkAfAYAnwHAZwDUjmfAxHwiIiKiGsCRMCIiIqIawCCMiIiIqAYwCCMiIiKqAQzCiIiIiGoAg7A66r333kO3bt2UInM6nQ7ffvutcv7atWuYOnUqmjZtiiZNmmDkyJE4d+6c6hqnT5/G4MGD4eHhgYCAAMycORPXr1+v7lupNK+88gokSUJCQoJyrL4/h3nz5kGSJNVXhw4dlPP1/f5lf//9N8aNG4emTZvC3d0dXbt2xa+//qqcF0Jgzpw5CAwMhLu7O6Kjo3H8+HHVNXJycjB27Fh4eXnB29sb8fHxuHz5cnXfSoWEhIRY/RxIkoSpU6cCaBg/B8XFxXjuuecQGhoKd3d3hIWF4YUXXlDt3Vfffw4A0zY5CQkJaNWqFdzd3REVFYV9+/Yp5+vbM/jxxx8xdOhQBAUFQZIkfP3116rzlXW/hw4dQr9+/dCoUSMEBwdj4cKFlXMDguqkjRs3ii1btog///xTHDt2TDz99NPCxcVFpKWlCSGEeOihh0RwcLDYsWOH+PXXX0Xv3r1FVFSU8v7r16+LLl26iOjoaHHgwAGxdetW4efnJ5566qmauqWb8ssvv4iQkBDRrVs38dhjjynH6/tzmDt3rujcubM4e/as8nXhwgXlfH2/fyGEyMnJEa1atRITJ04UKSkp4q+//hLfffedOHHihNLmlVdeEVqtVnz99dfit99+E8OGDROhoaHin3/+UdoMGjRIdO/eXfz8889i9+7dok2bNmLMmDE1cUvldv78edXPQGJiogAgdu7cKYRoGD8HL730kmjatKnYvHmzyMjIEGvXrhVNmjQRixcvVtrU958DIYQYPXq06NSpk9i1a5c4fvy4mDt3rvDy8hJnzpwRQtS/Z7B161bxzDPPiA0bNggA4quvvlKdr4z7NRgMolmzZmLs2LEiLS1NfPHFF8Ld3V3873//u+n+MwirR3x8fMSyZctEbm6ucHFxEWvXrlXOHTlyRAAQycnJQgjTD65GoxHZ2dlKm/fee094eXmJgoKCau/7zcjPzxdt27YViYmJ4vbbb1eCsIbwHObOnSu6d+9u81xDuH8hhHjyySdF37597Z43Go2iefPm4rXXXlOO5ebmCjc3N/HFF18IIYQ4fPiwACD27duntPn222+FJEni77//rrrOV5HHHntMhIWFCaPR2GB+DgYPHiwmTZqkOjZixAgxduxYIUTD+Dm4evWqcHJyEps3b1YdDw8PF88880y9fwaWQVhl3e+7774rfHx8VP8vPPnkk6J9+/Y33WdOR9YDxcXF+PLLL3HlyhXodDqkpqaiqKgI0dHRSpsOHTqgZcuWSE5OBgAkJyeja9euaNasmdImNjYWeXl5+OOPP6r9Hm7G1KlTMXjwYNX9Amgwz+H48eMICgpC69atMXbsWJw+fRpAw7n/jRs3omfPnvj3v/+NgIAA9OjRAx9++KFyPiMjA9nZ2arnoNVqERkZqXoO3t7e6Nmzp9ImOjoaGo0GKSkp1XczlaCwsBCff/45Jk2aBEmSGszPQVRUFHbs2IE///wTAPDbb7/hp59+wt133w2gYfwcXL9+HcXFxWjUqJHquLu7O3766acG8QzMVdb9Jicno3///nB1dVXaxMbG4tixY7h06dJN9ZEbeNdhv//+O3Q6Ha5du4YmTZrgq6++QqdOnXDw4EG4urrC29tb1b5Zs2bIzs4GAGRnZ6v+wpXPy+fqii+//BL79+9X5TzIsrOz6/1ziIyMxPLly9G+fXucPXsW8+fPR79+/ZCWltYg7h8A/vrrL7z33nuYMWMGnn76aezbtw+PPvooXF1dMWHCBOU+bN2n+XMICAhQnXd2doavr2+deQ6yr7/+Grm5uZg4cSKAhvH/AQDMnj0beXl56NChA5ycnFBcXIyXXnoJY8eOBYAG8XPg6ekJnU6HF154AR07dkSzZs3wxRdfIDk5GW3atGkQz8BcZd1vdnY2QkNDra4hn/Px8alwHxmE1WHt27fHwYMHYTAYsG7dOkyYMAG7du2q6W5Vm8zMTDz22GNITEy0+pdfQyH/Kx8AunXrhsjISLRq1Qpr1qyBu7t7Dfas+hiNRvTs2RMvv/wyAKBHjx5IS0vD+++/jwkTJtRw76rfRx99hLvvvhtBQUE13ZVqtWbNGqxcuRKrVq1C586dcfDgQSQkJCAoKKhB/Rx89tlnmDRpEm655RY4OTkhPDwcY8aMQWpqak13jWzgdGQd5urqijZt2iAiIgILFixA9+7dsXjxYjRv3hyFhYXIzc1VtT937hyaN28OAGjevLnV6ij5tdymtktNTcX58+cRHh4OZ2dnODs7Y9euXViyZAmcnZ3RrFmzBvEczHl7e6Ndu3Y4ceJEg/k5CAwMRKdOnVTHOnbsqEzLyvdh6z7Nn8P58+dV569fv46cnJw68xwA4NSpU0hKSsLkyZOVYw3l52DmzJmYPXs27r33XnTt2hXjx4/H9OnTsWDBAgAN5+cgLCwMu3btwuXLl5GZmYlffvkFRUVFaN26dYN5BrLKut+q/P+DQVg9YjQaUVBQgIiICLi4uGDHjh3KuWPHjuH06dPQ6XQAAJ1Oh99//131w5eYmAgvLy+rX2i11V133YXff/8dBw8eVL569uyJsWPHKt83hOdg7vLly0hPT0dgYGCD+Tno06cPjh07pjr2559/olWrVgCA0NBQNG/eXPUc8vLykJKSonoOubm5qtGC77//HkajEZGRkdVwF5Xjk08+QUBAAAYPHqwcayg/B1evXoVGo/6V5uTkBKPRCKBh/RwAQOPGjREYGIhLly7hu+++w7/+9a8G9wwq6351Oh1+/PFHFBUVKW0SExPRvn37m5qKBMASFXXV7Nmzxa5du0RGRoY4dOiQmD17tpAkSWzfvl0IYVqS3rJlS/H999+LX3/9Veh0OqHT6ZT3y0vSBw4cKA4ePCi2bdsm/P3969SSdFvMV0cKUf+fw+OPPy5++OEHkZGRIfbs2SOio6OFn5+fOH/+vBCi/t+/EKbyJM7OzuKll14Sx48fFytXrhQeHh7i888/V9q88sorwtvbW3zzzTfi0KFD4l//+pfNZeo9evQQKSkp4qeffhJt27attcvybSkuLhYtW7YUTz75pNW5hvBzMGHCBHHLLbcoJSo2bNgg/Pz8xKxZs5Q2DeHnYNu2beLbb78Vf/31l9i+fbvo3r27iIyMFIWFhUKI+vcM8vPzxYEDB8SBAwcEAPHmm2+KAwcOiFOnTgkhKud+c3NzRbNmzcT48eNFWlqa+PLLL4WHhwdLVDRkkyZNEq1atRKurq7C399f3HXXXUoAJoQQ//zzj/jvf/8rfHx8hIeHh/i///s/cfbsWdU1Tp48Ke6++27h7u4u/Pz8xOOPPy6Kioqq+1YqlWUQVt+fQ1xcnAgMDBSurq7illtuEXFxcar6WPX9/mWbNm0SXbp0EW5ubqJDhw7igw8+UJ03Go3iueeeE82aNRNubm7irrvuEseOHVO10ev1YsyYMaJJkybCy8tL3H///SI/P786b+OmfPfddwKA1X0J0TB+DvLy8sRjjz0mWrZsKRo1aiRat24tnnnmGVVZgYbwc7B69WrRunVr4erqKpo3by6mTp0qcnNzlfP17Rns3LlTALD6mjBhghCi8u73t99+E3379hVubm7illtuEa+88kql9F8SwqycMBERERFVC+aEEREREdUABmFERERENYBBGBEREVENYBBGREREVAMYhBERERHVAAZhRERERDWAQRgRERFRDWAQRkRERFQDGIQRUb1yxx13ICEhoaa7UeXmzZuHW2+9taa7QUQ3gUEYEVEtUlhYWK2fJ4TA9evXq/UziciEQRgR1RsTJ07Erl27sHjxYkiSBEmScPLkSaSlpeHuu+9GkyZN0KxZM4wfPx4XL15U3nfHHXfgkUceQUJCAnx8fNCsWTN8+OGHuHLlCu6//354enqiTZs2+Pbbb5X3/PDDD5AkCVu2bEG3bt3QqFEj9O7dG2lpaao+/fTTT+jXrx/c3d0RHByMRx99FFeuXFHOh4SE4IUXXsB9990HLy8vTJkyBQDw5JNPol27dvDw8EDr1q3x3HPPoaioCACwfPlyzJ8/H7/99ptyn8uXL8fJkychSRIOHjyoXD83NxeSJOGHH35Q9fvbb79FREQE3Nzc8NNPP8FoNGLBggUIDQ2Fu7s7unfvjnXr1lX2fyIiMsMgjIjqjcWLF0On0+GBBx7A2bNncfbsWXh6euLOO+9Ejx498Ouvv2Lbtm04d+4cRo8erXrvihUr4Ofnh19++QWPPPIIHn74Yfz73/9GVFQU9u/fj4EDB2L8+PG4evWq6n0zZ87EG2+8gX379sHf3x9Dhw5VgqX09HQMGjQII0eOxKFDh7B69Wr89NNPmDZtmuoar7/+Orp3744DBw7gueeeAwB4enpi+fLlOHz4MBYvXowPP/wQb731FgAgLi4Ojz/+ODp37qzcZ1xcXLme1ezZs/HKK6/gyJEj6NatGxYsWIBPP/0U77//Pv744w9Mnz4d48aNw65du8p1XSIqh0rZBpyIqJa4/fbbxWOPPaa8fuGFF8TAgQNVbTIzMwUAcezYMeU9ffv2Vc5fv35dNG7cWIwfP145dvbsWQFAJCcnCyGE2LlzpwAgvvzyS6WNXq8X7u7uYvXq1UIIIeLj48WUKVNUn717926h0WjEP//8I4QQolWrVmL48OGl3tdrr70mIiIilNdz584V3bt3V7XJyMgQAMSBAweUY5cuXRIAxM6dO1X9/vrrr5U2165dEx4eHmLv3r2q68XHx4sxY8aU2jciqhjnmgwAiYiq2m+//YadO3eiSZMmVufS09PRrl07AEC3bt2U405OTmjatCm6du2qHGvWrBkA4Pz586pr6HQ65XtfX1+0b98eR44cUT770KFDWLlypdJGCAGj0YiMjAx07NgRANCzZ0+rvq1evRpLlixBeno6Ll++jOvXr8PLy6vc92+P+WeeOHECV69eRUxMjKpNYWEhevToUWmfSURqDMKIqF67fPkyhg4dildffdXqXGBgoPK9i4uL6pwkSapjkiQBAIxGY7k++8EHH8Sjjz5qda5ly5bK940bN1adS05OxtixYzF//nzExsZCq9Xiyy+/xBtvvOHw8zQaU4aJEEI5Jk+NWjL/zMuXLwMAtmzZgltuuUXVzs3NzeFnElHFMQgjonrF1dUVxcXFyuvw8HCsX78eISEhcHau/L/yfv75ZyWgunTpEv78809lhCs8PByHDx9GmzZtynXNvXv3olWrVnjmmWeUY6dOnVK1sbxPAPD39wcAnD17VhnBMk/St6dTp05wc3PD6dOncfvtt5err0RUcUzMJ6J6JSQkBCkpKTh58iQuXryIqVOnIicnB2PGjMG+ffuQnp6O7777Dvfff79VEFMRzz//PHbs2IG0tDRMnDgRfn5+GD58OADTCse9e/di2rRpOHjwII4fP45vvvnGKjHfUtu2bXH69Gl8+eWXSE9Px5IlS/DVV19Z3WdGRgYOHjyIixcvoqCgAO7u7ujdu7eScL9r1y48++yzpd6Dp6cnnnjiCUyfPh0rVqxAeno69u/fj7fffhsrVqyo8LMhIscYhBFRvfLEE0/AyckJnTp1gr+/PwoLC7Fnzx4UFxdj4MCB6Nq1KxISEuDt7a1M392MV155BY899hgiIiKQnZ2NTZs2wdXVFYApz2zXrl34888/0a9fP/To0QNz5sxBUFCQw2sOGzYM06dPx7Rp03Drrbdi7969yqpJ2ciRIzFo0CAMGDAA/v7++OKLLwAAH3/8Ma5fv46IiAgkJCTgxRdfLNN9vPDCC3juueewYMECdOzYEYMGDcKWLVsQGhpagadCRGUhCfPkASIiKpMffvgBAwYMwKVLl+Dt7V3T3SGiOogjYUREREQ1gEEYERERUQ3gdCQRERFRDeBIGBEREVENYBBGREREVAMYhBERERHVAAZhRERERDWAQRgRERFRDWAQRkRERFQDGIQRERER1QAGYUREREQ1gEEYERERUQ34f3233J2oic8WAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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", + "image/png": 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fVzrilwoEQcDw4cOxbds2HDx4UBJ6AKBRo0YwMjLCgQMHxFp0dDTu3r0Ld3d3TbdLRESk85RKoGdPwMlpoyT0vP/++/D19dVY6CkKrRnjM2zYMKxduxahoaGwtLQUx+1YW1vDzMwM1tbWGDBgAMaMGYNy5crBysoKI0aMgLu7e4k80UVERKTv5s17hp9++kVS69OnT76LE6WJ1gSfZcuWAQBatmwpqQcHB4uzBM+fPx8GBgbo3r27ZAJDIiIiUq+zZ8+iVasdktrEiRPVNt1KSdGa4KPKUCRTU1MsWbIES5Ys0UBHRERE+icnJwfz588X13IEci9K5E1eW9ppTfAhIiIiecXFxeG3336T1EaMGCHOqacNGHyIiIjorXbu3CmZoDY5uSJ++WWgrLMwFweDDxERERXqxYsXmD17tqR24cJX6NevNrQs8wBg8CEiIqJCREZG5lu+aMKECVo9pxmDDxEREUkIgoCVK1fi4cOHYq1x48bo2LGjjF2ph9ZMYEiln5eXF7p16yZut2zZEqNGjXqnc6rjHEREpBqlEmjb9gmmTZsmCT2DBg3SidAD8IqPXvDy8sLq1bkzahoZGcHZ2Rl9+vTBxIkTUaZMyf0R2Lp1q8rzORw+fBitWrXKt+RIUc5BRETFo1QCEyYAlSodQfPmh8W6hYUFxowZU2rW2VIHBh890b59ewQHByM9PR27du3CsGHDYGRkBB8fH8lxGRkZMDY2Vsv3VMfjjdr0iCQRkbYKDMzCt9/OlNQ6duyIxo0by9RRydGdCEdvZGJiAkdHR1StWhVDhgxB27ZtoVQqxdtTM2fORKVKleDm5gYAuHfvHr7++mvY2NigXLly6Nq1K2JiYsTzZWdnY8yYMbCxsUH58uUxfvz4fJNMvn6bKj09HRMmTICTkxNMTExQo0YN/P7774iJiUGrVq0AALa2tlAoFOJs3K+fIzExEX369IGtrS3Mzc3RoUMHXL9+XdwfEhICGxsbhIWFoVatWihbtizat2+P2NhY8ZjDhw+jSZMmsLCwgI2NDT7++GPcuXNHTb/TRETa5fbt2/j0U2noGTt2rE6GHoDBR2+ZmZkhIyMDAHDgwAFER0dj37592LFjBzIzM+Hp6QlLS0scO3YMJ06cEANE3mt++eUXhISEYNWqVTh+/DiePHmCbdu2vfF79unTB+vWrcOiRYsQGRmJ3377DWXLloWTkxO2bNkCIHdh2djYWCxcuLDAc3h5eeHMmTNQKpUIDw+HIAj47LPPkJmZKR6TlpaGuXPn4s8//8TRo0dx9+5d/PTTTwCArKwsdOvWDS1atMClS5cQHh6OQYMGad08FERE6rB+/Xr88ccf4nZ8vBsaNvRF2bJlZeyqZPFWl54RBAEHDhxAWFgYRowYgUePHsHCwgIrV64Ub3H99ddfyMnJwcqVK8VAEBwcDBsbGxw+fBjt2rXDggUL4OPjgy+++AIAEBQUhLCwsEK/77Vr17Bx40bs27cPbdu2BQBUq1ZN3J93S8ve3l4yxudV169fh1KpxIkTJ+Dh4QEAWLNmDZycnLB9+3Z89dVXAIDMzEwEBQWhevXqAIDhw4dj2rRpAICUlBQkJyejU6dO4v5atWoV/TeSiEiLPX36FPPmzZPU+vbtCxcXF3ka0iBe8ZGJUgl4eOT+qgk7duxA2bJlYWpqig4dOuCbb77B1KlTAQD16tWTjOu5ePEibty4AUtLS5QtWxZly5ZFuXLl8OLFC9y8eRPJycmIjY1F06ZNxdeUKVPmjZdFL1y4AENDw3dayyUyMhJlypSRfN/y5cvDzc0NkZGRYs3c3FwMNQBQsWJFJCQkAMgNWF5eXvD09ETnzp2xcOFCyW0wIiJdd+bMmXyhZ9++Sbh0yUWehjSMV3xkEhAAhIfn/tqlS8l/v1atWmHZsmUwNjZGpUqVJE9zWVhYSI599uwZGjVqhDVr1uQ7j52dXbG+v5mZWbFeVxyvPwWmUCgk44+Cg4MxcuRI7NmzBxs2bMDkyZOxb98+fPTRRxrrkYhI03JycuDv/wuystLE2vHjrXDvXnNER2vu80huvOIjE29vwN0991dNsLCwQI0aNeDs7PzWR9g/+OADXL9+Hfb29qhRo4bky9raGtbW1qhYsSL+/fdf8TVZWVk4e/ZsoeesV68ecnJycOTIkQL3511xys7OLvQctWrVQlZWluT7Pn78GNHR0ahdu/Ybf6bXNWzYED4+Pjh58iTq1q2LtWvXFun1RETaJDY2FtOnT5eEnqNHR2DEiOYIDNTs55HcGHxk0qULcPJk6UzXPXv2RIUKFdC1a1ccO3YMt2/fxuHDhzFy5Ejcv38fAPDjjz8iICAA27dvR1RUFIYOHYqkpKRCz+ni4oK+ffuif//+2L59u3jOvKnQq1atCoVCgR07duDRo0d49uxZvnO899576Nq1KwYOHIjjx4/j4sWL6NWrFypXroyuXbuq9LPdvn0bPj4+CA8Px507d7B3715cv36d43yISGft2LEDy5cvF7eTkyuhQYMp+PHHcggIyK2V1s+jksDgQ/mYm5vj6NGjcHZ2xhdffIFatWphwIABePHiBaysrADkPurYu3dv9O3bF+7u7rC0tMTnn3/+xvMuW7YMX375JYYOHYqaNWti4MCBSE1NBQBUrlwZfn5+8Pb2hoODA4YPH17gOYKDg9GoUSN06tQJ7u7uEAQBu3btUnmSQ3Nzc0RFRaF79+54//33MWjQIAwbNgw//PBDEX6HiIhKvxcvXsDPz09yNf7rr7/GvHkD0bWrQjLkQp8ohNcnX9FzKSkpsLa2RnJysvghD+T+Abp9+zZcXV21enE2Kjq+90Skba5evYpNmzZJagcOTMDRoy//DlMqc0OPt7duXO0p7PP7dRzcTEREpCMEQcDy5csRFxcn1s6e/RDXr3+G2bOlx3bpohuBp6gYfIiIiHTA48eP8euvv0pqv/32A4KCHPUy4BSGwYeIiEjLHT58WPLUbGqqJX79dRRGjzZg6HkNgw8REZGWyszMxKxZsyS1K1c6YdOmRnB3B2bOLOSFeozBp4g4Flz/8D0notLo1q1b+PPPPyW1bdvGYtq0srh/X3/m5SkqBh8V5T0unZaWptFZiEl+aWm5E36p+sg8EVFJW7t2La5fvy5uX71aCxs3fg03N/0dtKwqBh8VGRoawsbGRlzzydzcnCt66zhBEJCWloaEhATY2NjA0NBQ7paISM8VtLhocHBf3LnjAlNTIDBQpsa0CINPETg6OgKAGH5IP9jY2IjvPRGRXE6fPo1du3ZJajNmTEJ2dhlUrgwsXcorPapg8CkChUKBihUrwt7eHpmZmXK3QxpgZGTEKz1EJKucnBzMnTsXz58/F2sVK7aGo2MzfPih7kxAqCkMPsVgaGjID0MiIipxDx8+xIoVKyS1hQtHwsHBFpGRDDzFweBDRERUCv399984d+6cuH3vXhX8/nt/AArY28vXl7Zj8CEiIipFnj9/jsDXRimvX/8NoqJqolw5wM2Nj6q/CwYfIiKiUuLKlSvYvHmzpObv743MTBPUrAnMns3bW++KwYeIiEhmgiAgKChI8tTw6dNNUb9+e3zwAQcwqxODDxERkYw2bvwPkZFLJLVlywYjPt4BT54AJ0/K1JiOYvAhIiKSyaFDhxAZeVTcTk62xoIFIyEIBjA15VieksDgQ0REpGEFLS6qVHbBuXMNYWoKNGzI21slhcGHiIhIQ5RKYMWKm2jc+C9Jfc6cn5CRYYGyZYGRI7mqekli8CEiItKQvXvXoHHjG+J2REQdbN78JQBg4kQGHk1g8CEiIiphKSkpmD9/PuzsXtZWrfLC3btVAeTOzcPQoxkMPkRERCXo33//xZ49e8TtnBwFZs2aiKys3MVFnZ05iFmTGHyIiIhKQHZ2NubMmYP09HSxdupUGyQmfoLx44FDhziAWQ4MPkRERGr24MEDrFy5UlJbsCB3cdGoKJmaIgAMPkRERGoVGhqKCxcuiNt37jgjONgLCoUCq1fL1xflYvAhIiJSg4IWF1279lv895+b+Jg6b2vJj8GHiIjoHR04cADHjx+X1GbN8kZWlgm2bWPgKU0YfIiIiIpJEARMmzZNUgsP/whhYZ4AcufmYegpXRh8iIiIiuHu3bsIDg6W1Fav7oP7910BAJUrc26e0ojBh4iIqIjmzPkdaWn3JbXAwJ9ha2uAceNePqpOpQ+DDxERkYpevHiB2bNnS2rh4U1x4kR7pKbK1BQVCYMPERGRCk6fPo1du3ZJaosX/4j0dBuMHClTU1RkDD5ERERv4efnJ9nOyjLEzJmT4ePDcTzahsGHiIioEBs2PEJU1FJJzcWlO377rS62b+cTW9qIwYeIiKgAU6ZsgaFhhKS2ceNEXLlihL59ZWqK3hmDDxER0SuysrIwc+ZMGBq+rF29Wgu7dn2NNWvk64vUg8GHiIjo/0VGRmLjxo2S2rJlgxEX5yBTR6RuDD5EREQAAgICkJ6eLqlNnToFHh4KmTqiksDgQ0REei0lJQXz58+X1A4e9MTYsR9BEGRqikoMgw8REemtZcv2IyHhhKR28OB4jB1rxie2dBSDDxER6Z2CFheNi3NAhw6D4esrU1OkEQw+RESkV+7cuYOQkBBJLSSkLzIzXbBsmTw9keYw+BARkd6YM2cF0tIeSmoRET+jYkUDLiqqJxh8iIhI502a9ALGxtLFRW/fdscXX7TjrS09w+BDREQ67eef/4Wx8R5JLTR0FKZOteYAZj3E4ENERDopNFTAhQvTUOaVT7qMDCOsXj0RDx4AAQFca0sfMfgQEZHOSUhIwIUL0pHKmZlfYubMOmjaNDf0cEyPfjKQu4GSsGTJEri4uMDU1BRNmzbFqVOn5G6JiIg0ZNOmTVj22uNZS5ZMwowZdQDkXuU5eZJXe/SVzl3x2bBhA8aMGYOgoCA0bdoUCxYsgKenJ6Kjo2Fvby93e0REVEK2b8/CxYszJbWIiDrYvPlLuLnJ1BSVOjp3xWfevHkYOHAg+vXrh9q1ayMoKAjm5uZYtWqV3K0REVEJUCqBdu2u5As9S5cOwaFDX8LdHQgMlKk5KnV06opPRkYGzp49Cx8fH7FmYGCAtm3bIjw8vMDXpKenSxalS0lJKfE+iYhIfU6fnoWPP86U1Bo0mILq1RXw9uYtLZLSqeDz33//ITs7Gw4ODpK6g4MDoqKiCnyNv78//Pz8NNEeERGpUY8eyXBzWyB5amvXrvZISWkKX1+ga1f5eqPSS+dudRWVj48PkpOTxa979+7J3RIREb3FlCl74ea2QFKrV28CDA2bYvbsgl9DBOjYFZ8KFSrA0NAQ8fHxknp8fDwcHR0LfI2JiQlMTEw00R4REb2jSZNyYGw8HYaGL2tpaZUwe/ZAAMAXX8jUGGkNnbriY2xsjEaNGuHAgQNiLScnBwcOHIC7u7uMnRER0buYNAlwc4uBsfF0Sf3vv73E0EOkCp264gMAY8aMQd++fdG4cWM0adIECxYsQGpqKvr16yd3a0REVEyJib+hR484SW3u3ClYs0YhU0ekrXQu+HzzzTd49OgRpkyZgri4ODRo0AB79uzJN+CZiIhKv+fPnyMwMBCv/hV+4cLHiIpqizVr+MQWFZ1CEARB7iZKk5SUFFhbWyM5ORlWVlZyt0NEpLd+/vkflCkTJqmNHj2afzdTgVT9/Na5Kz5ERKTdClpcNDPTFDNmTJCvKdIZOjW4mYiItFt8fDwuXJgmqe3b9zWaNGHoIfXgFR8iIpKdUgns2bMRDg6RkvqGDZNw9So/qkh9+KeJiIhk9fXXmahTZ5ZkAHNMTF388093TkZIasfgQ0REslAqgV9/jcDHH2+R1JcuHYr4eDuZuiJdx+BDREQap1QCp0/PwMcfZ0vqU6f64ssvZWqK9AKDDxERaVSPHklwc1soeWorK+szHDjwIUJDOTcPlSwGHyIi0pilS/fAze1fSW3ChAkwNTXF9OmFvIhIjRh8iIioxOUtLvqq2NgqCAoaIFNHpK8YfIiIqERNnnwLxsZ/Smr79/fDuHHOMnVE+ozBh4iISoRSCRw9uhSWlo8k9bCwKTh5kouLkjwYfIiISO3S0tJw/vwcWFq+rJ061Qy3brXm3DwkKwYfIiJSqxUrTuLhw32S2pgxY2D5agoikgmDDxERqUXe4qKvSk01x99/j4Ovr0xNEb2GwYeIiN7Z+vVxiI7+7bXaN4iJqYkNG2RqiqgADD5ERPRO1q9fj+joaEltzpxJcHIqgw0bOCEhlS4MPkREVCyZmZmYNWuWpHbx4v9w6tTnWLuWgYdKJwYfIiIqskuXLmHbtm2S2q+/DkOFChVw/75MTRGpgMGHiIhUplQC585Ng0IhSOrTp/vivffAR9Wp1GPwISIilSQlJeH8+YVQvDL34N9/d0J0dCNs3cpbW6QdGHyIiOitli7djUePTklq/v7eUChMOICZtAqDDxERFSonJwfTX1s23dnZGeXL98MHHwDe3gw9pF0YfIiIqEA3b97EX3/9Jam9/35/fPedEwAGHtJODD5ERJRPQMASpKf/J6lNmTIFCgUXFyXtxuBDRESirVvTcPnyHEntyJHmOHiwlUwdEamXgdwNEBFR6XD8+PF8oWfu3LEoX56hh3QHr/gQEek5QRAwbZp0cdGnT8vi5MmxePpUpqaISgiDDxGRHouNjcXy5csltc2bv0VWlhsnIySdxOBDRKSn1qxZgxs3bkhq8+ZNxp9/GvKJLdJZDD5ERHomIyMD/v7+klp0dAPExHTFn3/yMXXSbQw+RER65OLFi9i+fbuktnjxcNjZlUdkpDw9EWkSgw8RkZ7w8/PLV5s1yxfVqnFxUdIfDD5ERDouMTERixYtktSUys44d+4DuLmBV3pIrzD4EBHpsJ07d+LMmTOS2qxZ3jAwMEHNmrzSQ/qHwYeISAcVtLjonTsuCA/vC1dXIDCQg5hJPzH4EBHpmBs3bmDNmjWS2urVA/Ddd1V4W4v0XpGDj6GhIWJjY2Fvby+pP378GPb29sjOzlZbc0REVDSLFi1CYmKipDZ16hS4uyswc6ZMTRGVIkUOPoIgFFhPT0+HsbHxOzdERERFl5qairlz50pq8fEtERLSApUrA97eMjVGVMqoHHzynghQKBRYuXIlypYtK+7Lzs7G0aNHUbNmTfV3SEREb7R8+THExh6U1ObMGYv//a8s0tJkaoqolFI5+MyfPx9A7hWfoKAgGBoaivuMjY3h4uKCoKAg9XdIREQFKmhx0eRkKyxYMBrvv8+rPEQFUTn43L59GwDQqlUrbN26Fba2tiXWFBERvdnDhw+xYsUKSW3Nmh64efM9+PiA43mIClHkMT6HDh0qiT6IiEhFf/31F27evCmpNWgwGWFhhpg7l4+pE71JkYNP//7937h/1apVxW6GiEjfKZVAQEDubarXA0xBi4vev98QnTt3QZcuQNeuGmyUSEsVOfi8/phkZmYmIiIikJSUhNatW6utMSIifRQQAISH5/6at+3tDTg7X0BoaKjk2JUrh+P+/fK4coVXeYhUVeTgs23btny1nJwcDBkyBNWrV1dLU0RE+uTVqzze3i//Oy8EnT/vh/Pnpa8JC/NFnz7AoUMcxExUFAqhsIl5iig6OhotW7ZEbGysOk4nm5SUFFhbWyM5ORlWVlZyt0NEesDDIzfguLsDJ0++rG/a9ARXry6WHOvs3AX9+jXUcIdEpZ+qn98G6vqGN2/eRFZWlrpOR0SkN7y9c0PPq1du/v7773yhZ9YsH6xYwdBD9C6KfKtrzJgxkm1BEBAbG4udO3eib9++amuMiEjXvXqLK+9KT3Z2NmbMmCE57v79ali5sjfMzHhbi+hdFflWV6tWrSTbBgYGsLOzQ+vWrdG/f3+UKaPd657yVhcRacrrt7iuX7+OtWvXSo5Zvvx7WFpWRrlyBT/pRUS5VP385jw+REQalnelJ+/fkd7ewIIFC5CcnCw5bsWKKbCyUmD2bAYeInUp9uWZhIQEREdHAwDc3NzyrdZOREQFy3taCwD27n2GX375RbL/0qXW2Lq1Wb7BzkT07oo8uDklJQW9e/dGpUqV0KJFC7Ro0QKVK1dGr1698v1rhYiI8ssbzOzldSRf6Jkz5ydcudIs32BnIlKPIgefgQMH4t9//8XOnTuRlJSEpKQk7NixA2fOnMEPP/xQEj0SEemUzp0FeHr6ITb2sFh78sQWU6f64n//s0BgYO6VHt7eIlK/Ig9utrCwQFhYGD755BNJ/dixY2jfvj1SU1PV2qCmcXAzEZWkBw8eYOXKlZJaTExP/PFHDXzxBbBpk0yNEWm5EhvcXL58eVhbW+erW1tbc8V2IqI3WL16NWJiYiS1vXsn48QJQwQHy9MTkb4p8q2uyZMnY8yYMYiLixNrcXFxGDduHH7++We1NkdEpI2UytxH1ZXK3O309HT4+flJQk+FCo0RFuaLCRMM5WmSSE8V+VZXw4YNcePGDaSnp8PZ2RkAcPfuXZiYmOC9996THHvu3Dn1daohvNVFRO/q1fl5liw5D2VeAvp/K1aMwNKl5TiGh0iNSuxWV9euXaFQKN6pOSIiXdaqFXD5MuDp6YfXMg/mzvXFs2e5j7Qz+BBpXpGDz9SpU0ugDSIi3fHvv4/x00+/SmrbtnWDuXl9rFnzcpkKItK8Io/xqVatGh4/fpyvnpSUhGrVqqmlKSIibRUaGopmzaShZ9YsH/z3X31xyQk+qk4knyJf8YmJiUF2dna+enp6Ou7fv6+WpoiItM327dm4eFG6uOj16zUQGtoTmzYx6BCVFioHn1cH54WFhUkeac/OzsaBAwfg6uqq3u6IiLTAtWvXcPHiOkktM3MQbt2qiDVrGHqIShOVg0+3bt0AAAqFAn379pXsMzIygouLS76p14mIdN28efPw9OlTSW3q1Clwd1dwnS2iUkjlMT45OTnIycmBs7MzEhISxO2cnBykp6cjOjoanTp1KpEmY2JiMGDAALi6usLMzAzVq1eHr68vMjIyJMddunQJzZo1g6mpKZycnBAYGFgi/RARPXv2DH5+fpLQc/hwG6xb54uaNRUcvExUShV5jM/t27dLoo83ioqKQk5ODn777TfUqFEDERERGDhwIFJTUzF37lwAuc/vt2vXDm3btkVQUBAuX76M/v37w8bGBoMGDdJ4z0Skuw4fPowjR45IanPn/oSRIy0wc6ZMTRGRSoo8geG0adPeuH/KlCnv1JCq5syZg2XLluHWrVsAgGXLlmHSpEmIi4uDsbExAMDb2xvbt29HVFSUyuflBIZEVJjQUAEXLkj/DkxKKo/du4cjMJBjeYjkVGITGG7btk2ynZmZidu3b6NMmTKoXr26xoJPcnIyypUrJ26Hh4ejefPmYugBAE9PT8yePRuJiYmFriOWnp6O9PR0cTslJaXkmiYiraNUAuPHAxUq3Menn/4u2ffHH71w61Z1uLsz9BBpiyIHn/Pnz+erpaSkwMvLC59//rlamnqbGzduYPHixeJtLiB3vbDXnypzcHAQ9xUWfPz9/eHn51dyzRKRVlEqX04w2KVL7n97eASjatW7kuNWrvwZFhYGqFmTkxESaZMiT2BYECsrK/j5+RV5kVJvb28oFIo3fr1+m+rBgwdo3749vvrqKwwcOPCde/fx8UFycrL4de/evXc+JxFpr4CA3HW2AgJyrwh7evpJQs/Zsx9i6lRfWFgYICoKiIzk1R4ibVLkKz6FyQsORTF27Fh4eXm98ZhXZ4N++PAhWrVqBQ8PDyxfvlxynKOjI+Lj4yW1vG1HR8dCz29iYgITE5Mi9U1EukmpBJ48AWrWBL7//iwCAnZI9h89OhIffWSL//7jVR4ibVXk4LNo0SLJtiAIiI2NxZ9//okOHToU6Vx2dnaws7NT6dgHDx6gVatWaNSoEYKDg2FgIL1Y5e7ujkmTJiEzMxNGRkYAgH379sHNza3Q21xERMDL21tPngDR0cDUqX54/eKvr6+vPM0RkVoV+amu18fRGBgYwM7ODq1bt4aPjw8sLS3V2iCQG3patmyJqlWrYvXq1TA0NBT35V3NSU5OhpubG9q1a4cJEyYgIiIC/fv3x/z584v0ODuf6iLSPx4eube3PvzwP3TsuESyLzz8cwwd+j/eziIq5VT9/C5y8JFDSEgI+vXrV+C+V9u/dOkShg0bhtOnT6NChQoYMWIEJkyYUKTvxeBDpB9eHcQMADt2bEflyhclx8ycORGNGxtxBmYiLVCiwScpKQk3btwAANSoUQM2NjbFbrS0YfAh0g95V3k8PLLRrp10cdHo6Pdx7tx3KFfu5dNdRFS6lcg8PjExMRg2bBjCwsLEKy0KhQLt27fHr7/+ChcXl3dqmohIU7y9gd9/j8IHH2yQ1IOCfsDjx47YvJmBh0gXqRx87t27h48++ghGRkaYPn06atWqBQC4evUqli1bBnd3d5w+fRpVqlQpsWaJiNTl2rW5+OCDVElt6tQpMDVVMPQQ6TCVb3UNGDAAN27cQFhYGExNTSX7nj9/jvbt2+O9997DypUrS6RRTeGtLiLd9vTpU8ybN09SO3/+U9Sp44FDh3hri0hbqf1W1549e7Bhw4Z8oQcAzMzMMH36dHz77bfF65aISAMOHjyIY8eOSWqzZ49Dgwbm2L5dnp6ISLNUDj7//fffG8fwVKtWDU+ePFFHT0REaiUIQr4Flk1N7RAcPBRVq3IyQiJ9onLwqVixIq5evVroGJ6IiIg3zpBMRKRJeY+rDx9+F9evB0v2nT7dGzt2VEMRZ7sgIh2g8lpd3bp1w08//YRHjx7l25eQkIAJEyagW7du6uyNiKjYAgKA2rV/zxd6pk//GfXrVyvkVUSk61Qe3JyYmIimTZsiLi4OvXr1Qs2aNSEIAiIjI7F27Vo4Ojrin3/+Qbly5Uq65xLFwc1E2u/FixeYPXu2pGZn1xQTJrTHs2eAuzs4KSGRjlH74GZbW1v8+++/mDhxItavX4+kpCQAgI2NDXr06IFZs2ZpfeghIu13+vRp7Nq1S1ILDf0R587ZoEoV6WzNRKR/ijVzsyAI4i0vOzs7KBQKtTcmF17xIdJefn5+ku3sbANMn/4zatYEIiNlaoqINKJEZm7Oo1AoYG9vX+zmiIjU6dGjR1i6dKmktnlzd2Rl1YW7O6/wENFLxQo+RESlxaJFW5GYeFlSW7VqIszNjTB7NicjJCIpBh8i0kpZWVmYOXOmpBYfXxPLln3DwctEVCgGHyLSOpGRkdi4caOktnv3YEyc6IALF3hri4gKx+BDRFpl9uzZePHihaS2fv0UzJ6tQJcuvLVFRG+mUvBZtGiRyiccOXJksZshIipMSkoK5s+fL6mdO9cOSqU73N0ZeIhINSo9zu7q6qrayRQK3Lp1652bkhMfZycqffbv348TJ05IarNnj4ezsxnKleOK6kSk5sfZb9++rbbGiIhUVdDionFxDggKGgxTUyAwkIGHiIqm2GN8MjIycPv2bVSvXh1lynCoEBGp1507dxASEiKpHTzYFwkJLnBzY+ghouIpcmJJS0vDiBEjsHr1agDAtWvXUK1aNYwYMQKVK1eGNx+nIKJ3tGLFCjx8+FBS27DhZwQEGDDsENE7UXl19jw+Pj64ePEiDh8+DFNTU7Hetm1bbNiwQa3NEZF+efHiBfz8/CSh5/Rpd2Rk+OLqVYYeInp3Rb7is337dmzYsAEfffSRZI2uOnXq4ObNm2ptjoj0x6lTp7B7925J7ciRUTh0yBpPnsjUFBHpnCIHn0ePHhW4TldqaqpOLVZKRJpR0ABmIyMjTJw4EUol8OIFJyQkIvUp8q2uxo0bY+fOneJ2XthZuXIl3N3d1dcZEem8hISEfKHHxeVLTJw4EUDu4OWTJzmImYjUp8hXfGbNmoUOHTrg6tWryMrKwsKFC3H16lWcPHkSR44cKYkeiUgHbd68GVeuXJHUZsyYhA8/LIO+fWVqioh0XpGv+HzyySe4cOECsrKyUK9ePezduxf29vYIDw9Ho0aNSqJHItIhWVlZ8PPzk4SeuLjaaNjQFx9+WIa3tYioRKk0c7M+4czNRCXn6tWr2LRpk6QWEjIECxfa83YWEb0Ttc7cnJKSovI3ZlggooL4+/sjIyNDUluxYgqWLlUw9BCRxqgUfGxsbFR+Yis7O/udGiIi3ZKcnIwFCxZIapGR7bFhQ1MuLkpEGqdS8Dl06JD43zExMfD29oaXl5f4FFd4eDhWr14Nf3//kumSiLTSvn37cPLkSUlt/Pjx2LfPDHfv8jF1ItK8Io/xadOmDb7//nt89913kvratWuxfPlyHD58WJ39aRzH+BC9u9DQHFy4MF1Sq1ixIgYNGiRTR0Sk61T9/C7yU13h4eFo3Lhxvnrjxo1x6tSpop6OiHRMTExMvtDj5eXF0ENEpUKRg4+TkxNWrFiRr75y5Uo4OTmppSki0k7Lly8XFzDO8/PPP+Pixarw8ACUSpkaIyL6f0W+1bVr1y50794dNWrUQNOmTQHkrrFz/fp1bNmyBZ999lmJNKopvNVFVHTPnz9HYGCgpHb8uAeOHPkUJiaAtTXw4AHg7p47EzMRkbqp9XH2V3322We4fv06li5diqioKABA586dMXjwYF7xIdJDK1f+gwcPwiS1RYtG4+nT3L94nj0DsrJyQw8HMxOR3DiB4Wt4xYdINQUtLpqZaYLNm70RHZ0bdFq1AhYtAkaOBGbOlKlRItILJXbFBwCSkpLw+++/IzIyEgBQp04d9O/fH9bW1sXrloi0Snx8PIKCgiQ1V9ev0KdPbTRpAgQE5F7d6dKFgYeISpciD24+c+YMqlevjvnz5+PJkyd48uQJ5s2bh+rVq+PcuXMl0SMRlSILF27MF3rq15+EPn1qy9QREZHqinyrq1mzZqhRowZWrFiBMmVyLxhlZWXh+++/x61bt3D06NESaVRTeKuLqGCZmZmYNWuWpBYbWxdBQd0lNQ8PIDycA5mJSLNK7FbXmTNnJKEHAMqUKYPx48cXOL8PEWm/iIgIbNmyRVLbuXMIJk+2z3est/fLW11ERKVNkYOPlZUV7t69i5o1a0rq9+7dg6WlpdoaI6LSYcqUrTA0vPxabQp8fQtev69LF66/RUSlV5HH+HzzzTcYMGAANmzYgHv37uHevXtYv359gctYEJH2SktLg5+fnyT07Nz5GcLCfFVetJiIqLQp8hWfuXPnQqFQoE+fPsjKygIAGBkZYciQIQgICFB7g0SkeRcvXsT27dsltaVLfWBra4zX5ikkItIqxZ7HJy0tDTdv3gQAVK9eHebm5mptTC4c3Ez6LDRUwMmTi2BuniTWPvnkE7Rp00a+poiIVFCi8/gAgLm5OerVq1fclxNRKZOQkIALF5bh1X/DDBs2DBUqVJCvKSIiNVM5+PTv31+l41atWlXsZohIHnv37kV4eLi4LQgV4Os7VDKWR6mUTkxIRKSNVL7VZWBggKpVq6Jhw4Z400u2bdumtubkwFtdpE8yMjLg7+8vqW3e/AUsLevlm4OH8/MQUWmm9ltdQ4YMwbp163D79m3069cPvXr1Qrly5dTSLBFp3rVr17Bu3TpJrV698QgLMytwDh7Oz0NEuqBIg5vT09OxdetWrFq1CidPnkTHjh0xYMAAtGvXTmceb+UVH9J1giAgJCQEd+/eFWv37zdA585deQuLiLSWqp/fxX6q686dOwgJCcEff/yBrKwsXLlyBWXLli12w6UFgw/psk2bknD16kJJ7Z9/vseQIZUZeohIq5X4U10GBgZQKBQQBAHZ2dnFPQ0RlbC8Qclt2pxAmTL7xbqJiQn+/nscTp40RHIyBywTkX4o0szN6enpWLduHT799FO8//77uHz5Mn799VfcvXtXJ672EOkapRLo0ycLn346TRJ6qlRpD29vb0yYYAh3d47bISL9ofKtrqFDh2L9+vVwcnJC//790bNnT52c34O3ukiXNGt2F23bBktqdeqMxpdf8s82EekWtY/xMTAwgLOzMxo2bPjGgcxbt24terelCIMP6YqFCzcjKemKuF2jRg307NmT8/EQkU5S+xifPn366MyTW0S6LDU1FXPnzpXUqlfvhZ49qwPIDT3h4bm/MvgQkb5ROfiEhISUYBtEpA7nz5+HUqmU1OrXn4hu3YzEbc7HQ0T6rNhPdRFR6REamoPw8EUwM0sWa82aNUPr1q3F7VdvcXHmZSLSV0V6qouIShelEmjXLh4XLkyXhJ7hw4dLQg8gvcVFRKSvGHyItNSkScCyZXvw8cdBYs3U1B5TpkxB+fLl8x3v7Y0CH11XKnPX4XrtDhkRkU4q9szNuopPdZE2SE9PR8Brl266d++OunXrFvlcXHyUiHRBic/cTETyiI6Oxvr16yW18ePHw8zMrFjn42BnItInDD5EWkIQBAQHB+PevXtirWHDhujyjs+kd+nCx9qJSH8w+BBpgcTERCxatEhSGzhwICpVqiRTR0RE2knrBjenp6ejQYMGUCgUuHDhgmTfpUuX0KxZM5iamsLJyQmBgYHyNEmkRsePH5eEnrQ0M+zd+zNDDxFRMWjdFZ/x48ejUqVKuHjxoqSekpKCdu3aoW3btggKCsLly5fRv39/2NjYYNCgQTJ1S1R8WVlZmDlzpqR25sxnePz4Q47HISIqJq0KPrt378bevXuxZcsW7N69W7JvzZo1yMjIwKpVq2BsbIw6dergwoULmDdvHoMPaZ07d+7kmy398OExGD3akuNxiIjegdYEn/j4eAwcOBDbt2+Hubl5vv3h4eFo3rw5jI2NxZqnpydmz56NxMRE2NraFnje9PR0pKeni9spKSnqb56oCDZt2oSrV6+K2++99x569OgBX18ZmyIi0hFaEXwEQYCXlxcGDx6Mxo0bIyYmJt8xcXFxcHV1ldQcHBzEfYUFH39/f/j5+am9Z6Ki2rIlFRER0sVFe/fujWrVqsnUERGR7pF1cLO3tzcUCsUbv6KiorB48WI8ffoUPj4+au/Bx8cHycnJ4terjwoTlbS8WZODg8/lCz0TJ05k6CEiUjNZr/iMHTsWXl5ebzymWrVqOHjwIMLDw2FiYiLZ17hxY/Ts2ROrV6+Go6Mj4uPjJfvzth0dHQs9v4mJSb7zEmlKQEAO3N0X4O7dp2ItIqIFNm1qKV9TREQ6TNbgY2dnBzs7u7cet2jRIsyYMUPcfvjwITw9PbFhwwY0bdoUAODu7o5JkyYhMzMTRkZGAIB9+/bBzc2t0NtcRHKKi4uDp+dvktqxY8MxcmT+dbaIiEg9tGKMj7Ozs2S7bNmyAIDq1aujSpUqAIAePXrAz88PAwYMwIQJExAREYGFCxdi/vz5Gu+XqDBKZe7yEL167cajR6fEuqOjIwYNGgSFQiFjd0REuk/rJjAsjLW1Nfbu3Yvbt2+jUaNGGDt2LKZMmcJH2alUmTQpHZ6efpLQ8+WXX+KHH34oMPRw5XQiIvXi6uyv4ersVFIiIyOxceNGSW3ChAkwNTUt9DVcOZ2ISDWqfn7rzBUfotJKEASsXLlSEnrKl28EX19fMfQUdmXH2zs39HCmZiIi9WDwISpBiYmJmDZtGh48eCDWBg0ahOHDO0nCTkBA7pWdgADp67t0eXmlh7e8iIjeHYMPUQk5duyYZHHR9HRz/Pzzz6hYsSIAadh525WdwoIREREVjVY81UWkTQpaXPTKlY7o1asxDF75p4a398vQ06UL3rgG16vHEhFR8TH4EKlB3u2qYcNicOPGasm+MWPGwNLSssDj80LP27wtGBERkWp4q4tIDQICAGfnDZLQ4+bmBl9fX1haWuYbvJx366pnT47bISLSJAYfonegVAItWz6Dp6cfatWKEut9+vTBt99+K26/PkbH2xsoWxZ49ozjdoiINInBh+gd/PXXWbRq9YukNmnSJLi6ukpqrw9e7tIFWLOGj6oTEWkaJzB8DScwpLfJHZ+Tg44d5yErK1Wst2zZEi1atJCxMyIi/aXq5zcHNxMV0ZIlsfD0XI6srJe1ESNGoFy5cvI1RUREKmHwISqCnTt3wsPjjLhdsWJFDBw4kIuLEhFpCQYfordQKoG5c1+gTZvZkrqr61cICqoNR0c+ak5EpC04uJnoLVatiswXeiZMmICgoNqcTZmISMvwig9RIQRBwIoVK9CwYaxYq1DhQ6xZ8xkaNuRsykRE2ojBh6gAT548weLFiyW1QYMGoXv3iuJVnpMneYuLiEjbMPgQvebIkSM4fPiwuF22bFmMHj0aBgYGvMpDRKTlGHyI/l9mZiZmzZolqXXq1AmNGjUSt7lmFhGRduPgZiIAt2/fzhd6xo4dKwk9r6+3RURE2odXfEivKZVAWNh62NtHi7WaNWvim2++yXfsq+tt8aoPEZF2YvAhvfX06VOcPz8P9vYva3379oWLi0uBx3N8DxGR9mPwIb105swZ7Ny5U1KbNGkSypQp/H8Jju8hItJ+DD6kV0JDc3Dq1C8wNk4Ta61atULz5s1l7IqIiDSFwYf0glKZu7ioh8dyGBu/rI8cORK2trbyNUZERBrFp7pIJ73+BJZSuQMeHsvF/ebmlTFlyhSGHiIiPcMrPqST8p7Amjv3Bc6fnw0np5f7vvnmG9SsWVO+5oiISDYMPqSTvL2B4OAraNBg82t1b5iYmMjUFRERyY3Bh3SOIAiIjV2OBg3ixFqTJk3QoUMHGbsiIqLSgMGHdIZSCSxa9BjNmv0qqf/www9wdHSUqSsiIipNOLiZdMYffxyWhJ7kZCuEhf3M0ENERCJe8SGtl7e4aL16L2vOzp2xYsUHnGWZiIgkeMWHtEZBi4TeunUr3+KideuORb9+H+DkSc60TEREUrziQ1rj1UVCAWDv3rWws7su7q9duza++uormbojIiJtwCs+VGq9foXH2xtwdwfGjn2K8+f9JKHHy8uLoYeIiN5KIQiCIHcTpUlKSgqsra2RnJwMKysrudvRax4euVd43N2Bkydza6dPn8auXbskx71tcVEiItJ9qn5+89OCSi1v79zbWt7eQE5ODubMmYMXL16I+9u0aYNPPvlExg6JiEjb8IrPa3jFp/R5+PAhVqxYIalxcVEiInoVr/iQVlMqc6/29OihxOPH58W6k5MT+vXrB4VCIWN3RESkrRh8qFSaO/c5PD0D8fjxy9q3334LNzc3+ZoiIiKtx+BDpU5ERATatNkiqXFxUSIiUgcGH5JF3q0sb++XkwwKgoCgoCAkJCSIxzVt2hTt27eXqUsiItI1DD4ki1cnI+zSBfjvv/+wZMkSyTGDBw+Gg4ODTB0SEZEuYvAhWbz6qPrBgwdx7NgxcZ+1tTVGjhwJAwPOr0lEROrF4EMal3eba/z4TJw/L11nq0uXLmjYsKFMnRERka5j8CGNUiqBnj0Be/ubuHjxL8m+n376CRYWFjJ1RkRE+oDBhzQqIADo2nUN3nvvhlirW7cuunfvLmNXRESkLziIgtTi9QVFC6qnpKTA09NPEnr69evH0ENERBrD4EMqKyzcANKntAqqr137L+bPny/WDQwMMHnyZDg7O5dw10RERC/xVhep7PVH0PMolcCTJ0DNmrlPab1aT0zMxsSJc2BsnC7W27Zti48//liDnRMREeXiFR9Smbc34O4uDTdAbhCKjgZsbaWBaOnSB/j22xmS0PPjjz8y9BARkWx4xYdU1qWLNNjkeXVOnjyhoaFwd78gbjs7O8PLy4uLixIRkawYfOidvRqInj9/jsDAQMn+7777Du+//74MnREREUkx+JDaXL58GVu3bpXUuLgoERGVJgw+9M4EQcDSpUvx33//iTV3d3e0a9dOxq6IiIjyY/ChYslbdmLUqP8QGSldXHTIkCGwt7eXqTMiIqLCMfhQsQQEAObmBxAZeVys2draYsSIERzATEREpRaDDxVZRkYGPD39JbVu3bqhfv36MnVERESkGgYfKpIbN25gzZo1ktq4ceNgbm4uU0dERESqY/AhlQiCgL/++gu3bt0Sa/Xq1cMXX3whY1dERERFw+BDb5WSkiJZZwsA+vfvDycnJ5k6IiIiKh4GH3qjf/75B2FhYeJ2mTJl4O3tDUNDQxm7IiIiKh4GHypQdnY2Zs+ejczMTLH26aefwsPDQ8auiIiI3g2DD+Vz//59/P7775LaqFGjYG1tLVNHRERE6sHgQxLbt2/HxYsXxW0XFxf06dOHc/MQEZFOMJC7gaLYuXMnmjZtCjMzM9ja2qJbt26S/Xfv3kXHjh1hbm4Oe3t7jBs3DllZWfI0q2XS0tLg5+cnCT09evRA3759GXqIiEhnaM0Vny1btmDgwIGYNWsWWrdujaysLERERIj7s7Oz0bFjRzg6OuLkyZOIjY1Fnz59YGRkhFmzZsnYeel36dIlbNu2TVLz8fGBsbGxTB0RERGVDIUgCILcTbxNVlYWXFxc4OfnhwEDBhR4zO7du9GpUyc8fPgQDg4OAICgoCBMmDABjx49UvlDPCUlBdbW1khOToaVlZXafobSSBAELFmyBI8fPxZrHh4e+PTTT2XsioiIqOhU/fzWiltd586dw4MHD2BgYICGDRuiYsWK6NChg+SKT3h4OOrVqyeGHgDw9PRESkoKrly5Uui509PTkZKSIvnSB48ePcK0adMkoWfo0KEMPUREpNO0IvjkzRY8depUTJ48GTt27ICtrS1atmyJJ0+eAADi4uIkoQeAuB0XF1fouf39/WFtbS1+lfZJ+ZRKwMMj99fivm7//v1YunSpuK98+fKYMmUK7Ozs1NwtERFR6SJr8PH29oZCoXjjV1RUFHJycgAAkyZNQvfu3dGoUSMEBwdDoVBg06ZN79SDj48PkpOTxa979+6p40crMQEBQHh47q9Ffd2ZMxk4f94PJ06cEOuff/45hg8fzgHMRESkF2Qd3Dx27Fh4eXm98Zhq1aohNjYWAFC7dm2xbmJigmrVquHu3bsAAEdHR5w6dUry2vj4eHFfYUxMTGBiYlKc9mXh7Z0bYry9i/a6IUOu49attZIaFxclIiJ9I2vwsbOzU+n2SqNGjWBiYoLo6Gh88sknAIDMzEzExMSgatWqAAB3d3fMnDkTCQkJsLe3BwDs27cPVlZWksCk7bp0yf1SlSAI+OOPPxATEyPWypWrjxEjuqm9NyIiotJOKx5nt7KywuDBg+Hr6wsnJydUrVoVc+bMAQB89dVXAIB27dqhdu3a6N27NwIDAxEXF4fJkydj2LBhWnVFR52Sk5OxYMECSW3lygFwcqqCESPk6YmIiEhOWhF8AGDOnDkoU6YMevfujefPn6Np06Y4ePAgbG1tAQCGhobYsWMHhgwZAnd3d1hYWKBv376YNm2azJ3LIzw8HHv37hW3jYyMUKfOBDg5GRb5NhkREZGu0Ip5fDSpNM7jo1S+HNfztttc2dnZ8Pf3R3Z2tlirXNkT33//UQl3SUREJB+dmsdH36n6JNe9e/cwY8YMSeiZN28UVq1i6CEiIgK06FaXPlPlSa6tW7fi8uXL4vbNm9Vw6lQv1Kmj4K0tIiKi/8fgowXe9CRXWlqaONA7T/XqPREWVgOBgUV7AoyIiEjXMfhosYsXL2L79u2S2v79PvD1NUavXvL0REREVJox+GghQRCwePFiJCYmijUHh0/wxx9teFuLiIjoDRh8tExCQgKWLVsmqQ0dOhR2dnYYPFimpoiIiLQEg4+WUCqBrVv3wtU1XKxVqFABQ4cO5TpbREREKmLw0QIZGRk4f94frq4va1WrfoHly+vByYkDmImIiFTF4FPKXbt2DevWrZPUxo8fjzZtzMS5fRh8iIiIVMPgU0oJgoDVq1fjzp07Yu3+/Qbo3LkrzMyKv0o7ERGRPmPwKYWSkpKwcOFCSe37779H5cqVxe2irtJOREREXLKi1Dlx4oQk9JiYmKBBg8n46qvKUCplbIyIiEgH8IqPjF5dfLRjx2zMmjULOTk54v727dujadOm8PAAx/MQERGpAYOPjPIWHw0Kuovz54Ml+0aPHi2uLsvxPEREROqhEARBkLuJ0kTVZe3VQakEdu3agooVI8Ra9erV0YvrTRARERWJqp/fvOIjk9TUVJw/PxcVK76s9erVC9WrV5evKSIiIh3H4CODCxcuIDQ0VFKbOHEijIyMZOqIiIhIPzD4aJAgCFi0aBGSkpLEWrNmzdC6dWv5miIiItIjDD4aEh8fj6CgIElt2LBhqFChgkwdERER6R/O46Mhr4Yee3t7TJkyBRUqVIBSCXh4gHP0EBERaQCDj4Y0atQIANC9e3cMGTJEXFE975H2gAA5uyMiItIPfJz9NZp8nB2QTmLIyQmJiIiKh4+zawmuuUVERKQ5vNVFREREeoPBh4iIiPQGgw8RERHpDQYfDeFj60RERPJj8NGQCRNyH1ufMEHuToiIiPQXg4+G5E0awMkDiIiI5MPgoyGBgYC7e+6vREREJA/O46MhnK+HiIhIfrziQ0RERHqDwYeIiIj0BoMPERER6Q0GHyIiItIbDD5ERESkNxh8iIiISG8w+BAREZHeYPAhIiIivcHgQ0RERHqDwYeIiIj0BoMPERER6Q0GHyIiItIbDD5ERESkN7g6+2sEQQAApKSkyNwJERERqSrvczvvc7wwDD6vefr0KQDAyclJ5k6IiIioqJ4+fQpra+tC9yuEt0UjPZOTk4OHDx/C0tISCoVC7nZKVEpKCpycnHDv3j1YWVnJ3Q69gu9N6cT3pXTi+1J6afK9EQQBT58+RaVKlWBgUPhIHl7xeY2BgQGqVKkidxsaZWVlxb8sSim+N6UT35fSie9L6aWp9+ZNV3rycHAzERER6Q0GHyIiItIbDD56zMTEBL6+vjAxMZG7FXoN35vSie9L6cT3pfQqje8NBzcTERGR3uAVHyIiItIbDD5ERESkNxh8iIiISG8w+BAREZHeYPDRYzt37kTTpk1hZmYGW1tbdOvWTbL/7t276NixI8zNzWFvb49x48YhKytLnmb1UHp6Oho0aACFQoELFy5I9l26dAnNmjWDqakpnJycEBgYKE+TeiImJgYDBgyAq6srzMzMUL16dfj6+iIjI0NyHN8XeSxZsgQuLi4wNTVF06ZNcerUKblb0iv+/v748MMPYWlpCXt7e3Tr1g3R0dGSY168eIFhw4ahfPnyKFu2LLp37474+HhZ+mXw0VNbtmxB79690a9fP1y8eBEnTpxAjx49xP3Z2dno2LEjMjIycPLkSaxevRohISGYMmWKjF3rl/Hjx6NSpUr56ikpKWjXrh2qVq2Ks2fPYs6cOZg6dSqWL18uQ5f6ISoqCjk5Ofjtt99w5coVzJ8/H0FBQZg4caJ4DN8XeWzYsAFjxoyBr68vzp07h/r168PT0xMJCQlyt6Y3jhw5gmHDhuGff/7Bvn37kJmZiXbt2iE1NVU8ZvTo0fj777+xadMmHDlyBA8fPsQXX3whT8MC6Z3MzEyhcuXKwsqVKws9ZteuXYKBgYEQFxcn1pYtWyZYWVkJ6enpmmhTr+3atUuoWbOmcOXKFQGAcP78eXHf0qVLBVtbW8n7MGHCBMHNzU2GTvVXYGCg4OrqKm7zfZFHkyZNhGHDhonb2dnZQqVKlQR/f38Zu9JvCQkJAgDhyJEjgiAIQlJSkmBkZCRs2rRJPCYyMlIAIISHh2u8P17x0UPnzp3DgwcPYGBggIYNG6JixYro0KEDIiIixGPCw8NRr149ODg4iDVPT0+kpKTgypUrcrStN+Lj4zFw4ED8+eefMDc3z7c/PDwczZs3h7GxsVjz9PREdHQ0EhMTNdmqXktOTka5cuXEbb4vmpeRkYGzZ8+ibdu2Ys3AwABt27ZFeHi4jJ3pt+TkZAAQ//84e/YsMjMzJe9TzZo14ezsLMv7xOCjh27dugUAmDp1KiZPnowdO3bA1tYWLVu2xJMnTwAAcXFxktADQNyOi4vTbMN6RBAEeHl5YfDgwWjcuHGBx/C9kd+NGzewePFi/PDDD2KN74vm/ffff8jOzi7w952/5/LIycnBqFGj8PHHH6Nu3boAcv/8Gxsbw8bGRnKsXO8Tg48O8fb2hkKheONX3lgFAJg0aRK6d++ORo0aITg4GAqFAps2bZL5p9BNqr43ixcvxtOnT+Hj4yN3y3pB1fflVQ8ePED79u3x1VdfYeDAgTJ1TlQ6DRs2DBEREVi/fr3crRSqjNwNkPqMHTsWXl5ebzymWrVqiI2NBQDUrl1brJuYmKBatWq4e/cuAMDR0THfkxF5I/AdHR3V2LV+UPW9OXjwIMLDw/Ota9O4cWP07NkTq1evhqOjY76nIfjeFI+q70uehw8folWrVvDw8Mg3aJnvi+ZVqFABhoaGBf6+8/dc84YPH44dO3bg6NGjqFKlilh3dHRERkYGkpKSJFd9ZHufND6qiGSXnJwsmJiYSAY3Z2RkCPb29sJvv/0mCMLLwc3x8fHiMb/99ptgZWUlvHjxQuM964s7d+4Ily9fFr/CwsIEAMLmzZuFe/fuCYLwchBtRkaG+DofHx8Ooi1h9+/fF9577z3h22+/FbKysvLt5/sijyZNmgjDhw8Xt7Ozs4XKlStzcLMG5eTkCMOGDRMqVaokXLt2Ld/+vMHNmzdvFmtRUVGyDW5m8NFTP/74o1C5cmUhLCxMiIqKEgYMGCDY29sLT548EQRBELKysoS6desK7dq1Ey5cuCDs2bNHsLOzE3x8fGTuXL/cvn0731NdSUlJgoODg9C7d28hIiJCWL9+vWBubi6GVlK/+/fvCzVq1BDatGkj3L9/X4iNjRW/8vB9kcf69esFExMTISQkRLh69aowaNAgwcbGRvJEKpWsIUOGCNbW1sLhw4cl/2+kpaWJxwwePFhwdnYWDh48KJw5c0Zwd3cX3N3dZemXwUdPZWRkCGPHjhXs7e0FS0tLoW3btkJERITkmJiYGKFDhw6CmZmZUKFCBWHs2LFCZmamTB3rp4KCjyAIwsWLF4VPPvlEMDExESpXriwEBATI06CeCA4OFgAU+PUqvi/yWLx4seDs7CwYGxsLTZo0Ef755x+5W9Irhf2/ERwcLB7z/PlzYejQoYKtra1gbm4ufP7555J/OGiS4v+bJiIiItJ5fKqLiIiI9AaDDxEREekNBh8iIiLSGww+REREpDcYfIiIiEhvMPgQERGR3mDwISIiIr3B4ENEVEIOHz4MhUKBpKQkuVshov/H4ENEWmvq1Klo0KCB3G0QkRZh8CEinZeZmSl3C0RUSjD4EJFscnJy4O/vD1dXV5iZmaF+/frYvHkzgJe3iQ4cOIDGjRvD3NwcHh4eiI6OBgCEhITAz88PFy9ehEKhgEKhQEhICABAoVBg2bJl6NKlCywsLDBz5sw39pH3vcLCwtCwYUOYmZmhdevWSEhIwO7du1GrVi1YWVmhR48eSEtLE1+Xnp6OkSNHwt7eHqampvjkk09w+vTpkvnNIiL1kGWFMCIiQRBmzJgh1KxZU9izZ49w8+ZNITg4WDAxMREOHz4sHDp0SAAgNG3aVDh8+LBw5coVoVmzZoKHh4cgCIKQlpYmjB07VqhTp06+1aABCPb29sKqVauEmzdvCnfu3HljH3nf66OPPhKOHz8unDt3TqhRo4bQokULoV27dsK5c+eEo0ePCuXLl5csPDpy5EihUqVKwq5du4QrV64Iffv2FWxtbYXHjx9LzpuYmFgyv4FEVGQMPkQkixcvXgjm5ubCyZMnJfUBAwYI3333nRga9u/fL+7buXOnAEB4/vy5IAiC4OvrK9SvXz/fuQEIo0aNUrmXgr6Xv7+/AEC4efOmWPvhhx8ET09PQRAE4dmzZ4KRkZGwZs0acX9GRoZQqVIlITAwUHJeBh+i0qOMXFeaiEi/3bhxA2lpafj0008l9YyMDDRs2FDc/t///if+d8WKFQEACQkJcHZ2fuP5GzduXOSeXv1eDg4OMDc3R7Vq1SS1U6dOAQBu3ryJzMxMfPzxx+J+IyMjNGnSBJGRkUX+3kSkGQw+RCSLZ8+eAQB27tyJypUrS/aZmJjg5s2bAHLDRB6FQgEgd2zQ21hYWBS5p9e/16vbeTVVvjcRlV4c3ExEsqhduzZMTExw9+5d1KhRQ/Ll5OSk0jmMjY2RnZ1dwp0WrHr16jA2NsaJEyfEWmZmJk6fPo3atWvL0hMRvR2v+BCRLCwtLfHTTz9h9OjRyMnJwSeffILk5GScOHECVlZWqFq16lvP4eLigtu3b+PChQuoUqUKLC0tYWJiooHuc68oDRkyBOPGjUO5cuXg7OyMwMBApKWlYcCAARrpgYiKjsGHiGQzffp02NnZwd/fH7du3YKNjQ0++OADTJw4UaVbSt27d8fWrVvRqlUrJCUlITg4GF5eXiXf+P8LCAhATk4OevfujadPn6Jx48YICwuDra2txnogoqJRCIIgyN0EERERkSZwjA8RERHpDQYfItJ5gwcPRtmyZQv8Gjx4sNztEZEG8VYXEem8hIQEpKSkFLjPysoK9vb2Gu6IiOTC4ENERER6g7e6iIiISG8w+BAREZHeYPAhIiIivcHgQ0RERHqDwYeIiIj0BoMPERER6Q0GHyIiItIbDD5ERESkN/4P2nXFlq5Tpg8AAAAASUVORK5CYII=", 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rprVEcrRaLaqqqmqieA5JTk6GVqvF7t27kZCQAKB6+o39+/fj+eefr9WyMCAiIiJygTFt4eV1x1ElCDWethAcHIwXXngB06ZNg8FgQM+ePaHT6bB7926EhISIgYUtiYmJyM/PR05ODpo1a4bg4GAEBATUSHltadiwIZ555hm8+OKLCA8PR/PmzbFw4UKUlZVh3LhxtVoWBkREREQuqu20hddffx2RkZHIysrCL7/8grCwMNxzzz14+eWXFTVHDR06FOvWrUPfvn1RXFyMlStXYvTo0TVaZjnz58+HwWDAk08+id9//x1dunTBf//7XzRu3LhWy6ERBEGwvxjp9XqEhoZCp9MhJCTE08UhIiIX3bhxA/n5+UhKSkKDBg08XRxyga3vUun1m73MiIiIyOcxICIiIiK7nn76abNu/qaPp59+2tPFc5lHA6KdO3fiwQcfRFxcnOQMu8YhxS0fixYtEpdJTEy0en3+/Plm6zl69Ch69eqFBg0aID4+HgsXLqyNj0dERFRnzJ07Fzk5OZKPuXPnerp4LvNoUnVpaSlSU1MxduxYDBkyxOr1wsJCs783bdqEcePGYejQoWbPz507FxMmTBD/Dg4OFv+t1+sxYMAA9O/fH8uXL8exY8cwduxYhIWFYeLEiW7+RERERHVTVFQUoqKiPF2MGuPRgCgzMxOZmZmyr8fExJj9/c0336Bv375mI14C1QGQ5bJGn376KSorK/Hhhx9Cq9Xi7rvvRk5ODt58800GRERERATAi3KILl68iI0bN0qOSzB//nw0adIEnTp1wqJFi3Dr1i3xtezsbKSnp0Or1YrPZWRkIDc3F9evX5fdXkVFBfR6vdmDiIjqHjWOmkyOccd36DXjEH300UcIDg62alqbOnUq7rnnHoSHh2PPnj2YNWsWCgsL8eabbwIAioqKkJSUZPae6Oho8TW5cQ6ysrIwZ86cGvgkRESkBlqtFn5+frhw4QIiIyOh1WrFKS7IOwiCgMrKSly+fBl+fn5mlR+O8pqA6MMPP8Tjjz9uNb7A9OnTxX936NABWq0WTz31FLKyslwadXPWrFlm69br9YiPj3d6fUREpC5+fn5ISkpCYWEhLly44OnikAuCgoLQvHlz+Pk53/DlFQHR//73P+Tm5mLt2rV2l+3evTtu3bqFs2fPonXr1oiJicHFixfNljH+LZd3BFRPkOeJYcyJiKj2aLVaNG/eHLdu3VLF3F7kOH9/f9SrV8/l2j2vCIg++OADdO7cGampqXaXzcnJgZ+fn5gJn5aWhr/+9a+4efOmOOPuli1b0Lp161ofFpyIiNTHOCO75azs5Fs8mlRdUlIijmEAQJxorqCgQFxGr9fj888/x/jx463en52djcWLF+PIkSP45Zdf8Omnn2LatGl44oknxGBnxIgR0Gq1GDduHE6cOIG1a9fi7bffNmsOIyIiIt/m0RqiAwcOoG/fvuLfxiBl1KhRWLVqFQDgs88+gyAIGD58uNX7AwIC8Nlnn2H27NmoqKhAUlISpk2bZhbshIaGYvPmzZg0aRI6d+6MiIgIvPrqq+xyT0RERCJO7qoQJ3clIiLyPpzclYiIiEghBkRERETk8xgQERERkc9jQEREREQ+jwERERER+TwGREREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBEREZHPY0BEREREPo8BEREREfk8BkRERETk8xgQERERkc9jQEREREQ+jwERERER+TwGREREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBEREZHPY0BEREREPo8BEREREfk8BkRERETk8xgQERERkc9jQEREREQ+jwERERER+TwGREREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBEREZHP82hAtHPnTjz44IOIi4uDRqPB119/bfb66NGjodFozB4DBw40W+batWt4/PHHERISgrCwMIwbNw4lJSVmyxw9ehS9evVCgwYNEB8fj4ULF9b0RyMiIiIv4tGAqLS0FKmpqVi6dKnsMgMHDkRhYaH4WLNmjdnrjz/+OE6cOIEtW7Zgw4YN2LlzJyZOnCi+rtfrMWDAACQkJODgwYNYtGgRZs+ejX//+9819rmIiIjIu9Tz5MYzMzORmZlpc5mAgADExMRIvvbzzz/j+++/x/79+9GlSxcAwJIlS/DAAw/gH//4B+Li4vDpp5+isrISH374IbRaLe6++27k5OTgzTffNAuciIiIyHepPodo+/btiIqKQuvWrfHMM8/g6tWr4mvZ2dkICwsTgyEA6N+/P/z8/LB3715xmfT0dGi1WnGZjIwM5Obm4vr167LbraiogF6vN3sQERFR3aTqgGjgwIH4+OOP8eOPP2LBggXYsWMHMjMzUVVVBQAoKipCVFSU2Xvq1auH8PBwFBUVictER0ebLWP827iMlKysLISGhoqP+Ph4d340IiIiUhGPNpnZ89hjj4n/TklJQYcOHZCcnIzt27ejX79+NbrtWbNmYfr06eLfer2eQREREVEdpeoaIkstWrRAREQEzpw5AwCIiYnBpUuXzJa5desWrl27JuYdxcTE4OLFi2bLGP+Wy00CqnOXQkJCzB5ERERUN3lVQPTrr7/i6tWriI2NBQCkpaWhuLgYBw8eFJfZunUrDAYDunfvLi6zc+dO3Lx5U1xmy5YtaN26NRo3bly7H4CIiIhUyaMBUUlJCXJycpCTkwMAyM/PR05ODgoKClBSUoIXX3wRP/30E86ePYsff/wRf/rTn9CyZUtkZGQAANq2bYuBAwdiwoQJ2LdvH3bv3o3JkyfjscceQ1xcHABgxIgR0Gq1GDduHE6cOIG1a9fi7bffNmsOIyIiIt+mEQRB8NTGt2/fjr59+1o9P2rUKCxbtgyDBw/G4cOHUVxcjLi4OAwYMACvv/66WZL0tWvXMHnyZHz77bfw8/PD0KFD8c4776BRo0biMkePHsWkSZOwf/9+REREYMqUKZgxY4ZDZdXr9QgNDYVOp2PzGRERkZdQev32aEDkTRgQEREReR+l12+vyiEiIiIiqgkMiIiIiMjnMSAiIiIin8eAiIiIiHweAyIiIiLyeQyIiIiIyOcxICIiIiKfx4CIiIiIfB4DIiIiIvJ5DIiIiIjI5zEgIiIiIp/HgIiIiIh8HgMiIiIi8nkMiIiIiMjnMSAiIiIin8eAiIiIiHweAyIiIiLyeQyIiIiIyOcxICIiIiKfx4DIBxTqyrEn7woKdeWeLgoREZEq1fN0Aahmrd1fgFnrjsEgAH4aIGtICoZ1be7pYhEREakKa4jqsEJduRgMAYBBAF5ed5w1RURERBYYENVh+VdKxWDIqEoQcPZKmWcKREREpFIMiOqwpIiG8NOYP+ev0SAxIsgzBSIiIlIpBkR1WGxoILKGpMBfUx0V+Ws0mDekPWJDA22+j0nYRETka5hUXccN69oc6a0icfZKGRIjguwGQ0zCJiIiX8QaIh8QGxqItOQmimqGmIRNRES+iAERiZiETUREvooBEYmYhE1ERL6KARGJnE3CJiIi8nZMqiYzjiZhExER1QUMiMhKbGggAyEiIvIpbDIjIiIin8eAiIiIiHweAyIiIiLyeR4NiHbu3IkHH3wQcXFx0Gg0+Prrr8XXbt68iRkzZiAlJQUNGzZEXFwcRo4ciQsXLpitIzExERqNxuwxf/58s2WOHj2KXr16oUGDBoiPj8fChQtr4+MRERGRl/BoQFRaWorU1FQsXbrU6rWysjIcOnQIr7zyCg4dOoR169YhNzcXDz30kNWyc+fORWFhofiYMmWK+Jper8eAAQOQkJCAgwcPYtGiRZg9ezb+/e9/1+hnIyIiIu/h0V5mmZmZyMzMlHwtNDQUW7ZsMXvuX//6F7p164aCggI0b35nfq3g4GDExMRIrufTTz9FZWUlPvzwQ2i1Wtx9993IycnBm2++iYkTJ7rvw6hIoa4c+VdKkRTRkL3FiIiIFPCqHCKdTgeNRoOwsDCz5+fPn48mTZqgU6dOWLRoEW7duiW+lp2djfT0dGi1WvG5jIwM5Obm4vr167LbqqiogF6vN3t4g7X7C9Bj/laMWLEXPeZvxdr9BZ4uEhERkep5TUB048YNzJgxA8OHD0dISIj4/NSpU/HZZ59h27ZteOqppzBv3jy89NJL4utFRUWIjo42W5fx76KiItntZWVlITQ0VHzEx8e7+RO5HydnJSIico5XDMx48+ZNPProoxAEAcuWLTN7bfr06eK/O3ToAK1Wi6eeegpZWVkICAhwepuzZs0yW7der1d9UGRrclY2nREREclTfUBkDIbOnTuHrVu3mtUOSenevTtu3bqFs2fPonXr1oiJicHFixfNljH+LZd3BAABAQEuBVSeYJyc1TQo4uSsRERE9qm6ycwYDJ0+fRo//PADmjRpYvc9OTk58PPzQ1RUFAAgLS0NO3fuxM2bN8VltmzZgtatW6Nx48Y1VnZP4OSsREREzvFoDVFJSQnOnDkj/p2fn4+cnByEh4cjNjYWjzzyCA4dOoQNGzagqqpKzPkJDw+HVqtFdnY29u7di759+yI4OBjZ2dmYNm0annjiCTHYGTFiBObMmYNx48ZhxowZOH78ON5++2289dZbHvnMNY2TsxIRETlOIwiCYH+xmrF9+3b07dvX6vlRo0Zh9uzZSEpKknzftm3b0KdPHxw6dAjPPvssTp06hYqKCiQlJeHJJ5/E9OnTzZq7jh49ikmTJmH//v2IiIjAlClTMGPGDIfKqtfrERoaCp1OZ7fZjoiIiNRB6fXbowGRN2FARERE5H2UXr9VnUNEREREVBsYEBEREZHPY0BEREREPo8BEREREfk8BkRERETk8xgQERERkc9jQEREREQ+jwERERER+TwGREREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBEREZHPY0BEREREPo8BEREREfk8BkRERETk8xgQERERkc9jQEREREQ+jwERERER+TwGREREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PPqKV1Qr9crXmlISIhThSEiIiLyBMUBUVhYGDQajc1lBEGARqNBVVWVywUjIiIiqi2KA6Jt27bVZDmIiIiIPEZxQNS7d++aLAcRERGRxygOiCwVFxfjgw8+wM8//wwAuPvuuzF27FiEhoa6rXBEREREtcGpXmYHDhxAcnIy3nrrLVy7dg3Xrl3Dm2++ieTkZBw6dMjdZSQiIiKqURpBEARH39SrVy+0bNkSK1asQL161ZVMt27dwvjx4/HLL79g586dbi+op+n1eoSGhkKn07EXHRERkZdQev12KiAKDAzE4cOH0aZNG7PnT548iS5duqCsrMzxEqscAyIiIiLvo/T67VSTWUhICAoKCqyeP3/+PIKDg51ZJREREZHHOBUQDRs2DOPGjcPatWtx/vx5nD9/Hp999hnGjx+P4cOHu7uMRERERDXKqYDoH//4B4YMGYKRI0ciMTERiYmJGD16NB555BEsWLBA8Xp27tyJBx98EHFxcdBoNPj666/NXhcEAa+++ipiY2MRGBiI/v374/Tp02bLXLt2DY8//jhCQkIQFhaGcePGoaSkxGyZo0ePolevXmjQoAHi4+OxcOFCZz42ERER1VFOBURarRZvv/02rl+/jpycHOTk5ODatWt46623EBAQoHg9paWlSE1NxdKlSyVfX7hwId555x0sX74ce/fuRcOGDZGRkYEbN26Iyzz++OM4ceIEtmzZgg0bNmDnzp2YOHGi+Lper8eAAQOQkJCAgwcPYtGiRZg9ezb+/e9/O/PRiYiIqC4SVAKA8NVXX4l/GwwGISYmRli0aJH4XHFxsRAQECCsWbNGEARBOHnypABA2L9/v7jMpk2bBI1GI/z222+CIAjCu+++KzRu3FioqKgQl5kxY4bQunVrh8qn0+kEAIJOp3Pm4xEREZEHKL1+O1VDdOPGDSxatAgPPPAAunTpgnvuucfs4Q75+fkoKipC//79xedCQ0PRvXt3ZGdnAwCys7MRFhaGLl26iMv0798ffn5+2Lt3r7hMeno6tFqtuExGRgZyc3Nx/fp12e1XVFRAr9ebPYiIiKhucmqk6nHjxmHz5s145JFH0K1bN7uTvjqjqKgIABAdHW32fHR0tPhaUVERoqKizF6vV68ewsPDzZZJSkqyWofxtcaNG0tuPysrC3PmzHH9gxAREZHqORUQbdiwAd999x169Ojh7vKoxqxZszB9+nTxb71ej/j4eA+WiIiIiGqKU01mTZs2rfHxhmJiYgAAFy9eNHv+4sWL4msxMTG4dOmS2eu3bt3CtWvXzJaRWofpNqQEBAQgJCTE7EFERER1k1MB0T//+U/MmDED586dc3d5RElJSYiJicGPP/4oPqfX67F3716kpaUBANLS0lBcXIyDBw+Ky2zduhUGgwHdu3cXl9m5cydu3rwpLrNlyxa0bt1atrmMiIiIfItTAVGXLl1w48YNtGjRAsHBwQgPDzd7KFVSUiJ22weqE6lzcnJQUFAAjUaD559/Hm+88QbWr1+PY8eOYeTIkYiLi8PgwYMBAG3btsXAgQMxYcIE7Nu3D7t378bkyZPx2GOPIS4uDgAwYsQIaLVajBs3DidOnMDatWvx9ttvmzWHERERkW9zKodo+PDh+O233zBv3jxER0c7nVR94MAB9O3bV/zbGKSMGjUKq1atwksvvYTS0lJMnDgRxcXF6NmzJ77//ns0aNBAfM+nn36KyZMno1+/fvDz88PQoUPxzjvviK+HhoZi8+bNmDRpEjp37oyIiAi8+uqrZmMVERERkW9zanLXoKAgZGdnIzU1tSbKpEqc3JWIiMj71Ojkrm3atEF5ebnThSMiIiJSE6cCovnz5+Mvf/kLtm/fjqtXr3IAQyIiIvJqTjWZ+flVx1GWuUOCIECj0aCqqso9pVMRNpkRERF5H6XXb6eSqrdt2+Z0wYiIiIjUxqmAqHfv3oqWe/bZZzF37lxEREQ4sxkiIiKiWuFUDpFSn3zyCXOKiIiISPVqNCByIj2JiIiIqNbVaEBERERE5A0YEBEREZHPY0BEREREPo8BEREREfm8Gg2InnjiCQ5iSKpTqCvHnrwrKNRx+hkiIqrm1DhEAFBcXIx9+/bh0qVLMBgMZq+NHDkSALBs2TLXSkfkZmv3F2DWumMwCICfBsgakoJhXZt7ulhERORhTk3d8e233+Lxxx9HSUkJQkJCzKbw0Gg0uHbtmlsLqQacusP7FerK0WP+VhhMjnh/jQa7ZvZFbGig5wpGREQ1pkZnu//LX/6CsWPHoqSkBMXFxbh+/br4qIvBENUN+VdKzYIhAKgSBJy9UuaZAhERkWo4FRD99ttvmDp1KoKCgtxdHqIakxTREH7m8xHDX6NBYgSPYyIiX+dUQJSRkYEDBw64uyxENSo2NBBZQ1Lgf7uJ11+jwbwh7dlcRkREypOq169fL/570KBBePHFF3Hy5EmkpKSgfv36Zss+9NBD7ishkRsN69oc6a0icfZKGRIjghgMERERAAeSqv38lFUmaTQaVFVVuVQoNWJStW8r1JUj/0opkiIaMogiIvIiSq/fimuILLvWE/kKdtUnIqr7nMoh+vjjj1FRUWH1fGVlJT7++GOXC0WkFoW6cjEYAgCDALy87jgHdSRyEgdGJbVyKiAaM2YMdDqd1fO///47xowZ43KhiNSCXfWJ3Gft/gL0mL8VI1bsRY/5W7F2f4Gni0QkciogEgTBbDBGo19//RWhoaEuF4pILdhVn8g9WNtKaufQ1B2dOnWCRqOBRqNBv379UK/enbdXVVUhPz8fAwcOdHshiTzF2FX/5XXHUSUI7KpP5CRbta38PZEaOBQQDR48GACQk5ODjIwMNGrUSHxNq9UiMTERQ4cOdWsBiTyNXfWJXGesbbWcOoe1raQWDgVEr732GgAgMTERw4YNQ4MGDWqkUERqExsayECIyAWsbSW1c2pyV6PKykrJ2e6bN697XZI5DhERkesKdeWsbaVa5fZxiEydPn0aY8eOxZ49e8yeNyZb18WBGYmIyHWsbSW1ciogGj16NOrVq4cNGzYgNjZWsscZERERkbdwKiDKycnBwYMH0aZNG3eXh4iIiKjWOTUOUbt27XDlyhV3l4WIiIjII5wKiBYsWICXXnoJ27dvx9WrV6HX680eRERERN7EqV5mpjPfm+YP1eWkavYyIyIi8j412sts27ZtTheMqCYU6sqRf6UUSREN2YOFiIgc5lSTWe/eveHn54cVK1Zg5syZaNmyJXr37o2CggL4+/u7u4xENnHCSCIicpVTAdGXX36JjIwMBAYG4vDhw6ioqAAA6HQ6zJs3z60FTExMFOdPM31MmjQJANCnTx+r155++mmzdRQUFGDQoEEICgpCVFQUXnzxRdy6dcut5STP4ISRRFSbCnXl2JN3heeYOsipJrM33ngDy5cvx8iRI/HZZ5+Jz/fo0QNvvPGG2woHAPv37zfLSTp+/Dj+8Ic/4M9//rP43IQJEzB37lzx76CgO3PjVFVVYdCgQYiJicGePXtQWFiIkSNHon79+m4P3qj2ccJIIqota/cXiDdgfhoga0gKhnWtezMz+Cqnaohyc3ORnp5u9XxoaCiKi4tdLZOZyMhIxMTEiI8NGzYgOTkZvXv3FpcJCgoyW8Y0aWrz5s04efIkPvnkE3Ts2BGZmZl4/fXXsXTpUlRWVrq1rFT7jBNGmuKEkUTkbqyNrvucCohiYmJw5swZq+d37dqFFi1auFwoOZWVlfjkk08wduxYs95tn376KSIiItC+fXvMmjULZWVl4mvZ2dlISUlBdHS0+FxGRgb0ej1OnDghu62KigoOJ+AFjBNG+t8+HjhhJBHVBFu10VQ3ONVkNmHCBDz33HP48MMPodFocOHCBWRnZ+OFF17AK6+84u4yir7++msUFxdj9OjR4nMjRoxAQkIC4uLicPToUcyYMQO5ublYt24dAKCoqMgsGAIg/l1UVCS7raysLMyZM8f9H4IAuLdX2LCuzZHeKpITRhJRjTHWRpsGRayNrlucCohmzpwJg8GAfv36oaysDOnp6QgICMALL7yAKVOmuLuMog8++ACZmZmIi4sTn5s4caL475SUFMTGxqJfv37Iy8tDcnKy09uaNWsWpk+fLv6t1+sRHx/v9ProjppohzedMJJd8InI3Yy10S+vO44qQWBtdB3kVECk0Wjw17/+FS+++CLOnDmDkpIStGvXDo0aNXJ3+UTnzp3DDz/8INb8yOnevTsA4MyZM0hOTkZMTAz27dtntszFixcBVDf9yQkICEBAQICLpSZLcu3w6a0i3XJiYdIjEdUU1kbXbU7lEBlptVq0a9cO3bp1q9FgCABWrlyJqKgoDBo0yOZyOTk5AIDY2FgAQFpaGo4dO4ZLly6Jy2zZsgUhISFo165djZWXpNVkOzyTHomopsWGBiItuQmDoTrIpYCothgMBqxcuRKjRo1CvXp3KrXy8vLw+uuv4+DBgzh79izWr1+PkSNHIj09HR06dAAADBgwAO3atcOTTz6JI0eO4L///S/+9re/YdKkSawB8oCa7BXGpEciInKWVwREP/zwAwoKCjB27Fiz57VaLX744QcMGDAAbdq0wV/+8hcMHToU3377rbiMv78/NmzYAH9/f6SlpeGJJ57AyJEjzcYtotpTk73C2AWfiIic5dTkrr6Ik7u6V6GuvEba4dfuL7BKemQOERGR76rRyV2JXGXaK8ydmPRIRETOYEBEdU5NBVtERFR3eUUOEREREVFNYkBEREREPo8BEREREfk8BkRERETk8xgQERERkc9jQEREREQ+jwERERER+TwGREREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBHVokJdOfbkXUGhrtzTRSEiIhOc7Z6olqzdX4BZ647BIAB+GiBrSAqGdW3u6WIRERFYQ0RUKwp15WIwBAAGAXh53XHWFBERqQQDIqJakH+lVAyGjKoEAWevlHmmQEREZIYBEVEtSIpoCD+N+XP+Gg0SI4I8UyAiIjLDgEglmGxbt8WGBiJrSAr8NdVRkb9Gg3lD2iM2NNDDJSMiIoBJ1apgmWw7I7MNUpqGIimiIS+Ydciwrs2R3ioSZ6+UITEiiN8tEZGKMCDyMKlk26zvTgFgT6S6KDY0kIEQEZEKscnMw6SSbY3YE4mIiKh2MCDyMKlkW1PsiURERFTzGBB5mGWyrSX2RCIiIqp5zCFSAdNk26O/FmPh97moEgT2RPIRhbpy5F8pZRI9EZEHMSBSCWOybVpyEzzUMY49kXwEp/MgIlIHNpmpkDEwYjBUt3E6DyIi9WBAROQhnM6DiEg9GBAReQin8yAiUg8GREQewuk8iIjUg0nVRB7E6TyIiNSBARGRh3E6DyIiz2OTGalKoa4ce/KusKcVERHVKtYQkWpwTB4iIvIU1dcQzZ49GxqNxuzRpk0b8fUbN25g0qRJaNKkCRo1aoShQ4fi4sWLZusoKCjAoEGDEBQUhKioKLz44ou4detWbX8UsoFj8hARkSd5RQ3R3XffjR9++EH8u169O8WeNm0aNm7ciM8//xyhoaGYPHkyhgwZgt27dwMAqqqqMGjQIMTExGDPnj0oLCzEyJEjUb9+fcybN6/WPwtJszUmD/NryJdxahei2uEVAVG9evUQExNj9bxOp8MHH3yA1atX4/777wcArFy5Em3btsVPP/2Ee++9F5s3b8bJkyfxww8/IDo6Gh07dsTrr7+OGTNmYPbs2dBqtbX9cUiCcUwe06CIY/LUHl501YnNyES1R/VNZgBw+vRpxMXFoUWLFnj88cdRUFAAADh48CBu3ryJ/v37i8u2adMGzZs3R3Z2NgAgOzsbKSkpiI6OFpfJyMiAXq/HiRMnZLdZUVEBvV5v9vBmak9W5pg8nrN2fwF6zN+KESv2osf8rVi7v8DTRSKwGZmotqm+hqh79+5YtWoVWrdujcLCQsyZMwe9evXC8ePHUVRUBK1Wi7CwMLP3REdHo6ioCABQVFRkFgwZXze+JicrKwtz5sxx74fxEG+5y+SYPLVP7qKb3iqS+9/D2IxMVLtUHxBlZmaK/+7QoQO6d++OhIQE/N///R8CA2vupDBr1ixMnz5d/Fuv1yM+Pr7GtldTvO2CxzF5ahcvuurFZmSi2uUVTWamwsLC0KpVK5w5cwYxMTGorKxEcXGx2TIXL14Uc45iYmKsep0Z/5bKSzIKCAhASEiI2cMbcQLRmqP2ZkglOJ+aerEZmah2eV1AVFJSgry8PMTGxqJz586oX78+fvzxR/H13NxcFBQUIC0tDQCQlpaGY8eO4dKlS+IyW7ZsQUhICNq1a1fr5a9tvODVjLqSd8OLrroN69ocu2b2xZoJ92LXzL6qbOomqis0giAI9hfznBdeeAEPPvggEhIScOHCBbz22mvIycnByZMnERkZiWeeeQbfffcdVq1ahZCQEEyZMgUAsGfPHgDV3e47duyIuLg4LFy4EEVFRXjyyScxfvx4h7rd6/V6hIaGQqfTeV1t0dr9BXh53XFUCYJ4weOJ1XmFunL0mL/Vqilj18y+XhtIFOrKmbtFRHWS0uu36nOIfv31VwwfPhxXr15FZGQkevbsiZ9++gmRkZEAgLfeegt+fn4YOnQoKioqkJGRgXfffVd8v7+/PzZs2IBnnnkGaWlpaNiwIUaNGoW5c+d66iPVOiYru1ddzLth7hYR+TrV1xCphTfXEJF71cUaIiKiukrp9dvrcoiIPI15N0REdY/qm8yI1IjNkEREdQsDIiInuTPvhlNnEBF5FgMiIg/zlpHEiYjqMuYQEXkQ56siIlIHBkREHsSRxImI1IEBEZEHSY0k7gfgamkFa4mIiGoRAyIiD7Lswq8BIACYvPqwV08JQkTkbRgQEXmYcb6qfw3vBI2mOiACmE9ERFSbGBARqUBsaCDCG2mZT0RE5CEMiMghhbpy7Mm7wlqLGiCVT+Sv0SAxIsgzBSIi8iEMiEixtfsL0GP+VoxYsZf5LTWAU4IQEXkOJ3dVyNcnd+WEprWnUFfOKUGIiNxE6fWbI1WTIrbGy+FF273cOSUIEREpwyYzUoT5LUREZKku5ZUyICJF5PJbANSZHwMRESlX1/JKmUOkkK/nEBmZ5rfs/H+XOSkpEZEP8qa8UqXXb9YQkUNiQwORltwEADgpKRGRj6qL8zAyIFIRb2qLrYs/BiIiUqYu5pUyIFIJb2uLrekfgzcFh0REvqYujpvGHCKFajKHSO1tsYW6cuRfKUVSREOz8qzdX4CX1x1HlSCIPwZ35BCt3V/A3CQiF8n9boncyRvGTeM4RF5EzWP82ApOhnVtjvRWkW79MRTqyiVzk9JbRXp8XxB5C95UUG2pS+OmsclMBdTaFisXnJg2YxmTrN31g2BuEpFrlPxuicgaAyIVUNoWW9t5NZ4ITtQaHKqR1PHA3CviTQWRc9hkphL2mp88UQVuDE4sc5tqMjgxBoeWuUl1pUrWXSyPh3E9kxARHIAFm06xmcTHeeJ3S1QXMKlaIU8OzOippOtCXTlW7srH+//LhwFwa+K0km2rPVHPU6SOBylqSsyn2lVTHR6IvBGTqusQTyRdW9ZATOzZAmN6Jirenqs9XCwT9dhj5g6p40GKWhLz1a4uHls10eGBqK5jQOQFarsKXCop84Nd+RjTM1HR+93dvMceM+akjgcpbCaxry4fW3Wp9w9RbWBStReo7QGwXEnKdHcPF/aYsWY8HiyTz00x98o+HltEtccbOnywhshL1GYVuCs1Uu5u3lPzGE2eZDweVu7Ox/s77+R4vTSwNTo0C2MziQI8tohqh7fUxDIg8iK1VQXuSk8vdzfvsceMvNjQQLz8QDuM6ZHEXBEn+PKxVRfzpkidvGmwXQZEXqqmT2jO1ki5u9s8u+Hbx1wR53j7seXsOcBb7tapdtT0tcSbamLZ7V4hT3a7t+QNJzR3d5tnN3yqKd54bDl7DlD7vIlUu2rjWqKGY07p9ZtJ1V7GWxJB3T2lh7vXR2TkbceWK+cAubv1Q+eumyW82kuA9YYEWbKttq4ltd0pyBVsMvMy3lT9SETu58o5QCpvSqMBJq8+DAHVtQQPd2qKrw7/Jltr4A011GRfbV5LvGVcLNXXEGVlZaFr164IDg5GVFQUBg8ejNzcXLNl+vTpA41GY/Z4+umnzZYpKCjAoEGDEBQUhKioKLz44ou4detWbX4Ut+BcX+QrWAshzZVzgOXdup8GgAAYr4sGAfjy0G+ytQbeUkPtzWrruK/ta4k31MSqPiDasWMHJk2ahJ9++glbtmzBzZs3MWDAAJSWlpotN2HCBBQWFoqPhQsXiq9VVVVh0KBBqKysxJ49e/DRRx9h1apVePXVV2v747jMm6ofiZy1dn8BeszfihEr9qLH/K1Yu7/A00VSDVfPAcO6NseumX2xZsK9ePuxjrCXRGo6Bhknjq1ZtXnc81pizeuSqi9fvoyoqCjs2LED6enpAKpriDp27IjFixdLvmfTpk344x//iAsXLiA6OhoAsHz5csyYMQOXL1+GVqu1u101JVUD3pkIKoddgMmUGpIwvYE7zgFK5sUz3feFunLcl7XVLIjSANgz635+Ny7y5JyVdeVaIqfOJlXrdDoAQHh4uNnzn376KSIiItC+fXvMmjULZWV37liys7ORkpIiBkMAkJGRAb1ejxMnTkhup6KiAnq93uyhJt5Q/agEawLIEmshlHHHOUCqlmDoPU0dqzWwMWI6Keep476uXEvcwauSqg0GA55//nn06NED7du3F58fMWIEEhISEBcXh6NHj2LGjBnIzc3FunXrAABFRUVmwRAA8e+ioiLJbWVlZWHOnDk19EkI8K4Bu4xYm1XzfHnARE+QSnh9IaO1ZK1B/pVSqyY2QQA7dbgBj3vP86qAaNKkSTh+/Dh27dpl9vzEiRPFf6ekpCA2Nhb9+vVDXl4ekpOTndrWrFmzMH36dPFvvV6P+Ph45wpew7z1Iq20l4NaPh9719QOTw+YqJbjrTZZDu4pN9inrYu2L+43d/L0cU9eFBBNnjwZGzZswM6dO9GsWTOby3bv3h0AcObMGSQnJyMmJgb79u0zW+bixYsAgJiYGMl1BAQEICAgwA0ldw+5k403X6SV3BGp5fN5Y22WN/NUN121HG9qJXfR3vn/LtfKAH91PeDylu7pdZXqAyJBEDBlyhR89dVX2L59O5KSkuy+JycnBwAQGxsLAEhLS8Pf//53XLp0CVFRUQCALVu2ICQkBO3atauxsruL3Em6UFeOmV8eM+syO/PLY2gTE4zU+MYeLbMS9u6I1BSEcPyn2lfbU5Ko6XhTM8uLNgCzZGCDAMxad8yt+82XAlVOxeM5qg+IJk2ahNWrV+Obb75BcHCwmPMTGhqKwMBA5OXlYfXq1XjggQfQpEkTHD16FNOmTUN6ejo6dOgAABgwYADatWuHJ598EgsXLkRRURH+9re/YdKkSaqqBZJi6yR98Nx16/Z8AH9augcLhtbcCcOdd2q27ojUFISwqaDuU9PxpnamF+09eVes9ptBAFbuzsfLD7h2w1moK8eBs9d8MlDleaX2qT4gWrZsGYDqrvWmVq5cidGjR0Or1eKHH37A4sWLUVpaivj4eAwdOhR/+9vfxGX9/f2xYcMGPPPMM0hLS0PDhg0xatQozJ07tzY/ilNsnaRtjZhQUyeMmrhTcyZfobZ5sqmAaoeajjdvIrXfAOD9nfkY0yPJ6XOQ6bnGkjOBqjcFGI6eZ73ps6mZ6gMie8MkxcfHY8eOHXbXk5CQgO+++85dxao1tk7SiRFBuD3QrJWauLOt7SYFtSUZKmkqcHV/8MTmOWo73rxFbGggxvVMwor/5Zs9b4Dzvc8szzWWHA1UvanJzdHzrDd9NktqO9+pPiDydcaTtOkBb3qSnj80xSyPyKgm7mzlaqsOnr2OP6bWzMGstiRDe00FrgSi3nxiqyvUdrypia2L19ieSXj/f/lm5yFXzkFS5xrT9ToSqHpbbpgjTbfe9tlMqfF853UDM/oqY0WZZYXZsK7NsWfW/ZiYniR+mX4aYFi3eBw4e82t8+FIzX0DAFM/O1zjQ8ybDhymljmu3DkXUE3NEaWWfeVNOFCdNXsDqMaGBmL+UPdNAyH12/IDsHREJ+ya2dehC6e3DfTpyHnF2z6bkVrnxGNApHLGA8d4zAuwPnBiQwPx8gPtsPt2YGQQgNV7CzBlTQ7uy3Lf6M/G2irLg6Y2D2Y1jWztzrmAauLEpqZ9Rd5L6cXLdI40R4MWS1K/rayhKRjUIc7h35e3TYjtyHnF2z6bkVoDOTaZqZyjPV/et2jHFwDM+tJ9XWCHdW2OhgH1MHn1YcVlchc1Vg+7q4nF3Qm9atxX5J0cOQe5s8u4u35bSob3UFMeC6D8s3tr3ptaOzAwIFI5Rw4cuXZ3V5IbpXROaOyRg1mt3aLdcRFw94lNrfuKvI8nL17uCrDkAgw15rEYKf3sxs926Nx1GAQBXRLD7b5HCWOg2FDrj9LKKrcGjGoN5BgQqZwjB45c91c/wK0nL08dzGq9q3AXdyb0qmlfqfEOnJRT68XLUZYBhppqUY3jLWk0GnROaOzw9t09/IfUkAfuDhjV2IFBI9jr104AqucyCw0NhU6nQ0hISK1vv1BXrujAWbu/ADPXHROTrzUAZj7QBk+lm8/p5o6LlNIySb3P2W2v3V9gdWJWyx2d2qhhX6n5Dpwc4+zvXa325F3BiBV7rZ5fM+FepCU3qdFtm54Dd/6/y2Y9hTWo7j2s9HdSqCs3G/4DqL752TWzr+z3ZOscLLU+peu1t25PUXr9Zg2Rl3C0+nTJj6exet95CAAWbDqFsMD64g/MXRcpZ6qzXd22Gu8qlKrtE4Wn95Wa7sDJdXVtSglP1aJangMtAw8Bjk194mjzuNQ5OL1VpHhusjXkgb1md2+/AWJAVEd9tv+8+G/TCxEAj12k3HWB9MYTs6dOFI7sK3cHbMxjUg9H8kHs1R6o7e7fWZ5oCpQ6B0oxCMrzPh0J7KS2P3PdMUCoDsT8NMCMgW1kB/y1FTDWhRsgBkQeVhMnGJvTfUDw2EXK1Qukq/vK2fe7Y7tqP1HURMCmpjwmX2F6rALVv7ljv+mwYNMpRfkgto6D2grqazPoqu1aVFu1L6b8NMrzPh0J7KS2b5o0YxCABd+fkgyGLAcFVrJub7sBYkDkQTV1grF3IfLURcqVC6Sr+8rZ97vjO1L7icIdAZvURayuJON6C9Nj1Tg0jdy1V+o7tnUcXNLfMMtNtKx1diUhWO4z1FZNam3WOEudAzUa86BEc/tzx4YGKg4OlQZ2ch1vTMm99s5jnfDH1DiHPpuSCbDVVOvIgMhDarLWwN6FyFMXKWcvkK7uK2ff767vSO01Ja4GbLYuYp7OY/JWjl4kLI9VJT1lLL9jueNg5e58rNiZb7VOqdccTQi29RnUWJPqKrlzYHqrSBw8ex0aDXBPQmMAwLyNJ7Hi9nQoSoJDJYGd5fb9bgdjpt+t1HP+Gg06JzZ26rPZ6gGntpwjBkQeUtO1BrYuRJ68SDmzbVf3lbPvd9d3pPaaElcCNiUXMW/M+aoNckGPMxcJpU0xpiy/Y6njwE8DyWDI6N87883+djQh2JTaa1LdRe4caJwPUqrLu+XvypVaFcvt7/x/l+8ESKjOIQoLqu/UjWt8eBDWPZuGskqD3QmwL+lvmPWuU0MAzIDIQ2qj1sD0zs/0b+O/HT3o3NVV39F1OJo0aLn+Y7/qrJZTsq/d+R2puabElYDNVy5i7iYX9DhbS6KkKcSU1HcsdRyM7ZloNYu9PY4kBJvyZE2qkvOSkmYfpYMYyp1/Lb9/U8bflTvGHDLd/rCuzVFcfhPzb+eZLfj+FLKGpGDXzL6Kz1dSx3NachPZCbBX7jqLFbt+kax19OS5gwGRh9RGrYEzd5ruvGt1R3kA5ftKrjvpgu9PWa3zpczWDlcvu/odqbmmxDRgC9L6obSyCoW6crvlVXtzoJqYXjTlgh65APPQuesY1EG+ZsDyWNXc7iYkoPr7eGlga3RoFoYgrZ949y713VoG7gDwwa58h2qfHEkINuXu35vSmy8l5yVbXdUdSVq3x1ZNn79GgyCtn9PN/7aCuQWbTlnlh+2a2VfReEy2gnjJWkcA7+/6xWqicuNn9OS5gwGRB9VkrYEzd5ruvmt1tTym7O0rufUvfixV8gTToWmYonKruWbH3WJDAx2++1R7c2BNcbSm0yzh2SKJFrhzZyxX0zN59WFsPXUJXx3+Tfa7kQpmnDluLQN30+/Xkq2EYGfY+r0prcU5cPYasvOuYs3tcdik9pWt4HTWl8fQMKCemCBur6u6FHfmGwLVQcS8Ie1RWlnlcI2svYBvpUTAq7SmplBXjg1HL8i+Py25idX5YVzPRPxbotbR+Bk9ee5gQORhNVVr4GhThq2AxR3NIu5Yh619Jbd+P43G5RoMNdfsuJOzQasvBY2A4zWdVgnPNu6MjQGmZbOJAODLQ7+Jf8t9N5bHqrvzEY/+VoyFm3JtJgS7o1OI5Tre25mH+bdrMaQGE4wNDawepd8kJ8XIcl+ZrktqvB0DqgNQ43biw4NsdlWXYzp7u9LgWSrpeXzPFhjTMxGxoYE4cv66VRDq6NhAs9YdQ5DWX5zzTKpJVEktn1Suk1SZpAL19y2CMD8N8NWz9yE13nbidk1jQFRHOdqUYStgcUeziFzV6dXSCkVNM86s31+jwT0JjZ3u2aaWrqC1Re4YOHj2upjwKcfXg8Y2McGyuSNyzSB+qL74Wh6Tw7o2R5DWH1PW5NgsS23mWxi/37TkJngoNU42IdgWZ7tev7cjD1mb7jR7GwRg5pfHoLn9e/fTADMy22D+d9Lj5wB39tX6nAtm67IV1xi/23XPpjmUn2Xkr9Hg6G/FePz9nxxKE0hvFYnFj6XC7/b5y7g/jAGIZTAkdT47cv469p29hsqbBqtyGwRgypoc+GmAcT2TJPfB+J4t7DbByQVDGonxiozHj/F7njGwDRZ+bx5YezoYAhgQqY67LsRKmjJMt2Ur6HFHs4hVjgOqT0amd2KudLe0VUalNRjG/WGaE6CGrqDuZOv4kquun7LmMEorb9WZfeAKuaBx8NI9EFBd4zChVxLG9EwS96/cb8u0N47ld9ElMVx2tGDTdXgi38KZ4NfZAR8LdeWYv8k6B1AAzHJeFmySD4aMdp+5jKXb8iRfkwt4qgQBv14vt9tV3ZIxb8s0t8idaQtAdUC97tk0q0DiL/+XY1abKMcgVOeHWR5nfgDG9Ey0+V5buU4aAeL4VID8eXVGZht0aBqmqlplBkQq4u4xGWwFAlLberhTU7Mf0uBOcWZ3ra42ixjXcfDsdUz97LBLOUmOfl57J3G56l81dAV1F3vHV2xoIGZktkHWd+YXIAHOd6X2NvZuSOSCRsHk///+Xz7e35Uv7l+5YN3WHfHO/3fZ7G8NgCH3NMXXhy94Xa6WrVq189fLbXa9zr9SqmhMJSW1N+9uz5NclwbAipGdUV5pMDsvGU1efRjzh5r3ujLtqi6XtF7TaQsGAGWVBrPnjpy/rigYEtchABPTk/DB/846dFzZ6tVowJ1ehrbOqws35dqdKLa2MSBSiZoalEwqEDhy/rrVSUjqoP368AW8kHGnN5Y7mkViQwMR3qjmumo7U0Zb1b/uKpunph0xfb+S4yulaajk+w0CxJ5OrlJrc6SSGxK5mk5LlvvXkRsK43dlud6+baIwMi0BZZUGh3oCOsvVruhGsrVq7+6RzMUx9qpr3LA66Vnqwmu5341ByXwbNUUGmZwhAcCEjw8ia0iKbP6WZa8rpd+nI6kGB89ddzltYd/ZazKfXpq/RoMxPZIwpkeSQze7Yq7bl8dgsHjNdHTqmj6vuhsDIpWorfFc1u4vqB6C3+J5uerig2evI7zRnROeOy5mauuqbW9QO1fLVlvTjtj6bpQeX0kRDWUv8pNXH0ZJxS2rZFZHKPkscmO6ODN6s+Xycutw5IbEtKZzymeHZdtOLPev0mBdcr4p3GlefrhTU5u9zdzB2a7oxuYd030sNQ4YIJ+YrNFUf1YB1esd2D4G3x8vqt4OqptawoLqm2173pD2GNa1OR7qGIeDZ6+juLwSr3x9wqopaEbmndwVU8bve9fMvnj7sY5W+VtSvxV732dsaKDNWnfLfTnzy2NWz9tKW3hpYGurMea63U6UVsJybjJHf8vG38HKXWfx/q5fYBDMc5qkxiCS+mxqwoBIJWojSBDvPBUmB2oAsQpZyYlY6QXLHTlJ7mSr+leubEo/q9ILrSMX6plfHkNllQH920ZbJVzKfTdKj6/Y0EDMH5oi2VtHgHUyq62AxpmgQ6qK3ZkgQGp/ADB7bsbANkhpFoqkiIYO35AYazpt/Zac/f3abI4QrHubzfryGNrEBLstKVXJ9yS3THHZTSz4/pTZPpYaB0yOZX6OQQC+O1YEoPp8NCOzDZ7qnYy1+wvEfS8IwJlLJWJtmTHBu76/n9lxLAAIC6qPXTP7YuPRQryx8WezbRu/7y6J4W45FxfqyvHVYfPmq68O/4aRaQlm35VcjaBlwGLZ2880H2dczySM7ZmE1PjGGHpPU6tmM9NxqfwAjE+vrhVyR438y4PaYkzPRKsaJmfOq56mEQSll0ffptfrERoaCp1Oh5CQkBrZxtr9BVZBgjvv/PbkXcGIFXsVL68kqdPYBuzsIJBq6aptue+NOQFSZXPks8rt8zUT7hWr322tz9Z3pkH1vFHprSLNhscHzL8bManxV514d2y8W36oY5xs8LJmbwHe2XrG5n4z3Y7UZzGeqI13jLb2RaGu3Opz2NsucKdLs/HfDbX+ePjdPWbrsXUsm164Lbc96/YFWIqt8tr7/doLqE2PRyU0GmC+wppDe9tWcszKLSOVoGvZpCLFT1M9eagAwWbvOmMyuuX3a9y26TxqUt+P6XFj6zfj6LlYap/a2kem5ZRb7l/DzSdTNa05tff5j5y/jgNnryMxIghB2voujUvlCkfOqzVJ6fWbNUQqItUube/k5UgzguRMy7f/b3natRcMAebjbEiNdWEv/8kdOUnuIDUHj62cCUdyvezVzNhbn627LAHVtQNvD+8oW7thOdDiwPYx2HS7+WH+plNizoWx10dK01DxWBrevTmWbD1j8zgwrUU5cv661YzoK/6Xj/f/ly8GblJzZQVp/QA4Nh9XlSBgyY9n8Nn+AjE3xLhP5PJE5BgEYOH3uXi2TzL+ZdELaeH3ubi3Rbhkl3rJZozM1nZ7ztjrVZV/pRTprSKxa2ZfsVnOXlwkCObjy8gFqEpq2pTUJsrNeyaV+Cv1fWgAq27zf0yNQ6Gu3GYX9ypBwP6z1rk2wJ1cH9OEbLnfhdSAgXK1MfYu3nLfp60EfHu/ccvJVM0G9pTYn5brTY1vLFljqPR86648P28bo4wBkcqYBgn2aiIcrZWxPIHL3b0Zaw+k7phNGU+Skj0gBGDl7ny8/EA75R++Flj+0OXm4JHjTNOK1IkXqL4zvFpSYXN9UnkIpgyA7OCTUsP8G5sfAPOTqkGA2LvMtInJlFTwbDwG5AbFMy4/a90xfPXsfVajHhsE4OF394gD7SkJxI1W7yuQ/CzOVHlXCQLCgrSSzxuTfzUAhneLx5R+dzl14QRsB8CmwatGA8zMbIOn0pNx5NdiRfOJGYQ748sY96fltpQO7mivSVsuGLScwsL4/HyTqSGMNRnFZXfmz5r/3SlAAJ7qnWxzZGx/jQZdExvb7CZv/O3YC+zsfXdKbtjs3dBIJWlbltPe/rYa2NNGedyRd+ru3s5Kb3zV0NmCAZFK2fuhuWNU4TOXfscr35ywWmbu4LvxxL2JZomLliyHWZc6Qb2/M19RO7WzPwRHJ1S0/KHPyGzj8DghSmp8LD+L1OzSxup6P410jxnT9VnmIZjyA2QHn5Qa5l8JY14KNObl0txuWrIcUA2AZA6E5ToHL92DmZlt8NpDbfHaNyetulrvmtkXMzPbmA2cZ5TZPgb/PV6kqPnFGbYutILJhWj1vvNYve88JpqMNaT0wpl/pRTXSislL44Hz163Gs0663aQMLZnEt7/n/mM83I1u8Cd/SlVc2hJ7gKqJNCTWiYs0HqW9GFdm+Oh1DgcOncdggCx5qPH/K1m+T1Zm04BGuCp9OQ7uTK/FksO4CcXaJj+dpQGdq5cfO3dIA3r2hwFV8uwdLt5zaNljZut/S07sKfEsepq3qkz1xV3BDLuDsKcxYBIpez90FzplWY8CVwpuSH5elhg9Z3ysK7N0SYmWBx0zshymPXY0ECM65lkdRdrAOyOcuzsD0Eu+dZWDyzLH7rl3Sxgvg+lfui2TrK28meMD6lyaHAnKLI8aUt1xTXSAMgamiKeeKWaWx2pcTFlgPUbDQLQoVmY1SzY9nqTGIkXPQnG/f5U72RAA6sB3J5KT8Z/ss9KBvCuMiavGi+09mpQAeuxhqSY5m6ZdgeXCoAhU+OxYNMpPNQxDvOHmg8MOCOzDQBYjRllVHU7M9neCMu2LqBKggXLZeQu7LGhgWZDNsgdMws2ncJDqXHietOSm+ChjtYjY4s9nHbn4/2d+ZIjftsqjxx7+VaWfyu5QVq2w3owSKnJpeX2t62BPdceOI81e89Lnjuc4eh1xd75W0n+Wk0NOeMMBkQqtfvMFcnnjfkW7uiVJjUargYwa7tOjW9sdjKWG1RO6i4WqO6lJjfKsbM/BLnxLWy9X65ZT652xtYPXS74sPwsxvyZmQ/cyc2R61KtATCx1505iwDrKQuM/DTAnIfuRv920VYnGSWzUyvhB1hdpE27AJvu34Zaf5e3Z3rsPpWebDY9BADM23hSckJIV/mhOriPCmmAPXlXxNyds1fKEKT1kx0rB7B9vNma50nAnUDF+HvqnNBYctJX4yB3w7o2N2tiWrDpFMb1TJL9XMYcFMvgfXCnOIcHd3S0BsD0+JB7r9zwDgYBVhdfuUAhNjQQLz/Qzu4YOkprgezlW0nlX6W3isS4nkn44PbcXJb7VK52R+nk0sbyS92EnSr6HZ/dnsBWA+Clga1drlWRu64Eaf2wJ++K2Hnh4LnruFZagdnrT8qev5Xmr9XWkDNKMCBSoUJdOd7dLj3EvHFkUiXVwfbEhlZ3sTYOruWHOzUOppTcZVmuy8jRIEXJD8FW8q3c++XmUnusWzw+23deTP4c1q0ZLulv2A3ULE+ycmUSYJ6bM2NgG9lEyw925YtD5r+3Uz4YGtczSQyGbAVuSkf5BYAR3eOxdt+vVk1h9o4v4/alKK2dMl23ZXBnK7BwlB+AZ/okY/mOX6wuLKZTb5j2AJopMXK3Kanjzd6AdEB1QNsyKlgcYFFuW6aD3C34/pRZM6PUtAuAeXdtqd/uCxmtFdeYuNKUYeu9saGBks2jzjT5KAnA7JG6obHMt7L823TGew2qR3y2TBFwx42rVKcPwLyXnIDqDgAPdZQe58h0Xbb2j9R1ZXCnOLFXm73ftPH3cEl/w6qDhVz+mprGpWNApEL5V6THN/EDFLc7K6V0HZYBgFyuTMOAepi8+rDZex0JUpT8EOyNbyH1fssfOlB9971633mzfIzVe89j9d7zVu+vEgT8+PNFtIhs5NA8YKYMQvVJyzJ3yXQbxl57UvM3Gdex4n/5+GBXvlVXcYNQnctjHJPG1iCLpvw1Gky5/y5Muf8us1qZ/CulNnve2brwSwVVlvwALBnRSZzA0l6OlytM81meSEsw+5z3ZZnnssz88k4PyafSkwFBvqnPT2M9K7iS3nLhDbUouFZqFTDMMn5mmAeKUk1MBsF82gW58WUsf7umzbfGu36p370rTRlK3ivVPGralORocONK8OZID0cj00NaAPDB/85iTA/zWjtXb1zlOn1IHQ/2biaV7h/Ta0KQ1s+si7+Sc8nR34ptTrRrWt7qlIo41YxLx4BIheQurjMy29gNSpzhaGKh1A/LOHpxfONAxUGO5cnC8oRoq7xSPVH8AIyzMSmh8Ye+5Mczsj2UbPnb19X5K3Ink8z2Mdho0otLSpUgoGloIOY8dLdVPoxprz173azFHCiJ5wcv3SPWckjdhWs01RMwSuVdyNU6GccJMj3e5C4irwxqiwc6xIrrNB1MbuEm8wTZQR2qx1lRmuPlDM3t48q0dsJYtm+P/CbZRd809+2p3smIC2sgOT6O1KzgSoLjBvX9MGWNdcCwa2ZfyZwZuZuHMT2SMCglFvvPXkfXROmu1lLk8t2AO+M6udKUofS9xoDTtCkwLLA+ADgU3Liah6LkO7PHlQR1KbY+k5KmLVu1lvb2j/E3ojQ/0Lh944S2SnejaUqFGrrnMyBSIalAwZhYauTK3ZArgZTcyMmmY4o83Ml8EkqpIeaNpHIjwgLrm43LcvDcdQiCYDa+SnqrSCx+LBV+Gg2aNQ7ExqNFeH/XL4qSXdfsL5B83pKt0YJNRwd2pFlHgzujf5vWTFkGJkpOzgZAMu9EwJ0xaR7qGGd2F27cTpuYYMmLqNIRiI1BsNRYNFEhAWblMUuQTbW+2BfqyrHh6AVFOV72DL2nKUamJZh1BBCE6okkjcm6pjQajfVKcHtkXxNSoxfLzQoeGyrdycDU+I8PWn1vVYKAjUcLMahDrFUumFxNg+U4U0pGj5fLd1vxvztNcHLNu0qbMpTW/ko1BRpH0zd9zl5w42oeitQ5t2tiY+zNvy65vOWI2nKfz3T9jp5r7Y2jNGOgeW2iadOWVPO5M/unodbf7m9QA+Bft2t6bfWIe7hTU3x16DebKRWeCoSMGBCplK2I2ZW7IVe7N8olBZu2FX99+ILY1GI5xLzUCVvqhBik9ccF3Q2zqldjfgdgPQWDcS4d0/3RJibYqju+ktoXoHq9sx9qh1e/OSn5ugHA4Hf3YKbMCMeS67y9r0yrn/00wJLHOomJ7Ma7O3sXVODOGC9SNSkGwXxMmt0z7zfr9u/oidO0l5RpbYbZRKe3LxKm25WaHNX0GLUVTBqDaXF0bQ2Q2T4WG48VWi07pFMcOjYPwx/axUjmTsmd/DsnNLbuWKCpHsrAstxKq/ULdeVIjQ+z/kAm5I7BNzb+jHnf/Sy57yzPCYB5HonleUDut26recj0OzY271rW6jkTYMi9V66zgyV7F29HAjC5m0HTnmsrdubLBkPP3d8Sd8UE47fr5WY3Ce5u5rH1mdbuL6jeNqqP16f7tMCy7Xmyx4Iz6QnG48deMDR/aIpY0wtY38wZOy6kxjdG3zZRilMqPIEBkYrJRczORvvu6N6opGq5ShDEvJPH3//J5vbkTohSzRMCzGujjMtKzXBdJQhiLYGS0WNNaUxqQEx7UViVR5ButpLyyqC2iA5tYHUyMAhAk0YBVnf6MzLbSPYANH5205yYe5PCrYZGMF2/6UzdUseA6ajiSkcgNh5vxovIoXPXxQk5Tbdrb/wSezlIxgk7D527DoMgoHl4EDYdLzR7jwbA1zkXsO7wBcxef9JuzYblRXH+0BSrwEGqzEqq9eVGFHakpsvWvjM9J8jlkVTvq2uSv/U2McG4WlKhqAayShDQoan1MAtKKdlfcsebI7UvgLIATOnNoFRvWSMNgCXbzlh9v3IBriu18XKfCTCfGUAQgGXb8qzOQ44M/ihVblu13hoAc/90p6ervTIba6E7J1iP9aWmSV59KiBaunQpFi1ahKKiIqSmpmLJkiXo1q2bp4vlMGeTkd3RvdHygJc60dsawdpye4623ZvWRpk+J7cscKdHiOnosZbNkQ+lVs+SbawdMJbP1qi5gHyzlSljM5JUfhUA7D5zGe9a3N0t3JSLmZnWgyBKXWAsh0awZG/8KoNwZ1RxqROa3AjExt5P1esUFNfKAPLNZADwXL+WeKxbc/F9lsGiaZOsZa2bvZoNuYui0vwFqZsU0wFCLScTFd3O27L8uBoAU+5vaTVnnJLfpeRUPBqYBaaW6zTtTWcvSJMbZsFIycXeXjOIrYu+o0m27qhVV5JcbVrDayTA9kTFcgGYvX0o9ZkkE+xhfR5yZPBHS7b2g7253YzbOXj2OqCpDoKMXE0yr2k+ExCtXbsW06dPx/Lly9G9e3csXrwYGRkZyM3NRVRUlKeL5xBnDyp3dW80PeCnSsyz9NLAO4nR9rYnlyAtR3P7P0qavUwJAnDo3HUM6iDdFRmA5ACSpsuWVd60yv0wBgymF1/TsV4sm5Ey7q6eS8yUaTBkVCUIkoMgAtLzEcnV1BjLaNzncgGo6aji9kYg9gPwdO8WWLmrOu/EWAtneYGVO7bs5Vy98+MZxIVVl0PqQmbaJHu1tEKyCl6qZsPeRdGZk7KSOaYAiFN/WD0PoFV0sFO/S6ng3rJmRWp7xv/7ARjePR6f7b0z7ITxt2XvnOLOkYXlfo/OJNkav0fLHnRKbwZt3aDZCyAtB3W1F4BZHjszZSYStjw25c7jluchqe9P6XEuV3P3zu3mfXvrsJXbppYEaik+ExC9+eabmDBhAsaMGQMAWL58OTZu3IgPP/wQM2fO9HDpHOfMQeXO6Dw2NBDhjWQGHWsW5tD2bF3MTRnbq89cKlE0t5Ml00DGkQug6bLzJT6PcWoCy7FepJqR/nvcuhea1MXU3t25XDkHdQhEScUt2X0ul/BrHADQdDnT7Q7r2hzF5XeS3y2nIjAI1XeofoJ07zUjJWP0mN5ty13IyioNYhOgXDBh+RncPQCcI3NMyb1uHEDRcr66wZ1sjydjZHoeuFJyQ3ameKmLvAHAgx2aWg23YO+cUhMjC0sd5+4IUm11AJAKOuVu0MQeVDbyBU3XZ+9Ykzp2TKcusUXuvCp1HnKW3Db+mBpn971Kjg9nv9ua5hMBUWVlJQ4ePIhZs2aJz/n5+aF///7Izs6WfE9FRQUqKirEv/V6fY2X01HOHFTujM6V1Dg5Ms6R1MX8pYGt0axxoDgHkvFkYqudX4oG5iNwO8vW1ASWbemNG1on98pVbZsmD7tajWxvn0uNKm6vRqJQV17dndZWICNU9zYJbxgg+10rHe/FePGwd4w5EuS7ewA4R8eukfueAVjNV/f14Qt4IcP+EBSAea2IVELrkhGd0KxxoNl4MsbySAWO9rapppGFLcldjC07ANg6TizH4TEdhyss6E5Nqeb2XYwA6xsAe8ea3LFjOnWJLUrPQ65w9lqh5uPDHp8IiK5cuYKqqipER0ebPR8dHY1Tp6QHXMvKysKcOXNqo3i1zl0/GqUXI0e258io2LamJBjcKU7s4ik3ArezXKl2lqvaNiYPu6sa2VYZpfafvQBMycXfX6Mxy7+S4mgSrZJjzJGg2535C3I5PKajF1s2QUl9z84MsufI5zP2AHLXZ1fTyMKWbF2MXc0VA6R7+kmtz96x5sjUJY6W0Z2c2Yaajw97NILgaDaG97lw4QKaNm2KPXv2IC0tTXz+pZdewo4dO7B3716r90jVEMXHx0On0yEkJKRWyu0tCnXlHmkPttyuvb89Ye3+AsmqbTWUzZEyFOrKzbp4WzIGnUon5lWSRGs5PIO79pc71yX1WZRcMC3LY7lv/TUa7JrZ1+mmPLltuuuzyx3XnubufemO8sjtb6m5Cj1ZVndS2/Gh1+sRGhpq9/rtEwFRZWUlgoKC8MUXX2Dw4MHi86NGjUJxcTG++eYbu+tQukOJLKkh+HEH05OckZ+merRm00lplZDaJ966n9xRbrVdQJRQ6/flTfvyvZ15VoOmqrWsjlLT8cGAyEL37t3RrVs3LFmyBABgMBjQvHlzTJ48WVFSNQMiojsnOcvcCnKdmi4g3s6b9qU3ldVbKb1++0QOEQBMnz4do0aNQpcuXdCtWzcsXrwYpaWlYq8zIrJPrb1D6gLuW/fxpn3pTWWt63wmIBo2bBguX76MV199FUVFRejYsSO+//57q0RrIiIi8j0+02TmKjaZEREReR+l12+/WiwTERERkSoxICIiIiKfx4CIiIiIfB4DIiIiIvJ5DIiIiIjI5zEgIiIiIp/HgIiIiIh8HgMiIiIi8nkMiIiIiMjn+czUHa4yDuit1+s9XBIiIiJSynjdtjcxBwMihX7//XcAQHx8vIdLQkRERI76/fffERoaKvs65zJTyGAw4MKFCwgODoZGo/F0cWqdXq9HfHw8zp8/z7ncXMD96DruQ/fgfnQP7kf3qMn9KAgCfv/9d8TFxcHPTz5TiDVECvn5+aFZs2aeLobHhYSE8EfvBtyPruM+dA/uR/fgfnSPmtqPtmqGjJhUTURERD6PARERERH5PAZEpEhAQABee+01BAQEeLooXo370XXch+7B/ege3I/uoYb9yKRqIiIi8nmsISIiIiKfx4CIiIiIfB4DIiIiIvJ5DIiIiIjI5zEgItHOnTvx4IMPIi4uDhqNBl9//bXZ64Ig4NVXX0VsbCwCAwPRv39/nD592jOFVTF7+3H06NHQaDRmj4EDB3qmsCqWlZWFrl27Ijg4GFFRURg8eDByc3PNlrlx4wYmTZqEJk2aoFGjRhg6dCguXrzooRKrk5L92KdPH6tj8umnn/ZQidVp2bJl6NChgzhwYFpaGjZt2iS+zmPRPnv70NPHIQMiEpWWliI1NRVLly6VfH3hwoV45513sHz5cuzduxcNGzZERkYGbty4UcslVTd7+xEABg4ciMLCQvGxZs2aWiyhd9ixYwcmTZqEn376CVu2bMHNmzcxYMAAlJaWistMmzYN3377LT7//HPs2LEDFy5cwJAhQzxYavVRsh8BYMKECWbH5MKFCz1UYnVq1qwZ5s+fj4MHD+LAgQO4//778ac//QknTpwAwGNRCXv7EPDwcSgQSQAgfPXVV+LfBoNBiImJERYtWiQ+V1xcLAQEBAhr1qzxQAm9g+V+FARBGDVqlPCnP/3JI+XxZpcuXRIACDt27BAEofr4q1+/vvD555+Ly/z8888CACE7O9tTxVQ9y/0oCILQu3dv4bnnnvNcobxU48aNhffff5/HoguM+1AQPH8csoaIFMnPz0dRURH69+8vPhcaGoru3bsjOzvbgyXzTtu3b0dUVBRat26NZ555BlevXvV0kVRPp9MBAMLDwwEABw8exM2bN82OyTZt2qB58+Y8Jm2w3I9Gn376KSIiItC+fXvMmjULZWVlniieV6iqqsJnn32G0tJSpKWl8Vh0guU+NPLkccjJXUmRoqIiAEB0dLTZ89HR0eJrpMzAgQMxZMgQJCUlIS8vDy+//DIyMzORnZ0Nf39/TxdPlQwGA55//nn06NED7du3B1B9TGq1WoSFhZkty2NSntR+BIARI0YgISEBcXFxOHr0KGbMmIHc3FysW7fOg6VVn2PHjiEtLQ03btxAo0aN8NVXX6Fdu3bIycnhsaiQ3D4EPH8cMiAiqmWPPfaY+O+UlBR06NABycnJ2L59O/r16+fBkqnXpEmTcPz4cezatcvTRfFqcvtx4sSJ4r9TUlIQGxuLfv36IS8vD8nJybVdTNVq3bo1cnJyoNPp8MUXX2DUqFHYsWOHp4vlVeT2Ybt27Tx+HLLJjBSJiYkBAKteExcvXhRfI+e0aNECEREROHPmjKeLokqTJ0/Ghg0bsG3bNjRr1kx8PiYmBpWVlSguLjZbnsekNLn9KKV79+4AwGPSglarRcuWLdG5c2dkZWUhNTUVb7/9No9FB8jtQym1fRwyICJFkpKSEBMTgx9//FF8Tq/XY+/evWbtv+S4X3/9FVevXkVsbKyni6IqgiBg8uTJ+Oqrr7B161YkJSWZvd65c2fUr1/f7JjMzc1FQUEBj0kT9vajlJycHADgMWmHwWBARUUFj0UXGPehlNo+DtlkRqKSkhKzSDw/Px85OTkIDw9H8+bN8fzzz+ONN97AXXfdhaSkJLzyyiuIi4vD4MGDPVdoFbK1H8PDwzFnzhwMHToUMTExyMvLw0svvYSWLVsiIyPDg6VWn0mTJmH16tX45ptvEBwcLOZihIaGIjAwEKGhoRg3bhymT5+O8PBwhISEYMqUKUhLS8O9997r4dKrh739mJeXh9WrV+OBBx5AkyZNcPToUUybNg3p6eno0KGDh0uvHrNmzUJmZiaaN2+O33//HatXr8b27dvx3//+l8eiQrb2oSqOQ4/1byPV2bZtmwDA6jFq1ChBEKq73r/yyitCdHS0EBAQIPTr10/Izc31bKFVyNZ+LCsrEwYMGCBERkYK9evXFxISEoQJEyYIRUVFni626kjtQwDCypUrxWXKy8uFZ599VmjcuLEQFBQkPPzww0JhYaHnCq1C9vZjQUGBkJ6eLoSHhwsBAQFCy5YthRdffFHQ6XSeLbjKjB07VkhISBC0Wq0QGRkp9OvXT9i8ebP4Oo9F+2ztQzUchxpBEITaCb2IiIiI1Ik5REREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBEREZHPY0BEREREPo8BEREREfk8BkRERETk8xgQEZHXq6ys9HQRrKixTEQkjwEREalOnz59MHnyZEyePBmhoaGIiIjAK6+8AuNMQ4mJiXj99dcxcuRIhISEYOLEiQCAXbt2oVevXggMDER8fDymTp2K0tJScb3vvvsu7rrrLjRo0ADR0dF45JFHxNe++OILpKSkIDAwEE2aNEH//v3F9/bp0wfPP/+8WRkHDx6M0aNHi387WyYiUgcGRESkSh999BHq1auHffv24e2338abb76J999/X3z9H//4B1JTU3H48GG88soryMvLw8CBAzF06FAcPXoUa9euxa5duzB58mQAwIEDBzB16lTMnTsXubm5+P7775Geng4AKCwsxPDhwzF27Fj8/PPP2L59O4YMGQJHp3p0tExEpB6c3JWIVKdPnz64dOkSTpw4AY1GAwCYOXMm1q9fj5MnTyIxMRGdOnXCV199Jb5n/Pjx8Pf3x3vvvSc+t2vXLvTu3RulpaX47rvvMGbMGPz6668IDg42296hQ4fQuXNnnD17FgkJCZLl6dixIxYvXiw+N3jwYISFhWHVqlUA4FSZGjRo4NJ+IiL3YQ0REanSvffeKwZDAJCWlobTp0+jqqoKANClSxez5Y8cOYJVq1ahUaNG4iMjIwMGgwH5+fn4wx/+gISEBLRo0QJPPvkkPv30U5SVlQEAUlNT0a9fP6SkpODPf/4zVqxYgevXrztcZkfLRETqwYCIiLxSw4YNzf4uKSnBU089hZycHPFx5MgRnD59GsnJyQgODsahQ4ewZs0axMbG4tVXX0VqaiqKi4vh7++PLVu2YNOmTWjXrh2WLFmC1q1bi0GLn5+fVfPZzZs3XS4TEakHAyIiUqW9e/ea/f3TTz/hrrvugr+/v+Ty99xzD06ePImWLVtaPbRaLQCgXr166N+/PxYuXIijR4/i7Nmz2Lp1KwBAo9GgR48emDNnDg4fPgytVis2f0VGRqKwsFDcVlVVFY4fP273MygpExGpAwMiIlKlgoICTJ8+Hbm5uVizZg2WLFmC5557Tnb5GTNmYM+ePZg8eTJycnJw+vRpfPPNN2IC84YNG/DOO+8gJycH586dw8cffwyDwYDWrVtj7969mDdvHg4cOICCggKsW7cOly9fRtu2bQEA999/PzZu3IiNGzfi1KlTeOaZZ1BcXGz3M9grExGpRz1PF4CISMrIkSNRXl6Obt26wd/fH88995zYlV1Khw4dsGPHDvz1r39Fr169IAgCkpOTMWzYMABAWFgY1q1bh9mzZ+PGjRu46667sGbNGtx99934+eefsXPnTixevBh6vR4JCQn45z//iczMTADA2LFjceTIEYwcORL16tXDtGnT0LdvX7ufwV6ZiEg92MuMiFRHqlcXEVFNYpMZERER+TwGREREROTz2GRGREREPo81REREROTzGBARERGRz2NARERERD6PARERERH5PAZERERE5PMYEBEREZHPY0BEREREPo8BEREREfk8BkRERETk8/4/NXj9NDYxwzoAAAAASUVORK5CYII=", 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", 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", 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", 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sbCw0Gg0OHDiAqKgoADXTb6Snp2PWrFl1WhYGRERERLWgTYF4ZuMpVAnh9BQIPz8/PPXUU5g9ezaqq6vRu3dvFBUV4cCBA/D395cCC3Oio6ORk5ODjIwM3HLLLfDz84O3t7dTymtO48aN8dhjj2Hu3Llo1qwZWrZsiWXLlqG0tBSTJk2q07IwICIiIqqluk6BeOGFFxAcHIyUlBT88ssvCAwMxG233YZnnnnGquao4cOHY+PGjejfvz8KCwuxdu1ajB8/3qllNiU1NRXV1dUYM2YMrl27hu7du+Obb75B06ZN67QcKiGEsLwaFRcXIyAgAEVFRfD393d1cYiIqJZu3LiBnJwcxMTEoFGjRq4uDtWCuc/S2vs3e5kRERGR4jEgIiIiIoseffRRvW7+uo9HH33U1cWrNZcGRPv27cM999yDiIgI2Rl2tUOKGz6WL18urRMdHW20PDU1VW87mZmZ6NOnDxo1aoTIyEgsW7asLnaPiIio3liyZAkyMjJkH0uWLHF18WrNpUnV169fR3x8PCZOnIhhw4YZLc/Ly9P7e9u2bZg0aRKGDx+u9/ySJUswZcoU6W8/Pz/p/8XFxRg0aBAGDhyI1atX4+TJk5g4cSICAwMxdepUB+8RERFR/RQSEoKQkBBXF8NpXBoQJScnIzk52eTysLAwvb+/+OIL9O/fX2/ES6AmADJcV+vDDz9ERUUF3n33XWg0GnTs2BEZGRl45ZVXGBARERERAA/KIbp48SK2bt0qOy5Bamoqmjdvjq5du2L58uWorKyUlqWlpSExMREajUZ6LikpCVlZWbh69arJ9ysvL0dxcbHeg4iI6h93HDWZbOOIz9BjxiF677334OfnZ9S0NnPmTNx2221o1qwZDh48iAULFiAvLw+vvPIKACA/Px8xMTF6rwkNDZWWmRrnICUlBYsXL3bCnhARkTvQaDRQq9W4cOECgoODodFopCkuyDMIIVBRUYHLly9DrVbrVX7YymMConfffRejR482Gl9gzpw50v87d+4MjUaDRx55BCkpKbUadXPBggV62y4uLkZkZKTd2yMiIveiVqsRExODvLw8XLhwwdXFoVrw9fVFy5YtoVbb3/DlEQHRd999h6ysLGzYsMHiuj179kRlZSXOnTuHtm3bIiwsDBcvXtRbR/u3qbwjoGaCPFcMY05ERHVHo9GgZcuWqKysdIu5vch2Xl5eaNCgQa1r9zwiIHrnnXfQrVs3xMfHW1w3IyMDarVayoRPSEjAs88+i5s3b0oz7u7YsQNt27at82HBiYjI/WhnZDeclZ2UxaVJ1SUlJdIYBgCkieZyc3OldYqLi/HJJ59g8uTJRq9PS0vDihUrcOLECfzyyy/48MMPMXv2bDz88MNSsDNq1ChoNBpMmjQJp0+fxoYNG7By5Uq95jAiIiJSNpfWEB05cgT9+/eX/tYGKePGjcO6desAAOvXr4cQAiNHjjR6vbe3N9avX49FixahvLwcMTExmD17tl6wExAQgO3bt2PatGno1q0bgoKC8Nxzz7HLPREREUk4uauVOLkrERGR5+HkrkRERERWYkBEREREiseAiIiIiBSPAREREREpHgMiIiIiUjwGRERERKR4DIiIiIhI8RgQERERkeIxICIiIiLFY0BEREREiseAiIiIiBSPAREREREpHgMiIiIiUjwGRERERKR4DIiIiIhI8RgQERERkeIxICIiIiLFY0BEREREiseAiIiIiBSPAREREREpHgMiIiIiUjwGRERERKR4DIiIiIhI8RgQERERkeIxICIiIiLFY0BEREREiseAiIiIiBSPAREREREpHgMiIiIiUjwGRERERKR4DIiIiIhI8RgQERERkeIxICIiIiLFY0BEREREiseAiIiIiBTPpQHRvn37cM899yAiIgIqlQqbNm3SWz5+/HioVCq9x+DBg/XWuXLlCkaPHg1/f38EBgZi0qRJKCkp0VsnMzMTffr0QaNGjRAZGYlly5Y5e9eIiIjIg7g0ILp+/Tri4+PxxhtvmFxn8ODByMvLkx4ff/yx3vLRo0fj9OnT2LFjB7Zs2YJ9+/Zh6tSp0vLi4mIMGjQIUVFROHr0KJYvX45FixbhP//5j9P2i4iIiDxLA1e+eXJyMpKTk82u4+3tjbCwMNllP/74I77++mukp6eje/fuAIDXX38dd999N/7v//4PERER+PDDD1FRUYF3330XGo0GHTt2REZGBl555RW9wImIiIiUy+1ziPbs2YOQkBC0bdsWjz32GP744w9pWVpaGgIDA6VgCAAGDhwItVqNQ4cOSeskJiZCo9FI6yQlJSErKwtXr141+b7l5eUoLi7WexAREVH95NYB0eDBg/H+++9j586dWLp0Kfbu3Yvk5GRUVVUBAPLz8xESEqL3mgYNGqBZs2bIz8+X1gkNDdVbR/u3dh05KSkpCAgIkB6RkZGO3DUiIiJyIy5tMrPkoYcekv4fFxeHzp07IzY2Fnv27MGAAQOc+t4LFizAnDlzpL+Li4sZFBEREdVTbl1DZKhVq1YICgrC2bNnAQBhYWG4dOmS3jqVlZW4cuWKlHcUFhaGixcv6q2j/dtUbhJQk7vk7++v9yAiIqL6yaMCot9++w1//PEHwsPDAQAJCQkoLCzE0aNHpXV27dqF6upq9OzZU1pn3759uHnzprTOjh070LZtWzRt2rRud4CIiIjckksDopKSEmRkZCAjIwMAkJOTg4yMDOTm5qKkpARz587F999/j3PnzmHnzp2477770Lp1ayQlJQEA2rdvj8GDB2PKlCk4fPgwDhw4gOnTp+Ohhx5CREQEAGDUqFHQaDSYNGkSTp8+jQ0bNmDlypV6zWFERESkbCohhHDVm+/Zswf9+/c3en7cuHFYtWoVhg4diuPHj6OwsBAREREYNGgQXnjhBb0k6StXrmD69On48ssvoVarMXz4cLz22mto0qSJtE5mZiamTZuG9PR0BAUFYcaMGZg3b55NZS0uLkZAQACKiorYfEZEROQhrL1/uzQg8iQMiIiIiDyPtfdvj8ohIiIiInIGBkRERESkeAyIiIiISPEYEBEREZHiMSAiIiIixWNARERERIrHgIiIiIgUjwERERERKR4DIiIiIlI8BkRERESkeAyIiIiISPEYEBEREZHiMSAiIiIixWNARERERIrHgIiIiIgUjwERERERKR4DIiIiIlI8BkRERESkeAyIiIiISPEYEClEXlEZDmYXIK+ozNVFISIicjsNXF0Acr4N6blYsPEkqgWgVgEpw+Iw4vaWri4WERGR22ANUT2XV1QmBUMAUC2AZzaeYk0RERGRDgZE9VxOwXUpGNKqEgLnCkpdUyAiIiI3xIConosJagy1Sv85L5UK0UG+rikQERGRG2JAVI/lFZUhp+A65iW3g5eqJiryUqnw8rBOCA/wcXHpiIiI3AeTquspw0TqeYPbofMtgYgO8mUwREREZIA1RPWQXCL1sq+zGAwRERGZwICoHmIiNRERkW0YENVDTKQmIiKyDQOieig8wAcpw+KYSE1ERGQlJlXXUyNub4nENsE4V1DK3CEiIiILGBDVY+EBPgyEiIiIrMAmMyIiIlI8BkRERESkeAyIiIiISPFcGhDt27cP99xzDyIiIqBSqbBp0yZp2c2bNzFv3jzExcWhcePGiIiIwNixY3HhwgW9bURHR0OlUuk9UlNT9dbJzMxEnz590KhRI0RGRmLZsmV1sXtERETkIVwaEF2/fh3x8fF44403jJaVlpbi2LFjWLhwIY4dO4aNGzciKysL9957r9G6S5YsQV5envSYMWOGtKy4uBiDBg1CVFQUjh49iuXLl2PRokX4z3/+49R9IyIiIs/h0l5mycnJSE5Oll0WEBCAHTt26D3373//Gz169EBubi5atmwpPe/n54ewsDDZ7Xz44YeoqKjAu+++C41Gg44dOyIjIwOvvPIKpk6d6rid8SDaSV9jghqzFxoRERE8LIeoqKgIKpUKgYGBes+npqaiefPm6Nq1K5YvX47KykppWVpaGhITE6HRaKTnkpKSkJWVhatXr5p8r/LychQXF+s96oMN6bnolboLo9YcQq/UXdiQnuvqIhEREbmcxwREN27cwLx58zBy5Ej4+/tLz8+cORPr16/H7t278cgjj+Dll1/G008/LS3Pz89HaGio3ra0f+fn55t8v5SUFAQEBEiPyMhIB+9R3ZOb9PWZjaeQV1Tm2oIRERG5mEcMzHjz5k08+OCDEEJg1apVesvmzJkj/b9z587QaDR45JFHkJKSAm9vb7vfc8GCBXrbLi4u9vigyNykr2w6IyIiJXP7gEgbDP3666/YtWuXXu2QnJ49e6KyshLnzp1D27ZtERYWhosXL+qto/3bVN4RAHh7e9cqoHJH2klfdYMiTvpKRETk5k1m2mDozJkz+Pbbb9G8eXOLr8nIyIBarUZISAgAICEhAfv27cPNmzeldXbs2IG2bduiadOmTiu7O+Kkr0RERPJcWkNUUlKCs2fPSn/n5OQgIyMDzZo1Q3h4OB544AEcO3YMW7ZsQVVVlZTz06xZM2g0GqSlpeHQoUPo378//Pz8kJaWhtmzZ+Phhx+Wgp1Ro0Zh8eLFmDRpEubNm4dTp05h5cqVePXVV12yz67GSV+JiIiMqYQQwvJqzrFnzx7079/f6Plx48Zh0aJFiImJkX3d7t270a9fPxw7dgyPP/44fvrpJ5SXlyMmJgZjxozBnDlz9Jq7MjMzMW3aNKSnpyMoKAgzZszAvHnzbCprcXExAgICUFRUZLHZjoiIiNyDtfdvlwZEnoQBERERkeex9v7t1jlERERERHWBAREREREpHgMiIiIiUjwGRERERKR4DIiIiIhI8RgQERERkeIxICIiIiLFY0BEREREiseAiIiIiBSPAREREREpHgMiIiIiUjwGRERERKR4DIiIiIhI8RgQERERkeIxICIiIiLFY0BEREREiseAiIiIiBSPAREREREpHgMiIiIiUjwGRERERKR4DIiIiIhI8RgQERERkeIxICIiIiLFY0BEREREiseAiIiIiBSvgbUrFhcXW71Rf39/uwpDRERE5ApWB0SBgYFQqVRm1xFCQKVSoaqqqtYFIyIiIqorVgdEu3fvdmY5iIiIiFzG6oCob9++ziwHERERkctYHRAZKiwsxDvvvIMff/wRANCxY0dMnDgRAQEBDiscERERUV2wq5fZkSNHEBsbi1dffRVXrlzBlStX8MorryA2NhbHjh1zdBmJiIiInEolhBC2vqhPnz5o3bo11qxZgwYNaiqZKisrMXnyZPzyyy/Yt2+fwwvqasXFxQgICEBRURF70REREXkIa+/fdgVEPj4+OH78ONq1a6f3/A8//IDu3bujtLTU9hK7OQZEREREnsfa+7ddTWb+/v7Izc01ev78+fPw8/OzZ5NERERELmNXQDRixAhMmjQJGzZswPnz53H+/HmsX78ekydPxsiRIx1dRiIiIiKnsisg+r//+z8MGzYMY8eORXR0NKKjozF+/Hg88MADWLp0qdXb2bdvH+655x5ERERApVJh06ZNesuFEHjuuecQHh4OHx8fDBw4EGfOnNFb58qVKxg9ejT8/f0RGBiISZMmoaSkRG+dzMxM9OnTB40aNUJkZCSWLVtmz24TERFRPWVXQKTRaLBy5UpcvXoVGRkZyMjIwJUrV/Dqq6/C29vb6u1cv34d8fHxeOONN2SXL1u2DK+99hpWr16NQ4cOoXHjxkhKSsKNGzekdUaPHo3Tp09jx44d2LJlC/bt24epU6dKy4uLizFo0CBERUXh6NGjWL58ORYtWoT//Oc/9uw6ERER1UfCTQAQn3/+ufR3dXW1CAsLE8uXL5eeKywsFN7e3uLjjz8WQgjxww8/CAAiPT1dWmfbtm1CpVKJ33//XQghxJtvvimaNm0qysvLpXXmzZsn2rZta1P5ioqKBABRVFRkz+4RERGRC1h7/7arhujGjRtYvnw57r77bnTv3h233Xab3sMRcnJykJ+fj4EDB0rPBQQEoGfPnkhLSwMApKWlITAwEN27d5fWGThwINRqNQ4dOiStk5iYCI1GI62TlJSErKwsXL161eT7l5eXo7i4WO9BRERE9ZNdI1VPmjQJ27dvxwMPPIAePXpYnPTVHvn5+QCA0NBQvedDQ0OlZfn5+QgJCdFb3qBBAzRr1kxvnZiYGKNtaJc1bdpU9v1TUlKwePHi2u8IERERuT27AqItW7bgq6++Qq9evRxdHrexYMECzJkzR/q7uLgYkZGRLiwREREROYtdTWYtWrRw+nhDYWFhAICLFy/qPX/x4kVpWVhYGC5duqS3vLKyEleuXNFbR24buu8hx9vbG/7+/noPIiIiqp/sCoj+9a9/Yd68efj1118dXR5JTEwMwsLCsHPnTum54uJiHDp0CAkJCQCAhIQEFBYW4ujRo9I6u3btQnV1NXr27Cmts2/fPty8eVNaZ8eOHWjbtq3J5jIiIiJSFrsCou7du+PGjRto1aoV/Pz80KxZM72HtUpKSqRu+0BNInVGRgZyc3OhUqkwa9YsvPjii9i8eTNOnjyJsWPHIiIiAkOHDgUAtG/fHoMHD8aUKVNw+PBhHDhwANOnT8dDDz2EiIgIAMCoUaOg0WgwadIknD59Ghs2bMDKlSv1msOIiIhI2ezKIRo5ciR+//13vPzyywgNDbU7qfrIkSPo37+/9Lc2SBk3bhzWrVuHp59+GtevX8fUqVNRWFiI3r174+uvv0ajRo2k13z44YeYPn06BgwYALVajeHDh+O1116TlgcEBGD79u2YNm0aunXrhqCgIDz33HN6YxURERGRstk1uauvry/S0tIQHx/vjDK5JU7uSkRE5HmcOrlru3btUFZWZnfhiIiIiNyJXQFRamoqnnzySezZswd//PEHBzAkIiIij2ZXk5laXRNHGeYOCSGgUqlQVVXlmNK5ETaZEREReR5r7992JVXv3r3b7oIRERERuRu7AqK+fftatd7jjz+OJUuWICgoyJ63ISIiIqoTduUQWeuDDz5gThERERG5PacGRHakJxERERHVOacGRERERESegAERERERKR4DIiIiIlI8BkRERESkeE4NiB5++GEOYuggeUVlOJhdgLwiTplCRETkaHaNQwQAhYWFOHz4MC5duoTq6mq9ZWPHjgUArFq1qnalIwDAhvRcLNh4EtUCUKuAlGFxGHF7S1cXi4iIqN6wa+qOL7/8EqNHj0ZJSQn8/f31pvBQqVS4cuWKQwvpDlw1dUdeURl6pe5Ctc6n5KVSYf/8/ggP8KmzchAREXkip852/+STT2LixIkoKSlBYWEhrl69Kj3qYzDkSjkF1/WCIQCoEgLnCkpdUyAiIqJ6yK6A6Pfff8fMmTPh6+vr6PKQgZigxlDrz6ELL5UK0UE89kRERI5iV0CUlJSEI0eOOLosJCM8wAcpw+Lg9WezpJdKhZeHdWJzGRERkQNZnVS9efNm6f9DhgzB3Llz8cMPPyAuLg4NGzbUW/fee+91XAkJI25vicQ2wThXUIroIF8GQ0RERA5mdVK1Wm1dZZJKpUJVVVWtCuWOnJ1UnVdUhpyC64gJasyAh4iIyEGsvX9bXUNk2LWeHIfd6omIiFzLrhyi999/H+Xl5UbPV1RU4P333691oZQkr6hMCoYAoFoACzae5ACMREREdciugGjChAkoKioyev7atWuYMGFCrQulJHLd6qsFsPZAjmsKREREpEB2BURCCL3BGLV+++03BAQE1LpQSiLXrR4A3t6Xw1oiIiKiOmLT1B1du3aFSqWCSqXCgAED0KDBXy+vqqpCTk4OBg8e7PBC1mfhAT6Y1DsGa77TrxGqBnCuoJQJ1kRERHXApoBo6NChAICMjAwkJSWhSZMm0jKNRoPo6GgMHz7coQVUgom9Y/D2dznQbTnj4ItERER1x6aA6PnnnwcAREdHY8SIEWjUqJFTCqU04QE+SB0eh2c2nkKVEBx8kYiIqI7ZNbmrVkVFhexs9y1b1r8u43UxuWteURkHXyQiInIgh49DpOvMmTOYOHEiDh48qPe8Ntm6Pg7MWBfCA3wYCBEREbmAXQHR+PHj0aBBA2zZsgXh4eGyPc6IiIiIPIVdAVFGRgaOHj2Kdu3aObo8RERERHXOrnGIOnTogIKCAkeXhYiIiMgl7AqIli5diqeffhp79uzBH3/8geLiYr0HERERkSexq5eZ7sz3uvlD9Tmpui56mREREZFjObWX2e7du+0uGLmvvKIy5BRcR0xQY/Z2IyIiRbGryaxv375Qq9VYs2YN5s+fj9atW6Nv377Izc2Fl5eXo8tIdWBDei56pe7CqDWH0Ct1Fzak57q6SERERHXGroDos88+Q1JSEnx8fHD8+HGUl5cDAIqKivDyyy87tIDR0dHS/Gm6j2nTpgEA+vXrZ7Ts0Ucf1dtGbm4uhgwZAl9fX4SEhGDu3LmorKx0aDmdJa+oDAezC5w60WteURkWbDyJ6j8bT6sF8MzGU5xcloiIFMOuJrMXX3wRq1evxtixY7F+/Xrp+V69euHFF190WOEAID09XS8n6dSpU/jb3/6Gf/zjH9JzU6ZMwZIlS6S/fX3/mgOsqqoKQ4YMQVhYGA4ePIi8vDyMHTsWDRs2dHjw5mgb0nOlQEWtAlKGxWHE7Y4fBTyn4LoUDGlVCcHJZYmISDHsqiHKyspCYmKi0fMBAQEoLCysbZn0BAcHIywsTHps2bIFsbGx6Nu3r7SOr6+v3jq6SVPbt2/HDz/8gA8++ABdunRBcnIyXnjhBbzxxhuoqKhwaFkdqS5rbWKCGkNtMLYmJ5clIiIlsSsgCgsLw9mzZ42e379/P1q1alXrQplSUVGBDz74ABMnTtTr3fbhhx8iKCgInTp1woIFC1BaWiotS0tLQ1xcHEJDQ6XnkpKSUFxcjNOnT5t8r/LycpcOJ2Cu1sbRwgN8kDIsDl5/HlNOLktEREpjV5PZlClT8MQTT+Ddd9+FSqXChQsXkJaWhqeeegoLFy50dBklmzZtQmFhIcaPHy89N2rUKERFRSEiIgKZmZmYN28esrKysHHjRgBAfn6+XjAEQPo7Pz/f5HulpKRg8eLFjt8JK2lrbXSDIku1NrXpJTbi9pZIbBPMyWWJiEiR7AqI5s+fj+rqagwYMAClpaVITEyEt7c3nnrqKcyYMcPRZZS88847SE5ORkREhPTc1KlTpf/HxcUhPDwcAwYMQHZ2NmJjY+1+rwULFmDOnDnS38XFxYiMjLR7e7bS1to8s/EUqoSwWGtjb76RYRDFQIiIiJTIroBIpVLh2Wefxdy5c3H27FmUlJSgQ4cOaNKkiaPLJ/n111/x7bffSjU/pvTs2RMAcPbsWcTGxiIsLAyHDx/WW+fixYsAapr+TPH29oa3t3ctS1071tbamMo3SmwTbDbAqaukbSIiIndnVw6RlkajQYcOHdCjRw+nBkMAsHbtWoSEhGDIkCFm18vIyAAAhIeHAwASEhJw8uRJXLp0SVpnx44d8Pf3R4cOHZxWXkcJD/BBQmxzs4GNPflG7GpPRET0l1oFRHWluroaa9euxbhx49CgwV+VWtnZ2XjhhRdw9OhRnDt3Dps3b8bYsWORmJiIzp07AwAGDRqEDh06YMyYMThx4gS++eYb/POf/8S0adNcXgPkKPb0EqvLpG0iIiJ35xEB0bfffovc3FxMnDhR73mNRoNvv/0WgwYNQrt27fDkk09i+PDh+PLLL6V1vLy8sGXLFnh5eSEhIQEPP/wwxo4dqzdukaezp5cYu9oTERH9xa7JXZXIEyZ3zSsqs6mX2Ib0XKOkbeYQERFRfeLUyV3JPdnaS4xd7YmIiGowIFI4drUnIiLykBwiIiIiImdiQERERESKx4CIiIiIFI8BERERESkeAyIiIiJSPAZEREREpHgMiIiIiEjxGBARERGR4jEgIiIiIsVjQERERESKx4CIiIiIFI8BERERESkeAyIPlldUhoPZBcgrKnN1UYiIiDwaZ7v3UBvSc7Fg40lUC0CtAlKGxWHE7S1dXSwiIiKPxBoiD5RXVCYFQwBQLYBnNp5iTREREZGdGBB5oJyC61IwpFUlBM4VlLqmQERERB6OAZEHiglqDLVK/zkvlQrRQb6uKRAREZGHY0DkgcIDfJAyLA5eqpqoyEulwsvDOiE8wMfFJVMeJrYTEdUPTKr2UCNub4nENsE4V1CK6CBfBkMuYC6xPa+oDDkF1xET1JifDRGRB2BA5MHCA3x4s3URU4ntiW2Cse/ny+wBSETkYdhkRmQHU4ntx369yh6AREQeiAERkR1MJbZXC8EegEREHogBEZEdTCW2d49uxh6AREQeiDlEboJJuJ7HVGJ7yrA4PLPxFKqEYA9AIiIPwYDIDXAaDs8ll9jOHoBERJ6HTWYuxmk46qfwAB8kxDZnMERE5CEYELkYp+EgIiJyPQZELsZpOIiIiFyPAZGLcRoOIiIi12NStRtgEi4REZFrMSByE5yGg4iIyHXYZEYOxdnfiYjIE7GGiByG4ykREZGncvsaokWLFkGlUuk92rVrJy2/ceMGpk2bhubNm6NJkyYYPnw4Ll68qLeN3NxcDBkyBL6+vggJCcHcuXNRWVlZ17tSr3E8JSIi8mQeUUPUsWNHfPvtt9LfDRr8VezZs2dj69at+OSTTxAQEIDp06dj2LBhOHDgAACgqqoKQ4YMQVhYGA4ePIi8vDyMHTsWDRs2xMsvv1zn+2KJp07hYW48JU/aDyIiUiaPCIgaNGiAsLAwo+eLiorwzjvv4KOPPsJdd90FAFi7di3at2+P77//HnfccQe2b9+OH374Ad9++y1CQ0PRpUsXvPDCC5g3bx4WLVoEjUZT17tjkrVNTu4YNGnHU9INijieEhEReQq3bzIDgDNnziAiIgKtWrXC6NGjkZubCwA4evQobt68iYEDB0rrtmvXDi1btkRaWhoAIC0tDXFxcQgNDZXWSUpKQnFxMU6fPm3yPcvLy1FcXKz3cCZrm5w2pOeiV+oujFpzCL1Sd2FDeq5Ty2UtjqdkGhPNiYjcn9vXEPXs2RPr1q1D27ZtkZeXh8WLF6NPnz44deoU8vPzodFoEBgYqPea0NBQ5OfnAwDy8/P1giHtcu0yU1JSUrB48WLH7owZ1jQ5mQqaEtsEu0XgwfGUjDHRnIjIM7h9QJScnCz9v3PnzujZsyeioqLwv//9Dz4+zrvhLliwAHPmzJH+Li4uRmRkpNPez5omJ0/I0+F4Sn9x9wCWiIj+4hFNZroCAwPRpk0bnD17FmFhYaioqEBhYaHeOhcvXpRyjsLCwox6nWn/lstL0vL29oa/v7/ew5msaXLylHnP2ERUgxP3EhF5Do8LiEpKSpCdnY3w8HB069YNDRs2xM6dO6XlWVlZyM3NRUJCAgAgISEBJ0+exKVLl6R1duzYAX9/f3To0KHOy2/OiNtbYv/8/vh4yh3YP7+/UdOKJ+TpuGuOkyt4SgBLRESASgghLK/mOk899RTuueceREVF4cKFC3j++eeRkZGBH374AcHBwXjsscfw1VdfYd26dfD398eMGTMAAAcPHgRQ0+2+S5cuiIiIwLJly5Cfn48xY8Zg8uTJNnW7Ly4uRkBAAIqKipxeW2RJXlGZW+bp5BWVoVfqLqNmv/3z+7tVOevShvRcPLPxFKqEkAJY5hAREdUda+/fbp9D9Ntvv2HkyJH4448/EBwcjN69e+P7779HcHAwAODVV1+FWq3G8OHDUV5ejqSkJLz55pvS6728vLBlyxY89thjSEhIQOPGjTFu3DgsWbLEVbtUa+6ap+MJOU51jYnmRESewe1riNyFO9UQuSvWEBERkbux9v7tcTlE5L48IceJiIhIjts3mZFnYRMRERF5IgZE9Yi7TOnhrjlOREREpjAgqic4IjIREZH9mENUD1g7DxoRERHJY0BUD3BEZCIiotphQFQP1OWIyJyWg4iI6iMGRPVAXXV357QcRERUX3FgRit5wsCMzpzSg4MuEhGRJ6o3U3eQ9ZzZ3Z3TchARUX3GJjOyCmdudzzmYxERuQ8GRGQVTsvhWMzHIiJyL8whspIn5BDVBWfmKSkF87GIiOoOc4jIKTgtR+0xH4uIyP2wyYychjky8piPRUTkfhgQkVMwR8Z0QMh8LCIi98McIisxh8h6zJGxbrJd5mMRETmftfdv1hCRwyl9bjVrJ9sND/BBQmxzBkNERG6AARE5jLaJqLHGS9E5MkoPCImIPBF7mZFDGDYR3d+1BTYdv4AqIRSXI6NNmjZsMlRKQEhEZKu8ojLkFFxHTFBjl90rGBB5AHc4UcyRayLadPwCNj6egNKKasXlyGiTpp/ZeMotAkJ3P3+ISNmsybmsCwyI3Jy7nCjmmGoiKq2oRkJsc9cUysVG3N4SiW2CXZ407QnnDxEpl6mcy8Q2wXV+3WQOkRuzNjnX1TiujjxXJ017yvlDRMrlTjmXDIjcmDudKLoMx9exZ1wdRw3ayMEfTXPX84eISMudflCzycyNuVNyrjYP5eTvRVi67SejJhhbmogc1YzD5iDz3On8ISKS4045lxyY0UquGphxQ3qu0YlS1zd93cDDkK0DLjpq0MbabkcpicbucP4QEVnizIFqOblrPWGq5sXcDd2RN3vDPBRDtk5K6qiJTWuznfpUs2Tps3aX5G4iInPcYeJwBkQewPBEMXdDd/TNXi7w0GVrE4yjmnHs3Y479WioLWs/a3e40BARuTsmVXsYcz2HHN2rKK+oDFeuV0BlYrk9bb2OmtjU3u3Ul0Rj9iAjInIs1hB5GHM3dAHhkOYoQL/2QYWah0BN4PH04LbofEug3U0wjmrGsWc79SXR2FFNj0REVIMBkYexdEOv7c0+r6gMR85d0at9EH9u9/WHuqJbdFOH3HAd1Yxj63bcqUdDbdSXwI6IyF0wIPIwlm7o1t7s5ZJxzfUmqxaASlVTM6EthyvVJnG8PiQa15fAjojIXbDbvZVc1e3eFHNdFC11X5RLxk1sE2zUjV2X6s82M21tkSt7ZtWnXmK15cyuqkRE9YG1928GRFZyh4DIEd3p5cbvUauAaf1j8fqubNnXqFWA+DMY0vJSqbDx8QRcr6iq07F8HDWOERERKQPHIapnHFUrIpeMWy0gGwypAbw+qiuqhcCMjzP0llUJgaFvHKzzGiMmExMRkTO4fbf7lJQU3H777fDz80NISAiGDh2KrKwsvXX69esHlUql93j00Uf11snNzcWQIUPg6+uLkJAQzJ07F5WVlXW5K3aT62K94LOTdnWxlps3Ro6XSoWU4XEY0jkC3aObyb5GG5c4osu3tXOSudO8N0REVH+4fUC0d+9eTJs2Dd9//z127NiBmzdvYtCgQbh+/breelOmTEFeXp70WLZsmbSsqqoKQ4YMQUVFBQ4ePIj33nsP69atw3PPPVfXu2MX2VodAGv3n7N5W4bj98hZOKQ99s/vL9X4GL5GLjiqEgJbM/PsCoo2pOeiV+oujFpzCL1Sd2FDeq7V5WcyMREROYLH5RBdvnwZISEh2Lt3LxITEwHU1BB16dIFK1askH3Ntm3b8Pe//x0XLlxAaGgoAGD16tWYN28eLl++DI1GY/F9XZlDlFdUhjtTdsHwg1KrgAPz77IrGMgrKsOxX69i+kfHjXKDTOXjaBN4fTVq3P/mQdkEbFubz+zNCXJ1MrFS5kIjIvJ01t6/3b6GyFBRUREAoFmzZnrPf/jhhwgKCkKnTp2wYMEClJb+NfJwWloa4uLipGAIAJKSklBcXIzTp0/Lvk95eTmKi4v1Hq4SHuCDKX1ijJ6vFrB7hOXwAB8M6RyB1OHW17aEB/ggIbY54iObmqxlsrX5zN6Ro7VlcUUwYkuNFhEReQaPSqqurq7GrFmz0KtXL3Tq1El6ftSoUYiKikJERAQyMzMxb948ZGVlYePGjQCA/Px8vWAIgPR3fn6+7HulpKRg8eLFTtoT203oHYO39+c4fCA+e8fk0b5ua2YeXtz6o94yW5KcrRlg0J1qY+rTXGhERPQXj6ohmjZtGk6dOoX169frPT916lQkJSUhLi4Oo0ePxvvvv4/PP/8c2dny3citsWDBAhQVFUmP8+fP17b4teLM3BnD2hZrE5xrapnCzSY5W9qWpf1yt9oYZ8yFZu3xJqL6jdcC1/KYGqLp06djy5Yt2LdvH2655Raz6/bs2RMAcPbsWcTGxiIsLAyHDx/WW+fixYsAgLCwMNlteHt7w9vb2wEldxxnjrCsrYU5+VsRln79k9Xd+82NmGztUAGm9ssda2McPWUGB5kkpXGnGl93osRrgbudC24fEAkhMGPGDHz++efYs2cPYmKMc2kMZWRkAADCw8MBAAkJCXjppZdw6dIlhISEAAB27NgBf39/dOjQwWlldwZHzQGmy9SUHdYGIHIBjexQARtPol2YH+IjmxptQ26/3HHMIUdOmeGOAR+RMynxpq/LVACgxGuBO54Lbh8QTZs2DR999BG++OIL+Pn5STk/AQEB8PHxQXZ2Nj766CPcfffdaN68OTIzMzF79mwkJiaic+fOAIBBgwahQ4cOGDNmDJYtW4b8/Hz885//xLRp09yuFqiuGX4RDVkbgBgGNKYGgBz6xkGkDq858S39OpCrjVEBOHvpmkunqnBUTZ07BnxEzqLEm74ucwFAfb4WyF3n3fVccPuAaNWqVQBqutbrWrt2LcaPHw+NRoNvv/0WK1aswPXr1xEZGYnhw4fjn//8p7Sul5cXtmzZgsceewwJCQlo3Lgxxo0bhyVLltTlrjiVvVWPcl9EXfY2B8kFM0DNYI7PbDyFwtKbFpvmDGtjtK9f+MVpPPfFaaQOr5mDzRVVro6oqeOM9aQk9fmmb4mlAKC+XgtMBYHuei64fUBkaZikyMhI7N271+J2oqKi8NVXXzmqWG6lNlWPpgIXoHaJ29pgRq72qUoIpG77yWika7lfByNub4l2YX64742Des8LAPM/Oyn9312qXG3BGetJSay96btbXokjWAoA6uO1wFwQ6K4BoNsHRGRebase5b6ITye3RecWgbVultIGM9o5z7RUf04Wq8vcr4PrFVWy29fdhLtUudrKmYnyRLXlyODEmpu+O+aVOII1AUB9uxaYCwITYpu7ZQDIgMjD2VL1aOri5swvYnxkU6QOj8P8jSf/CoJETS6Q4QjZpn4dxAQ1NlpfjjtUudrDUYny9fGXNbmOM4ITc9cad80rkaP9rjXWeOF6RZXF75y1NUDO6DTjKid/LzJ6Tvc6744BIAMiD2dt1aOli5szv4iJbYL1ohmBmloitaiZk82aEbJTh8dh/mcnzQZF7lDl6ir19Zc1uYYzgxNT1xp3zSsxJNcr15rvnDsGAM6SV1SGpdt+Mnr+6cFt9fbb3QJAjxqYkYxZM2CjqYubdvAvZw8GllNw3SiQEQJ4fVRXfDzlDr2JZE0ZcXtLHFxwF14c2lF2uVoFt6hydQVLny+RrZwxAKkl2h93utztR46pXrnWfudcOeVQXTLVWafzLYF1XhZbsIaoHrD0y8PcxW3fz5edXrNgqhbrtqimNl0YwgN88PAd0WjopZaqntUAJifGYEKvmHp/kTGlLn9Z17ZZTvf1ANjE56ZckfTqCYnF5nrlumNtlqu4a9K0JQyI6glzVY+mTk5fjbpO2uxre6EzvAkrqerZGnV18bG1Wc7wc9N9vbYiwFN7CNZ3rgpO3P27balXrrvf8OuKJwS3clTCUr92AgAUFxcjICAARUVF8Pf3d3VxbLYhPdfo5Ixs5otRaw4ZrfvxlDuQENvc4WXIKyqz6kKneyOVq8Gqi7GHPC1BWe7zdWSAkVdUhl6pu4yCrv3z+8seH8Pgad7gdtK4U3K02wJgdbKqp31Gnsja76yS6H7XtJzxnasP3OX8sfb+zYDISp4eEAHGJ6etN7m6YKoWQUuFmoRsZzbxeWqCsiMuPqZGld2SeQEvbTVOkpQLnuXOKzVqEujNmdqnFd7e/4tVyaqe+hlR/aD9rvlq1CitqHb5Dd8T1eUPGgZEDlYfAiI5lmoW6vKkPXH+qtGYRZY4OoBzxyCxrsgFGQBMTu1i6rgczC6QrXk0N3SC+s8VrHkfJX9GnsrcdcTWLuzk+er6B42192/mECmcuTb7ujxpN6Tn1oxVZOPrHJ3I6Kquv65u/jE1Ga8Q8kGMuZwAUzlNj/VrhX/vzjZaX60CJveOwX++y5Etm+Hx94Tu2a7+PN2JYa3vlD4xmNA7xiivTEutAh7q0RIJrZqhe3QzxR+/+kI38HXX8aYYENVDtl6M5RKyLY1D4sgLvva95Ooq9ZrIUHNztnZAR3u4oneEOzT/mJqMV87CIe1xd+dws+NGySVUjri9JfwaNcTSbT+hGtDrIQgAb+/PsSpZ1dbPqK6DE3f4PJ0lr6gMR3+9CiGEVcGK4XVEAPjPdzl4e38O5iW3qzkXZM67jw7l4qNDuVAB0mTQ5Ln0gmIbZyqoSwyI6hlHXYzrsqu+ua6s4s9/pv5549z382Wbey7YckOs694R7jI6r7neM7q8VCqzwZCWqZrHR/rG4t4uEbI1koYT+WoN7RphNJibtZ9RXQcn7vJ5OoJcL0HdwVHNBSva1/5RUi57TlULyAZDhgSABZ+d9MjjRzWMgmI37qHHgKgeceTFuC676lu6GQsA73x3DhN6xdjcLdeeG2Jddv11RvOPqZwMc4FheIAPJvWOwRqZZivtZ2NrcKitedQO/Kl9X1NDRJia+27T8Qt4Kkl/hFtrPqMT56/q3cDrIjjxhOY8axj1EvyzNkd310wFK4avNZU7pm1Cs9RMXg143PGjv5j6wavtaOFOXfIZENUjjrwYm/oVfr2iyuEXfMP3kguODGeGtqZW6Mi5K3YHb3U1pLyjm+hM5WTc37UFPj/+u2xgqA2U/t45HG9/l2PUJLnx8QS7e9KYyx+Rc72iyugGWSUEjp67ir/HWz/vk6mcNGcHJ546IJ0uuR9WpmpzDIMVudeqVIBKJhdNDeCxfrF4c0+22aBIDXjU8XNHtjYbO7KZ2dR3ojbXFWdhQFSPOPpinNgmGCtHdgEE0C26qfRrvzbvYc0Es74aNe5/86Dd7yEXFGi52691RzbRmZtW4LNjv+v9rQ0MDZs/h93WApuOX9ArS3xkU7v2zVz+iKmausYaL9kcg5nrj+N6RaVVzV3mctI42rJltuSTGQYrr+86Y7SuEMAbo7riYPYf+OhwrvS5VANYtdc4yV6XCkDK8DjZ42fqWlLXOWPunkBvay25o5uZTX0n7L2uOBMDonrEkRdjU1+K2ryHLRPM2vsehs0khtzx17qjmujM5WIZqhICx369avRrftPxC0a/3Oy94Jsqj6maOu35IRfI2FK7Z66KnqMtW2ZtPplhsPLW3mx8dOi80XpqFfDb1TJ8rBMMaZl6jycGtMatIX6IbOaD6xVVyCsqs6oHrKNv5pbOfXdPoLe1c8yJ81dralYdnAPnKd8JBkT1jCNOPEtfInvew9b8Jnvew1LXfXf+te6IJjprb2RAzbGoFkK2abK0oloabLE2F3xz5TGsqTNVu2XuNba8r1oFfP74nUa/Si3NrWZvMFhXTa7OYPijx5AKwJL7OmJgh1C945QqM7s5ANwbH4FUg/wjc7xUKjzUoyX2/XxZqinWPfdMXUvahflZfY2x5nO1dO57QgK9uTSKzScu1Hwu4q9m9Y3HfndaM7MnfCcYENVDtT3xrMlFsvU97MlvsuU9zDWTqAG8PqqrzZPJOpMjqtkNt2HqRualUmFo1wijprDu0c1kAwdfjVrafm0u+Pt+viz7eWjLFB3ka7E3ktxrzB0DwPoqektzqwFw61//zqT9QfLxoVy8tuus3jIBoKmvRq+G4Y+ScpMBzxcZF8wGQ9ocI90EWwAmzz1T15KdP10yeY0B/gp2DQMBuc/VmnPf3RPo84rKkH25xOh5L5UK+89cxht7/mquNGxWN1zfV6PW6xhRXzEgIiPOSAx1drKpuWaSlOFxGNI5wiHvY461QY4jqtlNbUM37yuymY9e09dTSW2NatwMA6hqAdz/5kGkDItDZDNfuy/4UoAqs0x70zPMXzLscaQ7BpVc7Z6542iphlEuv0mrWtT0ntIdObu2tQ2uZKl8ppaHB/igTZif7DZVKut6k1nqRab9XA0/q4PZBSbPPblriUoFvLZTP3DTbj/z90KMfvt7m5pvrQl23DWBPq+oDO/uzzHqIAHUlO/p5LZI/Uq+Ns+QWlUz7IVcTV19xICIjDgjMdTZyaa2NJM4g7VBjiOq2U1to7D0pjSBqqUcLS25ru7a7W18PMHuC76pAFU7qCMAvek3tL2R1AY1BaaCGmuOo7kaRkv5VtWA0Z1cLhh09xwSufLpTo5saUyxblFNjQNVFXBLUx+9jg+Gn58awEM9IvHRYZmcItR04+98S6De56p7XE/+VmT0Ou25J9crVbZmWAU82rcVUr8y31wn97laE+y4YwK94VhRumrGjeqEwrKbVjVfqgCsGdsNU94/6tbNgo7EgKies/fXqzOS4JyZWGd0kUTNdBAh/o0c9h6m2BLkOKKa3dQ2dPM0bLlwmerqXlpRbfcFXzZABRAa0MjkPggBvDC0I2KD/YxulIZjGZk6Bsd+vYqmjS2f75byrXRrp7QMb4julENialJew/LN/+yk3uTIulOzaGvG2oX5ST8iwgN8kDo8zihokht+Qwjg36O6olljb+k4rU8/b/OPlLyiMiz92rgG47F+raR9072WFJTcwIyPM4zWf+j2lnhzr/ku/doyGQb51gY7zrqm2XPdNlcrC9R8znM/PWl1GaYkxsBH08Ds96y+zT/HgMiN1bYqvra/Xp2RBOfMxDrtxWnt/nN4e/8vJrt4WzPRpC3H3JYgxxHV7KZqw+TKsDUzD0MsjCxtrkwJsc3tvuBP6h2Dd/6cjkNbyzD9o+M1A/0Nbidb5oWbTiN1eJyU1A3IDxLoq/GSrbmY/tFxvTwgU+e79oZnKpF7Sp9WiA1pbPaG6C45JKa+57JBJ6DXg8hQNYChbx5EqoXmx7dkust7qVRGeXr2dLc2VXv3xu5sRDbzlcqlvZbIDQWiBrA+3bhXm5xqAWzOuIC4WwL0BjGNbOZr1Vg5jr6mWZPMLXeNOnLuilUdKqyhVkGaUkeueVL7PdNd39k9+uoCZ7u3Ul3Pdl/bYEZuRnC1Clj5UJd6PWGipZnQzR1Xe4+5rbOvb0jPlZ3nyxaG23g6ua3JwfMs7YvRlAwq6N0Q5Zi7eL21N1uqrVIBGNkzEusPnzc6PqbKrHvs5I6tHLlcFe12AJicTf3E+atGI2OrARxYcJf0/ubykO5M2WWU93Twz9c6k+5o5HJjdmn325pjJ8dcTY6pz2TB3e3wSGKs7Pq29kg1VW5T3yvD78Ok3tEmJws2RwWgz61B2H+2oM6aQQ17OhqeU9Zcv8yNvWaO9nuj+vM/AjC6JukeW8NaRV263xtbObvpmbPdezBHVMWbGlxtxscZbpnrUFvai8qV6xUmq3irhfHI1Qs2noSvxgstm/nWalRrW5qWHFHNLreNQJ+Gsl2lLSUEG1azq0TNoJymgh5zeSkHzhbgDZ0Z7QWA9YfO1+Tk6KgSAp1bBGLlQ12Mmjt0a1msHVtJbpUqIaTaQsMfBtrzPz6yKVKHm/7sdGshDmYX6AVVslTyTzuSYe84uebOcwWlSIhtbtSMLGBcqwaZG1y1AIa+cVB2rjJTn0nnFoGy5bW1BsVc7Z1cDZxcbQ5gerJgcwSAfWcKpL+11whHN4Nqv1snfyvSy/vr1TrI5OcJGPe8W/DZSQQ10dgVDBmOFg1A9ppkTfMkUFO7uHb/OTwzpL1N5XCnpmcGRG7IEVXx5nIk3Dkxzp5qU8MbhLmmFEPaINHcjcWactga5Fh7k7A0/5jchWtrZh5e3PqjxX3JKyrDlswLxoEzoBdIGI4BYzg/2PzPavISTF2Pq2E8w7W2SS46yFe2uUN7gW4s0zRmLRVgFAxpy2zLuFqmfn3fEdPUqFxCOHfeLXO947R0m2AN901ucuR2YX4Y+uZBo+YlgZobsG5OEWD62pL5e6FeU2dtmJrXzrB5WS4415bB3FhKtqgWwNoDOXjm7g612o6WqfOpWgDf6QRjWtrvg+yPXACT3jsq+z4vDu2I5744bbKmTa750lKz4Fv7zI8svua7XzChd7RN57+7ND0DNcea3Iz2gqPL1jwT7a8sL5X8T1bdXx3uYkN6Lnql7sKoNYfQK3UXNqTnWnyNqRuE9vipTfwCNmTpxqL7fgezC5BXVGa0fniADxJim9v9JTbctj3HIzzAB0M6hxudP2oAZy9fw5bMC8grKsNbe7NxZ8ouvLTVOHlVDf1AQhtA5BWV4eivV42DAJg/vmoA85PbSeeibi2M9jzVLa5AzRhGG9JzjW6ItugZ09Tkr2bD89/UZ2duwMjvc64aPefsbtcmh5dQ/fX+hrWTuvuW2CYYKx6KxxujumL//P5/1ZINizM6Z4C/aop0z73wAB/MG9zOaN1l27Jkvxf20tbeyZ03gOmaBW0ZRtzeEvvn98c/TdRY2HLze3tfjtG+mbsWmFrHmgFIDU1OrJn7T+6+YIqXSoUB7UP17gFqANP6xeLjKXdIn70t8orKsNTE4JtaAjU/pmzhiPudo7CGyA2ZaoIBYNPgWNpfh8d+vWpUQ+IO42Xosrfa1FTi6OsPdUXzJt5mq3jlmJrZXTu2hzZJ2NHNjqZmF5dr3rMmB2xy7xi8/V1OTe0Man5JLtx02mI5TOVfaAMIe1IO5yXX5JbcGx8hWwuT2CZYrwZJWzNhKlfBWnIBi64DZy9brNGwZToUAHh6cFun/qq1d6LMvKIyrN2fgzV/jk1jeP4mtgnG4vs6yp4jAsbfxbhbAozWM1ULWZtEWXO1d9YOIDukczhe/upHk8cs8/dCLP3qJ6NmXV3aSWy172vY1KVbg2puSAO5sb0sad7EW9qXlGFxWPDZSbNl1b12WVNzbe1nZO134e39ttUS2Zpy4EwMiNyUXFW3NtHQlptxzQXBByXllW5xwgHyX0B7q01N3SDMTUZrqglGBWDxfR3RzFeD26Ka4lLxDaz5LhuF128azcjtyGZHuWBQbuwU3ea9+cnt8Ehf4wRWw8Bq1O0t8fFhyzVLgP4YQXL5F5m/F+Le+Airm7C0gZ020dZUM6Etk4kabv/zx+/ET/nXzM5fZ8qbe7Ix+o6oWnXPN+SlVhnNu+Vour33rOm5JddEoz1/24X5YUtmnl5vQMBy87E1vSUdlShr6ryxVAbd64y53m4Jsc1xb3yEbL6Z7nYzfyvEqDXfy34vF3x2Eid+K8THh85LnQkA/SENTI3tpatdWBP8lK8/uvSybVm4I6YZcq+UorF3A6wZ1w2T3ztq8hq28fEEvfPB8PhZCtpMfUZyY0PJqbah2VhblsQ2wdg/v7/L5zpjLzMr1XUvM1229mIytx1Xn3CmLpK12UdLvbaMemQNbiv9upOjVgFdIgNxLLfQ4v58POUOm/MmDAPCg9kFGLXmkE3bAPR79eQVleHIuSt4Yn2GUQKxVcEF9HuIvLU3GykG1ePaz2Pfz5elX6lqAPff1kKaFkTLXNBmyFSPSGtqiLTHX6681nhjVFeLo5jrnj/WUKn+3HeDHle21JbIrWuYKzclMQYTesWY3Za1vfR0yZ0zct9Fc9872c8UwGujuqKbA6fQMVUGU4n/lq5/2mtk5m+FWPZ1ll4vTksDPFrj4yl3IPfKdZPNZi/c1xELv7Bck9u/XTB2/3TZ5HuYuiaZmq5Gy9Q115bzyFIPRXuCsdpiL7N6xFFJZ44eL8NWcjUhus1A9labWqoWlu2R5SvfI0tbLmuCIVubHU01uyW2CbYrcXjptp9wb3yE3oXFkLU3wnnJ7fSOm7kmEbnjOTYhSi/fRwBY9nUW7u0SYXcvPQB6M28b0p0PTW4gPy21Cnisb6ze3E1a0z86jpLyStm5rLQXbt399dWo8a/tP+v1RDIkBJDy1U+AgBQQ2lJbYhT49InBkM7hRrly73x3ThorxhRbm/yAmnNmamIM3vnunN2DEppKANaOQ+Wom59cGUw1v++f39/ijxftPggIvWbII+eu1DoYMhzba96nmXrn0fDbWmBgh1A8v1k+EVrX7p8u485WzXDwlyuy7yHHmoR8U/cVc+eRXG3YfW8cxAKDH0TmgjFn9eazFQMiF7PmV6Mz5sxxxSBY1gwFYG+1qalgT3c/dS+G2gvp0XNXMeNj+R5o5qhVsKnZUW5Ife2F+unktja++1+vP/brVbNJmtoaMd1RrHUvRobNWlqmBn/UTvxqeLxNjXZd21562hw4IYDfi8qwbFuW0U1abt4rXdr98/dtaDTukWF+jLlcMW2Z3p/UEyfOX8WRc1cRHeSLz4/9ji0n843ed+m2n3Bvl5raJ1tmYTe8af3nu79yf+SOLwCT32VTTX7mAnAvlQoTetXUPln6LtrSnKXl6F6uhmWw9gekpVo43V5rKhOdUyzRHmdtLVNOwXVcKr6B6xVVWPpAZ1wqvoEj566ie3RTqUbFcFoSU+f29zlXMK1/LFbtyZaaT58eXPMe2uOiy5rg2NR9xdT1YHr/1oiPDJDt6Zay7SdABTySGGtVMObo3nz2YEDkQtb+arSUdGZrcOOq+ZesuUha8yvOWpb2MzzAB82aXLfrl9+8we30mgcsTZ5pakh9wyk3bOGlUqFaCLPBkLYJ4d4uETh67ipUKuC2qJoLr7mbneE5B+hP/Gp4vtQmaDcVtGrLMaTzX+WTS8w2d9PXbbp6JDEWEQGNZMc9OnruKjJ/yzFKJjd1846PbIoQ/0Y4cu4KtsoEQ8BfibgCxp+Rdmws3X0DTN+0ZPNFVEDmb39NXGrqHDe8wU7u3Qo9WzWVzUMxDPTtDVjkzh9dttZwSz0chbDYqcDe/KbENsEmA1e5Od20zAWX2ucHdQw1Csa17zupTyu91xjWSJrqaVktgN6tg/HwHVE1zXy/F0rvIXcuyE6KC/MTKAN/fT/nDW4nNSNqO0G8tusszMWK2lpsqxOy9+VYbAZ2JgZELmJrrypTv6BtDW5cOQiWoy+S5li7n401XrKvbxvaBFkXS2SXAX81B1nTDm6putmWLD7dX5wvD+uEC0U3jNZRA3h9VFe9aRTCA3zw93jjcYzMMTfxq+FxtLeniK3nr1yNhKmbvlxPl+7RzWRvCjPWHzf5OVia1NUU7U34UvENo3GYAGDaR8eRcb4QE3vHSLVTZy9ds7r5VAgYzV83/7OTKK+sQrPG3lKujvZzTD93FbdHN8VP+dcw5X39YEiNmu7djrwZ6dbCzlx/3O4abqOR1AHZASN16fayVKuAib2jpWWmrg0rHoo3WbOUENsc8+9uV9MUqkNb86OtuTRl2ynjoNnctVf3PE8dHod5nxnPQaY72S0AKTA2tW1T31FzEygb1pbOS26HWwJ99Hotm7t+aROsTQVjhi/V/ohgQKQw9uQFyfUWsDW4cfUgWHU1FIC1k39er6iSff2iezuhtOKmyUHPtLUK5o6/9pfVwbNm8k1s2Cc1gM+n3ak3smyv1F1G681LbmcxUdhatjSF2To4pSODc2vfOzygZgydpdt+kpLCBcxf1L1UKvhq1NKQFwAsBkNq1HTB1+3qLmfNdzl4+7scDLutBTYe+93mmkLD9QWA5774AcBfgYNueeUSaa2ZcNWQtbXS2kD8eoV9vVzlalcFanp1yZ0nhgF2YusgfHemAGu+q7mxm+r6XhNIq8zWLNXUTl7H+sPnjaa4uDc+QvaaZok1117tuT1rfQYO5fyVMzS061/5edZe1019T+SOo1wT/7JtWVjxULzV+6gbtBkGY3KJ6q4eDoYBkYvY0mXUkcGNM/KRbKVtBjE1FIAj8ptkf5GojCf/TGwTbPJ4hAf4YOlw+XE/vFQqQKaZRnv8zSU620MFIGV4nN5Ny1TuTOdbAh3zprD9fLElcd/W89fSeaH73uamHVn69U/S+Ez924dg54+XTJZRrQKSOoVKIzmrANwdF2byc9XWTjX305icW86QAPDZsd+NnjfVBd5aAsbjOZlqeimtMDeyjT57mtztna7GVO2qYU2CtknNcBR1w2k4THV9105Ma6qW0zAheKpBbZr2mvZbYZlRLZI5uh0DLF3z0s/pJ1BvOn4BTyXVjHtly/fU0nfUUhO/qcBx4+MJ2JD+G9an58o2wVmabsjVw8EACguI3njjDSxfvhz5+fmIj4/H66+/jh49erikLOaaGKy94NgT3NjbtOEMcl8QR45dYtiMontj0M1ZMnc8tGXUHaNEu063qKYmE48dGQwBNcFcYptgveecFdwaXpyddb7YUn57e2kZDu1gmNhpKhjSdmtvqFbr9U4TgGzOkG4zJWD/pKq6HHH6WFMGW86Z2tTq2RIsa5nKD9Od3sWWiU2rhEBpRbXJc9qaXmsCpnv4xbUIkH3fxFuDcODsH3rNatr3tbfZXffHg9z1TreZ0BbmmvjNBY7xkTWJ4TMGtDYZ+BqeA46Y19GRFBMQbdiwAXPmzMHq1avRs2dPrFixAklJScjKykJISIhLymRLl1FT7cz23Kzc6SQ0/FXvyPwmS5MSai8o2ukM1H9+2eWO8zND2mNC72ijY5YyLE6va3i1ANbskx/cTUtujB1LuQjatnhH5O2YYyqYcMb5Ym35bTkvzK1rTWKnbv4RUDPzuCXacmubKV/e+oNDg+HakDvXrEmkNaWum9y154jud0xbWyp3vbTEsOu74bVXLrnfln021Rtr6QOdAUBKlC6tqIavRo3cK6V6Y4eZOret+fEg/Xg7kIM1+3L0mglt+VFpMgjVSbg3d02wNfC1J1B2FsUERK+88gqmTJmCCRMmAABWr16NrVu34t1338X8+fNdVi7Dk8HWC469Nyt3Ogm1nHGx1e6n3IjV2tFnzfXSkduWrsQ2wUY/5eW6YOual9xOtqpYm4tgSxKqI4MVS4GHM84Xa8pvy3lhbl1Lo05rR+rWbvNgdoHFWhrD1+QVlWGNQU81LTWAeXe3w/UblXht11kLW3aM+7u2QI+YZlYn0lriiiZ33bxDISCNQg9Y7kquWzNjGPzpntPmaiBtbY4yF+TLvZ8huXPblh8/b+vkrNnzo9KotgnyCffueA+pLUUERBUVFTh69CgWLFggPadWqzFw4ECkpaXJvqa8vBzl5eXS38XFxU4vJ2B/M1h9ODGdebE1ldRnOF+YrRePnALbu+13bhEo+wtVW05bk1Ad9fm7KuHeUvltOS/Mras9B+RuRF4qlV5gY2pbll5j6nz4e1w4nv17eyk4//fus+Z7HsovMuuJu1rjtd1n9RLEtXkmcuN72fOZuqrJXZujY8jcZ6SGfs2Mubm8LP0QsGWfLQX5lmq1avPjx1HfYXdqRahLigiICgoKUFVVhdDQUL3nQ0ND8dNP8glwKSkpWLx4cV0UT4875fjUNWfvu+GX3BEXD5vnutK52JkLBFxxQXKHhHs5tpwXltbVbVZ4e19Nt2xT2zPclm6Ss6nXyDaZAFIwpLtduVG45QJ1Q2rAKKHfS6XCrWF+RtvT7TburO+Rq5vc5YbyMFUzI8ea64Ct+2zuu20pR6c2P34c+R2uLz+0baGIgMgeCxYswJw5c6S/i4uLERkZWSfv7U4XnLrm7H03/JLX9uIh1TqY6In29OC2enMi2RLg1fUFyZ2DcVvOC0vrhgf44Jm7O1g1ErPhtgDbBrS0NO2FthkospmP3mz1uk2qqj+rjHQDMQBG7yGX5O+sgNadbpaGAxnqHkdrWBtEOGqfTQXNhmOH2cOdv8OeQBGTu1ZUVMDX1xeffvophg4dKj0/btw4FBYW4osvvrC4DVdO7krOY2liWGvlFZXJ9kTT9m7ypODW08rrjhxxDHW3ARgHYnLv4ajzWWnq+rg5+/34HdZn7f1bEQERAPTs2RM9evTA66+/DgCorq5Gy5YtMX36dKuSqhkQ1V+OvHjwQkSuxnPQPnV93Pg51R3Odm9gzpw5GDduHLp3744ePXpgxYoVuH79utTrjJTLkdX/7tSUQMrEc9A+rmii5ufkXhQTEI0YMQKXL1/Gc889h/z8fHTp0gVff/21UaI1ERERKY9imsxqi01mREREnsfa+7e6DstERERE5JYYEBEREZHiMSAiIiIixWNARERERIrHgIiIiIgUjwERERERKR4DIiIiIlI8BkRERESkeAyIiIiISPEUM3VHbWkH9C4uLnZxSYiIiMha2vu2pYk5GBBZ6dq1awCAyMhIF5eEiIiIbHXt2jUEBASYXM65zKxUXV2NCxcuwM/PDyqVyu7tFBcXIzIyEufPn1fsnGg8BjV4HHgMAB4DgMdAi8fBOcdACIFr164hIiICarXpTCHWEFlJrVbjlltucdj2/P39FXvCa/EY1OBx4DEAeAwAHgMtHgfHHwNzNUNaTKomIiIixWNARERERIrHgKiOeXt74/nnn4e3t7eri+IyPAY1eBx4DAAeA4DHQIvHwbXHgEnVREREpHisISIiIiLFY0BEREREiseAiIiIiBSPAREREREpHgMiB1i1ahU6d+4sDSSVkJCAbdu2Sctv3LiBadOmoXnz5mjSpAmGDx+Oixcv6m0jNzcXQ4YMga+vL0JCQjB37lxUVlbW9a44TGpqKlQqFWbNmiU9V9+Pw6JFi6BSqfQe7dq1k5bX9/3X9fvvv+Phhx9G8+bN4ePjg7i4OBw5ckRaLoTAc889h/DwcPj4+GDgwIE4c+aM3jauXLmC0aNHw9/fH4GBgZg0aRJKSkrqelfsEh0dbXQuqFQqTJs2DYAyzoWqqiosXLgQMTEx8PHxQWxsLF544QW9+aTq+3kA1EwXMWvWLERFRcHHxwd33nkn0tPTpeX18Rjs27cP99xzDyIiIqBSqbBp0ya95Y7a58zMTPTp0weNGjVCZGQkli1bVruCC6q1zZs3i61bt4qff/5ZZGVliWeeeUY0bNhQnDp1SgghxKOPPioiIyPFzp07xZEjR8Qdd9wh7rzzTun1lZWVolOnTmLgwIHi+PHj4quvvhJBQUFiwYIFrtqlWjl8+LCIjo4WnTt3Fk888YT0fH0/Ds8//7zo2LGjyMvLkx6XL1+Wltf3/de6cuWKiIqKEuPHjxeHDh0Sv/zyi/jmm2/E2bNnpXVSU1NFQECA2LRpkzhx4oS49957RUxMjCgrK5PWGTx4sIiPjxfff/+9+O6770Tr1q3FyJEjXbFLNrt06ZLeebBjxw4BQOzevVsIoYxz4aWXXhLNmzcXW7ZsETk5OeKTTz4RTZo0EStXrpTWqe/ngRBCPPjgg6JDhw5i79694syZM+L5558X/v7+4rfffhNC1M9j8NVXX4lnn31WbNy4UQAQn3/+ud5yR+xzUVGRCA0NFaNHjxanTp0SH3/8sfDx8RFvvfWW3eVmQOQkTZs2FW+//bYoLCwUDRs2FJ988om07McffxQARFpamhCi5uRRq9UiPz9fWmfVqlXC399flJeX13nZa+PatWvi1ltvFTt27BB9+/aVAiIlHIfnn39exMfHyy5Twv5rzZs3T/Tu3dvk8urqahEWFiaWL18uPVdYWCi8vb3Fxx9/LIQQ4ocffhAARHp6urTOtm3bhEqlEr///rvzCu8kTzzxhIiNjRXV1dWKOReGDBkiJk6cqPfcsGHDxOjRo4UQyjgPSktLhZeXl9iyZYve87fddpt49tlnFXEMDAMiR+3zm2++KZo2bar3fZg3b55o27at3WVlk5mDVVVVYf369bh+/ToSEhJw9OhR3Lx5EwMHDpTWadeuHVq2bIm0tDQAQFpaGuLi4hAaGiqtk5SUhOLiYpw+fbrO96E2pk2bhiFDhujtLwDFHIczZ84gIiICrVq1wujRo5GbmwtAOfsPAJs3b0b37t3xj3/8AyEhIejatSvWrFkjLc/JyUF+fr7esQgICEDPnj31jkVgYCC6d+8urTNw4ECo1WocOnSo7nbGASoqKvDBBx9g4sSJUKlUijkX7rzzTuzcuRM///wzAODEiRPYv38/kpOTASjjPKisrERVVRUaNWqk97yPjw/279+viGNgyFH7nJaWhsTERGg0GmmdpKQkZGVl4erVq3aVjZO7OsjJkyeRkJCAGzduoEmTJvj888/RoUMHZGRkQKPRIDAwUG/90NBQ5OfnAwDy8/P1Lnza5dplnmL9+vU4duyYXvu4Vn5+fr0/Dj179sS6devQtm1b5OXlYfHixejTpw9OnTqliP3X+uWXX7Bq1SrMmTMHzzzzDNLT0zFz5kxoNBqMGzdO2he5fdU9FiEhIXrLGzRogGbNmnnUsQCATZs2obCwEOPHjwegjO8CAMyfPx/FxcVo164dvLy8UFVVhZdeegmjR48GAEWcB35+fkhISMALL7yA9u3bIzQ0FB9//DHS0tLQunVrRRwDQ47a5/z8fMTExBhtQ7usadOmNpeNAZGDtG3bFhkZGSgqKsKnn36KcePGYe/eva4uVp05f/48nnjiCezYscPo15BSaH/5AkDnzp3Rs2dPREVF4X//+x98fHxcWLK6VV1dje7du+Pll18GAHTt2hWnTp3C6tWrMW7cOBeXru698847SE5ORkREhKuLUqf+97//4cMPP8RHH32Ejh07IiMjA7NmzUJERISizoP//ve/mDhxIlq0aAEvLy/cdtttGDlyJI4ePerqopEBNpk5iEajQevWrdGtWzekpKQgPj4eK1euRFhYGCoqKlBYWKi3/sWLFxEWFgYACAsLM+phov1bu467O3r0KC5duoTbbrsNDRo0QIMGDbB371689tpraNCgAUJDQxVxHHQFBgaiTZs2OHv2rGLOAwAIDw9Hhw4d9J5r37691Hyo3Re5fdU9FpcuXdJbXllZiStXrnjUsfj111/x7bffYvLkydJzSjkX5s6di/nz5+Ohhx5CXFwcxowZg9mzZyMlJQWAcs6D2NhY7N27FyUlJTh//jwOHz6MmzdvolWrVoo5Broctc/O+I4wIHKS6upqlJeXo1u3bmjYsCF27twpLcvKykJubi4SEhIAAAkJCTh58qTeCbBjxw74+/sb3Vjc1YABA3Dy5ElkZGRIj+7du2P06NHS/5VwHHSVlJQgOzsb4eHhijkPAKBXr17IysrSe+7nn39GVFQUACAmJgZhYWF6x6K4uBiHDh3SOxaFhYV6v6J37dqF6upq9OzZsw72wjHWrl2LkJAQDBkyRHpOKedCaWkp1Gr9W4yXlxeqq6sBKOs8AIDGjRsjPDwcV69exTfffIP77rtPcccAcNznnpCQgH379uHmzZvSOjt27EDbtm3tai4DwG73jjB//nyxd+9ekZOTIzIzM8X8+fOFSqUS27dvF0LUdLFt2bKl2LVrlzhy5IhISEgQCQkJ0uu1XWwHDRokMjIyxNdffy2Cg4M9qoutHN1eZkLU/+Pw5JNPij179oicnBxx4MABMXDgQBEUFCQuXbokhKj/+691+PBh0aBBA/HSSy+JM2fOiA8//FD4+vqKDz74QFonNTVVBAYGii+++EJkZmaK++67T7bbbdeuXcWhQ4fE/v37xa233urWXY0NVVVViZYtW4p58+YZLVPCuTBu3DjRokULqdv9xo0bRVBQkHj66aeldZRwHnz99ddi27Zt4pdffhHbt28X8fHxomfPnqKiokIIUT+PwbVr18Tx48fF8ePHBQDxyiuviOPHj4tff/1VCOGYfS4sLBShoaFizJgx4tSpU2L9+vXC19eX3e5dbeLEiSIqKkpoNBoRHBwsBgwYIAVDQghRVlYmHn/8cdG0aVPh6+sr7r//fpGXl6e3jXPnzonk5GTh4+MjgoKCxJNPPilu3rxZ17viUIYBUX0/DiNGjBDh4eFCo9GIFi1aiBEjRuiNvVPf91/Xl19+KTp16iS8vb1Fu3btxH/+8x+95dXV1WLhwoUiNDRUeHt7iwEDBoisrCy9df744w8xcuRI0aRJE+Hv7y8mTJggrl27Vpe7USvffPONAGC0X0Io41woLi4WTzzxhGjZsqVo1KiRaNWqlXj22Wf1ukkr4TzYsGGDaNWqldBoNCIsLExMmzZNFBYWSsvr4zHYvXu3AGD0GDdunBDCcft84sQJ0bt3b+Ht7S1atGghUlNTa1VulRA6w4YSERERKRBziIiIiEjxGBARERGR4jEgIiIiIsVjQERERESKx4CIiIiIFI8BERERESkeAyIiIiJSPAZEREREpHgMiIjIafr164dZs2a5uhhOt2jRInTp0sXVxSCiWmBARERkQkVFRZ2+nxAClZWVdfqeRFSDAREROcX48eOxd+9erFy5EiqVCiqVCufOncOpU6eQnJyMJk2aIDQ0FGPGjEFBQYH0un79+mHGjBmYNWsWmjZtitDQUKxZswbXr1/HhAkT4Ofnh9atW2Pbtm3Sa/bs2QOVSoWtW7eic+fOaNSoEe644w6cOnVKr0z79+9Hnz594OPjg8jISMycORPXr1+XlkdHR+OFF17A2LFj4e/vj6lTpwIA5s2bhzZt2sDX1xetWrXCwoULpVm2161bh8WLF+PEiRPSfq5btw7nzp2DSqVCRkaGtP3CwkKoVCrs2bNHr9zbtm1Dt27d4O3tjf3796O6uhopKSmIiYmBj48P4uPj8emnnzr6IyIiHQyIiMgpVq5ciYSEBEyZMgV5eXnIy8uDn58f7rrrLnTt2hVHjhzB119/jYsXL+LBBx/Ue+17772HoKAgHD58GDNmzMBjjz2Gf/zjH7jzzjtx7NgxDBo0CGPGjEFpaane6+bOnYt//etfSE9PR3BwMO655x4pcMnOzsbgwYMxfPhwZGZmYsOGDdi/fz+mT5+ut43/+7//Q3x8PI4fP46FCxcCAPz8/LBu3Tr88MMPWLlyJdasWYNXX30VADBixAg8+eST6Nixo7SfI0aMsOlYzZ8/H6mpqfjxxx/RuXNnpKSk4P3338fq1atx+vRpzJ49Gw8//DD27t1r03aJyAa1mhqWiMiMvn37iieeeEL6+4UXXhCDBg3SW+f8+fN6s8L37dtX9O7dW1peWVkpGjduLMaMGSM9l5eXJwCItLQ0IcRfs2uvX79eWuePP/4QPj4+YsOGDUIIISZNmiSmTp2q997fffedUKvVoqysTAghRFRUlBg6dKjF/Vq+fLno1q2b9Pfzzz8v4uPj9dbJyckRAMTx48el565evSoAiN27d+uVe9OmTdI6N27cEL6+vuLgwYN625s0aZIYOXKkxbIRkX0auDIYIyJlOXHiBHbv3o0mTZoYLcvOzkabNm0AAJ07d5ae9/LyQvPmzREXFyc9FxoaCgC4dOmS3jYSEhKk/zdr1gxt27bFjz/+KL13ZmYmPvzwQ2kdIQSqq6uRk5OD9u3bAwC6d+9uVLYNGzbgtddeQ3Z2NkpKSlBZWQl/f3+b998U3fc8e/YsSktL8be//U1vnYqKCnTt2tVh70lE+hgQEVGdKSkpwT333IOlS5caLQsPD5f+37BhQ71lKpVK7zmVSgUAqK6utum9H3nkEcycOdNoWcuWLaX/N27cWG9ZWloaRo8ejcWLFyMpKQkBAQFYv349/vWvf5l9P7W6JiNBCCE9p22+M6T7niUlJQCArVu3okWLFnrreXt7m31PIrIfAyIichqNRoOqqirp79tuuw2fffYZoqOj0aCB4y8/33//vRTcXL16FT///LNU83Pbbbfhhx9+QOvWrW3a5sGDBxEVFYVnn31Weu7XX3/VW8dwPwEgODgYAJCXlyfV7OgmWJvSoUMHeHt7Izc3F3379rWprERkPyZVE5HTREdH49ChQzh37hwKCgowbdo0XLlyBSNHjkR6ejqys7PxzTffYMKECUYBhT2WLFmCnTt34tSpUxg/fjyCgoIwdOhQADU9xQ4ePIjp06cjIyMDZ86cwRdffGGUVG3o1ltvRW5uLtavX4/s7Gy89tpr+Pzzz432MycnBxkZGSgoKEB5eTl8fHxwxx13SMnSe/fuxT//+U+L++Dn54ennnoKs2fPxnvvvYfs7GwcO3YMr7/+Ot577z27jw0RmceAiIic5qmnnoKXlxc6dOiA4OBgVFRU4MCBA6iqqsKgQYMQFxeHWbNmITAwUGpiqo3U1FQ88cQT6NatG/Lz8/Hll19Co9EAqMlL2rt3L37++Wf06dMHXbt2xXPPPYeIiAiz27z33nsxe/ZsTJ8+HV26dMHBgwel3mdaw4cPx+DBg9G/f38EBwfj448/BgC8++67qKysRLdu3TBr1iy8+OKLVu3HCy+8gIULFyIlJQXt27fH4MGDsXXrVsTExNhxVIjIGiqh28BNROSB9uzZg/79++Pq1asIDAx0dXGIyAOxhoiIiIgUjwERERERKR6bzIiIiEjxWENEREREiseAiIiIiBSPAREREREpHgMiIiIiUjwGRERERKR4DIiIiIhI8RgQERERkeIxICIiIiLFY0BEREREivf/P0yxqwCkR+gAAAAASUVORK5CYII=", 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XXR8yMjIQERGBoqIi9OvXz+51y1oiR8LDw1FTU+OL4rmldevWCA8Px3fffYeWLVsCqJ1+Iz8/H1OnTvVrWRgQaZyjqti+beOcBjkcyk9E5B/mrg9PrdiHGiF83vWhcePGePzxx/Hoo4/CZDKhd+/eMBqN+O677xAdHS0HFs6kpaWhsLAQu3btwlVXXYXGjRsjIiLCJ+V1JioqCg8++CCeeOIJNG3aFKmpqZg/fz4qKysxYcIEv5aFAZHGeVIV62kQRUREnlHT9cGbnnvuOcTFxSEnJwc///wzYmNjcd111+Gpp55S1Rw1fPhwrFixAv3790dZWRmWLFmCcePG+bTMjuTm5sJkMuGee+7B+fPn0a1bN3z11Vdo0qSJX8shCSGE69WovLwcMTExMBqNiI6O9tvnFhur0Ct3vV1VbF52f4c/uM0FpRi9eKvd8mUTeyCrdTNfFZWIKKhcvHgRhYWFSE9PR8OGDQNdHKoDZ9+l2vs3R5lpnCej0DiUn4iIyD1sMgsC7lbF+rs9m4iI6r+//OUvePfddxVfu/vuu7Fo0SI/l8i72GSmUqCazOqi2Fjlt/ZsIqJgwyYz95w+fRrl5eWKr0VHRyM+Pt7PJfqdN5rMWENUjzkbyk9EROSO+Pj4gAY9vsY+RERERKR7DIiIiEjXtJg1mdzjje+QTWZERKRL4eHhCAkJwcmTJxEXF4fw8HB5igsKDkIIXLp0CWfOnEFISAjCw8M93hYDIiIi0qWQkBCkp6ejuLgYJ0+eDHRxqA4iIyORmpqKkBDPG74YEBERkW6Fh4cjNTUVV65c0cTcXuS+0NBQhIWF1bl2jwERERHpmnlGdttZ2Ulf2KmaiIiIdI8BEREREekeAyIiIiLSPQZEREREpHsMiIiIiEj3GBARERGR7jEgIiIiIt1jQERERES6x4CIiIiIdC+gAdGmTZtw++23Izk5GZIk4dNPP7V6XQiBZ555BklJSTAYDBgwYAAOHz5stc7Zs2cxZswYREdHIzY2FhMmTEBFRYXVOnv27EGfPn3QsGFDpKSkYP78+b7eNSIiIgoiAQ2ILly4gM6dO+PVV19VfH3+/PlYsGABFi1ahK1btyIqKgoDBw7ExYsX5XXGjBmD/fv3Y+3atVi1ahU2bdqEBx54QH69vLwct9xyC1q2bInt27fjb3/7G2bPno033njD5/unVcXGKmwuKEWxsSrQRSEiItIESQghAl0IoHYumU8++QRDhw4FUFs7lJycjMceewyPP/44AMBoNCIhIQFLly7FyJEjceDAAWRkZCA/Px/dunUDAHz55Ze49dZb8csvvyA5ORkLFy7E008/jZKSEoSHhwMAsrOz8emnn+Knn35SXb7y8nLExMTAaDQiOjrauzvvR8vzizBjxV6YBBAiATnDMjHi+tRAF4uIiMgn1N6/NduHqLCwECUlJRgwYIC8LCYmBt27d8eWLVsAAFu2bEFsbKwcDAHAgAEDEBISgq1bt8rr9O3bVw6GAGDgwIE4ePAgzp075/Dzq6urUV5ebvUX7IqNVXIwBAAmATy1Yh9rioiISPc0GxCVlJQAABISEqyWJyQkyK+VlJQgPj7e6vWwsDA0bdrUah2lbVh+hpKcnBzExMTIfykpKXXbIQ0oLL0gB0NmNULgaGllYApERESkEZoNiAJtxowZMBqN8t/x48cDXaQ6S28ehRDJelmoJCGteWRgCkRERKQRmg2IEhMTAQCnTp2yWn7q1Cn5tcTERJw+fdrq9StXruDs2bNW6yhtw/IzlERERCA6OtrqL9glxRiQMywToVJtVBQqSZg3rCOSYgwBLhkREVFgaTYgSk9PR2JiItatWycvKy8vx9atW5GVlQUAyMrKQllZGbZv3y6vs379ephMJnTv3l1eZ9OmTbh8+bK8ztq1a9GuXTs0adLET3ujHSOuT0Vedn8sm9gDedn92aGaiIgIAQ6IKioqsGvXLuzatQtAbUfqXbt2oaioCJIkYerUqZg7dy5WrlyJvXv34t5770VycrI8Eq1Dhw4YNGgQJk6ciB9++AHfffcdJk+ejJEjRyI5ORkAMHr0aISHh2PChAnYv38/li9fjpdffhnTpk0L0F4HXlKMAVmtm7FmiIiIyEwE0IYNGwQAu7+xY8cKIYQwmUxi5syZIiEhQURERIibb75ZHDx40Gobv/76qxg1apRo1KiRiI6OFuPHjxfnz5+3Wmf37t2id+/eIiIiQrRo0ULk5ua6XVaj0SgACKPR6PH+EhERkX+pvX9rJg+R1tWXPERERER6EvR5iIiIiIj8hQERERER6R4DIiIiItI9BkRERESkewyIiIiISPcYEBEREZHuMSAiIiIi3WNARERERLrHgIiIiIh0jwERERER6R4DIiIiItI9BkRERESkewyIiIiISPcYEBEREZHuMSAiIiIi3WNARERERLrHgIiIiIh0jwERERER6R4DIiIiItI9BkRERESkewyIiIiISPcYEBEREZHuMSAiIiIi3WNARERERLrHgIiIiIh0jwERERER6R4DIiIiItI9BkRERESkewyIiIiISPcYEBEREZHuMSAiIiIi3WNARERERLrHgIiIiIh0jwERERER6R4DIiIiItI9BkRERESkewyIiIiISPcYEBEREZHuMSAiIiIi3WNARERERLrHgIiIiIh0jwERERER6R4DIiIiItI9BkRERESkewyIiIiISPcYEBEREZHuMSAiIiIi3WNARERERLrHgIiIiIh0jwERERER6Z6mA6KamhrMnDkT6enpMBgMaN26NZ577jkIIeR1hBB45plnkJSUBIPBgAEDBuDw4cNW2zl79izGjBmD6OhoxMbGYsKECaioqPD37qhWbKzC5oJSFBurAl0UIiIiXdB0QPTCCy9g4cKF+Ne//oUDBw7ghRdewPz58/HKK6/I68yfPx8LFizAokWLsHXrVkRFRWHgwIG4ePGivM6YMWOwf/9+rF27FqtWrcKmTZvwwAMPBGKXXFqeX4ReuesxevFW9Mpdj+X5RYEuEhERUb0nCcvqFo257bbbkJCQgLfeekteNnz4cBgMBrz77rsQQiA5ORmPPfYYHn/8cQCA0WhEQkICli5dipEjR+LAgQPIyMhAfn4+unXrBgD48ssvceutt+KXX35BcnKyqrKUl5cjJiYGRqMR0dHR3t9Z1NYM9cpdD5PFNxIqScjL7o+kGINPPpOIiKg+U3v/1nQNUc+ePbFu3TocOnQIALB7927k5eVh8ODBAIDCwkKUlJRgwIAB8ntiYmLQvXt3bNmyBQCwZcsWxMbGysEQAAwYMAAhISHYunWrw8+urq5GeXm51Z+vFZZesAqGAKBGCBwtrfT5ZxMREelZWKAL4Ex2djbKy8vRvn17hIaGoqamBs8//zzGjBkDACgpKQEAJCQkWL0vISFBfq2kpATx8fFWr4eFhaFp06byOkpycnIwZ84cb+6OS+nNoxAiwa6GKK15pF/LQUREpDeariH673//i/feew/vv/8+duzYgXfeeQd///vf8c477/j8s2fMmAGj0Sj/HT9+3OefmRRjQM6wTIRKEoDaYGjesI5sLiMiIvIxTdcQPfHEE8jOzsbIkSMBAJmZmTh27BhycnIwduxYJCYmAgBOnTqFpKQk+X2nTp3CtddeCwBITEzE6dOnrbZ75coVnD17Vn6/koiICERERHh5j1wbcX0q+raNw9HSSqQ1j2QwRERE5AeariGqrKxESIh1EUNDQ2EymQAA6enpSExMxLp16+TXy8vLsXXrVmRlZQEAsrKyUFZWhu3bt8vrrF+/HiaTCd27d/fDXrgvKcaArNbNGAwRERH5iaZriG6//XY8//zzSE1NxTXXXIOdO3fin//8J+677z4AgCRJmDp1KubOnYurr74a6enpmDlzJpKTkzF06FAAQIcOHTBo0CBMnDgRixYtwuXLlzF58mSMHDlS9QgzIiIiqt80HRC98sormDlzJh566CGcPn0aycnJ+POf/4xnnnlGXufJJ5/EhQsX8MADD6CsrAy9e/fGl19+iYYNG8rrvPfee5g8eTJuvvlmhISEYPjw4ViwYEEgdsmhYmMVCksvIL15FGuGiIiI/EzTeYi0xJd5iJbnF2HGir0wCSBEAnKGZWLE9ale/QwiIiI9qhd5iPSg2FglB0NA7ZD7p1bs47QdREREfsSAKMCYjJGIiCjwGBAFmDkZoyUmYyQiIvIvBkQBxmSMREREgafpUWZ6wWSMREREgcWASCOSYgwMhIiIiAKETWZERESkewyIiIiISPcYEBEREZHuMSAiIiIi3WNApCHFxipsLihllmoiIiI/4ygzjeB8ZkRERIHDGiIN0Mp8ZqyhIiIivWINkQY4m8/MWW6iYmMVCksvIL15VJ1zGLGGioiI9IwBkQaY5zOzDIpczWfmzQDGUQ1V37ZxTBZJRES6wCYzDXB3PjNvN7E5q6EiIiLSA9YQaYQ785l52sTmiCc1VERERPUJa4g0JCnGgKzWzVwGNeYAxlJdAhh3a6iIiIjqG9YQBSFzAPPUin2oEcIrAYw7NVRERET1DQOiIOWLACYpxsBAiIiIdIkBURBjAENEROQd7ENEREREuseAiIiIiHSPARERERHpHgMiIiIi0j0GRERERKR7DIiIiIhI9xgQERERke4xINK5YmMVNheUejwxLBERUX3AxIw6tjy/CDNW7IVJACESkDMsEyOuTw10sYiIiPyONUQ6VWyskoMhoHam+6dW7GNNERER6RIDIp0qLL0gB0NmNULgaGllYApEREQUQAyIdCq9eRRCJOtloZKEtOaRgSkQERFRADEg0qmkGANyhmUiVKqNikIlCfOGdeRksUREpEvsVK1jI65PRd+2cThaWom05pEMhoiISLcYEOlcUoyBgRAREekem8yIiIhI9xgQERERke4xICIiIiLdU92HqLy8XPVGo6OjPSoMERERUSCoDohiY2MhSZLTdYQQkCQJNTU1dS4YERERkb+oDog2bNjgy3IQERERBYzqgKhfv36+LAcRERFRwHich6isrAxvvfUWDhw4AAC45pprcN999yEmJsZrhSMiIiLyB49GmW3btg2tW7fGiy++iLNnz+Ls2bP45z//idatW2PHjh3eLiM5UWyswuaCUs5ST0REVAeSEEK4Xs1anz590KZNGyxevBhhYbWVTFeuXMH999+Pn3/+GZs2bfJ6QQOtvLwcMTExMBqNmhlFtzy/CDNW7IVJACESkDMsEyOuTw10sYiIiDRD7f3bo4DIYDBg586daN++vdXyH3/8Ed26dUNlZaX7JdY4rQVExcYq9MpdD5PFtxcqScjL7s+pOIiIiH6j9v7tUZNZdHQ0ioqK7JYfP34cjRs39mST5KbC0gtWwRAA1AiBo6X1LxglIiLyNY8CohEjRmDChAlYvnw5jh8/juPHj+ODDz7A/fffj1GjRnm7jKQgvXkUQmzSQoVKEtKaRwamQDrFPlxERPWDR6PM/v73v0OSJNx77724cuUKAKBBgwZ48MEHkZub69UCkrKkGANyhmXiqRX7UCMEQiUJ84Z1ZHOZH7EPFxFR/eFRDVF4eDhefvllnDt3Drt27cKuXbtw9uxZvPjii4iIiPBqAU+cOIG7774bzZo1g8FgQGZmJrZt2ya/LoTAM888g6SkJBgMBgwYMACHDx+22sbZs2cxZswYREdHIzY2FhMmTEBFRYVXyxkII65PRV52fyyb2AN52f15M/ajYmOVHAwBgEkAT63Yx5oiIqIgVafJXSMjI5GZmYnMzExERnq/qebcuXPo1asXGjRogDVr1uDHH3/EP/7xDzRp0kReZ/78+ViwYAEWLVqErVu3IioqCgMHDsTFixfldcaMGYP9+/dj7dq1WLVqFTZt2oQHHnjA6+UNhKQYA7JaN2PNkJ+xDxcRUf3i0Sizixcv4pVXXsGGDRtw+vRpmEwmq9e9lYsoOzsb3333Hb799lvF14UQSE5OxmOPPYbHH38cAGA0GpGQkIClS5di5MiROHDgADIyMpCfn49u3boBAL788kvceuut+OWXX5CcnKyqLFobZUaBxVF+RETBwaejzCZMmID58+ejZcuWuO222/CHP/zB6s9bVq5ciW7duuFPf/oT4uPj0aVLFyxevFh+vbCwECUlJRgwYIC8LCYmBt27d8eWLVsAAFu2bEFsbKwcDAHAgAEDEBISgq1btzr87OrqapSXl1v9EZmZ+3CF/jbhMftwEREFN486Va9atQpffPEFevXq5e3yWPn555+xcOFCTJs2DU899RTy8/MxZcoUhIeHY+zYsSgpKQEAJCQkWL0vISFBfq2kpATx8fFWr4eFhaFp06byOkpycnIwZ84cL+8R1Scjrk9F37ZxOFpaibTmkVbBULGxCoWlF5DePIpBEhFREPAoIGrRooVf8g2ZTCZ069YN8+bNAwB06dIF+/btw6JFizB27FiffvaMGTMwbdo0+d/l5eVISUnx6WdS8EmKMdgFPBx9RkQUfDxqMvvHP/6B6dOn49ixY94uj5WkpCRkZGRYLevQoYOcFDIxMREAcOrUKat1Tp06Jb+WmJiI06dPW71+5coVnD17Vl5HSUREBKKjo63+iFzh6DMiouDkUUDUrVs3XLx4Ea1atULjxo3RtGlTqz9v6dWrFw4ePGi17NChQ2jZsiUAID09HYmJiVi3bp38enl5ObZu3YqsrCwAQFZWFsrKyrB9+3Z5nfXr18NkMqF79+5eKysRwNFnRETByqMms1GjRuHEiROYN28eEhISIEmS6zd54NFHH0XPnj0xb9483HXXXfjhhx/wxhtv4I033gAASJKEqVOnYu7cubj66quRnp6OmTNnIjk5GUOHDgVQW6M0aNAgTJw4EYsWLcLly5cxefJkjBw5UvUIMyK1zBnEbUefMYM4EZG2eTTsPjIyElu2bEHnzp19USYrq1atwowZM3D48GGkp6dj2rRpmDhxovy6EAKzZs3CG2+8gbKyMvTu3RuvvfYa2rZtK69z9uxZTJ48GZ9//jlCQkIwfPhwLFiwAI0aNVJdDg67J7WW5xfZZRBnHyIiosDw6Wz31113HV577TX06NGjToUMJgyIyB3FxirF0WdERORfPs1DlJubi8ceewzffPMNfv31V+brIbLBDOJERMHFoxqikJDaOMq275AQApIkoaamxjul0xDWEBEREQUftfdvjzpVb9iwweOCEREREWmNRwFRv379VK330EMP4dlnn0Xz5s09+RgiIiIiv6jTbPeuvPvuu+xTRERERJrn04DIg+5JVAfFxipsLihlVmQiIiI3edRkRtrD+bOIiIg859MaIvIPzp9FRERUNwyI6gHOn0VERFQ3DIjqAfP8WZY4fxYREZF6Pg2I7r77biYx9IOkGANyhmUi9LdEmeb5swKRJZkdu4mIKBh5lKkaAMrKyvDDDz/g9OnTMJlMVq/de++9XimclgRDpupAz5/Fjt1ERKQ1Pp3c9fPPP8eYMWNQUVGB6Ohoqyk8JEnC2bNnPSu1hgVDQBRIxcYq9Mpdb9WXKQTAdzNu4nxeREQUMD6d3PWxxx7Dfffdh4qKCpSVleHcuXPyX30Mhsg1pY7dJgBL8o4GojhERERu8SggOnHiBKZMmYLISHbarY886QeU3jwKksLyN/N+Zn8iIiLSPI8CooEDB2Lbtm3eLgtpwPL8IvTKXY/Ri7eiV+56LM8vUvW+pBgDJvZJt1tuEuDwfyIi0jzVmapXrlwp//eQIUPwxBNP4Mcff0RmZiYaNGhgte4dd9zhvRKS3zhK8Ni3bZyqfkDje6fjzbxCq6YzDv8nIqJgoDogGjp0qN2yZ5991m6ZJEmoqampU6EoMJwleFQTEJmH/z+1Yh9qhAjo8H8iIiJ3qA6IbIfWk/YUG6tQWHoB6c2jPApCzAke61LDM+L6VPRtGxfQ4f9ERETu8qgP0b///W9UV1fbLb906RL+/e9/17lQ5D5P+/5Y8laCx6QYA7JaN2MwREREQcOjPEShoaEoLi5GfHy81fJff/0V8fHx9bLJTGt5iCxrgwDY5QAKlSTkZff3KCgJdIJHIiIib1F7/1bdZGZJCGGVjNHsl19+QUxMjCebJDfYZoSe0Du9Tn1/bCXFGBgIERGRrrgVEHXp0gWSJEGSJNx8880IC/v97TU1NSgsLMSgQYO8Xkj6ndJIsLfyCiEBsIyJOLqLiIhIPbcCIvNIs127dmHgwIFo1KiR/Fp4eDjS0tIwfPhwrxaQrClmhBbAA33T8da3Rzm6i4iIyANuBUSzZs0CAKSlpWHEiBFo2LChTwpFjjkaCTa+VzrG90r3e9+fuo5sIyIi0gKP+hCNHTsWQO2oMqXZ7lNTOcO5r7jK9ePPoISz2xMRUX3h0Sizw4cP47777sPmzZutlps7W3OUme8FeiSY0uz2dRnZRkRE5As+HWU2btw4hIWFYdWqVUhKSlIccUa+FeiRYHXNak1ERKQlHgVEu3btwvbt29G+fXtvl4eChDeyWhMREWmFR5mqMzIyUFpa6u2yUBDxVlZrIiIiLfCoD9H69evx17/+FfPmzVOc7V4LfWy8TWt9iLQi0H2ZiIiInFF7//YoIAoJ+b1iybL/EDtVExERkZb4tFP1hg0bPC4YERERkdZ41IeoX79+CAkJweLFi5GdnY02bdqgX79+KCoqQmhoqLfLSERERORTHgVEH3/8MQYOHAiDwYCdO3eiuroaAGA0GjFv3jyvFpC0pdhYhc0FpSg2VgW6KEGDx4yISPs8ajKbO3cuFi1ahHvvvRcffPCBvLxXr16YO3eu1wpH2sLM1O7jMSMiCg4e1RAdPHgQffv2tVseExODsrKyupaJNKjYWCXf2IHa/ENPrdjHWg8neMyIiIKHRwFRYmIijhw5Yrc8Ly8PrVq1qnOhSHucZaYmZTxmRETBw6OAaOLEiXjkkUewdetWSJKEkydP4r333sPjjz+OBx980NtlJA0wZ6a2xMzUzvGYEREFD4/6EGVnZ8NkMuHmm29GZWUl+vbti4iICDz++ON4+OGHvV1G0gBzZuqnVuxDjRDMTK0CjxkRUfDwKDGj2aVLl3DkyBFUVFQgIyMDjRo18mbZNIWJGWsxM7X7eMyIiALHp4kZzcLDw5GRkVGXTVCQSYox8KbuJh4zIiLt86gPEREREVF9woCIiIiIdI8BEREREekeAyIiIiLSPQZEGsN5r4iIiPyvTqPMyLuCed6rYmMVCksvIL15FEdUERFR0GFApBGO5r3q2zZO8wFGoAM5BmNERFRXDIg0wtm8V1q+yRcbq5D98V6Yi+7vQC7QwRgREdUPQdWHKDc3F5IkYerUqfKyixcvYtKkSWjWrBkaNWqE4cOH49SpU1bvKyoqwpAhQxAZGYn4+Hg88cQTuHLlip9L71ywznv1dl4hbFOd+2sCU84mT0RE3hI0AVF+fj5ef/11dOrUyWr5o48+is8//xwffvghNm7ciJMnT2LYsGHy6zU1NRgyZAguXbqEzZs345133sHSpUvxzDPP+HsXnDLPexUq1UZFwTDvVbGxCm/lFdotDwFcBnLe6DzO2eSJiMhbgqLJrKKiAmPGjMHixYsxd+5cebnRaMRbb72F999/HzfddBMAYMmSJejQoQO+//579OjRA19//TV+/PFH/O9//0NCQgKuvfZaPPfcc5g+fTpmz56N8PDwQO2WnRHXp6Jv27igmfdKKSABgPv7pjstu7eaucy1apZlCIZaNSIi0p6gqCGaNGkShgwZggEDBlgt3759Oy5fvmy1vH379khNTcWWLVsAAFu2bEFmZiYSEhLkdQYOHIjy8nLs37/f4WdWV1ejvLzc6s8fkmIMyGrdTPPBEKDczBciAeN7pTt8jzebuYKxVo2IiLRJ8zVEH3zwAXbs2IH8/Hy710pKShAeHo7Y2Fir5QkJCSgpKZHXsQyGzK+bX3MkJycHc+bMqWPp6zdzQPLUin2oEcIuIFEa/eXtzuPBVqtGRETapOmA6Pjx43jkkUewdu1aNGzY0K+fPWPGDEybNk3+d3l5OVJSUvxahmDgKCBx1Czmi2YuziZPRER1pekms+3bt+P06dO47rrrEBYWhrCwMGzcuBELFixAWFgYEhIScOnSJZSVlVm979SpU0hMTAQAJCYm2o06M//bvI6SiIgIREdHW/2RMttmPmfNYmzmIiIiLdJ0DdHNN9+MvXv3Wi0bP3482rdvj+nTpyMlJQUNGjTAunXrMHz4cADAwYMHUVRUhKysLABAVlYWnn/+eZw+fRrx8fEAgLVr1yI6OhoZGRn+3SGdcNUsxmYuIiLSGk0HRI0bN0bHjh2tlkVFRaFZs2by8gkTJmDatGlo2rQpoqOj8fDDDyMrKws9evQAANxyyy3IyMjAPffcg/nz56OkpAR//etfMWnSJERERPh9n/RATbMYm7mIiEhLNN1kpsaLL76I2267DcOHD0ffvn2RmJiIFStWyK+HhoZi1apVCA0NRVZWFu6++27ce++9ePbZZwNY6vqNzWJEpEecnDu4SUIIhUwyZKu8vBwxMTEwGo2670+kdu6wYmMVm8WISBc4jZB2qb1/a7rJjLTHnR89m8WISA+CeXJu+l3QN5mR/3DuMCIie5xGqH5gQESq1fVHz/Z1IqqPgnVybrLGgIhUKTZW4eyFS7D5zav+0S/PL0Kv3PUYvXgreuWux/L8It8UlIjIzziQpH5gHyJyybLfkITaPwH1P3pftq+r7eBNRORLzK8W/BgQkVO2wYxAbWfqV0Z2Qde0Jqp+9N6ev8yMozqISEs4kCS4scmMnFIKZkwCaNYoQvUP3xft6+zgTURE3sSAiJzyRjDji/Z1juogIiJvYpMZOWUOZp5asQ81QngczHi7fV3N9CBERERqMSAil7wVzHizfd1bgVogsUM4EZF2MCAiVbTYWTCYR3WwQzgRkbawDxEFtaQYA7JaNwuqYIgdwomItIcBEZGfsUM4EZH2MCAi8jOm+Sci0h4GRER+xjT/RETaw07V5BBHQflOMHcIJyKqjxgQBRl/BSkcBeV7Why5R0SkVwyIgoi/ghRfTsZKRESkRexDFCT8OVQ7GEZBFRursLmgVNND1YOhjEREVIs1REHCnRnj69qspvVpMYKhOS8YykhERL9jDVGQUDtUe3l+EXrlrsfoxVvRK3c9lucXuf1ZWh4FFQxJDYOhjEREZI01REFCzdxd3uz7o8VRUMXGKqzac1J1TVmguFObR0RE2sCAKIi4ClK8fSPW0igoyyYoW1pqzgO03+RIRET22GQWZJzN3VVfMyDb1nxZ0lJznpmWmxyJiEgZa4jqETXNasFIqeYLAGYO6YBbOyVpcv+02ORIRESOMSCqZ+rjjVipCSoEQEJMw4CVSQ0tNTkSEZFzbDKrh5w1qwUj2yYoCYAAMPn9nR6PpCMiIrLEgIiCwojrU5GX3R//GtUFklQbEAEc0k5ERN7BgEjjmO34d0kxBjRtFK75LNpERBR82IdIw5jt2B6HtBMRkS+whkijmO1YGYe0ExGRL7CGSKOY7dix+jiSjoiIAosBkUaxaci5ug5pr+sEuFpQH/aBiEgrGBBpVH1NsqgF9aFvVn3YByKq34LtoU0SQijkACZb5eXliImJgdFoRHR0tN8+t9hYxaYhLyo2VqFX7nq7mre87P5Bc3zrwz4QUf3m7KHN34GS2vs3a4g0zlfZjh2dkMEW0burPvTNqg/7QET1l6NBQX3bxmHToTOard1mQKRDjiJ3rTbDeDNIqw99s+rDPhBR/eXooW370XMOAyUtPMxx2L3OKEXuM1bsxX++P4rsj7U3zH95fhF65a7H6MVbvTJNR30Ytl8f9oGI6i/zQ5ulUEkCbB7kAG0l1mUNkc4oRe4mAcz8dL/duoFuhnEUvLVPbIzOKU083m59GLZfH/aBiOqv+3un481vC2HC7w9tXVs20XTtNgMinVFqbnEk0Ceqo+Bt6KubkTu8tjnP0+a0+jATfX3YByKqX2y7XjzQuxXG906Tr1VaHj3NgEhnbIfzO6KFE9VR8CZQ25xXVnUZL6z5SXN9nohIWX0ftKF3SrX6b+UVYnzvNHkdLdduMyDSIfMJuePYOUx+fycs440QCVgwsgu6pjUJ+IlqDt4sf2BmNUIgd81PEBrtnEdE1rQ6aIO8R+0IWK3WbrNTtU4lxRgwpFMycodbd87NGZaJ2zonqzpZi41V2FxQ6tOO1yOuT8UnD/WETf88hACwreDSUuc8Ivod52bUB0edqbXSR8gV1hDpnKfVl/582uuc0gS5w63bnZ8c1A4vfPmTZjvnEdHvmDtLH4J9hgUGROR29aWzpFu+OvGVArfYyAZB+8Mj0hPmztIPLfcRcoUBEbktUE97toFbMP/wiPQk2GsOyD1a7SPkCgOiIKKVERpaetoL1h8ekdZ5+3rDBxjSOgZEAab2ouPLPjvuXviUnvaeHNwOhaUX5NeJKHj56nrDBxjSMs52r5IvZrtXe9Hx5ezmdbnwFRurcLS0Ent+KZM7OGttOK03n3K1UkNH5Eu+vN4QBQJnu9c4dzom+6rPTl07R5vXGfPm95qcrM+bT7nMoUJ6wRFhpFeaz0OUk5OD66+/Ho0bN0Z8fDyGDh2KgwcPWq1z8eJFTJo0Cc2aNUOjRo0wfPhwnDp1ymqdoqIiDBkyBJGRkYiPj8cTTzyBK1eu+HNXrDi76NjyRm4HpZxB7pTBEW9swxe8mfeEOVRIT4I9lwyRpzQfEG3cuBGTJk3C999/j7Vr1+Ly5cu45ZZbcOHCBXmdRx99FJ9//jk+/PBDbNy4ESdPnsSwYcPk12tqajBkyBBcunQJmzdvxjvvvIOlS5fimWeeCcQuAXDvolPX2c0dzRjvjQufVi+e3gzUtBr0EflCXa83RMEq6PoQnTlzBvHx8di4cSP69u0Lo9GIuLg4vP/++/jjH/8IAPjpp5/QoUMHbNmyBT169MCaNWtw22234eTJk0hISAAALFq0CNOnT8eZM2cQHh7u8nN91YfIdhiqs2YYc58dd0ZouOoP4KoMavrNuLsf/uDNfhDsU0F65Mn1hkiL6m0fIqPRCABo2rQpAGD79u24fPkyBgwYIK/Tvn17pKamygHRli1bkJmZKQdDADBw4EA8+OCD2L9/P7p06WL3OdXV1aiurpb/XV5e7vV9cXcYqicjNFz1B3BWBrX9ZjwdTuvLTsrezHvCHCqkRxwRRnoTVAGRyWTC1KlT0atXL3Ts2BEAUFJSgvDwcMTGxlqtm5CQgJKSEnkdy2DI/Lr5NSU5OTmYM2eOl/fAnq8vOmpyBimVwd0O1472w1HQ449Oyt7Me8IcKv7D0XxEFAhBFRBNmjQJ+/btQ15ens8/a8aMGZg2bZr87/LycqSkpPj8c73N09oNb4w0cRT0+HPqD28GnHxi9j2O5iOiQAmagGjy5MlYtWoVNm3ahKuuukpenpiYiEuXLqGsrMyqlujUqVNITEyU1/nhhx+stmcehWZex1ZERAQiIiK8vBeB4W7tRrGxCr9WVNcpG7WzoIfDeklJIObIIyIy0/woMyEEJk+ejE8++QTr169Henq61etdu3ZFgwYNsG7dOnnZwYMHUVRUhKysLABAVlYW9u7di9OnT8vrrF27FtHR0cjIyPDPjgRYUowBWa2bubyxmEekPbxsF4QAfhto4na/GUdBz45j5zQ7Mo0Ci6P5iCiQNF9DNGnSJLz//vv47LPP0LhxY7nPT0xMDAwGA2JiYjBhwgRMmzYNTZs2RXR0NB5++GFkZWWhR48eAIBbbrkFGRkZuOeeezB//nyUlJTgr3/9KyZNmlRvaoG8wfYJXQAIEcC/RnfBdS2buPWUrtR3CQAmv78TucMzXTbjab0fidbLF4y0NEceEemP5ofdS5KkuHzJkiUYN24cgNrEjI899hiWLVuG6upqDBw4EK+99ppVc9ixY8fw4IMP4ptvvkFUVBTGjh2L3NxchIWpiwl9MexeazYXlGL04q12y5dN7IGs1s1cvt82SLDsD2LJPGQdQJ1GtwWK1ssXzLyZwoFBKwULnqu+pfb+rfmASCv0EBDVJd+OoyDh890n8PCyXXbrOwqyfJHzx9vzmTEnkW95I/9NIINW3tzIHXzA8j2192/N9yEi//E0Q62zqS26pTV1q7+Qt/uROMrS7Slvl09pShW9cLTvavu7OduuO1OtePM78Pb5RvUbpwXSFs33ISL/8iTfjrMgIat1M7eG/XuzH4kvRi15s3x6fjL05b67M4rRm+XgKDlyF0fcagtriMiO+QkdgKonZ1ejxkZcn4q87P5YNrEH8rL7O73heHMeJU9rc8w1BruPn7Pbf2+VT89Phr7ed7WjGL1dDo6SI3dxxK22sIaIFLnz5Kwm+aM7SQ29lRXak9ocpY7gtvvvjfLp+cnQ1/uuNhmpt8vBUXLkLk4LpC0MiMiOJ1X/3p7awlkApbbTqrsXG9v9NlPa/7pmrdbzzdMf+67mfHSUGmLPiTJVoypt8eamD97uNM9pgbSDARHZ8fTJ2VWQ4I0Libt9Pty52Cjtt5m3a2/0fPP01767Oh+TYgyYPqg9ctb8ZLV8/pqDuKNzskfl4c2tfvNV37f6PC1QMI26ZEBEdnzxBO+NC4mnnVbVXmwc1RgAvqm90fPN09m++/MCmnlVjN2yuga/9fnmpmfsNO++YBs4wk7VZMebHZsB73VedVZz5Y2h05sOnYFSVi5f1t7UdYh5MFPad38PW2enVlKLnebdE4wDR1hDRIq8WXvhrc6rjmqu9pwow5g3v/dK7ZNlMSV4Nm0JeSYQT+B6brok9+i535+ZufY2KjwURWcrIUkSujq4PgbjwBEGRPWEo2aGujQ/eKvq31sXEqWb15OD2uGFNT/V+Saq9OMVAJpGRWj2x+tNvjh/3BWoC6iemy5JPb0Hz46mYpIA5A63fwh1dN2PDA/B5oJSTfYpYkBUDzhqp9VK+603LyS2Ny9HN9HVe4oxpFOS6s/Q89OfVs6fQH4H7PdDaugteLasEVIKhoDaB8cZK/baPYQqXfeHdknGna9tDvg9yRHOZaaSVucyczS31oqHsuQTz3J5IOfc8sYcVUrbtN1/M1c/OKXJaL01sWiw0Nr5E8zfgTdq04JpRA7Vb5YPRBIAV4GCs/kpj5ZWIjI8JGD3JLX3b9YQBTlHNST5R89prv3WF0/htk8hlpw1nzmq/dDT0x+gvfMnWL8Db9SmaaVGl8i2P5+rYChEglyTaxvUm/82F5Rq7p5kiwFRkHPUzHB9WhPdNAGZb6Kr9xRj7uoDVq8p/eAcdd5tn9gYFy7V6OrpXIvnT7A1X3mjMziHdJOWOMrJppSWRAIwfXB7uZbdUVAfDN0SOOw+yDkaIt85pYnXhs4Hw4zsSTEGDOmUpGoItaNakaGvblYc7h0M++8p2/MnRAKeHNxO8fx5cnA7FJZeqJfHoS68MRybQ7pJSxylo/jkoZ5YNrEHPpvUE6O7pwCorT16Yc1PeH1jgdNh9t5O5+ILrCGqBxw1M3ij+SGYqvEVR6H9dhM3vw44TsBo/qfl0/mmQ2eCZv89NeL6VJRVXkbub6P1cr/4Cb+er8b43unIy+6Po6WV2PNLmTyar74eB1tq+vMUG6vwa0V1nZ98g+Hp2VPOjqM7fabYv8q3bI+v0kCYzilN5HU/+OG4/F6TqA2KTDbbtAzqC0svoG/bOPmaosUmcXaqVkmrnap9yVGH20B2zFbD3Ilvzy9leOFL5Zu4ZeddR9mpXx3dBQ8v2xl0+++Is6H1Sh3Tzcesb9s4v5wHWrrhqXkQsO10CgkQAh53Bg+GDuXufke2x2hin3SM751u17xifm1IpyTFZutgejDTErXfl6Pj62ggzOaCUoxevNVuO9JvvwEz80NpoB+m2Kma6iwYE2sBv9cEmZM1AvZ9MixrzxyNfjAJEZT7r8TZDcVRfwHzMXt51LU+Pw5K5evbNi4gAZKa/jxKnU5DRN0SeWq5Q3mxsQpv5xXirbxC1Tc2pWP0xreFWPxtIUZ1T8GyrcflWlnza298WwigNkDKHtwef+7Xmv2rPKQ2iHR1fJWOsaMazScHt8P8NQe9nifOXxgQaZQWnpaDuRpfTTBn+WNXqh7ultY0aPffkqsLnrM53GqEAIR9Z0p3j4OrZhPb8mV/vBfSb5/p76dKNeeO0jom1D2Rp1Y6lFt+X5sOnUH2x9ZZ3J3d2Mzv/bWi2mHemve3Hrd/wWadnDU/ARKQ2SKm3jyY+Is7QaSj833HsXMY0kn5+DpqUhtxfSru6JzsMk+cVr87BkQapJXq4WDLzGp5EXc3mHP0dO5o/7UQsKrl6qJk/p5nfLzXrg9AqCSha1qTOp0Hrs5nR1nCRYCeKtWcO8H8sOCKXVMglIddK93YbL9rNflrnHlhzU/45KGe9fZY+4qrTvrbjp6Vp91w9EA0+f2dqKi+4vDe4+iaaRvUB9N3x4BIY7RWPRzIanx3gg6lm66aDtaWlJ7OlfZfKwGrWmpu3ub9XJJ3FG/m/QyTsB4FYn59+9FzgAR0bdlE1WerOZ+d1VCZ+fOpUs2DQLA9LLjiKCOxs2DG9hxS+q4lCZCE50GRSQCVl0z16lj7g8N5H38pw+jF38vfh3najZxhmXaZqAV+/60CULwWu6rRDLbfCTtVq+SvTtWOOqs5ygJaX7kTdDjr/A3AZQdr8zbUBF/B2tH89U0Fclu+qw67jjpSetLPR+35bNXJHb/VEFmsH4hjrCazui+yr/ubVY2QTadYR0IkYELvdNz3WwdpwPF3/eroLth93IjF3/7sdmBk+b3Xh2PtT7ad9J8c3A65X/xk9x2ESMB32Tdh29GzeHjZLrvtPNCnlfyQ5M4DoOU1FUBAvzt2qg5S9bkqXi13a8mcVQ+bb7rOOli7E3wFW5s4UHthNAdDEoAnB7VzekFTeupT7OezYi/w29O/o+Om9ny2rYnbdOhMwJ8q1fTn0UqfH0/ZdXx2EbGEAOh9dXN8e7gUi7+t7WRt/t6jwkPtmshCJQnXtWyCIZ2SMb53GpbkHZUDI0fNaeagzJwTy1FTDDmnNO+j0vE2idpgpVtaU7vvJASQgyHzujM+3ouoiDCHs9wD2un24S4mZtSYYEhe5WvuJqlzlETMfNN1tj1HwZej5IOuPssT3kz8aLstpZE+87886PZnKfbzEfa5m2y36875nBRjQFbrZnITXV52fyyb2AN52f2D4mIajByNMJTsFyFEAhaP7Yq8I6V23/vfv/oJQ1/bbBcMWX7XSTEGtI6PgmTeuGT/OaGShIf6tYaE33PbWCZJpd+puW5Y/qbSm0c5/F7NDyGWJAD390lXHDww+f2ddglsLcvlzjVVS1hDpEF16bfjrSRogeRuLZmrdmpn23O3xsfbbeLefJJS2lZK00iv1GjVpZ+Pp+czawR8z9FvY/YdGZj52X6rdU0CKCytVDyf/rWhwGpZiATMuqOD3P8EUK6NkqTadAUmWOessQy4ZnxsP5O63nly3UiKMSB3eKbViEHpt/cCtTPWW361kgQM6ZSEN39LtWDLUc19MNaimzEg0ihPbgbOfiTBVIXpSdDh7KbranvuNlF6q6O5NzvQO9rWioeyvNIEa3sMQyTrGiJX23XUDBcMAbojWi9/sbEK24+dgxAC3dKaOizj/b3T8ea3hXJQMm9YR/RtG4dZK/fb3djOXbjkMjAGal9/5rMfMXvlj/K1xlEt479Gd0HTqAiHDygmAEvyjuKpIR3k/Qr2h766UJsnS+k4WA6OkCTIObOUJl41d2ifPqh9bQoEBZaBjmXH/GDt9sGAqJ5w9iMBoKmRa2p4EnQ4CyKdDRH1pMbHG7UX3nyScrQtb47Q8WY/H60E6J7eQLVSfkeW5xdZ1wSgdjSRZRlt9+GB3q0wvneafBymD26PnC+sb4SLNv6M6YPby8n3XA2rt7zWOKqNsk1kqbTNxd/+jE4pMThxrspp9nktfyfeUGyswqo9J51eN1wdh6QYA27rbH2uO6tF//VCtcPymNex/cw7u7TApztPBl26EgZE9YSzm6tAcGZc9naTiXl75rZ3848yUKkFvNmB3tm2slo389r+WX4nnh43raSW8PQGqlT+GSv2on1iY3mup0Ayl8/yJy9g3fSktA9v5RVifO80+T2ZLWLstl0jBDq1iK0dsbTmJ1Uj0iwHOKhJZzCxT7qcsdqy/JPf32m1zHzetE9sjOPnqqwCQHNTm1a+E2+wPF9tmX/r7v62LAMUpe/GnJRTSYgEzBvWEYD9A/enO09ixUNZqLxkCqp0JQyI6glXN9dgrcL0ttc3FcgXcssfpb+adNRMoOjJZ7nalq/643iyXS30MahLUKbYrCOAoa9utquF8TWlc9ThVCyAfIxdfQfFxiqcvXBJcdRYZHhIbT8fB52xbRdbXmvUBNHje6dj8beFqobo1whR25nbwf4OfW0zsge1R+ZVMV6vlfBnbYft+WrJ8rf++e4Tqn9bSgGK5cSrANArd73d9yABmNg3HeN7pTtsbjPXTptH+WrlIcgVBkT1hKsbYjAlx/KUqwvU6xsLrNrCnf0offE042ib3qq90fJcWJYcBe+R4SFWNXe+VJegzFEHcwH/XuQdnU+OyhcCyMfYWT8P20zV5iDHfN24cKlG8cY8c0gH3NopyaopNQS1aR5sa4G8eXyc1VIJAfk3r5Q7yVP+rO1w1EwG/H7MLWtgbCk9/DoKUPKy+8tBjFKgAwCvjOqC2zony/9WU9Ot5vemheY0BkT1iLMbYrDcLD3l6gJVbKxCrkLHQKWboDtPM+4kdHR3AkVPuLutQFyElIL3oV2S5Ql2/VGdXpfmSnP5lZ7YfVnTZdlJOrVppNPzKWdYJrJX7JWDBQnAnde1sDrGSv08AOvmD4HadV8Z2QVd05rItUdKx858Yx5xfSrKqi4j97fcVy98+RNiIxu4nAjWfB46ypdjKwSwm2rGGZMAFn9biDe/LUT24N9rjQDlLMyOfhv+qu2wnVBXSTeL70TpfAwBFB9+1QQojn4jXdOsmyDV1HQrbStEAkorLqLYWIVNh85oojmNAVE94+yG6KtmE29z9ybtqE9HZHioPLrG0UXWnIPDktrag2BP6Kim/L4KmCwD9MjwEPlGDfinOr2uzZUjrk9F+8TGGPqqfe4dXzRF23aSVmJ5PpmP745j5yAEkNLUYHeMlfp5OBpt1KzR75PWJsUYcGeXFvh4xwl5naFdkq2e9C2b1Fx9n7bn4fTB7R2OZDMPze/UItbuvAHsuwYoEfi91shyrjbL34Cz34avf8vmQOhNFc2GlZdMDssEAK+M7oIhnZLtlqt5IHD2G7G9Lrh64Lbdljn55sPLdtmNWA1kcxoDItIUT6qiHfXpMP/YzFNMKF0spw9ub/ejU3OxcPcp0d0aCdsLjrcDEzXlr2uzgKsymwN0R30QXN1g6npM6lpr2jmlCXKH+74pWqmTtBKlG5p5tnI1/TwA9ef+JztPWG4Kn+48iccHtnPaP2n1nmIM+a0WyXbfLM/D+WsOYvqg9pj/5cHfp50Y1A6droq1+56UbthlVZftRsc5YllM84NU80bhTn8bvpxNQE3gq/SZzkbwKVH7QODOXI6uHrgtg/TJ7++0CoBsBephkQER+Z23q6KdJQ20bBu3yqOD2mDoz31b271HzcXCmwkdbY+H0hDWT3bWdpaUJCDbQbnVUjN019V34SoYcSeY8uQGY9s5fvrg9shs4X7H2brWmvqjKdrR078l84gfR59vzlLsqDbL1Wgjd859R7/HuasPYN4XB1TVtnS6Ktaqg6+j/VI6/psLSp0fLCdMApjwzna75Zb7V9faRUfUBr6AchZwb+Zus2T5G6lrc2FSjAFNolw3iQZq0A8DIvIrX1RF214MbJm34c7Ny9W6ntzE1TxtTR/UXs6zAtRu37JpQgjUPv0K4M/93A+K1AzdBZx/F67a+929aLq6mFsmfLtwqQbfHSnFqxaZkU3mYwL78vijj5Svm6LVZAlfMNK6o6slcxOMJQm/B1BKIy+dBSOuzn1nv0d3alvUHlfb9RwdL1c5k5yx/W37IhBWE/gCtf2CVjyUZZdOwNu529SWsUYI7Dh2Dk2i1P3OlL4fSQIkAavkoIHoSsCAiPzG1Y2yLlXRjqpjbbdh+7SjpklHie1F33YiSkdcPW29sOYnVR1FX1jzE+64Ntmti4baobuA85FgrmqOXNU+KenbNg4vj7oWEJA77wLOAzglluXRSkfNujKfa5adpC0pdXQ1c9QEI0lA+8TGmLFiD5b9cFxerjTayFF5nNVGmPtYfbjtF7y71Xq+K1/XtjjapjlY2HOizCqxJOA8UJIc/LbrEggrXXvUBL5AbdBg7jtkKxDBuSRBvuaq+Z25+n4COeiHARH5zRKF0RJqL45qnvTNfSYqqq+4vMC606TjLA1+WaXFaJo1PyHW4Hw0jSVH0xSYOxw6Y5lTxlV5zf/+taJa8WJ7W2YSnr6tg9W2HH0XSkOulWqObIUA+PVCNYqNVXZldpQbylkA50yNENh+9JxPRwL5e3SeZcD/XUEpPth63OXTtLMmGJMA/vDqZsXPUlMr66o2Qm1NpJptecJZZvqs1s1wR+dk+bUfTxoVm8nMhAe/bWec9cEZeX0K3rcIUJX4ujnJ2bmt9CDoSYdoZ99PIDEgIr8oNlZhsU32WcB+lJc7nfgccXWBdadJx9lnFxtrpxFw92Lgas6fJwe3k59gQyUJAzsm4Iu9JVbbUMrbYztM17b/UYgERV/sK8bTt3VweRwBYPuxcw6T9TkLXgRqnyJtj6Gz3FBqmxBshUoSoPCkXZeOmsXGKmw7ehaSJOFEWe0oKn/XPJkD/iGdkvHwTVe7DCA8PX5KIy8dlcdZIKamJtLVturCVQ2v5bXFFW8F1LuPn7PLqG1Zo/lBvn0wJKH2IckkfN+cpOZaa3ldKK24iIeX7bJ6Xe3vzNe1WZ5gQER+4WjY+/29Wzm9OHraic/Zj01tXyVXn+1Jnyc1c/6MuD7V6gk2KcaA1zcWyM1pSnl77uzSAit2nLAbNWPZ/8gklPtRmATsRgBZPiVmtW7mcbI+M6UbAACnuaHUNiHY+ku/VhC/Pb1avjdEqk1M6C5nI3+8XfOklpqbicMEjS6OqdLIS3c4CsTu7pGKP3W9SjNTaSgFbs6a0Oo68ml5flFts6fCdnccO6cYRIYAyBme6bIGra41luYcV46CNUfXaEc5qWxHJQY64aJaDIjILxQTcwFWcycp8UXOD7V9lTwZTeMoK6y5RkjNnD+A/Q3vz/1a445rkx3m7bEMfJxxdB+0HAEEwLqj9+D2co2I7TaeHNRObt5S22nVco49pfXNNRTm6vkZH+91KwHfa98UQOD3J2vLfDh3vrbZrRqdYmOVW/l/POWLGdztmjcA3N83HUMyk+zy9wC1x6uuIxgBx4HYu98X4f2tRZrpy6X0+xYAXh3dBQCc9kW05eo7kpsvFU4kCcC2o2cVg0jLPEJqH7LczSfmrHnT1bntqg9YMMxfZokBEfmFp50nfZHzQ21Z3B1No7Qdq5oVhb5BSrlgnJU7KUY5b483mETthJiWzU1yR28Hnzf/y4O449pkbDp0RvH1ENTeZBzdWJzlhio2ViGlaSTm/OEazPxsv922LZsSLAmL/5dsasXU1Oi4mzVZgromJtttq7lpqMnA7uxGrNR8XGyswoTe6b83raI2UDLPTVVXtr8LS4GqUVPiLHdPUoy6voiAuu/I0dQbQO25uWTzMbvlzvIIWW67LvnEXPXTU3OtddRFIVjmL7PEgIj8xtNhob7I+aGmLGpH0zjaju0FwdEIIXefOj1tSlLDBNhV8zhqagOsq/stX5cA/Gt0F1zXsonV3FbO5tizzA3lqInOUvbg9oiNbGDVwdNRcGRbZkdPvUpZk9XWfHmSm6lv2ziHNw3AfhZxyxuK2qdvy9pG2+P6gBcDIUvm38XqPcWYu/qA1Wvu1qj5qsnF1e9bzTXC1U3f3VGSZq6uc84GSriTT8xZPzN3rrVKTbhazM7vCgMi8itPOtL5KvmdmrKYP3v70XOABHS1eGKz7Wdjy1WnVk+fOpUu5EO7JMt9kZRMuakNXll/xOrGbjtCBKitLbDtkGzOFGyZH8nyNZMQioFI06ja6R7cnWPPLpCsLZIc8JgDFXPTjqMpQH7bFcVgRqkvkaOsydmD29eOglPYjrl8S76rnWrBNtgxnx+AcnDz0sjOijeN1XuKkRjT0OENxdH2nM23Z9tkKwC89e1RjO+V7mDP6qa2E3gS5n1xwOMaXk+aXNwJoNRMOeFsG85u+gA8Cobu6Z6KP3Zz3NfK9pjYnuMh+L3G0pNm/xCpNq+VZfoLT/gyo7evMCCioBDIEQlK+WwAKD7tW16Io8JDHQ6hlwDM/kOGXBNgSU1Vs9KF/PGB7RzmYRrVPRUtmhjsnoYBqFo24vpU3HFtcu2Nf1Oh1ZDvbmlNVc2LpLYzqKO+Ha+M7IJmjSLsblyW27YNFCf0TsMbCqMblfK4OMuavHnGTfjfj6cUm+5CJGDxpkKrZrnsj/fKzXnmWdaVtm2suuwwq7NSzZi5Y7gn8+05arL15RN7XWp4PWlyUUp0ap7E1Vk/GHc6KlsujwoPdZgB3NNRfv/ZWoT3flDua6V0TMxJDS2bijcdOoMR16d63OzvKMmnO3xVu+9LkhCuMp4QAJSXlyMmJgZGoxHR0dGBLg75SbGxCr1y19t1BretRZF+uyoK2A93d8Y27465Gtx2KCsALJvYQ1Vfo+X5RYoBjXl/lNr61SyzPCa2rzn7TGflVHr6VzrmoZKEvOz+qm+klqkC1G5LzefajjiTJOD+3umKKSUsWZ4ftm5Ia4Ifjp5Tfh/s+0kpZTQ3L/8u+yar79B2f2zZ7p9SEOCNJitn55MjmwtKMXrxVrvljn4HzvbX3Q69jpo3LdNaKI1Iszz3nZXHXKu7YucJhznHlM5TR8dEKXD+5KGe6JzSRNVv05PvRy1fblsttfdvBkQqMSDSJ0cXIG8y5x6yzG1j25TlTkAABOYi5M5nugo+PAmwHLHd1pOD2yE5piEkSULXltbNArbpDRzdPLYfPQdJgtzp1VXgYaa2P5Ir5v3ItZhVXgKQO/z3m76jczcEUNw/pSAAsK8J9dcoIXcCY3PH5edXO57UVe1vSOlznQ3HNwuRgMX3doUhPMxqXkLbc69Ti1g5WN929CyMVZdxurwaC9YfsdumbfDn6AFNaRSm5fmghaAkkNTev9lkRuSEo3QBSon/PFUjhNWNzVwNHiI8n9snEE2M7nymqyYfb/Ybs9xW3uEzVjOhW940lucX1da64PfpGhx1Ur6ts3V5nM2lZ8lbT581QuCqWIPVBgVqm5TaJzbGhUs1DhN/rngoC8fPVln1iVNqilEacWi5fV/nlVHb5KK247La5kFHTbaumARw/zvb5T5vE/ukY3zvdMV54ZSa9hxNlWOZfFXpmDjq32c+H8xNjP64HgRTziElDIiInHB0UQbgdHSTO0KgMDpK1I7Sahpl32fGVjBehNR0uPTmRTwpxoCVu0/i1W8KrJYL1NaA1M7rZT0icP6ag7ijs7r54sxzdw19bbPLaVe8wdyZ3fajaoSQp+RwlPjzp5LzdrU+KU0j7c5BpRGHNUJg6KubFeet8sV56G7WeWfUduityyhOy348b3xbiMXfFuKhG1uj19XNHZbZJGrTV0wf3N4qQ71t8lXzsVY6JrGRDRSPgz9HdXmac0hL1y8GREFESyeON2l9vxxdlC2XLckrVOy8Cyjn4jFz9IRnmQ+l2Fhl9ZRoKdgSn5n5u8NlsbFKMSs2UHtDyj96TvFmsv3oObvaIEcuXKrxSjDk6mYcIsFhZ3ZLJmGf+BOwbt4z1/qseChLdU2oZedxf0ykaxkY214rHHVcnjmkA64IYRVguDOEXE2Nn/Tb/5jn4FMqhwDw6jcFePWbAvm4REWEKXfebxEr1yYpJV81B+6dU5rYPSzIAflvwaqZqyDQW9deT3MOae36xYAoSGjtxPEWb++XL3OWOBuSO753Ot60mbw2RALm3HENmkSF40RZldXF2bIvwaZDZ6xupNJvNzzbPCaukqqpvQi54q8A1VfpFJQUll5wGqykN49UvKk9/MFO7P6lDPf1Tpe34+i4OJoJXPqt6dNZYGz2QN90NIuKcDrM/7Fb2qL84mWcLr/o8sZtm/hTKamneR1PakL9MZGumdJvoX1iY8VRXrf+Ng2N7RQ4tiznqDM3HxaWXkDftnHIy+6PuasOYPXeYrv3mSdEBuAw5YMtk0Dt1B0OOlmby5gUo5x81SSAoa9uRu5w+xGtANA5pQlyh6t/yKhr0k9LnuQc0mLiRl0FRK+++ir+9re/oaSkBJ07d8Yrr7yCG264IdDFcsjZlA+BPnG8wds/iLoEV3UNAhzlBpq1cr9VP4FOV8UqZnO1vJZIorb2yRdzqbni78DbF30blL5LV80gkeENFKcJEQJY/FvTh/nG6+i4OKr1sgz6LJNUKnWwfvPbQrsO9bb+9tUh+b+HX9fitxv3j1htMwEwYD9Rq7OmyqzWzZzWhCrd9EMAHDp93udNNUq/hewVe+1G7tkGAc7OL6U56iy/4+mD2mPNPvtgSALw9G0drD4DgKppZpSCoRCLByAzR+ergH1KB8tzUe1DhjsJJdVcBzzJOaTFxI26CYiWL1+OadOmYdGiRejevTteeuklDBw4EAcPHkR8fHygi2fHNqOsUl8By2ykWm5ycsSbP4i6BFfeCgIsL0ZKVd7zvzxoN8pF6RiYAHmuL2fHJ715lN25IUH9NBK2tPjE5i5H36U5WFG6YVkGBFERYZj8/k7FbSs1FdkeF0c3JPP/W77+64Vqu89yt9/KxztO4NbMRKzZZx8MAfYTtbpqqnRVE2r5XvO5t2Cd/egobyfgU+zobPPvEAlY8VCWqsljHc1RZ/kdm0cb2prYVzmzt/m7fWXdEbz/Q5HLMpgtGNnFLu+PfL4q9AsSsJ6fz/ZcdBYEmu8VZy9ccnhtAdQn/bQtrztN4HtPGO2WBTpxo24Con/+85+YOHEixo8fDwBYtGgRVq9ejbfffhvZ2dkBLp01pUy9tswnTjA3pXkzk6mnwZW3gwBnVd5K5XF1DNw+PpLjl1zR4hObO1x9l+Yb1pK8o3gz72eYhH2NQteWTVR1qHV2XFzVeplfV5opXCnlAlD7td7UPg7rfrKfM27DwTOK5R3dPUVxota6NFVaZm6f8sFOxc/1RX8wNR2dTUI54aYSNXPUmUcbCpvvx1lm76QYA+YNy0TL5pF2cwA6SqfRNU05gHPUL8iW2t+oq+lwnCWUVPMZ7pxXxcYqvKDQp+/JQe0Ceq2xz19fD126dAnbt2/HgAED5GUhISEYMGAAtmzZovie6upqlJeXW/35i6OOgiG/3ews2/eVbgDFxio/lbRuzE8VoVLtjtXlQmq+YFpSE1y5Sr3vKbXlcXYMXB0fpYu6EPC47J4eQ61Q810mxRjw1JAO+C77Jiyb2AN52f2tHiDMx9z2ONjyxnFR+n5zhmUid/jvy0Ik4IE+rbB5xk2YcvPVitvp3y7OrrwhEvDwTcrrmz87q3Uzj4P+po3CHXZmtj2m3mB7rMxTVlhy5zsx1646EypJyB7c3u77UXPM/ty3tXyOfTapJ5ZN7IHvsm+y+m7VXO/M/YLk/YZn++3oIdv2nmKuefb0OqD2vHJ0j+t0VazLz/AlXdQQlZaWoqamBgkJCVbLExIS8NNPyiNPcnJyMGfOHH8Uz46jWgPL0SLu1EJombc61no6aslX8+24Ux535/ryVdn9PfLL29w5Hs5qceSaJItpSixHFHnzuKgZwWjZFDL8uhb4eMcJ+f3Dr2uBmzsk+v17c3SszZ2ZfcH2WDmbNNiVpBgDcodnWmcd/+3/BayTVrrqmO3sM9Q2qfp6v92ZDscf1wGtznOmi0zVJ0+eRIsWLbB582ZkZWXJy5988kls3LgRW7faZ3Otrq5GdXW1/O/y8nKkpKT4LVO12nTrdZnioD7yJCOrN7Mie6M87vBF2YM5q623j4ftNCBaOC67j5/DtqPn0C2tiVV/GX9/b7783ahV131Wyjquhe/YFXf325N7RTBeuxzh1B0WLl26hMjISHz00UcYOnSovHzs2LEoKyvDZ5995nIbgZi6Q80JqYWLUn0QzEFAMJfdF3g8/IfHOnho8V7hr/OHAZGN7t2744YbbsArr7wCADCZTEhNTcXkyZNVdarW8lxmvCgREZErer1XcC4zG9OmTcPYsWPRrVs33HDDDXjppZdw4cIFedRZMPNFLhciIqpfeK9wTjcB0YgRI3DmzBk888wzKCkpwbXXXosvv/zSrqM1ERER6Y9umszqSstNZkRERKRM7f1bF3mIiIiIiJxhQERERES6x4CIiIiIdI8BEREREekeAyIiIiLSPQZEREREpHsMiIiIiEj3GBARERGR7jEgIiIiIt3TzdQddWVO6F1eXh7gkhAREZFa5vu2q4k5GBCpdP78eQBASkpKgEtCRERE7jp//jxiYmIcvs65zFQymUw4efIkGjduDEmSPN5OeXk5UlJScPz4cd3OicZjwGMA8BgAPAYAjwHAYwD49hgIIXD+/HkkJycjJMRxTyHWEKkUEhKCq666ymvbi46O1u2Jb8ZjwGMA8BgAPAYAjwHAYwD47hg4qxkyY6dqIiIi0j0GRERERKR7DIj8LCIiArNmzUJERESgixIwPAY8BgCPAcBjAPAYADwGgDaOATtVExERke6xhoiIiIh0jwERERER6R4DIiIiItI9BkRERESkewyIvGDhwoXo1KmTnFAqKysLa9askV+/ePEiJk2ahGbNmqFRo0YYPnw4Tp06ZbWNoqIiDBkyBJGRkYiPj8cTTzyBK1eu+HtXvCY3NxeSJGHq1Knysvp+HGbPng1Jkqz+2rdvL79e3/ff7MSJE7j77rvRrFkzGAwGZGZmYtu2bfLrQgg888wzSEpKgsFgwIABA3D48GGrbZw9exZjxoxBdHQ0YmNjMWHCBFRUVPh7VzySlpZmdx5IkoRJkyYB0Md5UFNTg5kzZyI9PR0GgwGtW7fGc889ZzWXVH0/D4DaqSKmTp2Kli1bwmAwoGfPnsjPz5dfr2/HYNOmTbj99tuRnJwMSZLw6aefWr3urf3ds2cP+vTpg4YNGyIlJQXz58/3zg4IqrOVK1eK1atXi0OHDomDBw+Kp556SjRo0EDs27dPCCHEX/7yF5GSkiLWrVsntm3bJnr06CF69uwpv//KlSuiY8eOYsCAAWLnzp3iiy++EM2bNxczZswI1C7VyQ8//CDS0tJEp06dxCOPPCIvr+/HYdasWeKaa64RxcXF8t+ZM2fk1+v7/gshxNmzZ0XLli3FuHHjxNatW8XPP/8svvrqK3HkyBF5ndzcXBETEyM+/fRTsXv3bnHHHXeI9PR0UVVVJa8zaNAg0blzZ/H999+Lb7/9VrRp00aMGjUqELvkttOnT1udA2vXrhUAxIYNG4QQ+jgPnn/+edGsWTOxatUqUVhYKD788EPRqFEj8fLLL8vr1PfzQAgh7rrrLpGRkSE2btwoDh8+LGbNmiWio6PFL7/8IoSof8fgiy++EE8//bRYsWKFACA++eQTq9e9sb9Go1EkJCSIMWPGiH379olly5YJg8EgXn/99TqXnwGRjzRp0kS8+eaboqysTDRo0EB8+OGH8msHDhwQAMSWLVuEELUnUUhIiCgpKZHXWbhwoYiOjhbV1dV+L3tdnD9/Xlx99dVi7dq1ol+/fnJApIfjMGvWLNG5c2fF1/Sw/0IIMX36dNG7d2+Hr5tMJpGYmCj+9re/ycvKyspERESEWLZsmRBCiB9//FEAEPn5+fI6a9asEZIkiRMnTviu8D7yyCOPiNatWwuTyaSb82DIkCHivvvus1o2bNgwMWbMGCGEPs6DyspKERoaKlatWmW1/LrrrhNPP/10vT8GtgGRt/b3tddeE02aNLH6LUyfPl20a9euzmVmk5mX1dTU4IMPPsCFCxeQlZWF7du34/LlyxgwYIC8Tvv27ZGamootW7YAALZs2YLMzEwkJCTI6wwcOBDl5eXYv3+/3/ehLiZNmoQhQ4ZY7S8A3RyHw4cPIzk5Ga1atcKYMWNQVFQEQD/7v3LlSnTr1g1/+tOfEB8fjy5dumDx4sXy64WFhSgpKbE6DjExMejevbvVcYiNjUW3bt3kdQYMGICQkBBs3brVfzvjBZcuXcK7776L++67D5Ik6eY86NmzJ9atW4dDhw4BAHbv3o28vDwMHjwYgD7OgytXrqCmpgYNGza0Wm4wGJCXl6eLY2DJW/u7ZcsW9O3bF+Hh4fI6AwcOxMGDB3Hu3Lk6lZGTu3rJ3r17kZWVhYsXL6JRo0b45JNPkJGRgV27diE8PByxsbFW6yckJKCkpAQAUFJSYnXxM79ufi1YfPDBB9ixY4dVG7lZSUlJvT8O3bt3x9KlS9GuXTsUFxdjzpw56NOnD/bt26eL/QeAn3/+GQsXLsS0adPw1FNPIT8/H1OmTEF4eDjGjh0r74fSfloeh/j4eKvXw8LC0LRp06A5DmaffvopysrKMG7cOAD6+B0AQHZ2NsrLy9G+fXuEhoaipqYGzz//PMaMGQMAujgPGjdujKysLDz33HPo0KEDEhISsGzZMmzZsgVt2rTRxTGw5K39LSkpQXp6ut02zK81adLE4zIyIPKSdu3aYdeuXTAajfjoo48wduxYbNy4MdDF8pvjx4/jkUcewdq1a+2eiPTC/PQLAJ06dUL37t3RsmVL/Pe//4XBYAhgyfzHZDKhW7dumDdvHgCgS5cu2LdvHxYtWoSxY8cGuHT+99Zbb2Hw4MFITk4OdFH86r///S/ee+89vP/++7jmmmuwa9cuTJ06FcnJybo6D/7zn//gvvvuQ4sWLRAaGorrrrsOo0aNwvbt2wNdNFLAJjMvCQ8PR5s2bdC1a1fk5OSgc+fOePnll5GYmIhLly6hrKzMav1Tp04hMTERAJCYmGg3ysT8b/M6Wrd9+3acPn0a1113HcLCwhAWFoaNGzdiwYIFCAsLQ0JCgi6Og6XY2Fi0bdsWR44c0c15kJSUhIyMDKtlHTp0kJsOzfuhtJ+Wx+H06dNWr1+5cgVnz54NmuMAAMeOHcP//vc/3H///fIyvZwHTzzxBLKzszFy5EhkZmbinnvuwaOPPoqcnBwA+jkPWrdujY0bN6KiogLHjx/HDz/8gMuXL6NVq1a6OQZm3tpfX/4+GBD5iMlkQnV1Nbp27YoGDRpg3bp18msHDx5EUVERsrKyAABZWVnYu3ev1Ymwdu1aREdH291ctOrmm2/G3r17sWvXLvmvW7duGDNmjPzfejgOlioqKlBQUICkpCTdnAe9evXCwYMHrZYdOnQILVu2BACkp6cjMTHR6jiUl5dj69atVsehrKzM6il6/fr1MJlM6N69ux/2wjuWLFmC+Ph4DBkyRF6ml/OgsrISISHWt5fQ0FCYTCYA+joPACAqKgpJSUk4d+4cvvrqK/zhD3/Q3THw1v5mZWVh06ZNuHz5srzO2rVr0a5duzo1lwHgsHtvyM7OFhs3bhSFhYViz549Ijs7W0iSJL7++mshRO0w29TUVLF+/Xqxbds2kZWVJbKysuT3m4fZ3nLLLWLXrl3iyy+/FHFxcUE1zFaJ5SgzIer/cXjsscfEN998IwoLC8V3330nBgwYIJo3by5Onz4thKj/+y9EbcqFsLAw8fzzz4vDhw+L9957T0RGRop3331XXic3N1fExsaKzz77TOzZs0f84Q9/UBx626VLF7F161aRl5cnrr76as0ONVZSU1MjUlNTxfTp0+1e08N5MHbsWNGiRQt52P2KFStE8+bNxZNPPimvo4fz4MsvvxRr1qwRP//8s/j6669F586dRffu3cWlS5eEEPXvGJw/f17s3LlT7Ny5UwAQ//znP8XOnTvFsWPHhBDe2d+ysjKRkJAg7rnnHrFv3z7xwQcfiMjISA6714r77rtPtGzZUoSHh4u4uDhx8803y8GQEEJUVVWJhx56SDRp0kRERkaKO++8UxQXF1tt4+jRo2Lw4MHCYDCI5s2bi8cee0xcvnzZ37viVbYBUX0/DiNGjBBJSUkiPDxctGjRQowYMcIq/05933+zzz//XHTs2FFERESI9u3bizfeeMPqdZPJJGbOnCkSEhJERESEuPnmm8XBgwet1vn111/FqFGjRKNGjUR0dLQYP368OH/+vD93o06++uorAcBuv4TQx3lQXl4uHnnkEZGamioaNmwoWrVqJZ5++mmrodJ6OA+WL18uWrVqJcLDw0ViYqKYNGmSKCsrk1+vb8dgw4YNAoDd39ixY4UQ3tvf3bt3i969e4uIiAjRokULkZub65XyS0JYpA4lIiIi0iH2ISIiIiLdY0BEREREuseAiIiIiHSPARERERHpHgMiIiIi0j0GRERERKR7DIiIiIhI9xgQERERke4xICIin7nxxhsxderUQBfD52bPno1rr7020MUgojpgQERE5MClS5f8+nlCCFy5csWvn0lEtRgQEZFPjBs3Dhs3bsTLL78MSZIgSRKOHj2Kffv2YfDgwWjUqBESEhJwzz33oLS0VH7fjTfeiIcffhhTp05FkyZNkJCQgMWLF+PChQsYP348GjdujDZt2mDNmjXye7755htIkoTVq1ejU6dOaNiwIXr06IF9+/ZZlSkvLw99+vSBwWBASkoKpkyZggsXLsivp6Wl4bnnnsO9996L6OhoPPDAAwCA6dOno23btoiMjESrVq0wc+ZMebbtpUuXYs6cOdi9e7e8n0uXLsXRo0chSRJ27dolb7+srAySJOGbb76xKveaNWvQtWtXREREIC8vDyaTCTk5OUhPT4fBYEDnzp3x0UcfefsrIiILDIiIyCdefvllZGVlYeLEiSguLkZxcTEaN26Mm266CV26dMG2bdvw5Zdf4tSpU7jrrrus3vvOO++gefPm+OGHH/Dwww/jwQcfxJ/+9Cf07NkTO3bswC233IJ77rkHlZWVVu974okn8I9//AP5+fmIi4vD7bffLgcuBQUFGDRoEIYPH449e/Zg+fLlyMvLw+TJk6228fe//x2dO3fGzp07MXPmTABA48aNsXTpUvz44494+eWXsXjxYrz44osAgBEjRuCxxx7DNddcI+/niBEj3DpW2dnZyM3NxYEDB9CpUyfk5OTg3//+NxYtWoT9+/fj0Ucfxd13342NGze6tV0icoNXpoglIlLQr18/8cgjj8j/fu6558Qtt9xitc7x48etZobv16+f6N27t/z6lStXRFRUlLjnnnvkZcXFxQKA2LJlixDi91m2P/jgA3mdX3/9VRgMBrF8+XIhhBATJkwQDzzwgNVnf/vttyIkJERUVVUJIYRo2bKlGDp0qMv9+tvf/ia6du0q/3vWrFmic+fOVusUFhYKAGLnzp3ysnPnzgkAYsOGDVbl/vTTT+V1Ll68KCIjI8XmzZuttjdhwgQxatQol2UjIs+EBTIYIyJ92b17NzZs2IBGjRrZvVZQUIC2bdsCADp16iQvDw0NRbNmzZCZmSkvS0hIAACcPn3aahtZWVnyfzdt2hTt2rXDgQMH5M/es2cP3nvvPXkdIQRMJhMKCwvRoUMHAEC3bt3syrZ8+XIsWLAABQUFqKiowJUrVxAdHe32/jti+ZlHjhxBZWUl/u///s9qnUuXLqFLly5e+0wissaAiIj8pqKiArfffjteeOEFu9eSkpLk/27QoIHVa5IkWS2TJAkAYDKZ3PrsP//5z5gyZYrda6mpqfJ/R0VFWb22ZcsWjBkzBnPmzMHAgQMRExODDz74AP/4xz+cfl5ISG2PBCGEvMzcfGfL8jMrKioAAKtXr0aLFi2s1ouIiHD6mUTkOQZEROQz4eHhqKmpkf993XXX4eOPP0ZaWhrCwrx/+fn+++/l4ObcuXM4dOiQXPNz3XXX4ccff0SbNm3c2ubmzZvRsmVLPP300/KyY8eOWa1ju58AEBcXBwAoLi6Wa3YsO1g7kpGRgYiICBQVFaFfv35ulZWIPMdO1UTkM2lpadi6dSuOHj2K0tJSTJo0CWfPnsWoUaOQn5+PgoICfPXVVxg/frxdQOGJZ599FuvWrcO+ffswbtw4NG/eHEOHDgVQO1Js8+bNmDx5Mnbt2oXDhw/js88+s+tUbevqq69GUVERPvjgAxQUFGDBggX45JNP7PazsLAQu3btQmlpKaqrq2EwGNCjRw+5s/TGjRvx17/+1eU+NG7cGI8//jgeffRRvPPOOygoKMCOHTvwyiuv4J133vH42BCRcwyIiMhnHn/8cYSGhiIjIwNxcXG4dOkSvvvuO9TU1OCWW25BZmYmpk6ditjYWLmJqS5yc3PxyCOPoGvXrigpKcHnn3+O8PBwALX9kjZu3IhDhw6hT58+6NKlC5555hkkJyc73eYdd9yBRx99FJMnT8a1116LzZs3y6PPzIYPH45Bgwahf//+iIuLw7JlywAAb7/9Nq5cuYKuXbti6tSpmDt3rqr9eO655zBz5kzk5OSgQ4cOGDRoEFavXo309HQPjgoRqSEJywZuIqIg9M0336B///44d+4cYmNjA10cIgpCrCEiIiIi3WNARERERLrHJjMiIiLSPdYQERERke4xICIiIiLdY0BEREREuseAiIiIiHSPARERERHpHgMiIiIi0j0GRERERKR7DIiIiIhI9xgQERERke79P40qAxlJLkdNAAAAAElFTkSuQmCC", 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", 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", 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" ] @@ -472,9 +478,9 @@ ], "source": [ "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(poly_surr, data_training, filename=\"pysmo_poly_train_scatter2D.pdf\")\n", - "surrogate_parity(poly_surr, data_training, filename=\"pysmo_poly_train_parity.pdf\")\n", - "surrogate_residual(poly_surr, data_training, filename=\"pysmo_poly_train_residual.pdf\")" + "surrogate_scatter2D(poly_surr, data_training)\n", + "surrogate_parity(poly_surr, data_training)\n", + "surrogate_residual(poly_surr, data_training)" ] }, { @@ -493,7 +499,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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" ] @@ -594,9 +600,9 @@ ], "source": [ "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(poly_surr, data_validation, filename=\"pysmo_poly_val_scatter2D.pdf\")\n", - "surrogate_parity(poly_surr, data_validation, filename=\"pysmo_poly_val_parity.pdf\")\n", - "surrogate_residual(poly_surr, data_validation, filename=\"pysmo_poly_val_residual.pdf\")" + "surrogate_scatter2D(poly_surr, data_validation)\n", + "surrogate_parity(poly_surr, data_validation)\n", + "surrogate_residual(poly_surr, data_validation)" ] }, { From f84cc4d051e3307b0f81d8b28ebcb63834420930 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Fri, 25 Aug 2023 01:03:50 -0400 Subject: [PATCH 06/75] Revert "Updated the HDA example exercise and solution" This reverts commit 87fa04dfe4f7a097f01033c362f2bd6e5e7b46bd. --- ...flowsheet_with_distillation_solution.ipynb | 65 +------------------ 1 file changed, 2 insertions(+), 63 deletions(-) diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb index 19cb655d..5bc30ee9 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb @@ -26,7 +26,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -59,7 +58,7 @@ "example, toluene will be reacted with hydrogen gas at high temperatures\n", " to form benzene via the following reaction:\n", "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", "\n", "\n", "This reaction is often accompanied by an equilibrium side reaction\n", @@ -83,7 +82,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -95,7 +93,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -134,7 +131,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -172,7 +168,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -219,7 +214,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -244,7 +238,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -268,7 +261,6 @@ ] }, { - 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"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -612,7 +594,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -631,7 +612,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -673,7 +653,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -695,7 +674,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -712,7 +690,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -746,7 +723,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -773,7 +749,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -799,7 +774,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -832,7 +806,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -852,7 +825,6 @@ ] }, { - 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"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3006,7 +2948,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3073,7 +3014,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3118,7 +3058,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3162,4 +3101,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} +} \ No newline at end of file From a2dc179e21dfda779e711cbb2d4425059a39f5d5 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Fri, 25 Aug 2023 01:06:12 -0400 Subject: [PATCH 07/75] Revert "Removed HDA examples" This reverts commit 9a628901d88ab173e865cccdc66f355972a2436c. --- .../OMLT/SCO2_keras_surrogate.ipynb | 498 +++++++++--------- .../PySMO/SCO2_pysmo_surrogate.ipynb | 132 +++-- 2 files changed, 312 insertions(+), 318 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb index 0e763b7f..fd6f6ce2 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb @@ -164,505 +164,505 @@ "output_type": "stream", "text": [ "Epoch 1/250\n", - "13/13 - 5s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 5s/epoch - 368ms/step\n", + "13/13 - 2s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 2s/epoch - 173ms/step\n", "Epoch 2/250\n", - "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 475ms/epoch - 37ms/step\n", + "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 220ms/epoch - 17ms/step\n", "Epoch 3/250\n", - "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 307ms/epoch - 24ms/step\n", + "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 228ms/epoch - 18ms/step\n", "Epoch 4/250\n", - "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 392ms/epoch - 30ms/step\n", + "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 239ms/epoch - 18ms/step\n", "Epoch 5/250\n", - "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 329ms/epoch - 25ms/step\n", + "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 229ms/epoch - 18ms/step\n", "Epoch 6/250\n", - "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 246ms/epoch - 19ms/step\n", + "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 202ms/epoch - 16ms/step\n", "Epoch 7/250\n", - "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 476ms/epoch - 37ms/step\n", + "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 241ms/epoch - 19ms/step\n", "Epoch 8/250\n", - "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 272ms/epoch - 21ms/step\n", + "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 233ms/epoch - 18ms/step\n", "Epoch 9/250\n", - "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 248ms/epoch - 19ms/step\n", + "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 227ms/epoch - 17ms/step\n", "Epoch 10/250\n", - "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 443ms/epoch - 34ms/step\n", + "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 240ms/epoch - 18ms/step\n", "Epoch 11/250\n", - "13/13 - 0s - 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289ms/epoch - 22ms/step\n", + "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 234ms/epoch - 18ms/step\n", "Epoch 15/250\n", - "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 348ms/epoch - 27ms/step\n", + "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 207ms/epoch - 16ms/step\n", "Epoch 16/250\n", - "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 307ms/epoch - 24ms/step\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 215ms/epoch - 17ms/step\n", "Epoch 17/250\n", - "13/13 - 1s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 515ms/epoch - 40ms/step\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 227ms/epoch - 17ms/step\n", "Epoch 18/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 396ms/epoch - 30ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 234ms/epoch - 18ms/step\n", "Epoch 19/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 404ms/epoch - 31ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 111ms/epoch - 9ms/step\n", "Epoch 20/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 300ms/epoch - 23ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 246ms/epoch - 19ms/step\n", "Epoch 21/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 315ms/epoch - 24ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 172ms/epoch - 13ms/step\n", "Epoch 22/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 240ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 209ms/epoch - 16ms/step\n", "Epoch 23/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 135ms/epoch - 10ms/step\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", "Epoch 24/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 260ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 219ms/epoch - 17ms/step\n", "Epoch 25/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 223ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 212ms/epoch - 16ms/step\n", "Epoch 26/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 244ms/epoch - 19ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 220ms/epoch - 17ms/step\n", "Epoch 27/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 250ms/epoch - 19ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 224ms/epoch - 17ms/step\n", "Epoch 28/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 111ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", "Epoch 29/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 106ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 112ms/epoch - 9ms/step\n", "Epoch 30/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 229ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 223ms/epoch - 17ms/step\n", "Epoch 31/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 208ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 219ms/epoch - 17ms/step\n", "Epoch 32/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 231ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 228ms/epoch - 18ms/step\n", "Epoch 33/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 120ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 112ms/epoch - 9ms/step\n", "Epoch 34/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 363ms/epoch - 28ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 244ms/epoch - 19ms/step\n", "Epoch 35/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 226ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 202ms/epoch - 16ms/step\n", "Epoch 36/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 206ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 224ms/epoch - 17ms/step\n", "Epoch 37/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 119ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - 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22ms/step\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 108ms/epoch - 8ms/step\n", "Epoch 48/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 263ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 221ms/epoch - 17ms/step\n", "Epoch 49/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.2521e-04 - val_mae: 0.0218 - val_mse: 9.2521e-04 - 116ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.2521e-04 - val_mae: 0.0218 - val_mse: 9.2521e-04 - 113ms/epoch - 9ms/step\n", "Epoch 50/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7818e-04 - val_mae: 0.0231 - val_mse: 9.7818e-04 - 179ms/epoch - 14ms/step\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7818e-04 - val_mae: 0.0231 - val_mse: 9.7818e-04 - 114ms/epoch - 9ms/step\n", "Epoch 51/250\n", - "13/13 - 0s - loss: 9.9977e-04 - mae: 0.0232 - mse: 9.9977e-04 - val_loss: 9.4350e-04 - val_mae: 0.0221 - val_mse: 9.4350e-04 - 182ms/epoch - 14ms/step\n", + "13/13 - 0s - loss: 9.9977e-04 - mae: 0.0232 - mse: 9.9977e-04 - val_loss: 9.4350e-04 - val_mae: 0.0221 - val_mse: 9.4350e-04 - 119ms/epoch - 9ms/step\n", "Epoch 52/250\n", - "13/13 - 0s - loss: 9.8599e-04 - mae: 0.0229 - mse: 9.8599e-04 - val_loss: 9.0638e-04 - val_mae: 0.0230 - val_mse: 9.0638e-04 - 266ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 9.8599e-04 - mae: 0.0229 - mse: 9.8599e-04 - val_loss: 9.0638e-04 - val_mae: 0.0230 - val_mse: 9.0638e-04 - 219ms/epoch - 17ms/step\n", "Epoch 53/250\n", - "13/13 - 0s - loss: 9.8295e-04 - mae: 0.0228 - mse: 9.8295e-04 - val_loss: 9.0667e-04 - val_mae: 0.0215 - val_mse: 9.0667e-04 - 187ms/epoch - 14ms/step\n", + "13/13 - 0s - loss: 9.8295e-04 - mae: 0.0228 - mse: 9.8295e-04 - 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val_mse: 8.3750e-04 - 115ms/epoch - 9ms/step\n", "Epoch 72/250\n", - "13/13 - 0s - loss: 8.5576e-04 - mae: 0.0218 - mse: 8.5576e-04 - val_loss: 8.1156e-04 - val_mae: 0.0210 - val_mse: 8.1156e-04 - 288ms/epoch - 22ms/step\n", + "13/13 - 0s - loss: 8.5576e-04 - mae: 0.0218 - mse: 8.5576e-04 - val_loss: 8.1156e-04 - val_mae: 0.0210 - val_mse: 8.1156e-04 - 211ms/epoch - 16ms/step\n", "Epoch 73/250\n", - "13/13 - 0s - loss: 8.4688e-04 - mae: 0.0216 - mse: 8.4688e-04 - val_loss: 8.0221e-04 - val_mae: 0.0210 - val_mse: 8.0221e-04 - 354ms/epoch - 27ms/step\n", + "13/13 - 0s - loss: 8.4688e-04 - mae: 0.0216 - mse: 8.4688e-04 - val_loss: 8.0221e-04 - val_mae: 0.0210 - val_mse: 8.0221e-04 - 216ms/epoch - 17ms/step\n", "Epoch 74/250\n", - "13/13 - 0s - loss: 8.3636e-04 - mae: 0.0211 - mse: 8.3636e-04 - val_loss: 7.9384e-04 - val_mae: 0.0208 - val_mse: 7.9384e-04 - 252ms/epoch - 19ms/step\n", + "13/13 - 0s - loss: 8.3636e-04 - mae: 0.0211 - mse: 8.3636e-04 - val_loss: 7.9384e-04 - val_mae: 0.0208 - val_mse: 7.9384e-04 - 219ms/epoch - 17ms/step\n", "Epoch 75/250\n", - "13/13 - 0s - loss: 8.4758e-04 - mae: 0.0222 - mse: 8.4758e-04 - val_loss: 8.2932e-04 - val_mae: 0.0212 - val_mse: 8.2932e-04 - 300ms/epoch - 23ms/step\n", + "13/13 - 0s - loss: 8.4758e-04 - mae: 0.0222 - mse: 8.4758e-04 - val_loss: 8.2932e-04 - val_mae: 0.0212 - val_mse: 8.2932e-04 - 111ms/epoch - 9ms/step\n", "Epoch 76/250\n", - "13/13 - 0s - loss: 8.4142e-04 - mae: 0.0213 - mse: 8.4142e-04 - val_loss: 8.0552e-04 - val_mae: 0.0209 - val_mse: 8.0552e-04 - 265ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 8.4142e-04 - mae: 0.0213 - mse: 8.4142e-04 - val_loss: 8.0552e-04 - val_mae: 0.0209 - val_mse: 8.0552e-04 - 118ms/epoch - 9ms/step\n", "Epoch 77/250\n", - "13/13 - 0s - loss: 8.5035e-04 - mae: 0.0215 - mse: 8.5035e-04 - val_loss: 8.6014e-04 - val_mae: 0.0215 - val_mse: 8.6014e-04 - 254ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 8.5035e-04 - mae: 0.0215 - mse: 8.5035e-04 - val_loss: 8.6014e-04 - val_mae: 0.0215 - val_mse: 8.6014e-04 - 115ms/epoch - 9ms/step\n", "Epoch 78/250\n", - "13/13 - 1s - loss: 8.9015e-04 - mae: 0.0228 - mse: 8.9015e-04 - val_loss: 9.2548e-04 - val_mae: 0.0225 - val_mse: 9.2548e-04 - 536ms/epoch - 41ms/step\n", + "13/13 - 0s - loss: 8.9015e-04 - mae: 0.0228 - mse: 8.9015e-04 - val_loss: 9.2548e-04 - val_mae: 0.0225 - val_mse: 9.2548e-04 - 108ms/epoch - 8ms/step\n", "Epoch 79/250\n", - "13/13 - 0s - loss: 8.1577e-04 - mae: 0.0212 - mse: 8.1577e-04 - val_loss: 8.4703e-04 - val_mae: 0.0211 - val_mse: 8.4703e-04 - 311ms/epoch - 24ms/step\n", + "13/13 - 0s - loss: 8.1577e-04 - mae: 0.0212 - mse: 8.1577e-04 - val_loss: 8.4703e-04 - val_mae: 0.0211 - val_mse: 8.4703e-04 - 112ms/epoch - 9ms/step\n", "Epoch 80/250\n", - "13/13 - 0s - loss: 8.0555e-04 - mae: 0.0211 - mse: 8.0555e-04 - val_loss: 8.5652e-04 - val_mae: 0.0214 - val_mse: 8.5652e-04 - 203ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 8.0555e-04 - mae: 0.0211 - mse: 8.0555e-04 - val_loss: 8.5652e-04 - val_mae: 0.0214 - val_mse: 8.5652e-04 - 108ms/epoch - 8ms/step\n", "Epoch 81/250\n", - "13/13 - 0s - loss: 8.3478e-04 - mae: 0.0219 - mse: 8.3478e-04 - val_loss: 9.1057e-04 - val_mae: 0.0222 - val_mse: 9.1057e-04 - 276ms/epoch - 21ms/step\n", + "13/13 - 0s - loss: 8.3478e-04 - mae: 0.0219 - mse: 8.3478e-04 - val_loss: 9.1057e-04 - val_mae: 0.0222 - val_mse: 9.1057e-04 - 114ms/epoch - 9ms/step\n", "Epoch 82/250\n", - "13/13 - 0s - loss: 8.2593e-04 - mae: 0.0217 - mse: 8.2593e-04 - val_loss: 8.1172e-04 - val_mae: 0.0209 - val_mse: 8.1172e-04 - 224ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 8.2593e-04 - mae: 0.0217 - mse: 8.2593e-04 - val_loss: 8.1172e-04 - val_mae: 0.0209 - val_mse: 8.1172e-04 - 113ms/epoch - 9ms/step\n", "Epoch 83/250\n", - "13/13 - 0s - loss: 8.2887e-04 - mae: 0.0213 - mse: 8.2887e-04 - val_loss: 8.2033e-04 - val_mae: 0.0211 - val_mse: 8.2033e-04 - 184ms/epoch - 14ms/step\n", + "13/13 - 0s - loss: 8.2887e-04 - mae: 0.0213 - mse: 8.2887e-04 - val_loss: 8.2033e-04 - val_mae: 0.0211 - val_mse: 8.2033e-04 - 165ms/epoch - 13ms/step\n", "Epoch 84/250\n", - "13/13 - 0s - loss: 8.1454e-04 - mae: 0.0219 - mse: 8.1454e-04 - val_loss: 8.1589e-04 - val_mae: 0.0211 - val_mse: 8.1589e-04 - 181ms/epoch - 14ms/step\n", + "13/13 - 0s - loss: 8.1454e-04 - mae: 0.0219 - mse: 8.1454e-04 - val_loss: 8.1589e-04 - val_mae: 0.0211 - val_mse: 8.1589e-04 - 109ms/epoch - 8ms/step\n", "Epoch 85/250\n", - "13/13 - 0s - loss: 8.0777e-04 - mae: 0.0212 - mse: 8.0777e-04 - val_loss: 7.8637e-04 - val_mae: 0.0208 - val_mse: 7.8637e-04 - 306ms/epoch - 24ms/step\n", + "13/13 - 0s - loss: 8.0777e-04 - mae: 0.0212 - mse: 8.0777e-04 - val_loss: 7.8637e-04 - val_mae: 0.0208 - val_mse: 7.8637e-04 - 177ms/epoch - 14ms/step\n", "Epoch 86/250\n", - "13/13 - 0s - loss: 7.8107e-04 - mae: 0.0213 - mse: 7.8107e-04 - val_loss: 7.8138e-04 - val_mae: 0.0212 - val_mse: 7.8138e-04 - 266ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 7.8107e-04 - mae: 0.0213 - mse: 7.8107e-04 - val_loss: 7.8138e-04 - val_mae: 0.0212 - val_mse: 7.8138e-04 - 223ms/epoch - 17ms/step\n", "Epoch 87/250\n", - "13/13 - 0s - loss: 7.9729e-04 - mae: 0.0210 - mse: 7.9729e-04 - val_loss: 7.3667e-04 - val_mae: 0.0204 - val_mse: 7.3667e-04 - 281ms/epoch - 22ms/step\n", + "13/13 - 0s - loss: 7.9729e-04 - mae: 0.0210 - mse: 7.9729e-04 - val_loss: 7.3667e-04 - val_mae: 0.0204 - val_mse: 7.3667e-04 - 237ms/epoch - 18ms/step\n", "Epoch 88/250\n", - "13/13 - 0s - loss: 7.5931e-04 - mae: 0.0205 - mse: 7.5931e-04 - val_loss: 7.5522e-04 - val_mae: 0.0210 - val_mse: 7.5522e-04 - 129ms/epoch - 10ms/step\n", + "13/13 - 0s - loss: 7.5931e-04 - mae: 0.0205 - mse: 7.5931e-04 - val_loss: 7.5522e-04 - val_mae: 0.0210 - val_mse: 7.5522e-04 - 108ms/epoch - 8ms/step\n", "Epoch 89/250\n", - "13/13 - 0s - loss: 7.6036e-04 - mae: 0.0211 - mse: 7.6036e-04 - val_loss: 7.5503e-04 - val_mae: 0.0207 - val_mse: 7.5503e-04 - 124ms/epoch - 10ms/step\n", + "13/13 - 0s - loss: 7.6036e-04 - mae: 0.0211 - mse: 7.6036e-04 - val_loss: 7.5503e-04 - val_mae: 0.0207 - val_mse: 7.5503e-04 - 106ms/epoch - 8ms/step\n", "Epoch 90/250\n", - "13/13 - 0s - loss: 7.6322e-04 - mae: 0.0204 - mse: 7.6322e-04 - val_loss: 7.7629e-04 - val_mae: 0.0203 - val_mse: 7.7629e-04 - 133ms/epoch - 10ms/step\n", + "13/13 - 0s - loss: 7.6322e-04 - mae: 0.0204 - mse: 7.6322e-04 - val_loss: 7.7629e-04 - val_mae: 0.0203 - val_mse: 7.7629e-04 - 117ms/epoch - 9ms/step\n", "Epoch 91/250\n", - "13/13 - 0s - loss: 7.5436e-04 - mae: 0.0208 - mse: 7.5436e-04 - val_loss: 7.4549e-04 - val_mae: 0.0210 - val_mse: 7.4549e-04 - 156ms/epoch - 12ms/step\n", + "13/13 - 0s - loss: 7.5436e-04 - mae: 0.0208 - mse: 7.5436e-04 - val_loss: 7.4549e-04 - val_mae: 0.0210 - val_mse: 7.4549e-04 - 109ms/epoch - 8ms/step\n", "Epoch 92/250\n", - "13/13 - 0s - loss: 7.8479e-04 - mae: 0.0208 - mse: 7.8479e-04 - val_loss: 8.0607e-04 - val_mae: 0.0208 - val_mse: 8.0607e-04 - 184ms/epoch - 14ms/step\n", + "13/13 - 0s - loss: 7.8479e-04 - mae: 0.0208 - mse: 7.8479e-04 - val_loss: 8.0607e-04 - val_mae: 0.0208 - val_mse: 8.0607e-04 - 112ms/epoch - 9ms/step\n", "Epoch 93/250\n", - "13/13 - 0s - loss: 7.7194e-04 - mae: 0.0211 - mse: 7.7194e-04 - val_loss: 7.7994e-04 - val_mae: 0.0206 - val_mse: 7.7994e-04 - 137ms/epoch - 11ms/step\n", + "13/13 - 0s - loss: 7.7194e-04 - mae: 0.0211 - mse: 7.7194e-04 - val_loss: 7.7994e-04 - val_mae: 0.0206 - val_mse: 7.7994e-04 - 109ms/epoch - 8ms/step\n", "Epoch 94/250\n", - "13/13 - 0s - loss: 7.4802e-04 - mae: 0.0205 - mse: 7.4802e-04 - val_loss: 7.2386e-04 - val_mae: 0.0201 - val_mse: 7.2386e-04 - 248ms/epoch - 19ms/step\n", + "13/13 - 0s - loss: 7.4802e-04 - mae: 0.0205 - mse: 7.4802e-04 - val_loss: 7.2386e-04 - val_mae: 0.0201 - val_mse: 7.2386e-04 - 190ms/epoch - 15ms/step\n", "Epoch 95/250\n", - "13/13 - 0s - loss: 7.2616e-04 - mae: 0.0203 - mse: 7.2616e-04 - val_loss: 7.2728e-04 - val_mae: 0.0204 - val_mse: 7.2728e-04 - 199ms/epoch - 15ms/step\n", + "13/13 - 0s - loss: 7.2616e-04 - mae: 0.0203 - mse: 7.2616e-04 - val_loss: 7.2728e-04 - val_mae: 0.0204 - val_mse: 7.2728e-04 - 121ms/epoch - 9ms/step\n", "Epoch 96/250\n", - "13/13 - 1s - loss: 7.2310e-04 - mae: 0.0204 - mse: 7.2310e-04 - val_loss: 7.1349e-04 - val_mae: 0.0206 - val_mse: 7.1349e-04 - 583ms/epoch - 45ms/step\n", + "13/13 - 0s - loss: 7.2310e-04 - mae: 0.0204 - mse: 7.2310e-04 - val_loss: 7.1349e-04 - val_mae: 0.0206 - val_mse: 7.1349e-04 - 219ms/epoch - 17ms/step\n", "Epoch 97/250\n", - "13/13 - 0s - loss: 7.0905e-04 - mae: 0.0201 - mse: 7.0905e-04 - val_loss: 7.6242e-04 - val_mae: 0.0205 - val_mse: 7.6242e-04 - 178ms/epoch - 14ms/step\n", + "13/13 - 0s - loss: 7.0905e-04 - mae: 0.0201 - mse: 7.0905e-04 - val_loss: 7.6242e-04 - val_mae: 0.0205 - val_mse: 7.6242e-04 - 117ms/epoch - 9ms/step\n", "Epoch 98/250\n", - "13/13 - 0s - loss: 7.1839e-04 - mae: 0.0200 - mse: 7.1839e-04 - val_loss: 7.7098e-04 - val_mae: 0.0202 - val_mse: 7.7098e-04 - 136ms/epoch - 10ms/step\n", + "13/13 - 0s - loss: 7.1839e-04 - mae: 0.0200 - mse: 7.1839e-04 - val_loss: 7.7098e-04 - val_mae: 0.0202 - val_mse: 7.7098e-04 - 111ms/epoch - 9ms/step\n", "Epoch 99/250\n", - "13/13 - 0s - loss: 7.3924e-04 - mae: 0.0208 - mse: 7.3924e-04 - val_loss: 7.8554e-04 - val_mae: 0.0206 - val_mse: 7.8554e-04 - 167ms/epoch - 13ms/step\n", + "13/13 - 0s - loss: 7.3924e-04 - mae: 0.0208 - mse: 7.3924e-04 - val_loss: 7.8554e-04 - val_mae: 0.0206 - val_mse: 7.8554e-04 - 114ms/epoch - 9ms/step\n", "Epoch 100/250\n", - "13/13 - 0s - loss: 7.5556e-04 - mae: 0.0209 - mse: 7.5556e-04 - val_loss: 8.6021e-04 - val_mae: 0.0215 - val_mse: 8.6021e-04 - 270ms/epoch - 21ms/step\n", + "13/13 - 0s - loss: 7.5556e-04 - mae: 0.0209 - mse: 7.5556e-04 - val_loss: 8.6021e-04 - val_mae: 0.0215 - val_mse: 8.6021e-04 - 111ms/epoch - 9ms/step\n", "Epoch 101/250\n", - "13/13 - 0s - loss: 7.9288e-04 - mae: 0.0213 - mse: 7.9288e-04 - val_loss: 7.2968e-04 - val_mae: 0.0203 - val_mse: 7.2968e-04 - 157ms/epoch - 12ms/step\n", + "13/13 - 0s - loss: 7.9288e-04 - mae: 0.0213 - mse: 7.9288e-04 - val_loss: 7.2968e-04 - val_mae: 0.0203 - val_mse: 7.2968e-04 - 110ms/epoch - 8ms/step\n", "Epoch 102/250\n", - "13/13 - 0s - loss: 7.1861e-04 - mae: 0.0204 - mse: 7.1861e-04 - val_loss: 7.0941e-04 - val_mae: 0.0207 - val_mse: 7.0941e-04 - 264ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 7.1861e-04 - mae: 0.0204 - mse: 7.1861e-04 - val_loss: 7.0941e-04 - val_mae: 0.0207 - val_mse: 7.0941e-04 - 227ms/epoch - 17ms/step\n", "Epoch 103/250\n", - "13/13 - 1s - loss: 7.5092e-04 - mae: 0.0208 - mse: 7.5092e-04 - val_loss: 6.8788e-04 - val_mae: 0.0198 - val_mse: 6.8788e-04 - 522ms/epoch - 40ms/step\n", + "13/13 - 0s - loss: 7.5092e-04 - mae: 0.0208 - mse: 7.5092e-04 - val_loss: 6.8788e-04 - val_mae: 0.0198 - val_mse: 6.8788e-04 - 218ms/epoch - 17ms/step\n", "Epoch 104/250\n", - "13/13 - 0s - loss: 7.0460e-04 - mae: 0.0200 - mse: 7.0460e-04 - val_loss: 7.2570e-04 - val_mae: 0.0200 - val_mse: 7.2570e-04 - 154ms/epoch - 12ms/step\n", + "13/13 - 0s - loss: 7.0460e-04 - mae: 0.0200 - mse: 7.0460e-04 - val_loss: 7.2570e-04 - val_mae: 0.0200 - val_mse: 7.2570e-04 - 115ms/epoch - 9ms/step\n", "Epoch 105/250\n", - "13/13 - 0s - loss: 6.9255e-04 - mae: 0.0202 - mse: 6.9255e-04 - val_loss: 6.7411e-04 - val_mae: 0.0199 - val_mse: 6.7411e-04 - 259ms/epoch - 20ms/step\n", + "13/13 - 0s - loss: 6.9255e-04 - mae: 0.0202 - mse: 6.9255e-04 - val_loss: 6.7411e-04 - val_mae: 0.0199 - val_mse: 6.7411e-04 - 193ms/epoch - 15ms/step\n", "Epoch 106/250\n", - "13/13 - 0s - loss: 6.8175e-04 - mae: 0.0196 - mse: 6.8175e-04 - val_loss: 6.7593e-04 - val_mae: 0.0196 - val_mse: 6.7593e-04 - 186ms/epoch - 14ms/step\n", + "13/13 - 0s - loss: 6.8175e-04 - mae: 0.0196 - mse: 6.8175e-04 - val_loss: 6.7593e-04 - val_mae: 0.0196 - val_mse: 6.7593e-04 - 112ms/epoch - 9ms/step\n", "Epoch 107/250\n", - "13/13 - 0s - loss: 6.7018e-04 - mae: 0.0196 - mse: 6.7018e-04 - val_loss: 6.8702e-04 - val_mae: 0.0196 - val_mse: 6.8702e-04 - 161ms/epoch - 12ms/step\n", + "13/13 - 0s - loss: 6.7018e-04 - mae: 0.0196 - mse: 6.7018e-04 - val_loss: 6.8702e-04 - val_mae: 0.0196 - val_mse: 6.8702e-04 - 110ms/epoch - 8ms/step\n", "Epoch 108/250\n", - 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0s - loss: 3.5933e-04 - mae: 0.0144 - mse: 3.5933e-04 - val_loss: 3.5900e-04 - val_mae: 0.0134 - val_mse: 3.5900e-04 - 105ms/epoch - 8ms/step\n", "Epoch 226/250\n", - "13/13 - 0s - loss: 3.6466e-04 - mae: 0.0144 - mse: 3.6466e-04 - val_loss: 3.5378e-04 - val_mae: 0.0135 - val_mse: 3.5378e-04 - 204ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 3.6466e-04 - mae: 0.0144 - mse: 3.6466e-04 - val_loss: 3.5378e-04 - val_mae: 0.0135 - val_mse: 3.5378e-04 - 232ms/epoch - 18ms/step\n", "Epoch 227/250\n", - "13/13 - 0s - loss: 3.5876e-04 - mae: 0.0144 - mse: 3.5876e-04 - val_loss: 3.6523e-04 - val_mae: 0.0133 - val_mse: 3.6523e-04 - 96ms/epoch - 7ms/step\n", + "13/13 - 0s - loss: 3.5876e-04 - mae: 0.0144 - mse: 3.5876e-04 - val_loss: 3.6523e-04 - val_mae: 0.0133 - val_mse: 3.6523e-04 - 112ms/epoch - 9ms/step\n", "Epoch 228/250\n", - "13/13 - 0s - loss: 3.4559e-04 - mae: 0.0142 - mse: 3.4559e-04 - val_loss: 3.5907e-04 - val_mae: 0.0139 - val_mse: 3.5907e-04 - 96ms/epoch - 7ms/step\n", + "13/13 - 0s - loss: 3.4559e-04 - mae: 0.0142 - mse: 3.4559e-04 - val_loss: 3.5907e-04 - val_mae: 0.0139 - val_mse: 3.5907e-04 - 162ms/epoch - 12ms/step\n", "Epoch 229/250\n", - "13/13 - 0s - loss: 3.4162e-04 - mae: 0.0142 - mse: 3.4162e-04 - val_loss: 4.2194e-04 - val_mae: 0.0141 - val_mse: 4.2194e-04 - 96ms/epoch - 7ms/step\n", + "13/13 - 0s - loss: 3.4162e-04 - mae: 0.0142 - mse: 3.4162e-04 - val_loss: 4.2194e-04 - val_mae: 0.0141 - val_mse: 4.2194e-04 - 101ms/epoch - 8ms/step\n", "Epoch 230/250\n", - "13/13 - 0s - loss: 3.6967e-04 - mae: 0.0146 - mse: 3.6967e-04 - val_loss: 3.7720e-04 - val_mae: 0.0138 - val_mse: 3.7720e-04 - 96ms/epoch - 7ms/step\n", + "13/13 - 0s - loss: 3.6967e-04 - mae: 0.0146 - mse: 3.6967e-04 - val_loss: 3.7720e-04 - val_mae: 0.0138 - val_mse: 3.7720e-04 - 105ms/epoch - 8ms/step\n", "Epoch 231/250\n", - "13/13 - 0s - loss: 3.3735e-04 - mae: 0.0136 - mse: 3.3735e-04 - val_loss: 3.3976e-04 - val_mae: 0.0129 - val_mse: 3.3976e-04 - 401ms/epoch - 31ms/step\n", + "13/13 - 0s - loss: 3.3735e-04 - mae: 0.0136 - mse: 3.3735e-04 - val_loss: 3.3976e-04 - val_mae: 0.0129 - val_mse: 3.3976e-04 - 227ms/epoch - 17ms/step\n", "Epoch 232/250\n", - "13/13 - 0s - loss: 3.3844e-04 - mae: 0.0141 - mse: 3.3844e-04 - val_loss: 3.8716e-04 - val_mae: 0.0135 - val_mse: 3.8716e-04 - 95ms/epoch - 7ms/step\n", + "13/13 - 0s - loss: 3.3844e-04 - mae: 0.0141 - mse: 3.3844e-04 - val_loss: 3.8716e-04 - val_mae: 0.0135 - val_mse: 3.8716e-04 - 109ms/epoch - 8ms/step\n", "Epoch 233/250\n", - "13/13 - 0s - loss: 3.6741e-04 - mae: 0.0145 - mse: 3.6741e-04 - val_loss: 3.8668e-04 - val_mae: 0.0136 - val_mse: 3.8668e-04 - 89ms/epoch - 7ms/step\n", + "13/13 - 0s - loss: 3.6741e-04 - mae: 0.0145 - mse: 3.6741e-04 - val_loss: 3.8668e-04 - val_mae: 0.0136 - val_mse: 3.8668e-04 - 117ms/epoch - 9ms/step\n", "Epoch 234/250\n", - "13/13 - 0s - loss: 3.4129e-04 - mae: 0.0139 - mse: 3.4129e-04 - val_loss: 3.4933e-04 - val_mae: 0.0133 - val_mse: 3.4933e-04 - 102ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.4129e-04 - mae: 0.0139 - mse: 3.4129e-04 - val_loss: 3.4933e-04 - val_mae: 0.0133 - val_mse: 3.4933e-04 - 118ms/epoch - 9ms/step\n", "Epoch 235/250\n", - "13/13 - 0s - loss: 3.2338e-04 - mae: 0.0137 - mse: 3.2338e-04 - val_loss: 3.4566e-04 - val_mae: 0.0133 - val_mse: 3.4566e-04 - 96ms/epoch - 7ms/step\n", + "13/13 - 0s - loss: 3.2338e-04 - mae: 0.0137 - mse: 3.2338e-04 - val_loss: 3.4566e-04 - val_mae: 0.0133 - val_mse: 3.4566e-04 - 108ms/epoch - 8ms/step\n", "Epoch 236/250\n", - "13/13 - 0s - loss: 3.1652e-04 - mae: 0.0134 - mse: 3.1652e-04 - val_loss: 3.9728e-04 - val_mae: 0.0136 - val_mse: 3.9728e-04 - 98ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.1652e-04 - mae: 0.0134 - mse: 3.1652e-04 - val_loss: 3.9728e-04 - val_mae: 0.0136 - val_mse: 3.9728e-04 - 111ms/epoch - 9ms/step\n", "Epoch 237/250\n", - "13/13 - 0s - loss: 3.2047e-04 - mae: 0.0136 - mse: 3.2047e-04 - val_loss: 3.3756e-04 - val_mae: 0.0130 - val_mse: 3.3756e-04 - 161ms/epoch - 12ms/step\n", + "13/13 - 0s - loss: 3.2047e-04 - mae: 0.0136 - mse: 3.2047e-04 - val_loss: 3.3756e-04 - val_mae: 0.0130 - val_mse: 3.3756e-04 - 225ms/epoch - 17ms/step\n", "Epoch 238/250\n", - "13/13 - 0s - loss: 3.3167e-04 - mae: 0.0138 - mse: 3.3167e-04 - val_loss: 3.3191e-04 - val_mae: 0.0126 - val_mse: 3.3191e-04 - 186ms/epoch - 14ms/step\n", + "13/13 - 0s - loss: 3.3167e-04 - mae: 0.0138 - mse: 3.3167e-04 - val_loss: 3.3191e-04 - val_mae: 0.0126 - val_mse: 3.3191e-04 - 228ms/epoch - 18ms/step\n", "Epoch 239/250\n", - "13/13 - 0s - loss: 3.2033e-04 - mae: 0.0134 - mse: 3.2033e-04 - val_loss: 3.2969e-04 - val_mae: 0.0128 - val_mse: 3.2969e-04 - 190ms/epoch - 15ms/step\n", + "13/13 - 0s - loss: 3.2033e-04 - mae: 0.0134 - mse: 3.2033e-04 - val_loss: 3.2969e-04 - val_mae: 0.0128 - val_mse: 3.2969e-04 - 215ms/epoch - 17ms/step\n", "Epoch 240/250\n", - "13/13 - 0s - loss: 3.5224e-04 - mae: 0.0141 - mse: 3.5224e-04 - val_loss: 3.9061e-04 - val_mae: 0.0148 - val_mse: 3.9061e-04 - 103ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.5224e-04 - mae: 0.0141 - mse: 3.5224e-04 - val_loss: 3.9061e-04 - val_mae: 0.0148 - val_mse: 3.9061e-04 - 110ms/epoch - 8ms/step\n", "Epoch 241/250\n", - "13/13 - 0s - loss: 3.9777e-04 - mae: 0.0153 - mse: 3.9777e-04 - val_loss: 3.7065e-04 - val_mae: 0.0137 - val_mse: 3.7065e-04 - 99ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.9777e-04 - mae: 0.0153 - mse: 3.9777e-04 - val_loss: 3.7065e-04 - val_mae: 0.0137 - val_mse: 3.7065e-04 - 107ms/epoch - 8ms/step\n", "Epoch 242/250\n", - "13/13 - 0s - loss: 3.2502e-04 - mae: 0.0138 - mse: 3.2502e-04 - val_loss: 3.3236e-04 - val_mae: 0.0124 - val_mse: 3.3236e-04 - 101ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.2502e-04 - mae: 0.0138 - mse: 3.2502e-04 - val_loss: 3.3236e-04 - val_mae: 0.0124 - val_mse: 3.3236e-04 - 109ms/epoch - 8ms/step\n", "Epoch 243/250\n", - "13/13 - 0s - loss: 3.0734e-04 - mae: 0.0133 - mse: 3.0734e-04 - val_loss: 3.2635e-04 - val_mae: 0.0126 - val_mse: 3.2635e-04 - 198ms/epoch - 15ms/step\n", + "13/13 - 0s - loss: 3.0734e-04 - mae: 0.0133 - mse: 3.0734e-04 - val_loss: 3.2635e-04 - val_mae: 0.0126 - val_mse: 3.2635e-04 - 227ms/epoch - 17ms/step\n", "Epoch 244/250\n", - "13/13 - 0s - loss: 3.2928e-04 - mae: 0.0137 - mse: 3.2928e-04 - val_loss: 3.2871e-04 - val_mae: 0.0125 - val_mse: 3.2871e-04 - 100ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.2928e-04 - mae: 0.0137 - mse: 3.2928e-04 - val_loss: 3.2871e-04 - val_mae: 0.0125 - val_mse: 3.2871e-04 - 104ms/epoch - 8ms/step\n", "Epoch 245/250\n", - "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 92ms/epoch - 7ms/step\n", + "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 112ms/epoch - 9ms/step\n", "Epoch 246/250\n", - "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 95ms/epoch - 7ms/step\n", + "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 107ms/epoch - 8ms/step\n", "Epoch 247/250\n", - "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 95ms/epoch - 7ms/step\n", + "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 106ms/epoch - 8ms/step\n", "Epoch 248/250\n", - "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 110ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 118ms/epoch - 9ms/step\n", "Epoch 249/250\n", - "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 105ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 106ms/epoch - 8ms/step\n", "Epoch 250/250\n", - "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 248ms/epoch - 19ms/step\n" + "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 109ms/epoch - 8ms/step\n" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb index aca9aa02..fe6bd96f 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb @@ -151,13 +151,7 @@ "Default parameter estimation method is used.\n", "Parameter estimation method: pyomo \n", "\n", - "No iterations will be run.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "No iterations will be run.\n", "WARNING: Loading a SolverResults object with a warning status into\n", "model.name=\"unknown\";\n", " - termination condition: maxIterations\n", @@ -209,37 +203,37 @@ " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", " Exceeded.\n", "\n", - "Best surrogate model is of order 5 with a cross-val S.S. Error of 22655.436683\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 20466.657669\n", "\n", "------------------------------------------------------------\n", "The final coefficients of the regression terms are: \n", "\n", - "k | -509611.829792\n", - "(x_ 1 )^ 1 | -3514.24009\n", - "(x_ 2 )^ 1 | 881.280222\n", - "(x_ 1 )^ 2 | 115.258156\n", - "(x_ 2 )^ 2 | -2.391388\n", - "(x_ 1 )^ 3 | -2.712279\n", - "(x_ 2 )^ 3 | 0.003345\n", - "(x_ 1 )^ 4 | 0.037981\n", - "(x_ 2 )^ 4 | -2e-06\n", - "(x_ 1 )^ 5 | -0.000196\n", + "k | -534397.59515\n", + "(x_ 1 )^ 1 | -2733.579691\n", + "(x_ 2 )^ 1 | 1036.106357\n", + "(x_ 1 )^ 2 | 32.409203\n", + "(x_ 2 )^ 2 | -2.852387\n", + "(x_ 1 )^ 3 | 0.893563\n", + "(x_ 2 )^ 3 | 0.004018\n", + "(x_ 1 )^ 4 | -0.045284\n", + "(x_ 2 )^ 4 | -3e-06\n", + "(x_ 1 )^ 5 | 0.000564\n", "(x_ 2 )^ 5 | 0.0\n", - "x_ 1 .x_ 2 | 4.574188\n", + "x_ 1 .x_ 2 | 4.372684\n", "\n", "The coefficients of the extra terms in additional_regression_features are:\n", "\n", - "Coeff. additional_regression_features[ 1 ]: -0.003097\n", - "Coeff. additional_regression_features[ 2 ]: 4.9e-05\n", - "Coeff. additional_regression_features[ 3 ]: -0.066624\n", - "Coeff. additional_regression_features[ 4 ]: 117026.221822\n", - "Coeff. additional_regression_features[ 5 ]: -62.034801\n", + "Coeff. additional_regression_features[ 1 ]: -0.002723\n", + "Coeff. additional_regression_features[ 2 ]: 3.6e-05\n", + "Coeff. additional_regression_features[ 3 ]: -0.050607\n", + "Coeff. additional_regression_features[ 4 ]: 169668.814595\n", + "Coeff. additional_regression_features[ 5 ]: -44.726026\n", "\n", "Regression model performance on training data:\n", - "Order: 5 / MAE: 124.816299 / MSE: 43122.530042 / R^2: 0.999692\n", + "Order: 5 / MAE: 134.972465 / MSE: 54613.278159 / R^2: 0.999601\n", "\n", "Results saved in solution.pickle\n", - "2023-08-22 10:19:11 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", + "2023-08-19 23:48:46 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", "\n", "===========================Polynomial Regression===============================================\n", "\n", @@ -301,37 +295,37 @@ " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", " Exceeded.\n", "\n", - "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.176582\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.156437\n", "\n", "------------------------------------------------------------\n", "The final coefficients of the regression terms are: \n", "\n", - "k | -431.52518\n", - "(x_ 1 )^ 1 | -11.451609\n", - "(x_ 2 )^ 1 | 3.128102\n", - "(x_ 1 )^ 2 | 0.469184\n", - "(x_ 2 )^ 2 | -0.008586\n", - "(x_ 1 )^ 3 | -0.013135\n", - "(x_ 2 )^ 3 | 1.2e-05\n", - "(x_ 1 )^ 4 | 0.000209\n", + "k | -519.862457\n", + "(x_ 1 )^ 1 | -8.820865\n", + "(x_ 2 )^ 1 | 3.676641\n", + "(x_ 1 )^ 2 | 0.18002\n", + "(x_ 2 )^ 2 | -0.010217\n", + "(x_ 1 )^ 3 | -0.000783\n", + "(x_ 2 )^ 3 | 1.4e-05\n", + "(x_ 1 )^ 4 | -6.9e-05\n", "(x_ 2 )^ 4 | -0.0\n", - "(x_ 1 )^ 5 | -1e-06\n", + "(x_ 1 )^ 5 | 1e-06\n", "(x_ 2 )^ 5 | 0.0\n", - "x_ 1 .x_ 2 | 0.010646\n", + "x_ 1 .x_ 2 | 0.010367\n", "\n", "The coefficients of the extra terms in additional_regression_features are:\n", "\n", - "Coeff. additional_regression_features[ 1 ]: -8e-06\n", + "Coeff. additional_regression_features[ 1 ]: -7e-06\n", "Coeff. additional_regression_features[ 2 ]: 0.0\n", - "Coeff. additional_regression_features[ 3 ]: -0.000162\n", - "Coeff. additional_regression_features[ 4 ]: 277.590963\n", - "Coeff. additional_regression_features[ 5 ]: -0.183622\n", + "Coeff. additional_regression_features[ 3 ]: -0.000112\n", + "Coeff. additional_regression_features[ 4 ]: 484.312223\n", + "Coeff. additional_regression_features[ 5 ]: -0.1166\n", "\n", "Regression model performance on training data:\n", - "Order: 5 / MAE: 0.357715 / MSE: 0.361988 / R^2: 0.999176\n", + "Order: 5 / MAE: 0.398072 / MSE: 0.495330 / R^2: 0.998873\n", "\n", "Results saved in solution.pickle\n", - "2023-08-22 10:20:14 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" + "2023-08-19 23:49:20 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" ] } ], @@ -377,7 +371,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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", 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7dy7Cw8ORlJSEzMxMpKam4sWLF++cVDg4OBjTpk3D5cuX8ezZM2lwQ1RUFOrVq1cs74OIiFTX0qURcHTcDgCoX/8GOnVqAkA95sBUamgzMzODmZnZBz0358s4LS0NAPDZZ58hMzMTd+7cQY0aNQAAN2/eBABUrVoVAGBvb4/Dhw/LtnP8+HHY29sDAHR0dNCkSRP4+/uja9eu0uv4+/tj+PDhAIAmTZpAW1sb/v7+6NatGwAgIiICUVFR0nbs7e0xe/Zs6XZDOa9jbGxcZMFBW1sb7u7uRbLt/Lx2YQgLC4ONjQ3u3buHjh07YujQoZg9ezbKlSuHv/76Cy4uLkhPT39raEtJSYGTkxOcnJywZcsWmJmZISoqCk5OTkhPTy+UGomISD35+AicPLkRrVvfl9patWqlVpOWq8W9RwMDAxEUFISWLVuibNmyuHPnDqZMmYIaNWpIQcnR0RGNGzfGgAEDsGTJEmRnZ2PYsGH46quvpKNvQ4YMwYoVKzBhwgQMGDAAJ0+exM6dO3Ho0CHptcaMGYP+/fujadOmaNasGZYsWYKUlBT89NNPAAATExO4uLhgzJgxKFeuHIyNjTFixAjY29ujRYsWAICvv/4a9erVQ9++feHl5YWYmBhMnjwZw4YNK7IjaQqFolBOUSrLyZMncfXqVYwePRrBwcHIzs7GwoULoaHx6rLLnTt3yvrr6OggKytL1hYeHo6nT5/C09NTOq186dKl4nkDRESkshITExEaugTlyv3XVqfOz2jb1lJ5RX0AtQhtBgYG2Lt3Lzw8PJCSkgJLS0u0a9cOkydPlkKQhoYGDhw4gBEjRqB169YwNDRE+/btsXDhQmk7NjY2OHToEEaPHo2lS5eiSpUqWLduHZycnKQ+PXr0QFxcHKZOnYqYmBjY2dnh6NGjsoEFixcvhoaGBrp164a0tDQ4OTlh1apV0npNTU0cPHgQQ4cOhb29PQwNDdG/f3/MmDGjGD4t1ZeWloaYmBhkZWUhNjYWR48exdy5c9GxY0f069cP165dQ0ZGBpYvX45OnTrh/PnzWLNmjWwb1apVw/Pnz+Hv74+GDRvCwMAA1tbW0NHRwfLlyzFkyBBcu3YNM2fOVNK7JCIiVRAYGIijR49Ky0LoYerU8dJBAXWiEIUx9I+KRFJSEkxMTJCYmAhjY2OpPTU1FZGRkbCxsYGenp4SKyw4Z2dnbNy4EQCgpaWFsmXLomHDhujduzf69+8v/RAtXrwY8+fPR0JCAlq3bo0+ffqgX79+ePbsmXQt4dChQ7Fr1y48ffoUHh4emDZtGrZt24aJEyciOjoajRs3hru7Ozp37oyQkBDY2dkp6V0XHnXe90RExWnSpGxkZc2Hvn6q1Hb0qBMUiha4cKFoX/tt398fi6FNhZXE0EYfh/ueiOj9tm+PRkTE77I2X99RSEkxgZcX0Llz0b5+UYU2tTg9SkRERJQfR44cQUTEfzM4pKRYY948Z3h4qM+Ag7dhaCMiIiK1l5aWBs83Zsrdtq0HypWzhRoNEH0nhjYiIiJSazdv3sS2bdtkbf/7nyv8/PTg5qakoooAQxsRERGpJSEEFi3ahOfPI6W2xo0bo1OnTgCAb79VVmVFg6GNiIiI1E5SUhIWL14saxs4cCAqV66spIqKnvpNUkJERESlWlBQkCywpadrY8eOySU6sAE80kZERERqIjs7G3PnLkRm5guprVKlr7BhgwPeGINQIjG0ERERkcqLiYnBb7/9Jmv7/fdf8eiRKQYNUlJRxYyhjYiIiFSan58fLl68KC0/fFgZf/zhAnf3EjKXRz4xtFGJ5OzsjISEBOzfvx8A8MUXX8DOzg5Lliz54G0WxjaIiCj/0tPTMXfuXFnbjh0/ICKiLvbvL/o7G6gahjYqVq/fe1RbWxvW1tbo168fJk6cCC2tovvnuHfvXmhra+er7+nTp9GmTRvZfU4Lug0iIvo4t2/fxpYtW2Rtnp6uAF7NvVbaAhvA0EZK0K5dO2zYsAFpaWk4fPgwhg0bBm1tbbi7u8v6paenQ0dHp1Bes1y5ciqxDSIier/Nmzfjzp070nJWlh0WL+6CMWOA2bOVWJiSccoPKna6urqwsLBA1apVMXToUDg6OsLX1xfOzs7o2rUrZs+ejUqVKqFOnToAgAcPHuCHH36AqakpypUrhy5duuDevXvS9rKysjBmzBiYmpqifPnymDBhAoQQstf84osvMGrUKGk5LS0Nrq6usLKygq6uLmrWrIk//vgD9+7dQ5s2bQAAZcuWhUKhgLOzc57bePbsGfr164eyZcvCwMAA7du3x61bt6T13t7eMDU1hZ+fH+rWrQsjIyO0a9cO0dHRUp/Tp0+jWbNmMDQ0hKmpKT777DPcv3+/kD5pIiL1kpycjOnTp8sC2+bNLpgxowuSk0t3YAMY2kgF6OvrIz09HQDg7++PiIgIHD9+HAcPHkRGRgacnJxQpkwZnDt3DufPn5fCT85zFi5cCG9vb6xfvx5//fUX4uPjsW/fvne+Zr9+/bBt2zYsW7YMYWFh+O2332BkZAQrKyvs2bMHABAREYHo6GgsXbo0z204Ozvj0qVL8PX1RUBAAIQQ+Oabb5CRkSH1efHiBRYsWIBNmzbh7NmziIqKwrhx4wAAmZmZ6Nq1Kz7//HNcuXIFAQEB+Pnnn6EoKTfJIyIqgODgYCxatEhazsrSwKxZk/HDD1WUWJVq4elRUhohBPz9/eHn54cRI0YgLi4OhoaGWLdunXRadPPmzcjOzsa6deukMLNhwwaYmpri9OnT+Prrr7FkyRK4u7vju+++AwCsWbMGfn5+b33dmzdvYufOnTh+/DgcHR0BANWrV5fW55wGrVixouyattfdunULvr6+OH/+PBwcHAAAW7ZsgZWVFfbv34/vv/8eAJCRkYE1a9agRo0aAIDhw4djxowZAF7N5p2YmIiOHTtK6+vWrVvwD5KISI1lZ2djyZIlSE5OltoqVXLEhg2fYc+e0nnt2tvwSBvB1xdwcHj1Z3E4ePAgjIyMoKenh/bt26NHjx6YNm0aAKBBgway69guX76M27dvo0yZMjAyMoKRkRHKlSuH1NRU3LlzB4mJiYiOjkbz5s2l52hpaaFp06Zvff3Q0FBoamri888//+D3EBYWBi0tLdnrli9fHnXq1EFYWJjUZmBgIAUyALC0tMSTJ08AvAqHzs7OcHJyQqdOnbB06VLZqVMiopIuNjYWM2fOlAW2kSNHYtCgz3DhAgPbmxjaCJ6eQEAAim026TZt2iA0NBS3bt3Cy5cvsXHjRhgaGgKA9GeO58+fo0mTJggNDZU9bt68id69e3/Q6+vr63/0e8ivN0ebKhQK2fV2GzZsQEBAABwcHLBjxw7Url1bNhcREVFJdfz4caxZs0ZafvzYEn5+U1G2bFklVqXaGNoIbm6Avf2rP4uDoaEhatasCWtr6/dO89G4cWPcunULFStWRM2aNWUPExMTmJiYwNLSEoGBgdJzMjMzERwc/NZtNmjQANnZ2Thz5kye63OO9GVlZb11G3Xr1kVmZqbsdZ8+fYqIiAjUq1fvne/pTY0aNYK7uzsuXLiATz75BFu3bi3Q84mI1El6ejqmT5+OCxcuSG27dnXHunU/w82N1/S+C0MboXNnqOxh6D59+qBChQro0qULzp07h8jISJw+fRojR47Ew4cPAQC//vorPD09sX//foSHh+OXX35BQkLCW7dZrVo19O/fHwMGDMD+/fulbe7cuRMAULVqVSgUChw8eBBxcXF4/vx5rm3UqlULXbp0waBBg/DXX3/h8uXL+PHHH1G5cmV06dIlX+8tMjIS7u7uCAgIwP3793Hs2DHcunWL17URUYl1586dXJPlzps3AWFh9Uvt3GsFwdBGKs3AwABnz56FtbU1vvvuO9StWxcuLi5ITU2FsbExAGDs2LHo27cv+vfvD3t7e5QpUwbffvvtO7e7evVqdO/eHb/88gtsbW0xaNAgpKSkAAAqV66M6dOnw83NDebm5hg+fHie29iwYQOaNGmCjh07wt7eHkIIHD58ON8T8BoYGCA8PBzdunVD7dq18fPPP2PYsGEYPHhwAT4hIiL1sG3bNmzevFlaDg1tiO3bPWBnp499+zidR34oxJsTWpHKSEpKgomJCRITE6WAAgCpqamIjIyEjY0N9PT0lFghFTfueyJSN8+fP8fChQtlbX/88RMePbLGvn0l8+ja276/Pxan/CAiIqIiERISAt/XpiYQApg1axKysrQwcWLJDGxFiaGNiIiICpUQAkuXLkViYqLU5u/fBnfvtoa+PjByJE+HfgiGNiIiIio0cXFxWLVqlaxt2bIRSE8vhy1beHTtYzC0ERERUaHw9/fHX3/9JS3HxJhjzZrB0NNTYMcOBraPxdCmxjiGpPThPiciVZSRkYE5c+bI2nbv/g63bzeArS0wbx4DW2FgaFNDOVNKvHjxolhn9yfle/HiBYDcd1ogIlKWLVsicfv2n7K2pUvHo2JFAx5dK2QMbWpIU1MTpqam0j0sDQwMpJupU8kkhMCLFy/w5MkTmJqaQlNTU9klERFhx44duH07XFq+evUT+Pl147VrRYShTU1ZWFgAgBTcqHQwNTWV9j0RkbKkpKRgwYIFsra7d53x+HFVBrYixNCmphQKBSwtLVGxYkVkZGQouxwqBtra2jzCRkRKFxoaCh8fH1nbrFmT8OmnWnjtdqJUBBja1Jympia/yImIqMgJIbBixQrEx8dLbVevfo5+/b7Ap58Cbm5KLK6UYGgjIiKid/r333+xcuVKWdvy5cNhZlYenTvzdGhxYWgjIiKitzp16hTOnj0rLcfFVYC//y+oXVvBo2vFjKGNiIiIcsnMzMTsN+41tXfvt7h163/Yvp1H15SBoY2IiIhk7t27h40bN8raFi0ah/r1DRnYlIihjYiIiCS7du3CjRs3pOXr1+th167voa0Njg5VMoY2IiIiwosXLzB//nxZW82a/bF2bTXo6QFjxiipMJIwtBEREZVyV65cwb59+2RtS5ZMxLNn2ujTR0lFUS4MbURERKWUEAKrVq3Cv//+K7WdOdMKZ8605chQFcTQRkREVAo9ffoUK1askLWtWDEMqakVsG8fBxuoIoY2IiKiUubMmTM4ffq0tPz0aTmsWDEcCoWCgU2FMbQRERGVEnnNvbZ/fxdERdnB0BAYOZKBTZUxtBEREZUC9+/fh7e3t6xt+fJxqF3bEBs2MKypA4Y2IiKiEm7Pnj24du2atJydbYtFi3pg5EjgjQNvpMIY2oiIiEqovOZe69u3L6pXr47p05VUFH0wDWUXQERERIXv2rVruQLb8eMTUb16dSVVRB+LR9qIiIhKECEEvLx+Q2pqrNR27txnCA93xKpVSiyMPhpDGxERUQkRHx+P5cuXy9pWrvwFcXFmsLfnYAN1x9BGRERUApw7dw4nT56UlhMSTLF06UhoaSlgawve4aAEYGgjIiJSY3nNvZaZ2Qlt2jRGYOCrsMYjbCUDQxsREZGaevDgAdavXy9rW7BgLAAjJCczrJU0DG1ERERqaP/+/bh8+bK0/PhxLRw61BtZWcDo0UosjIoMQxsREZEaefnyJby8vGRtu3b9iDlzauC335RUFBULhjYiIiI1cf36dezevVvWduKEO+bM0eGp0FKAoY2IiEjFCSEwf/5avHwZLbVFRtrD2/treHgosTAqVgxtREREKuzZs2dYtmyZrO3w4SGYNMlcSRWRsjC0ERERqai1a8/j8eMT0nJSUhksXjwKLVpo8HRoKcTQRkREpGKysrIwZ84cZGdnS20HD3aAjk5TtGjBiXJLK4Y2IiIiFfLw4UP88ccfsrb9+8cgJKSMkioiVcHQRkREpCKWL/dBfHyotHz7dg3s3/8jtmxRXk2kOhjaiIiIlGzv3lRcvTpP1lajRh/4+dXEli28swG9wtBGRESkRJMnh0Fbe6es7X//c8e33+rgxx+VVBSpJIY2IiIiJfDxETh9+g+Ymj6S2oKCmuPnn9vxyBrlSUPZBeRX586dYW1tDT09PVhaWqJv3754/PixtH7atGlQKBS5HoaGhrLt7Nq1C7a2ttDT00ODBg1w+PBh2XohBKZOnQpLS0vo6+vD0dERt27dkvWJj49Hnz59YGxsDFNTU7i4uOD58+eyPleuXEGrVq2gp6cHKyurXLccISKi0ishIQGhoTNkgW3PnsEMbPROahPa2rRpg507dyIiIgJ79uzBnTt30L17d2n9uHHjEB0dLXvUq1cP33//vdTnwoUL6NWrF1xcXBASEoKuXbuia9euuHbtmtTHy8sLy5Ytw5o1axAYGAhDQ0M4OTkhNTVV6tOnTx9cv34dx48fx8GDB3H27Fn8/PPP0vqkpCR8/fXXqFq1KoKDgzF//nxMmzYNv//+exF/SkREpOrWrr2ApUuXSsspKQaYMmUKrlyxYGCjd1IIIYSyi/gQvr6+6Nq1K9LS0qCtrZ1r/eXLl2FnZ4ezZ8+iVatWAIAePXogJSUFBw8elPq1aNECdnZ2WLNmDYQQqFSpEsaOHYtx48YBABITE2Fubg5vb2/07NkTYWFhqFevHoKCgtC0aVMAwNGjR/HNN9/g4cOHqFSpElavXo1JkyYhJiYGOjo6AAA3Nzfs378f4eHh+X6PSUlJMDExQWJiIoyNjT/4syIiIuXLysrC7NmeECJTajtxoj1at26G2bOVWBgVuqL6/labI22vi4+Px5YtW+Dg4JBnYAOAdevWoXbt2lJgA4CAgAA4OjrK+jk5OSEgIAAAEBkZiZiYGFkfExMTNG/eXOoTEBAAU1NTKbABgKOjIzQ0NBAYGCj1ad26tRTYcl4nIiICz549+8h3T0RE6ubRo0eYNWuWLLCtWjUa584xsFH+qVVoc3V1haGhIcqXL4+oqCj4+Pjk2S81NRVbtmyBi4uLrD0mJgbm5vJ7tZmbmyMmJkZan9P2rj4VK1aUrdfS0kK5cuVkffLaxuuvkZe0tDQkJSXJHkREpN4OHjyIdevWScuRkTbw9JyKgQN5BoUKRqmhzc3NLc/BA68/Xj+dOH78eISEhODYsWPQ1NREv379kNfZ3X379iE5ORn9+/cvzrfz0ebOnQsTExPpYWVlpeySiIjoA6WmpmL69OkIDg6W2v75pxe++64fXr5U8AgbFZhSp/wYO3YsnJ2d39mnevXq0t8rVKiAChUqoHbt2qhbty6srKxw8eJF2Nvby56zbt06dOzYMdfRLgsLC8TGxsraYmNjYWFhIa3PabO0tJT1sbOzk/o8efJEto3MzEzEx8fLtpPX67z+Gnlxd3fHmDFjpOWkpCQGNyIiNRQeHo4dO3bI2tzc3KCrq6ukiqgkUGpoMzMzg5mZ2Qc9N+cmumlpabL2yMhInDp1Cr6+vrmeY29vD39/f4waNUpqO378uBT6bGxsYGFhAX9/fymkJSUlITAwEEOHDpW2kZCQgODgYDRp0gQAcPLkSWRnZ6N58+ZSn0mTJiEjI0O65u748eOoU6cOypYt+9b3pKuryx9oIiI1JoTAwoXeSEmJktoCAz9FQsI38PBQYmFUIqjF5LqBgYEICgpCy5YtUbZsWdy5cwdTpkxBjRo1ch1lW79+PSwtLdG+fftc2/n111/x+eefY+HChejQoQO2b9+OS5cuSVNxKBQKjBo1CrNmzUKtWrVgY2ODKVOmoFKlSujatSsAoG7dumjXrh0GDRqENWvWICMjA8OHD0fPnj1RqVIlAEDv3r0xffp0uLi4wNXVFdeuXcPSpUuxePHiov2giIhIaRITE7FkyRJZ22+//YzkZEveO5QKhVqENgMDA+zduxceHh5ISUmBpaUl2rVrh8mTJ8uOTGVnZ8Pb2xvOzs7Q1NTMtR0HBwds3boVkydPxsSJE1GrVi3s378fn3zyidRnwoQJSElJwc8//4yEhAS0bNkSR48ehZ6entRny5YtGD58OL788ktoaGigW7duWLZsmbTexMQEx44dw7Bhw9CkSRNUqFABU6dOlc3lRkREJce6dRfx6JGftPzypR6WLBmPxo014ObGe4dS4VDbedpKA87TRkSk2rKzs+Hl5SW7VCcz0wlLlrTAyJHgYINSqqi+v9XiSBsREZGq2b49GhER8jvd1K8/Ct27m2DmTCUVRSUaQxsREVEB+PoCPj6HYW0dJLVZW1vD2dkZCoVCiZVRSadWk+sSEREpi68vYGOThpCQ6bLAZmPTAz/99BMDGxU5HmkjIiJ6D19fYMqUm3B23iZrd3V1lQ1UIypKDG1ERETvIISAv/+f+O67e1JbfHwTBAV1RKNGHBlKxYenR4mIiN4iKSkJM2bMQLly96S2QYMGISioIwICAE9P5dVGpQ+PtBEREb3B1xdYu/ZvNG16RGrT1taGq6srNDU14eb2KrC5uSmxSCp1GNqIiIhek52djcDAhWja9IXUFh7+NbZt++8OPJ0787QoFT+GNiIiov+3fXsMIiJ+g47Of22+vr/Cw8NUaTUR5WBoIyIiArBq1VHExQVKy8+eVcHixQPg4cGpPEg1MLQREVGpNmlSOnR05srazp37ASNH1gWnXiNVwtBGRESl1q1bt6Cjs1XW1qCBKzw8OPcaqR6GNiIiKnV8fYHDhzfD0vKO1PbPP40QF9cZHh5KLIzoHRjaiIioVNmzJxnXri2CpeV/bceOuSA+vgrmzVNeXUTvw9BGRESlxqVLl3Dt2iFpOStLE40bu8PDQ1OJVRHlD0MbERGVeD4+2bh4cTH09J5LbZUqOWLQoM+UWBVRwTC0ERFRibZjRyzCw9fg9fu616s3Et9/X1Z5RRF9AIY2IiIqkXx9gb17j8HGJkBqe/SoEtauHQh7ewW+/16JxRF9AIY2IiIqcdLT0xESMhc2Nv+1ZWR0x7lz9VGnDu8ZSuqJoY2IiEqUzZvv4M6dzbK2CRMmQF9fH7NmKakookLA0EZERCWCry9w7NhWmJndktoePWqIjh27Ql9fiYURFRKGNiIiUnvPnz9HSMhCmJn911ar1gB4eFgpryiiQsbQRkREau2ff/7BgQMHpOXsbAWmTp0ETU3OvUYlC0MbERGpJSEEJk9eCh2dRKnt7Nm2+PXXVmBeo5KIoY2IiNTOkydPsHr1aujo/Nd29uwI/PprOXTurLy6iIoSQxsREamVEydO4Pz589JyTIw5/v13MPz9FUqsiqjoMbQREZFayMjIwJw5c2RtBw50g5PTJ1i9WklFERUjhjYiIlJ5W7bcxe3bm2Rt48ePh4eHgZIqIip+DG1ERKSyfH2Bo0e3w9w8Qmq7caMB+vT5DgbMa1TKMLQREZFKSklJQUjIApib/9d24sRPGD/emoMNqFRiaCMiIpWzYUMIoqJ8ZW2TJk2Chwe/tqj04r9+IiJSGUIILF++HM+ePZPaTp/+AqNHfw4tfmNRKccfASIiUglxcXFYtWqVrO2334bjp5/K83QoERjaiIhIBaxZcxKxseekZTMzMwwdOhQeHpx7jSgHQxsRESlNXnOvffvtt/jf//6npIqIVJeGsgsgIqLS6d69e7kC29Kl4xnYiN6CR9qIiKjYLV26EwkJYdJyVlZ9LF7cHSNHKrEoIhXH0EZERMUmJSUFCxYskLX1798f1apVw4wZSiqKSE3w9CgRERWLy5cv5wpsO3ZMQrVq1ZRTEJGa4ZE2IiIqUkIIzJu3EmlpT6W206dbIzCwDbZvV2JhRGqmwEfaNDU18eTJk1ztT58+haamZqEURUREJcPTp08xY8YMWWBbsWIYYmJeBTbOv0aUfwU+0iaEyLM9LS0NOjo6H10QERGVDL/9dhoxMWek5adPy2HduuEYPVqB2bOVWBiRmsp3aFu2bBkAQKFQYN26dTAyMpLWZWVl4ezZs7C1tS38ComISK1kZmZi9hupbP/+LvjmGzu8eKGkoohKgHyHtsWLFwN4daRtzZo1slOhOjo6qFatGtasWVP4FRIRkdq4f/8+vL29ZW379o3DjBmGPBVK9JEU4m3nO9+iTZs22Lt3L8qWLVtUNdH/S0pKgomJCRITE2FsbKzscoiI3mnPnj24du2atBwba4tVq3oosSIi5Siq7+8CX9N26tSpQntxIiJSfy9evMD8+fNlbdu29YOXl42SKiIqmQoc2gYMGPDO9evXr//gYoiISL14e1/F/ft7ZW2zZk1EzZraPB1KVMgKHNqePXsmW87IyMC1a9eQkJCAtm3bFlphRESkunKub359CqizZ1vi8eMv8emngJubEosjKqEKHNr27duXqy07OxtDhw5FjRo1CqUoIiJSXfHx8Vi+fLmsbdeuX5CZaQYvL869RlRUCjwQ4W0iIiLwxRdfIDo6ujA2R+BABCJSPWfPnpVd2/zsmSn++GMkkpMVSqyKSLWozECEt7lz5w4yMzMLa3NERKRC8pp77dChTggLa4yRI5VUFFEpU+DQNmbMGNmyEALR0dE4dOgQ+vfvX2iFERGRanjw4EGuQWarVo3FwIFG+PtvJRVFVAoVOLSFhITIljU0NGBmZoaFCxe+d2QpERGpl3379uHKlSvSckREbdy71wuxsUosiqiU4jxtRESUy8uXL+Hl5SVr27TpR2hp1cAbzURUTD74mrYnT54gIiICAFCnTh1UrFix0IoiIiLluXbtGvbs2SNrW7JkIn75RZs3eidSogKHtqSkJAwbNgzbtm1DdnY2AEBTUxM9evTAypUrYWJiUuhFEhFR0RNC4Pfff0dMTIzUdv68A54//wpvTNFJREqgUdAnDBo0CIGBgTh06BASEhKQkJCAgwcP4tKlSxg8eHBR1EhEREXs2bNnmDFjhiywrVo1FOfOfcWJcolURIHnaTM0NISfnx9atmwpaz937hzatWuHlJSUQi2wNOM8bURUHP766y/4+/tLy0lJZbBkySjUqqXByXKJPoDKzNNWvnz5PE+BmpiYoGzZsoVSFBERFb2srCzMmjUbwH//dz9woCNCQprAzQ28fo1IxRT49OjkyZMxZswY2SH0mJgYjB8/HlOmTCnU4oiIqGg8fPgQs2bNwuuBbeHCMYiMbIJ9+xjYiFRRgU+PNmrUCLdv30ZaWhqsra0BAFFRUdDV1UWtWrVkff/555/Cq7QU4ulRIioKPj4+CA0NlZb//bcGvvrqR54GJSokKnN6tEuXLlAoeI85IiJ1k5qainnz5snagoP7wNe3ppIqIqKCKHBomzZtWhGUQURERenGjRvYtWuXrG3pUnd4e+soqSIiKqgCh7bq1asjKCgI5cuXl7UnJCSgcePGuHv3bqEVR0REH0cIgQUL1uHFi8dSW0BAC5w/74QtWzgylEidFDi03bt3D1lZWbna09LS8PDhw0IpioiIPl5CQgKWLl0qa6tTZzD8/CwY2IjUUL5Hj/r6+sLX1xcA4OfnJy37+vpi3759mDlzJmxsbIqs0M6dO8Pa2hp6enqwtLRE37598fjxY1kfPz8/tGjRAmXKlIGZmRm6deuGe/fuyfqcPn0ajRs3hq6uLmrWrAlvb+9cr7Vy5UpUq1YNenp6aN68Of7++2/Z+tTUVAwbNgzly5eHkZERunXrhtg37p4cFRWFDh06wMDAABUrVsT48eORmZlZKJ8FEdH7rF17QRbYnj83xI4dU9CzpwUuXGBgI1JH+R49qqHxKt8pFAq8+RRtbW1Uq1YNCxcuRMeOHQu/SgCLFy+Gvb09LC0t8ejRI4wbNw4AcOHCBQBAZGQk6tatizFjxsDFxQWJiYkYPXo0kpOTpVGskZGR+OSTTzBkyBAMHDgQ/v7+GDVqFA4dOgQnJycAwI4dO9CvXz+sWbMGzZs3x5IlS7Br1y5ERERI91cdOnQoDh06BG9vb5iYmGD48OHQ0NDA+fPnAbya+8jOzg4WFhaYP38+oqOj0a9fPwwaNAhz5szJ93vm6FEiKqisrCzMnu0JIf77T+Jff32Dhw8/xbx5DGtExaGovr8LPOWHjY0NgoKCUKFChUIr4kP4+vqia9euSEtLg7a2Nnbv3o1evXohLS1NCpgHDhxAly5dpD6urq44dOgQrl27Jm2nZ8+eSEhIwNGjRwEAzZs3x6effooVK1YAALKzs2FlZYURI0bAzc0NiYmJMDMzw9atW9G9e3cAQHh4OOrWrYuAgAC0aNECR44cQceOHfH48WOYm5sDANasWQNXV1fExcVBRyd/F/4ytBFRQTx69Ajr1q2TtS1cOBqffGKM////LREVg6L6/i7w5LqRkZFKD2zx8fHYsmULHBwcoK2tDQBo0qQJNDQ0sGHDBmRlZSExMRGbNm2Co6Oj1CcgIACOjo6ybTk5OSEgIAAAkJ6ejuDgYFkfDQ0NODo6Sn2Cg4ORkZEh62Nrawtra2upT0BAABo0aCAFtpzXSUpKwvXr19/6vtLS0pCUlCR7EBHlx4EDB2SB7c6d6pg2bSqEMOa9Q4lKiAIPRJgxY8Y710+dOvWDi3kfV1dXrFixAi9evECLFi1w8OBBaZ2NjQ2OHTuGH374AYMHD0ZWVhbs7e1x+PBhqU9MTIwsSAGAubk5kpKS8PLlSzx79gxZWVl59gkPD5e2oaOjA1NT01x9cu4S8bbXyVn3NnPnzsX06dPz+WkQEeU999qWLb1x+3Yt1KkD3juUqAQp8JG2ffv2yR47d+7EvHnzsHDhQuzfv79A23Jzc4NCoXjnIycsAcD48eMREhKCY8eOQVNTE/369ZOur4uJicGgQYPQv39/BAUF4cyZM9DR0UH37t1zXYOnqtzd3ZGYmCg9Hjx4oOySiEiFhYeH5wps3t5uePiwFtzdgfBwBjaikqTAR9pCQkJytSUlJcHZ2RnffvttgbY1duxYODs7v7NP9erVpb9XqFABFSpUQO3atVG3bl1YWVnh4sWLsLe3x8qVK2FiYgIvLy+p/+bNm2FlZYXAwEC0aNECFhYWuUZ5xsbGwtjYGPr6+tDU1ISmpmaefSwsLAAAFhYWSE9PR0JCguxo25t93hxxmrPNnD550dXVha6u7js/DyIiIQQ2bNgg+49dYGAzJCS0R2SkEgsjoiJV4NCWF2NjY0yfPh2dOnVC37598/08MzMzmJmZfdBrZmdnA3h1HRgAvHjxQhqAkENTU1PW983TpQBw/Phx2NvbAwB0dHTQpEkT+Pv7o2vXrtJz/f39MXz4cACvrp3T1taGv78/unXrBgCIiIhAVFSUtB17e3vMnj0bT548kUacHj9+HMbGxqhXr94HvV8iIgCYNCkROjpLZG1r1vyMJ08ssW+fcmoiouJRKKENgHRKrygEBgYiKCgILVu2RNmyZXHnzh1MmTIFNWrUkIJShw4dsHjxYsyYMQO9evVCcnIyJk6ciKpVq6JRo0YAgCFDhmDFihWYMGECBgwYgJMnT2Lnzp04dOiQ9FpjxoxB//790bRpUzRr1gxLlixBSkoKfvrpJwCAiYkJXFxcMGbMGJQrVw7GxsYYMWIE7O3t0aJFCwDA119/jXr16qFv377w8vJCTEwMJk+ejGHDhvFIGhF9sIsXL0JHx09aTk3Vw+bN45GQoAE3N54KJSrpChzali1bJlsWQiA6OhqbNm1C+/btC62w1xkYGGDv3r3w8PBASkoKLC0t0a5dO0yePFkKQW3btsXWrVvh5eUFLy8vGBgYwN7eHkePHoW+vj6AV4MVDh06hNGjR2Pp0qWoUqUK1q1bJ83RBgA9evRAXFwcpk6dipiYGNjZ2eHo0aOygQWLFy+GhoYGunXrhrS0NDg5OWHVqlXSek1NTRw8eBBDhw6Fvb09DA0N0b9///cO4iAiyktWVha8vLyQnp4utR0+3A4hIc3xWhMRlXAfNE/b6zQ0NGBmZoa2bdvC3d0dZcqUKdQCSzPO00ZEjx8/xtq1a2VtixePQmKiCSpXBnj3QCLVU1Tf3wU+0hbJq1yJiIrFoUOHcOnSJWn5yZOq+Prr/vjzTwU8PcH514hKmQ+6pi0hIQG3b98GANSsWTPXnGVERPTh9u1Lw5UrnrK2bdt6IiKiDkJCwHuHEpVSBZqn7d69e+jQoQMqVKiA5s2bo3nz5qhQoQI6duyY68bsRERUcBEREbkC2+7dbgDqwNaWR9eISrN8H2l78OABWrRoAW1tbcycORN169YFANy4cQOrV6+Gvb09goKCUKVKlSIrloiopBJCYOPGjbh//77UFhTUBGfOdMSWLTyyRkQFGIjg4uKC27dvw8/PD3p6erJ1L1++RLt27VCrVq1cNyumD8eBCESlw+7dSbh+fbGszc9vEIBKnMqDSA0pfSDC0aNHsWPHjlyBDQD09fUxc+ZM9OzZs9AKIyIqDf7++29cv35EWk5L04GX1wTs3avJsEZEMvkObf/++y+qVav21vXVq1dHfHx8YdRERFTiZWdnY8GCBXj58qXUduzY17hwwR6VK/PoGhHllu/QZmlpiRs3brz1mrVr1669876aRET0SnR0NH7//XdZ26+//opGjUw5lQcRvVW+Q1vXrl0xbtw4+Pv757pf6JMnT+Dq6irdr5OIiPJ25MgR/P3339KylZUVfvrpJygUCnTuzCNsRPR2+R6I8OzZMzRv3hwxMTH48ccfYWtrCyEEwsLCsHXrVlhYWODixYsoV65cUddcanAgAlHJkZ6ejrlz58rafvjhB2kkPhGVHEofiFC2bFkEBgZi4sSJ2L59OxISEgAApqam6N27N+bMmcPARkSUh1u3bmHr1q2yNldX1zwHdhERvU2B7z0KvJpPKC4uDgBgZmYGhUJR6IURj7QRlQSbNm3C3bt3peXg4EY4daozkpOVWBQRFSmlH2l7nUKhQMWKFQutCCKikiYpKQmLF8vnXlu7diAePaqMiROVVBQRqbUPCm1ERPR2QUFBOHz4sLSsUGghLc0NiYmamDgRmD1bicURkdpiaCMiKiTZ2dlYtGgRUlJSpLZjxxwhxGe4cIFhjYg+DkMbEVEhiI2NxZo1a2Rt27ePRNmyZTnvGhEVCoY2IqKP5Ofnh4sXL0rLjx5Vwtq1A2Frq8CFC0osjIhKlHyFtmXLluV7gyNHjvzgYoiI1MnevS9x9aqXrG3nzu9x40Y9aGgA8+YpqTAiKpHyFdreHAH1NgqFgqGNiEqFoKAgXL16WNa2ePEEaGrqQ08PGDOGdzcgosKVr9AWGRlZ1HUQEamN6dOny5ZfvtTDvHmuAAB7e/CUKBEViQ++pi09PR2RkZGoUaMGtLR4aRwRlWy+vsCyZU/RqtWKN9q7IyysPrp3Bx494s3eiajoFDhtvXjxAiNGjMDGjRsBADdv3kT16tUxYsQIVK5cGW78jUVEJYSvL+Dp+SqI+foeRKtWwbL1s2ZNhJ6eNl68UFKBRFSqaBT0Ce7u7rh8+TJOnz4tu2+eo6MjduzYUajFEREpk6cnEBiYjZCQ6bCy+i+w3bxZC8uWeUBPTxu8jJeIikuBj7Tt378fO3bsQIsWLWT3HK1fvz7u3LlTqMURESnTsGGRcHL6U9bm5zcIQCVs2MCBBkRUvAoc2uLi4vK872hKSgpvHE9EJcbvv/+O6OhoWZuf31S4uSkY1ohIKQp8erRp06Y4dOiQtJwT1NatWwd7e/vCq4yISAlSU1Mxffp0WWBr1aoVPDw8cOECAxsRKU+Bj7TNmTMH7du3x40bN5CZmYmlS5fixo0buHDhAs6cOVMUNRIRFYvg4GAcPHhQ1jZ69GgYGxsrqSIiov8U+Ehby5YtERoaiszMTDRo0ADHjh1DxYoVERAQgCZNmhRFjURERW769OmywKZQaMHDw4OBjYhUhkIIIZRdBOUtKSkJJiYmSExM5BcHURGYNAlYv/4ZhgyR36pv9+7vUKZMA06SS0QfpKi+v/N1ejQpKSnfG2S4ICJ1ce3aEQwZ8resbfbsicjK0sa+fUoqiojoLfIV2kxNTfM9MjQrK+ujCiIiKmrZ2dmYOXMmGjf+r+3p0+rYu7cvtLSA8eM5nQcRqZ58hbZTp05Jf7937x7c3Nzg7OwsjRYNCAjAxo0bMXfu3KKpkoiokNy/fx/e3t6ytrVrXZCYWAXJycqpiYgoPwp8TduXX36JgQMHolevXrL2rVu34vfff8fp06cLs75Sjde0ERWuP/74Aw8fPpS1rV07FYmJCowcCcyeraTCiKhEUeo1ba8LCAjAmjVrcrU3bdoUAwcOLJSiiIgKU1paGjw9PWVtN258hgcPHLFqFU+FEpF6KPCUH1ZWVli7dm2u9nXr1sHKyqpQiiIiKiwhISG5AtvKlaPQp48jLlxgYCMi9VHgI22LFy9Gt27dcOTIETRv3hwA8Pfff+PWrVvYs2dPoRdIRPShpk+fLlvOzlZgxoypsLVlWCMi9VPgI23ffPMNbt26hU6dOiE+Ph7x8fHo1KkTbt68iW+++aYoaiQiei9fX8DB4dWfCQkJuQLb3r1dsXPnVNjbA/PmKalIIqKPwMl1VRgHIhDln4MDEBAA9OzpB1vbi7J1u3e7o0wZHbi58QgbERU9lRmIALz6X+wff/yBsLAwAED9+vUxYMAAmJiYFFphRETv4usLTJgAKBSvjpy5ugqEhs6Q9TEyqoo9e5wxezbDGhGpvwIfabt06RKcnJygr6+PZs2aAQCCgoLw8uVLHDt2DI1fn62SPgqPtBG9Xc6RNQCwsYlC//4bZOtr1RqA3r05OIqIil9RfX8XOLS1atUKNWvWxNq1a6Gl9epAXWZmJgYOHIi7d+/i7NmzhVZcacfQRvR2OUfaPvvMG9bW92Xr/Pym4sKF/N3FhYiosKnM6dFLly7JAhsAaGlpYcKECWjatGmhFUZE9DpfX8DTE9J1ae3apSMkRH4XlgsXWuDECSfeN5SISqQChzZjY2NERUXB1tZW1v7gwQOUKVOm0AojInqdp+er06F9+gDLl1/G/fv7Zes3bPgVT5+acrABEZVYBQ5tPXr0gIuLCxYsWAAHBwcAwPnz5zF+/Phct7YiIiosbm6vAtuYMTNw/778qo5p0zxQpw5471AiKtEKHNoWLFgAhUKBfv36ITMzEwCgra2NoUOH5pp1nIiosHz+eSLGjVsia7O27ox58xrB1pZzrxFRyffB87S9ePECd+7cAQDUqFEDBgYGhVoYcSACUY4TJ07g/Pnzsrb//c8N336rq6SKiIjeTmUGIuQwMDBAgwYNCq0QIqI3CSEwY4Z87rUHD6rgxg0XeHgoqSgiIiXJd2gbMGBAvvqtX7/+g4shIsrx8OFD/PHHH7K2DRuc8fRpVWzZoqSiiIiUKN+hzdvbG1WrVkWjRo3AO18RUVHatGkT7t69K2uzs5uCSpU0sGwZR4cSUemU79A2dOhQbNu2DZGRkfjpp5/w448/oly5ckVZGxGVIr6+wPz56XB0lM+9FhjYDAkJ7eHhAXTpoqTiiIhUgEZ+O65cuRLR0dGYMGECDhw4ACsrK/zwww/w8/PjkTci+mje3ldzBbYDB0YiIaE93NyUVBQRkQr54NGj9+/fh7e3N/78809kZmbi+vXrMDIyKuz6SjWOHqXSYs6cOcjIyJC1+fl5cKJcIlJLKjd6VENDAwqFAkIIZGVlFVpBRFR6JCUlYfHixbK2Awc6IjKyCZ4+VVJRREQqKt+nRwEgLS0N27Ztw1dffYXatWvj6tWrWLFiBaKioniUjYgK5OTJk7kC2//+5wodnSbYsEFJRRERqbB8H2n75ZdfsH37dlhZWWHAgAHYtm0bKlSoUJS1EVEJlNfca48fW+Lq1Z/h4QF8+62SCiMiUnH5vqZNQ0MD1tbWaNSoERQKxVv77d27t9CKK+14TRuVNI8fP8batWtlbSdP9kNsrA28vHj9GhGVDEq/pq1fv37vDGtERG/y9QU8PV/d7P358624deuWbP3ChVOQlFSgqzSIiEqtAk2uS0RUEJ6eQFBQBkJC5sjaL11qgsOHO3IqDyKiAvjg0aNERO8zePB1ODntlrXVrTscfn7lsW8fT4cSERUEQxsRFYl58+YhNTVV1ubx/3d5/+EHZVRERKTeGNqIqFAlJydj0aJFsrYbN77Bjh2fKqkiIqKSgaGNiArNmTNncPr0aVmbv/8EjBunr5yCiIhKEIY2IvpgOaNDXV0FQkPlc6/FxlbEqVNDERampOKIiEoYhjYi+mCenkBkZDRCQ3+Xtdeo8SP8/Gpg3jwlFUZEVAKpzQRJnTt3hrW1NfT09GBpaYm+ffvi8ePHsj47d+6EnZ0dDAwMULVqVcyfPz/Xdk6fPo3GjRtDV1cXNWvWzHMqk5UrV6JatWrQ09ND8+bN8ffff8vWp6amYtiwYShfvjyMjIzQrVs3xMbGyvpERUWhQ4cOMDAwQMWKFTF+/HhkZmZ+/AdBpAJ8fQEHB+Crr3ZgyBB5YJs/fzJ+/LEGLlzg6FAiosKkNqGtTZs22LlzJyIiIrBnzx7cuXMH3bt3l9YfOXIEffr0wZAhQ3Dt2jWsWrUKixcvxooVK6Q+kZGR6NChA9q0aYPQ0FCMGjUKAwcOhJ+fn9Rnx44dGDNmDDw8PPDPP/+gYcOGcHJywpMnT6Q+o0ePxoEDB7Br1y6cOXMGjx8/xnfffSetz8rKQocOHZCeno4LFy5g48aN8Pb2xtSpU4v4UyIqHl5emXBymg4NjXCpLSvLDgsWeODXXzWVWBkRUcmV79tYqRpfX1907doVaWlp0NbWRu/evZGRkYFdu3ZJfZYvXw4vLy9ERUVBoVDA1dUVhw4dwrVr16Q+PXv2REJCAo4ePQoAaN68OT799FMp7GVnZ8PKygojRoyAm5sbEhMTYWZmhq1bt0qhMTw8HHXr1kVAQABatGiBI0eOoGPHjnj8+DHMzc0BAGvWrIGrqyvi4uKgo6OTr/fI21iRKgoLC8POnTtlbcOGDeO9iImI/l9RfX+rzZG218XHx2PLli1wcHCAtrY2ACAtLQ16enqyfvr6+nj48CHu378PAAgICICjo6Osj5OTEwICAgAA6enpCA4OlvXR0NCAo6Oj1Cc4OBgZGRmyPra2trC2tpb6BAQEoEGDBlJgy3mdpKQkXL9+/a3vKy0tDUlJSbIHkarw9QXc3BbkCmx+fh4MbERExUCtQpurqysMDQ1Rvnx5REVFwcfHR1rn5OSEvXv3wt/fH9nZ2bh58yYWLlwIAIiOjgYAxMTEyIIUAJibmyMpKQkvX77Ev//+i6ysrDz7xMTESNvQ0dGBqanpO/vktY2cdW8zd+5cmJiYSA8rK6v8fjRERer58+cICZkOff0Uqe3ChXbYts2Dt6IiIiomSg1tbm5uUCgU73yEh/93zcz48eMREhKCY8eOQVNTE/369UPO2d1BgwZh+PDh6NixI3R0dNCiRQv07NkTwKujZerA3d0diYmJ0uPBgwfKLokI586dk/4DlGPevPFITm6O8HAONiAiKi5KnfJj7NixcHZ2fmef6tWrS3+vUKECKlSogNq1a6Nu3bqwsrLCxYsXYW9vD4VCgXnz5mHOnDmIiYmBmZkZ/P39ZduwsLDINcozNjYWxsbG0NfXh6amJjQ1NfPsY2FhIW0jPT0dCQkJsqNtb/Z5c8RpzjZz+uRFV1cXurq67/w8iIqLEAIzZsjnXktJKY+WLYfDzg48wkZEVMyUGtrMzMxgZmb2Qc/Nzs4G8Oo6sNdpamqicuXKAIBt27bB3t5eeg17e3scPnxY1v/48eOwt7cHAOjo6KBJkybw9/dH165dpdfx9/fH8OHDAQBNmjSBtrY2/P390a1bNwBAREQEoqKipO3Y29tj9uzZePLkCSpWrCi9jrGxMerVq/dB75eoOMXGxmLNmjWytj17emPWrFro3JlH14iIlEEtJtcNDAxEUFAQWrZsibJly+LOnTuYMmUKatSoIQWlf//9F7t378YXX3yB1NRUbNiwQZqSI8eQIUOwYsUKTJgwAQMGDMDJkyexc+dOHDp0SOozZswY9O/fH02bNkWzZs2wZMkSpKSk4KeffgIAmJiYwMXFBWPGjEG5cuVgbGyMESNGwN7eHi1atAAAfP3116hXrx769u0LLy8vxMTEYPLkyRg2bBiPpJHKW7p0FxISbsjaFi2ajE2bNBnWiIiUSC1Cm4GBAfbu3QsPDw+kpKTA0tIS7dq1w+TJk2UhaOPGjRg3bhyEELC3t8fp06fRrFkzab2NjQ0OHTqE0aNHY+nSpahSpQrWrVsHJycnqU+PHj0QFxeHqVOnIiYmBnZ2djh69KhsYMHixYuhoaGBbt26IS0tDU5OTli1apW0XlNTEwcPHsTQoUNhb28PQ0ND9O/fP9epJiJVkpmZidmzZ8vaLl/+H44f/xZbtvDoGhGRsqntPG2lAedpo+IyeXIEtLW3y9p27x6KzMyKmDePgY2IqCCK6vtbLY60EVHRWbx4MbS15XMC2tlNhYeHQkkVERFRXhjaiEqplJQULFiwQNZ28eJXOHLEQUkVERHRuzC0EZVC58+fx4kTJ2Rt8+ePx9atBkqqiIiI3oehjagU8fERCA2VD4hJSDDBsmWj4ObGa9eIiFQZQxtRKfHkyROEhq6WtW3d2guPH9fGvn0MbEREqo6hjagU2Lt3L65evSprmzlzMrS1NbFjBwMbEZE6YGgjKsGysrIwa9YsWVt0dH0cPNgdWVlArVoMbERE6oKhjaiEunXrFrZu3SprO39+CI4fN0edOoC9Pe8fSkSkThjaiEqgZcuW4dmzZ7I2O7upaNRIgefPwUEHRERqiKGNqAR58eIF5s+fL2s7ceJLhIa2lG5FxbBGRKSeNJRdABEVjnXrLuYKbJ98Mg6hoS3x/Dng6amkwoiIqFDwSBuRmstr7jUjIyOMHTsWAKCt/Sqw8fo1IiL1xtBGpMb+/fdfhIaulLXZ2PRAv3620jJPiRIRlQwMbURqaupUH2hqhsraGjachK5d+WNNRFQS8bc7kZrJmXtNU/O/ttjYuli9+gfY2wNduyqtNCIiKkIMbURq5M6dO9i8ebOsLSNjMNq1s0BoKK9bIyIqyRjaiNTEqlWrEBcXJ2ubOnUqFAoFAF63RkRU0jG0Eam4ly9fwsvLS9bWpk0btG7dWkkVERGRMjC0Eamwv//+G0eOHJG1jRkzBmXKlFFSRUREpCwMbUQqavr06bJlTU19HD48AY0avVrOmXuNp0WJiEoHhjYiFbNz51OEha2QtX3//fcYOLAeAgL+u7NBzt8Z2oiISgeGNiIVcuDAAYSF/SNrmzhxIrS1teHmJr+zAe9yQERUuiiEEELZRVDekpKSYGJigsTERBgbGyu7HCpC2dnZmDlzpqwtIqI27t3rhQsXlFQUERF9kKL6/uaRNiIl8vUFFi6MRNu2f8ra69QZBD+/SjySRkREEg1lF0BUmp0581uuwDZ16lQYGFRSUkVERKSqeKSNSAlSU1Mxb948vH7U3MKiNQYPbgPg1fVqHGhARESvY2gjKmbr11/CgweHZG2jR4+WXffw5qADIiIihjaiYvTm3GuZmTr49FN3vHmdaufOPMJGRERyDG1ExSA+Ph7Lly+XtVWr1g39+3+ipIqIiEjdMLQRFbHDhw8jKChI1pYz9xoREVF+MbQRFREfn2yEhsrnXqtevTr69u2rpIqIiEidMbQRFSJfX8DVFahQ4R4cHTfK1g0cOBCVK1dWUmVERKTuGNqICpGnJ9Cy5TpUqfJI1j516lQoFAolVUVERCUBQxtRIfD1BebPT4OTk6esvWXLlvjyyy+VVBUREZUkvCMCUSHYtOkfODrKA9uoUaPeGdh8fQEHh1d/EhERvQ9vGK/CeMN41efrC4SEyOde09DQwJQpU977XAeHV3c9sLcHbwpPRFSCFNX3N4+0EX2gZ8+e5QpsVat+i61bp8DW9v1H0NzcXgU23vWAiIjyg0faVBiPtKkmX1/gzz9D0aCBj6zd3d0dX3yhg4CAV8s8gkZEVDrxSBuRChBC4K+/lssCm5FRNXh4eEBHRwdubkDlyoCeHtCmjRILJSKiEoehjSif/v33X8yYMQOGhvFS2/LlwzBtWn/pVGjnzoC1NZCaCpw69d9zOeiAiIg+FkMbUT6cOnUKK1eulJbLly8PO7upSEurgOfPX83PliOva9U8PV8NOvCUDzAlIiLKN87TRvQOmZmZmD17tqyta9euaNiwIQBgy5ZXQez1gNa586vH69zccvcjIiIqCA5EUGEciKAcvr6vAtawYfdw+7b8VlTjxo2DoaGhkiojIiJ1wIEIREUs57ozV1egcuXdssAWE1MXjRp5yAIbr1MjIqLixCNtKoxH2oqXgwNw+fILTJgwX9bu798P587Z5JrCg5PjEhFRXnikjaiI5Bwx+/LLK7kC26xZExEXZ5PnJLg5Aw7atOERNyIiKno80qbCeKSteDg4CDRuvApmZv9Kba1atcLz522lwQNvDiyQP59H3IiI6D9F9f3N0aNUqj19+hROTitkbb/88gvMzMwAvDus5eDIUCIiKg4MbVRqnTlzBqdPn5aWy5YtixEjRkChUBRoO3lN8UFERFTYGNqo1Mlr7rXOnTujUaNGSqqIiIjo/RjaqFSJiorChg0bZG1jx46FkZGRkioiIiLKH4Y2KjX27t2Lq1evSst16tRBz549lVgRERFR/jG0UYnm6wssXPgCbdvKp/Lo27cvqlevrqSqiIiICo6hjUq0FSuuoW3bPbK2iRMnQltbW0kVERERfRhOrkslSs5EuT4+AmvWrMFnn/0X2BwcHODh4SEFNt6GioiI1AlDG5Uonp5AeHg8QkNnIDY2Vmq3tR2Kr776KlffgIBXfxIREak6nh6lEsXZ+Ryio09Ky8bGxvj111+hoZH7/yecFJeIiNQJQxupPV9fwN09C99/Pwuvz4trZdUJAwY0fuvzOCkuERGpE4Y2UnteXg/www/rZW1jxoxBmTJllFQRERFR4WNoI7W2f/9+fPXVZWnZ2LgmRo/uo8SKiIiIigZDG6kdX19gwYKX+PJLL1l7jRo/4scfayipKiIioqLF0EZqw9f31cABI6Pr+PLL3bJ17u7u0NHRUVJlRERERY+hjVReTliLihLo0GEtKlWKltbZ29vj66+/VmJ1RERExYOhjVSary/Qowegp/cMo0Ytk60bMmQIzM3NlVQZERFR8VK7yXXT0tJgZ2cHhUKB0NBQ2borV66gVatW0NPTg5WVFby8vHI9f9euXbC1tYWenh4aNGiAw4cPy9YLITB16lRYWlpCX18fjo6OuHXrlqxPfHw8+vTpA2NjY5iamsLFxQXPnz8vcC30fp6eQJMm52WBTUvLCFOmTGFgIyKiUkXtQtuECRNQqVKlXO1JSUn4+uuvUbVqVQQHB2P+/PmYNm0afv/9d6nPhQsX0KtXL7i4uCAkJARdu3ZF165dce3aNamPl5cXli1bhjVr1iAwMBCGhoZwcnJCamqq1KdPnz64fv06jh8/joMHD+Ls2bP4+eefC1QLvV3O7aX2789Cu3az8NVXJ6R1HTp0wKRJY/OcLJeIiKhEE2rk8OHDwtbWVly/fl0AECEhIdK6VatWibJly4q0tDSpzdXVVdSpU0da/uGHH0SHDh1k22zevLkYPHiwEEKI7OxsYWFhIebPny+tT0hIELq6umLbtm1CCCFu3LghAIigoCCpz5EjR4RCoRCPHj3Kdy35kZiYKACIxMTEAj1P3dnbC1G58gMxbdo02SMpKUnZpREREb1XUX1/q83hitjYWAwaNAibNm2CgYFBrvUBAQFo3bq1bAShk5MTIiIi8OzZM6mPo6Oj7HlOTk4ICAgAAERGRiImJkbWx8TEBM2bN5f6BAQEwNTUFE2bNpX6ODo6QkNDA4GBgfmuJS9paWlISkqSPUqj3r19MWjQH9Jy9erV4eHhwclyiYioVFOL0CaEgLOzM4YMGSILS6+LiYnJdY1TznJMTMw7+7y+/vXnva1PxYoVZeu1tLRQrly5977O66+Rl7lz58LExER6WFlZvbVvSZSamorp06fj6dMQqa13797o27evEqsiIiJSDUoNbW5ublAoFO98hIeHY/ny5UhOToa7u7syyy1y7u7uSExMlB4PHjxQdknFJiwsDPPmzZO1ubu7o1atWkqqiIiISLUodcqPsWPHwtnZ+Z19qlevjpMnTyIgIAC6urqydU2bNkWfPn2wceNGWFhYIDY2VrY+Z9nCwkL6M68+r6/PabO0tJT1sbOzk/o8efJEto3MzEzEx8e/93Vef4286Orq5nqPJU3OnGtubq9u1i6EwPr16/Hw4UOpT7NmzdC+fXslVklERKR6lBrazMzMYGZm9t5+y5Ytw6xZs6Tlx48fw8nJCTt27EDz5s0BvJpkddKkScjIyIC2tjYA4Pjx46hTpw7Kli0r9fH398eoUaOkbR0/fhz29vYAABsbG1hYWMDf318KaUlJSQgMDMTQoUOlbSQkJCA4OBhNmjQBAJw8eRLZ2dkFqqW08vQEAgKAPn2ADRsScP36Utn6wYMHvzPYEhERlVqFOqyhmERGRuYaPZqQkCDMzc1F3759xbVr18T27duFgYGB+O2336Q+58+fF1paWmLBggUiLCxMeHh4CG1tbXH16lWpj6enpzA1NRU+Pj7iypUrokuXLsLGxka8fPlS6tOuXTvRqFEjERgYKP766y9Rq1Yt0atXrwLVkh+qNnrUx+fVyE4fnw/v6+MjhJGREPb2F2QjQ728vERWVlbRFE5ERFSMiur7u8SENiGEuHz5smjZsqXQ1dUVlStXFp6enrmeu3PnTlG7dm2ho6Mj6tevLw4dOiRbn52dLaZMmSLMzc2Frq6u+PLLL0VERISsz9OnT0WvXr2EkZGRMDY2Fj/99JNITk4ucC3vo2qhzd5eCODVnx/aNzMzU8yYMVsW2AIDA4umYCIiIiUoqu9vhRBCKPNIH71dUlISTExMkJiYCGNjY2WXk+t6tIL2ffz4MdauXSvrN3r0aJV4b0RERIWlqL6/GdpUmKqFto9x8OBBBAcHS8vVqlVDv379oFAolFgVERFR4Suq72+1mKeN1EfOLah8fV8tp6WlYfr06bLA1rNnT/Tv35+BjYiIqACUOnqUSp6c0aGenkCdOhHYvn27bL2bm1uJn9aEiIioKDC0UaFycwM8PQW++84b27dHSe1NmzZFhw4dlFgZERGRemNoo0L1+eeJCAlZgpSU/9oGDRqESpUqKa8oIiKiEoChjQpNYGAgjh49Ki3r6upi/Pjx0NTUVGJVREREJQNDG3207OxseHl5IS0tTWqrXNkJAwe2UGJVREREJQtDG30wX19g5cpoODj8LmtfvHgU6tUzwcCBSiqMiIioBGJoow/m43MYDg5B0nJcnBVWrvwJenoKuLkpsTAiIqISiKGNCiwtLQ2enp6wtv6vrUePHrh50xb//JO/OyYQERFRwTC0UYHcvHkT27Ztk7W5urpCT08PtrYMa0REREWFd0QgyZt3M3idEAJ//vmnLLA1btwYHh4e0NPTK8YqiYiISieGNpK8fjeD1yUlJWHGjBmIjIyU2gICBqJTp07FXCEREVHpxdBGEjc3wN4eskEEf//9NxYvXiwta2ho49ixyfjll8pKqJCIiKj04jVtBODVKVFPz/8GEWRnZ2PhwoV48eKF1Cc8/Cv06uWAKVOUWCgREVEpxdBGAOSnRps1i8Fvv/0mW3/mzK84dcoU9+9zsAEREZEyMLQRgJwbvQM//uiH3367KLUbGFTGuHEuOHBAgdRUcP41IiIiJWFoIwBAu3bpCAmZi7i4/9p27PgeDx7UQ506r46u8QgbERGR8nAgAuHWrVuYO3eurG3PHldERtbD8+e5R5MSERFR8eORtlJu8+bNuHPnjrRsZ2eHLl26wMNDPjiBiIiIlIuhrZRKTk7GokWLZG0uLi6oUqWKtMxTokRERKqDoa0U2rAhFFFRPtKyhoYGJk6cCE1NTSVWRURERO/Ca9pKoXv3Dkh///LLLzFlyhQpsL3rVlZERESkPAxtpZCV1ddITLREvXoj0bJlS1lQe9utrIiIiEi5FEIIoewiKG9JSUkwMTFBYmIijI2Ni+x1HBxeBbWcW1i9fmcEIiIiKpii+v7mkTaS3XO0c2fgwgUGNiIiIlXDgQjEUaJERERqgEfaSjEOOiAiIlIfDG2lGAcdEBERqQ+GtlLs9WvZiIiISLXxmrZSjNeyERERqQ8eaSMiIiJSAwxtRERERGqAoY2IiIhIDTC0EREREakBhjYiIiIiNcDQRkRERKQGGNqIiIiI1ABDGxEREZEaYGgjIiIiUgMMbURERERqgKGNiIiISA0wtBERERGpAYY2IiIiIjWgpewC6O2EEACApKQkJVdCRERE+ZXzvZ3zPV5YGNpUWHJyMgDAyspKyZUQERFRQSUnJ8PExKTQtqcQhR0DqdBkZ2fj8ePHKFOmDBQKhbLLKVZJSUmwsrLCgwcPYGxsrOxy6DXcN6qL+0Z1cd+otsLeP0IIJCcno1KlStDQKLwr0XikTYVpaGigSpUqyi5DqYyNjfkLTkVx36gu7hvVxX2j2gpz/xTmEbYcHIhAREREpAYY2oiIiIjUAEMbqSRdXV14eHhAV1dX2aXQG7hvVBf3jerivlFt6rJ/OBCBiIiISA3wSBsRERGRGmBoIyIiIlIDDG1EREREaoChjYiIiEgNMLRRoUtLS4OdnR0UCgVCQ0Nl665cuYJWrVpBT08PVlZW8PLyyvX8Xbt2wdbWFnp6emjQoAEOHz4sWy+EwNSpU2FpaQl9fX04Ojri1q1bsj7x8fHo06cPjI2NYWpqChcXFzx//rzAtZQUnTt3hrW1NfT09GBpaYm+ffvi8ePHsj47d+6EnZ0dDAwMULVqVcyfPz/Xdk6fPo3GjRtDV1cXNWvWhLe3d64+K1euRLVq1aCnp4fmzZvj77//lq1PTU3FsGHDUL58eRgZGaFbt26IjY2V9YmKikKHDh1gYGCAihUrYvz48cjMzPz4D0IF5Wff+Pn5oUWLFihTpgzMzMzQrVs33Lt3T9aH+6ZovG//TJs2DQqFItfD0NBQth3+Xit8+fnZEUJgwYIFqF27NnR1dVG5cmXMnj1b1ketfnYEUSEbOXKkaN++vQAgQkJCpPbExERhbm4u+vTpI65duya2bdsm9PX1xW+//Sb1OX/+vNDU1BReXl7ixo0bYvLkyUJbW1tcvXpV6uPp6SlMTEzE/v37xeXLl0Xnzp2FjY2NePnypdSnXbt2omHDhuLixYvi3LlzombNmqJXr14FqqUkWbRokQgICBD37t0T58+fF/b29sLe3l5af/jwYaGlpSVWr14t7ty5Iw4ePCgsLS3F8uXLpT53794VBgYGYsyYMeLGjRti+fLlQlNTUxw9elTqs337dqGjoyPWr18vrl+/LgYNGiRMTU1FbGys1GfIkCHCyspK+Pv7i0uXLokWLVoIBwcHaX1mZqb45JNPhKOjowgJCRGHDx8WFSpUEO7u7kX8KSnH+/bN3bt3ha6urnB3dxe3b98WwcHBonXr1qJRo0ayPtw3ReN9+yc5OVlER0fLHvXq1RP9+/eX+vD3WtF4374RQogRI0aIOnXqCB8fH3H37l1x6dIlcezYMWm9uv3sMLRRoTp8+LCwtbUV169fzxXaVq1aJcqWLSvS0tKkNldXV1GnTh1p+YcffhAdOnSQbbN58+Zi8ODBQgghsrOzhYWFhZg/f760PiEhQejq6opt27YJIYS4ceOGACCCgoKkPkeOHBEKhUI8evQo37WUZD4+PkKhUIj09HQhhBC9evUS3bt3l/VZtmyZqFKlisjOzhZCCDFhwgRRv359WZ8ePXoIJycnablZs2Zi2LBh0nJWVpaoVKmSmDt3rhDi1b7S1tYWu3btkvqEhYUJACIgIEAI8erfkIaGhoiJiZH6rF69WhgbG8v2V0n15r7ZtWuX0NLSEllZWVIfX19fWR/um+Lz5v55U2hoqAAgzp49K7Xx91rxeHPf3LhxQ2hpaYnw8PC3PkfdfnZ4epQKTWxsLAYNGoRNmzbBwMAg1/qAgAC0bt0aOjo6UpuTkxMiIiLw7NkzqY+jo6PseU5OTggICAAAREZGIiYmRtbHxMQEzZs3l/oEBATA1NQUTZs2lfo4OjpCQ0MDgYGB+a6lpIqPj8eWLVvg4OAAbW1tAK9Oaevp6cn66evr4+HDh7h//z6A9++b9PR0BAcHy/poaGjA0dFR6hMcHIyMjAxZH1tbW1hbW8v2X4MGDWBubi57naSkJFy/fr2wPgaVlNe+adKkCTQ0NLBhwwZkZWUhMTERmzZtgqOjo9SH+6Z45LV/3rRu3TrUrl0brVq1ktr4e63o5bVvDhw4gOrVq+PgwYOwsbFBtWrVMHDgQMTHx0vPU7efHYY2KhRCCDg7O2PIkCGyXyqvi4mJkf2DBSAtx8TEvLPP6+tff97b+lSsWFG2XktLC+XKlXvv67z+GiWNq6srDA0NUb58eURFRcHHx0da5+TkhL1798Lf3x/Z2dm4efMmFi5cCACIjo4G8PbPLCkpCS9fvsS///6LrKys9+4bHR0dmJqavrMP981/+8bGxgbHjh3DxIkToaurC1NTUzx8+BA7d+6U+nDfFK137Z/XpaamYsuWLXBxcZG18/da0XnXvrl79y7u37+PXbt24c8//4S3tzeCg4PRvXt3qY+6/ewwtNE7ubm55XmR7euP8PBwLF++HMnJyXB3d1d2yaVGfvdNjvHjxyMkJATHjh2DpqYm+vXrB/H/N0QZNGgQhg8fjo4dO0JHRwctWrRAz549Abz6XyUVTGHum5iYGAwaNAj9+/dHUFAQzpw5Ax0dHXTv3l3qQwVTmPvndfv27UNycjL69+9fnG+nRCnMfZOdnY20tDT8+eefaNWqFb744gv88ccfOHXqFCIiIpT1Fj+KlrILINU2duxYODs7v7NP9erVcfLkSQQEBOS6b1vTpk3Rp08fbNy4ERYWFrlG0+QsW1hYSH/m1ef19TltlpaWsj52dnZSnydPnsi2kZmZifj4+Pe+zuuvoeryu29yVKhQARUqVEDt2rVRt25dWFlZ4eLFi7C3t4dCocC8efMwZ84cxMTEwMzMDP7+/rJtvO0zMzY2hr6+PjQ1NaGpqfne/Zeeno6EhATZ/0rf7PPmyKzSvG9WrlwJExMT2SjAzZs3w8rKCoGBgWjRogX3TQEV5v553bp169CxY8dcR1T4ey3/CnPfWFpaQktLC7Vr15b6161bF8CrkZx16tRRv5+dfF/9RvQO9+/fF1evXpUefn5+AoDYvXu3ePDggRDiv4tkX7+A193dPddAhI4dO8q2bW9vn+uC3QULFkjrExMT87xg99KlS1IfPz+/PC/YfVctJdn9+/cFAHHq1Km39unbt69sJNaECRPEJ598IuvTq1evXBfsDh8+XFrOysoSlStXznXB7u7du6U+4eHheV6w+/rIrN9++00YGxuL1NTUD3vDauTNfTNmzBjRrFkzWZ/Hjx8LAOL8+fNCCO6b4vS2n527d+8KhUIhDhw4kOs5/L1WPN7cNznfQ7dv35b65AwUiYiIEEKo388OQxsVicjIyFyjRxMSEoS5ubno27evuHbtmti+fbswMDDINeWHlpaWWLBggQgLCxMeHh55Do03NTUVPj4+4sqVK6JLly55Do1v1KiRCAwMFH/99ZeoVauWbGh8fmopKS5evCiWL18uQkJCxL1794S/v79wcHAQNWrUkH5ZxMXFidWrV4uwsDAREhIiRo4cKfT09ERgYKC0nZyh8ePHjxdhYWFi5cqVeQ6N19XVFd7e3uLGjRvi559/FqamprIRU0OGDBHW1tbi5MmT4tKlS7mG6ecMjf/6669FaGioOHr0qDAzMyuR00rkZ9/4+/sLhUIhpk+fLm7evCmCg4OFk5OTqFq1qnjx4oUQgvumqORn/+SYPHmyqFSpksjMzMy1Hf5eK3z52TdZWVmicePGonXr1uKff/4Rly5dEs2bNxdfffWVtB11+9lhaKMikVdoE0KIy5cvi5YtWwpdXV1RuXJl4enpmeu5O3fuFLVr1xY6Ojqifv364tChQ7L12dnZYsqUKcLc3Fzo6uqKL7/8UvpfU46nT5+KXr16CSMjI2FsbCx++uknkZycXOBaSoIrV66INm3aiHLlygldXV1RrVo1MWTIEPHw4UOpT1xcnGjRooUwNDQUBgYG4ssvvxQXL17Mta1Tp04JOzs7oaOjI6pXry42bNiQq8/y5cuFtbW10NHREc2aNcu1nZcvX4pffvlFlC1bVhgYGIhvv/1WREdHy/rcu3dPtG/fXujr64sKFSqIsWPHioyMjML5QFRIfvaNEEJs27ZNNGrUSBgaGgozMzPRuXNnERYWJuvDfVP48rt/srKyRJUqVcTEiRPfui3+Xitc+d03jx49Et99950wMjIS5ubmwtnZWTx9+lTWR51+dhRC8EpWIiIiIlXHYWFEREREaoChjYiIiEgNMLQRERERqQGGNiIiIiI1wNBGREREpAYY2oiIiIjUAEMbERERkRpgaCMiKgIKhQL79+9Xdhkyp0+fhkKhQEJCgrJLIaIPwNBGRPQRpk2bJt3Um4ioKDG0EREREakBhjYiKtWys7Mxd+5c2NjYQF9fHw0bNsTu3bsB/Hc60d/fH02bNoWBgQEcHBwQEREBAPD29sb06dNx+fJlKBQKKBQKeHt7S9v+999/8e2338LAwAC1atWCr69vvmrKeV0/Pz80atQI+vr6aNu2LZ48eYIjR46gbt26MDY2Ru/evfHixQvpeWlpaRg5ciQqVqwIPT09tGzZEkFBQYX3YRGRUjG0EVGpNnfuXPz5559Ys2YNrl+/jtGjR+PHH3/EmTNnpD6TJk3CwoULcenSJWhpaWHAgAEAgB49emDs2LGoX78+oqOjER0djR49ekjPmz59On744QdcuXIF33zzDfr06YP4+Ph81zZt2jSsWLECFy5cwIMHD/DDDz9gyZIl2Lp1Kw4dOoRjx45h+fLlUv8JEyZgz5492LhxI/755x/UrFkTTk5OBXpNIlJhBbq9PBFRCZKamioMDAzEhQsXZO0uLi6iV69e4tSpUwKAOHHihLTu0KFDAoB4+fKlEEIIDw8P0bBhw1zbBiAmT54sLT9//lwAEEeOHHlvXXm97ty5cwUAcefOHalt8ODBwsnJSdq+tra22LJli7Q+PT1dVKpUSXh5ecm2++zZs/fWQESqR0uJeZGISKlu376NFy9e4KuvvpK1p6eno1GjRtLy//73P+nvlpaWAIAnT57A2tr6ndt//XmGhoYwNjbGkydP8l3f6883NzeHgYEBqlevLmv7+++/AQB37txBRkYGPvvsM2m9trY2mjVrhrCwsHy/JhGpLoY2Iiq1nj9/DgA4dOgQKleuLFunq6uLO3fuAHgVfnIoFAoAr66Fe5/Xn5fz3Pw8L6/nKxSKj94eEak3XtNGRKVWvXr1oKuri6ioKNSsWVP2sLKyytc2dHR0kJWVVcSVvl+NGjWgo6OD8+fPS20ZGRkICgpCvXr1lFgZERUWHmkjolKrTJkyGDduHEaPHo3s7Gy0bNkSiYmJOH/+PIyNjVG1atX3bqNatWqIjIxEaGgoqlSpgjJlykBXV7cYqpczNDTE0KFDMX78eJQrVw7W1tbw8vLCixcv4OLiUuz1EFHhY2gjolJt5syZMDMzw9y5c3H37l2YmpqicePGmDhxYr5OPXbr1g179+5FmzZtkJCQgA0bNsDZ2bnoC8+Dp6cnsrOz0bdvXyQnJ6Np06bw8/ND2bJllVIPERUuhRBCKLsIIiIiIno3XtNGREREpAYY2oiIitmQIUNgZGSU52PIkCHKLo+IVBRPjxIRFbMnT54gKSkpz3XGxsaoWLFiMVdEROqAoY2IiIhIDfD0KBEREZEaYGgjIiIiUgMMbURERERqgKGNiIiISA0wtBERERGpAYY2IiIiIjXA0EZERESkBhjaiIiIiNTA/wGshenDJO+9bAAAAABJRU5ErkJggg==", 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", 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SnD5R0gr7/H6d1lzqEgQB33//PX777TeEh4dLQg8ANGnSBIaGhjh69Ch69uwJALhx4wbu378PNzc3OVomIiIqs/LW5Dl/PgsTJszCq7NKunTpgkaNGsnX3BtoTfDx9vbGpk2bsGfPHlhaWorzdqytrWFqagpra2sMHDgQY8aMQbly5WBlZYXvv/8ebm5uJXJHFxERkS7z9QUmTLiNXr02SOo//vgjzM3NZerq7bQm+CxfvhwA0KZNG0k9ODhYXCV4wYIF0NPTQ8+ePSULGBIREZF6paRsQK9et8Xt999/H59//rmMHalGa4KPKlORTExMsHTpUixdulQDHREREemelJQULFiwQFL79ttv4ezsLFNHRaM1wYeIiIjkdebMGcmCwnp6evDz84OBgfbECe3plIiIiGSRk5ODoKAgyYr8N254YNOmD2XsqngYfIiIiKhQDx48wOrVqyW1Eyd+wKhRNvI09I4YfIiIiKhAe/bswaVLl8TtqlWron///rI+a+tdMfgQERGRRFpaGubMmSOpVa/eG337vidTR+qjlY+soNLJy8sL3bp1E7fbtGmDUaNGvdM51XEOIiJSTWgo0LPn5XyhZ9YsPyxfrv2hB+CIj07w8vLCunXrAACGhoZwdnZGv379MGHChBKdib9r1y4YGhqqdGx4eDjatm2b75EjRTkHEREVT+7jJwS4uS3FBx88Eevu7u5IT/8/NGmSu2BhWcDgoyM6dOiA4OBgZGRk4Pfff4e3tzcMDQ3h5+cnOS4zMxNGRkZqec1y5cqVinMQEdGbLV78GJ6e0gV/hw8fDjs7OwBAly5ydFUyeKlLRxgbG8PBwQFVq1bFsGHD4OHhgdDQUPHy1MyZM+Ho6AhXV1cAQFxcHL744gvY2NigXLly6Nq1K2JjY8Xz5eTkYMyYMbCxsUH58uUxbty4fItMvn6ZKiMjA+PHj4eTkxOMjY1Rs2ZNrFmzBrGxsWjbti0AwNbWFgqFQlyN+/VzPH36FP369YOtrS3MzMzQsWNH3Lx5U9wfEhICGxsbhIWFoU6dOrCwsECHDh0QHx8vHhMeHo7mzZvD3NwcNjY2+PDDD3Hv3j01/U0TEWmXw4cP46OPXoae1NRymDx5shh6yhoGHx1lamoqrsdw9OhR3LhxA4cPH8a+ffuQlZUFT09PWFpa4tSpUzh9+rQYIPJ+Zt68eQgJCcHatWvxxx9/4L///sNvv/32xtfs168fNm/ejEWLFuH69etYuXIlLCws4OTkhJ07dwLIfbBsfHw8Fi5cWOA5vLy8cO7cOYSGhiIyMhKCIODTTz9FVlaWeExaWhrmzp2L9evX4+TJk7h//z5+/PFHAEB2dja6deuG1q1b4/Lly4iMjMSQIUO0+g4FIqLiyMzMxNSpUxERESHWLl/ujo8++r5M/zeRl7p0jCAIOHr0KMLCwvD999/j8ePHMDc3x+rVq8VLXBs2bIBSqcTq1avFf/iDg4NhY2OD8PBwtG/fHj///DP8/PzQo0cPAMCKFSsQFhZW6Ov+/fff2LZtGw4fPgwPDw8AQPXq1cX9eZe07O3tJXN8XnXz5k2Ehobi9OnTcHd3BwBs3LgRTk5O2L17N3r16gUAyMrKwooVK1CjRg0AwIgRIzBt2jQAuUutJycno1OnTuL+OnXqFP0vkohIi928eRObNm2S1OrXHwt/fzOZOtIcjvjIJDQUcHfP/a4J+/btg4WFBUxMTNCxY0d8+eWXmDJlCgCgfv36knk9UVFRuHXrFiwtLWFhYQELCwuUK1cOL168wO3bt5GcnIz4+Hi0aNFC/BkDAwM0bdq00Ne/dOkS9PX10bp162L/DtevX4eBgYHkdcuXLw9XV1dcv35drJmZmYmhBgAqVaqExMREALkBy8vLC56enujcuTMWLlwouQxGRFSWCYKAdevWSULPlSsNMGWKPyZMKPuhB+CIj2wCA4HIyNzvmpg01rZtWyxfvhxGRkZwdHSU3M1lbm4uOfb58+do0qQJNm7cmO88xb3ma2pqWqyfK47X7wJTKBSS+UfBwcEYOXIkDh48iK1bt2LSpEk4fPgwWrZsqbEeiYg0LSkpKd80gg0bBiI9vQoAoAxf3ZLgiI9MfH0BNzfN3R5obm6OmjVrwtnZ+a23sDdu3Bg3b96Evb09atasKfmytraGtbU1KlWqhDNnzog/k52djfPnzxd6zvr160OpVOLEiRMF7s8bccrJySn0HHXq1EF2drbkdZ88eYIbN26gbt26b/ydXteoUSP4+fkhIiIC9erVyzfkS0RUlkREREhCjyAY4dChSZg3rwqWLcv9PJo9W8YGNYgjPjLp0qX03h749ddfY86cOejatSumTZuGKlWq4N69e9i1axfGjRuHKlWq4IcffkBgYCBq1aqF2rVrY/78+UhKSir0nC4uLujfvz8GDBiARYsWoUGDBrh37x4SExPxxRdfoGrVqlAoFNi3bx8+/fRTmJqawsLCQnKOWrVqoWvXrhg8eDBWrlwJS0tL+Pr6onLlyujatatKv9vdu3exatUqdOnSBY6Ojrhx4wZu3ryJfv36vctfGRFRqbR7dw4uXpwFPT2lWDtwoAOuXm2BjRtffg6V1s+jksARH8rHzMwMJ0+ehLOzM3r06IE6depg4MCBePHiBaysrAAAPj4+6Nu3L/r37w83NzdYWlqie/fubzzv8uXL8fnnn2P48OGoXbs2Bg8ejNTUVABA5cqVMXXqVPj6+qJixYoYMWJEgecIDg5GkyZN0KlTJ7i5uUEQBPz+++8qL3JoZmaGmJgY9OzZE++99x6GDBkCb29vfPfdd0X4GyIiKv3u37+PqKgZktDz/vujcfVqCzx/njvVQhcphNcXX9FxKSkpsLa2RnJysvghDwAvXrzA3bt3Ua1aNZiYmMjYIWka33si0jY7duzA1atXxe1bt2rg9u1vEBGRt0pz7lSLsjTSU9jn9+t4qYuIiKiMSE1Nxdy5cyW1rKxvcPt2DXFOaWmeaqEJDD5ERERlwKVLl7Bnzx5JbebMCWja1BCvrFGo8xh8iIiItJhSqcSiRYuQnJws1i5caIUDB9rB3r7sPFxUXRh8iIiItNSjR4+wYsUKSe3UKW8cPVoBAODsrNuXtQrC4FNEnAuue/ieE1FpdPDgQcm6ZgkJFREe/h1mz1bgwQNAEDjaUxAGHxXl3S6dlpam0VWISX5paWkA8q8ITUQkh4yMDAS+di/6gQM9ceZMPbi6cvLy2zD4qEhfXx82NjbiM5/MzMzK9NNrKXekJy0tDYmJibCxsYG+vr7cLRGRjouJicHWrVsltcDAcQBMNfo0AG3G4FMEDg4OACCGH9INNjY24ntPRCQHQRCwZs0aPHjwQKw1btwYf/7ZGQYGwMiRwMyZMjaoRbiA4WtUWQApJycHWVlZGu6M5GBoaMiRHiKS1dOnT7Fo0SJJbfDgwXB0dJSpo9KJCxiWIH19fX4YEhFRiTt16hSOHTsmbqemmmHfPh/4+/OJU8XF4ENERFTKZGdnY+Zr16727fsU5841g6urTE2VEQw+REREpUhsbCzWrVsnqc2dOwbPn1vCxAQICpKpsTKCwYeIiKiU2Lp1K2JiYsTtGzfew+bNvQEAlSsDy5bxVvV3xeBDREQks+fPn2PevHmSWkhIP2RlVYOFBe/aUicGHyIiIhmdO3cO+/fvl9RmzJiI7GwD7NnDER51Y/AhIiKSgVKpxPz585GamirWjh9vg9OnWyMnB/j8c4aeksDgQ0REpGHx8fFYtWqVpLZly/ewtS2HnTsZeEoSgw8REZEG7d+/H+fOnRO3HzxwxC+/DELt2gpERMjYmI5g8CEiItKAFy9eYPbs2ZLa1q1f4Pr1OgCA13ZRCWHwISIiKmHXrl3D9u3bJbWAgPHIyDCBqSkwejQvb2kKgw8REVEJEQQBq1atQkJCglg7c6YZjhz5FAYGwIQJvE1d0xh8iIiISsCTJ0+wZMkSSS0r6zskJTlgxw6O8MiFwYeIiEjNjh8/jpMnT4rbyclW+PnnH9CypR4nMMuMwYeIiEhNsrKyMGvWLEktNLQzLlxoDADw9ZWjK3oVgw8REZEa3LlzB+vXr5fU5szxQVqaBfT0gB49eHmrNGDwISIiekebNm3CzZs3xe2rV+ti+/ZeADiBubRh8CEiIiqmlJQULFiwQFJbu9YL9+9XBcDQUxox+BARERXD2bNnceDAAXFbqVRg5swJsLY24BPVSzEGHyIioiLIycnBrFlzoFRmiLXDhz1w+vSHMDEBgoM5l6c005O7ASIiIm3x4MEDzJgxQxJ6Vq36AZUqfQg3N2DrVoae0o4jPkRERCrYs2cPLl26JG7fu+eMyEgvPHigkK8pKjIGHyIiojdIT09HUFCQpLZp01eIi3PFli0yNUXFxuBDRERUiJ9+ugIDg12S2gcf+CIszBhz5vCyljZi8CEiInqNIAiYMGEZTEz+FWuRkW4YPrw9unQBuneXsTl6Jww+REREr/j333+xdOlSmJi8rC1bNgyJifZISeEoj7Zj8CEiIvqfI0eO4PTp0+L2f//ZYu3a77FxowKBgXzWVlnA4ENERDrvt98ycflywGu1boiKaoDatXNHeTjSUzYw+BARkU67efMmLl/eJKnNmTMWHTuawcyMozxlDYMPERHppD17BBw9+ivKl48VazdvfoA7d7pj0yaO8JRVDD5ERKRzkpOTcenSzyhf/mVt48YBmDvXiYGnjGPwISIinREaCmzeHInatQ+JtexsAxw75ou5c/UZenQAgw8REemE3btzcP58AGrXzhFrMTHt0bu3G6ZPl7Ex0igGHyIiKvM2bYrDzZtrYfDKp96oUaNgbW0tX1MkCwYfIiIqk0JDgcBAwMNjF/T1r4j1f/6pjqtXv0GjRgpe2tJBDD5ERFQm/fRTKnr0mCupHT/+NRISauLGjdxQxOCje/TkboCIiEjdfvrpUr7Q88EHfggPr4mgIMDNjevz6KoyGXyWLl0KFxcXmJiYoEWLFjh79qzcLRERkQYIgoCFCxfCwGCPWDt58iNs2eKP7t2NAOSO8kREcLRHV5W54LN161aMGTMG/v7+uHDhAho0aABPT08kJibK3RoREZWQiRMBZ+dHmDZtGpKSksT62rXeePjwE8yeLV9vVLooBEEQ5G5CnVq0aIFmzZphyZIlAAClUgknJyd8//338FVhXDMlJQXW1tZITk6GlZVVSbdLRETvKDQUWL48DC1b/inWnj+3w7x5w9CypQIRETI2Rxqj6ud3mRrxyczMxPnz5+Hh4SHW9PT04OHhgcjIyAJ/JiMjAykpKZIvIiIq/UJDgVatMnDx4lRJ6ImI6IlWrYajZUsF5/FQPmUq+Pz777/IyclBxYoVJfWKFSsiISGhwJ8JCAiAtbW1+OXk5KSJVomI6B2EhgITJ96Ah0egpL5kyTh4e9fjPB4qVJkKPsXh5+eH5ORk8SsuLk7uloiIqBATJwKWlgIOHFiLzz/fItbLl2+EsDB/rFljyrBDb1Sm1vGpUKEC9PX18ejRI0n90aNHcHBwKPBnjI2NYWxsrIn2iIjoHYSGAitWPMWPPy6S1N97bxB6966MESNkaoy0Spka8TEyMkKTJk1w9OhRsaZUKnH06FG4ubnJ2BkREb2LiROBuXP/wMiRL0NPZqYpfvrpJ/TuXVnGzkjblKkRHwAYM2YM+vfvj6ZNm6J58+b4+eefkZqaim+//Vbu1oiIqBiys7NhZDQTn3zysnbkyKcYO7YZ9MrU/76TJpS54PPll1/i8ePHmDx5MhISEtCwYUMcPHgw34RnIiIq/TZtuoebN0MktfDwMRg71pJzeahYytw6Pu+K6/gQEZUOCxduQ1LSdXG7Vq1a6NOnj4wdUWmm6ud3mRvxISIi7bZz53NER8+T1Pbv74uzZ6vL1BGVJbw6SkREsgsNBdzdgbVrL+QLPTNmTMCzZww9pB4c8SEiItkFBirh7r4AcXHPxdrx421gZ9cazZrxSeqkPgw+REQki9BQIDAQsLJKgKfnSsm+U6e+x5gx5TiBmdSOwYeIiGQxfjxQvfp+NG9+Tqw9fFgJ69cPRlqaQsbOqCxj8CEiIo2aOBFYuvQFRo+eLalv3doLt27VxdixMjVGOoHBh4iINCY0FNi9+xpGj94uqe/cOR6BgSa8tEUljsGHiIg0QhAEnDixCl98kSDWzp5tiqdPP8PlyzI2RjqFwYeIiErc9u3/4dq1xXh1XbmsrCF4+rQS79gijWLwISKiEvXTT+EwMDghbr94YYGZM0dDjw/aIhkw+BARUYnYvTsLUVGzYPDKJ82BA50wYUITPlyUZMPgQ0REanfnzh1ERa2X1DZs8MG8eRacwEyyYvAhIiK1WrBgE1JSborbd+7Uwbp1X8DfX8amiP6HwYeIiNRi585niI6eL6kFB3vBxKSqTB0R5cerrERE9E5CQ4G2bc/mCz1r1kyEqWlVBAXJ1BhRATjiQ0RExaZUKnHmzBy0afNCrB050g45Oa1w/76MjREVgsGHiIiK5eHDh/jll19gZPSy9ssvI+HsbMu1eajUYvAhIqIiCw0NxcWLF8XthAQnhId/i2XLFLxri0o1Bh8iIlJZeno6gl6btLN581coV84V16/L1BRRETD4EBGRSqKjo7Fz505JbdYsXxgZGXMCM2kNBh8iInojQRAQFLQcL148FmvnzrXEvn2e0NMDtm8HL2+R1uDt7EREVKh///0X06ZNk4SeYcOG4YMPPGFhAfj6MvSQdlEIgiDI3URpkpKSAmtrayQnJ8Pq1ccIExHpmMmTj0Jf/w9xOy3NFu7u36NrV4WMXREVTNXPb17qIiIiUWgoMGdOJjw8AqCv/7J+5UpX7NjRULa+iNSFwYeIiES//HILHh4bJbX163/E/PnmMnVEpF4MPkREBEEQsGDBBjRtekesxcbWR/fuPfhwUSpTGHyIiHRccnIyfv75Z0ltzZoBqFLFiROXqcxh8CEi0mF//vknwsLCxO3sbAOsW+cLc3N9PnaCyiQGHyIiHbR7dw4uXAiEvn62WGvfvj3c3NwwfbqMjRGVMK7jQ0SkY+Li4hAVNUMSelauHAU3NzcZuyLSDI74EBHpkF27duHKlSvi9p071bBhQ1/4+nJtHtINDD5ERDogLS0Nc+bMkdQ2bOiD9PRa+O03rr5MuoPBh4iojIuKisLu3bsltZkz/ZCVZQQ3N4Ye0i0MPkREZZQgCFi8eDGePn0q1k6d+hBHj3rAxASoUQO8c4t0Dic3ExGVAaGhgLt77ncASExMxLRp0yShZ/ny4Th2zAPlygFbtwLXr3O0h3QPR3yIiMqAwEAgMjL3u4nJIURGRor7/vuvAlatGo4xYxSYOVPGJolKgSKP+Ojr6yMxMTFf/cmTJ9B/9Yl2RESkMb6+wEcfZcLTc6ok9OzY0QOLFnmjUSOGHiKgGCM+giAUWM/IyICRkdE7N0REREUTGgqsWXMDHh5bJPWFC8fh6VNTmJpyLg9RHpWDz6JFiwAACoUCq1evhoWFhbgvJycHJ0+eRO3atdXfIRERFUoQBBw7FozGjePE2oULDfH3310REpJ76cvXl3N5iPKoHHwWLFgAIPdfshUrVkguaxkZGcHFxQUrVqxQf4dERFSgpKQkLFy4ELa2L2u//DII8fGVxbV5GHiIpFQOPnfv3gUAtG3bFrt27YLtq/+mERGRRp0+fRpHjhwRt7OyjBEQMBaAPkd4iN6gyHN8jh8/XhJ9EBGRCnbvzsalS7OgULycb3n9ekf06dMcLVrwshbR2xQ5+AwYMOCN+9euXVvsZoiIdF1o6Mt5OYB0js69e/cQFRUCxSuP1QoPH43Ro614WYtIRUUOPq8uhgUAWVlZiI6ORlJSEtq1a6e2xoiIdNGr6/EAL/+ckbEd165dE4+7ebMm9uz5Gs+eydQokZYqcvD57bff8tWUSiWGDRuGGjVqqKUpIiJd8uooj6+vdMRn/vxUtG07F69kHvz6a1/ExlbnLepExaAQCluYp4hu3LiBNm3aID4+Xh2nk01KSgqsra2RnJwMKysrudshIh3g7p47suPmBkREvKxfuHABe/fulRybmTkBx48bci4P0WtU/fxW2yMrbt++jezsbHWdjohIZ7w+yqNUKhEY+DOysl5exwoP/xjh4W3zhSMiKpoiB58xY8ZItgVBQHx8PPbv34/+/furrTEiorLs1ctbeROTQ0MBT88EuLuvlBy7b98InDtXniswE6lBkYPPxYsXJdt6enqws7PDvHnz3nrHFxER5Qacr78Gnj/PDT95l6x27/4d7u5/icfFxzvg11+HYMsWBVdgJlITtc3xKSs4x4eISlrenB4LC2DjRiA7+wWuXJktOWbbtl6IiakLX1/w4aJEKijxOT6JiYm4ceMGAMDV1RX29vbFPRURkU55dU6Pre0pHDt2TLI/MHA8DAxMxMdOEJH6FDn4pKSkwNvbG5s3b4ZSqQQA6Ovr48svv8TSpUthbW2t9iaJiMqSLl2Azp0FTJs2TVK/cKEJzp/vBBcXYPZshh6ikqBX1B8YPHgwzpw5g/379yMpKQlJSUnYt28fzp07h++++64keiQiKlMePHiQL/Rs3NgHoaGd4OwMXL/O0ENUUoo8x8fc3BxhYWH46KOPJPVTp06hQ4cOSE1NVWuDmsY5PkRUkn799Vfxoc95liyZhCdP9GFrCwQHM/QQFUeJzfEpX758gZezrK2t+cR2IiLkv1UdADIyMhCY9xyK/zl3rjGePOmMx49laJJIRxX5UtekSZMwZswYJCQkiLWEhASMHTsWP/30k1qbIyLSRq8/b+vixYv5Qk9m5vd48qQz1+Uh0rAiX+pq1KgRbt26hYyMDDg7OwMA7t+/D2NjY9SqVUty7IULF9TXqYbwUhcRvauJE4FFi4CRIwEjo6n59k+Z4s8VmInUrMQudXXt2hUKheKdmiMiKsuOHweMjZ/AyGiJpH7lSlfs3NkQFhZcgZlILkUOPlOmTCmBNoiIyo7evffgv/8uSWrLl/th4EAjuLlxBWYiORU5+FSvXh1//fUXypcvL6knJSWhcePGuHPnjtqaIyLSFqGhwOzZOWjffoakfvNmTWzc+DWA3JEgXt4ikleRg09sbCxycnLy1TMyMvDPP/+opSkiIm2zZs0NtG+/RVL79dchuHOnEhQKwNGRl7eISgOVg09oaKj457CwMMkt7Tk5OTh69CiqVaum3u6IiLTAzJnz0Ljxc0mtYcPJaNSIDxclKm1UvqtLTy/3zneFQoHXf8TQ0BAuLi6YN28eOnXqpP4uNYh3dRGRqp49e4b58+dLaidOeOD48Q951xaRhqn6+a3yOj5KpRJKpRLOzs5ITEwUt5VKJTIyMnDjxo0SCz2xsbEYOHAgqlWrBlNTU9SoUQP+/v7IzMyUHHf58mW0atUKJiYmcHJyQlBQUIn0Q0R07NixfKEnM3MsRo36UJzATESlT5Hn+Ly+1LomxMTEQKlUYuXKlahZsyaio6MxePBgpKamYu7cuQByk1779u3h4eGBFStW4MqVKxgwYABsbGwwZMgQjfdMRGVP7orMAjw9pc/Zevy4ApYu9YabGzBzJi9rEZVmRV7A8PUH671u8uTJ79SQqubMmYPly5eLd5EtX74cEydOREJCAoyMjAAAvr6+2L17N2JiYlQ+Ly91EdGrXn38xIoVcWjRYq1k//79ffHwYXVYWABBQQw9RHIpsQUMf/vtN8l2VlYW7t69CwMDA9SoUUNjwSc5ORnlypUTtyMjI/Hxxx+LoQcAPD09MXv2bDx9+rTQ54hlZGQgIyND3E5JSSm5polI6+Q9fuLYsbVo0SJOsm/atJ+gVOpxPg+RFily8Ll48WK+WkpKCry8vNC9e3e1NPU2t27dwuLFi8XLXEDu88Jev6usYsWK4r7Cgk9AQACmTs2/pDwREQCMHfsCly/PltTOn2+O06c7QqkETEw4n4dImxT5IaUFsbKywtSpU4v8kFJfX18oFIo3fr1+merBgwfo0KEDevXqhcGDB79z735+fkhOTha/4uLi3v5DRFQmhYYC7u653wHgr7/+yhd69u79AXv3doS9PeDmBmzdystbRNqkyCM+hckLDkXh4+MDLy+vNx5TvXp18c8PHz5E27Zt4e7ujlWrVkmOc3BwwKNHjyS1vG0HB4dCz29sbAxjY+Mi9U1EZdOrT1W/eFE6EpyTo4fp039CuXLgYyeItFiRg8+iRYsk24IgID4+HuvXr0fHjh2LdC47OzvY2dmpdOyDBw/Qtm1bNGnSBMHBweK6Qnnc3NwwceJEZGVlwdDQEABw+PBhuLq6FnqZi4gIeDmBuXJloGrVx/D0XCbZHxXVEwcP1gMApKdzPg+RNivyXV2vz6PR09ODnZ0d2rVrBz8/P1haWqq1QSA39LRp0wZVq1bFunXroK+vL+7LG81JTk6Gq6sr2rdvj/HjxyM6OhoDBgzAggULinQ7O+/qItI97u65Iz1ffrkTdepES/YdPjwB48YZ4swZYNEiYOTI3FvWiah0UfXzu8jBRw4hISH49ttvC9z3avuXL1+Gt7c3/vrrL1SoUAHff/89xo8fX6TXYvAh0g2v3qauVGYjKkqaZh49qoMOHb7g5SwiLVGiwScpKQm3bt0CANSsWRM2NjbFbrS0YfAh0g15ozzdul1Dw4bbJfuWLx+KR48q8jZ1Ii1SIuv4xMbGwtvbG2FhYeJIi0KhQIcOHbBkyRK4uLi8U9NERJri6wucPRsIQ8MMSX3KlMkAFLCw4G3qRGWRysEnLi4OLVu2hKGhIaZPn446deoAAK5du4bly5fDzc0Nf/31F6pUqVJizRIRqUNycjIuXvwZ/7sPAgBw/bon+vRpCVdXQKEAZs/mXVtEZZHKl7oGDhyIW7duISwsDCYmJpJ96enp6NChA2rVqoXVq1eXSKOawktdRGXb4cOHEfHa9aujR8fhxx9NGXSItJjaL3UdPHgQW7duzRd6AMDU1BTTp0/HV199VbxuiYhKmFKpxPTp0yU1U9NK2L17CNfkIdIhKq/c/O+//75xDk/16tXx33//qaMnIiK1io2NzRd6vLy8sHv3EHHBQiLSDSoHn0qVKuHatWuF7o+Ojn7jCslERJqU9/iJoKAVWLdunWRfWNhkVK1aFb6+L1dhJiLdoPKlrm7duuHHH3/E0aNH8622nJiYiPHjx6Nbt27q7o+IqFjmzUuHp2cQ0tNf1q5d+xCXL3tg9v8ev9WlCy9xEekalSc3P336FC1atEBCQgK++eYb1K5dG4Ig4Pr169i0aRMcHBzw559/oly5ciXdc4ni5GYi7RcZGYlDhw5JauHhoxEebsW1eYjKKLVPbra1tcWZM2cwYcIEbNmyBUlJSQAAGxsb9OnTB7NmzdL60ENE2k0QBEybNk1Se/HCBHp64zF6NJCRwctaRLquWCs3C4KAx48fA8h90KhCoVB7Y3LhiA+RdkpISMDKlSsltW3bvsC1a3VgYQE8eyZTY0SkESWycnMehUIBe3v7YjdHRKROW7duRUxMjKQ2d+5EdOhggPv3cx8sSkQEFDP4EBGVBllZWZg1a5akdvlyfRw40ANbtnDiMhHlx+BDRFrpypUr2LVrl6RWp85whIXZMfQQUaEYfIhI60ybNg2vT0+cO9cfz54BX3whU1NEpBUYfIhIazx9+hSLFi2S1I4f/wxnzjTFmDEyNUVEWkWl4PP6f2jeZCRnERJRCVi27AAePz4rqQUE+CIjwxhubsDMmTI1RkRaRaXgs2DBApVOplAoGHyISK0KerjovXvO2LDhW9jbA5aWXJuHiFSnUvC5e/duSfdBRJTP7du3sWHDBkltz54BSEtzwo4dnMBMREVX7Dk+mZmZuHv3LmrUqAEDA04VIiL1WrJkCZ48eSKpNWw4Gf7+ZWfBVCLSPJWfzp4nLS0NAwcOhJmZGd5//33cv38fAPD9998jMDBQ7Q0SkW5JTU3F1KlTJaEnIqI1GjXyR9euDD1E9G6KHHz8/PwQFRWF8PBwmJiYiHUPDw9s3bpVrc0RkW45deoU5s6dK6kdP+4Db+82vKxFRGpR5GtUu3fvxtatW9GyZUvJM7ref/993L59W63NEZFuKOjhoikploiMHMMnqRORWhV5xOfx48cFPqcrNTW1TD2slIg04+HDh/lCT/XqvREZOQZt2wLu7kBoqEzNEVGZU+Tg07RpU+zfv1/czgs7q1evhpubm/o6I6Iyb8OGDfjll18ktUOHJqFv3/cQEQEcPw5ERgKcPkhE6lLkS12zZs1Cx44dce3aNWRnZ2PhwoW4du0aIiIicOLEiZLokYjKmMzMTAQEBEhqt241wrlzXTB79suar29u6OE6PUSkLgrh9QfeqOD27dsIDAxEVFQUnj9/jsaNG2P8+PGoX79+SfSoUSkpKbC2tkZycjKsrKzkboeozLl48SJCX7t2tXr1CPzzT3m4uYFzeoioWFT9/C7WAjw1atTINzxNRPQ2U6dOzVcLDPTHmDG5l7U4skNEJU2l4JOSkqLyCTlKQkSve/LkCZYsWSKp7d7dFbduNcTWrVyBmYg0R6XgY2Njo/IdWzk5Oe/UEBGVLaGhobh48aKk9sEHfggLM8LGjQw9RKRZKgWf48ePi3+OjY2Fr68vvLy8xLu4IiMjsW7dunyTFYlId+Xk5GDGjBmS2q1bNXD79jfw9we6d5epMSLSaUWe3PzJJ59g0KBB6N27t6S+adMmrFq1CuHh4ersT+M4uZno3f3999/YvHmzpObqOhiLFjnC15ejPESkfqp+fhc5+JiZmSEqKgq1atWS1P/++280bNgQaWlpxeu4lGDwIXo38+fPx7NnzyS1hg0n8zlbRFSiVP38LvIChk5OTgXe0bV69Wo4OTkV9XREVEY8f/4cU6dOlYSeo0c/wZQp/pg9m6GHiEqHIt/OvmDBAvTs2RMHDhxAixYtAABnz57FzZs3sXPnTrU3SESlX3h4eL4FTIOCfkTDhuZwc+Nt6kRUehQ5+Hz66ae4efMmli1bhpiYGABA586dMXToUI74EOmYgh8uWh7z54/4334uSEhEpUuxFjCsUqUKZs2ape5eiEiL/PPPP1izZo2k9s033+Dq1Rr4/ffc0MORHiIqbYoVfJKSkrBmzRpcv34dAPD+++9jwIABsLa2VmtzRFQ6jRsXDHPz+5LaTz/9BD09PdSowbu2iKj0KvJdXefOnYOnpydMTU3RvHlzAMBff/2F9PR0HDp0CI0bNy6RRjWFd3URFe7FixeY/epTRAGcOdMMd+58iv9d+SYikkWJ3c7eqlUr1KxZE7/88gsMDHIHjLKzszFo0CDcuXMHJ0+efLfOZcbgQ1Swc+fOYf/+/ZLaqVMjkZZmy7V5iEh2JRZ8TE1NcfHiRdSuXVtSv3btGpo2bcp1fIjKmNBQ4OJF6cNFlUoFpk6dLFNHRET5ldg6PlZWVrh//36+elxcHCwtLYt6OiIqxR4/fpwv9Ozb1wNNmjD0EJF2KvLk5i+//BIDBw7E3Llz4e7uDgA4ffo0xo4dm+8xFkSkvXbt2oUrV65Iatu2TUBAgCEvaxGR1ipy8Jk7dy4UCgX69euH7OxsAIChoSGGDRuGwMBAtTdIRJqVnZ2NmTNnSmqPHrmiQ4ev4O8vU1NERGpS5Dk+edLS0nD79m0AQI0aNWBmZqbWxuTCOT6ky3799Tru3t0mqX333XdwcHCQqSMiItWo+vldrHV8gNyHldavX7+4P05EpcyMGbORk/NCUps8eTIUitznbIWGAoGB4B1cRKTVVA4+AwYMUOm4tWvXFrsZItK8HTtScPXqAkntwgVP7NnTUlILDAQiI3O/M/gQkbZSOfiEhISgatWqaNSoEYp5dYyISpnDhw/j6lXpw7R27RqH6dNN8x3r6/tyxIeISFupPMfH29sbmzdvRtWqVfHtt9/im2++Qbly5Uq6P43jHB/SBQU9XNTUtCLGjRsqU0dERO9G7ev4LF26FPHx8Rg3bhz27t0LJycnfPHFFwgLC+MIEJEWuXfvXr7QU7Nmf4YeItIJxb6r6969ewgJCcGvv/6K7OxsXL16FRYWFuruT+M44kNlWVDQKqSnx0tqU6f+hJYt9RARUcgPERFpgRK/q0tPTw8KhQKCICAnJ6e4pyEiDUhPT0dQUJCk5u7ujvT0/0PLlpy3Q0S6o0jBJyMjA7t27cLatWvxxx9/oFOnTliyZAk6dOgAPb0iP/2CiEpYaCiwadOfqFMnTFJ///1R+L//swbAO7SISLeoHHyGDx+OLVu2wMnJCQMGDMDmzZtRoUKFkuyNiN7Bnj0CLl2ahjp1XtaMjY1Rt64vAgMBIyOGHiLSPSrP8dHT04OzszMaNWokLmhWkF27dqmtOTlwjg+VBY8ePcKKFSsktV69eqFu3bpwd89dj8fNDZzXQ0Rlhtrn+PTr1++NgYeISodt27bh+vXrktrEiRNhYJD7rzvX4yEiXVakBQyJqPTKysrCrFmzJLV69eqhZ8+eAKSPnOBIDxHpqmLf1UVEpUd0dDR27twpqQ0fPhx2dnbiNh85QUTE4EOktUJDgXHjgC++mAF9femSEv7+/vmO5yUuIqJ3WMCwrOLkZtIWjRsnoWvXhZLap59+imbNmsnUERGRfEp8AUMiks/BgwfRtesZSW38+PEwMTGRqSMiIu3A4EOkRZRKJaZPny6pmZs74ccfB8jUERGRduFyy0Ra4s6dO/lCz4ABA9459ISGAu7uud+JiMo6jvgQaYGlS5fi33//ldQmT56slrW1eLcXEekSrRvxycjIQMOGDaFQKHDp0iXJvsuXL6NVq1YwMTGBk5NTvocyEmmbtLQ0TJ06VRJ6bt/+GI0a+attQVFf39xVnHm3FxHpAq0b8Rk3bhwcHR0RFRUlqaekpKB9+/bw8PDAihUrcOXKFQwYMAA2NjYYMmSITN0SFd/p06dx5MgRSW3MmDGwtLRU6+t06cKRHiLSHVoVfA4cOIBDhw5h586dOHDggGTfxo0bkZmZibVr18LIyAjvv/8+Ll26hPnz5zP4kFYRBAHTpk2T1CwsLODj4yNTR0REZYfWBJ9Hjx5h8ODB2L17N8zMzPLtj4yMxMcffwwjIyOx5unpidmzZ+Pp06ewtbUt8LwZGRnIyMgQt1NSUtTfPJEKch8pEQ9Pz1WS+ldffQVXV1eZuiIiKlu0Yo6PIAjw8vLC0KFD0bRp0wKPSUhIQMWKFSW1vO2EhIRCzx0QEABra2vxy8nJSX2NExXBoUMb84WeSZMmMfQQEamRrMHH19cXCoXijV8xMTFYvHgxnj17Bj8/P7X34Ofnh+TkZPErLi5O7a9BVJjQUKBVq0xMnToVdna3xHq5cg3h7+8PfX39dz4/b1UnInpJ1ktdPj4+8PLyeuMx1atXx7FjxxAZGQljY2PJvqZNm+Lrr7/GunXr4ODggEePHkn25207ODgUen5jY+N85yXSlF9/vQQPjz2S2ogRI1C+fHm1nJ+3qhMRSckafOzs7CRPjy7MokWLMGPGDHH74cOH8PT0xNatW9GiRQsAgJubGyZOnIisrCwYGhoCAA4fPgxXV9dC5/cQySF3Lg/g6TkV9etL9xX0cNF3wQeTEhFJaeVDSmNjY1GtWjVcvHgRDRs2BAAkJyfD1dUV7du3x/jx4xEdHY0BAwZgwYIFRbqriw8ppZIUGgp4e/+HQYMWS+qdO3dG48aNZeqKiEj7qfr5rRWTm1VhbW2NQ4cO4e7du2jSpAl8fHwwefJk3spOpUpo6N58ocfPz6/Q0MM5OkRE6qWVIz4liSM+VBJycnIkl2sBoFq1aujXrx+Al5e/fH2lc3Hc3XPn6Li5ARERmuyYiEi76NyID1FptX79zXyhZ9CgQbCx6SeO5rw6CflVfJwEEZF6ac0ChkTaaP/+/bhz55yklvdw0V69XoadwiYh83ESRETqxeBDVAJevHiB2bNnS2qVKrXDkCGtxO1Xww4DDhGRZjD4EKnZtWvXsH37dklt/PjxMDExkdQYdoiINI9zfIjUIPfuKwFBQSsloadZs2bw9/fHoUMmxb47i3d2ERGpD4MP0TvICyXTpj2Bp+c0pKe/fC5cRMR3yM7+FEDBk5dVDTSFTXwmIqKiY/AhegeBgYCxcTg6d14i1iwtLREW9hMOHXIQw0pBd2epGmh4ZxcRkfpwHZ/XcB0fepu828/HjctCVNQsyb68FZgLW5enoPO86RgiIlKNqp/fDD6vYfCht6ldG8jKuoN+/dZL6j4+PrCwsJCpKyIi3abq5zfv6iIqotatN8HR8aa4XbduXfTq1UvGjoiISFUMPkRvkXdJasyYFFy9ugCOji/3nTnjhUaNqsrXHBERFQmDD9FbBAYCOTlncfXqAbGmUCgQFjYBEREGSEriHB0iIm3B4EP0Bjk5OejYcQ6Uygyx9vffn+DLLz9Cw4YFP2aCiIhKLwYfokI8ePAAq1evltROnPgBx4/b4O7d3Kelc6SHiEi7MPgQFWDPnj24dOmSuO3s7AwvLy/s3avAixcc5SEi0lYMPkSvSE9PR1BQkKT21VdfwdXVFQCfr0VEpO0YfIj+58qVK9i1a5ek5uvrC2NjY5k6IiIidWPwIZ2We6u6gC5dliEj41+x7ubmhvbt28vYGRERlQQGH9Jpixf/C0/Ppch4edMWhg0bBnt7e/maIiKiEsOHlJLOOnLkCD76aKm4bWtri8mTJxcaelR9mjoREZVeHPEhnZOZmYmAgABJrVu3bmjQoMEbf+7Vp6lzgjMRkXZi8CGdkPfYiWHDbuLOnU2SfWPHjoWZmdlbz+HrywULiYi0HZ/O/ho+nb1scncX8N57v6JatVix9sEHH6B79+7yNUVERGqj6uc35/hQmfTqfJzk5GR4ek6ThJ4BAwYw9BAR6SBe6qIyKW8+zubNkbh48ZBYNzAwgK+vL/T19WXsjoiI5MLgQ2XSuHE5uHAhAPr6OWItJqY9Nm92k7ErIiKSG4MPlTmbNsXh5s21eHVQ58SJURg1ylq+poiIqFRg8KEyIzQU2L9/Fxwdr4i16tWr45tvvoFCoZCxMyIiKi0YfKhMSE1NxcWLc+Ho+LJWo8bX+OabmvI1RUREpQ6DD2m9S5cuYc+ePZKan58fjIyMZOqIiIhKKwYf0kp5Dxft3HkRMjOTxPqdOx+hZ89PwMxDREQFYfAhrbRkSSI8PZcjM/NlzdvbGxUqVJCvKSIiKvUYfEjrhIWF4cMP/xS37ezsMGzYME5gJiKit2LwIa2RkZGBwMBASa1nz56oV6+eTB0REZG24SMrqNR69bETN27cyBd6xo0bx9BDRERFwhEfKpVCQ4GvvwaePxdw7FgwLl6ME/c1atQIXbp0kbE7IiLSVgw+VCoFBgIGBk8xZcoiSX3QoEGoXLmyTF0REZG2Y/ChUiP3FnXA1xfw8voD8fFHxX0mJiYYO3Ys9PR4dZaIiIqPwYdKjcBA4OzZbFy8OFNS79ixI5o3by5TV0REVJbwf59JFq9OXM4zYsQ9/PSTNPSMGTOGoYeIiNSGIz4ki8BAIDIy93uXLsC2bdtw8+Z1cX+tWrXQp08fGTskIqKyiMGHZOHrmxt6fHyeY+rUeZJ9ffv2RfXq1WXqjIiIyjIGH9K4vEnMgwZdQHT0Xsm+CRMmwNDQUKbOiIiorGPwIY0KDQW++UaJIUMWIC7uuVhv06YNWrduLWNnRESkCzi5WYcVNMG4pF9v1KgE+PhMh6Xly9Dz/fffM/QQEZFGMPjosFcnGJeUV8PVnj370b//SnFfpUqVMHnyZJQrV67kGiAiInoFg48O8/UF3Nxyv7+rwkaPAgOBCxde4OLFqXB2PifWe/XqhSFDhvCJ6kREpFEKQRAEuZsoTVJSUmBtbY3k5GRYWVnJ3Y7WcHfPHT1ycwMiInJroaHAokXX0arVNsmx48ePh4mJiQxdEhFRWaXq5zdHfEglb5sP9ProkSAICA9fJQk9TZs2hb+/P0MPERHJhiM+r+GIT8EKGtEpzH///YfFixdLakOGDEGlSpVKsEMiItJlqn5+83Z2UknegoNvmw8UHh6OEydOiNsWFhYYPXo0Hy5KRESlAoMPqaRLl9yvwmRlZWHWrFmSWqdOndCkSZMS7oyIiEh1DD70zu7cuYP169dLaj4+PrCwsJCpIyIiooIx+FCR5T1ywtcXeP58E27evCnuq1OnDr744gsZuyMiIiocgw8VWWAgEB39DBcvzpfU+/fvDxcXF3maIiIiUgGDDxXZwIF/4Z9/fpfUJk6cCAMD/uNERESlGz+pSGVKpRJz585Fenq6WGvXrh1atWolY1dERESqY/AhlTx8+BC//PKLpDZy5EjY2trK1BEREVHRcXEVkihohebQ0FBJ6HFycsLkyZMZeoiISOtwxIckXn1i+//9XzqCgoIk+7/66iu4urrK1B0REdG7YfAhibwVmr/7LhpBQTtf2+cLY2NjmTojIiJ6dww+JNG5s4B//lmO2NjHYq1ly5bw9PSUsSsiIiL1YPAh0b///oulS5dKakOHDkXFihVl6oiIiEi9tGpy8/79+9GiRQuYmprC1tYW3bp1k+y/f/8+PvvsM5iZmcHe3h5jx45Fdna2PM1qmaNHj0pCj42NDX766SeGHiIiKlO0ZsRn586dGDx4MGbNmoV27dohOzsb0dHR4v6cnBx89tlncHBwQEREBOLj49GvXz8YGhrme3gmvVTQw0W7du2Khg0bytMQERFRCVIIgiDI3cTbZGdnw8XFBVOnTsXAgQMLPObAgQPo1KkTHj58KI5SrFixAuPHj8fjx49hZGSk0mulpKTA2toaycnJsLKyUtvvUBrdunULGzdulNR+/PFHmJuby9QRERFR8aj6+a0Vl7ouXLiABw8eQE9PD40aNUKlSpXQsWNHyYhPZGQk6tevL7k04+npiZSUFFy9erXQc2dkZCAlJUXyVdYJgoD169dLQk/9+vXh7+/P0ENERGWaVgSfO3fuAACmTJmCSZMmYd++fbC1tUWbNm3w33//AQASEhLyzUfJ205ISCj03AEBAbC2tha/nJycSui3kFfewoQ7diRj2rRp4t8pAAwYMAA9evSQsTsiIiLNkDX4+Pr6QqFQvPErJiYGSqUSQO6DMHv27IkmTZogODgYCoUC27dvf6ce/Pz8kJycLH7FxcWp41crdQIDAUH4E1ev/izWDAwMMGnSpDIb9oiIiF4n6+RmHx8feHl5vfGY6tWrIz4+HgBQt25dsW5sbIzq1avj/v37AAAHBwecPXtW8rOPHj0S9xXG2Ni4zC/Kl5OTgw4dAiEIL+9wa9++Pdzc3GTsioiISPNkDT52dnaws7N763FNmjSBsbExbty4gY8++ghA7t1IsbGxqFq1KgDAzc0NM2fORGJiIuzt7QEAhw8fhpWVlSQwabvQ0NzRG19foEuXtx8fFxeHtWvXSmonToyCv791CXVIRERUemnF7exWVlYYOnQo/P394eTkhKpVq2LOnDkAgF69egHIHcGoW7cu+vbti6CgICQkJGDSpEnw9vYuUyM6rz5L623B57fffsPly5fFbQuLati5sy98fRUl3CUREVHppBXBBwDmzJkDAwMD9O3bF+np6WjRogWOHTsmPiFcX18f+/btw7Bhw+Dm5gZzc3P0798f06ZNk7lz9cp7lpavb+HHpKWlicEwT58+fVCrVi34+JRwg0RERKWYVqzjo0navo5PVFQUdu/eLan5+fmpvI4RERGRNlL181trRnzozQRBwOLFi/H06VOxlpPzIY4c8UCjRqrNByIiIirrtGIdH12XtwZPaGjB+xMTEzFt2jRJ6FmyZDgWLPAQ5wMRERERR3y0wpsmNB86dAiRkZHitolJBdSuPRy1ainQti1w/Pib5wMRERHpEgYfLVDQhObMzEwEBARIjtuxowcsLetj/Higa1cNN0lERKQFGHy0QJcu0pGeGzduYMuWLZJj6tcfh7AwU47uEBERvQGDjxYRBAEhISHiatUA0LBhQ3T93/AOH7dFRET0Zgw+WiIpKQkLFy6U1AYNGoTKlSvL1BEREZH2YfDRAqdPn8aRI0fEbX19Yxw4MBaNGumDuYeIiEh1DD6lWHZ2NmbNmoVX15g8f74j/v23ucqPrSAiIqKXGHxKqXv37iEkJERSmzdvNBwdrRAU9PbHVhAREVF+DD6l0Pbt23Ht2jVxu2bNmrC0/Br16r18KjtHeoiIiIqOwacUSU1Nxdy5cyW1vn37Ijq6ujjCw8BDRERUfAw+pcSFCxewd+9eSW3ChAkwNDTEN98UvnIzERERqY7BR2ZKpRI///wznj17JtY+/vhjtG3bVtwuaOVmIiIiKjoGHxlt2ZKAGzdWSmojRoxA+fLlJTXO6SEiIlIPBh+ZHDhwADdunBW3HRwcMGTIECgUChm7IiIiKtsYfDQsIyMDgYGBkpqLSy/0719Xpo6IiIh0B4OPBl2/fh3btm2T1MaPHw8TExOZOiIiItItDD4asnfvXly4cEHcbtq0KT777DMZOyIiItI9enI3oCuuX78u/nnIkCFi6AkNBdzdc78TERFRyVIIrz4IipCSkgJra2skJyfDyspKbeeNjY3Fw4cP0bJlS+jpvcyb7u65a/S4uQEREWp7OSIiIp2i6uc3L3VpiIuLC1xcXPLV27YFrlzJ/U5EREQli5e6ZHb8OPD8ee53IiIiKlkMPjLz9c29zMVVmYmIiEoeL3XJjKsyExERaQ5HfIiIiEhnMPgQERGRzmDw0RCu10NERCQ/Bh8NCQzMXa/ntcd0ERERkQYx+GgI794iIiKSH+/q0hDevUVERCQ/jvgQERGRzmDwISIiIp3B4ENEREQ6g8GHiIiIdAaDDxEREekMBh8iIiLSGQw+REREpDMYfIiIiEhnMPgQERGRzmDwISIiIp3B4ENEREQ6g8GHiIiIdAaDDxEREekMPp39NYIgAABSUlJk7oSIiIhUlfe5nfc5XhgGn9c8e/YMAODk5CRzJ0RERFRUz549g7W1daH7FcLbopGOUSqVePjwISwtLaFQKORup0SlpKTAyckJcXFxsLKykrsdegXfm9KJ70vpxPel9NLkeyMIAp49ewZHR0fo6RU+k4cjPq/R09NDlSpV5G5Do6ysrPgfi1KK703pxPeldOL7Unpp6r1500hPHk5uJiIiIp3B4ENEREQ6g8FHhxkbG8Pf3x/GxsZyt0Kv4XtTOvF9KZ34vpRepfG94eRmIiIi0hkc8SEiIiKdweBDREREOoPBh4iIiHQGgw8RERHpDAYfHbZ//360aNECpqamsLW1Rbdu3ST779+/j88++wxmZmawt7fH2LFjkZ2dLU+zOiYjIwMNGzaEQqHApUuXJPsuX76MVq1awcTEBE5OTggKCpKnSR0SGxuLgQMHolq1ajA1NUWNGjXg7++PzMxMyXF8b+SxdOlSuLi4wMTEBC1atMDZs2flbkmnBAQEoFmzZrC0tIS9vT26deuGGzduSI558eIFvL29Ub58eVhYWKBnz5549OiRLP0y+OionTt3om/fvvj2228RFRWF06dPo0+fPuL+nJwcfPbZZ8jMzERERATWrVuHkJAQTJ48Wcaudce4cePg6OiYr56SkoL27dujatWqOH/+PObMmYMpU6Zg1apVMnSpO2JiYqBUKrFy5UpcvXoVCxYswIoVKzBhwgTxGL438ti6dSvGjBkDf39/XLhwAQ0aNICnpycSExPlbk1nnDhxAt7e3vjzzz9x+PBhZGVloX379khNTRWPGT16NPbu3Yvt27fjxIkTePjwIXr06CFPwwLpnKysLKFy5crC6tWrCz3m999/F/T09ISEhASxtnz5csHKykrIyMjQRJs66/fffxdq164tXL16VQAgXLx4Udy3bNkywdbWVvIejB8/XnB1dZWhU90WFBQkVKtWTdzmeyOP5s2bC97e3uJ2Tk6O4OjoKAQEBMjYlW5LTEwUAAgnTpwQBEEQkpKSBENDQ2H79u3iMdevXxcACJGRkRrvjyM+OujChQt48OAB9PT00KhRI1SqVAkdO3ZEdHS0eExkZCTq16+PihUrijVPT0+kpKTg6tWrcrStEx49eoTBgwdj/fr1MDMzy7c/MjISH3/8MYyMjMSap6cnbty4gadPn2qyVZ2XnJyMcuXKidt8bzQvMzMT58+fh4eHh1jT09ODh4cHIiMjZexMtyUnJwOA+O/H+fPnkZWVJXmfateuDWdnZ1neJwYfHXTnzh0AwJQpUzBp0iTs27cPtra2aNOmDf777z8AQEJCgiT0ABC3ExISNNuwjhAEAV5eXhg6dCiaNm1a4DF8X0qHW7duYfHixfjuu+/EGt8bzfv333+Rk5NT4N87/87loVQqMWrUKHz44YeoV68egNx//o2MjGBjYyM5Vq73icGnDPH19YVCoXjjV95cBQCYOHEievbsiSZNmiA4OBgKhQLbt2+X+bcoe1R9XxYvXoxnz57Bz89P7pZ1hqrvzasePHiADh06oFevXhg8eLBMnROVTt7e3oiOjsaWLVvkbqVQBnI3QOrj4+MDLy+vNx5TvXp1xMfHAwDq1q0r1o2NjVG9enXcv38fAODg4JDvzoi8GfgODg5q7LrsU/V9OXbsGCIjI/M906Zp06b4+uuvsW7dOjg4OOS7E4LvS/Gp+t7kefjwIdq2bQt3d/d8k5b53mhehQoVoK+vX+DfO//ONW/EiBHYt28fTp48iSpVqoh1BwcHZGZmIikpSTLqI9v7pPFZRSS75ORkwdjYWDK5OTMzU7C3txdWrlwpCMLLyc2PHj0Sj1m5cqVgZWUlvHjxQuM964J79+4JV65cEb/CwsIEAMKOHTuEuLg4QRBeTqDNzMwUf87Pz48TaDXgn3/+EWrVqiV89dVXQnZ2dr79fG/k0bx5c2HEiBHidk5OjlC5cmVObtYgpVIpeHt7C46OjsLff/+db3/e5OYdO3aItZiYGNkmNzP46KgffvhBqFy5shAWFibExMQIAwcOFOzt7YX//vtPEARByM7OFurVqye0b99euHTpknDw4EHBzs5O8PPzk7lz3XH37t18d3UlJSUJFStWFPr27StER0cLW7ZsEczMzMTASiXjn3/+EWrWrCl88sknwj///CPEx8eLX3n43shjy5YtgrGxsRASEiJcu3ZNGDJkiGBjYyO5I5VK1rBhwwRra2shPDxc8u9GWlqaeMzQoUMFZ2dn4dixY8K5c+cENzc3wc3NTZZ+GXx0VGZmpuDj4yPY29sLlpaWgoeHhxAdHS05JjY2VujYsaNgamoqVKhQQfDx8RGysrJk6lj3FBR8BEEQoqKihI8++kgwNjYWKleuLAQGBsrToA4JDg4WABT49Sq+N/JYvHix4OzsLBgZGQnNmzcX/vzzT7lb0imF/bsRHBwsHpOeni4MHz5csLW1FczMzITu3btL/sdBkxT/a5qIiIiozONdXURERKQzGHyIiIhIZzD4EBERkc5g8CEiIiKdweBDREREOoPBh4iIiHQGgw8RERHpDAYfIqISEh4eDoVCgaSkJLlbIaL/YfAhIq01ZcoUNGzYUO42iEiLMPgQUZmXlZUldwtEVEow+BCRbJRKJQICAlCtWjWYmpqiQYMG2LFjB4CXl4mOHj2Kpk2bwszMDO7u7rhx4wYAICQkBFOnTkVUVBQUCgUUCgVCQkIAAAqFAsuXL0eXLl1gbm6OmTNnvrGPvNcKCwtDo0aNYGpqinbt2iExMREHDhxAnTp1YGVlhT59+iAtLU38uYyMDIwcORL29vYwMTHBRx99hL/++qtk/rKISD1keUIYEZEgCDNmzBBq164tHDx4ULh9+7YQHBwsGBsbC+Hh4cLx48cFAEKLFi2E8PBw4erVq0KrVq0Ed3d3QRAEIS0tTfDx8RHef//9fE+DBiDY29sLa9euFW7fvi3cu3fvjX3kvVbLli2FP/74Q7hw4YJQs2ZNoXXr1kL79u2FCxcuCCdPnhTKly8vefDoyJEjBUdHR+H3338Xrl69KvTv31+wtbUVnjx5Ijnv06dPS+YvkIiKjMGHiGTx4sULwczMTIiIiJDUBw4cKPTu3VsMDUeOHBH37d+/XwAgpKenC4IgCP7+/kKDBg3ynRuAMGrUKJV7Kei1AgICBADC7du3xdp3330neHp6CoIgCM+fPxcMDQ2FjRs3ivszMzMFR0dHISgoSHJeBh+i0sNArpEmItJtt27dQlpaGv7v//5PUs/MzESjRo3E7Q8++ED8c6VKlQAAiYmJcHZ2fuP5mzZtWuSeXn2tihUrwszMDNWrV5fUzp49CwC4ffs2srKy8OGHH4r7DQ0N0bx5c1y/fr3Ir01EmsHgQ0SyeP78OQBg//79qFy5smSfsbExbt++DSA3TORRKBQAcucGvY25uXmRe3r9tV7dzqup8tpEVHpxcjMRyaJu3bowNjbG/fv3UbNmTcmXk5OTSucwMjJCTk5OCXdasBo1asDIyAinT58Wa1lZWfjrr79Qt25dWXoiorfjiA8RycLS0hI//vgjRo8eDaVSiY8++gjJyck4ffo0rKysULVq1beew8XFBXfv3sWlS5dQpUoVWFpawtjYWAPd544oDRs2DGPHjkW5cuXg7OyMoKAgpKWlYeDAgRrpgYiKjsGHiGQzffp02NnZISAgAHfu3IGNjQ0aN26MCRMmqHRJqWfPnti1axfatm2LpKQkBAcHw8vLq+Qb/5/AwEAolUr07dsXz549Q9OmTREWFgZbW1uN9UBERaMQBEGQuwkiIiIiTeAcHyIiItIZDD5EVOYNHToUFhYWBX4NHTpU7vaISIN4qYuIyrzExESkpKQUuM/Kygr29vYa7oiI5MLgQ0RERDqDl7qIiIhIZzD4EBERkc5g8CEiIiKdweBDREREOoPBh4iIiHQGgw8RERHpDAYfIiIi0hkMPkRERKQz/h8ZE1xgsXhGfQAAAABJRU5ErkJggg==", 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", 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", 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", + "image/png": 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", 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", 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", 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", 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", 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" ] @@ -478,9 +472,9 @@ ], "source": [ "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(poly_surr, data_training)\n", - "surrogate_parity(poly_surr, data_training)\n", - "surrogate_residual(poly_surr, data_training)" + "surrogate_scatter2D(poly_surr, data_training, filename=\"pysmo_poly_train_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_training, filename=\"pysmo_poly_train_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_training, filename=\"pysmo_poly_train_residual.pdf\")" ] }, { @@ -499,7 +493,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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P0ddTl5qaKpKSkvR+paammnTdogwdOlR07dpVJCcni7t374rTp0+L+fPni0qVKolu3bqJ3Nxco67z7bffCrlcXip9LOx5nzUREZUPqs++jz9+KGxslAIQwsZGKT7++GGJfvYZ+vxWZ5ElKtLT07Fu3Tq0adNGKhmwY8cO1KpVCzt37kTXrl0hhEBYWBgWLVoET09PAEBcXBzCwsI0rhUeHo6YmBgABcVPT58+jRkzZkjnbWxsEBYWhri4OADA6dOnkZubq3Gd+vXro3r16oiLi0Pr1q0RFxeHxo0bw9fXV+N9xo4di4sXL6JZs2al8lyMlZaWhhUrVhTZLjo6Gl5eXiX+/o6OjvDz8wMAVK1aFSEhIWjdujU6deqENWvWYOTIkfjkk0/w7bff4q+//oKnpyd69uyJRYsWoVKlSjhy5AiGDx8O4FnS/Jw5czB37lx8//33WLZsGa5evQpXV1d07NgRS5cuRZUqVUr8PoiIyHKoPvsUCjcsXRoDIQo+P5RKGaZMccfff38DuTyr1D77dLGY6UgAmDZtGlxdXeHl5YXbt2/jv//9r3Tur7/+wq1bt7B582Z89913WLNmDU6fPq0xZZmSkqIRGAGAr68vMjMz8c8//yA1NRX5+fk626SkpEjXcHBw0JoKK9xG1zVU5/TJzs5GZmamxldpMHabo7LcDqljx45o0qQJtm7dCqAg+F2+fDkuXryItWvX4tChQ5g6dSoAoE2bNli6dCnc3d2RnJyM5ORkTJ48GQCQm5uL9957D+fOncO2bdtw8+ZNDBs2rMzug4iIyifVZ1p6uheE0Ax/hLBBerqnRruyYNYgbPr06VJ+kL6vK1euSO2nTJmCs2fPYv/+/bC1tcWQIUMghABQUNk8Ozsb3333Hdq2bYtXX30Vq1evxuHDh3H16lVz3WKxLFiwAHK5XPoKDAw0d5fKVP369XHz5k0AQExMDDp06ICaNWuiY8eOeP/997Fp0yYABXW75HI5ZDIZ/Pz84Ofnh0qVKgEARowYgW7duqFWrVpo3bo1li9fjj179uDRo0fmui0iIipHPD3TIJNp5iDLZEp4eqaXeV/MOh05adKkIkcpatWqJX3v7e0Nb29v1K1bFw0aNEBgYCB+//13hIaGwt/fH3Z2dqhbt67UvkGDBgAKVirWq1cPfn5+WqsY7927B3d3dzg7O8PW1ha2trY626imz/z8/JCTk4OMjAyN0bDCbQqvqFRdU9VGlxkzZmDixInSz5mZmVYViAkhpOnFAwcOYMGCBbhy5QoyMzORl5eHp0+f4smTJwZ3CDh9+jTmzp2Lc+fO4eHDh1Ky/+3bt9GwYcMyuQ8iIiq/5PIs9Oy5Ezt29IAQNpDJlOjZcyfk8qwy74tZgzAfHx/4+PiY9FrVh2t2djYA4OWXX0ZeXh4SEhIQHBwMALh27RoAoEaNGgCA0NBQ7N69W+M6sbGxCA0NBVAwwtK8eXMcPHgQvXv3lt7n4MGDiI6OBgA0b94c9vb2OHjwICIiIgAAV69exe3bt6XrhIaGYv78+bh//76UixQbGwt3d3eDgYCjoyMcHR1Neh4VweXLlxEUFISbN2+iR48eGDt2LObPnw9PT08cO3YMUVFRyMnJ0RuEPX78GOHh4QgPD8e6devg4+OD27dvIzw8vEyHl4mIyLzS0tK0/t5PTU2Vvg8JOYvg4BtIT/eEp2e6WQIwwEL2jjxx4gROnTqFV155BZUrV0ZCQgJmzZqF4OBgKfAJCwtDSEgIRowYgaVLl0KpVGLcuHHo3LmzNDo2ZswYrFixAlOnTsWIESNw6NAhbNq0Cbt27ZLea+LEiRg6dChatGiBli1bYunSpXj8+LGUCC6XyxEVFYWJEyfC09MT7u7uePPNNxEaGorWrVsDALp06YKGDRti8ODBWLRoEVJSUvDOO+9g3LhxVh1kGXLo0CH8+eefmDBhAk6fPg2lUonFixdLG5OrpiJVHBwctLYRunLlCtLS0rBw4UJpBPGPP/4omxsgIqJywdjFZ3J5ltmCLxWLSMx3cXHB1q1b0alTJ9SrVw9RUVF48cUXcfToUSmosbGxwY4dO+Dt7Y127dqhe/fuaNCgATZs2CBdJygoCLt27UJsbCyaNGmCxYsX4+uvv0Z4eLjUJjIyEh9//DFmz56Npk2bIj4+Hnv37tVItF+yZAl69OiBiIgItGvXDn5+flJCOQDY2tpi586dsLW1RWhoKAYNGoQhQ4bg3XffLYOnVf5lZ2cjJSUFf//9N86cOYMPPvgAr732Gnr06IEhQ4agdu3ayM3Nxaeffoq//voL33//PVatWqVxjZo1a+LRo0c4ePAgUlNT8eTJE1SvXh0ODg7S67Zv34733nvPTHdJRETmYEkzHzKhymynciczMxNyuRwKhQLu7u4a554+fYrExEQEBQXBycmpWNdNTk7Gl19+WWS70aNHw9/fv1jXLsqwYcOwdu1aAICdnR0qV66MJk2a4PXXX8fQoUOlka8lS5bgo48+QkZGBtq1a4eBAwdiyJAhePjwoZSLN3bsWGzevBlpaWlSiYoff/wRb7/9NpKTkxESEoIZM2agV69eOHv2LJo2bWpSn5/nWRMRUdkq/BmnULghPd0Lnp5pRo18lcRnn6HPb3UMwsqx0grCzF0nzNIwCCMishzqQdiZM820EvBDQs4afH1JfPYZG4RZRE4YlSwvLy9ER0cbHLJ1cHBgAEZERBZLoXCTAjCgoBbYjh09EBx8A3J5Fvr06QNvb2+N15T1Zx+DMCvFAIuIiCoyQ0VZ5fIseHt7l3jKTXExCCMiIiKLoKv0hDoHBwfpe1VRVvVAzFxFWfVhEEZERETlnrH5zP369QNQvoqy6sMgjIiIiMq9wiNg+lY9qnZeAQwXZVUfNTMXBmFERERkUQytepTL5Raz+IxBGBEREVmMolY9Apaz+MwiKuYTERERAYZXPVoajoQRERGRWVW0VY/GYhBGFcqRI0fQoUMHje2NilKzZk3ExMQgJiamVPtGRETajF31GBkZCcAyVj0ai9ORVKaGDRsGmUyGMWPGaJ0bN24cZDIZhg0bVvYdIyIiszB2w+3c3Fzp+5CQs4iJWYqhQ9cgJmZpkVsRlVcMwqjMBQYGYsOGDfjnn3+kY0+fPsX69etRvXp1M/aMiIjMTaFwQ2JiTSgUbhrH7ew0J+/k8iwEBd3SGgErD6UnjMXpSCpzISEhSEhIwNatWzFw4EAAwNatW1G9enUEBQVJ7bKzszFlyhRs2LABmZmZaNGiBZYsWYKXXnpJarN7927ExMTgzp07aN26NYYOHar1fseOHcOMGTPwxx9/wNvbG//617+wYMECuLq6lv7NEhGR0QyVnvDw8LCY0hPG4kgY4e5d4PDhgj/LyogRI/Dtt99KP3/zzTcYPny4RpupU6diy5YtWLt2Lc6cOYPatWsjPDwc6ekFyZd37txBnz590LNnT8THx2PkyJGYPn26xjUSEhLQtWtXRERE4Pz589i4cSOOHTuG6Ojo0r9JIiLSkJaWhuTkZI2v1NRUAPpLT6iPiHl5ecHf31/vlyUFYABHwqze6tXA6NGAUgnY2ABffglERZX++w4aNAgzZszArVu3AAC//fYbNmzYgCNHjgAAHj9+jJUrV2LNmjXo1q0bAOCrr75CbGwsVq9ejSlTpmDlypUIDg7G4sWLAQD16tXDn3/+iQ8//FB6nwULFmDgwIFS0n2dOnWwfPlytG/fHitXroSTk1Pp3ywRERWZgF/UhtsVEYMwK3b37rMADCj48403gPBwoFq10n1vHx8fdO/eHWvWrIEQAt27d4e3t7d0PiEhAbm5uXj55ZelY/b29mjZsiUuX74MALh8+TJatWqlcd3Q0FCNn8+dO4fz589j3bp10jEhBJRKJRITE9GgQYPSuD0iIqujr8yEQqFAbm4usrIMB1IVqfSEsRiEWbHr158FYCr5+cCNG6UfhAEFU5KqacHPPvusVN7j0aNHeOONN/DWW29pneMiACKikmFsmQl1hfd+rEilJ4zFIMyK1alTMAWpHojZ2gK1a5fN+3ft2hU5OTmQyWQIDw/XOBccHAwHBwf89ttvqFGjBoCC5cmnTp2SphYbNGiA7du3a7zu999/1/g5JCQEly5dQu2yuikiIitkbJkJFX0J+OV9w+2SxiDMilWrVpAD9sYbBSNgtrbAF1+UzSgYANja2kpTi7a2thrnXF1dMXbsWEyZMgWenp6oXr06Fi1ahCdPniDqf0lrY8aMweLFizFlyhSMHDkSp0+fxpo1azSuM23aNLRu3RrR0dEYOXIkXF1dcenSJcTGxhb7X21ERKQtLS1NSq5XKTzKpX78zp1Ag3s/Dh/eWSM9BbC8VY/GYhBm5aKiCnLAbtwoGAErqwBMxd3dXe+5hQsXQqlUYvDgwcjKykKLFi2wb98+VK5cGUDBdOKWLVswYcIEfPrpp2jZsiU++OADjBgxQrrGiy++iKNHj2LmzJlo27YthBAIDg6WKi8TEZHpdE1D6hvlUj9emHoCvre3N/z9/cvqFsxKJoQQ5u4E6ZaZmQm5XA6FQqEVrDx9+hSJiYkICgriCr9SxmdNRFSgcPJ9amoqtm7dKv2sULhh6dIYreT6/v1/xIYNA3QGYKo2MTFLIZdnYfTo0RYfhBn6/FbHkTAiIiIqUkJCAn744QeDbe7cCdRZZuLHHwdAX2lSa0jA14dBGBERERmUlpamFYAVzvtSTTfqZgNAAJCpHVOib9+fEBh4t8In4OvDIIyIiIgMKrz68bffQhEbGwagIO8rLOwADhwI0zvdWEAm1QFTjX698ELB4qw+ffrA29u7wibg68MgjIiIiIxWEIB1hmpUSwgbKSAzRCZTIirqa+TmOmiVnwgICLCq4EuFQZiF47qK0sdnTERUQKFww4EDYdCcVgR0Tzc+oxr5qlYtGcCzkS+g4pafMAaDMAulqquVk5MDZ2dnM/emYlMNwxeuZUZEZG107e9YQIm2bX/Fr7+2g3ogJpMpERGhnfdlrSNfhTEIs1B2dnZwcXHBgwcPYG9vDxsbw8PAZBqlUokHDx7AxcUFdnb834WIrJuu/R0Bgc6dD+Dll+Pg5JStkSvGvC/D+KlioWQyGfz9/ZGYmIhbt26ZuzsVmo2NDapXrw6ZTPcwOxGRtdC1v2NYWEEABgAvvxyHF164oHPbIY5+aWMQZsEcHBxQp06dYu/ZRcXj4ODAkUYiov8xtL8jAIwa1Q0eHh4axzj6pRuDMAtnY2PDKu5ERFSqCtfuksuzdBZXHTRoEIKDg8uqWxaPQRgREREZ5OXlhejoaIMzLxztKj4GYURERFQkBlglj4kuRERERGbAkTAiIiIrkZaWxinFcoRBGBERUQWiL9BSKBTYuHFjka+Pjo5mIFZGGIQRERFVEGlpaVixYoVRbRUKN6Sne8HTM01jpSPLHpUdBmFEREQVhLEB1JkzzTQKrvbsuRMhIWdLuXdUGBPziYiIKiiFwg2JiTWhULhpHFMFYAAghA127Oih0YbKBkfCiIiIKiB9o126NuEWwgbp6Z46C7BS6eFIGBERUQVjaLRLtQm3OplMCU/PdHN01apxJIyIiMiCqK9+VCgUyM3Nlc49fPgQAAyOdgUF3dLahLtnz50cBTMDBmFERETlWOGgy5gyE6rRLvVATH20q6hNuKlsMAgjIiIqp4oqOaFeZgKARsmJoka79G3CXXizbio9DMKIiIjKKUMlJ9QT7wElABkAmUYSvr7Rrn79+sHDw0PrmqyYX7YYhBEREZUzqinI1NRUjeOqkS97+2yNxHv1dXaqJPzg4BuQy7MwfHhneHt7S+cZaJUfDMKIiIjKEX1TkNojX/oLHKiXnPD29oa/v3/pdZhMxhIVRERE5YjufR/ddIx8Cb3XYMkJy8AgjIiIqBxTKNxw8WIjrZITBTlg2lhywnJwOpKIiKic0pyCFNAMvFQFV200jkVFfY1q1ZLLrI9kOgZhRERE5ZD2FKQMqkBMNdoFQKsMReEAjCUnyi8GYUREROWQrqr3gAzh4XvRsOElabpRXxmKPn36ICAggCshyzEGYUREROWQvqr36gEYoL/oqre3NwOwco5BGBERkRmob0ekTlUbzJiq94ZwGrL8YxBGRERUSvQFWoX3gFTffkg9yDKl6j3AgqyWgkEYERFRCVIFXhkZGdi0aVOR7dVXQKpvOQQU5HWpV7tXYZBVMTAIIyIiKiGGNtzWNdpVeAVk4S2HWO2+YmMQRkREVEIKTz2qAq+kJH8cOBCmNdqlawWk+pZDVLExCCMiIiohGRkZ0vf6Cq2qj3bpWwHJLYesA7ctIiIiKgFpaWlSDpjuQqvPqI929ey5EzJZQfV7bjlkXTgSRkREVALUpyJ1F1p9Rn20y9AKSKrYGIQREREVk67SE6r6XgCQlOQP7b0eVQTCwg4YVXCVKjYGYUREREVISEjAkydPAABZWVmIjY3V21ahcMOBA2HQHYABgAwBAUlGvS8LrlZsDMKIiIgMSEhIwA8//KD3fOHSE8WZigT0F11lLbCKj0EYERGRAaoRMJW7d/1x+3YNVK9+C/fv+2kVWg0OvqG14lE1NVk48b5fv35o0KBB2d0MlSsWszqyV69eqF69OpycnODv74/BgwcjKenZcO7cuXMhk8m0vlxdXTWus3nzZtSvXx9OTk5o3Lgxdu/erXFeCIHZs2fD398fzs7OCAsLw/Xr1zXapKenY+DAgXB3d4eHhweioqLw6NEjjTbnz59H27Zt4eTkhMDAQCxatKiEnwgREZW1n39+DV9/PQr794fj669HYfv2nlqFVgForXjs3DkWQ4euQUzMUqkaPgC92w6RdbCYkbAOHTrg7bffhr+/P/7++29MnjwZffv2xfHjxwEAkydPxpgxYzRe06lTJ7z00kvSz8ePH8eAAQOwYMEC9OjRA+vXr0fv3r1x5swZvPDCCwCARYsWYfny5Vi7di2CgoIwa9YshIeH49KlS3BycgIADBw4EMnJyYiNjUVubi6GDx+O0aNHY/369QCAzMxMdOnSBWFhYVi1ahX+/PNPjBgxAh4eHhg9enRZPC4iIjKBroT7hw8fAigYATt3rgme5Xpp53ypSk9wxSMZQyaEEObuhCm2b9+O3r17Izs7G/b29lrnz507h6ZNm+KXX35B27ZtAQCRkZF4/Pgxdu7cKbVr3bo1mjZtilWrVkEIgYCAAEyaNAmTJ08GULDJqq+vL9asWYP+/fvj8uXLaNiwIU6dOoUWLVoAAPbu3Yv/+7//w927dxEQEICVK1di5syZSElJkZIqp0+fjm3btuHKlStG32NmZibkcjkUCgXc3d1NflZERFQ0Q1sOAcDx462xf3+4wWvIZErExCw1OuiKjo5m3lcFZOznt8WMhKlLT0/HunXr0KZNG50BGAB8/fXXqFu3rhSAAUBcXBwmTpyo0S48PBzbtm0DACQmJiIlJQVhYWHSeblcjlatWiEuLg79+/dHXFwcPDw8pAAMAMLCwmBjY4MTJ07gX//6F+Li4tCuXTuNVS3h4eH48MMP8fDhQ1SuXFlnn7Ozs5GdnS39nJmZafxDISKi56JvyyFVwn316regXXZC/O/LRivfq3PnznBzc9O4pp2dnTQFycR7sqggbNq0aVixYgWePHmC1q1ba4xoqXv69CnWrVuH6dOnaxxPSUmBr6+vxjFfX1+kpKRI51XHDLWpUqWKxnk7Ozt4enpqtAkKCtK6huqcviBswYIFmDdvnu6bJyKiMqO+5ZD6Xo9NmpxTm5IUaNLkHDp2PKRz2jEoKIibb5NBZk3Mnz59us5kevUv9em7KVOm4OzZs9i/fz9sbW0xZMgQ6JpN/fnnn5GVlYWhQ4eW5e08txkzZkChUEhfd+7cMXeXiIisTuEth1QJ93fv+qNp03MYMGA9wsP3YuTIr/Cvf/0XcnkWgoJuaU1BssYXFcWsI2GTJk3CsGHDDLapVauW9L23tze8vb1Rt25dNGjQAIGBgfj9998RGhqq8Zqvv/4aPXr00BrR8vPzw7179zSO3bt3D35+ftJ51TH1f73cu3cPTZs2ldrcv39f4xp5eXlIT0/XuI6u91F/D10cHR3h6Oio9zwREZU+XXW+hLDB6tUjNUbGqlVL1mjTrVs3BAYGAuBUIxnHrEGYj48PfHx8THqtUlmw9Fc9hwooyOs6fPgwtm/frvWa0NBQHDx4EDExMdKx2NhYKYgLCgqCn58fDh48KAVdmZmZOHHiBMaOHStdIyMjA6dPn0bz5s0BAIcOHYJSqUSrVq2kNjNnzkRubq6UsxYbG4t69erpnYokIqLywdMzTWedr8IjY8HBNyCXZ6FDhw6oWrUqgoODzdNhslgWUSfsxIkTWLFiBeLj43Hr1i0cOnQIAwYMQHBwsNYo2DfffAN/f39069ZN6zrjx4/H3r17sXjxYly5cgVz587FH3/8gejoaACATCZDTEwM3n//fWzfvh1//vknhgwZgoCAAPTu3RsA0KBBA3Tt2hWjRo3CyZMn8dtvvyE6Ohr9+/dHQEAAAOD111+Hg4MDoqKicPHiRWzcuBHLli3TWhRARETlj1yepVHnC1CicDkKVSkKAKhTpw4DMDKJRSTmu7i4YOvWrZgzZw4eP34Mf39/dO3aFe+8847G9J1SqcSaNWswbNgw2Nraal2nTZs2WL9+Pd555x28/fbbqFOnDrZt2ybVCAOAqVOn4vHjxxg9ejQyMjLwyiuvYO/evVKNMABYt24doqOj0alTJ9jY2CAiIgLLly+Xzsvlcuzfvx/jxo1D8+bN4e3tjdmzZ7NGGBGRhVCv82VvnyNNRaoU3nqIyBQWWyfMGrBOGBFR6dBVlDUjIwObNm3S2V7fakkAGD16NFdBkoYKXSeMiIjIVEUVZVXp3LkzYmNjAYAV8KlUMAgjIiKrUlRRVhVPT0+NdnJ5ls7gi6UoyFQMwoiIqEIrPPWYmpoqfW9omlEulyM6OloraFPHUhT0PBiEERFRhWVo6lFfUVZV6QkADLCoVFlEiQoiIiJT6Jp6TEysKU1B6irKqio9QVTaOBJGRERWofDUY1jYAa2irCw9QWWJI2FERFTh6Zp6PHAgTArEAEg5YVz5SGWFI2FERFTh6Zt6DAhIQkzMUpaeILNgEEZERBWerv0gVVOPLD1B5sIgjIiIKjzVfpCFy1Gogq8+ffrA29tbas/SE1QWGIQREZFVMFT13tvbm1sPUZljEEZERBZB136P6nSNXhWeUuTUI5UnDMKIiKjcK1x0Vd9WQ9HR0RqBmJeXF6veU7nFIIyIiMo99SDK0FZDuoItBlhUXrFOGBERWQx9Ww0pFG5m7hlR8TEIIyIii8GthqgiYRBGREQWQ1XvSx23GiJLxSCMiIgshqreF7caooqAiflERGRRDNX7IrIkDMKIiKjMmVLzS52+el9EloRBGBERlanCNb/0Ua/5ZWwxVRZdJUvCIIyIiMpU4REwfYVX1dux6CpVRAzCiIjIbAwVXi2MARZVNFwdSUREZsHCq2TtOBJGREQlzlDifWpqKgDDhVeZdE/WgEEYERGVKGMT71WFV9UDMRZeJWvC6UgiIipRuhLvExNrak0zsvAqWTuOhBERUbEZM90IFJ14z8KrZM0YhBERUbEUnm7UV2JCX+J9cPANjXb6Cq+y5hdVdAzCiIioWNRHwAyNdBWVeN+nTx94e3vrfA/W/CJrwCCMiIhMom+kq0qVFFSrllxk4r23tzf8/f3N0nei8oCJ+UREZBJ9I11ffz0SZ840Y+I9URE4EkZERCbRNdJV4FnuFxPvifTjSBgREZmk8EiXOlXul6pdUNAtrQCMifdk7TgSRkREJgsJOYsqVVLw9dcjof7vevXcL10J+Ey8J2IQRkREz6latWT06rVTa5WkauSLCfhEujEIIyKiYtE1jcjcL6LiYxBGRFSBGapsD5g2Lejl5YXo6Gjcv38fmzZtko6z6CpR8ciEEMLcnSDdMjMzIZfLoVAo4O7ubu7uEJGFMXYj7ejoaJPzs0ojyCOydMZ+fnMkjIioglEFRup7OAL6txcyFEQVhQEWkekYhBERVSD6Rr+K2kibiMoe64QREVUghUe1FAo3XLjQUOf2QgqFmzm6SET/w5EwIqIKSn30qzD1jbSJyDw4EkZEVAEV3ly7MPViqkRkHgzCiIgqIF2ba6twI22i8oHTkUREFsKYchAqujfXVqJv358QGHiXARhROcAgjIjIAhhb8ysyMhLAs821C6+IfOGFy1qvYTFVIvNgEEZEZAGMreWVm5srfW9oKyHVptospkpkPgzCiIgskL7CqykpKRrt9G0lFBAQwOCLyMwYhBERWRhDhVePHz9e5OsHDRrEAIyoHGAQRkRkJqbsu1i49ISq8Gpw8A1pxEvfKJmKi4tLCd4FEZmKQRgRkRmYurm2rtITqsKrAHDiRCscPx6KggpESrRpE4dWrU5wNSRROcQgjIjIDHRtL2TM5tq6Sk/IZEokJQXgu++GFArQbHD8+MuIiwvlXpFE5RCDMCIiMyvO5tq6Sk+EhR3AgQNheouz6pqyJCLzMzoIy8zMNPqi7u7uJnWGiMjaGJPjBWjW8ipcesJQdXwV7hVJVP4YHYR5eHhAJpMZbCOEgEwmQ35+/nN3jIjIGhjK8ZLLs5CamiodHzRokEZSfWpqKrZu3QoAOqrja+JekUTlj9FB2OHDh0uzH0REVklfjpcqYFIFWSqFE/UB3VOUtWol4K+/gjWmODkKRlS+GB2EtW/fvjT7QURklfRtLwQAiYk1i0zUV9FVHb8g2V+7Wj63KSIqH0xOzM/IyMDq1atx+XLBPmSNGjXCiBEjIJfLS6xzRETWoHAAlZBQG0uXxhSZqF84mCpcHV/1c+fOnVG5cmV4eHhwmyKicsSkIOyPP/5AeHg4nJ2d0bJlSwDAJ598gvnz52P//v0ICQkp0U4SEVU0+gIoYxP1AcDLywvR0dHFLvhKROWDSUHYhAkT0KtXL3z11Vewsyu4RF5eHkaOHImYmBj88ssvJdpJIqKKpnAApUqyLypRX9d1iMgymTwSph6AAYCdnR2mTp2KFi1alFjniIgqkqK2KQKKTtQnoorDpCDM3d0dt2/fRv369TWO37lzB25ubiXSMSKiisTYbYr0JepzZSNRxWNSEBYZGYmoqCh8/PHHaNOmDQDgt99+w5QpUzBgwIAS7SARUUVg7DZFgO6VjkRU8ZgUhH388ceQyWQYMmQI8vLyAAD29vYYO3YsFi5cWKIdJCKqaIzZpqjwSkcVlpcgqjgM73Ohh4ODA5YtW4aHDx8iPj4e8fHxSE9Px5IlS+Do6FjSfQQA9OrVC9WrV4eTkxP8/f0xePBgJCUlabTZt28fWrduDTc3N/j4+CAiIgI3b97UaHPkyBGEhITA0dERtWvXxpo1a7Te67PPPkPNmjXh5OSEVq1a4eTJkxrnnz59inHjxsHLywuVKlVCREQE7t27p9Hm9u3b6N69O1xcXFClShVMmTJFCliJyHrpW/2oUBSkcvTp0wejR4/W+aWrUCsRWS6TgjAVFxcXNG7cGI0bN9bYSqM0dOjQAZs2bcLVq1exZcsWJCQkoG/fvtL5xMREvPbaa+jYsSPi4+Oxb98+pKamok+fPhptunfvjg4dOiA+Ph4xMTEYOXIk9u3bJ7XZuHEjJk6ciDlz5uDMmTNo0qQJwsPDcf/+fanNhAkTsGPHDmzevBlHjx5FUlKSxvvk5+eje/fuyMnJwfHjx7F27VqsWbMGs2fPLtVnRETlQ1paGpKTkzW+VNsPGVr9CADe3t7w9/fX+cUAjKhikQkhRHFf9PTpU3z66ac4fPgw7t+/D6VSqXH+zJkzJdZBfbZv347evXsjOzsb9vb2+OmnnzBgwABkZ2fDxqbgL7gdO3bgtddek9pMmzYNu3btwoULF6Tr9O/fHxkZGdi7dy8AoFWrVnjppZekBFqlUonAwEC8+eabmD59OhQKBXx8fLB+/XopCLxy5QoaNGiAuLg4tG7dGnv27EGPHj2QlJQEX19fAMCqVaswbdo0PHjwwOjphMzMTMjlcigUCm6KTlSOqFY5KhQK5ObmapzLyspCbGys3tcqFG5SIVYVmUyJmJilkMuzMHr0aPj7+5da34mo9Bn7+W1STlhUVBT279+Pvn37omXLlkVu7F3S0tPTsW7dOrRp0wb29vYAgObNm8PGxgbffvsthg0bhkePHuH7779HWFiY1CYuLg5hYWEa1woPD0dMTAyAgsTZ06dPY8aMGdJ5GxsbhIWFIS4uDgBw+vRp5Obmalynfv36qF69uhSExcXFoXHjxlIApnqfsWPH4uLFi2jWrFmpPBciKn3GrnJUKZyAz9WPRKRiUhC2c+dO7N69Gy+//HJJ98egadOmYcWKFXjy5Alat26NnTt3SueCgoKwf/9+9OvXD2+88Qby8/MRGhqK3bt3S21SUlI0AiMA8PX1RWZmJv755x88fPgQ+fn5OttcuXJFuoaDgwM8PDy02qSkpBh8H9U5fbKzs5GdnS39nJmZWdQjIaISVlQtr4yMDI2fDa1y1JeAz9WPRASYGIRVrVq1ROqBTZ8+HR9++KHBNpcvX5bqkU2ZMgVRUVG4desW5s2bhyFDhmDnzp2QyWRISUnBqFGjMHToUAwYMABZWVmYPXs2+vbti9jY2DIfrTPFggULMG/ePHN3g8hqFXeUSz3IApRo0yYOrVqdMGr7Ia5+JCKTgrDFixdj2rRpWLVqFWrUqGHym0+aNAnDhg0z2KZWrVrS997e3vD29kbdunXRoEEDBAYG4vfff0doaCg+++wzyOVyLFq0SGr/ww8/IDAwECdOnEDr1q3h5+entYrx3r17cHd3h7OzM2xtbWFra6uzjZ+fHwDAz88POTk5yMjI0BgNK9ym8IpK1TVVbXSZMWMGJk6cKP2cmZmJwMBAg8+HiEpOcWp5FQ6yABscP/4y4uJC0bPnTlSu/NDg9kN9+vSBt7e3xnnu80hkXUwKwlq0aIGnT5+iVq1acHFxkXKuVNLTjdtew8fHBz4+PqZ0QVoMoJq+e/LkiZSQr2Jra6vRtvD0JADExsYiNDQUQMFfgM2bN8fBgwfRu3dv6bUHDx5EdHQ0gILcM3t7exw8eBAREREAgKtXr+L27dvSdUJDQzF//nzcv38fVapUkd7H3d0dDRs21HtPjo6OpVbig4iKp6haXrpWOQLPRry6ddsNQAn1Rejq2w+pVkESkfUyKQgbMGAA/v77b3zwwQfw9fUt9am+EydO4NSpU3jllVdQuXJlJCQkYNasWQgODpYCn+7du2PJkiV49913penIt99+GzVq1JAS4ceMGYMVK1Zg6tSpGDFiBA4dOoRNmzZh165d0ntNnDgRQ4cORYsWLdCyZUssXboUjx8/xvDhwwEAcrkcUVFRmDhxIjw9PeHu7o4333wToaGhaN26NQCgS5cuaNiwIQYPHoxFixYhJSUF77zzDsaNG8cgi8gCFDWVCABJSf4ABADtv/+EsMHu3T3+d74gEGMCPhEVZlIQdvz4ccTFxaFJkyYl3R+dXFxcsHXrVsyZMwePHz+Gv78/unbtinfeeUcKajp27Ij169dj0aJFWLRoEVxcXBAaGoq9e/fC2dkZQEHy/q5duzBhwgQsW7YM1apVw9dff43w8HDpvSIjI/HgwQPMnj0bKSkpaNq0Kfbu3auRaL9kyRLY2NggIiIC2dnZCA8Px+effy6dt7W1xc6dOzF27FiEhobC1dUVQ4cOxbvvvlsmz4uIno+hWl6qfK8DB8KgKwDTJINMJhARsQmBgXcZgBGRBpOCsPr16+Off/4p6b7o1bhxYxw6dKjIdv3790f//v0Ntnn11Vdx9uxZg22io6Ol6UddnJyc8Nlnn+Gzzz7T26ZGjRpaU59EZBk8PdMgkym1anmpphL1TUXqIoQNXF2faAVgTMAnIpMq5i9cuBCTJk3CkSNHkJaWhszMTI0vIiJLpqrlJZMV5JMWnkpUBWmalPi//9uBgunHZ9SDN9WWRNx+iIgAE0fCunbtCgDo1KmTxnEhBGQyGfLz85+/Z0REZSgtLU3aWgiAwVpe+gquhoSchZ2d0FuIlcn4RKTOpCDs8OHDJd0PIiKz0VcfTF8tL0B/kGYoeOMUJBGpMykIa9++vVHt/vOf/+Ddd9/VqoVDRFSeFKc+mDp9QdqoUd20dtVgDTAiKsykIMxYP/zwAyZPnswgjIhKXVHbDRkbBBmqD6YvOFMvvMpgi4iMVapBmBCiNC9PRATA+O2GikqIN1QfLCGhtt7gjLleRGSKUg3CiIjKgrHTiUlJSRptC49a6asPdudOtSKLtxIRFReDMCKqUAxNJ27dulWrvXpNQH31wQCZweKtRESmMKlOGBFReaRvOlGhcNP7GvWRMX31wQID72jVBVOv/0VEZAqOhBFRhWFouyHV+aJWPOorMaGrLpjqHEtPEJEpSjUIGzRoENzd3UvzLYiIJPqmE5OSAvDdd0N0TlEC2kGUrtITISFnERPTAOnpnqhZMw8BAS8BeImrIYnIZCYHYRkZGTh58iTu378PpVJzmH7IkCEAgJUrVz5f74iIikFXJfuwsAM4cCDMYFK9l5cXoqOjS6TEBRGRsUwKwnbs2IGBAwfi0aNHcHd3h0wmk87JZDIpCCMiKkn6aoEZ2m7I0BSl+mgXAywiKmsmBWGTJk3CiBEj8MEHH8DFxaWk+0REpMXYWmCA9nSirilKJtUTkbmZtDry77//xltvvcUAjIjKjKGpQnVdunTR+FnfikeWliAiczNpJCw8PBx//PEHatWqVdL9ISIrpmu6MSMjA3l5eXj48KHGcX0FWStVqqR13eDgG4iI2AJAIDDwLjfVJqJyweggbPv27dL33bt3x5QpU3Dp0iU0btwY9vb2Gm179epVcj0kIqtQnOlGQwVZr127ZnTbyMhI5oIRkdkYHYT17t1b69i7776rdUwmkyE/P/+5OkVE1sfYrYcM7e8ol2fhwoULGm23b+8BVeZF4bZyubz0b4yISA+jg7DCZSiIiEpKWlqaxgpHfaNXCoUbLl5sZPQWQidOtELh1FduN0RE5YVJOWHfffcdIiMj4ejoqHE8JycHGzZsYIkKItKir7xERkYGNm3aJP2sb6Trn3+c1Op9CQAytasI2NvnaIyeAUBcXKiOnnBlJBGVDyYFYcOHD0fXrl1RpUoVjeNZWVkYPnw4gzAi0lA430vfVCMA3LkTqHOkKzY2DM9GtWTQJMOlS40QFxf6v9cq0ajRJa3rAECbNnEcBSOicsGkIEwIoVGgVeXu3bvMsSCyUuojXUlJNkhMtENQUB4CApRGTTWqzhXkcGkqXOdLm1ItAAMAG1y8+AIKj5jJZEq0anVC+pkrI4nInIoVhDVr1gwymQwymQydOnWCnd2zl+fn5yMxMRFdu3Yt8U4SUfmmPtJlKMgylFQPADt2PEuiV9G19VCBggBLJlMiNDQOx4+/rKNnMo126vXBBg0axJWRRGRWxQrCVCsk4+PjER4erlGPx8HBATVr1kRERESJdpCIyr8HDx4AKHrloqEthACZztGuiIif8MILl+Hs/FRrT0i5PAOADB4eDwuNhKmTITx8Lxo2vIThwzvD25ubbhNR+VCsIGzOnDkAgJo1ayIyMhJOTk6l0ikishxpaWnYuHEjABS5T6OnZ5rBLYR0nQsMvAtAe0/IhITa2LKlrxSUvfjieZw79yJ0jaQ1bHgJcnkWvL294e/vXxqPgYio2Ezatmjo0KFwcnJCTk4O7t69i9u3b2t8EZH1UF/xmJTkj4Lpv2fUgyxDWwgVtb1Qhw4d0K1bYwQF3QIArRG38+dfxMiRX6NNm9/0XoM5YERUnpiUmH/9+nWMGDECx48f1ziuSthnsVYi66NQuOHAgTAULh1RMG34bDVi4REtY8/VqVMHAHD8+HG9I265uQ7o0uUAJkywRWZmFQQGZqNatTrw8OAUJBGVPyYFYcOGDYOdnR127twJf39/nSsliahiKlzvS7XyUVdgBMgQEJCkdQ3VyJcu+s45ODhI71vUtObLL9fgtCMRlXsmBWHx8fE4ffo06tevX9L9IaJyTH+9L7ciAyNjREZG6ixzoxrFSk5OBvBsWrPwKkzW/yIiS2JSENawYUONuj9EZB3UR8B0laIoKjDq06cPvL29dV7bmOlC9ZwuQ1OXzP0iIktgUhD24YcfYurUqfjggw/QuHFj2Nvba5x3d3cvkc4RUfmkrxRFTMxSxMQs1RkYAUBAQMBz5WV5eXkhOjpa5/ZHKsz9IiJLYVIQFhYWBgDo2LGjRj4YE/OJKg5dez0ayv9SlaIICrqlc1qwX79+JRIcMcAioorCpCDs8OHDJd0PIipHDO/1WHRivC4ymUzK6VLHkSsislYmBWHt27fHr7/+ii+++AIJCQn46aefULVqVXz//fcICgoq6T4SURkrKvcrJOSswfwvVe5XRkYGNm3aBABSQVddoqOjGYgRkdUxKQjbsmULBg8ejIEDB+Ls2bPIzs4GACgUCnzwwQfYvXt3iXaSiMzD0DZEhhLji1uZ3lCOFxFRRWVSxfz3338fq1atwldffaWRlP/yyy/jzJkzJdY5IjIvw3s9FpSK0JcDpotC4YbExJpQKNxKvK9ERJbGpJGwq1evol27dlrH5XI5MjIynrdPRFROmFr7S1eJCH3TmkRE1sqkIMzPzw83btxAzZo1NY4fO3YMtWrVKol+EVEZMbQKsqiiqLrqfulKtDc0rckCq0RkrUwKwkaNGoXx48fjm2++gUwmQ1JSEuLi4jB58mTMmjWrpPtIRKWk8CpIXYrK/VLfTggoyO9SrYI0pqQFgzAislYmBWHTp0+HUqlEp06d8OTJE7Rr1w6Ojo6YPHky3nzzzZLuIxGVksIjYJqlKJ4FR/r2c1Rf/WhISWxpRERU0ZgUhMlkMsycORNTpkzBjRs38OjRIzRs2BCVKlUq6f4RURkxJWdLvVizIdzrkYhIm0lBmIqDgwMaNmxYUn0hIjMpKmdLX+6XsSNpAPd6JCIq7LmCMCKqGIrK2dJX90u9Ar6hkbTn3bibiKgiYhBGREXmbGVkZBgsvlrUSFpxi7cSEVkDk4q1ElHFosrZksmUAKCVs7Vp0yakpaXpfX1RRV2JiEgbR8KICMCznK07d6oBkCEw8I7G+aSkJK0cMFVxZq5+JCIqPgZhRFascEJ8QkJtvXldW7du1Xsdrn4kIio+BmFEVszLywuRkZHYuHFjkXldhlY+Alz9SERUXAzCiKycXC4HYDivy9AIWWRkpHQNXbj6kYhINwZhRARAf16XvX2OwREyuVzOlY9ERCbg6kgiAqB/hWRuriNXPhIRlQKOhBGRpHBeFwDcuRMIQAn1f7Nx5SMR0fNjEEZkpdLS0pCTkyOVmVBRbdatXgEfEFAFYlz5SERUMhiEEVmhtLQ0rFixQu/5wislARlkMoGIiE0IDLzLAIyIqAQwJ4zIChUuulqYvpWSrq5PtAIwlp8gIjINR8KISKsGWFEV8FUbcrP8BBGR6RiEEVk59dwv9Rpghirgc0NuIqLnxyCMyIoZqpLPCvhERKWLQRhROaRauahPSU0DGqqSL5dnYfjwzvD29i6V9yYisnYMwojKmaJWLqpER0c/dzBUVO4Xpx2JiEoPV0cSlTNFrVwsbjtD9FXJZwkKIqLSx5EwonKu8MrFkqCe08XcLyIi82AQRlSO6Vu5+Ly8vLwQHR1dJnlnRESkG4MwonLK0MrFkhgRY4BFRGReDMKIyqmiVi6mpqbqfB1HsIiILAODMKJyqqiVi1u3btX72pJYOUlERKXLYlZH9urVC9WrV4eTkxP8/f0xePBgJCUlabTZtGkTmjZtChcXF9SoUQMfffSR1nWOHDmCkJAQODo6onbt2lizZo1Wm88++ww1a9aEk5MTWrVqhZMnT2qcf/r0KcaNGwcvLy9UqlQJERERuHfvnkab27dvo3v37nBxcUGVKlUwZcoU5OXlPf+DIKthzMpFhcINiYk1oVC4aby2JFZOEhFR6bKYkbAOHTrg7bffhr+/P/7++29MnjwZffv2xfHjxwEAe/bswcCBA/Hpp5+iS5cuuHz5MkaNGgVnZ2dER0cDABITE9G9e3eMGTMG69atw8GDBzFy5Ej4+/sjPDwcALBx40ZMnDgRq1atQqtWrbB06VKEh4fj6tWrqFKlCgBgwoQJ2LVrFzZv3gy5XI7o6Gj06dMHv/32GwAgPz8f3bt3h5+fH44fP47k5GQMGTIE9vb2+OCDD8zw9MiSGLtysbSS9omIqGzIhBDC3J0wxfbt29G7d29kZ2fD3t4er7/+OnJzc7F582apzaeffopFixbh9u3bkMlkmDZtGnbt2oULFy5Ibfr374+MjAzs3bsXANCqVSu89NJLUrFMpVKJwMBAvPnmm5g+fToUCgV8fHywfv169O3bFwBw5coVNGjQAHFxcWjdujX27NmDHj16ICkpCb6+vgCAVatWYdq0aXjw4IHRy/4zMzMhl8uhUCjg7u5eIs+NLIO+ivmpqanYunUrFAo3LF0aozVVGROzFHJ5FkaPHs0iq0REZmLs57fFTEeqS09Px7p169CmTRvY29sDALKzs+Hk5KTRztnZGXfv3sWtW7cAAHFxcQgLC9NoEx4ejri4OAAFUzinT5/WaGNjY4OwsDCpzenTp5Gbm6vRpn79+qhevbrUJi4uDo0bN5YCMNX7ZGZm4uLFi3rvKzs7G5mZmRpfZJ28vLzg7++v9aXaQshQ0j4REVkGiwrCpk2bBldXV3h5eeH27dv473//K50LDw/H1q1bcfDgQSiVSly7dg2LFy8GACQnJwMAUlJSNAIjAPD19UVmZib++ecfpKamIj8/X2eblJQU6RoODg7w8PAw2EbXNVTn9FmwYAHkcrn0FRgYaOyjoWJKS0tDcnKy3q+0tDRzd9EgVdK+OvWkfSIiKv/MmhM2ffp0fPjhhwbbXL58GfXr1wcATJkyBVFRUbh16xbmzZuHIUOGYOfOnZDJZBg1ahQSEhLQo0cP5Obmwt3dHePHj8fcuXNhY2MZseaMGTMwceJE6efMzEwGYqWgLPdmLC2qpP3COWHcboiIyHKYNQibNGkShg0bZrBNrVq1pO+9vb3h7e2NunXrokGDBggMDMTvv/+O0NBQyGQyfPjhh/jggw+QkpICHx8fHDx4UOMafn5+WqsY7927B3d3dzg7O8PW1ha2trY62/j5+UnXyMnJQUZGhsZoWOE2hVdUqq6paqOLo6MjHB0dDT4Pen6Fc630bQtU0isM9eV5qRS3vpehpH0iIir/zBqE+fj4wMfHx6TXKpUFUzHZ2dkax21tbVG1alUAwI8//ojQ0FDpPUJDQ7F7926N9rGxsQgNDQVQ8CHYvHlzHDx4EL1795be5+DBg9IKy+bNm8Pe3h4HDx5EREQEAODq1au4ffu2dJ3Q0FDMnz8f9+/fl1ZUxsbGwt3dHQ0bNjTpfql0lNUKQ2NH3yIjIyGXyzWOqQdnhRd1yOVZOoMv7vlIRFT+WUSJihMnTuDUqVN45ZVXULlyZSQkJGDWrFkIDg6WAp/U1FT89NNPePXVV/H06VN8++232Lx5M44ePSpdZ8yYMVixYgWmTp2KESNG4NChQ9i0aRN27doltZk4cSKGDh2KFi1aoGXLlli6dCkeP36M4cOHAwDkcjmioqIwceJEeHp6wt3dHW+++SZCQ0PRunVrAECXLl3QsGFDDB48GIsWLUJKSgreeecdjBs3jiNd5UhpbAtkaFVj4ffWNfq2ceNGnddVTY1yz0cioorDIoIwFxcXbN26FXPmzMHjx4/h7++Prl274p133tEIatauXYvJkydDCIHQ0FAcOXIELVu2lM4HBQVh165dmDBhApYtW4Zq1arh66+/lmqEAQUjEQ8ePMDs2bORkpKCpk2bYu/evRqJ9kuWLIGNjQ0iIiKQnZ2N8PBwfP7559J5W1tb7Ny5E2PHjkVoaChcXV0xdOhQvPvuu6X8pKg4itoWqLiMHe0yNPpmzNQoAywioorBYuuEWQPWCSsdV65cwcaNG4ustRUZGSktCjFGcnIyvvzyS4NtDL1nQkJtvcEZ634REVmOCl0njOh55ObmAih6WyBVO1Pp2lJI3+jbnTvVdE6NFt6OiIiIKg6LmI4kKi2GVhg+fPhQqjFX3DwrfVOO+jblBmQlOjVKRETlH4Mwsjp2drp+7WVaRw4fPozDhw9LPxtbN6yohH9d9b0CA+/oDM5YfJWIqOJiEEZWR72+W3FKVBhbN6yohH99o28svkpEZF0YhJHVKo0SFQD0Tjmqj2rpqu/F4qtERNaFQRhZtOepQm8oST49/R+tMhHGKmpLoT59+kgbcWdkZGDTpk0ar2XxVSIi68AgjCzW8+4BqW/E6qef+gIofgV99UDJ0KhWQECA1B9/f38WXyUislIMwshiGZujVbidKljSNWJVUDXPtOlJU6vZM8AiIrJODMLI6hQOlqKjr2LduhN4/NgVP/30b422xS0TwYCKiIiMxSCMrFLhYCko6BYUCjeWiSAiojLDivlUYeiqUG+MwtOT+iroMzmeiIhKEkfCqEIoTr2vwgpPT86e/QA3b9qhZs08BAS8BOAlJscTEVGJYxBGFq8k6n2pB1j+/kDz5qXSVSIiIgmnI8niGapQT0REVF4xCCOLpcrRUtX7UqeeUM9cLiIiKo84HUkWSz2Xq2rVTEybJkd+vgy2tgIffpiJ118fwFwuIiIqt2RCFJSnpPInMzMTcrkcCoUC7u7u5u5OuXf3LnDjBlC7NlCtmrl7Q0RE1srYz2+OhFGFUa0agy8iIrIczAkjIiIiMgMGYURERERmwCCMiIiIyAwYhBERERGZARPzySqkpaVJ2xLpwlIWRERU1hiEUYWXlpaGFStWFNkuOjqagRgREZUZBmFU4RUeAVMo3JCe7gVPzzSNvSWTkpKQk5PDUTEiIioTDMLIqpw500za7FsmUyIs7AACApLh6ZmGrVu3Su04KkZERKWNQRiVqPKce6VQuEkBGFCwyXdsbGcAMshkSvTsuRMhIWcBaI+eERERlTQGYVRiynvuVXq6lxSAPSMDUBCQ7djRA8HBNzSmKImIiEoLS1RQiTF29Mhco0yenmmQyZR6zwthg/R0zzLsERERWTMGYWQ15PIs9Oy5Uy0Q09y7XiZTwtMzvew7RkREVonTkWRVQkLOIjj4BtLTPfHXX0H49de2AGyknDBORRIRUVnhSBhVeA4ODho/y+VZePjQE8eOPQvAwsIOSEn5REREZYFBGFV4Xl5eiI6ORp8+fQDoXiV54EAYFAo3c3aTiIisDIMwsgpeXl7w9vYGoHuVZOGk/MKjZ0RERCWNOWFUrpVG3THVKkn1QEw9Kb9fv34s1EpERKWOQRiVGGNHj4xtV1p1x1SrJNUr56sn5Xt4eBh9LSIiIlMxCKMSo8q9Uo1cJSXZIDHRDkFBeQgIKCgLUZyRq9KsO6a+StLTM52rIomIqMwxCKMSpQqwVq8GRo8GlErAxgb48ksgKur5rq1v421TyeVZDL6IiMhsGIRRibt791kABhT8+cYbQHg4UK2aadcsvPG2+j6P+vpw/TpQp47p70lERFSauDqSStz1688CMJX8fODGDdOup6ukxI4dPbRKSqSlpSE5ORmLF2egRg2Bjh2BGjUEFi/OQHJyMp48eWLU+3FlJBERlQWOhFGJq1OnYApSPRCztQVq1zbteoZKSqimE1VJ/AqFG5YujYEQBRtzK5UyTJnijr///gZyeRYGDRoEFxcXve9lympLIiIiUzAIoxJXrVpBDtgbbxSMgNnaAl98Yfq0YFElJYBnyflFBWwuLi7w9/c3rSNEREQliNORVCqiooCbN4HDhwv+fJ6k/MIbbxva51EVsKnjxtxERFQecSSMSk21as+XFK+em2WopISDg4M0ElZUDTAiIqLygkEYlVuF647posrhSk5Olo6xBhgREVkCBmFUrpmaJM8aYEREVN4xJ4yIiIjIDBiEEREREZkBgzCqEEp683AiIqLSxpwwqhCKk8RPRERUHjAIowqDARYREVkSBmFUqtLS0jg6RUREpAODMCo1qv0cVRQKN6Sne8HTM02jfER0dDQDMSIisjoMwqjUqI+AnTnTTKuKfUjIWa12gOboWVKSDRIT7RAUlIeAgILtiDh6RkREFQGDMCp1CoWbFIABBRtq79jRA8HBN7QKqqqPnhkK3Dh6RkRElo4lKqjUpad7SQGYihA2SE/31GqrGgHTF7gpFG4a7YiIiCwVgzAqdZ6eaZDJlBrHZDIlPD3T9b6mOIEbERGRJWIQRqVOLs9Cz547pUBMNbVoaG9HUwI3IiIiS8KcMCoTISFnERx8A+npnvD0TC9yc21V4FY4J4ybchMRUUXBIIzKjFyeVawgqriBGxERkSVhEEalxth9GhUKhfR9RkaGxrniBm5ERESWgkEYlZrC+zlmZGRg06ZN0nlV8dYvv9zNQIuIiKwOgzAqVfpqeRmqAWYMY0fZiIiIyisGYVTmiireGhkZCblcrvf1rJhPREQVAYMwKnOGaoAV5IDJ4e/vb6beERERlQ2LqxOWnZ2Npk2bQiaTIT4+XuPc+fPn0bZtWzg5OSEwMBCLFi3Sev3mzZtRv359ODk5oXHjxti9e7fGeSEEZs+eDX9/fzg7OyMsLAzXr1/XaJOeno6BAwfC3d0dHh4eiIqKwqNHj4rdF2vFGmBEREQWGIRNnToVAQEBWsczMzPRpUsX1KhRA6dPn8ZHH32EuXPn4ssvv5TaHD9+HAMGDEBUVBTOnj2L3r17o3fv3rhw4YLUZtGiRVi+fDlWrVqFEydOwNXVFeHh4Xj69KnUZuDAgbh48SJiY2Oxc+dO/PLLLxg9enSx+mLNTCneSkREVNHIhBDC3J0w1p49ezBx4kRs2bIFjRo1wtmzZ9G0aVMAwMqVKzFz5kykpKRISdvTp0/Htm3bcOXKFQBAZGQkHj9+jJ07d0rXbN26NZo2bYpVq1ZBCIGAgABMmjQJkydPBlBQPsHX1xdr1qxB//79cfnyZTRs2BCnTp1CixYtAAB79+7F//3f/+Hu3bsICAgwqi/GyMzMhFwuh0KhgLu7+3M/P5W7d4Hr14E6dYBq1UrsskhLS5NWQiYl2SAx0Q5BQXkICCgIthQKBTZu3Ci1L1gdqV0DbPTo0ZyOJCIii2Xs57fFjITdu3cPo0aNwvfffw8XFxet83FxcWjXrp3Gqrnw8HBcvXoVDx8+lNqEhYVpvC48PBxxcXEAgMTERKSkpGi0kcvlaNWqldQmLi4OHh4eUgAGAGFhYbCxscGJEyeM7osu2dnZyMzM1PgqaatXAzVqAB07Fvy5enXJXDctLQ0rVqzAl19+iTFjTuGll3zw73974aWXfDBmzCl8+eWXGgEYUDAiFhR0iyNgRERklSwiCBNCYNiwYRgzZoxG8KMuJSUFvr6+GsdUP6ekpBhso35e/XX62lSpUkXjvJ2dHTw9PYt8H/X30GXBggWQy+XSV2BgoN62prh7Fxg9GlD+Lx1LqQTeeKPg+PNSjYDpW/moULgZfS2WnyAiImtg1tWR06dPx4cffmiwzeXLl7F//35kZWVhxowZZdQz85gxYwYmTpwo/ZyZmVmigdj1688CMJX8fODGjeeblkxLS0NqaioA4M6dQIMrH/v16wcPDw+912L5CSIishZmDcImTZqEYcOGGWxTq1YtHDp0CHFxcXB0dNQ416JFCwwcOBBr166Fn58f7t27p3Fe9bOfn5/0p6426udVx9Rzku7duyflnvn5+eH+/fsa18jLy0N6enqR76P+Hro4Ojpq3WNJqlMHsLHRDMRsbYHatU2/pmoaEnhWgLUw9ZWPHh4ezPciIiKCmacjfXx8UL9+fYNfDg4OWL58Oc6dO4f4+HjEx8dLZSU2btyI+fPnAwBCQ0Pxyy+/IDc3V7p+bGws6tWrh8qVK0ttDh48qNGH2NhYhIaGAgCCgoLg5+en0SYzMxMnTpyQ2oSGhiIjIwOnT5+W2hw6dAhKpRKtWrUyui/mUK0a8OWXBYEXUPDnF1883yiYvmnIZ7jykYiISBeLyAmrXr06XnjhBemrbt26AIDg4GBU+18E8frrr8PBwQFRUVG4ePEiNm7ciGXLlmlM740fPx579+7F4sWLceXKFcydOxd//PEHoqOjAQAymQwxMTF4//33sX37dvz5558YMmQIAgIC0Lt3bwBAgwYN0LVrV4waNQonT57Eb7/9hujoaPTv318qnWFMX8wlKgq4eRM4fLjgz6iokrmurgKsANC370/F2o6IiIjIWlSYivlyuRz79+/HuHHj0Lx5c3h7e2P27Nka9bvatGmD9evX45133sHbb7+NOnXqYNu2bXjhhRekNlOnTsXjx48xevRoZGRk4JVXXsHevXvh5OQktVm3bh2io6PRqVMn2NjYICIiAsuXLy9WX8ypWrWSLU0BPCvAqh6IyWRKBAaWQNY/ERFRBWRRdcKsTWnVCTOFvtpiycnJUhFaYzblZg0wIiKq6Iz9/K4wI2FUelavflbawsamIK9M1zRmSMhZBAff0FmAVYXlJ4iIiAowCCO90tLScPNmHkaPrgKlUgZAVVtMoGnT+6hZU/vXp2ADbu3gq0+fPggICGD5CSIiov9hEEY6qUpPJCbWhFI5VONcfr4Mn366B0FBt9CvXz+jruft7c0AjIiISI1FrI6ksqcqPaFKuFenXvdLJpMZdT1OQxIREWniSBgZJJdnoWfPnVoJ96opR7lcjujoaClo04VV8ImIiLQxCKMiFZVwzwCLiIio+BiEkVH0JdwTERGRaZgTRkRERGQGHAmzEmlpaczbIiIiKkcYhFkBVbmJokRHRzMQIyIiKiOcjrQChkbA9LUztqQES08QERGZhiNhpJOXlxdLTxAREZUiBmGkFwMsIiKi0sMgjCSpqal6z3HUi4iIqGQxCCPJ1q1bDZ5n4j4REVHJYWI+Gc3YBH8iIiIqGoMwIiIiIjNgEGYFWEaCiIio/GFOmBUoqtxEampqkflgREREVLIYhFkJJtQTERGVL5yOJC0KhRsSE2tCoXAzd1eIiIgqLI6EkYYzZ5phx44eEMIGMpkSPXvuREjIWXN3i4iIqMLhSBhJifsKhZsUgAGAEDbYsaOHNCLGBH8iIqKSIxNCCHN3gnTLzMyEXC6HQqGAu7t7qb5XWloaDh8G/v1v7dyxn35Kw6uvMq+MiIjIGMZ+fnM6kgAUBFitWwM2NoBS+ey4rS3QqpUXGH8RERGVLE5HkqRaNeDLLwsCL6Dgzy++KDhOREREJYsjYaQhKgoIDwdu3ABq12YARkREVFoYhJGWatUYfBEREZU2TkcSERERmQGDMCIiIiIzYBBGREREZAYMwoiIiIjMgEEYERERkRkwCCMiIiIyAwZhRERERGbAIIyIiIjIDBiEEREREZkBgzAiIiIiM2AQRkRERGQG3DuyHBNCAAAyMzPN3BMiIiIylupzW/U5rg+DsHIsKysLABAYGGjmnhAREVFxZWVlQS6X6z0vE0WFaWQ2SqUSSUlJcHNzg0wmM/k6mZmZCAwMxJ07d+Du7l6CPbQcfAZ8Bip8DnwGAJ+BCp9D6TwDIQSysrIQEBAAGxv9mV8cCSvHbGxsUK1atRK7nru7u9X+T6bCZ8BnoMLnwGcA8Bmo8DmU/DMwNAKmwsR8IiIiIjNgEEZERERkBgzCrICjoyPmzJkDR0dHc3fFbPgM+AxU+Bz4DAA+AxU+B/M+AybmExEREZkBR8KIiIiIzIBBGBEREZEZMAgjIiIiMgMGYURERERmwCDMQq1cuRIvvviiVFwuNDQUe/bskc4/ffoU48aNg5eXFypVqoSIiAjcu3dP4xq3b99G9+7d4eLigipVqmDKlCnIy8sr61spMQsXLoRMJkNMTIx0rKI/h7lz50Imk2l81a9fXzpf0e9f3d9//41BgwbBy8sLzs7OaNy4Mf744w/pvBACs2fPhr+/P5ydnREWFobr169rXCM9PR0DBw6Eu7s7PDw8EBUVhUePHpX1rZikZs2aWr8LMpkM48aNA2Advwv5+fmYNWsWgoKC4OzsjODgYLz33nsa+/dV9N8DoGCrnJiYGNSoUQPOzs5o06YNTp06JZ2viM/gl19+Qc+ePREQEACZTIZt27ZpnC+pez5//jzatm0LJycnBAYGYtGiRc/XcUEWafv27WLXrl3i2rVr4urVq+Ltt98W9vb24sKFC0IIIcaMGSMCAwPFwYMHxR9//CFat24t2rRpI70+Ly9PvPDCCyIsLEycPXtW7N69W3h7e4sZM2aY65aey8mTJ0XNmjXFiy++KMaPHy8dr+jPYc6cOaJRo0YiOTlZ+nrw4IF0vqLfv0p6erqoUaOGGDZsmDhx4oT466+/xL59+8SNGzekNgsXLhRyuVxs27ZNnDt3TvTq1UsEBQWJf/75R2rTtWtX0aRJE/H777+LX3/9VdSuXVsMGDDAHLdUbPfv39f4PYiNjRUAxOHDh4UQ1vG7MH/+fOHl5SV27twpEhMTxebNm0WlSpXEsmXLpDYV/fdACCH69esnGjZsKI4ePSquX78u5syZI9zd3cXdu3eFEBXzGezevVvMnDlTbN26VQAQP//8s8b5krhnhUIhfH19xcCBA8WFCxfEjz/+KJydncUXX3xhcr8ZhFUglStXFl9//bXIyMgQ9vb2YvPmzdK5y5cvCwAiLi5OCFHwC2tjYyNSUlKkNitXrhTu7u4iOzu7zPv+PLKyskSdOnVEbGysaN++vRSEWcNzmDNnjmjSpInOc9Zw/yrTpk0Tr7zyit7zSqVS+Pn5iY8++kg6lpGRIRwdHcWPP/4ohBDi0qVLAoA4deqU1GbPnj1CJpOJv//+u/Q6X0rGjx8vgoODhVKptJrfhe7du4sRI0ZoHOvTp48YOHCgEMI6fg+ePHkibG1txc6dOzWOh4SEiJkzZ1rFMygchJXUPX/++eeicuXKGv8/TJs2TdSrV8/kvnI6sgLIz8/Hhg0b8PjxY4SGhuL06dPIzc1FWFiY1KZ+/fqoXr064uLiAABxcXFo3LgxfH19pTbh4eHIzMzExYsXy/wense4cePQvXt3jfsFYDXP4fr16wgICECtWrUwcOBA3L59G4D13D8AbN++HS1atMC///1vVKlSBc2aNcNXX30lnU9MTERKSorGs5DL5WjVqpXGs/Dw8ECLFi2kNmFhYbCxscGJEyfK7mZKQE5ODn744QeMGDECMpnMan4X2rRpg4MHD+LatWsAgHPnzuHYsWPo1q0bAOv4PcjLy0N+fj6cnJw0jjs7O+PYsWNW8QwKK6l7jouLQ7t27eDg4CC1CQ8Px9WrV/Hw4UOT+sYNvC3Yn3/+idDQUDx9+hSVKlXCzz//jIYNGyI+Ph4ODg7w8PDQaO/r64uUlBQAQEpKisZftqrzqnOWYsOGDThz5oxGvoNKSkpKhX8OrVq1wpo1a1CvXj0kJydj3rx5aNu2LS5cuGAV96/y119/YeXKlZg4cSLefvttnDp1Cm+99RYcHBwwdOhQ6V503av6s6hSpYrGeTs7O3h6elrUswCAbdu2ISMjA8OGDQNgHf8vAMD06dORmZmJ+vXrw9bWFvn5+Zg/fz4GDhwIAFbxe+Dm5obQ0FC89957aNCgAXx9ffHjjz8iLi4OtWvXtopnUFhJ3XNKSgqCgoK0rqE6V7ly5WL3jUGYBatXrx7i4+OhUCjw008/YejQoTh69Ki5u1Vm7ty5g/HjxyM2NlbrX33WQvUvfAB48cUX0apVK9SoUQObNm2Cs7OzGXtWtpRKJVq0aIEPPvgAANCsWTNcuHABq1atwtChQ83cu7K3evVqdOvWDQEBAebuSpnatGkT1q1bh/Xr16NRo0aIj49HTEwMAgICrOr34Pvvv8eIESNQtWpV2NraIiQkBAMGDMDp06fN3TUqhNORFszBwQG1a9dG8+bNsWDBAjRp0gTLli2Dn58fcnJykJGRodH+3r178PPzAwD4+flprYxS/axqU96dPn0a9+/fR0hICOzs7GBnZ4ejR49i+fLlsLOzg6+vr1U8B3UeHh6oW7cubty4YTW/BwDg7++Phg0bahxr0KCBNDWruhdd96r+LO7fv69xPi8vD+np6Rb1LG7duoUDBw5g5MiR0jFr+V2YMmUKpk+fjv79+6Nx48YYPHgwJkyYgAULFgCwnt+D4OBgHD16FI8ePcKdO3dw8uRJ5ObmolatWlbzDNSV1D2Xxv8jDMIqEKVSiezsbDRv3hz29vY4ePCgdO7q1au4ffs2QkNDAQChoaH4888/NX7pYmNj4e7urvVhVl516tQJf/75J+Lj46WvFi1aYODAgdL31vAc1D169AgJCQnw9/e3mt8DAHj55Zdx9epVjWPXrl1DjRo1AABBQUHw8/PTeBaZmZk4ceKExrPIyMjQGC04dOgQlEolWrVqVQZ3UTK+/fZbVKlSBd27d5eOWcvvwpMnT2Bjo/mxZmtrC6VSCcC6fg8AwNXVFf7+/nj48CH27duH1157zeqeAVBy/91DQ0Pxyy+/IDc3V2oTGxuLevXqmTQVCYAlKizV9OnTxdGjR0ViYqI4f/68mD59upDJZGL//v1CiILl6NWrVxeHDh0Sf/zxhwgNDRWhoaHS61XL0bt06SLi4+PF3r17hY+Pj0UtR9dFfXWkEBX/OUyaNEkcOXJEJCYmit9++02EhYUJb29vcf/+fSFExb9/lZMnTwo7Ozsxf/58cf36dbFu3Trh4uIifvjhB6nNwoULhYeHh/jvf/8rzp8/L1577TWdS9SbNWsmTpw4IY4dOybq1KlTrpflF5afny+qV68upk2bpnXOGn4Xhg4dKqpWrSqVqNi6davw9vYWU6dOldpYw+/B3r17xZ49e8Rff/0l9u/fL5o0aSJatWolcnJyhBAV8xlkZWWJs2fPirNnzwoA4pNPPhFnz54Vt27dEkKUzD1nZGQIX19fMXjwYHHhwgWxYcMG4eLiwhIV1mjEiBGiRo0awsHBQfj4+IhOnTpJAZgQQvzzzz/iP//5j6hcubJwcXER//rXv0RycrLGNW7evCm6desmnJ2dhbe3t5g0aZLIzc0t61spUYWDsIr+HCIjI4W/v79wcHAQVatWFZGRkRq1sSr6/avbsWOHeOGFF4Sjo6OoX7+++PLLLzXOK5VKMWvWLOHr6yscHR1Fp06dxNWrVzXapKWliQEDBohKlSoJd3d3MXz4cJGVlVWWt/Fc9u3bJwBo3ZcQ1vG7kJmZKcaPHy+qV68unJycRK1atcTMmTM1SgpYw+/Bxo0bRa1atYSDg4Pw8/MT48aNExkZGdL5ivgMDh8+LABofQ0dOlQIUXL3fO7cOfHKK68IR0dHUbVqVbFw4cLn6rdMCLVSwkRERERUJpgTRkRERGQGDMKIiIiIzIBBGBEREZEZMAgjIiIiMgMGYURERERmwCCMiIiIyAwYhBERERGZAYMwIiIiIjNgEEZEFcqrr76KmJgYc3ej1M2dOxdNmzY1dzeI6DkwCCMiKkdycnLK9P2EEMjLyyvT9ySiAgzCiKjCGDZsGI4ePYply5ZBJpNBJpPh5s2buHDhArp164ZKlSrB19cXgwcPRmpqqvS6V199FW+++SZiYmJQuXJl+Pr64quvvsLjx48xfPhwuLm5oXbt2tizZ4/0miNHjkAmk2HXrl148cUX4eTkhNatW+PChQsafTp27Bjatm0LZ2dnBAYG4q233sLjx4+l8zVr1sR7772HIUOGwN3dHaNHjwYATJs2DXXr1oWLiwtq1aqFWbNmITc3FwCwZs0azJs3D+fOnZPuc82aNbh58yZkMhni4+Ol62dkZEAmk+HIkSMa/d6zZw+aN28OR0dHHDt2DEqlEgsWLEBQUBCcnZ3RpEkT/PTTTyX9n4iI1DAII6IKY9myZQgNDcWoUaOQnJyM5ORkuLm5oWPHjmjWrBn++OMP7N27F/fu3UO/fv00Xrt27Vp4e3vj5MmTePPNNzF27Fj8+9//Rps2bXDmzBl06dIFgwcPxpMnTzReN2XKFCxevBinTp2Cj48PevbsKQVLCQkJ6Nq1KyIiInD+/Hls3LgRx44dQ3R0tMY1Pv74YzRp0gRnz57FrFmzAABubm5Ys2YNLl26hGXLluGrr77CkiVLAACRkZGYNGkSGjVqJN1nZGRksZ7V9OnTsXDhQly+fBkvvvgiFixYgO+++w6rVq3CxYsXMWHCBAwaNAhHjx4t1nWJqBiea/tvIqJypn379mL8+PHSz++9957o0qWLRps7d+4IAOLq1avSa1555RXpfF5ennB1dRWDBw+WjiUnJwsAIi4uTgghxOHDhwUAsWHDBqlNWlqacHZ2Fhs3bhRCCBEVFSVGjx6t8d6//vqrsLGxEf/8848QQogaNWqI3r17F3lfH330kWjevLn085w5c0STJk002iQmJgoA4uzZs9Kxhw8fCgDi8OHDGv3etm2b1Obp06fCxcVFHD9+XON6UVFRYsCAAUX2jYhMY2fOAJCIqLSdO3cOhw8fRqVKlbTOJSQkoG7dugCAF198UTpua2sLLy8vNG7cWDrm6+sLALh//77GNUJDQ6XvPT09Ua9ePVy+fFl67/Pnz2PdunVSGyEElEolEhMT0aBBAwBAixYttPq2ceNGLF++HAkJCXj06BHy8vLg7u5e7PvXR/09b9y4gSdPnqBz584abXJyctCsWbMSe08i0sQgjIgqtEePHqFnz5748MMPtc75+/tL39vb22uck8lkGsdkMhkAQKlUFuu933jjDbz11lta56pXry597+rqqnEuLi4OAwcOxLx58xAeHg65XI4NGzZg8eLFBt/PxqYgw0QIIR1TTY0Wpv6ejx49AgDs2rULVatW1Wjn6Oho8D2JyHQMwoioQnFwcEB+fr70c0hICLZs2YKaNWvCzq7k/8r7/fffpYDq4cOHuHbtmjTCFRISgkuXLqF27drFuubx48dRo0YNzJw5Uzp269YtjTaF7xMAfHx8AADJycnSCJZ6kr4+DRs2hKOjI27fvo327dsXq69EZDom5hNRhVKzZk2cOHECN2/eRGpqKsaNG4f09HQMGDAAp06dQkJCAvbt24fhw4drBTGmePfdd3Hw4EFcuHABw4YNg7e3N3r37g2gYIXj8ePHER0djfj4eFy/fh3//e9/tRLzC6tTpw5u376NDRs2ICEhAcuXL8fPP/+sdZ+JiYmIj49HamoqsrOz4ezsjNatW0sJ90ePHsU777xT5D24ublh8uTJmDBhAtauXYuEhAScOXMGn376KdauXWvysyEiwxiEEVGFMnnyZNja2qJhw4bw8fFBTk4OfvvtN+Tn56NLly5o3LgxYmJi4OHhIU3fPY+FCxdi/PjxaN68OVJSUrBjxw44ODgAKMgzO3r0KK5du4a2bduiWbNmmD17NgICAgxes1evXpgwYQKio6PRtGlTHD9+XFo1qRIREYGuXbuiQ4cO8PHxwY8//ggA+Oabb5CXl4fmzZsjJiYG77//vlH38d5772HWrFlYsGABGjRogK5du2LXrl0ICgoy4akQkTFkQj15gIiIjHLkyBF06NABDx8+hIeHh7m7Q0QWiCNhRERERGbAIIyIiIjIDDgdSURERGQGHAkjIiIiMgMGYURERERmwCCMiIiIyAwYhBERERGZAYMwIiIiIjNgEEZERERkBgzCiIiIiMyAQRgRERGRGTAIIyIiIjKD/wfm0URWgsqaDAAAAABJRU5ErkJggg==", + "image/png": 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", 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", 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", 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", 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", 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", 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99FP5+vqqTZs2mjZtml544QW1bt1a7777rt3E7U6dOmno0KH6xz/+ofDwcL344osKDw/X/Pnz9cEHH6hly5ZKTU3V1KlTTfopAQAVQX5+vlJSUmxCz2233aZnnnlGvr6+JlYmWQxnPBftQbKyshQaGqrMzEybNX0uXLigffv2qVGjRgoMDDSxQrga9x4Aiu+7776z21/ziSeeKPeV/wv7/X05hroAAIBTJCcn27yvWrWBRo4caOrQ1uUY6gIAAGXyxx9/2IWe7767Th99FFehQo9Ejw8AACiDd999V7t377Zpa916lNLSqpq6UGFhCD4AAKDECgoKHD7Fm5SUJEnq29fVFRUPwQcAAJTItm3b7NZ6u+eee9SqVSuTKio+gg8AACi2y+fySNIzzzxjXfy2oiP4AACAKzp16pRmzJhh09a4cWP179/fpIpKh+ADAACKNHPmTLutJr79dpiSkiJMqqj0CD4AAMAhwzD07LPP2rWnpSVVyCe2isM9BuTgFuLi4tSnTx/r+1tvvVUjRowo0zWdcQ0AQMmtXbvWLvS0atVKSUlJWrdO6tXLpMLKiB4fLxAXF6e3335bkuTn56f69etrwIABGjt2rCpVKr//CXz88cfy8/Mr1rlr1qxR586dderUKZtlzUtyDQCAcziawDxmzBiP2LaH4OMlunfvrnnz5ik7O1ufffaZ4uPj5efnp8TERJvzcnJy5O/v75TPrF69eoW4BgCgeM6dO+dwo+m0tCRFR7tvL89fMdTlJQICAhQZGakGDRpo2LBh6tq1q5YsWWIdnnr++edVp04dNWvWTJJ08OBB/d///Z/CwsJUvXp19e7dW/v377deLz8/XyNHjlRYWJhq1Kihp59+Wpfvd3v5MFV2drbGjBmjqKgoBQQEqGnTpnrrrbe0f/9+de7cWZJUrVo1WSwWxcXFObzGqVOnNGDAAFWrVk1BQUHq0aOHdu3aZT0+f/58hYWFKS0tTS1atFDVqlXVvXt3HT161HrOmjVr1KFDB1WpUkVhYWG64YYb9NtvvznpbxoA3NO0adPsQk+3bt2Ulpak9HQpNdWkwpyM4OOlKleurJycHEnSypUrtWPHDq1YsUJLly5Vbm6uYmNjFRwcrG+++UZr1661BohL3/PSSy9p/vz5mjt3rr799ludPHlSn3zySZGfOWDAAL3//vuaMWOGtm3bpjfeeENVq1ZVVFSUPvroI0nSjh07dPToUb3yyisOrxEXF6cff/xRS5YsUXp6ugzD0O23367c3FzrOefPn9fUqVO1YMECff311zpw4ICeeuopSVJeXp769OmjW265RT///LPS09P18MMPV7i9ZADAlZKTk3XmzBmbtgkTJigmJkYJCVJMjNx2MvPlGOryMoZhaOXKlUpLS9Njjz2mEydOqEqVKpozZ451iOudd95RQUGB5syZYw0E8+bNU1hYmNasWaNu3brp5ZdfVmJiou6++25J0uzZs5WWllbo5+7cuVP/+c9/tGLFCnXt2lXSxfUfLrk0pBUREWEzx+evdu3apSVLlmjt2rXq1KmTpIt7xERFRWnx4sW69957JUm5ubmaPXu2mjRpIkkaPny4dYJeVlaWMjMzdeedd1qPt2jRouR/kQDgAdauXasvv/zSrv3SthPSxeEtTxjiuoQeH5MsWSJ16nTxqyssXbpUVatWVWBgoHr06KF//OMfmjhxoiTpmmuusZnXs3nzZu3evVvBwcGqWrWqqlatqurVq+vChQvas2ePMjMzdfToUXXs2NH6PZUqVVL79u0L/fxNmzbJ19dXt9xyS6l/hm3btqlSpUo2n1ujRg01a9ZM27Zts7YFBQVZQ40k1a5dW8ePH5d0MWDFxcUpNjZWPXv21CuvvGIzDAYA3iI5Odku9LRo8ahN6PFEBB+TpKbKpWOmnTt31qZNm7Rr1y79+eefevvtt1WlShVJsn695OzZs2rXrp02bdpk89q5c6f69etXqs+vXLlymX+G4rr8KTCLxWIz/2jevHlKT09Xp06dtGjRIl199dVav369y+oDADPl5OQ4fGpr4sQkvfxyuAkVuRbBxySuHjOtUqWKmjZtqvr161/xEfZrr71Wu3btUkREhJo2bWrzCg0NVWhoqGrXrq3vvvvO+j15eXnasGFDode85pprVFBQoK+++srh8Us9Tvn5+YVeo0WLFsrLy7P53D/++EM7duxQy5Yti/yZLhcdHa3ExEStW7dOrVu31nvvvVei7wcAd5ScnKyUlBSbtsDAQEVHJ3nUPJ6iEHxM0quXKuwCUP/85z9Vs2ZN9e7dW99884327dunNWvW6PHHH9ehQ4ckSU888YRSU1O1ePFibd++XY8++qhOnz5d6DUbNmyogQMHatCgQVq8eLH1mpd2923QoIEsFouWLl2qEydO6OzZs3bXuOqqq9S7d28NGTJE3377rTZv3qwHHnhAdevWVe/evYv1s+3bt0+JiYlKT0/Xb7/9pi+++EK7du1ing8Aj+eol2fcuHEaM2ZMhf6d5GwEH9gJCgrS119/rfr16+vuu+9WixYtNHjwYF24cEEhISGSpFGjRql///4aOHCgYmJiFBwcrLvuuqvI686aNUv33HOPHn30UTVv3lxDhgzRuXPnJEl169ZVcnKyEhISVKtWLQ0fPtzhNebNm6d27drpzjvvVExMjAzD0GeffVbsRQ6DgoK0fft29e3bV1dffbUefvhhxcfH65FHHinB3xAAuI/du3c7DD1JSUnluohtRWUxLl98xctlZWUpNDRUmZmZ1l/yknThwgXt27dPjRo18oiVK1F83HsA7spR4Ln99tt13XXXmVBN+Srs9/flvC/qAQDg4QrbXNTTn9gqDoIPAAAeZMGCBdq7d69dO6HnIoIPAAAewtHQ1siRIxUcHGxCNRUTwQcAADd36NAhvfXWW3bt9PLYI/iUEHPBvQ/3HEBF5qiX5/DhOnr//SEi99gj+BTTpcelz58/79JViGG+8+fPS7JfERoAzOYo9MyYkaScHOnxx00oyA0QfIrJ19dXYWFh1j2fgoKC2NHbwxmGofPnz+v48eMKCwuTr6+v2SUBgCRp/vz5+u233+za09KSNG+edyxEWFoEnxKIjIyUJGv4gXcICwuz3nsAMJujXp57771XLVu2ZGirGAg+JWCxWFS7dm1FREQoNzfX7HLgAn5+fvT0AKgQzp49q5deesmunQnMJUPwKQVfX19+GQIAXMZRL48kRUcTekqK4AMAQAXmKPSsWDFWa9f6KSaG+TwlRfABAKACWr58ub7//nu79ujoJEVHS6mpUkKCCYW5OYIPAAAVjOO1edrozTf7WN/T01M6PmYXAAAALiooKHAYetLSknTnnX1cX5AHoscHAIAKoLAJzElJSTym7kQEHwAATOYo9MTHx6tmzZomVOPZGOoCAMAk3333XaFDW4Se8kGPDwAAJihsaCs5OUmLF7u2Fm9C8AEAwMUchZ6JE5NksUiJiTyxVZ4IPgAAuEhhvTw5OUmKibm4Lg+hp3wRfAAAcAFHoWfv3hv07393VUyMtG6dCUV5IYIPAADl6OjRo3rzzTft2i+twLxrFyswuxLBBwCAclLU2jyXMLTlWgQfAADKgaPQM3bsWPn5+ZlQDS4h+AAA4ETF6eWBeVjAEAAAJyH0VHz0+AAAUEa5ubmaPHmyXTuBp+Ih+AAAUAb08rgXgg8AAKXkKPQMGTJEderUMaEaFAfBBwCAEkpLS9P69evt2unlqfgIPgAAlABDW+6N4AMAQDE5Cj0EHvdC8AEA4AoK6+WJjib0uBuCDwAARXAUemrVulFDh/7dhGpQVgQfAAAcOHDggObNm2fXHh2dxP5abozgAwDAZZjA7LkIPgAA/IWj0DN+/Hj5+vqaUA2cjeADAIDo5fEWBB8AgNdzFHqCgoI0evRoE6pBeSL4AAC81iefXNDPP79g184EZs9F8AEAeCWGtryTj9kFlIfXXntNDRs2VGBgoDp27Kjvv//e7JIAABWIo9AzdOhQQo8X8Lgen0WLFmnkyJGaPXu2OnbsqJdfflmxsbHasWOHIiIizC4PAGCijz/+WL/88otdO4HHe1gMwzDMLsKZOnbsqOuuu06vvvqqJKmgoEBRUVF67LHHlJCQcMXvz8rKUmhoqDIzMxUSElLe5QIAXIShLc9W3N/fHtXjk5OTow0bNigxMdHa5uPjo65duyo9Pd3h92RnZys7O9v6Pisrq9zrBAC4FpuL4hKPCj6///678vPzVatWLZv2WrVqafv27Q6/JyUlpdD/CgAAuDd6eXA5j5zcXBKJiYnKzMy0vg4ePGh2SQAAJ3AUenbu7MKO6l7Oo3p8atasKV9fXx07dsym/dixY4qMjHT4PQEBAQoICHBFeQAAF9i+fbsWLVpk104vDyQP6/Hx9/dXu3bttHLlSmtbQUGBVq5cqZiYGBMrAwC4QnJyMqEHRfKoHh9JGjlypAYOHKj27durQ4cOevnll3Xu3Dk9+OCDZpcGAChHjoa20tKeUUKCR/03PsrI44LPP/7xD504cUITJkxQRkaG2rZtq88//9xuwjMAwDMUNYGZjh5czuPW8Skr1vEBAPfhKPQEBgZqzJgxJlQDM3nlOj4AAO9w7tw5TZ061a6duTy4EoIPAMCtsDYPyoIZXwAAt+Eo9HzzzXDW5kGx0eMDAKjw3nzzTR09etSunV4elBTBBwBQoTG0BWci+AAAKqRPPzW0adOzdu0EHpQFwQcAUOHQy4PyQvABAFQojkLPbbfdpk6dOplQDTwNwQcAUCFs2LBBS5cutWtPS2MFZjgPwQcAYLrChrbS0pKUkODiYuDRCD4AAFM5Cj0TJkyQxWKhpwdOR/ABAJiCCcwwA8EHAOByjkKPn5+fxo4da0I18CYEHwCAy5w6dUozZsywa6eXB65C8AEAuARDW6gICD4AgHLnKPQ88cQTCgsLc30x8GoEHwBAuaGXBxWNj9kFAAA8E6EHFRE9PgAApzIMQ88+a7+5aE5Okp5/3oSCgL8g+AAAnIZeHlR0BB8AgFM4Cj3du3dXx44dTagGcIzgAwAok9WrV+vrr7+2a6eXBxURwQcAUGoMbcHdEHwAAKVS1OaiQEVF8AEAlAi9PHBnBB8AQLE5Cj3Z2VU1efIoE6oBSo7gAwC4ooyMDL3xxht27WlpSUpIMKEgoJQIPgCAIhU1tMXoFtwNwQcAUChHoWfkyJEKDg42oRqg7Ag+AAA7TGCGp2KTUgCADUIPPBk9PgAASVJ+fr6ee+45u3YCDzwJwQcAQC8PvAZDXQDg5RyFnl9+6a3oaEIPPA89PgDgpZYtW6Yff/zRrj0tLUnr1plQEOACBB8A8EKFDW2xICE8HcEHALyMo9CTmpqkkSNFTw88HsEHADzYkiVSaqqUkCBt3MgKzADBBwA8WGqqlJ7uOPRERkbqkUceMaEqwDwEHwDwYI89dlixsXPs2qOjk9SrlwkFASYj+ACAhypqAjNDW/BWJQ4+vr6+Onr0qCIiImza//jjD0VERCg/P99pxQEASsdR6PnwwzHKywvUCy+YUBBQQZQ4+BiG4bA9Oztb/v7+ZS4IAFB6Ra3ATC8PUILgM2PGDEmSxWLRnDlzVLVqVeux/Px8ff3112revLnzKwQAFAvbTgBXVuzgM336dEkXe3xmz54tX19f6zF/f381bNhQs2fPdn6FAIAiLV6cp82bn7drJ/AA9oodfPbt2ydJ6ty5sz7++GNVq1at3IoCABQPvTxAyZR4js/q1avLow4AQAksWeJ4bZ6PP75PkyY1M6EiwD2UOPgMGjSoyONz584tdTEAgCtbsGCB9u7da9eelpakSZPE+jxAEUocfE6dOmXzPjc3V1u2bNHp06fVpUsXpxUGALDHU1tA2ZQ4+HzyySd2bQUFBRo2bJiaNGnilKIAAPYchR7m8gAlYzEKW5inhHbs2KFbb71VR48edcblTJOVlaXQ0FBlZmYqJCTE7HIAeLnC5vJIhB7gr4r7+9vHWR+4Z88e5eXlOetyAAA5Dj2HD9dTdDShByiNEg91jRw50ua9YRg6evSoli1bpoEDBzqtMADwZrt27dJ7771n1z51apLefZcJzEBplTj4bNy40ea9j4+PwsPD9dJLL13xiS8AwJUVtbkooQcoG9bxAYAKxFHo+c9/EpWS4s9TW4ATlDj4XHL8+HHt2LFDktSsWTO73doBAMVXWC/PxIlJiomhlwdwlhJPbs7KylL//v1Vp04d3XLLLbrllltUt25dPfDAA8rMzCyPGgHAoxUWenJyLoaehAQXFwR4sBL3+AwZMkQbN27UsmXLFBMTI0lKT0/XE088oUceeUQLFy50epEA4Imys7OVmppq185j6kD5KfE6PlWqVFFaWppuvPFGm/ZvvvlG3bt317lz55xaoKuxjg8AV2BzUcC5ivv7u8Q9PjVq1FBoaKhde2hoKDu2A0AxOAo9q1YN0FdfNTKhGsC7lDj4jB8/XiNHjtSCBQsUGRkpScrIyNDo0aP1zDPPOL1AAPAU//rXv3TkyBG79rS0JObxAC5S4qGu6Oho7d69W9nZ2apfv74k6cCBAwoICNBVV11lc+5PP/3kvEpdhKEuAOWhsKGt6OgkntgCnKDchrp69+4ti8VSpuIAwJs4Cj1Tpybp7FnxqDrgYiUOPhMnTiyHMgDA8xS1AvPjj0urV/OoOuBqJQ4+jRs31g8//KAaNWrYtJ8+fVrXXnut9u7d67TiAMBdOQo9rVq10j333MMKzICJShx89u/fr/z8fLv27OxsHTp0yClFAYC7+uWXX/Txxx/btfOYOlAxFDv4LFmyxPrntLQ0m0fa8/PztXLlSjVqxKOYALwXa/MAFV+xg0+fPn0kSRaLRQMHDrQ55ufnp4YNG+qll15yanGX7N+/X5MmTdKqVauUkZGhOnXq6IEHHtC4cePk7+9vPe/nn39WfHy8fvjhB4WHh+uxxx7T008/XS41AcBfOQo948ePl6+vrwnVAChMsYNPQUGBJKlRo0b64YcfVLNmzXIr6nLbt29XQUGB3njjDTVt2lRbtmzRkCFDdO7cOU2dOlXSxcfYunXrpq5du2r27Nn65ZdfNGjQIIWFhenhhx92Wa0AvAu9PIB7KfE6PhXFlClTNGvWLOtk6lmzZmncuHHKyMiw9gIlJCRo8eLF2r59e7Gvyzo+AIqLtXmAiqPc1vF59tlnizw+YcKEkl6yVDIzM1W9enXr+/T0dN188802Q1+xsbF64YUXdOrUqUK308jOzlZ2drb1fVZWVvkVDcAjnD9/XlOmTLFrT0tLUno6a/MAFVmJg88nn3xi8z43N1f79u1TpUqV1KRJE5cEn927d2vmzJnWYS7p4rYZl0+urlWrlvVYYcEnJSWl0P9qA4DLFdXLEx0tpaayNg9QkZU4+GzcuNGuLSsrS3FxcbrrrrtKdK2EhAS98MILRZ6zbds2NW/e3Pr+8OHD6t69u+69914NGTKkRJ/nSGJiokaOHGl9n5WVpaioqDJfF4DncRR6mjUbovvuq2N9T08PULGVOPg4EhISouTkZPXs2VP9+/cv9veNGjVKcXFxRZ7TuHFj65+PHDmizp07q1OnTnrzzTdtzouMjNSxY8ds2i69v7SZqiMBAQEKCAgods0AvM+MGTN06tQpu/aJE5PUrJl0330mFAWgVJwSfKSLc24yMzNL9D3h4eEKDw8v1rmHDx9W586d1a5dO82bN08+Pj42x2NiYjRu3Djl5ubKz89PkrRixQo1a9as0GEuALiSwoa2Fi68+NQWWxcC7qXEwWfGjBk27w3D0NGjR7VgwQL16NHDaYX91eHDh3XrrbeqQYMGmjp1qk6cOGE9dqk3p1+/fkpOTtbgwYM1ZswYbdmyRa+88oqmT59eLjUB8HyOQs+lx9SZzwO4pxI/zn75BGIfHx+Fh4erS5cuSkxMVHBwsFMLlKT58+frwQcfdHjsr+X/dQHDmjVr6rHHHtOYMWNK9Fk8zg6Ax9QB91Pc399uu45PeSH4AN7NUejZu/cG/fvfXRUTI61bZ0JRAK6o3NbxkS7uxL57925JUtOmTRUWFlaqIgGgotiwYYOWLl1q137pMfVduxjWAjxBiYLP/v37FR8fr7S0NOsQk8ViUffu3fXqq6+qYcOG5VEjAJSrwoa2Jk5MsvbyMMQFeIZiB5+DBw/q+uuvl5+fnyZNmqQWLVpIkn799VfNmjVLMTEx+uGHH1SvXr1yKxYAnM1R6HnmmWe0dKmPYmLo5QE8TbHn+AwePFi7d+9WWlqaAgMDbY79+eef6t69u6666irNmTOnXAp1Feb4AN6BzUUBz+L0OT6ff/65Fi1aZBd6JKly5cqaNGmS7mMVLwBugNADeK9iB5/ff/+9yDk8jRs31smTJ51REwCUi6ysLIdrexF4AO9R7OBTu3Zt/frrr4XO4dmyZUuRW0MAgJno5QEglSD49OnTR0899ZRWrlxpt83E8ePHNWbMGPXp08fZ9QFAmTkKPfHx8apZs6YJ1QAwU7EnN586dUodO3ZURkaGHnjgATVv3lyGYWjbtm167733FBkZqfXr16t69erlXXO5YnIz4BmWLJHWrn1FQUGn7Y6lpSWxECHgYZw+ublatWr67rvvNHbsWC1cuFCnT5+WJIWFhalfv36aPHmy24ceAJ5j48ZkBQXZt6elJfGIOuDFSrVlhWEY1o1Cw8PDZfGg7Ynp8QHcm2EYevbZZ+3a2WcL8GzlumWFxWJRREREqYsDgPLABGYAV1Kq4AMAFY2j0BMbG6vrr7/ehGoAVFQEHwBu7fvvv9fy5cvt2unlAeAIwQeA22JoC0BJEXwAuCVHoSctbYLWrfOchy0AOF+xgs+MGTOKfcHHH3+81MUAwJUU1svDY+oAiqNYj7M3atSoeBezWLR3794yF2UmHmcHKi5HoSckJERPPvmkCdUAqEic+jj7vn37nFYYAJTUqVOnHPY8M5cHQEn5lPYbc3JytGPHDuXl5TmzHgCwkZyc7DD0pKURegCUXImDz/nz5zV48GAFBQWpVatWOnDggCTpscceU2pqqtMLBOC9HA1ttW49kvk8AEqtxMEnMTFRmzdv1po1axQYGGht79q1qxYtWuTU4gB4p5dfftlh6ElKSlLfvsFat05sPwGgVEocfBYvXqxXX31VN954o80eXa1atdKePXucWhwA75OcnKzMzEy7doa2ADhDidfxOXHihMN9us6dO+dRm5UCcK2iNhdNTRVDWwCcosTBp3379lq2bJkee+wxSbKGnTlz5igmJsa51QHwCldagZlhLQDOUuLgM3nyZPXo0UO//vqr8vLy9Morr+jXX3/VunXr9NVXX5VHjQA8mKPQ07DhPRo4sJUJ1QDwdCWe43PjjTdq06ZNysvL0zXXXKMvvvhCERERSk9PV7t27cqjRgAeaMOGDQ5Dz8SJSXrjDUIPgPJRqr26mjRpon/961/OrgWAlyhsaCsnJ0kxMcznAVB+ihV8srKyin1BtnkAUJTCHlMHAFcoVvAJCwsr9hNb+fn5ZSoIgGe60gRmAHCFYgWf1atXW/+8f/9+JSQkKC4uzvoUV3p6ut5++22lpKSUT5UA3Jqj0HPkyDV64427TagGgDcr1u7sf/X3v/9dDz30kO6//36b9vfee09vvvmm1qxZ48z6XI7d2QHnKWxz0UtbTvCYOgBnKe7v7xIHn6CgIG3evFlXXXWVTfvOnTvVtm1bnT9/vnQVVxAEH8A5GNoC4ErF/f1d4sfZo6KiHD7RNWfOHEVFRZX0cgA8kKPQM2bMGEVHJ6lTJ2nJEhOKAgCV4nH26dOnq2/fvlq+fLk6duwoSfr++++1a9cuffTRR04vEID7mDt3rg4ePGjXfqmXJzVVSk+/+JVhLgBmKHGPz+23365du3apZ8+eOnnypE6ePKmePXtq586duv3228ujRgBuIDk52WHoSUtLsvbwJCSIdXoAmKrEc3w8HXN8gJIpKCjQpEmT7NqTki4Oa6WnXww769aZUBwAr1Hc39+lWrn59OnTeuutt7Rt2zZJUqtWrTRo0CCFhoaWrloAbulKE5gTEsTO6gAqlBL3+Pz444+KjY1V5cqV1aFDB0nSDz/8oD///FNffPGFrr322nIp1FXo8QGKx1HoGTRoEA85ADBFuT3OftNNN6lp06b617/+pUqVLnYY5eXl6aGHHtLevXv19ddfl61ykxF8gKL9/PPP+uSTT+za09KSGM4CYJpyG+r68ccfbUKPJFWqVElPP/202rdvX7pqAbiFwoa2Li1ICAAVXYmf6goJCdGBAwfs2g8ePKjg4GCnFAWg4nEUetLSkhQdfbGnh8fTAbiDEvf4/OMf/9DgwYM1depUderUSZK0du1ajR492m4bCwDur6gJzCzCDMDdlDj4TJ06VRaLRQMGDFBeXp4kyc/PT8OGDVNqaqrTCwRgHkehp1u3btYNigHA3ZR6HZ/z589rz549kqQmTZooKCjIqYWZhcnNgHTy5EnNnDnTrp19tgBUVOW6jo90cbPSa665prTfDqCCYnNRAJ6s2MFn0KBBxTpv7ty5pS4GgLkchZ5x48bZPMUJAO6s2P9vNn/+fDVo0EDR0dFilwvAsyxatEjbt2+3a6eXB4CnKXbwGTZsmN5//33t27dPDz74oB544AFVr169PGsD4AKOenmCgupo9OghJlQDAOWrRJObs7Oz9fHHH2vu3Llat26d7rjjDg0ePFjdunWTxWIpzzpdhsnN8Bb5+fl67rnn7Nrp5QHgjspty4pLfvvtN82fP1///ve/lZeXp61bt6pq1aqlLriiIPjAGzCBGYCnKfenunx8fGSxWGQYhvLz80t7GQAu5ij0DB8+XDVq1DChGgBwrRJtWZGdna33339ft912m66++mr98ssvevXVV3XgwAGP6O0BPNmmTZschp6kpCRCDwCvUewen0cffVQLFy5UVFSUBg0apPfff181a9Ysz9oAOAlDWwBwUbHn+Pj4+Kh+/fqKjo4uciLzxx9/7LTizMAcH3iawnp5AMCTOH2Oz4ABAzzmyS3AG9DLAwD2SrSAIQD34Cj09OnTR23atDGhGgCoOFiHHvAgbC4KAEUj+AAegqEtALgygg/gARyFnmeeeUY+PiVasQIAPB7BB3BjbC4KACVD8AHclKNenrZt26p3794mVAMA7oHgA7iZvLw8Pf/883bt9PIAwJURfAA3wgRmACgbgg/gJhyFnhEjRig0NNSEagDAPRF8gArup59+0n//+1+7dnp5AKDk3O5Z1+zsbLVt21YWi0WbNm2yOfbzzz/rpptuUmBgoKKiovTiiy+aUyTgJMnJyXahx2KxEHoAoJTcLvg8/fTTqlOnjl17VlaWunXrpgYNGmjDhg2aMmWKJk6cqDfffNOEKoGyK2xz0QkTJpToOkuWSJ06XfwKAN7OrYa6li9fri+++EIfffSRli9fbnPs3XffVU5OjubOnSt/f3+1atVKmzZt0rRp0/Twww+bVDFQcs6ewJyaKqWnX/zaq1dZKgMA9+c2PT7Hjh3TkCFDtGDBAgUFBdkdT09P18033yx/f39rW2xsrHbs2KFTp04Vet3s7GxlZWXZvACzOAo9999/f5mGthISpJiYi18BwNu5RfAxDENxcXEaOnSo2rdv7/CcjIwM1apVy6bt0vuMjIxCr52SkqLQ0FDrKyoqynmFA8X0+++/Fzq0dfXVV5fp2r16SevW0dsDAJLJwSchIUEWi6XI1/bt2zVz5kydOXNGiYmJTq8hMTFRmZmZ1tfBgwed/hlAUZKTk/Xaa6/ZtTOBGQCcz9Q5PqNGjVJcXFyR5zRu3FirVq1Senq6AgICbI61b99e//znP/X2228rMjJSx44dszl+6X1kZGSh1w8ICLC7LuAqjnp50tImKCHBYkI1AOD5TA0+4eHhCg8Pv+J5M2bM0HPPPWd9f+TIEcXGxmrRokXq2LGjJCkmJkbjxo1Tbm6u/Pz8JEkrVqxQs2bNVK1atfL5AYBSKmpzUTp6AKD8uMVTXfXr17d5X7VqVUlSkyZNVK9ePUlSv379lJycrMGDB2vMmDHasmWLXnnlFU2fPt3l9QJFcdTLc9NNN6lLly4mVAMA3sUtgk9xhIaG6osvvlB8fLzatWunmjVrasKECTzKjgojNzdXkydPtmtnLg8AuI7FMAzD7CIqkqysLIWGhiozM1MhISFmlwMPweaiAFC+ivv722N6fICKylHoGT16tMP1qAAA5YvgA5STn3/+WZ988oldO708AGAegg9QDhz18jRo0OCKyzcAAMoXwQdwssJWYAYAmI/gAzjJlClTdP78ebt2Qg8AVBwEH8AJHPXyPPTQQ6pbt64J1QAACkPwAcrg999/d7jPVlpakqKjJXIPAFQsBB+glApbmyctLUnp6VJqKjuiA0BFQ/ABimnJkothJiFB2rjRPvRMmDBBFotF0dH/Ow8AULGwcvNlWLkZhenUSapZ879q1+4nu2NMYAYAc7FyM+BksbH2vTy9evVSdHS0CdUAAEqD4ANcQU5OjlJSUuza6eUBAPdD8AGKwOaiAOBZCD5AIRyFnsTERPn7+5tQDQDAGQg+wGW2bt2qDz/80K6dXh4AcH8EH+AvHPXytGvXTnfeeacJ1QAAnI3gA0gyDEPPPvusXTu9PADgWQg+8HpsLgoA3oPgA6/maGgrPj5eNWvWNKEaAEB58zG7AMBVliy5uPrykiXS8ePHHYaepKQkQg8AeDB6fOA1UlOl9PSL+2xt3Gh7LCQkRE8++aQ5hQEAXIbgA69xpc1FAQCej+ADr7BixQpt3LjOrp0JzADgXQg+8HiO5vL83//9n1q0aGFCNQAAMxF84LGys7OVmppq104vDwB4L4IPPBKbiwIAHCH4wOM4Cj3jxo1TpUr8zx0AvB3r+MBj7N6922HoSUtLIvQAACTR4wMP4SjwREXdoTlz2ishwYSCAAAVEsEHbu1Km4sOGuTqigAAFRnBB25rwYIF2rt3r107E5gBAIUh+MAtORraGjlypIKDg02oBgDgLgg+cCt//PGHXn31Vbt2enkAAMVB8IHbcNTL07ZtW/Xu3duEagAA7ojgA7fgKPTQywMAKCmCDyq0NWvW6KuvvrJrJ/QAAEqD4IMKy1Evz+DBg1WvXj0TqgEAeAKCDyocNhcFAJQXgo+XWLJESk2VEhKkXr3MrqZwjnp5QkNDNWLECNcXAwDwOAQfL5GaKqWnX/xakYLPXwPZxo32oWf8+PHy9fU1oTIAgCdik1IvkZAgxcSowu1blZoqnTq13WHoSUpKIvQAAJyKHh8v0atXxerpuSQ21j7w9O7dW23btnV9MQAAj0fwgSmutLkoAADlgeADl3vrrbd06NAhu3ZCDwCgvBF84FKOntp66qmnVKVKFROqAQB4G4IPXOL48eOaNWuWXTu9PAAAVyL4oNxcelTd0QTm6667TrfffrsJVQEAvBnBB+WmsNBDLw8AwCwEH5SLdevWKTZ2hV07oQcAYCaCD5zO0QTmRx55RJGRkSZUAwDA/xB84DRsLgoAqOgIPiiTSxOYb7/9BeXnX7A51qxZM913330mVQYAgD2CD8rk0gTm/Hzb9meeeUY+PmwFBwCoWAg+KLWDBw8qNnauXTtDWwCAiorgg1JxNIG5X79+uuqqq0yoBgCA4iH4oETYXBQA4M4IPii2tLQ0rV+/3qatQYMGiouLM6cgAABKiOCDK1qyRNq40X5oKzExUf7+/iZUBABA6RB8UKQzZ85o48Zpdu0MbQEA3BHBB4WaO3euDh48aNN211136W9/+5tJFQEAUDYstOLFliyROnW6+PVyycnJdqEnKSmJ0AMAcGv0+Hix1FQpPf3i1169Lrbt3LlT77//vs15jRo10oABA0yoEAAA5yL4eLGEhIuhJyHh4ntHa/M89dRTqlKliosrAwCgfBB8vFivXhdfubm5Sk6ebHecCcwAAE9D8PFyy5Yt048//mjT1rNnT1177bUmVQQAQPkh+HgxR0NbEyZMkMViMaEaAADKn1s91bVs2TJ17NhRlStXVrVq1dSnTx+b4wcOHNAdd9yhoKAgRUREaPTo0crLyzOn2Ars9OnTdqEnMDBQSUlJhB4AgEdzmx6fjz76SEOGDNHkyZPVpUsX5eXlacuWLdbj+fn5uuOOOxQZGal169bp6NGjGjBggPz8/DR5sv38FW/14YcfauvWrTZt8fHxqlmzpkkVAQDgOhbDMAyzi7iSvLw8NWzYUMnJyRo8eLDDc5YvX64777xTR44cUa1atSRJs2fP1pgxY3TixIlib62QlZWl0NBQZWZmKiQkxGk/g9nYXBQA4MmK+/vbLYa6fvrpJx0+fFg+Pj6Kjo5W7dq11aNHD5sen/T0dF1zzTXW0CNJsbGxysrKsuvh+Kvs7GxlZWXZvDzNiRMn7ELPXXfdRegBAHgdtwg+e/fulSRNnDhR48eP19KlS1WtWjXdeuutOnnypCQpIyPDJvRIsr7PyMgo9NopKSkKDQ21vqKiosrppzDH6tWr9frrr9u0jR8/nhWYAQBeydTgk5CQIIvFUuRr+/btKigokCSNGzdOffv2Vbt27TRv3jxZLBZ98MEHZaohMTFRmZmZ1tfl2zS4q4tr8yTr66+/trb97W9/U1JSknx9fU2sDAAA85g6uXnUqFGKi4sr8pzGjRvr6NGjkqSWLVta2wMCAtS4cWMdOHBAkhQZGanvv//e5nuPHTtmPVaYgIAABQQElKb8Cmvv3r1asGCBTRsrMAMAYHLwCQ8PV3h4+BXPa9eunQICArRjxw7deOONki72aOzfv18NGjSQJMXExOj555/X8ePHFRERIUlasWKFQkJCbAKTp3vvvfe0a9cu6/tWrVrpnnvuMbEiAAAqDrd4nD0kJERDhw5VUlKSoqKi1KBBA02ZMkWSdO+990qSunXrppYtW6p///568cUXlZGRofHjxys+Pt7jenQcycrK0vTp023aHnzwQdWvX9+kigAAqHjcIvhI0pQpU1SpUiX1799ff/75pzp27KhVq1apWrVqkiRfX18tXbpUw4YNU0xMjKpUqaKBAwc6fITb03z//fdavny59b3FYtHYsWNVqZLb3F4AAFzCLdbxcSV3WscnPz9fU6ZMUXZ2trWta9euuuGGG0ysCgAA1yvu72+6BNzU4cOHNWfOHJu2J554QmFhYeYUBACAGyD4uKFPP/1UmzZtsr6vX7++4uLi2GcLAIArIPi4kT///FMvvviiTdt9992nZs2amVQRAADuheDjJrZs2aKPPvrIpi0hIcErnlgDAMBZCD4VnGEYmjVrlk6cOGFtu/766xUbG2tiVQAAuCeCTwX2+++/67XXXrNpGzZsmHWBRgAAUDIEnwpq5cqV+vbbb63vq1Wrpscee4wJzAAAlAHBp4LJzc3V5MmTbdp69+6ttm3bmlMQAAAehOBTgezevVvvvvuuTRubiwIA4DwEnwrAMAy988472rt3r7Xtmmuu0d13321iVQAAeB6Cj8kcbS46aNAgRUVFmVQRAACei+BjovXr1ystLc36vlKlSkpISJCvr6+JVQEA4LkIPibIz8/XCy+8oNzcXGvbbbfdpk6dOplYFQAAno/g42KHDh3SW2+9ZdM2YsQIhYaGmlQRAADeg+DjQosXL9bmzZut7xs2bKgBAwawNg8AAC5C8HGRN998U0ePHrW+79evn6666ioTKwIAwPsQfFzkzz//tP45MTFR/v7+JlYDAIB3Ivi4yIMPPqgLFy6wzxYAACYi+LhISEiIQkJCzC4DAACv5mN2AQAAAK5C8AEAAF6D4AMAALwGwcdFliyROnW6+BUAAJiD4OMiqalSevrFrwAAwBwEHxdJSJBiYi5+BQAA5uBxdhfp1eviCwAAmIceHwAA4DUIPgAAwGsQfAAAgNcg+AAAAK9B8AEAAF6D4AMAALwGwQcAAHgNgg8AAPAaBB8AAOA1CD4AAMBrEHwAAIDXIPgAAACvQfABAABeg93ZL2MYhiQpKyvL5EoAAEBxXfq9fen3eGEIPpc5c+aMJCkqKsrkSgAAQEmdOXNGoaGhhR63GFeKRl6moKBAR44cUXBwsCwWi9nlOE1WVpaioqJ08OBBhYSEmF0OisC9ch/cK/fBvXIfpb1XhmHozJkzqlOnjnx8Cp/JQ4/PZXx8fFSvXj2zyyg3ISEh/KN3E9wr98G9ch/cK/dRmntVVE/PJUxuBgAAXoPgAwAAvAbBx0sEBAQoKSlJAQEBZpeCK+BeuQ/ulfvgXrmP8r5XTG4GAABegx4fAADgNQg+AADAaxB8AACA1yD4AAAAr0Hw8RLLli1Tx44dVblyZVWrVk19+vSxOX7gwAHdcccdCgoKUkREhEaPHq28vDxzioWys7PVtm1bWSwWbdq0yebYzz//rJtuukmBgYGKiorSiy++aE6RXmz//v0aPHiwGjVqpMqVK6tJkyZKSkpSTk6OzXncq4rjtddeU8OGDRUYGKiOHTvq+++/N7skr5aSkqLrrrtOwcHBioiIUJ8+fbRjxw6bcy5cuKD4+HjVqFFDVatWVd++fXXs2LEyfzbBxwt89NFH6t+/vx588EFt3rxZa9euVb9+/azH8/PzdccddygnJ0fr1q3T22+/rfnz52vChAkmVu3dnn76adWpU8euPSsrS926dVODBg20YcMGTZkyRRMnTtSbb75pQpXea/v27SooKNAbb7yhrVu3avr06Zo9e7bGjh1rPYd7VXEsWrRII0eOVFJSkn766Se1adNGsbGxOn78uNmlea2vvvpK8fHxWr9+vVasWKHc3Fx169ZN586ds57z5JNP6r///a8++OADffXVVzpy5Ijuvvvusn+4AY+Wm5tr1K1b15gzZ06h53z22WeGj4+PkZGRYW2bNWuWERISYmRnZ7uiTPzFZ599ZjRv3tzYunWrIcnYuHGj9djrr79uVKtWzea+jBkzxmjWrJkJleKvXnzxRaNRo0bW99yriqNDhw5GfHy89X1+fr5Rp04dIyUlxcSq8FfHjx83JBlfffWVYRiGcfr0acPPz8/44IMPrOds27bNkGSkp6eX6bPo8fFwP/30kw4fPiwfHx9FR0erdu3a6tGjh7Zs2WI9Jz09Xddcc41q1aplbYuNjVVWVpa2bt1qRtle69ixYxoyZIgWLFigoKAgu+Pp6em6+eab5e/vb22LjY3Vjh07dOrUKVeWistkZmaqevXq1vfcq4ohJydHGzZsUNeuXa1tPj4+6tq1q9LT002sDH+VmZkpSdZ/Qxs2bFBubq7NfWvevLnq169f5vtG8PFwe/fulSRNnDhR48eP19KlS1WtWjXdeuutOnnypCQpIyPDJvRIsr7PyMhwbcFezDAMxcXFaejQoWrfvr3Dc7hXFdPu3bs1c+ZMPfLII9Y27lXF8Pvvvys/P9/hveA+VAwFBQUaMWKEbrjhBrVu3VrSxX8j/v7+CgsLsznXGfeN4OOmEhISZLFYinxdmocgSePGjVPfvn3Vrl07zZs3TxaLRR988IHJP4V3KO69mjlzps6cOaPExESzS/Zaxb1Xf3X48GF1795d9957r4YMGWJS5YD7io+P15YtW7Rw4UKXfF4ll3wKnG7UqFGKi4sr8pzGjRvr6NGjkqSWLVta2wMCAtS4cWMdOHBAkhQZGWn3hMOlmfORkZFOrNo7FfderVq1Sunp6Xb707Rv317//Oc/9fbbbysyMtLuqQbulfMU915dcuTIEXXu3FmdOnWym7TMvaoYatasKV9fX4f3gvtgvuHDh2vp0qX6+uuvVa9ePWt7ZGSkcnJydPr0aZteH6fctzLNEEKFl5mZaQQEBNhMbs7JyTEiIiKMN954wzCM/01uPnbsmPWcN954wwgJCTEuXLjg8pq91W+//Wb88ssv1ldaWpohyfjwww+NgwcPGobxvwmzOTk51u9LTExkwqwJDh06ZFx11VXGfffdZ+Tl5dkd515VHB06dDCGDx9ufZ+fn2/UrVuXyc0mKigoMOLj4406deoYO3futDt+aXLzhx9+aG3bvn27UyY3E3y8wBNPPGHUrVvXSEtLM7Zv324MHjzYiIiIME6ePGkYhmHk5eUZrVu3Nrp162Zs2rTJ+Pzzz43w8HAjMTHR5Mq92759++ye6jp9+rRRq1Yto3///saWLVuMhQsXGkFBQdYQC9c4dOiQ0bRpU+Pvf/+7cejQIePo0aPW1yXcq4pj4cKFRkBAgDF//nzj119/NR5++GEjLCzM5klWuNawYcOM0NBQY82aNTb/fs6fP289Z+jQoUb9+vWNVatWGT/++KMRExNjxMTElPmzCT5eICcnxxg1apQRERFhBAcHG127djW2bNlic87+/fuNHj16GJUrVzZq1qxpjBo1ysjNzTWpYhiG4+BjGIaxefNm48YbbzQCAgKMunXrGqmpqeYU6MXmzZtnSHL4+ivuVcUxc+ZMo379+oa/v7/RoUMHY/369WaX5NUK+/czb9486zl//vmn8eijjxrVqlUzgoKCjLvuusvmPy5Ky/L/CwAAAPB4PNUFAAC8BsEHAAB4DYIPAADwGgQfAADgNQg+AADAaxB8AACA1yD4AAAAr0HwAYBysmbNGlksFp0+fdrsUgD8fwQfAG5r4sSJatu2rdllAHAjBB8AHi83N9fsEgBUEAQfAKYpKChQSkqKGjVqpMqVK6tNmzb68MMPJf1vmGjlypVq3769goKC1KlTJ+3YsUOSNH/+fCUnJ2vz5s2yWCyyWCyaP3++JMlisWjWrFnq1auXqlSpoueff77IOi59VlpamqKjo1W5cmV16dJFx48f1/Lly9WiRQuFhISoX79+On/+vPX7srOz9fjjjysiIkKBgYG68cYb9cMPP5TPXxYA5yjzbl8AUErPPfec0bx5c+Pzzz839uzZY8ybN88ICAgw1qxZY6xevdqQZHTs2NFYs2aNsXXrVuOmm24yOnXqZBiGYZw/f94YNWqU0apVK7udnSUZERERxty5c409e/YYv/32W5F1XPqs66+/3vj222+Nn376yWjatKlxyy23GN26dTN++ukn4+uvvzZq1Khhs9Ho448/btSpU8f47LPPjK1btxoDBw40qlWrZvzxxx821z116lT5/AUCKDGCDwBTXLhwwQgKCjLWrVtn0z548GDj/vvvt4aGL7/80nps2bJlhiTjzz//NAzDMJKSkow2bdrYXVuSMWLEiGLX4uizUlJSDEnGnj17rG2PPPKIERsbaxiGYZw9e9bw8/Mz3n33XevxnJwco06dOsaLL75oc12CD1BxVDKrpwmAd9u9e7fOnz+v2267zaY9JydH0dHR1vd/+9vfrH+uXbu2JOn48eOqX79+kddv3759iWv662fVqlVLQUFBaty4sU3b999/L0nas2ePcnNzdcMNN1iP+/n5qUOHDtq2bVuJPxuAaxB8AJji7NmzkqRly5apbt26NscCAgK0Z88eSRfDxCUWi0XSxblBV1KlSpUS13T5Z/31/aW24nw2gIqLyc0ATNGyZUsFBATowIEDatq0qc0rKiqqWNfw9/dXfn5+OVfqWJMmTeTv76+1a9da23Jzc/XDDz+oZcuWptQE4Mro8QFgiuDgYD311FN68sknVVBQoBtvvFGZmZlau3atQkJC1KBBgyteo2HDhtq3b582bdqkevXqKTg4WAEBAS6o/mKP0rBhwzR69GhVr15d9evX14svvqjz589r8ODBLqkBQMkRfACYZtKkSQoPD1dKSor27t2rsLAwXXvttRo7dmyxhpT69u2rjz/+WJ07d9bp06c1b948xcXFlX/h/19qaqoKCgrUv39/nTlzRu3bt1daWpqqVavmshoAlIzFMAzD7CIAAABcgTk+AADAaxB8AHi8oUOHqmrVqg5fQ4cONbs8AC7EUBcAj3f8+HFlZWU5PBYSEqKIiAgXVwTALAQfAADgNRjqAgAAXoPgAwAAvAbBBwAAeA2CDwAA8BoEHwAA4DUIPgAAwGsQfAAAgNcg+AAAAK/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", 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ec3cpAIAaZObMmerYsaO7y/AoHnEdIhS3YmeGpq/6XoWG5GORkgcnaOj1Td1dFgAANRItRB4o03rOFoYkqdCQnlq1l5YiAICky7e7Sk5OVlxcnAIDA9WhQwetXLlS0m+HmDZs2KDOnTsrKChIN954o9LS0iRJS5cu1axZs/Tdd9/JYrHIYrFo6dKltnmfPHlSd9xxh4KCgtSyZUutWbPGoZqKXvezzz7Tddddp8DAQN188806fvy4PvnkE7Vp00YhISEaPny43T1J8/PzNXHiREVGRqp27dq66aabtHPnTtetLAcRiDzQoZNnbWGoSIFh6PDJvJKfAABwu+rs5pCcnKy3335bixYt0r59+zR58mTde++92rJli22aP//5z3rxxRe1a9cu+fn5afTo0ZKkoUOH6rHHHtO1116rzMxMZWZmaujQobbnzZo1S3fddZf27NmjW265Rffcc49Onz7tcG0zZ87UP/7xD23fvl1HjhzRXXfdpfnz52vZsmVat26dPv/8cy1YsMA2/ZNPPqn//ve/euutt/TNN9+oRYsWSkpKqtBrugKByAPFNagjn6suluprsahZA66VAQCeaMXODHWbs1HDF+9QtzkbtWJnRpW9Vn5+vmbPnq0333xTSUlJat68uUaNGqV7771Xr7/+um26v/71r+rZs6fatm2radOmafv27Tp//rwCAwMVHBwsPz8/RUdHKzo62u42VqNGjdKwYcPUokULzZ49W7m5ucVukVWW559/Xt26ddN1112nMWPGaMuWLVq4cKGuu+46de/eXXfeeac2bdokSTp79qwWLlyoF154QQMGDFDbtm21ePFiBQYG6l//+pfrVpoDCEQeKCY0UMmDE+T7f5eQ97VYNHtwO8WElnzfNQCA+1R3N4eDBw8qLy9Pv//97xUcHGx7vP3220pPT7dNV3RHB+m3W1oU3eKiLFc+r06dOgoJCXHoeSU9PyoqSkFBQWrevLndsKL5paen6+LFi+rWrZttfK1atdSlSxf98MMPDr+mK9Cp2kMNvb6pelwTocMn89SsQRBhCAA8VFndHKpi352bmytJWrdunRo1amQ3LiAgwBaKatWqZRtedI+2wsLCcud/5fOKnuvI80p6vsViqfT8qguByIPFhAYShADAwxV1c7gyFFVlN4e2bdsqICBAGRkZ6tmzZ7HxV7YSlcbf318FBQVVUV6FxMfHy9/fX19++aViY2MlXb79xs6dOzVp0qRqrYVABABAJRR1c3hq1V4VGEaVd3OoW7euHn/8cU2ePFmFhYW66aabZLVa9eWXXyokJMQWLMrSrFkzHTp0SKmpqWrcuLHq1q1ruyF6dapTp47GjRunJ554QuHh4WratKnmzZunvLw8jRkzplprIRABAFBJ1d3N4bnnnlNERISSk5P1v//9T2FhYfrd736np556yqHDUUOGDNGqVavUu3dvZWdna8mSJRo1alSV1lyaOXPmqLCwUPfdd5/OnDmjzp0767PPPlO9evWqtQ5u7uogbu4KADULN3etObi5KwAAgAsQiAAAQLkeeughu9P8r3w89NBD7i6v0uhDBAAAyvXss8/q8ccfL3FcTehKQiACAADlioyMVGRkpLvLqDIcMgMAAKZHIAIAmJonXjUZFeOK95BDZgAAU/L395ePj4+OHj2qiIgI+fv7225xAe9gGIYuXLigEydOyMfHR/7+/k7Pi0AEADAlHx8fxcXFKTMzU0ePHnV3OaiEoKAgNW3aVD4+zh/4IhABAEzL399fTZs21aVLlzzi3l6oOF9fX/n5+VW6dY9ABAAwtaI7sl99V3aYC52qAQCA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6RGIAACA6bk1ECUnJ+v6669X3bp1FRkZqUGDBiktLc1umvPnz2v8+PGqX7++goODNWTIEB07dsxumoyMDA0cOFBBQUGKjIzUE088oUuXLtlNs3nzZv3ud79TQECAWrRooaVLl1b14gEAAC/h1kC0ZcsWjR8/Xl999ZXWr1+vixcvql+/fjp79qxtmsmTJ+ujjz7S+++/ry1btujo0aMaPHiwbXxBQYEGDhyoCxcuaPv27Xrrrbe0dOlSPf3007ZpDh06pIEDB6p3795KTU3VpEmTNHbsWH322WfVurwAAMAzWQzDMNxdRJETJ04oMjJSW7ZsUY8ePWS1WhUREaFly5bpzjvvlCT9+OOPatOmjVJSUnTDDTfok08+0R/+8AcdPXpUUVFRkqRFixZp6tSpOnHihPz9/TV16lStW7dOe/futb3W3XffrezsbH366acO1ZaTk6PQ0FBZrVaFhIS4fuEBAIDLOfr97VF9iKxWqyQpPDxckrR7925dvHhRffv2tU3TunVrNW3aVCkpKZKklJQUJSQk2MKQJCUlJSknJ0f79u2zTXPlPIqmKZpHSfLz85WTk2P3AAAANZPHBKLCwkJNmjRJ3bp1U7t27SRJWVlZ8vf3V1hYmN20UVFRysrKsk1zZRgqGl80rqxpcnJydO7cuRLrSU5OVmhoqO3RpEmTSi8jAADwTB4TiMaPH6+9e/fqvffec3cpkqTp06fLarXaHkeOHHF3SQAAoIr4ubsASZowYYLWrl2rrVu3qnHjxrbh0dHRunDhgrKzs+1aiY4dO6bo6GjbNF9//bXd/IrOQrtymqvPTDt27JhCQkIUGBhYYk0BAQEKCAio9LIBAADP59YWIsMwNGHCBK1evVobN25UXFyc3fhOnTqpVq1a2rBhg21YWlqaMjIylJiYKElKTEzU999/r+PHj9umWb9+vUJCQtS2bVvbNFfOo2iaonkAAABzc+tZZg8//LCWLVumDz/8UK1atbINDw0NtbXcjBs3Th9//LGWLl2qkJAQPfLII5Kk7du3S7p82n3Hjh3VsGFDzZs3T1lZWbrvvvs0duxYzZ49W9Ll0+7btWun8ePHa/To0dq4caMmTpyodevWKSkpyaFaOcsMAADv4+j3t1sDkcViKXH4kiVLNGrUKEmXL8z42GOPafny5crPz1dSUpJee+012+EwSfrpp580btw4bd68WXXq1NHIkSM1Z84c+fn9dkRw8+bNmjx5svbv36/GjRtrxowZttdwBIEIAADv4xWByJsQiAAA8D5eeR0iAAAAdyAQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA0yMQAQAA03NrINq6datuvfVWNWzYUBaLRR988IHd+FGjRslisdg9+vfvbzfN6dOndc899ygkJERhYWEaM2aMcnNz7abZs2ePunfvrtq1a6tJkyaaN29eVS8aAADwIm4NRGfPnlWHDh306quvljpN//79lZmZaXssX77cbvw999yjffv2af369Vq7dq22bt2qBx980DY+JydH/fr1U2xsrHbv3q0XXnhBM2fO1D//+c8qWy4AAOBd/Nz54gMGDNCAAQPKnCYgIEDR0dEljvvhhx/06aefaufOnercubMkacGCBbrlllv0t7/9TQ0bNtS7776rCxcu6M0335S/v7+uvfZapaam6qWXXrILTgAAwLw8vg/R5s2bFRkZqVatWmncuHE6deqUbVxKSorCwsJsYUiS+vbtKx8fH+3YscM2TY8ePeTv72+bJikpSWlpafr1119Lfd38/Hzl5OTYPQAAQM3k0YGof//+evvtt7VhwwbNnTtXW7Zs0YABA1RQUCBJysrKUmRkpN1z/Pz8FB4erqysLNs0UVFRdtMU/V00TUmSk5MVGhpqezRp0sSViwYAADyIWw+Zlefuu++2/T8hIUHt27dXfHy8Nm/erD59+lTpa0+fPl1Tpkyx/Z2Tk0MoAgCghvLoFqKrNW/eXA0aNNDBgwclSdHR0Tp+/LjdNJcuXdLp06dt/Y6io6N17Ngxu2mK/i6tb5J0ue9SSEiI3QMAANRMXhWIfv75Z506dUoxMTGSpMTERGVnZ2v37t22aTZu3KjCwkJ17drVNs3WrVt18eJF2zTr169Xq1atVK9evepdAAAA4JHcGohyc3OVmpqq1NRUSdKhQ4eUmpqqjIwM5ebm6oknntBXX32lw4cPa8OGDbr99tvVokULJSUlSZLatGmj/v3764EHHtDXX3+tL7/8UhMmTNDdd9+thg0bSpKGDx8uf39/jRkzRvv27dOKFSv097//3e5wGAAAMDeLYRiGu1588+bN6t27d7HhI0eO1MKFCzVo0CB9++23ys7OVsOGDdWvXz8999xzdp2kT58+rQkTJuijjz6Sj4+PhgwZoldeeUXBwcG2afbs2aPx48dr586datCggR555BFNnTq1QrXm5OQoNDRUVquVw2cAAHgJR7+/3RqIvAmBCAAA7+Po97dX9SECAACoCgQiAABgegQiAABgegQiAABgeg5fqboi9/Ki0zEAAPAmDgeisLAwWSyWMqcxDEMWi8V2rzEAAABv4HAg2rRpU1XWAQAA4DYOB6KePXtWZR0AAABu4/Td7rOzs/Wvf/1LP/zwgyTp2muv1ejRoxUaGuqy4gAAAKqDU2eZ7dq1S/Hx8Xr55Zd1+vRpnT59Wi+99JLi4+P1zTffuLpGAACAKuXUrTu6d++uFi1aaPHixfLzu9zIdOnSJY0dO1b/+9//tHXrVpcX6m7cugMAAO9TpfcyCwwM1LfffqvWrVvbDd+/f786d+6svLy8ilfs4QhEAAB4nyq9l1lISIgyMjKKDT9y5Ijq1q3rzCwBAADcxqlANHToUI0ZM0YrVqzQkSNHdOTIEb333nsaO3ashg0b5uoaAQAAqpRTZ5n97W9/k8Vi0YgRI3Tp0iVJUq1atTRu3DjNmTPHpQUCAABUNaf6EBXJy8tTenq6JCk+Pl5BQUEuK8zT0IcIAADv4+j3t9PXIZKkoKAgJSQkVGYWAAAAbudUIDp//rwWLFigTZs26fjx4yosLLQbz7WIAACAN3EqEI0ZM0aff/657rzzTnXp0qXcm74CAAB4MqcC0dq1a/Xxxx+rW7durq4HAACg2jl12n2jRo243hAAAKgxnApEL774oqZOnaqffvrJ1fUAAABUO6cOmXXu3Fnnz59X8+bNFRQUpFq1atmNP336tEuKAwAAqA5OBaJhw4bpl19+0ezZsxUVFUWnagAA4NWcCkTbt29XSkqKOnTo4Op6AAAAqp1TfYhat26tc+fOuboWAAAAt3AqEM2ZM0ePPfaYNm/erFOnTiknJ8fuAQAA4E2cupeZj8/lHHV13yHDMGSxWFRQUOCa6jwI9zIDAMD7VOm9zDZt2uR0YQAAAJ7GqUDUs2dPh6Z7+OGH9eyzz6pBgwbOvAwAAEC1cKoPkaPeeecd+hQBAACPV6WByInuSQAAANWuSgMRAACANyAQAQAA0yMQAQAA0yMQAQAA06vSQHTvvfdyEUMAAODxnLoOkSRlZ2fr66+/1vHjx1VYWGg3bsSIEZKkhQsXVq46AACAauBUIProo490zz33KDc3VyEhIXa38LBYLLZABAAA4A2cOmT22GOPafTo0crNzVV2drZ+/fVX2+P06dOurhEAAKBKORWIfvnlF02cOFFBQUGurgcAAKDaORWIkpKStGvXLlfXAgAA4BYO9yFas2aN7f8DBw7UE088of379yshIUG1atWym/a2225zXYUAAABVzGI4eMMxHx/HGpMsFosKCgoqVZQnysnJUWhoqKxWK5cSAADASzj6/e1wC9HVp9YDAADUFE71IXr77beVn59fbPiFCxf09ttvV7ooAACA6uTwIbMr+fr6KjMzU5GRkXbDT506pcjISA6ZAQAAj+Do97dTLUSGYdhdjLHIzz//rNDQUGdmiQrItJ7T9vSTyrSec3cpAADUCBW6UvV1110ni8Uii8WiPn36yM/vt6cXFBTo0KFD6t+/v8uLxG9W7MzQ9FXfq9CQfCxS8uAEDb2+qbvLAgDAq1UoEA0aNEiSlJqaqqSkJAUHB9vG+fv7q1mzZhoyZIhLC8RvMq3nbGFIkgoN6alVe9XjmgjFhAa6tzgAALxYhQLRM888I0lq1qyZhg4dqtq1a1dJUSjZoZNnbWGoSIFh6PDJPAIRAACV4NTNXUeOHCnp8lllJd3tvmlTDuFUhbgGdeRjkV0o8rVY1KwBt1ABAKAynOpUfeDAAXXv3l2BgYGKjY1VXFyc4uLi1KxZM8XFxbm6RvyfmNBAJQ9OkO//dWj3tVg0e3A7WocAAKgkp1qIRo0aJT8/P61du1YxMTElnnGGqjH0+qbqcU2EDp/MU7MGQYQhAABcwKlAlJqaqt27d6t169aurgcOiAkNJAgBAOBCTh0ya9u2rU6ePOnqWgAAANzCqUA0d+5cPfnkk9q8ebNOnTqlnJwcuwcAAIA3cerWHVfe+f7K/kNFV7Dm1h0AAMATVOmtOzZt2mR7bNy40fYo+ttRW7du1a233qqGDRvKYrHogw8+sBtvGIaefvppxcTEKDAwUH379tWBAwfspjl9+rTuuecehYSEKCwsTGPGjFFubq7dNHv27FH37t1Vu3ZtNWnSRPPmzXNmsQEAQA3lVCDq2bOnfHx8tHjxYk2bNk0tWrRQz549lZGRIV9fX4fnc/bsWXXo0EGvvvpqiePnzZunV155RYsWLdKOHTtUp04dJSUl6fz587Zp7rnnHu3bt0/r16/X2rVrtXXrVj344IO28Tk5OerXr59iY2O1e/duvfDCC5o5c6b++c9/OrPoAACgJjKcsHLlSiMwMNAYO3asERAQYKSnpxuGYRgLFiwwBgwY4MwsDUnG6tWrbX8XFhYa0dHRxgsvvGAblp2dbQQEBBjLly83DMMw9u/fb0gydu7caZvmk08+MSwWi/HLL78YhmEYr732mlGvXj0jPz/fNs3UqVONVq1aVag+q9VqSDKsVqsziwcAANzA0e9vp1qInn/+eS1atEiLFy9WrVq1bMO7deumb775xiVB7dChQ8rKylLfvn1tw0JDQ9W1a1elpKRIklJSUhQWFqbOnTvbpunbt698fHy0Y8cO2zQ9evSQv7+/bZqkpCSlpaXp119/dUmtAADAuzl1HaK0tDT16NGj2PDQ0FBlZ2dXtiZJUlZWliQpKirKbnhUVJRtXFZWliIjI+3G+/n5KTw83G6aq6+eXTTPrKws1atXr8TXz8/PV35+vu1vzp4DAKDmcqqFKDo6WgcPHiw2fNu2bWrevHmli/IEycnJCg0NtT2aNGni7pIAAEAVcSoQPfDAA3r00Ue1Y8cOWSwWHT16VO+++64ef/xxjRs3ziWFRUdHS5KOHTtmN/zYsWO2cdHR0Tp+/Ljd+EuXLun06dN205Q0jytfoyTTp0+X1Wq1PY4cOVK5BQIAAB7LqUNm06ZNU2Fhofr06aO8vDz16NFDAQEBevzxx/XII4+4pLC4uDhFR0drw4YN6tixo6TLh6127NhhC12JiYnKzs7W7t271alTJ0nSxo0bVVhYqK5du9qm+fOf/6yLFy/a+jutX79erVq1KvVwmSQFBAQoICDAJcsCAAA8m1MXZixy4cIFHTx4ULm5uWrbtq2Cg4Mr9Pzc3FzbobfrrrtOL730knr37q3w8HA1bdpUc+fO1Zw5c/TWW28pLi5OM2bM0J49e7R//37Vrl1bkjRgwAAdO3ZMixYt0sWLF3X//ferc+fOWrZsmSTJarWqVatW6tevn6ZOnaq9e/dq9OjRevnll+1Ozy8PF2YEAMD7OPz9XS3nvJVi06ZNhqRij5EjRxqGcfnU+xkzZhhRUVFGQECA0adPHyMtLc1uHqdOnTKGDRtmBAcHGyEhIcb9999vnDlzxm6a7777zrjpppuMgIAAo1GjRsacOXMqXCun3QMA4H0c/f6uVAuRmdBCBACA96nSW3cAAADUJAQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAABgegQiAAAclGk9p+3pJ5VpPefuUuBifu4uAAAAb7BiZ4amr/pehYbkY5GSBydo6PVN3V0WXIQWIgAAypFpPWcLQ5JUaEhPrdpLS1ENQiACAKAch06etYWhIgWGocMn89xTEFyOQAQAQDniGtSRj8V+mK/FomYNgtxTEFyOQAQAQDliQgOVPDhBvpbLqcjXYtHswe0UExro5srgKnSqBgDAAUOvb6oe10To8Mk8NWsQRBiqYQhEAAA4KCY0kCBUQ3HIDDABrp0CAGWjhQio4bh2CgCUjxYioAbj2ikA4BgCEVCDce0UAHAMgQiowbh2CgA4hkAE1GBcOwUAHEOnaqCG49opAFA+AhFgAlw7BQDKxiEzAABgeh4fiGbOnCmLxWL3aN26tW38+fPnNX78eNWvX1/BwcEaMmSIjh07ZjePjIwMDRw4UEFBQYqMjNQTTzyhS5cuVfeiAAAAD+UVh8yuvfZaffHFF7a//fx+K3vy5Mlat26d3n//fYWGhmrChAkaPHiwvvzyS0lSQUGBBg4cqOjoaG3fvl2ZmZkaMWKEatWqpdmzZ1f7sgAAAM/jFYHIz89P0dHRxYZbrVb961//0rJly3TzzTdLkpYsWaI2bdroq6++0g033KDPP/9c+/fv1xdffKGoqCh17NhRzz33nKZOnaqZM2fK39+/uhcHAAB4GI8/ZCZJBw4cUMOGDdW8eXPdc889ysjIkCTt3r1bFy9eVN++fW3Ttm7dWk2bNlVKSookKSUlRQkJCYqKirJNk5SUpJycHO3bt6/U18zPz1dOTo7dAwAA1EweH4i6du2qpUuX6tNPP9XChQt16NAhde/eXWfOnFFWVpb8/f0VFhZm95yoqChlZWVJkrKysuzCUNH4onGlSU5OVmhoqO3RpEkT1y4YAADwGB5/yGzAgAG2/7dv315du3ZVbGys/vOf/ygwsOpOI54+fbqmTJli+zsnJ4dQBABADeXxLURXCwsL0zXXXKODBw8qOjpaFy5cUHZ2tt00x44ds/U5io6OLnbWWdHfJfVLKhIQEKCQkBC7BwAAqJm8LhDl5uYqPT1dMTEx6tSpk2rVqqUNGzbYxqelpSkjI0OJiYmSpMTERH3//fc6fvy4bZr169crJCREbdu2rfb6AQCA5/H4Q2aPP/64br31VsXGxuro0aN65pln5Ovrq2HDhik0NFRjxozRlClTFB4erpCQED3yyCNKTEzUDTfcIEnq16+f2rZtq/vuu0/z5s1TVlaW/vKXv2j8+PEKCAhw89IBAABP4PGB6Oeff9awYcN06tQpRURE6KabbtJXX32liIgISdLLL78sHx8fDRkyRPn5+UpKStJrr71me76vr6/Wrl2rcePGKTExUXXq1NHIkSP17LPPumuRAACAh7EYhmG4uwhvkJOTo9DQUFmtVvoTAQDgJRz9/va6PkQAAACuRiACAACmRyACAACmRyACAACmRyACAACmRyACAACmRyACAACmRyACAACmRyACAACmRyACAACmRyAykUzrOW1PP6lM6zl3lwIAgEfx+Ju7wjVW7MzQ9FXfq9CQfCxS8uAEDb2+abXXkWk9p0MnzyquQR3FhAZW++sDAFASApEJZFrP2cKQJBUa0lOr9qrHNRHVGko8JZQBAHA1DpmZwKGTZ21hqEiBYejwybxqq6G0UMbhOwCAJyAQmUBcgzrysdgP87VY1KxBULXV4AmhDAC8HX1Bqw6ByARiQgOVPDhBvpbLqcjXYtHswe2q9XCZJ4QyAPBmK3ZmqNucjRq+eIe6zdmoFTszXDJfQtZlFsMwjPInQ05OjkJDQ2W1WhUSEuLucpySaT2nwyfz1KxBkFs6NK/YmaGnVu1VgWHYQpkn9SGiwzcAT5VpPaduczbatbT7WizaNq13pfZXZujb6ej3N52qTSQmNNCtX/RDr2+qHtdEuDWUlcYMOwUA3qusbgfO7ks95YQbT8EhMxNyZ/NoTGigEuPre9SHjQ7fADxdVXQ7oG+nPQKRyVTVMWhvxk4BgKerir6g9O20xyEzE6F5tGRFO4Wrj82bdaeA4uhfhupU2vbm6m4HRSHr6r6dZt3GCUQmUhXHoGsCdgooC/3LUJ3K295c3RfUk/t2VjcCkYnQElI6dgooCa2qqE7u2t7cfcKNp6APkYl4wvWIPJkndviGe9G/DNWJ7c29aCEyGVpCAMfRqorqxPbmXrQQmRAtIYBjaFVFdWJ7cy+uVO2gmnClagDOcfdV3mEubG+uxZWqAcBF6HSK6sT25h4cMgOAasSNNCuOdYbqQAsRAFQTrmlUcawzVBdaiACgGnDPvIpjnaE6EYjgEjRpA2XjGjMVxzpDdeKQGSqNJm3z4J5ezuMaMxXHOkN1ooUIlUKTtnms2JmhbnM2avjiHeo2Z6NW7Mxwd0lehWvMVBzrDNWJFiJUCjeMNQfu6eUaXCm+4lhnNZsntToTiFApNGmbA8HXdbjGTMWxzmomT+tuwSEzVApN2uZQFHyvRPAF4CxP7G5BC5GH8qRmxPJUpEnbm5YLvykKvk+t2qsCwyD4Aibj6n23J7Y6E4g8kKc1IzrCkSZtb1wu/Ia+HIA5Xb3vnjqgtRIahVYqHHlidwtu7uqg6rq5a6b1nLrN2VhsI1n1cKLOXijw2paV0pZr27TeXrk8AODNHG3xKWnfXaSyP2xX7Mwo1upcFT+SubmrlyqtGXHQq9tlyHtbVhxpHuVwGhxlpm3FTMuK6lGR1vqS9t1FKnu2qae1OhOIPExJzYiSVPSnt57uXF7zKIfT4CgzbStmWlZUj4peQqO076Qile3340lnEHKWmYe5+qytq8/skbzz0vVlnY3miWcbwDOZaVsx07Ki+lT0dihX77uv5u5+P65EC5EHKWoa73FNhLZN663DJ/MU5O+jO17b7lEdz5xVWvOoJ55tAM9kpm3FTMuK6uNMZ+Yr9917fs7WvE/TauTZpgQiD1FW03hNOt25pOZRTzzbAKVzZ58WM20r1bGs7u6fVJnXd3ftrlS0LHX8fYudPOPq5XT2EhpF++7E+Pq6rWNDj+n340qcZeagqjzLzJEzsDKt52rkBlikus42QOV4Qp8WM20rVbmsjr6Xrv5CLprf979YNfeTH53aljxhO3SVK5elSNEySaqy5azp3ylXcvT7m0DkoKoMRNvTT2r44h3Fhi9/4AYlxtd36Wt5MjN9QL2RJ106wUzbSlUsq6PvpauDR0lf/le/vqQyA5gnbYeVVeYp7ZJUQguhpyynN7XQcdq9FzHTYYCylHW2QXkfPm/6cHorT+rT4klnplS1qlhWRy+D4cob+l49v6sVGIaWbDusN7b9r8wA5knbYWWVeUq79Nvpxf/HU5azJrXQXYmzzDwA9wMr24qdGeo2Z6OGL96hbnM2asXOjAqNR+kyree0Pf2kQ2cucT+zmsOR97KiZyOVp6wvf+nyl1FRGJJKP6uuJm2HJS1LER8VP8vYE5azJp/9SCDyEEOvb6pt03pr+QM3aNu03jUibbtCeR++mvzhrGoVDZIE95rDkffS1cGjrC9/X4tFY7vHORTAYkIDNXVAa9u8qms7rMiPB0fm9dF3v2j3T79qav/WxU5p97VYlDwkweWfN1csg6uDsifhkJkHqarDAN58OKm85vGa1HxenZw9HOJpV5aF88p7L119Q9+S5vdk/1Zq3zjMFrLe2Hao3K4DK3Zm2DpjWyQ92b+Vwz8gnd0XuvIQ0YqdGZr23+9tR8Mskqbd0lrtG4UpyN9HeRcK7d6P0t6jii6Lq5ahJnfxIBDVcN5+rLe8D19N/nBWpcoESTP136npynsvXR2Ay5tfeQHs6iBvSJr3aZpu69iw3L6FW///E07tC13ZlyrTes4uDBUtw9xPftSX024ucX4lvUcV3a+7chlcHZSvrNHdP9wJRDVYeR8CT9gAy1Peh6+qPpw1XUlB0kdSkH/FjqJ7wzZUFm+vvzq4OgCXNb/yAlNFgvyVoaHogJQzt0ByZSv0oZNnr+4nbavH0fk5E25c3ZJe3vvkrtaryiIQeZGKbmRlfQic/bXkDuV9+DiMU1x528rVQVK6fFbLHa9td3hb8JSdWJGylrmkce6unzBWsrICk6MtwiW1JF3N0UDgylbouAZ1ZCmhHh+LHJ6fo+Hmyu2rKlrSS3uf3Nl6VVkEIi/hzM67tA9BkL+Px2yAjirvVyqHcX7j6LYy9Pqmah1dV4Ne2y6jgtuCJ+3EpLKXuaRxPa6JqLb6r/xiklTpixKamaMtwuWd0SY5Hggq2gpdVtCNCQ3UnCEJxfoQjbkprtw6ipTWunvqbL4yrecUExpY4jZf1S3pmdZz2nX4tNtbryqDQOQFnP3yKe2DfPZCgcdsgFXBm355V7RWR67HVJFt5eyFAl19aVZHtgVP2omVtcySShw3/+4OVVL/1e9PWYdtilS2T4q3bOuu4kiLcEmhwSLJ8n/DKhoIHG2FduTHSNG8dh/+Vdv/d1LLdxzR4v/vkP617ZBDwfjq/XpRi9OEZd/KxyJNHdDaFral37avbdN6a9u03vrmp19VaBjq3CzcoWV3RFkX3Czvc+VJ/UAJRF6gMl8+JX2QM63nKrQBununW5HXd/dhkIqoaK2OTF/RbcXZnZEzz6uq7aisZTZklDjOx2IptX5XnYk0tX9rzf30ty+mshosnAlj3rStl6Qy24MjLcYl/RiszKH18l6zIj9GYkID1amZNPG9b53q13RlqJr43rd2r3llGCpStH1lnD7r0m2mpFahq5W3X/CkfqAEIjdzZKdQ2QR99Qe5vA3QFWdmuEpFdvqedhjn6tqufJ8rWquj01d0W3F2Z1TR51Xll3d5y1zSuN/F1iuxfleeiTT3kx8vX23YAaW9R6XtHzx5W3dEdYS50lp1qmr9VPTHSGVbWWNCAxUeXHweRa2RVw6uaFeJsra7kr4bSuPo/sRT+oESiNzI0Z1CVSTo0jZAV52ZIVX+Tta7f/rV7lh7ea9f2g7mm59+1cD27vuSKOl9bhIeVKGd4e6ffnVoeme2FWd3Ro4+r6q/vMtb5tLGXV2/JLv7SlX2TKRCXT5EU97dIkt7j8raP3jSIcuKqs4wV519Cyv6Y8QVh4pKm8eT/Vtp3qdpTnWVKG27u3q4YZTe6ukjacHw6/S72HoOr39P6AdqqkD06quv6oUXXlBWVpY6dOigBQsWqEuXLm6ppaI7BVcl6KtDSlm/Okva2B3d6VbmF6Ajx6Ol4jeALGnnIF0+tp6bf6nY61fHocDS3udVDyc6vDMsupDb1Uqb3pltxdmdkSPPq44v77KWuaxxV9a/Pf2k03WW+sU0oJXdRQSly5+rqy9KWNIv9LL2D3X8fUtsBfCG6285eh81dx2md/a1K/pjxBU/dEubx9Drm+q2jg0r3FWitO2udXTdYsNLU1TDwPYNHV4OT2GaQLRixQpNmTJFixYtUteuXTV//nwlJSUpLS1NkZGR1V6PM18SlU3Q5YUUV52ZUZlfgOXdANJH0kd7juq9rzOKLUfRzuHq5xsq/vqlrQtX74hLe5/zLhQ6tDMsWh9Xrw4fi8rd2br711aR6uo0WdYyO7I+KlNnaV9Mkn0LUdEVicsLqo5cMuPqMOQt198qbz1X1eE0Rz7blX3tiv4YccUP3bIODVa09bi07W7n4eIt1FdzplXI05gmEL300kt64IEHdP/990uSFi1apHXr1unNN9/UtGnTqr2e6u5Z70hIcdWZGZVpESgrlFl0+TDEsh2/3XPr6uUYen1TBfn76pHlqaW+fmnrIvvcRZefCl3W+5wYX7/cnWFp6+OVu6/THzp4xy8wT+o0WZbK1lnaIbiit8+QNO+TNG2b1tvpcHZ1PxDp8ra66uFEdWhSrwJL6z5lreeqOpzmSNBx1WtX9MeIK368ODqP8gJYadvd9c3qFf9usEgW4/I+2Ztbha5kikB04cIF7d69W9OnT7cN8/HxUd++fZWSklLic/Lz85Wfn2/7Oycnx6U1VfeXhCMhpbSaKvoLpjJhr8RrbFikWbddq2fW7CuxP8bVy9G5WXiZr1/aupjzyY8Vvh5Pecp7n8vbkZW2Ljs1844vvyKe0mmyPJWt01WH4ErbbkrqB1JoSHkXHO2+7RlKW89VcXjV0aDjzf2yKqK8ltSStrsOTUo+CcEbPtMVYYpAdPLkSRUUFCgqKspueFRUlH788ccSn5OcnKxZs2ZVaV3V+SXhaEhxxZkZlQl7pT23pE7IpS1Hea9f2oXNqmpnWJn32VtaVxzhSYfxyuKqOivbCuyKS2Z4spLWc1W0nDsadDzpejjuVNr+qrrP2nMHUwQiZ0yfPl1Tpkyx/Z2Tk6MmTZq4/HWq60uiIl+srqipMiHA0S8CqfS+NOV1pC3prttXXjNGcu3OsDLr1FtaV2DPVZ1mK9oPxJtVxfI5GnRq+rqtiNL2V97yo8ZZFsMo76RQ73fhwgUFBQVp5cqVGjRokG34yJEjlZ2drQ8//LDceeTk5Cg0NFRWq1UhISFVWG3VyrSe89ov1hU7M2w7Kx9JY3vE6f5ucZVqSr9yXVw5/yvP1gAqoyo+c978OXaEq5evIp/tmr5uzcjR729TBCJJ6tq1q7p06aIFCxZIkgoLC9W0aVNNmDDBoU7VNSUQebuq3lmxMwRqJj7b5uXo97dpDplNmTJFI0eOVOfOndWlSxfNnz9fZ8+etZ11Bu9Q1U22Nb1JGDArPtsoj2kC0dChQ3XixAk9/fTTysrKUseOHfXpp58W62gNAADMxzSHzCqLQ2YAAHgfR7+/faqxJgAAAI9EIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZHIAIAAKZnmlt3VFbRBb1zcnLcXAkAAHBU0fd2eTfmIBA56MyZM5KkJk2auLkSAABQUWfOnFFoaGip47mXmYMKCwt19OhR1a1bVxaLxd3lVFpOTo6aNGmiI0eOcG82F2Gduh7r1LVYn67HOnU9V69TwzB05swZNWzYUD4+pfcUooXIQT4+PmrcuLG7y3C5kJAQPsQuxjp1Pdapa7E+XY916nquXKdltQwVoVM1AAAwPQIRAAAwPQKRSQUEBOiZZ55RQECAu0upMVinrsc6dS3Wp+uxTl3PXeuUTtUAAMD0aCECAACmRyACAACmRyACAACmRyACAACmRyCq4bZu3apbb71VDRs2lMVi0QcffGA33jAMPf3004qJiVFgYKD69u2rAwcOuKdYL1HeOh01apQsFovdo3///u4p1gskJyfr+uuvV926dRUZGalBgwYpLS3Nbprz589r/Pjxql+/voKDgzVkyBAdO3bMTRV7PkfWaa9evYptpw899JCbKvZsCxcuVPv27W0XCkxMTNQnn3xiG8/2WXHlrVN3bJ8Eohru7Nmz6tChg1599dUSx8+bN0+vvPKKFi1apB07dqhOnTpKSkrS+fPnq7lS71HeOpWk/v37KzMz0/ZYvnx5NVboXbZs2aLx48frq6++0vr163Xx4kX169dPZ8+etU0zefJkffTRR3r//fe1ZcsWHT16VIMHD3Zj1Z7NkXUqSQ888IDddjpv3jw3VezZGjdurDlz5mj37t3atWuXbr75Zt1+++3at2+fJLZPZ5S3TiU3bJ8GTEOSsXr1atvfhYWFRnR0tPHCCy/YhmVnZxsBAQHG8uXL3VCh97l6nRqGYYwcOdK4/fbb3VJPTXD8+HFDkrFlyxbDMC5vk7Vq1TLef/992zQ//PCDIclISUlxV5le5ep1ahiG0bNnT+PRRx91X1Ferl69esYbb7zB9ulCRevUMNyzfdJCZGKHDh1SVlaW+vbtaxsWGhqqrl27KiUlxY2Veb/NmzcrMjJSrVq10rhx43Tq1Cl3l+Q1rFarJCk8PFyStHv3bl28eNFuO23durWaNm3Kduqgq9dpkXfffVcNGjRQu3btNH36dOXl5bmjPK9SUFCg9957T2fPnlViYiLbpwtcvU6LVPf2yc1dTSwrK0uSFBUVZTc8KirKNg4V179/fw0ePFhxcXFKT0/XU089pQEDBiglJUW+vr7uLs+jFRYWatKkSerWrZvatWsn6fJ26u/vr7CwMLtp2U4dU9I6laThw4crNjZWDRs21J49ezR16lSlpaVp1apVbqzWc33//fdKTEzU+fPnFRwcrNWrV6tt27ZKTU1l+3RSaetUcs/2SSACXOzuu++2/T8hIUHt27dXfHy8Nm/erD59+rixMs83fvx47d27V9u2bXN3KTVGaev0wQcftP0/ISFBMTEx6tOnj9LT0xUfH1/dZXq8Vq1aKTU1VVarVStXrtTIkSO1ZcsWd5fl1Upbp23btnXL9skhMxOLjo6WpGJnQxw7dsw2DpXXvHlzNWjQQAcPHnR3KR5twoQJWrt2rTZt2qTGjRvbhkdHR+vChQvKzs62m57ttHylrdOSdO3aVZLYTkvh7++vFi1aqFOnTkpOTlaHDh3097//ne2zEkpbpyWpju2TQGRicXFxio6O1oYNG2zDcnJytGPHDrvjuKicn3/+WadOnVJMTIy7S/FIhmFowoQJWr16tTZu3Ki4uDi78Z06dVKtWrXsttO0tDRlZGSwnZaivHVaktTUVEliO3VQYWGh8vPz2T5dqGidlqQ6tk8OmdVwubm5don60KFDSk1NVXh4uJo2bapJkybp+eefV8uWLRUXF6cZM2aoYcOGGjRokPuK9nBlrdPw8HDNmjVLQ4YMUXR0tNLT0/Xkk0+qRYsWSkpKcmPVnmv8+PFatmyZPvzwQ9WtW9fW7yI0NFSBgYEKDQ3VmDFjNGXKFIWHhyskJESPPPKIEhMTdcMNN7i5es9U3jpNT0/XsmXLdMstt6h+/fras2ePJk+erB49eqh9+/Zurt7zTJ8+XQMGDFDTpk115swZLVu2TJs3b9Znn33G9umkstap27bPaj2nDdVu06ZNhqRij5EjRxqGcfnU+xkzZhhRUVFGQECA0adPHyMtLc29RXu4stZpXl6e0a9fPyMiIsKoVauWERsbazzwwANGVlaWu8v2WCWtS0nGkiVLbNOcO3fOePjhh4169eoZQUFBxh133GFkZma6r2gPV946zcjIMHr06GGEh4cbAQEBRosWLYwnnnjCsFqt7i3cQ40ePdqIjY01/P39jYiICKNPnz7G559/bhvP9llxZa1Td22fFsMwjKqLWwAAAJ6PPkQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAAMD0CEQAvN6FCxfcXUIxnlgTgNIRiAB4nF69emnChAmaMGGCQkND1aBBA82YMUNFdxpq1qyZnnvuOY0YMUIhISF68MEHJUnbtm1T9+7dFRgYqCZNmmjixIk6e/asbb6vvfaaWrZsqdq1aysqKkp33nmnbdzKlSuVkJCgwMBA1a9fX3379rU9t1evXpo0aZJdjYMGDdKoUaNsfztbEwDPQCAC4JHeeust+fn56euvv9bf//53vfTSS3rjjTds4//2t7+pQ4cO+vbbbzVjxgylp6erf//+GjJkiPbs2aMVK1Zo27ZtmjBhgiRp165dmjhxop599lmlpaXp008/VY8ePSRJmZmZGjZsmEaPHq0ffvhBmzdv1uDBg1XRWz1WtCYAnoObuwLwOL169dLx48e1b98+WSwWSdK0adO0Zs0a7d+/X82aNdN1112n1atX254zduxY+fr66vXXX7cN27Ztm3r27KmzZ8/q448/1v3336+ff/5ZdevWtXu9b775Rp06ddLhw4cVGxtbYj0dO3bU/PnzbcMGDRqksLAwLV26VJKcqql27dqVWk8AXIcWIgAe6YYbbrCFIUlKTEzUgQMHVFBQIEnq3Lmz3fTfffedli5dquDgYNsjKSlJhYWFOnTokH7/+98rNjZWzZs313333ad3331XeXl5kqQOHTqoT58+SkhI0B//+EctXrxYv/76a4VrrmhNADwHgQiAV6pTp47d37m5ufrTn/6k1NRU2+O7777TgQMHFB8fr7p16+qbb77R8uXLFRMTo6efflodOnRQdna2fH19tX79en3yySdq27atFixYoFatWtlCi4+PT7HDZxcvXqx0TQA8B4EIgEfasWOH3d9fffWVWrZsKV9f3xKn/93vfqf9+/erRYsWxR7+/v6SJD8/P/Xt21fz5s3Tnj17dPjwYW3cuFGSZLFY1K1bN82aNUvffvut/P39bYe/IiIilJmZaXutgoIC7d27t9xlcKQmAJ6BQATAI2VkZGjKlClKS0vT8uXLtWDBAj366KOlTj916lRt375dEyZMUGpqqg4cOKAPP/zQ1oF57dq1euWVV5SamqqffvpJb7/9tgoLC9WqVSvt2LFDs2fP1q5du5SRkaFVq1bpxIkTatOmjSTp5ptv1rp167Ru3Tr9+OOPGjdunLKzs8tdhvJqAuA5/NxdAACUZMSIETp37py6dOkiX19fPfroo7ZT2UvSvn17bdmyRX/+85/VvXt3GYah+Ph4DR06VJIUFhamVatWaebMmTp//rxatmyp5cuX69prr9UPP/ygrVu3av78+crJyVFsbKxefPFFDRgwQJI0evRofffddxoxYoT8/Pw0efJk9e7du9xlKK8mAJ6Ds8wAeJySzuoCgKrEITMAAGB6BCIAAGB6HDIDAACmRwsRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwPQIRAAAwvf8HTOyn0Ejzhn8AAAAASUVORK5CYII=", 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", 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", + "image/png": 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", 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", 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", 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", 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", 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" ] @@ -600,9 +594,9 @@ ], "source": [ "# visualize with IDAES surrogate plotting tools\n", - "surrogate_scatter2D(poly_surr, data_validation)\n", - "surrogate_parity(poly_surr, data_validation)\n", - "surrogate_residual(poly_surr, data_validation)" + "surrogate_scatter2D(poly_surr, data_validation, filename=\"pysmo_poly_val_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_validation, filename=\"pysmo_poly_val_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_validation, filename=\"pysmo_poly_val_residual.pdf\")" ] }, { From 33909f86bdbbd6c06a71ec701d4d1da1f37d0465 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Fri, 25 Aug 2023 01:25:24 -0400 Subject: [PATCH 08/75] Trying with replacing files from latest clone. --- .../hda_flowsheet_with_costing_test.ipynb | 1116 ++++++------ .../hda_flowsheet_with_costing_usr.ipynb | 1116 ++++++------ .../hda_flowsheet_with_distillation.ipynb | 1597 +---------------- ...flowsheet_with_distillation_solution.ipynb | 1498 +--------------- 4 files changed, 1259 insertions(+), 4068 deletions(-) diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_test.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_test.ipynb index 8f740cad..b9a6f2c2 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_test.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_test.ipynb @@ -1,560 +1,560 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Costing\n", + "\n", + "\n", + "## Note\n", + "\n", + "This example will demonstrate adding capital and operating costs to the two HDA examples, the basic [HDA with Flash](../tut/hda_flowsheet_solution_test.ipynb) and a comparison with the HDA with Distillation.\n", + "\n", + "\n", + "## Learning outcomes\n", + "\n", + "- Import external pre-built steady-state flowsheets using the IDAES unit model library\n", + "- Define and add costing blocks using the IDAES Process Costing Framework\n", + "- Fomulate and solve a process economics optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "Users may refer to the prior examples linked at the top of this notebook for detailed process descriptions of the two HDA configurations. As before, the properties required for this module are defined in\n", + "\n", + "- `hda_ideal_VLE.py`\n", + "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", + "- `hda_reaction.py`\n", + "\n", + "Additionally, we will be importing externally-defined flowsheets for the two HDA configurations from\n", + "\n", + "- `hda_flowsheets_for_costing_notebook.py`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import and run HDA Flowsheets\n", + "First, we will generate solved flowsheets for each HDA model. The external scripts build and set inputs for the flowsheets, initialize unit models and streams, and solve the flowsheets before returning the model objects. Note that the HDA flowsheets contain all unit models and stream connections, and no costing equations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The flowsheet utilizes the Wegstein method to iteratively solve circular dependencies such as recycle streams, and is intended to approach a feasible solution. As such, the calls below will fail to converge after 3 iterations and pass to IPOPT to obtain an optimal solution as expected:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Source file for prebuilt flowsheets\n", + "from hda_flowsheets_for_costing_notebook import hda_with_flash\n", + "\n", + "# Build hda model with second flash unit and return model object\n", + "m = hda_with_flash(tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IDAES Process Costing Framework\n", + "IDAES provides a library of capital costing correlations based on those in the following source:\n", + "\n", + "*Process and Product Design Principles: Synthesis, Analysis, and Evaluation*. Seider, Seader, Lewin, Windagdo, 3rd Ed. John Wiley and Sons Chapter 22. Cost Accounting and Capital Cost Estimation 22.2 Cost Indexes and Capital Investment.\n", + "\n", + "Currently, IDAES supports calculation of capital costing for a wide array of unit operations, vesseel sizing and material properties, and specific unit options such as column tray types and heat exchanger configurations. Users may find further information on specific costing methods and options in the IDAES Process Costing Framework documentation (link pending).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Operating Cost Equations\n", + "Before adding capital costing blocks, we will add operating cost equations taken from the basic [HDA with Flash](../tut/hda_flowsheet_solution_test.ipynb) and the HDA with Distillation examples. The examples assume constant cooling and heating coefficients over an annual cost basis. The IDAES Generic Costing Framework does not currently support variable cost calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Required imports\n", + "from pyomo.environ import Expression\n", + "\n", + "# Operating costs for HDA with second flash (model m)\n", + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", + ")\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", + ")\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Capital Costing\n", + "Below, we will add add capital costing blocks to the imported flowsheets and evaluate the economic impact of replacing the second Flash with a Distillation column. First, let's import and define the main flowsheet costing block:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import costing methods - classes, heaters, vessels, compressors, columns\n", + "from idaes.models.costing.SSLW import (\n", + " SSLWCosting,\n", + " SSLWCostingData,\n", + ")\n", + "from idaes.core import UnitModelCostingBlock\n", + "\n", + "# Costing block\n", + "m.fs.costing = SSLWCosting()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will build the relevant costing blocks for the equipment we wish to cost. Note how the costing block, methods and flags are passed as arguments in the costing block call itself. Each unit model will have a single costing block, but each flowsheet model (m and n) will also have a single costing block for flowsheet-level properties.\n", + "\n", + "Users should note that IDAES costing methods support a wide array of heating sources (e.g. fired, steam boiler, hot water) and do not support direct capital costing of coolers. If users wish to cost Heater units acting as coolers, it is necessary to cost a \"dummy\" [0D shell and tube exchanger](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html) with appropriate aliased hot stream properties and proper cooling water properties. This is not demonstrated here, as the HDA examples take advantage of Flash and Condenser operations to recover liquid product.\n", + "\n", + "Capital costing is independent of unit model connections, and building cost equations may be done piecewise in this fashion. Default options are passed explicitly to demonstrate proper syntax and usage. Now that all required properties are defined, let's cost our models connecting costing blocks, methods and unit models in each flowsheet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Flexibility of Costing Block Definitions\n", + "IDAES supports many ways to define batches of costing blocks, and several are shown in the example. Users may employ whichever method fits their modeling needs for explicit or concise code. In the code below, note how the unit model itself is never passed to the costing method; when the full model is executed, the costing block will automatically connect its parent block with child equation blocks.\n", + "\n", + "`Compressor` unit models with isothermal or adiabatic thermodynamics are too simple to cost and are therefore excluded from the economic analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define costing for the heater unit:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.costing.SSLW import (\n", + " HeaterMaterial,\n", + " HeaterSource,\n", + ")\n", + "\n", + "# Costing for heater - m.fs.H101\n", + "m.fs.H101.costing = UnitModelCostingBlock(\n", + " flowsheet_costing_block=m.fs.costing,\n", + " costing_method=SSLWCostingData.cost_fired_heater,\n", + " costing_method_arguments={\n", + " \"material_type\": HeaterMaterial.CarbonSteel,\n", + " \"heat_source\": HeaterSource.Fuel,\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The costing module provides a `unit_mapping` dictionary linking generic unit model classes with recommended costing methods. In this example, StoichiometricReactor and Flash vessels utilize different vessel costing methods with similar arguments. The diameter and length attributes need to exist in order to cost vessel sizing and material requirements, and we add them if they don't exist already. The `unit_mapping` method provides an opportunity to automatically select the correct vessel orientation (vertical or horizontal) based on the unit type; passing a `StoichiometricReactor` or `PFR` class object will call the `cost_horizontal_vessel` method, while passing a `Flash` or `CSTR` class object will call the `cost_vertical_vessel` method.\n", + "\n", + "All vessels are assigned costing succintly via a loop below - users may define each block individually if desired:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.costing.SSLW import (\n", + " VesselMaterial,\n", + " TrayType,\n", + " TrayMaterial,\n", + ")\n", + "\n", + "from idaes.core.util.constants import Constants\n", + "from pyomo.environ import Var, Constraint, units as pyunits, Param, value\n", + "from idaes.models.unit_models import StoichiometricReactor, Flash\n", + "\n", + "# Map unit models to unit classes\n", + "# Will pass to unit_mapping which calls costing methods based on unit class\n", + "unit_class_mapping = {\n", + " m.fs.R101: StoichiometricReactor,\n", + " m.fs.F101: Flash,\n", + " m.fs.F102: Flash,\n", + "}\n", + "\n", + "# Costing for vessels - m.fs.R101, m.fs.F101, m.fs.F102\n", + "\n", + "# Loop over units\n", + "for unit in [m.fs.R101, m.fs.F101, m.fs.F102]:\n", + " # Get correct unit class for unit model\n", + " unit_class = unit_class_mapping[unit]\n", + "\n", + " # Add dimension variables and constraint if they don't exist\n", + " if not hasattr(unit, \"diameter\"):\n", + " unit.diameter = Var(initialize=1, units=pyunits.m)\n", + " if not hasattr(unit, \"length\"):\n", + " unit.length = Var(initialize=1, units=pyunits.m)\n", + " if hasattr(unit, \"volume\"): # if volume exists, set diameter from volume\n", + " unit.volume_eq = Constraint(\n", + " expr=unit.volume[0]\n", + " == unit.length * unit.diameter**2 * 0.25 * Constants.pi\n", + " )\n", + " else: # fix diameter directly\n", + " unit.diameter.fix(0.2214 * pyunits.m)\n", + " # Either way, fix L/D to calculate L from D\n", + " unit.L_over_D = Constraint(expr=unit.length == 3 * unit.diameter)\n", + "\n", + " # Define vessel costing\n", + " unit.costing = UnitModelCostingBlock(\n", + " flowsheet_costing_block=unit.parent_block().costing, # e.g. m.fs.R101.costing\n", + " costing_method=SSLWCostingData.unit_mapping[\n", + " unit_class\n", + " ], # e.g. cost_vertical_vessel()\n", + " costing_method_arguments={\n", + " \"material_type\": VesselMaterial.CarbonSteel,\n", + " \"shell_thickness\": 1.25 * pyunits.inch,\n", + " },\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve Flowsheet Costing Blocks\n", + "Now, we may solve the full flowsheet for all costing blocks:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Eefine solver\n", + "from idaes.core.solvers import get_solver\n", + "\n", + "solver = get_solver()\n", + "\n", + "# Check that the degrees of freedom is zero\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Check physical units consistency, solve and check solver status\n", + "from pyomo.environ import TerminationCondition\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "\n", + "assert_units_consistent(m)\n", + "results = solver.solve(m, tee=True, symbolic_solver_labels=True)\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For comparison, we will call and build the HDA flowsheet replacing the second `Flash` with a `TrayColumn` distillation unit model. The flowsheet costing occurs in the external script `hda_flowsheets_for_costing_notebook.py` and is not shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from pyomo.common.log import LoggingIntercept\n", + "import logging\n", + "from io import StringIO\n", + "\n", + "stream = StringIO()\n", + "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", + " # Source file for prebuilt flowsheets\n", + " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", + "\n", + " # Build hda model with distillation column and return model object\n", + " n = hda_with_distillation(tee=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results Comparison and Visualization\n", + "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports and data gathering\n", + "from matplotlib import pyplot as plt\n", + "\n", + "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", + "\n", + "two_flash_unitlist = [\n", + " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", + "]\n", + "distillation_unitlist = [\n", + " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare equipment purchase costs (actual capital costs)\n", + "\n", + "two_flash_capcost = {\n", + " unit.name: value(unit.costing.capital_cost / 1e3) for unit in two_flash_unitlist\n", + "}\n", + "distillation_capcost = {\n", + " unit.name: value(unit.costing.capital_cost / 1e3) for unit in distillation_unitlist\n", + "}\n", + "\n", + "two_flash_capdf = pd.DataFrame(\n", + " list(two_flash_capcost.items()), columns=[\"Equipment\", \"Two Flash\"]\n", + ").set_index(\"Equipment\")\n", + "distillation_capdf = pd.DataFrame(\n", + " list(distillation_capcost.items()), columns=[\"Equipment\", \"Distillation\"]\n", + ").set_index(\"Equipment\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "capcosts = two_flash_capdf.add(distillation_capdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "# Sort according to an easier order to view\n", + "capcosts = capcosts[[\"fs.H101\", \"fs.R101\", \"fs.F101\", \"fs.F102\", \"fs.D101\", \"fs.H102\"]]\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(capcosts) # view dataframe before plotting\n", + "\n", + "capplot = capcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Total Capital Costs\", ylabel=\"$1000\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare operating costs (per year)\n", + "\n", + "two_flash_opcost = {\n", + " \"cooling\": value(3600 * 24 * 365 * m.fs.cooling_cost / 1e3),\n", + " \"heating\": value(3600 * 24 * 365 * m.fs.heating_cost / 1e3),\n", + "}\n", + "distillation_opcost = {\n", + " \"cooling\": value(3600 * 24 * 365 * n.fs.cooling_cost / 1e3),\n", + " \"heating\": value(3600 * 24 * 365 * n.fs.heating_cost / 1e3),\n", + "}\n", + "\n", + "two_flash_opdf = pd.DataFrame(\n", + " list(two_flash_opcost.items()), columns=[\"Utilities\", \"Two Flash\"]\n", + ").set_index(\"Utilities\")\n", + "distillation_opdf = pd.DataFrame(\n", + " list(distillation_opcost.items()), columns=[\"Utilities\", \"Distillation\"]\n", + ").set_index(\"Utilities\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "opcosts = two_flash_opdf.add(distillation_opdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(opcosts) # view dataframe before plotting\n", + "\n", + "opplot = opcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Operating Costs\", ylabel=\"$1000/year\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare total costs (capital costs and operating costs)\n", + "\n", + "two_flash_totcost = {\n", + " \"capital\": sum(two_flash_capcost[idx] for idx in two_flash_capcost),\n", + " \"operating\": value(m.fs.operating_cost) / 1e3,\n", + "}\n", + "distillation_totcost = {\n", + " \"capital\": sum(distillation_capcost[idx] for idx in distillation_capcost),\n", + " \"operating\": value(n.fs.operating_cost) / 1e3,\n", + "}\n", + "\n", + "two_flash_totdf = pd.DataFrame(\n", + " list(two_flash_totcost.items()), columns=[\"Costs\", \"Two Flash\"]\n", + ").set_index(\"Costs\")\n", + "distillation_totdf = pd.DataFrame(\n", + " list(distillation_totcost.items()), columns=[\"Costs\", \"Distillation\"]\n", + ").set_index(\"Costs\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "totcosts = two_flash_totdf.add(distillation_totdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(totcosts) # view dataframe before plotting\n", + "\n", + "totplot = totcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Total Plant Cost (TPC)\", ylabel=\"$1000/year\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's compare the total costs on a production basis. This will account for the greater efficiency provided by the distillation column relative to the less-expensive second flash unit:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "two_flash_cost = value(1e3 * sum(two_flash_totcost[idx] for idx in two_flash_totcost))\n", + "two_flash_prod = value(\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] * 365 * 24 * 3600\n", + ")\n", + "distillation_cost = value(\n", + " 1e3 * sum(distillation_totcost[idx] for idx in distillation_totcost)\n", + ")\n", + "distillation_prod = value(n.fs.D101.condenser.distillate.flow_mol[0] * 365 * 24 * 3600)\n", + "\n", + "print(\n", + " f\"Two flash case over one year: ${two_flash_cost/1e3:0.0f}K / {two_flash_prod/1e3:0.0f} kmol benzene = ${two_flash_cost/(two_flash_prod/1e3):0.2f} per kmol benzene produced\"\n", + ")\n", + "print(\n", + " f\"Distillation case over one year: ${distillation_cost/1e3:0.0f}K / {distillation_prod/1e3:0.0f} kmol benzene = ${distillation_cost/(distillation_prod/1e3):0.2f} per kmol benzene produced\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary\n", + "In this example, IDAES Process Costing Framework methods were applied to two HDA flowsheets for capital cost estimation. The costing blocks calls showcased multiple methods to define unit costing, demonstrating the flexibility and best practice of the costing framework. In the basic examples, the two-flash HDA did not include costing and the distillation HDA estimated a reactor capital cost comprising 3.3% of the total plant cost (TPC). With more rigorous costing, IDAES obtained total capital costs of 8.5% TPC (two flash HDA) and 9.6% (distillation HDA) and better modeled the impact of equipment cost on process economics. As printed above, the IDAES Process Costing Framework confirmed that replacing the second flash drum with a distillation column results in increased equipment costs, increased production and decreased cost per unit product." + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Costing\n", - "\n", - "\n", - "## Note\n", - "\n", - "This example will demonstrate adding capital and operating costs to the two HDA examples, the basic [HDA with Flash](../tut/hda_flowsheet_solution_test.ipynb) and a comparison with the HDA with Distillation.\n", - "\n", - "\n", - "## Learning outcomes\n", - "\n", - "- Import external pre-built steady-state flowsheets using the IDAES unit model library\n", - "- Define and add costing blocks using the IDAES Process Costing Framework\n", - "- Fomulate and solve a process economics optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "Users may refer to the prior examples linked at the top of this notebook for detailed process descriptions of the two HDA configurations. As before, the properties required for this module are defined in\n", - "\n", - "- `hda_ideal_VLE.py`\n", - "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", - "- `hda_reaction.py`\n", - "\n", - "Additionally, we will be importing externally-defined flowsheets for the two HDA configurations from\n", - "\n", - "- `hda_flowsheets_for_costing_notebook.py`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import and run HDA Flowsheets\n", - "First, we will generate solved flowsheets for each HDA model. The external scripts build and set inputs for the flowsheets, initialize unit models and streams, and solve the flowsheets before returning the model objects. Note that the HDA flowsheets contain all unit models and stream connections, and no costing equations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The flowsheet utilizes the Wegstein method to iteratively solve circular dependencies such as recycle streams, and is intended to approach a feasible solution. As such, the calls below will fail to converge after 3 iterations and pass to IPOPT to obtain an optimal solution as expected:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Source file for prebuilt flowsheets\n", - "from hda_flowsheets_for_costing_notebook import hda_with_flash\n", - "\n", - "# Build hda model with second flash unit and return model object\n", - "m = hda_with_flash(tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IDAES Process Costing Framework\n", - "IDAES provides a library of capital costing correlations based on those in the following source:\n", - "\n", - "*Process and Product Design Principles: Synthesis, Analysis, and Evaluation*. Seider, Seader, Lewin, Windagdo, 3rd Ed. John Wiley and Sons Chapter 22. Cost Accounting and Capital Cost Estimation 22.2 Cost Indexes and Capital Investment.\n", - "\n", - "Currently, IDAES supports calculation of capital costing for a wide array of unit operations, vesseel sizing and material properties, and specific unit options such as column tray types and heat exchanger configurations. Users may find further information on specific costing methods and options in the IDAES Process Costing Framework documentation (link pending).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add Operating Cost Equations\n", - "Before adding capital costing blocks, we will add operating cost equations taken from the basic [HDA with Flash](../tut/hda_flowsheet_solution_test.ipynb) and the HDA with Distillation examples. The examples assume constant cooling and heating coefficients over an annual cost basis. The IDAES Generic Costing Framework does not currently support variable cost calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Required imports\n", - "from pyomo.environ import Expression\n", - "\n", - "# Operating costs for HDA with second flash (model m)\n", - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", - ")\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", - ")\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add Capital Costing\n", - "Below, we will add add capital costing blocks to the imported flowsheets and evaluate the economic impact of replacing the second Flash with a Distillation column. First, let's import and define the main flowsheet costing block:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import costing methods - classes, heaters, vessels, compressors, columns\n", - "from idaes.models.costing.SSLW import (\n", - " SSLWCosting,\n", - " SSLWCostingData,\n", - ")\n", - "from idaes.core import UnitModelCostingBlock\n", - "\n", - "# Costing block\n", - "m.fs.costing = SSLWCosting()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will build the relevant costing blocks for the equipment we wish to cost. Note how the costing block, methods and flags are passed as arguments in the costing block call itself. Each unit model will have a single costing block, but each flowsheet model (m and n) will also have a single costing block for flowsheet-level properties.\n", - "\n", - "Users should note that IDAES costing methods support a wide array of heating sources (e.g. fired, steam boiler, hot water) and do not support direct capital costing of coolers. If users wish to cost Heater units acting as coolers, it is necessary to cost a \"dummy\" [0D shell and tube exchanger](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html) with appropriate aliased hot stream properties and proper cooling water properties. This is not demonstrated here, as the HDA examples take advantage of Flash and Condenser operations to recover liquid product.\n", - "\n", - "Capital costing is independent of unit model connections, and building cost equations may be done piecewise in this fashion. Default options are passed explicitly to demonstrate proper syntax and usage. Now that all required properties are defined, let's cost our models connecting costing blocks, methods and unit models in each flowsheet." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Flexibility of Costing Block Definitions\n", - "IDAES supports many ways to define batches of costing blocks, and several are shown in the example. Users may employ whichever method fits their modeling needs for explicit or concise code. In the code below, note how the unit model itself is never passed to the costing method; when the full model is executed, the costing block will automatically connect its parent block with child equation blocks.\n", - "\n", - "`Compressor` unit models with isothermal or adiabatic thermodynamics are too simple to cost and are therefore excluded from the economic analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define costing for the heater unit:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.costing.SSLW import (\n", - " HeaterMaterial,\n", - " HeaterSource,\n", - ")\n", - "\n", - "# Costing for heater - m.fs.H101\n", - "m.fs.H101.costing = UnitModelCostingBlock(\n", - " flowsheet_costing_block=m.fs.costing,\n", - " costing_method=SSLWCostingData.cost_fired_heater,\n", - " costing_method_arguments={\n", - " \"material_type\": HeaterMaterial.CarbonSteel,\n", - " \"heat_source\": HeaterSource.Fuel,\n", - " },\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The costing module provides a `unit_mapping` dictionary linking generic unit model classes with recommended costing methods. In this example, StoichiometricReactor and Flash vessels utilize different vessel costing methods with similar arguments. The diameter and length attributes need to exist in order to cost vessel sizing and material requirements, and we add them if they don't exist already. The `unit_mapping` method provides an opportunity to automatically select the correct vessel orientation (vertical or horizontal) based on the unit type; passing a `StoichiometricReactor` or `PFR` class object will call the `cost_horizontal_vessel` method, while passing a `Flash` or `CSTR` class object will call the `cost_vertical_vessel` method.\n", - "\n", - "All vessels are assigned costing succintly via a loop below - users may define each block individually if desired:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.costing.SSLW import (\n", - " VesselMaterial,\n", - " TrayType,\n", - " TrayMaterial,\n", - ")\n", - "\n", - "from idaes.core.util.constants import Constants\n", - "from pyomo.environ import Var, Constraint, units as pyunits, Param, value\n", - "from idaes.models.unit_models import StoichiometricReactor, Flash\n", - "\n", - "# Map unit models to unit classes\n", - "# Will pass to unit_mapping which calls costing methods based on unit class\n", - "unit_class_mapping = {\n", - " m.fs.R101: StoichiometricReactor,\n", - " m.fs.F101: Flash,\n", - " m.fs.F102: Flash,\n", - "}\n", - "\n", - "# Costing for vessels - m.fs.R101, m.fs.F101, m.fs.F102\n", - "\n", - "# Loop over units\n", - "for unit in [m.fs.R101, m.fs.F101, m.fs.F102]:\n", - " # Get correct unit class for unit model\n", - " unit_class = unit_class_mapping[unit]\n", - "\n", - " # Add dimension variables and constraint if they don't exist\n", - " if not hasattr(unit, \"diameter\"):\n", - " unit.diameter = Var(initialize=1, units=pyunits.m)\n", - " if not hasattr(unit, \"length\"):\n", - " unit.length = Var(initialize=1, units=pyunits.m)\n", - " if hasattr(unit, \"volume\"): # if volume exists, set diameter from volume\n", - " unit.volume_eq = Constraint(\n", - " expr=unit.volume[0]\n", - " == unit.length * unit.diameter**2 * 0.25 * Constants.pi\n", - " )\n", - " else: # fix diameter directly\n", - " unit.diameter.fix(0.2214 * pyunits.m)\n", - " # Either way, fix L/D to calculate L from D\n", - " unit.L_over_D = Constraint(expr=unit.length == 3 * unit.diameter)\n", - "\n", - " # Define vessel costing\n", - " unit.costing = UnitModelCostingBlock(\n", - " flowsheet_costing_block=unit.parent_block().costing, # e.g. m.fs.R101.costing\n", - " costing_method=SSLWCostingData.unit_mapping[\n", - " unit_class\n", - " ], # e.g. cost_vertical_vessel()\n", - " costing_method_arguments={\n", - " \"material_type\": VesselMaterial.CarbonSteel,\n", - " \"shell_thickness\": 1.25 * pyunits.inch,\n", - " },\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve Flowsheet Costing Blocks\n", - "Now, we may solve the full flowsheet for all costing blocks:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Eefine solver\n", - "from idaes.core.solvers import get_solver\n", - "\n", - "solver = get_solver()\n", - "\n", - "# Check that the degrees of freedom is zero\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Check physical units consistency, solve and check solver status\n", - "from pyomo.environ import TerminationCondition\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "assert_units_consistent(m)\n", - "results = solver.solve(m, tee=True, symbolic_solver_labels=True)\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For comparison, we will call and build the HDA flowsheet replacing the second `Flash` with a `TrayColumn` distillation unit model. The flowsheet costing occurs in the external script `hda_flowsheets_for_costing_notebook.py` and is not shown here:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from pyomo.common.log import LoggingIntercept\n", - "import logging\n", - "from io import StringIO\n", - "\n", - "stream = StringIO()\n", - "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", - " # Source file for prebuilt flowsheets\n", - " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", - "\n", - " # Build hda model with distillation column and return model object\n", - " n = hda_with_distillation(tee=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results Comparison and Visualization\n", - "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Imports and data gathering\n", - "from matplotlib import pyplot as plt\n", - "\n", - "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", - "\n", - "two_flash_unitlist = [\n", - " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", - "]\n", - "distillation_unitlist = [\n", - " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare equipment purchase costs (actual capital costs)\n", - "\n", - "two_flash_capcost = {\n", - " unit.name: value(unit.costing.capital_cost / 1e3) for unit in two_flash_unitlist\n", - "}\n", - "distillation_capcost = {\n", - " unit.name: value(unit.costing.capital_cost / 1e3) for unit in distillation_unitlist\n", - "}\n", - "\n", - "two_flash_capdf = pd.DataFrame(\n", - " list(two_flash_capcost.items()), columns=[\"Equipment\", \"Two Flash\"]\n", - ").set_index(\"Equipment\")\n", - "distillation_capdf = pd.DataFrame(\n", - " list(distillation_capcost.items()), columns=[\"Equipment\", \"Distillation\"]\n", - ").set_index(\"Equipment\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "capcosts = two_flash_capdf.add(distillation_capdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "# Sort according to an easier order to view\n", - "capcosts = capcosts[[\"fs.H101\", \"fs.R101\", \"fs.F101\", \"fs.F102\", \"fs.D101\", \"fs.H102\"]]\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(capcosts) # view dataframe before plotting\n", - "\n", - "capplot = capcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Total Capital Costs\", ylabel=\"$1000\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare operating costs (per year)\n", - "\n", - "two_flash_opcost = {\n", - " \"cooling\": value(3600 * 24 * 365 * m.fs.cooling_cost / 1e3),\n", - " \"heating\": value(3600 * 24 * 365 * m.fs.heating_cost / 1e3),\n", - "}\n", - "distillation_opcost = {\n", - " \"cooling\": value(3600 * 24 * 365 * n.fs.cooling_cost / 1e3),\n", - " \"heating\": value(3600 * 24 * 365 * n.fs.heating_cost / 1e3),\n", - "}\n", - "\n", - "two_flash_opdf = pd.DataFrame(\n", - " list(two_flash_opcost.items()), columns=[\"Utilities\", \"Two Flash\"]\n", - ").set_index(\"Utilities\")\n", - "distillation_opdf = pd.DataFrame(\n", - " list(distillation_opcost.items()), columns=[\"Utilities\", \"Distillation\"]\n", - ").set_index(\"Utilities\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "opcosts = two_flash_opdf.add(distillation_opdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(opcosts) # view dataframe before plotting\n", - "\n", - "opplot = opcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Operating Costs\", ylabel=\"$1000/year\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare total costs (capital costs and operating costs)\n", - "\n", - "two_flash_totcost = {\n", - " \"capital\": sum(two_flash_capcost[idx] for idx in two_flash_capcost),\n", - " \"operating\": value(m.fs.operating_cost) / 1e3,\n", - "}\n", - "distillation_totcost = {\n", - " \"capital\": sum(distillation_capcost[idx] for idx in distillation_capcost),\n", - " \"operating\": value(n.fs.operating_cost) / 1e3,\n", - "}\n", - "\n", - "two_flash_totdf = pd.DataFrame(\n", - " list(two_flash_totcost.items()), columns=[\"Costs\", \"Two Flash\"]\n", - ").set_index(\"Costs\")\n", - "distillation_totdf = pd.DataFrame(\n", - " list(distillation_totcost.items()), columns=[\"Costs\", \"Distillation\"]\n", - ").set_index(\"Costs\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "totcosts = two_flash_totdf.add(distillation_totdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(totcosts) # view dataframe before plotting\n", - "\n", - "totplot = totcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Total Plant Cost (TPC)\", ylabel=\"$1000/year\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's compare the total costs on a production basis. This will account for the greater efficiency provided by the distillation column relative to the less-expensive second flash unit:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "two_flash_cost = value(1e3 * sum(two_flash_totcost[idx] for idx in two_flash_totcost))\n", - "two_flash_prod = value(\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] * 365 * 24 * 3600\n", - ")\n", - "distillation_cost = value(\n", - " 1e3 * sum(distillation_totcost[idx] for idx in distillation_totcost)\n", - ")\n", - "distillation_prod = value(n.fs.D101.condenser.distillate.flow_mol[0] * 365 * 24 * 3600)\n", - "\n", - "print(\n", - " f\"Two flash case over one year: ${two_flash_cost/1e3:0.0f}K / {two_flash_prod/1e3:0.0f} kmol benzene = ${two_flash_cost/(two_flash_prod/1e3):0.2f} per kmol benzene produced\"\n", - ")\n", - "print(\n", - " f\"Distillation case over one year: ${distillation_cost/1e3:0.0f}K / {distillation_prod/1e3:0.0f} kmol benzene = ${distillation_cost/(distillation_prod/1e3):0.2f} per kmol benzene produced\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Summary\n", - "In this example, IDAES Process Costing Framework methods were applied to two HDA flowsheets for capital cost estimation. The costing blocks calls showcased multiple methods to define unit costing, demonstrating the flexibility and best practice of the costing framework. In the basic examples, the two-flash HDA did not include costing and the distillation HDA estimated a reactor capital cost comprising 3.3% of the total plant cost (TPC). With more rigorous costing, IDAES obtained total capital costs of 8.5% TPC (two flash HDA) and 9.6% (distillation HDA) and better modeled the impact of equipment cost on process economics. As printed above, the IDAES Process Costing Framework confirmed that replacing the second flash drum with a distillation column results in increased equipment costs, increased production and decreased cost per unit product." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_usr.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_usr.ipynb index d4a23407..4a2583ba 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_usr.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_usr.ipynb @@ -1,560 +1,560 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Costing\n", + "\n", + "\n", + "## Note\n", + "\n", + "This example will demonstrate adding capital and operating costs to the two HDA examples, the basic [HDA with Flash](../tut/hda_flowsheet_solution_usr.ipynb) and a comparison with the HDA with Distillation.\n", + "\n", + "\n", + "## Learning outcomes\n", + "\n", + "- Import external pre-built steady-state flowsheets using the IDAES unit model library\n", + "- Define and add costing blocks using the IDAES Process Costing Framework\n", + "- Fomulate and solve a process economics optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "Users may refer to the prior examples linked at the top of this notebook for detailed process descriptions of the two HDA configurations. As before, the properties required for this module are defined in\n", + "\n", + "- `hda_ideal_VLE.py`\n", + "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", + "- `hda_reaction.py`\n", + "\n", + "Additionally, we will be importing externally-defined flowsheets for the two HDA configurations from\n", + "\n", + "- `hda_flowsheets_for_costing_notebook.py`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import and run HDA Flowsheets\n", + "First, we will generate solved flowsheets for each HDA model. The external scripts build and set inputs for the flowsheets, initialize unit models and streams, and solve the flowsheets before returning the model objects. Note that the HDA flowsheets contain all unit models and stream connections, and no costing equations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The flowsheet utilizes the Wegstein method to iteratively solve circular dependencies such as recycle streams, and is intended to approach a feasible solution. As such, the calls below will fail to converge after 3 iterations and pass to IPOPT to obtain an optimal solution as expected:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Source file for prebuilt flowsheets\n", + "from hda_flowsheets_for_costing_notebook import hda_with_flash\n", + "\n", + "# Build hda model with second flash unit and return model object\n", + "m = hda_with_flash(tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IDAES Process Costing Framework\n", + "IDAES provides a library of capital costing correlations based on those in the following source:\n", + "\n", + "*Process and Product Design Principles: Synthesis, Analysis, and Evaluation*. Seider, Seader, Lewin, Windagdo, 3rd Ed. John Wiley and Sons Chapter 22. Cost Accounting and Capital Cost Estimation 22.2 Cost Indexes and Capital Investment.\n", + "\n", + "Currently, IDAES supports calculation of capital costing for a wide array of unit operations, vesseel sizing and material properties, and specific unit options such as column tray types and heat exchanger configurations. Users may find further information on specific costing methods and options in the IDAES Process Costing Framework documentation (link pending).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Operating Cost Equations\n", + "Before adding capital costing blocks, we will add operating cost equations taken from the basic [HDA with Flash](../tut/hda_flowsheet_solution_usr.ipynb) and the HDA with Distillation examples. The examples assume constant cooling and heating coefficients over an annual cost basis. The IDAES Generic Costing Framework does not currently support variable cost calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Required imports\n", + "from pyomo.environ import Expression\n", + "\n", + "# Operating costs for HDA with second flash (model m)\n", + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", + ")\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", + ")\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Capital Costing\n", + "Below, we will add add capital costing blocks to the imported flowsheets and evaluate the economic impact of replacing the second Flash with a Distillation column. First, let's import and define the main flowsheet costing block:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import costing methods - classes, heaters, vessels, compressors, columns\n", + "from idaes.models.costing.SSLW import (\n", + " SSLWCosting,\n", + " SSLWCostingData,\n", + ")\n", + "from idaes.core import UnitModelCostingBlock\n", + "\n", + "# Costing block\n", + "m.fs.costing = SSLWCosting()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will build the relevant costing blocks for the equipment we wish to cost. Note how the costing block, methods and flags are passed as arguments in the costing block call itself. Each unit model will have a single costing block, but each flowsheet model (m and n) will also have a single costing block for flowsheet-level properties.\n", + "\n", + "Users should note that IDAES costing methods support a wide array of heating sources (e.g. fired, steam boiler, hot water) and do not support direct capital costing of coolers. If users wish to cost Heater units acting as coolers, it is necessary to cost a \"dummy\" [0D shell and tube exchanger](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html) with appropriate aliased hot stream properties and proper cooling water properties. This is not demonstrated here, as the HDA examples take advantage of Flash and Condenser operations to recover liquid product.\n", + "\n", + "Capital costing is independent of unit model connections, and building cost equations may be done piecewise in this fashion. Default options are passed explicitly to demonstrate proper syntax and usage. Now that all required properties are defined, let's cost our models connecting costing blocks, methods and unit models in each flowsheet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Flexibility of Costing Block Definitions\n", + "IDAES supports many ways to define batches of costing blocks, and several are shown in the example. Users may employ whichever method fits their modeling needs for explicit or concise code. In the code below, note how the unit model itself is never passed to the costing method; when the full model is executed, the costing block will automatically connect its parent block with child equation blocks.\n", + "\n", + "`Compressor` unit models with isothermal or adiabatic thermodynamics are too simple to cost and are therefore excluded from the economic analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define costing for the heater unit:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.costing.SSLW import (\n", + " HeaterMaterial,\n", + " HeaterSource,\n", + ")\n", + "\n", + "# Costing for heater - m.fs.H101\n", + "m.fs.H101.costing = UnitModelCostingBlock(\n", + " flowsheet_costing_block=m.fs.costing,\n", + " costing_method=SSLWCostingData.cost_fired_heater,\n", + " costing_method_arguments={\n", + " \"material_type\": HeaterMaterial.CarbonSteel,\n", + " \"heat_source\": HeaterSource.Fuel,\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The costing module provides a `unit_mapping` dictionary linking generic unit model classes with recommended costing methods. In this example, StoichiometricReactor and Flash vessels utilize different vessel costing methods with similar arguments. The diameter and length attributes need to exist in order to cost vessel sizing and material requirements, and we add them if they don't exist already. The `unit_mapping` method provides an opportunity to automatically select the correct vessel orientation (vertical or horizontal) based on the unit type; passing a `StoichiometricReactor` or `PFR` class object will call the `cost_horizontal_vessel` method, while passing a `Flash` or `CSTR` class object will call the `cost_vertical_vessel` method.\n", + "\n", + "All vessels are assigned costing succintly via a loop below - users may define each block individually if desired:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.costing.SSLW import (\n", + " VesselMaterial,\n", + " TrayType,\n", + " TrayMaterial,\n", + ")\n", + "\n", + "from idaes.core.util.constants import Constants\n", + "from pyomo.environ import Var, Constraint, units as pyunits, Param, value\n", + "from idaes.models.unit_models import StoichiometricReactor, Flash\n", + "\n", + "# Map unit models to unit classes\n", + "# Will pass to unit_mapping which calls costing methods based on unit class\n", + "unit_class_mapping = {\n", + " m.fs.R101: StoichiometricReactor,\n", + " m.fs.F101: Flash,\n", + " m.fs.F102: Flash,\n", + "}\n", + "\n", + "# Costing for vessels - m.fs.R101, m.fs.F101, m.fs.F102\n", + "\n", + "# Loop over units\n", + "for unit in [m.fs.R101, m.fs.F101, m.fs.F102]:\n", + " # Get correct unit class for unit model\n", + " unit_class = unit_class_mapping[unit]\n", + "\n", + " # Add dimension variables and constraint if they don't exist\n", + " if not hasattr(unit, \"diameter\"):\n", + " unit.diameter = Var(initialize=1, units=pyunits.m)\n", + " if not hasattr(unit, \"length\"):\n", + " unit.length = Var(initialize=1, units=pyunits.m)\n", + " if hasattr(unit, \"volume\"): # if volume exists, set diameter from volume\n", + " unit.volume_eq = Constraint(\n", + " expr=unit.volume[0]\n", + " == unit.length * unit.diameter**2 * 0.25 * Constants.pi\n", + " )\n", + " else: # fix diameter directly\n", + " unit.diameter.fix(0.2214 * pyunits.m)\n", + " # Either way, fix L/D to calculate L from D\n", + " unit.L_over_D = Constraint(expr=unit.length == 3 * unit.diameter)\n", + "\n", + " # Define vessel costing\n", + " unit.costing = UnitModelCostingBlock(\n", + " flowsheet_costing_block=unit.parent_block().costing, # e.g. m.fs.R101.costing\n", + " costing_method=SSLWCostingData.unit_mapping[\n", + " unit_class\n", + " ], # e.g. cost_vertical_vessel()\n", + " costing_method_arguments={\n", + " \"material_type\": VesselMaterial.CarbonSteel,\n", + " \"shell_thickness\": 1.25 * pyunits.inch,\n", + " },\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve Flowsheet Costing Blocks\n", + "Now, we may solve the full flowsheet for all costing blocks:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Eefine solver\n", + "from idaes.core.solvers import get_solver\n", + "\n", + "solver = get_solver()\n", + "\n", + "# Check that the degrees of freedom is zero\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Check physical units consistency, solve and check solver status\n", + "from pyomo.environ import TerminationCondition\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "\n", + "assert_units_consistent(m)\n", + "results = solver.solve(m, tee=True, symbolic_solver_labels=True)\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For comparison, we will call and build the HDA flowsheet replacing the second `Flash` with a `TrayColumn` distillation unit model. The flowsheet costing occurs in the external script `hda_flowsheets_for_costing_notebook.py` and is not shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from pyomo.common.log import LoggingIntercept\n", + "import logging\n", + "from io import StringIO\n", + "\n", + "stream = StringIO()\n", + "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", + " # Source file for prebuilt flowsheets\n", + " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", + "\n", + " # Build hda model with distillation column and return model object\n", + " n = hda_with_distillation(tee=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results Comparison and Visualization\n", + "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports and data gathering\n", + "from matplotlib import pyplot as plt\n", + "\n", + "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", + "\n", + "two_flash_unitlist = [\n", + " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", + "]\n", + "distillation_unitlist = [\n", + " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare equipment purchase costs (actual capital costs)\n", + "\n", + "two_flash_capcost = {\n", + " unit.name: value(unit.costing.capital_cost / 1e3) for unit in two_flash_unitlist\n", + "}\n", + "distillation_capcost = {\n", + " unit.name: value(unit.costing.capital_cost / 1e3) for unit in distillation_unitlist\n", + "}\n", + "\n", + "two_flash_capdf = pd.DataFrame(\n", + " list(two_flash_capcost.items()), columns=[\"Equipment\", \"Two Flash\"]\n", + ").set_index(\"Equipment\")\n", + "distillation_capdf = pd.DataFrame(\n", + " list(distillation_capcost.items()), columns=[\"Equipment\", \"Distillation\"]\n", + ").set_index(\"Equipment\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "capcosts = two_flash_capdf.add(distillation_capdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "# Sort according to an easier order to view\n", + "capcosts = capcosts[[\"fs.H101\", \"fs.R101\", \"fs.F101\", \"fs.F102\", \"fs.D101\", \"fs.H102\"]]\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(capcosts) # view dataframe before plotting\n", + "\n", + "capplot = capcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Total Capital Costs\", ylabel=\"$1000\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare operating costs (per year)\n", + "\n", + "two_flash_opcost = {\n", + " \"cooling\": value(3600 * 24 * 365 * m.fs.cooling_cost / 1e3),\n", + " \"heating\": value(3600 * 24 * 365 * m.fs.heating_cost / 1e3),\n", + "}\n", + "distillation_opcost = {\n", + " \"cooling\": value(3600 * 24 * 365 * n.fs.cooling_cost / 1e3),\n", + " \"heating\": value(3600 * 24 * 365 * n.fs.heating_cost / 1e3),\n", + "}\n", + "\n", + "two_flash_opdf = pd.DataFrame(\n", + " list(two_flash_opcost.items()), columns=[\"Utilities\", \"Two Flash\"]\n", + ").set_index(\"Utilities\")\n", + "distillation_opdf = pd.DataFrame(\n", + " list(distillation_opcost.items()), columns=[\"Utilities\", \"Distillation\"]\n", + ").set_index(\"Utilities\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "opcosts = two_flash_opdf.add(distillation_opdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(opcosts) # view dataframe before plotting\n", + "\n", + "opplot = opcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Operating Costs\", ylabel=\"$1000/year\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare total costs (capital costs and operating costs)\n", + "\n", + "two_flash_totcost = {\n", + " \"capital\": sum(two_flash_capcost[idx] for idx in two_flash_capcost),\n", + " \"operating\": value(m.fs.operating_cost) / 1e3,\n", + "}\n", + "distillation_totcost = {\n", + " \"capital\": sum(distillation_capcost[idx] for idx in distillation_capcost),\n", + " \"operating\": value(n.fs.operating_cost) / 1e3,\n", + "}\n", + "\n", + "two_flash_totdf = pd.DataFrame(\n", + " list(two_flash_totcost.items()), columns=[\"Costs\", \"Two Flash\"]\n", + ").set_index(\"Costs\")\n", + "distillation_totdf = pd.DataFrame(\n", + " list(distillation_totcost.items()), columns=[\"Costs\", \"Distillation\"]\n", + ").set_index(\"Costs\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "totcosts = two_flash_totdf.add(distillation_totdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(totcosts) # view dataframe before plotting\n", + "\n", + "totplot = totcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Total Plant Cost (TPC)\", ylabel=\"$1000/year\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's compare the total costs on a production basis. This will account for the greater efficiency provided by the distillation column relative to the less-expensive second flash unit:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "two_flash_cost = value(1e3 * sum(two_flash_totcost[idx] for idx in two_flash_totcost))\n", + "two_flash_prod = value(\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] * 365 * 24 * 3600\n", + ")\n", + "distillation_cost = value(\n", + " 1e3 * sum(distillation_totcost[idx] for idx in distillation_totcost)\n", + ")\n", + "distillation_prod = value(n.fs.D101.condenser.distillate.flow_mol[0] * 365 * 24 * 3600)\n", + "\n", + "print(\n", + " f\"Two flash case over one year: ${two_flash_cost/1e3:0.0f}K / {two_flash_prod/1e3:0.0f} kmol benzene = ${two_flash_cost/(two_flash_prod/1e3):0.2f} per kmol benzene produced\"\n", + ")\n", + "print(\n", + " f\"Distillation case over one year: ${distillation_cost/1e3:0.0f}K / {distillation_prod/1e3:0.0f} kmol benzene = ${distillation_cost/(distillation_prod/1e3):0.2f} per kmol benzene produced\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary\n", + "In this example, IDAES Process Costing Framework methods were applied to two HDA flowsheets for capital cost estimation. The costing blocks calls showcased multiple methods to define unit costing, demonstrating the flexibility and best practice of the costing framework. In the basic examples, the two-flash HDA did not include costing and the distillation HDA estimated a reactor capital cost comprising 3.3% of the total plant cost (TPC). With more rigorous costing, IDAES obtained total capital costs of 8.5% TPC (two flash HDA) and 9.6% (distillation HDA) and better modeled the impact of equipment cost on process economics. As printed above, the IDAES Process Costing Framework confirmed that replacing the second flash drum with a distillation column results in increased equipment costs, increased production and decreased cost per unit product." + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Costing\n", - "\n", - "\n", - "## Note\n", - "\n", - "This example will demonstrate adding capital and operating costs to the two HDA examples, the basic [HDA with Flash](../tut/hda_flowsheet_solution_usr.ipynb) and a comparison with the HDA with Distillation.\n", - "\n", - "\n", - "## Learning outcomes\n", - "\n", - "- Import external pre-built steady-state flowsheets using the IDAES unit model library\n", - "- Define and add costing blocks using the IDAES Process Costing Framework\n", - "- Fomulate and solve a process economics optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 → C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "Users may refer to the prior examples linked at the top of this notebook for detailed process descriptions of the two HDA configurations. As before, the properties required for this module are defined in\n", - "\n", - "- `hda_ideal_VLE.py`\n", - "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", - "- `hda_reaction.py`\n", - "\n", - "Additionally, we will be importing externally-defined flowsheets for the two HDA configurations from\n", - "\n", - "- `hda_flowsheets_for_costing_notebook.py`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import and run HDA Flowsheets\n", - "First, we will generate solved flowsheets for each HDA model. The external scripts build and set inputs for the flowsheets, initialize unit models and streams, and solve the flowsheets before returning the model objects. Note that the HDA flowsheets contain all unit models and stream connections, and no costing equations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The flowsheet utilizes the Wegstein method to iteratively solve circular dependencies such as recycle streams, and is intended to approach a feasible solution. As such, the calls below will fail to converge after 3 iterations and pass to IPOPT to obtain an optimal solution as expected:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Source file for prebuilt flowsheets\n", - "from hda_flowsheets_for_costing_notebook import hda_with_flash\n", - "\n", - "# Build hda model with second flash unit and return model object\n", - "m = hda_with_flash(tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IDAES Process Costing Framework\n", - "IDAES provides a library of capital costing correlations based on those in the following source:\n", - "\n", - "*Process and Product Design Principles: Synthesis, Analysis, and Evaluation*. Seider, Seader, Lewin, Windagdo, 3rd Ed. John Wiley and Sons Chapter 22. Cost Accounting and Capital Cost Estimation 22.2 Cost Indexes and Capital Investment.\n", - "\n", - "Currently, IDAES supports calculation of capital costing for a wide array of unit operations, vesseel sizing and material properties, and specific unit options such as column tray types and heat exchanger configurations. Users may find further information on specific costing methods and options in the IDAES Process Costing Framework documentation (link pending).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add Operating Cost Equations\n", - "Before adding capital costing blocks, we will add operating cost equations taken from the basic [HDA with Flash](../tut/hda_flowsheet_solution_usr.ipynb) and the HDA with Distillation examples. The examples assume constant cooling and heating coefficients over an annual cost basis. The IDAES Generic Costing Framework does not currently support variable cost calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Required imports\n", - "from pyomo.environ import Expression\n", - "\n", - "# Operating costs for HDA with second flash (model m)\n", - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", - ")\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", - ")\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add Capital Costing\n", - "Below, we will add add capital costing blocks to the imported flowsheets and evaluate the economic impact of replacing the second Flash with a Distillation column. First, let's import and define the main flowsheet costing block:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import costing methods - classes, heaters, vessels, compressors, columns\n", - "from idaes.models.costing.SSLW import (\n", - " SSLWCosting,\n", - " SSLWCostingData,\n", - ")\n", - "from idaes.core import UnitModelCostingBlock\n", - "\n", - "# Costing block\n", - "m.fs.costing = SSLWCosting()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will build the relevant costing blocks for the equipment we wish to cost. Note how the costing block, methods and flags are passed as arguments in the costing block call itself. Each unit model will have a single costing block, but each flowsheet model (m and n) will also have a single costing block for flowsheet-level properties.\n", - "\n", - "Users should note that IDAES costing methods support a wide array of heating sources (e.g. fired, steam boiler, hot water) and do not support direct capital costing of coolers. If users wish to cost Heater units acting as coolers, it is necessary to cost a \"dummy\" [0D shell and tube exchanger](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html) with appropriate aliased hot stream properties and proper cooling water properties. This is not demonstrated here, as the HDA examples take advantage of Flash and Condenser operations to recover liquid product.\n", - "\n", - "Capital costing is independent of unit model connections, and building cost equations may be done piecewise in this fashion. Default options are passed explicitly to demonstrate proper syntax and usage. Now that all required properties are defined, let's cost our models connecting costing blocks, methods and unit models in each flowsheet." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Flexibility of Costing Block Definitions\n", - "IDAES supports many ways to define batches of costing blocks, and several are shown in the example. Users may employ whichever method fits their modeling needs for explicit or concise code. In the code below, note how the unit model itself is never passed to the costing method; when the full model is executed, the costing block will automatically connect its parent block with child equation blocks.\n", - "\n", - "`Compressor` unit models with isothermal or adiabatic thermodynamics are too simple to cost and are therefore excluded from the economic analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define costing for the heater unit:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.costing.SSLW import (\n", - " HeaterMaterial,\n", - " HeaterSource,\n", - ")\n", - "\n", - "# Costing for heater - m.fs.H101\n", - "m.fs.H101.costing = UnitModelCostingBlock(\n", - " flowsheet_costing_block=m.fs.costing,\n", - " costing_method=SSLWCostingData.cost_fired_heater,\n", - " costing_method_arguments={\n", - " \"material_type\": HeaterMaterial.CarbonSteel,\n", - " \"heat_source\": HeaterSource.Fuel,\n", - " },\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The costing module provides a `unit_mapping` dictionary linking generic unit model classes with recommended costing methods. In this example, StoichiometricReactor and Flash vessels utilize different vessel costing methods with similar arguments. The diameter and length attributes need to exist in order to cost vessel sizing and material requirements, and we add them if they don't exist already. The `unit_mapping` method provides an opportunity to automatically select the correct vessel orientation (vertical or horizontal) based on the unit type; passing a `StoichiometricReactor` or `PFR` class object will call the `cost_horizontal_vessel` method, while passing a `Flash` or `CSTR` class object will call the `cost_vertical_vessel` method.\n", - "\n", - "All vessels are assigned costing succintly via a loop below - users may define each block individually if desired:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.costing.SSLW import (\n", - " VesselMaterial,\n", - " TrayType,\n", - " TrayMaterial,\n", - ")\n", - "\n", - "from idaes.core.util.constants import Constants\n", - "from pyomo.environ import Var, Constraint, units as pyunits, Param, value\n", - "from idaes.models.unit_models import StoichiometricReactor, Flash\n", - "\n", - "# Map unit models to unit classes\n", - "# Will pass to unit_mapping which calls costing methods based on unit class\n", - "unit_class_mapping = {\n", - " m.fs.R101: StoichiometricReactor,\n", - " m.fs.F101: Flash,\n", - " m.fs.F102: Flash,\n", - "}\n", - "\n", - "# Costing for vessels - m.fs.R101, m.fs.F101, m.fs.F102\n", - "\n", - "# Loop over units\n", - "for unit in [m.fs.R101, m.fs.F101, m.fs.F102]:\n", - " # Get correct unit class for unit model\n", - " unit_class = unit_class_mapping[unit]\n", - "\n", - " # Add dimension variables and constraint if they don't exist\n", - " if not hasattr(unit, \"diameter\"):\n", - " unit.diameter = Var(initialize=1, units=pyunits.m)\n", - " if not hasattr(unit, \"length\"):\n", - " unit.length = Var(initialize=1, units=pyunits.m)\n", - " if hasattr(unit, \"volume\"): # if volume exists, set diameter from volume\n", - " unit.volume_eq = Constraint(\n", - " expr=unit.volume[0]\n", - " == unit.length * unit.diameter**2 * 0.25 * Constants.pi\n", - " )\n", - " else: # fix diameter directly\n", - " unit.diameter.fix(0.2214 * pyunits.m)\n", - " # Either way, fix L/D to calculate L from D\n", - " unit.L_over_D = Constraint(expr=unit.length == 3 * unit.diameter)\n", - "\n", - " # Define vessel costing\n", - " unit.costing = UnitModelCostingBlock(\n", - " flowsheet_costing_block=unit.parent_block().costing, # e.g. m.fs.R101.costing\n", - " costing_method=SSLWCostingData.unit_mapping[\n", - " unit_class\n", - " ], # e.g. cost_vertical_vessel()\n", - " costing_method_arguments={\n", - " \"material_type\": VesselMaterial.CarbonSteel,\n", - " \"shell_thickness\": 1.25 * pyunits.inch,\n", - " },\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve Flowsheet Costing Blocks\n", - "Now, we may solve the full flowsheet for all costing blocks:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Eefine solver\n", - "from idaes.core.solvers import get_solver\n", - "\n", - "solver = get_solver()\n", - "\n", - "# Check that the degrees of freedom is zero\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Check physical units consistency, solve and check solver status\n", - "from pyomo.environ import TerminationCondition\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "assert_units_consistent(m)\n", - "results = solver.solve(m, tee=True, symbolic_solver_labels=True)\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For comparison, we will call and build the HDA flowsheet replacing the second `Flash` with a `TrayColumn` distillation unit model. The flowsheet costing occurs in the external script `hda_flowsheets_for_costing_notebook.py` and is not shown here:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from pyomo.common.log import LoggingIntercept\n", - "import logging\n", - "from io import StringIO\n", - "\n", - "stream = StringIO()\n", - "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", - " # Source file for prebuilt flowsheets\n", - " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", - "\n", - " # Build hda model with distillation column and return model object\n", - " n = hda_with_distillation(tee=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results Comparison and Visualization\n", - "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Imports and data gathering\n", - "from matplotlib import pyplot as plt\n", - "\n", - "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", - "\n", - "two_flash_unitlist = [\n", - " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", - "]\n", - "distillation_unitlist = [\n", - " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare equipment purchase costs (actual capital costs)\n", - "\n", - "two_flash_capcost = {\n", - " unit.name: value(unit.costing.capital_cost / 1e3) for unit in two_flash_unitlist\n", - "}\n", - "distillation_capcost = {\n", - " unit.name: value(unit.costing.capital_cost / 1e3) for unit in distillation_unitlist\n", - "}\n", - "\n", - "two_flash_capdf = pd.DataFrame(\n", - " list(two_flash_capcost.items()), columns=[\"Equipment\", \"Two Flash\"]\n", - ").set_index(\"Equipment\")\n", - "distillation_capdf = pd.DataFrame(\n", - " list(distillation_capcost.items()), columns=[\"Equipment\", \"Distillation\"]\n", - ").set_index(\"Equipment\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "capcosts = two_flash_capdf.add(distillation_capdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "# Sort according to an easier order to view\n", - "capcosts = capcosts[[\"fs.H101\", \"fs.R101\", \"fs.F101\", \"fs.F102\", \"fs.D101\", \"fs.H102\"]]\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(capcosts) # view dataframe before plotting\n", - "\n", - "capplot = capcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Total Capital Costs\", ylabel=\"$1000\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare operating costs (per year)\n", - "\n", - "two_flash_opcost = {\n", - " \"cooling\": value(3600 * 24 * 365 * m.fs.cooling_cost / 1e3),\n", - " \"heating\": value(3600 * 24 * 365 * m.fs.heating_cost / 1e3),\n", - "}\n", - "distillation_opcost = {\n", - " \"cooling\": value(3600 * 24 * 365 * n.fs.cooling_cost / 1e3),\n", - " \"heating\": value(3600 * 24 * 365 * n.fs.heating_cost / 1e3),\n", - "}\n", - "\n", - "two_flash_opdf = pd.DataFrame(\n", - " list(two_flash_opcost.items()), columns=[\"Utilities\", \"Two Flash\"]\n", - ").set_index(\"Utilities\")\n", - "distillation_opdf = pd.DataFrame(\n", - " list(distillation_opcost.items()), columns=[\"Utilities\", \"Distillation\"]\n", - ").set_index(\"Utilities\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "opcosts = two_flash_opdf.add(distillation_opdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(opcosts) # view dataframe before plotting\n", - "\n", - "opplot = opcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Operating Costs\", ylabel=\"$1000/year\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare total costs (capital costs and operating costs)\n", - "\n", - "two_flash_totcost = {\n", - " \"capital\": sum(two_flash_capcost[idx] for idx in two_flash_capcost),\n", - " \"operating\": value(m.fs.operating_cost) / 1e3,\n", - "}\n", - "distillation_totcost = {\n", - " \"capital\": sum(distillation_capcost[idx] for idx in distillation_capcost),\n", - " \"operating\": value(n.fs.operating_cost) / 1e3,\n", - "}\n", - "\n", - "two_flash_totdf = pd.DataFrame(\n", - " list(two_flash_totcost.items()), columns=[\"Costs\", \"Two Flash\"]\n", - ").set_index(\"Costs\")\n", - "distillation_totdf = pd.DataFrame(\n", - " list(distillation_totcost.items()), columns=[\"Costs\", \"Distillation\"]\n", - ").set_index(\"Costs\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "totcosts = two_flash_totdf.add(distillation_totdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(totcosts) # view dataframe before plotting\n", - "\n", - "totplot = totcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Total Plant Cost (TPC)\", ylabel=\"$1000/year\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's compare the total costs on a production basis. This will account for the greater efficiency provided by the distillation column relative to the less-expensive second flash unit:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "two_flash_cost = value(1e3 * sum(two_flash_totcost[idx] for idx in two_flash_totcost))\n", - "two_flash_prod = value(\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] * 365 * 24 * 3600\n", - ")\n", - "distillation_cost = value(\n", - " 1e3 * sum(distillation_totcost[idx] for idx in distillation_totcost)\n", - ")\n", - "distillation_prod = value(n.fs.D101.condenser.distillate.flow_mol[0] * 365 * 24 * 3600)\n", - "\n", - "print(\n", - " f\"Two flash case over one year: ${two_flash_cost/1e3:0.0f}K / {two_flash_prod/1e3:0.0f} kmol benzene = ${two_flash_cost/(two_flash_prod/1e3):0.2f} per kmol benzene produced\"\n", - ")\n", - "print(\n", - " f\"Distillation case over one year: ${distillation_cost/1e3:0.0f}K / {distillation_prod/1e3:0.0f} kmol benzene = ${distillation_cost/(distillation_prod/1e3):0.2f} per kmol benzene produced\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Summary\n", - "In this example, IDAES Process Costing Framework methods were applied to two HDA flowsheets for capital cost estimation. The costing blocks calls showcased multiple methods to define unit costing, demonstrating the flexibility and best practice of the costing framework. In the basic examples, the two-flash HDA did not include costing and the distillation HDA estimated a reactor capital cost comprising 3.3% of the total plant cost (TPC). With more rigorous costing, IDAES obtained total capital costs of 8.5% TPC (two flash HDA) and 9.6% (distillation HDA) and better modeled the impact of equipment cost on process economics. As printed above, the IDAES Process Costing Framework confirmed that replacing the second flash drum with a distillation column results in increased equipment costs, increased production and decreased cost per unit product." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 3 -} + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb index a8ed1642..b1250e16 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb @@ -26,7 +26,6 @@ ] }, { - 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"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -695,7 +674,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -712,7 +690,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -746,7 +723,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -759,15 +735,7 @@ "cell_type": "code", "execution_count": 28, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "29\n" - ] - } - ], + "outputs": [], "source": [ "print(degrees_of_freedom(m))" ] @@ -787,7 +755,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -813,7 +780,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -846,7 +812,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -866,7 +831,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -916,7 +880,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -937,7 +900,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -958,7 +920,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -979,7 +940,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -990,42 +950,7 @@ "cell_type": "code", "execution_count": 38, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" - ] - } - ], + "outputs": [], "source": [ "# Set scaling factors for heat duty, reaction extent and volume\n", "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", @@ -1043,7 +968,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1076,15 +1000,7 @@ "solution" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], + "outputs": [], "source": [ "# Todo: Check the degrees of freedom\n", "print(degrees_of_freedom(m))" @@ -1105,7 +1021,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1134,99 +1049,6 @@ ] }, { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['__class__',\n", - " '__delattr__',\n", - " '__dict__',\n", - " '__dir__',\n", - " '__doc__',\n", - " '__eq__',\n", - " '__format__',\n", - " '__ge__',\n", - " '__getattribute__',\n", - " '__gt__',\n", - " '__hash__',\n", - " '__init__',\n", - " '__init_subclass__',\n", - " '__le__',\n", - " '__lt__',\n", - " '__module__',\n", - " '__ne__',\n", - " '__new__',\n", - " '__reduce__',\n", - " '__reduce_ex__',\n", - " '__repr__',\n", - " '__setattr__',\n", - " '__sizeof__',\n", - " '__str__',\n", - " '__subclasshook__',\n", - " '__weakref__',\n", - " '_run_impl',\n", - " 'adj_lists',\n", - " 'all_cycles',\n", - " 'arc_to_edge',\n", - " 'cache',\n", - " 'cacher',\n", - " 'calculation_order',\n", - " 'check_tear_set',\n", - " 'check_value_fix',\n", - " 'combine_and_fix',\n", - " 'compute_err',\n", - " 'create_graph',\n", - " 'cycle_edge_matrix',\n", - " 'edge_to_idx',\n", - " 'fixed_inputs',\n", - " 'generate_first_x',\n", - " 'generate_gofx',\n", - " 'idx_to_edge',\n", - " 'idx_to_node',\n", - " 'indexes_to_arcs',\n", - " 'load_guesses',\n", - " 'load_values',\n", - " 'node_to_idx',\n", - " 'options',\n", - " 'pass_edges',\n", - " 'pass_single_value',\n", - " 'pass_tear_direct',\n", - " 'pass_tear_wegstein',\n", - " 'pass_values',\n", - " 'run',\n", - " 'run_order',\n", - " 'scc_calculation_order',\n", - " 'scc_collect',\n", - " 'select_tear_heuristic',\n", - " 'select_tear_mip',\n", - " 'select_tear_mip_model',\n", - " 'set_guesses_for',\n", - " 'set_tear_set',\n", - " 'solve_tear_direct',\n", - " 'solve_tear_wegstein',\n", - " 'source_dest_peer',\n", - " 'sub_graph_edges',\n", - " 'tear_diff_direct',\n", - " 'tear_set',\n", - " 'tear_set_arcs',\n", - " 'tear_upper_bound',\n", - " 'tree_order']" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dir(seq)" - ] - }, - { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1235,24 +1057,15 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.s03\n" - ] - } - ], + "outputs": [], "source": [ "for o in heuristic_tear_set:\n", " print(o.name)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1261,29 +1074,15 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 44, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.H101\n", - "fs.R101\n", - "fs.F101\n", - "fs.S101\n", - "fs.C101\n", - "fs.M101\n" - ] - } - ], + "outputs": [], "source": [ "for o in order:\n", " print(o[0].name)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1294,7 +1093,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1318,7 +1117,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1327,7 +1125,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1336,7 +1134,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1345,140 +1142,16 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 47, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-07-27 11:23:51 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-07-27 11:23:52 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-27 11:23:56 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:56 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:56 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:56 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:56 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:58 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-27 11:23:58 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:58 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:58 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:58 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:58 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:00 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-27 11:24:00 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:00 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:00 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-27 11:24:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:02 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:02 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:04 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:04 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:04 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-27 11:24:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "WARNING: Wegstein failed to converge in 3 iterations\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" - ] - } - ], + "outputs": [], "source": [ "seq.run(m, function)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1490,125 +1163,9 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 48, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 1097\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 877\n", - "\n", - "Total number of variables............................: 363\n", - " variables with only lower bounds: 8\n", - " variables with lower and upper bounds: 155\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 363\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", - " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", - " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.43e+03 - 1.00e+00 9.82e-01h 1\n", - " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 6.42e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 2.97e-05 2.81e+03 -3.8 2.13e-07 - 1.00e+00 1.00e+00H 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.7855284385533683e+04 1.7855284385533683e+04\n", - "Constraint violation....: 2.4734281289795490e-10 2.9668448405573148e-05\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.4734281289795490e-10 1.7855284385533683e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 12\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 12\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.025\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], + "outputs": [], "source": [ "# Create the solver object\n", "solver = get_solver()\n", @@ -1619,7 +1176,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 49, "metadata": { "tags": [ "testing" @@ -1634,7 +1191,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1655,619 +1211,9 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 50, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", - "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101: Begin initialization.\n", - "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", - "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", - "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", - "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", - "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" - ] - } - ], + "outputs": [], "source": [ "# Add distillation column to the flowsheet\n", "m.fs.D101 = TrayColumn(\n", @@ -2305,7 +1251,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2316,7 +1261,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -2343,7 +1288,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2352,7 +1296,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 52, "metadata": { "tags": [ "testing" @@ -2366,140 +1310,16 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 53, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.D101.condenser.control_volume.properties_out[0.0].scaling_factor' that\n", - "contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4042\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 2376\n", - "\n", - "Total number of variables............................: 1169\n", - " variables with only lower bounds: 112\n", - " variables with lower and upper bounds: 365\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", - " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", - " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", - " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 7.45e-09 5.93e-02 -3.8 3.58e-08 - 1.00e+00 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042542854672720e+04 1.5042542854672720e+04\n", - "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 5.8207660913467407e-11 1.5042542854672720e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.057\n", - "Total CPU secs in NLP function evaluations = 0.006\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.14362859725952148}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "solver.solve(m, tee=True)" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 54, "metadata": { "tags": [ "testing" @@ -2514,7 +1334,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2525,28 +1344,9 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 55, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 442301.47075252124\n", - "operating cost = $ 427596.7305680538\n", - "capital cost = $ 14704.740184467468\n", - "\n", - "Distillate flowrate = 0.16196898920633476 mol/s\n", - "Benzene purity = 89.49161665800843 %\n", - "Residue flowrate = 0.10515007120697811 mol/s\n", - "Toluene purity = 43.32260291055274 %\n", - "\n", - "Conversion = 75.0 %\n", - "\n", - "Overhead benzene loss in F101 = 42.161938483603166 %\n" - ] - } - ], + "outputs": [], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -2582,22 +1382,13 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 56, "metadata": { "tags": [ "testing" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "427596.7305680538\n", - "14704.740184467468\n" - ] - } - ], + "outputs": [], "source": [ "import pytest\n", "\n", @@ -2608,7 +1399,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2617,48 +1407,14 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 57, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 0.0000 : watt : True : (None, None)\n", - " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", - " temperature kelvin 600.00 771.85\n", - " pressure pascal 3.5000e+05 3.5000e+05\n", - "====================================================================================\n" - ] - } - ], + "outputs": [], "source": [ "m.fs.R101.report()" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2667,48 +1423,14 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 58, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.F101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -70343. : watt : False : (None, None)\n", - " Pressure Change : 0.0000 : pascal : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Vapor Outlet Liquid Outlet\n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", - " temperature kelvin 771.85 325.00 325.00 \n", - " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", - "====================================================================================\n" - ] - } - ], + "outputs": [], "source": [ "m.fs.F101.report()" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2722,27 +1444,9 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 59, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Units Reactor Light Gases\n", - "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", - "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", - "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", - "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", - "temperature kelvin 771.85 325.00 \n", - "pressure pascal 3.5000e+05 3.5000e+05 \n" - ] - } - ], + "outputs": [], "source": [ "from idaes.core.util.tables import (\n", " create_stream_table_dataframe,\n", @@ -2754,7 +1458,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2767,7 +1470,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2792,7 +1494,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2801,7 +1502,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -2809,7 +1510,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2818,7 +1518,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -2831,7 +1531,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2846,7 +1545,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 62, "metadata": { "tags": [ "exercise" @@ -2859,7 +1558,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 63, "metadata": { "tags": [ "solution" @@ -2872,7 +1571,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2889,7 +1587,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -2918,7 +1616,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2932,7 +1629,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 65, "metadata": { "tags": [ "exercise" @@ -2948,7 +1645,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 66, "metadata": { "tags": [ "solution" @@ -2966,7 +1663,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2975,7 +1671,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -2987,7 +1683,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3001,7 +1696,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 68, "metadata": { "tags": [ "exercise" @@ -3014,7 +1709,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 69, "metadata": { "tags": [ "solution" @@ -3027,7 +1722,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3036,7 +1730,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -3046,7 +1740,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3058,155 +1751,16 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 71, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4073\n", - "Number of nonzeros in inequality constraint Jacobian.: 6\n", - "Number of nonzeros in Lagrangian Hessian.............: 2391\n", - "\n", - "Total number of variables............................: 1176\n", - " variables with only lower bounds: 113\n", - " variables with lower and upper bounds: 372\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 3\n", - " inequality constraints with only lower bounds: 2\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 1\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", - " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", - " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", - " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", - " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", - " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", - " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", - " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", - " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", - " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", - " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", - " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", - " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", - " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", - " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", - " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", - " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", - " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", - " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", - " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", - " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", - " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", - " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", - " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", - " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", - " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", - " 29 4.3884157e+05 6.56e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", - " 31 4.3883992e+05 3.05e-07 1.25e-05 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", - " 32 4.3883990e+05 5.46e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", - " 33 4.3883990e+05 1.49e-08 1.07e-07 -8.0 5.40e-05 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 33\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 4.3883989842627057e+02 4.3883989842627058e+05\n", - "Dual infeasibility......: 1.0693122464843572e-07 1.0693122464843573e-04\n", - "Constraint violation....: 5.8207660913467407e-11 1.4901161193847656e-08\n", - "Complementarity.........: 9.0909948039747601e-09 9.0909948039747593e-06\n", - "Overall NLP error.......: 9.0909948039747601e-09 1.0693122464843573e-04\n", - "\n", - "\n", - "Number of objective function evaluations = 35\n", - "Number of objective gradient evaluations = 34\n", - "Number of equality constraint evaluations = 35\n", - "Number of inequality constraint evaluations = 35\n", - "Number of equality constraint Jacobian evaluations = 34\n", - "Number of inequality constraint Jacobian evaluations = 34\n", - "Number of Lagrangian Hessian evaluations = 33\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.310\n", - "Total CPU secs in NLP function evaluations = 0.050\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], + "outputs": [], "source": [ "results = solver.solve(m, tee=True)" ] }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 72, "metadata": { "tags": [ "testing" @@ -3221,7 +1775,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3232,28 +1785,9 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 73, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 438839.8984262706\n", - "operating cost = $ 408883.53148307273\n", - "capital cost = $ 29956.366943197827\n", - "\n", - "Distillate flowrate = 0.17999999002639896 mol/s\n", - "Benzene purity = 98.99999900049087 %\n", - "Residue flowrate = 0.10851616424263705 mol/s\n", - "Toluene purity = 15.67617808620809 %\n", - "\n", - "Conversion = 93.38705916369607 %\n", - "\n", - "Overhead benzene loss in F101 = 17.340617931156185 %\n" - ] - } - ], + "outputs": [], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -3289,22 +1823,13 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 74, "metadata": { "tags": [ "testing" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "408883.53148307273\n", - "29956.366943197827\n" - ] - } - ], + "outputs": [], "source": [ "import pytest\n", "\n", @@ -3316,7 +1841,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3325,25 +1849,9 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 75, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "H101 outlet temperature = 568.9232042951996 K\n", - "\n", - "R101 outlet temperature = 790.3655425698917 K\n", - "\n", - "F101 outlet temperature = 298.0 K\n", - "\n", - "H102 outlet temperature = 368.74143399528367 K\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -3361,7 +1869,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3400,7 +1907,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.8.16" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb index 5bc30ee9..247e5621 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 71, + "execution_count": 1, "metadata": { "tags": [ "header", @@ -115,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 3, "metadata": { "tags": [ "exercise" @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 4, "metadata": { "tags": [ "solution" @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -195,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -222,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -271,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -291,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -320,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -352,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 12, "metadata": { "tags": [ "exercise" @@ -365,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 13, "metadata": { "tags": [ "solution" @@ -391,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -431,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -460,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -496,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 17, "metadata": { "tags": [ "exercise" @@ -509,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 18, "metadata": { "tags": [ "solution" @@ -525,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -564,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 20, "metadata": { "tags": [ "exercise" @@ -577,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 21, "metadata": { "tags": [ "solution" @@ -604,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -627,7 +627,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 23, "metadata": { "tags": [ "exercise" @@ -640,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 24, "metadata": { "tags": [ "solution" @@ -661,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -682,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -705,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -733,17 +733,9 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 28, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "29\n" - ] - } - ], + "outputs": [], "source": [ "print(degrees_of_freedom(m))" ] @@ -757,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -789,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -816,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -842,7 +834,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 33, "metadata": { "tags": [ "exercise" @@ -858,7 +850,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 34, "metadata": { "tags": [ "solution" @@ -882,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -902,7 +894,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -922,7 +914,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -942,44 +934,9 @@ }, { "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap]\n" - ] - } - ], + "execution_count": 38, + "metadata": {}, + "outputs": [], "source": [ "# Set scaling factors for heat duty, reaction extent and volume\n", "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", @@ -1010,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 39, "metadata": { "tags": [ "exercise" @@ -1023,21 +980,13 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 40, "metadata": { "tags": [ "solution" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], + "outputs": [], "source": [ "# Todo: Check the degrees of freedom\n", "print(degrees_of_freedom(m))" @@ -1056,7 +1005,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1080,17 +1029,9 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 43, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.s03\n" - ] - } - ], + "outputs": [], "source": [ "for o in heuristic_tear_set:\n", " print(o.name)" @@ -1105,22 +1046,9 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 44, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.H101\n", - "fs.R101\n", - "fs.F101\n", - "fs.S101\n", - "fs.C101\n", - "fs.M101\n" - ] - } - ], + "outputs": [], "source": [ "for o in order:\n", " print(o[0].name)" @@ -1137,7 +1065,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1169,7 +1097,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1186,134 +1114,11 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 47, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-06-26 12:37:47 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:47 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:48 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:49 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-06-26 12:37:54 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:54 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:54 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:54 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:56 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:56 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-06-26 12:37:56 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-06-26 12:37:57 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:57 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:57 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:57 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:57 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:58 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:58 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-06-26 12:37:58 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-06-26 12:37:58 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:58 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:58 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:59 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-06-26 12:37:59 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:00 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:00 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:00 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:00 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-06-26 12:38:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:02 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:02 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:04 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:04 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:04 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-06-26 12:38:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "WARNING: Wegstein failed to converge in 3 iterations\n", - "2023-06-26 12:38:04 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:04 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" - ] - } - ], + "outputs": [], "source": [ "seq.run(m, function)" ] @@ -1330,125 +1135,9 @@ }, { "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 1097\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 877\n", - "\n", - "Total number of variables............................: 363\n", - " variables with only lower bounds: 8\n", - " variables with lower and upper bounds: 155\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 363\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", - " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", - " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.43e+03 - 1.00e+00 9.82e-01h 1\n", - " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 6.42e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 2.97e-05 2.81e+03 -3.8 2.13e-07 - 1.00e+00 1.00e+00H 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.7855284385533683e+04 1.7855284385533683e+04\n", - "Constraint violation....: 2.4734281289795490e-10 2.9668448405573148e-05\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.4734281289795490e-10 1.7855284385533683e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 12\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 12\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.036\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], + "execution_count": 48, + "metadata": {}, + "outputs": [], "source": [ "# Create the solver object\n", "solver = get_solver()\n", @@ -1478,619 +1167,9 @@ }, { "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101: Begin initialization.\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", - "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", - "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", - "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", - "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", - "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" - ] - } - ], + "execution_count": 50, + "metadata": {}, + "outputs": [], "source": [ "# Add distillation column to the flowsheet\n", "m.fs.D101 = TrayColumn(\n", @@ -2138,7 +1217,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -2173,133 +1252,9 @@ }, { "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.D101.condenser.control_volume.properties_out[0.0].scaling_factor' that\n", - "contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4042\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 2376\n", - "\n", - "Total number of variables............................: 1169\n", - " variables with only lower bounds: 112\n", - " variables with lower and upper bounds: 365\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", - " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", - " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", - " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 7.45e-09 5.93e-02 -3.8 3.58e-08 - 1.00e+00 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042542854672720e+04 1.5042542854672720e+04\n", - "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 5.8207660913467407e-11 1.5042542854672720e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.068\n", - "Total CPU secs in NLP function evaluations = 0.009\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.16938281059265137}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 53, + "metadata": {}, + "outputs": [], "source": [ "solver.solve(m, tee=True)" ] @@ -2315,28 +1270,9 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 55, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 442301.47075252124\n", - "operating cost = $ 427596.7305680538\n", - "capital cost = $ 14704.740184467468\n", - "\n", - "Distillate flowrate = 0.16196898920633476 mol/s\n", - "Benzene purity = 89.49161665800843 %\n", - "Residue flowrate = 0.10515007120697811 mol/s\n", - "Toluene purity = 43.32260291055274 %\n", - "\n", - "Conversion = 75.0 %\n", - "\n", - "Overhead benzene loss in F101 = 42.161938483603166 %\n" - ] - } - ], + "outputs": [], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -2379,42 +1315,9 @@ }, { "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 0.0000 : watt : True : (None, None)\n", - " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", - " temperature kelvin 600.00 771.85\n", - " pressure pascal 3.5000e+05 3.5000e+05\n", - "====================================================================================\n" - ] - } - ], + "execution_count": 57, + "metadata": {}, + "outputs": [], "source": [ "m.fs.R101.report()" ] @@ -2428,42 +1331,9 @@ }, { "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.F101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -70343. : watt : False : (None, None)\n", - " Pressure Change : 0.0000 : pascal : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Vapor Outlet Liquid Outlet\n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", - " temperature kelvin 771.85 325.00 325.00 \n", - " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", - "====================================================================================\n" - ] - } - ], + "execution_count": 58, + "metadata": {}, + "outputs": [], "source": [ "m.fs.F101.report()" ] @@ -2482,27 +1352,9 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 59, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Units Reactor Light Gases\n", - "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", - "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", - "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", - "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", - "temperature kelvin 771.85 325.00 \n", - "pressure pascal 3.5000e+05 3.5000e+05 \n" - ] - } - ], + "outputs": [], "source": [ "from idaes.core.util.tables import (\n", " create_stream_table_dataframe,\n", @@ -2558,7 +1410,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -2574,7 +1426,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -2601,7 +1453,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 62, "metadata": { "tags": [ "exercise" @@ -2614,7 +1466,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 63, "metadata": { "tags": [ "solution" @@ -2643,7 +1495,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -2685,7 +1537,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 65, "metadata": { "tags": [ "exercise" @@ -2701,7 +1553,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 66, "metadata": { "tags": [ "solution" @@ -2727,7 +1579,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -2752,7 +1604,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 68, "metadata": { "tags": [ "exercise" @@ -2765,7 +1617,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 69, "metadata": { "tags": [ "solution" @@ -2786,7 +1638,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -2807,142 +1659,9 @@ }, { "cell_type": "code", - "execution_count": 135, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4073\n", - "Number of nonzeros in inequality constraint Jacobian.: 6\n", - "Number of nonzeros in Lagrangian Hessian.............: 2391\n", - "\n", - "Total number of variables............................: 1176\n", - " variables with only lower bounds: 113\n", - " variables with lower and upper bounds: 372\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 3\n", - " inequality constraints with only lower bounds: 2\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 1\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", - " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", - " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", - " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", - " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", - " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", - " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", - " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", - " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", - " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", - " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", - " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", - " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", - " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", - " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", - " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", - " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", - " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", - " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", - " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", - " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", - " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", - " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", - " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", - " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", - " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", - " 29 4.3884157e+05 6.56e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", - " 31 4.3883992e+05 3.05e-07 1.25e-05 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", - " 32 4.3883990e+05 5.46e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", - " 33 4.3883990e+05 1.49e-08 1.07e-07 -8.0 5.40e-05 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 33\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 4.3883989842627057e+02 4.3883989842627058e+05\n", - "Dual infeasibility......: 1.0693122464843572e-07 1.0693122464843573e-04\n", - "Constraint violation....: 5.8207660913467407e-11 1.4901161193847656e-08\n", - "Complementarity.........: 9.0909948039747601e-09 9.0909948039747593e-06\n", - "Overall NLP error.......: 9.0909948039747601e-09 1.0693122464843573e-04\n", - "\n", - "\n", - "Number of objective function evaluations = 35\n", - "Number of objective gradient evaluations = 34\n", - "Number of equality constraint evaluations = 35\n", - "Number of inequality constraint evaluations = 35\n", - "Number of equality constraint Jacobian evaluations = 34\n", - "Number of inequality constraint Jacobian evaluations = 34\n", - "Number of Lagrangian Hessian evaluations = 33\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.522\n", - "Total CPU secs in NLP function evaluations = 0.078\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], + "execution_count": 71, + "metadata": {}, + "outputs": [], "source": [ "results = solver.solve(m, tee=True)" ] @@ -2958,28 +1677,9 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 73, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 438839.8984262706\n", - "operating cost = $ 408883.53148307273\n", - "capital cost = $ 29956.366943197827\n", - "\n", - "Distillate flowrate = 0.17999999002639896 mol/s\n", - "Benzene purity = 98.99999900049087 %\n", - "Residue flowrate = 0.10851616424263705 mol/s\n", - "Toluene purity = 15.67617808620809 %\n", - "\n", - "Conversion = 93.38705916369607 %\n", - "\n", - "Overhead benzene loss in F101 = 17.340617931156185 %\n" - ] - } - ], + "outputs": [], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -3022,25 +1722,9 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 75, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "H101 outlet temperature = 568.9232042951996 K\n", - "\n", - "R101 outlet temperature = 790.3655425698917 K\n", - "\n", - "F101 outlet temperature = 298.0 K\n", - "\n", - "H102 outlet temperature = 368.74143399528367 K\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -3096,7 +1780,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.8.16" } }, "nbformat": 4, From af9312cb1678a4e6cd4eecb948501c9b95a1f756 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Fri, 25 Aug 2023 01:33:59 -0400 Subject: [PATCH 09/75] Revert "Trying with replacing files from latest clone." This reverts commit 33909f86bdbbd6c06a71ec701d4d1da1f37d0465. --- .../hda_flowsheet_with_costing_test.ipynb | 1116 ++++++------ .../hda_flowsheet_with_costing_usr.ipynb | 1116 ++++++------ .../hda_flowsheet_with_distillation.ipynb | 1597 ++++++++++++++++- ...flowsheet_with_distillation_solution.ipynb | 1498 +++++++++++++++- 4 files changed, 4068 insertions(+), 1259 deletions(-) diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_test.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_test.ipynb index b9a6f2c2..8f740cad 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_test.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_test.ipynb @@ -1,560 +1,560 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Costing\n", - "\n", - "\n", - "## Note\n", - "\n", - "This example will demonstrate adding capital and operating costs to the two HDA examples, the basic [HDA with Flash](../tut/hda_flowsheet_solution_test.ipynb) and a comparison with the HDA with Distillation.\n", - "\n", - "\n", - "## Learning outcomes\n", - "\n", - "- Import external pre-built steady-state flowsheets using the IDAES unit model library\n", - "- Define and add costing blocks using the IDAES Process Costing Framework\n", - "- Fomulate and solve a process economics optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "Users may refer to the prior examples linked at the top of this notebook for detailed process descriptions of the two HDA configurations. As before, the properties required for this module are defined in\n", - "\n", - "- `hda_ideal_VLE.py`\n", - "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", - "- `hda_reaction.py`\n", - "\n", - "Additionally, we will be importing externally-defined flowsheets for the two HDA configurations from\n", - "\n", - "- `hda_flowsheets_for_costing_notebook.py`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import and run HDA Flowsheets\n", - "First, we will generate solved flowsheets for each HDA model. The external scripts build and set inputs for the flowsheets, initialize unit models and streams, and solve the flowsheets before returning the model objects. Note that the HDA flowsheets contain all unit models and stream connections, and no costing equations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The flowsheet utilizes the Wegstein method to iteratively solve circular dependencies such as recycle streams, and is intended to approach a feasible solution. As such, the calls below will fail to converge after 3 iterations and pass to IPOPT to obtain an optimal solution as expected:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Source file for prebuilt flowsheets\n", - "from hda_flowsheets_for_costing_notebook import hda_with_flash\n", - "\n", - "# Build hda model with second flash unit and return model object\n", - "m = hda_with_flash(tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IDAES Process Costing Framework\n", - "IDAES provides a library of capital costing correlations based on those in the following source:\n", - "\n", - "*Process and Product Design Principles: Synthesis, Analysis, and Evaluation*. Seider, Seader, Lewin, Windagdo, 3rd Ed. John Wiley and Sons Chapter 22. Cost Accounting and Capital Cost Estimation 22.2 Cost Indexes and Capital Investment.\n", - "\n", - "Currently, IDAES supports calculation of capital costing for a wide array of unit operations, vesseel sizing and material properties, and specific unit options such as column tray types and heat exchanger configurations. Users may find further information on specific costing methods and options in the IDAES Process Costing Framework documentation (link pending).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add Operating Cost Equations\n", - "Before adding capital costing blocks, we will add operating cost equations taken from the basic [HDA with Flash](../tut/hda_flowsheet_solution_test.ipynb) and the HDA with Distillation examples. The examples assume constant cooling and heating coefficients over an annual cost basis. The IDAES Generic Costing Framework does not currently support variable cost calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Required imports\n", - "from pyomo.environ import Expression\n", - "\n", - "# Operating costs for HDA with second flash (model m)\n", - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", - ")\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", - ")\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add Capital Costing\n", - "Below, we will add add capital costing blocks to the imported flowsheets and evaluate the economic impact of replacing the second Flash with a Distillation column. First, let's import and define the main flowsheet costing block:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import costing methods - classes, heaters, vessels, compressors, columns\n", - "from idaes.models.costing.SSLW import (\n", - " SSLWCosting,\n", - " SSLWCostingData,\n", - ")\n", - "from idaes.core import UnitModelCostingBlock\n", - "\n", - "# Costing block\n", - "m.fs.costing = SSLWCosting()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will build the relevant costing blocks for the equipment we wish to cost. Note how the costing block, methods and flags are passed as arguments in the costing block call itself. Each unit model will have a single costing block, but each flowsheet model (m and n) will also have a single costing block for flowsheet-level properties.\n", - "\n", - "Users should note that IDAES costing methods support a wide array of heating sources (e.g. fired, steam boiler, hot water) and do not support direct capital costing of coolers. If users wish to cost Heater units acting as coolers, it is necessary to cost a \"dummy\" [0D shell and tube exchanger](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html) with appropriate aliased hot stream properties and proper cooling water properties. This is not demonstrated here, as the HDA examples take advantage of Flash and Condenser operations to recover liquid product.\n", - "\n", - "Capital costing is independent of unit model connections, and building cost equations may be done piecewise in this fashion. Default options are passed explicitly to demonstrate proper syntax and usage. Now that all required properties are defined, let's cost our models connecting costing blocks, methods and unit models in each flowsheet." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Flexibility of Costing Block Definitions\n", - "IDAES supports many ways to define batches of costing blocks, and several are shown in the example. Users may employ whichever method fits their modeling needs for explicit or concise code. In the code below, note how the unit model itself is never passed to the costing method; when the full model is executed, the costing block will automatically connect its parent block with child equation blocks.\n", - "\n", - "`Compressor` unit models with isothermal or adiabatic thermodynamics are too simple to cost and are therefore excluded from the economic analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define costing for the heater unit:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.costing.SSLW import (\n", - " HeaterMaterial,\n", - " HeaterSource,\n", - ")\n", - "\n", - "# Costing for heater - m.fs.H101\n", - "m.fs.H101.costing = UnitModelCostingBlock(\n", - " flowsheet_costing_block=m.fs.costing,\n", - " costing_method=SSLWCostingData.cost_fired_heater,\n", - " costing_method_arguments={\n", - " \"material_type\": HeaterMaterial.CarbonSteel,\n", - " \"heat_source\": HeaterSource.Fuel,\n", - " },\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The costing module provides a `unit_mapping` dictionary linking generic unit model classes with recommended costing methods. In this example, StoichiometricReactor and Flash vessels utilize different vessel costing methods with similar arguments. The diameter and length attributes need to exist in order to cost vessel sizing and material requirements, and we add them if they don't exist already. The `unit_mapping` method provides an opportunity to automatically select the correct vessel orientation (vertical or horizontal) based on the unit type; passing a `StoichiometricReactor` or `PFR` class object will call the `cost_horizontal_vessel` method, while passing a `Flash` or `CSTR` class object will call the `cost_vertical_vessel` method.\n", - "\n", - "All vessels are assigned costing succintly via a loop below - users may define each block individually if desired:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.costing.SSLW import (\n", - " VesselMaterial,\n", - " TrayType,\n", - " TrayMaterial,\n", - ")\n", - "\n", - "from idaes.core.util.constants import Constants\n", - "from pyomo.environ import Var, Constraint, units as pyunits, Param, value\n", - "from idaes.models.unit_models import StoichiometricReactor, Flash\n", - "\n", - "# Map unit models to unit classes\n", - "# Will pass to unit_mapping which calls costing methods based on unit class\n", - "unit_class_mapping = {\n", - " m.fs.R101: StoichiometricReactor,\n", - " m.fs.F101: Flash,\n", - " m.fs.F102: Flash,\n", - "}\n", - "\n", - "# Costing for vessels - m.fs.R101, m.fs.F101, m.fs.F102\n", - "\n", - "# Loop over units\n", - "for unit in [m.fs.R101, m.fs.F101, m.fs.F102]:\n", - " # Get correct unit class for unit model\n", - " unit_class = unit_class_mapping[unit]\n", - "\n", - " # Add dimension variables and constraint if they don't exist\n", - " if not hasattr(unit, \"diameter\"):\n", - " unit.diameter = Var(initialize=1, units=pyunits.m)\n", - " if not hasattr(unit, \"length\"):\n", - " unit.length = Var(initialize=1, units=pyunits.m)\n", - " if hasattr(unit, \"volume\"): # if volume exists, set diameter from volume\n", - " unit.volume_eq = Constraint(\n", - " expr=unit.volume[0]\n", - " == unit.length * unit.diameter**2 * 0.25 * Constants.pi\n", - " )\n", - " else: # fix diameter directly\n", - " unit.diameter.fix(0.2214 * pyunits.m)\n", - " # Either way, fix L/D to calculate L from D\n", - " unit.L_over_D = Constraint(expr=unit.length == 3 * unit.diameter)\n", - "\n", - " # Define vessel costing\n", - " unit.costing = UnitModelCostingBlock(\n", - " flowsheet_costing_block=unit.parent_block().costing, # e.g. m.fs.R101.costing\n", - " costing_method=SSLWCostingData.unit_mapping[\n", - " unit_class\n", - " ], # e.g. cost_vertical_vessel()\n", - " costing_method_arguments={\n", - " \"material_type\": VesselMaterial.CarbonSteel,\n", - " \"shell_thickness\": 1.25 * pyunits.inch,\n", - " },\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve Flowsheet Costing Blocks\n", - "Now, we may solve the full flowsheet for all costing blocks:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Eefine solver\n", - "from idaes.core.solvers import get_solver\n", - "\n", - "solver = get_solver()\n", - "\n", - "# Check that the degrees of freedom is zero\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Check physical units consistency, solve and check solver status\n", - "from pyomo.environ import TerminationCondition\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "assert_units_consistent(m)\n", - "results = solver.solve(m, tee=True, symbolic_solver_labels=True)\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For comparison, we will call and build the HDA flowsheet replacing the second `Flash` with a `TrayColumn` distillation unit model. The flowsheet costing occurs in the external script `hda_flowsheets_for_costing_notebook.py` and is not shown here:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from pyomo.common.log import LoggingIntercept\n", - "import logging\n", - "from io import StringIO\n", - "\n", - "stream = StringIO()\n", - "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", - " # Source file for prebuilt flowsheets\n", - " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", - "\n", - " # Build hda model with distillation column and return model object\n", - " n = hda_with_distillation(tee=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results Comparison and Visualization\n", - "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Imports and data gathering\n", - "from matplotlib import pyplot as plt\n", - "\n", - "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", - "\n", - "two_flash_unitlist = [\n", - " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", - "]\n", - "distillation_unitlist = [\n", - " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare equipment purchase costs (actual capital costs)\n", - "\n", - "two_flash_capcost = {\n", - " unit.name: value(unit.costing.capital_cost / 1e3) for unit in two_flash_unitlist\n", - "}\n", - "distillation_capcost = {\n", - " unit.name: value(unit.costing.capital_cost / 1e3) for unit in distillation_unitlist\n", - "}\n", - "\n", - "two_flash_capdf = pd.DataFrame(\n", - " list(two_flash_capcost.items()), columns=[\"Equipment\", \"Two Flash\"]\n", - ").set_index(\"Equipment\")\n", - "distillation_capdf = pd.DataFrame(\n", - " list(distillation_capcost.items()), columns=[\"Equipment\", \"Distillation\"]\n", - ").set_index(\"Equipment\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "capcosts = two_flash_capdf.add(distillation_capdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "# Sort according to an easier order to view\n", - "capcosts = capcosts[[\"fs.H101\", \"fs.R101\", \"fs.F101\", \"fs.F102\", \"fs.D101\", \"fs.H102\"]]\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(capcosts) # view dataframe before plotting\n", - "\n", - "capplot = capcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Total Capital Costs\", ylabel=\"$1000\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare operating costs (per year)\n", - "\n", - "two_flash_opcost = {\n", - " \"cooling\": value(3600 * 24 * 365 * m.fs.cooling_cost / 1e3),\n", - " \"heating\": value(3600 * 24 * 365 * m.fs.heating_cost / 1e3),\n", - "}\n", - "distillation_opcost = {\n", - " \"cooling\": value(3600 * 24 * 365 * n.fs.cooling_cost / 1e3),\n", - " \"heating\": value(3600 * 24 * 365 * n.fs.heating_cost / 1e3),\n", - "}\n", - "\n", - "two_flash_opdf = pd.DataFrame(\n", - " list(two_flash_opcost.items()), columns=[\"Utilities\", \"Two Flash\"]\n", - ").set_index(\"Utilities\")\n", - "distillation_opdf = pd.DataFrame(\n", - " list(distillation_opcost.items()), columns=[\"Utilities\", \"Distillation\"]\n", - ").set_index(\"Utilities\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "opcosts = two_flash_opdf.add(distillation_opdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(opcosts) # view dataframe before plotting\n", - "\n", - "opplot = opcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Operating Costs\", ylabel=\"$1000/year\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare total costs (capital costs and operating costs)\n", - "\n", - "two_flash_totcost = {\n", - " \"capital\": sum(two_flash_capcost[idx] for idx in two_flash_capcost),\n", - " \"operating\": value(m.fs.operating_cost) / 1e3,\n", - "}\n", - "distillation_totcost = {\n", - " \"capital\": sum(distillation_capcost[idx] for idx in distillation_capcost),\n", - " \"operating\": value(n.fs.operating_cost) / 1e3,\n", - "}\n", - "\n", - "two_flash_totdf = pd.DataFrame(\n", - " list(two_flash_totcost.items()), columns=[\"Costs\", \"Two Flash\"]\n", - ").set_index(\"Costs\")\n", - "distillation_totdf = pd.DataFrame(\n", - " list(distillation_totcost.items()), columns=[\"Costs\", \"Distillation\"]\n", - ").set_index(\"Costs\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "totcosts = two_flash_totdf.add(distillation_totdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(totcosts) # view dataframe before plotting\n", - "\n", - "totplot = totcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Total Plant Cost (TPC)\", ylabel=\"$1000/year\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's compare the total costs on a production basis. This will account for the greater efficiency provided by the distillation column relative to the less-expensive second flash unit:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "two_flash_cost = value(1e3 * sum(two_flash_totcost[idx] for idx in two_flash_totcost))\n", - "two_flash_prod = value(\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] * 365 * 24 * 3600\n", - ")\n", - "distillation_cost = value(\n", - " 1e3 * sum(distillation_totcost[idx] for idx in distillation_totcost)\n", - ")\n", - "distillation_prod = value(n.fs.D101.condenser.distillate.flow_mol[0] * 365 * 24 * 3600)\n", - "\n", - "print(\n", - " f\"Two flash case over one year: ${two_flash_cost/1e3:0.0f}K / {two_flash_prod/1e3:0.0f} kmol benzene = ${two_flash_cost/(two_flash_prod/1e3):0.2f} per kmol benzene produced\"\n", - ")\n", - "print(\n", - " f\"Distillation case over one year: ${distillation_cost/1e3:0.0f}K / {distillation_prod/1e3:0.0f} kmol benzene = ${distillation_cost/(distillation_prod/1e3):0.2f} per kmol benzene produced\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Summary\n", - "In this example, IDAES Process Costing Framework methods were applied to two HDA flowsheets for capital cost estimation. The costing blocks calls showcased multiple methods to define unit costing, demonstrating the flexibility and best practice of the costing framework. In the basic examples, the two-flash HDA did not include costing and the distillation HDA estimated a reactor capital cost comprising 3.3% of the total plant cost (TPC). With more rigorous costing, IDAES obtained total capital costs of 8.5% TPC (two flash HDA) and 9.6% (distillation HDA) and better modeled the impact of equipment cost on process economics. As printed above, the IDAES Process Costing Framework confirmed that replacing the second flash drum with a distillation column results in increased equipment costs, increased production and decreased cost per unit product." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Costing\n", + "\n", + "\n", + "## Note\n", + "\n", + "This example will demonstrate adding capital and operating costs to the two HDA examples, the basic [HDA with Flash](../tut/hda_flowsheet_solution_test.ipynb) and a comparison with the HDA with Distillation.\n", + "\n", + "\n", + "## Learning outcomes\n", + "\n", + "- Import external pre-built steady-state flowsheets using the IDAES unit model library\n", + "- Define and add costing blocks using the IDAES Process Costing Framework\n", + "- Fomulate and solve a process economics optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 → C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "Users may refer to the prior examples linked at the top of this notebook for detailed process descriptions of the two HDA configurations. As before, the properties required for this module are defined in\n", + "\n", + "- `hda_ideal_VLE.py`\n", + "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", + "- `hda_reaction.py`\n", + "\n", + "Additionally, we will be importing externally-defined flowsheets for the two HDA configurations from\n", + "\n", + "- `hda_flowsheets_for_costing_notebook.py`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import and run HDA Flowsheets\n", + "First, we will generate solved flowsheets for each HDA model. The external scripts build and set inputs for the flowsheets, initialize unit models and streams, and solve the flowsheets before returning the model objects. Note that the HDA flowsheets contain all unit models and stream connections, and no costing equations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The flowsheet utilizes the Wegstein method to iteratively solve circular dependencies such as recycle streams, and is intended to approach a feasible solution. As such, the calls below will fail to converge after 3 iterations and pass to IPOPT to obtain an optimal solution as expected:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Source file for prebuilt flowsheets\n", + "from hda_flowsheets_for_costing_notebook import hda_with_flash\n", + "\n", + "# Build hda model with second flash unit and return model object\n", + "m = hda_with_flash(tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IDAES Process Costing Framework\n", + "IDAES provides a library of capital costing correlations based on those in the following source:\n", + "\n", + "*Process and Product Design Principles: Synthesis, Analysis, and Evaluation*. Seider, Seader, Lewin, Windagdo, 3rd Ed. John Wiley and Sons Chapter 22. Cost Accounting and Capital Cost Estimation 22.2 Cost Indexes and Capital Investment.\n", + "\n", + "Currently, IDAES supports calculation of capital costing for a wide array of unit operations, vesseel sizing and material properties, and specific unit options such as column tray types and heat exchanger configurations. Users may find further information on specific costing methods and options in the IDAES Process Costing Framework documentation (link pending).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Operating Cost Equations\n", + "Before adding capital costing blocks, we will add operating cost equations taken from the basic [HDA with Flash](../tut/hda_flowsheet_solution_test.ipynb) and the HDA with Distillation examples. The examples assume constant cooling and heating coefficients over an annual cost basis. The IDAES Generic Costing Framework does not currently support variable cost calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Required imports\n", + "from pyomo.environ import Expression\n", + "\n", + "# Operating costs for HDA with second flash (model m)\n", + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", + ")\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", + ")\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Capital Costing\n", + "Below, we will add add capital costing blocks to the imported flowsheets and evaluate the economic impact of replacing the second Flash with a Distillation column. First, let's import and define the main flowsheet costing block:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import costing methods - classes, heaters, vessels, compressors, columns\n", + "from idaes.models.costing.SSLW import (\n", + " SSLWCosting,\n", + " SSLWCostingData,\n", + ")\n", + "from idaes.core import UnitModelCostingBlock\n", + "\n", + "# Costing block\n", + "m.fs.costing = SSLWCosting()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will build the relevant costing blocks for the equipment we wish to cost. Note how the costing block, methods and flags are passed as arguments in the costing block call itself. Each unit model will have a single costing block, but each flowsheet model (m and n) will also have a single costing block for flowsheet-level properties.\n", + "\n", + "Users should note that IDAES costing methods support a wide array of heating sources (e.g. fired, steam boiler, hot water) and do not support direct capital costing of coolers. If users wish to cost Heater units acting as coolers, it is necessary to cost a \"dummy\" [0D shell and tube exchanger](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html) with appropriate aliased hot stream properties and proper cooling water properties. This is not demonstrated here, as the HDA examples take advantage of Flash and Condenser operations to recover liquid product.\n", + "\n", + "Capital costing is independent of unit model connections, and building cost equations may be done piecewise in this fashion. Default options are passed explicitly to demonstrate proper syntax and usage. Now that all required properties are defined, let's cost our models connecting costing blocks, methods and unit models in each flowsheet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Flexibility of Costing Block Definitions\n", + "IDAES supports many ways to define batches of costing blocks, and several are shown in the example. Users may employ whichever method fits their modeling needs for explicit or concise code. In the code below, note how the unit model itself is never passed to the costing method; when the full model is executed, the costing block will automatically connect its parent block with child equation blocks.\n", + "\n", + "`Compressor` unit models with isothermal or adiabatic thermodynamics are too simple to cost and are therefore excluded from the economic analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define costing for the heater unit:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.costing.SSLW import (\n", + " HeaterMaterial,\n", + " HeaterSource,\n", + ")\n", + "\n", + "# Costing for heater - m.fs.H101\n", + "m.fs.H101.costing = UnitModelCostingBlock(\n", + " flowsheet_costing_block=m.fs.costing,\n", + " costing_method=SSLWCostingData.cost_fired_heater,\n", + " costing_method_arguments={\n", + " \"material_type\": HeaterMaterial.CarbonSteel,\n", + " \"heat_source\": HeaterSource.Fuel,\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The costing module provides a `unit_mapping` dictionary linking generic unit model classes with recommended costing methods. In this example, StoichiometricReactor and Flash vessels utilize different vessel costing methods with similar arguments. The diameter and length attributes need to exist in order to cost vessel sizing and material requirements, and we add them if they don't exist already. The `unit_mapping` method provides an opportunity to automatically select the correct vessel orientation (vertical or horizontal) based on the unit type; passing a `StoichiometricReactor` or `PFR` class object will call the `cost_horizontal_vessel` method, while passing a `Flash` or `CSTR` class object will call the `cost_vertical_vessel` method.\n", + "\n", + "All vessels are assigned costing succintly via a loop below - users may define each block individually if desired:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.costing.SSLW import (\n", + " VesselMaterial,\n", + " TrayType,\n", + " TrayMaterial,\n", + ")\n", + "\n", + "from idaes.core.util.constants import Constants\n", + "from pyomo.environ import Var, Constraint, units as pyunits, Param, value\n", + "from idaes.models.unit_models import StoichiometricReactor, Flash\n", + "\n", + "# Map unit models to unit classes\n", + "# Will pass to unit_mapping which calls costing methods based on unit class\n", + "unit_class_mapping = {\n", + " m.fs.R101: StoichiometricReactor,\n", + " m.fs.F101: Flash,\n", + " m.fs.F102: Flash,\n", + "}\n", + "\n", + "# Costing for vessels - m.fs.R101, m.fs.F101, m.fs.F102\n", + "\n", + "# Loop over units\n", + "for unit in [m.fs.R101, m.fs.F101, m.fs.F102]:\n", + " # Get correct unit class for unit model\n", + " unit_class = unit_class_mapping[unit]\n", + "\n", + " # Add dimension variables and constraint if they don't exist\n", + " if not hasattr(unit, \"diameter\"):\n", + " unit.diameter = Var(initialize=1, units=pyunits.m)\n", + " if not hasattr(unit, \"length\"):\n", + " unit.length = Var(initialize=1, units=pyunits.m)\n", + " if hasattr(unit, \"volume\"): # if volume exists, set diameter from volume\n", + " unit.volume_eq = Constraint(\n", + " expr=unit.volume[0]\n", + " == unit.length * unit.diameter**2 * 0.25 * Constants.pi\n", + " )\n", + " else: # fix diameter directly\n", + " unit.diameter.fix(0.2214 * pyunits.m)\n", + " # Either way, fix L/D to calculate L from D\n", + " unit.L_over_D = Constraint(expr=unit.length == 3 * unit.diameter)\n", + "\n", + " # Define vessel costing\n", + " unit.costing = UnitModelCostingBlock(\n", + " flowsheet_costing_block=unit.parent_block().costing, # e.g. m.fs.R101.costing\n", + " costing_method=SSLWCostingData.unit_mapping[\n", + " unit_class\n", + " ], # e.g. cost_vertical_vessel()\n", + " costing_method_arguments={\n", + " \"material_type\": VesselMaterial.CarbonSteel,\n", + " \"shell_thickness\": 1.25 * pyunits.inch,\n", + " },\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve Flowsheet Costing Blocks\n", + "Now, we may solve the full flowsheet for all costing blocks:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Eefine solver\n", + "from idaes.core.solvers import get_solver\n", + "\n", + "solver = get_solver()\n", + "\n", + "# Check that the degrees of freedom is zero\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Check physical units consistency, solve and check solver status\n", + "from pyomo.environ import TerminationCondition\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "\n", + "assert_units_consistent(m)\n", + "results = solver.solve(m, tee=True, symbolic_solver_labels=True)\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For comparison, we will call and build the HDA flowsheet replacing the second `Flash` with a `TrayColumn` distillation unit model. The flowsheet costing occurs in the external script `hda_flowsheets_for_costing_notebook.py` and is not shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from pyomo.common.log import LoggingIntercept\n", + "import logging\n", + "from io import StringIO\n", + "\n", + "stream = StringIO()\n", + "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", + " # Source file for prebuilt flowsheets\n", + " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", + "\n", + " # Build hda model with distillation column and return model object\n", + " n = hda_with_distillation(tee=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results Comparison and Visualization\n", + "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports and data gathering\n", + "from matplotlib import pyplot as plt\n", + "\n", + "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", + "\n", + "two_flash_unitlist = [\n", + " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", + "]\n", + "distillation_unitlist = [\n", + " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare equipment purchase costs (actual capital costs)\n", + "\n", + "two_flash_capcost = {\n", + " unit.name: value(unit.costing.capital_cost / 1e3) for unit in two_flash_unitlist\n", + "}\n", + "distillation_capcost = {\n", + " unit.name: value(unit.costing.capital_cost / 1e3) for unit in distillation_unitlist\n", + "}\n", + "\n", + "two_flash_capdf = pd.DataFrame(\n", + " list(two_flash_capcost.items()), columns=[\"Equipment\", \"Two Flash\"]\n", + ").set_index(\"Equipment\")\n", + "distillation_capdf = pd.DataFrame(\n", + " list(distillation_capcost.items()), columns=[\"Equipment\", \"Distillation\"]\n", + ").set_index(\"Equipment\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "capcosts = two_flash_capdf.add(distillation_capdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "# Sort according to an easier order to view\n", + "capcosts = capcosts[[\"fs.H101\", \"fs.R101\", \"fs.F101\", \"fs.F102\", \"fs.D101\", \"fs.H102\"]]\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(capcosts) # view dataframe before plotting\n", + "\n", + "capplot = capcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Total Capital Costs\", ylabel=\"$1000\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare operating costs (per year)\n", + "\n", + "two_flash_opcost = {\n", + " \"cooling\": value(3600 * 24 * 365 * m.fs.cooling_cost / 1e3),\n", + " \"heating\": value(3600 * 24 * 365 * m.fs.heating_cost / 1e3),\n", + "}\n", + "distillation_opcost = {\n", + " \"cooling\": value(3600 * 24 * 365 * n.fs.cooling_cost / 1e3),\n", + " \"heating\": value(3600 * 24 * 365 * n.fs.heating_cost / 1e3),\n", + "}\n", + "\n", + "two_flash_opdf = pd.DataFrame(\n", + " list(two_flash_opcost.items()), columns=[\"Utilities\", \"Two Flash\"]\n", + ").set_index(\"Utilities\")\n", + "distillation_opdf = pd.DataFrame(\n", + " list(distillation_opcost.items()), columns=[\"Utilities\", \"Distillation\"]\n", + ").set_index(\"Utilities\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "opcosts = two_flash_opdf.add(distillation_opdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(opcosts) # view dataframe before plotting\n", + "\n", + "opplot = opcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Operating Costs\", ylabel=\"$1000/year\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare total costs (capital costs and operating costs)\n", + "\n", + "two_flash_totcost = {\n", + " \"capital\": sum(two_flash_capcost[idx] for idx in two_flash_capcost),\n", + " \"operating\": value(m.fs.operating_cost) / 1e3,\n", + "}\n", + "distillation_totcost = {\n", + " \"capital\": sum(distillation_capcost[idx] for idx in distillation_capcost),\n", + " \"operating\": value(n.fs.operating_cost) / 1e3,\n", + "}\n", + "\n", + "two_flash_totdf = pd.DataFrame(\n", + " list(two_flash_totcost.items()), columns=[\"Costs\", \"Two Flash\"]\n", + ").set_index(\"Costs\")\n", + "distillation_totdf = pd.DataFrame(\n", + " list(distillation_totcost.items()), columns=[\"Costs\", \"Distillation\"]\n", + ").set_index(\"Costs\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "totcosts = two_flash_totdf.add(distillation_totdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(totcosts) # view dataframe before plotting\n", + "\n", + "totplot = totcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Total Plant Cost (TPC)\", ylabel=\"$1000/year\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's compare the total costs on a production basis. This will account for the greater efficiency provided by the distillation column relative to the less-expensive second flash unit:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "two_flash_cost = value(1e3 * sum(two_flash_totcost[idx] for idx in two_flash_totcost))\n", + "two_flash_prod = value(\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] * 365 * 24 * 3600\n", + ")\n", + "distillation_cost = value(\n", + " 1e3 * sum(distillation_totcost[idx] for idx in distillation_totcost)\n", + ")\n", + "distillation_prod = value(n.fs.D101.condenser.distillate.flow_mol[0] * 365 * 24 * 3600)\n", + "\n", + "print(\n", + " f\"Two flash case over one year: ${two_flash_cost/1e3:0.0f}K / {two_flash_prod/1e3:0.0f} kmol benzene = ${two_flash_cost/(two_flash_prod/1e3):0.2f} per kmol benzene produced\"\n", + ")\n", + "print(\n", + " f\"Distillation case over one year: ${distillation_cost/1e3:0.0f}K / {distillation_prod/1e3:0.0f} kmol benzene = ${distillation_cost/(distillation_prod/1e3):0.2f} per kmol benzene produced\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary\n", + "In this example, IDAES Process Costing Framework methods were applied to two HDA flowsheets for capital cost estimation. The costing blocks calls showcased multiple methods to define unit costing, demonstrating the flexibility and best practice of the costing framework. In the basic examples, the two-flash HDA did not include costing and the distillation HDA estimated a reactor capital cost comprising 3.3% of the total plant cost (TPC). With more rigorous costing, IDAES obtained total capital costs of 8.5% TPC (two flash HDA) and 9.6% (distillation HDA) and better modeled the impact of equipment cost on process economics. As printed above, the IDAES Process Costing Framework confirmed that replacing the second flash drum with a distillation column results in increased equipment costs, increased production and decreased cost per unit product." + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_usr.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_usr.ipynb index 4a2583ba..d4a23407 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_usr.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_costing_usr.ipynb @@ -1,560 +1,560 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "header", - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "###############################################################################\n", - "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", - "# Framework (IDAES IP) was produced under the DOE Institute for the\n", - "# Design of Advanced Energy Systems (IDAES).\n", - "#\n", - "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", - "# University of California, through Lawrence Berkeley National Laboratory,\n", - "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", - "# University, West Virginia University Research Corporation, et al.\n", - "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", - "# for full copyright and license information.\n", - "###############################################################################" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# HDA Flowsheet Costing\n", - "\n", - "\n", - "## Note\n", - "\n", - "This example will demonstrate adding capital and operating costs to the two HDA examples, the basic [HDA with Flash](../tut/hda_flowsheet_solution_usr.ipynb) and a comparison with the HDA with Distillation.\n", - "\n", - "\n", - "## Learning outcomes\n", - "\n", - "- Import external pre-built steady-state flowsheets using the IDAES unit model library\n", - "- Define and add costing blocks using the IDAES Process Costing Framework\n", - "- Fomulate and solve a process economics optimization problem\n", - " - Defining an objective function\n", - " - Setting variable bounds\n", - " - Adding additional constraints \n", - "\n", - "\n", - "## Problem Statement\n", - "\n", - "Hydrodealkylation is a chemical reaction that often involves reacting\n", - "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", - "simpler aromatic hydrocarbon devoid of functional groups. In this\n", - "example, toluene will be reacted with hydrogen gas at high temperatures\n", - " to form benzene via the following reaction:\n", - "\n", - "**C6H5CH3 + H2 \u2192 C6H6 + CH4**\n", - "\n", - "\n", - "This reaction is often accompanied by an equilibrium side reaction\n", - "which forms diphenyl, which we will neglect for this example.\n", - "\n", - "This example is based on the 1967 AIChE Student Contest problem as\n", - "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", - "McGraw-Hill.\n", - "\n", - "Users may refer to the prior examples linked at the top of this notebook for detailed process descriptions of the two HDA configurations. As before, the properties required for this module are defined in\n", - "\n", - "- `hda_ideal_VLE.py`\n", - "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", - "- `hda_reaction.py`\n", - "\n", - "Additionally, we will be importing externally-defined flowsheets for the two HDA configurations from\n", - "\n", - "- `hda_flowsheets_for_costing_notebook.py`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import and run HDA Flowsheets\n", - "First, we will generate solved flowsheets for each HDA model. The external scripts build and set inputs for the flowsheets, initialize unit models and streams, and solve the flowsheets before returning the model objects. Note that the HDA flowsheets contain all unit models and stream connections, and no costing equations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The flowsheet utilizes the Wegstein method to iteratively solve circular dependencies such as recycle streams, and is intended to approach a feasible solution. As such, the calls below will fail to converge after 3 iterations and pass to IPOPT to obtain an optimal solution as expected:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Source file for prebuilt flowsheets\n", - "from hda_flowsheets_for_costing_notebook import hda_with_flash\n", - "\n", - "# Build hda model with second flash unit and return model object\n", - "m = hda_with_flash(tee=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IDAES Process Costing Framework\n", - "IDAES provides a library of capital costing correlations based on those in the following source:\n", - "\n", - "*Process and Product Design Principles: Synthesis, Analysis, and Evaluation*. Seider, Seader, Lewin, Windagdo, 3rd Ed. John Wiley and Sons Chapter 22. Cost Accounting and Capital Cost Estimation 22.2 Cost Indexes and Capital Investment.\n", - "\n", - "Currently, IDAES supports calculation of capital costing for a wide array of unit operations, vesseel sizing and material properties, and specific unit options such as column tray types and heat exchanger configurations. Users may find further information on specific costing methods and options in the IDAES Process Costing Framework documentation (link pending).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add Operating Cost Equations\n", - "Before adding capital costing blocks, we will add operating cost equations taken from the basic [HDA with Flash](../tut/hda_flowsheet_solution_usr.ipynb) and the HDA with Distillation examples. The examples assume constant cooling and heating coefficients over an annual cost basis. The IDAES Generic Costing Framework does not currently support variable cost calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Required imports\n", - "from pyomo.environ import Expression\n", - "\n", - "# Operating costs for HDA with second flash (model m)\n", - "m.fs.cooling_cost = Expression(\n", - " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", - ")\n", - "m.fs.heating_cost = Expression(\n", - " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", - ")\n", - "m.fs.operating_cost = Expression(\n", - " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add Capital Costing\n", - "Below, we will add add capital costing blocks to the imported flowsheets and evaluate the economic impact of replacing the second Flash with a Distillation column. First, let's import and define the main flowsheet costing block:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Import costing methods - classes, heaters, vessels, compressors, columns\n", - "from idaes.models.costing.SSLW import (\n", - " SSLWCosting,\n", - " SSLWCostingData,\n", - ")\n", - "from idaes.core import UnitModelCostingBlock\n", - "\n", - "# Costing block\n", - "m.fs.costing = SSLWCosting()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will build the relevant costing blocks for the equipment we wish to cost. Note how the costing block, methods and flags are passed as arguments in the costing block call itself. Each unit model will have a single costing block, but each flowsheet model (m and n) will also have a single costing block for flowsheet-level properties.\n", - "\n", - "Users should note that IDAES costing methods support a wide array of heating sources (e.g. fired, steam boiler, hot water) and do not support direct capital costing of coolers. If users wish to cost Heater units acting as coolers, it is necessary to cost a \"dummy\" [0D shell and tube exchanger](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html) with appropriate aliased hot stream properties and proper cooling water properties. This is not demonstrated here, as the HDA examples take advantage of Flash and Condenser operations to recover liquid product.\n", - "\n", - "Capital costing is independent of unit model connections, and building cost equations may be done piecewise in this fashion. Default options are passed explicitly to demonstrate proper syntax and usage. Now that all required properties are defined, let's cost our models connecting costing blocks, methods and unit models in each flowsheet." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Flexibility of Costing Block Definitions\n", - "IDAES supports many ways to define batches of costing blocks, and several are shown in the example. Users may employ whichever method fits their modeling needs for explicit or concise code. In the code below, note how the unit model itself is never passed to the costing method; when the full model is executed, the costing block will automatically connect its parent block with child equation blocks.\n", - "\n", - "`Compressor` unit models with isothermal or adiabatic thermodynamics are too simple to cost and are therefore excluded from the economic analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define costing for the heater unit:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.costing.SSLW import (\n", - " HeaterMaterial,\n", - " HeaterSource,\n", - ")\n", - "\n", - "# Costing for heater - m.fs.H101\n", - "m.fs.H101.costing = UnitModelCostingBlock(\n", - " flowsheet_costing_block=m.fs.costing,\n", - " costing_method=SSLWCostingData.cost_fired_heater,\n", - " costing_method_arguments={\n", - " \"material_type\": HeaterMaterial.CarbonSteel,\n", - " \"heat_source\": HeaterSource.Fuel,\n", - " },\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The costing module provides a `unit_mapping` dictionary linking generic unit model classes with recommended costing methods. In this example, StoichiometricReactor and Flash vessels utilize different vessel costing methods with similar arguments. The diameter and length attributes need to exist in order to cost vessel sizing and material requirements, and we add them if they don't exist already. The `unit_mapping` method provides an opportunity to automatically select the correct vessel orientation (vertical or horizontal) based on the unit type; passing a `StoichiometricReactor` or `PFR` class object will call the `cost_horizontal_vessel` method, while passing a `Flash` or `CSTR` class object will call the `cost_vertical_vessel` method.\n", - "\n", - "All vessels are assigned costing succintly via a loop below - users may define each block individually if desired:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from idaes.models.costing.SSLW import (\n", - " VesselMaterial,\n", - " TrayType,\n", - " TrayMaterial,\n", - ")\n", - "\n", - "from idaes.core.util.constants import Constants\n", - "from pyomo.environ import Var, Constraint, units as pyunits, Param, value\n", - "from idaes.models.unit_models import StoichiometricReactor, Flash\n", - "\n", - "# Map unit models to unit classes\n", - "# Will pass to unit_mapping which calls costing methods based on unit class\n", - "unit_class_mapping = {\n", - " m.fs.R101: StoichiometricReactor,\n", - " m.fs.F101: Flash,\n", - " m.fs.F102: Flash,\n", - "}\n", - "\n", - "# Costing for vessels - m.fs.R101, m.fs.F101, m.fs.F102\n", - "\n", - "# Loop over units\n", - "for unit in [m.fs.R101, m.fs.F101, m.fs.F102]:\n", - " # Get correct unit class for unit model\n", - " unit_class = unit_class_mapping[unit]\n", - "\n", - " # Add dimension variables and constraint if they don't exist\n", - " if not hasattr(unit, \"diameter\"):\n", - " unit.diameter = Var(initialize=1, units=pyunits.m)\n", - " if not hasattr(unit, \"length\"):\n", - " unit.length = Var(initialize=1, units=pyunits.m)\n", - " if hasattr(unit, \"volume\"): # if volume exists, set diameter from volume\n", - " unit.volume_eq = Constraint(\n", - " expr=unit.volume[0]\n", - " == unit.length * unit.diameter**2 * 0.25 * Constants.pi\n", - " )\n", - " else: # fix diameter directly\n", - " unit.diameter.fix(0.2214 * pyunits.m)\n", - " # Either way, fix L/D to calculate L from D\n", - " unit.L_over_D = Constraint(expr=unit.length == 3 * unit.diameter)\n", - "\n", - " # Define vessel costing\n", - " unit.costing = UnitModelCostingBlock(\n", - " flowsheet_costing_block=unit.parent_block().costing, # e.g. m.fs.R101.costing\n", - " costing_method=SSLWCostingData.unit_mapping[\n", - " unit_class\n", - " ], # e.g. cost_vertical_vessel()\n", - " costing_method_arguments={\n", - " \"material_type\": VesselMaterial.CarbonSteel,\n", - " \"shell_thickness\": 1.25 * pyunits.inch,\n", - " },\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve Flowsheet Costing Blocks\n", - "Now, we may solve the full flowsheet for all costing blocks:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Eefine solver\n", - "from idaes.core.solvers import get_solver\n", - "\n", - "solver = get_solver()\n", - "\n", - "# Check that the degrees of freedom is zero\n", - "from idaes.core.util.model_statistics import degrees_of_freedom\n", - "\n", - "assert degrees_of_freedom(m) == 0" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Check physical units consistency, solve and check solver status\n", - "from pyomo.environ import TerminationCondition\n", - "from pyomo.util.check_units import assert_units_consistent\n", - "\n", - "assert_units_consistent(m)\n", - "results = solver.solve(m, tee=True, symbolic_solver_labels=True)\n", - "assert results.solver.termination_condition == TerminationCondition.optimal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For comparison, we will call and build the HDA flowsheet replacing the second `Flash` with a `TrayColumn` distillation unit model. The flowsheet costing occurs in the external script `hda_flowsheets_for_costing_notebook.py` and is not shown here:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from pyomo.common.log import LoggingIntercept\n", - "import logging\n", - "from io import StringIO\n", - "\n", - "stream = StringIO()\n", - "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", - " # Source file for prebuilt flowsheets\n", - " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", - "\n", - " # Build hda model with distillation column and return model object\n", - " n = hda_with_distillation(tee=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Results Comparison and Visualization\n", - "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Imports and data gathering\n", - "from matplotlib import pyplot as plt\n", - "\n", - "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", - "\n", - "two_flash_unitlist = [\n", - " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", - "]\n", - "distillation_unitlist = [\n", - " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare equipment purchase costs (actual capital costs)\n", - "\n", - "two_flash_capcost = {\n", - " unit.name: value(unit.costing.capital_cost / 1e3) for unit in two_flash_unitlist\n", - "}\n", - "distillation_capcost = {\n", - " unit.name: value(unit.costing.capital_cost / 1e3) for unit in distillation_unitlist\n", - "}\n", - "\n", - "two_flash_capdf = pd.DataFrame(\n", - " list(two_flash_capcost.items()), columns=[\"Equipment\", \"Two Flash\"]\n", - ").set_index(\"Equipment\")\n", - "distillation_capdf = pd.DataFrame(\n", - " list(distillation_capcost.items()), columns=[\"Equipment\", \"Distillation\"]\n", - ").set_index(\"Equipment\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "capcosts = two_flash_capdf.add(distillation_capdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "# Sort according to an easier order to view\n", - "capcosts = capcosts[[\"fs.H101\", \"fs.R101\", \"fs.F101\", \"fs.F102\", \"fs.D101\", \"fs.H102\"]]\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(capcosts) # view dataframe before plotting\n", - "\n", - "capplot = capcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Total Capital Costs\", ylabel=\"$1000\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare operating costs (per year)\n", - "\n", - "two_flash_opcost = {\n", - " \"cooling\": value(3600 * 24 * 365 * m.fs.cooling_cost / 1e3),\n", - " \"heating\": value(3600 * 24 * 365 * m.fs.heating_cost / 1e3),\n", - "}\n", - "distillation_opcost = {\n", - " \"cooling\": value(3600 * 24 * 365 * n.fs.cooling_cost / 1e3),\n", - " \"heating\": value(3600 * 24 * 365 * n.fs.heating_cost / 1e3),\n", - "}\n", - "\n", - "two_flash_opdf = pd.DataFrame(\n", - " list(two_flash_opcost.items()), columns=[\"Utilities\", \"Two Flash\"]\n", - ").set_index(\"Utilities\")\n", - "distillation_opdf = pd.DataFrame(\n", - " list(distillation_opcost.items()), columns=[\"Utilities\", \"Distillation\"]\n", - ").set_index(\"Utilities\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "opcosts = two_flash_opdf.add(distillation_opdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(opcosts) # view dataframe before plotting\n", - "\n", - "opplot = opcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Operating Costs\", ylabel=\"$1000/year\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare total costs (capital costs and operating costs)\n", - "\n", - "two_flash_totcost = {\n", - " \"capital\": sum(two_flash_capcost[idx] for idx in two_flash_capcost),\n", - " \"operating\": value(m.fs.operating_cost) / 1e3,\n", - "}\n", - "distillation_totcost = {\n", - " \"capital\": sum(distillation_capcost[idx] for idx in distillation_capcost),\n", - " \"operating\": value(n.fs.operating_cost) / 1e3,\n", - "}\n", - "\n", - "two_flash_totdf = pd.DataFrame(\n", - " list(two_flash_totcost.items()), columns=[\"Costs\", \"Two Flash\"]\n", - ").set_index(\"Costs\")\n", - "distillation_totdf = pd.DataFrame(\n", - " list(distillation_totcost.items()), columns=[\"Costs\", \"Distillation\"]\n", - ").set_index(\"Costs\")\n", - "\n", - "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", - "totcosts = two_flash_totdf.add(distillation_totdf, fill_value=0).fillna(0).transpose()\n", - "\n", - "print(\"Costs in $1000:\")\n", - "display(totcosts) # view dataframe before plotting\n", - "\n", - "totplot = totcosts.plot(\n", - " kind=\"bar\", stacked=True, title=\"HDA Total Plant Cost (TPC)\", ylabel=\"$1000/year\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's compare the total costs on a production basis. This will account for the greater efficiency provided by the distillation column relative to the less-expensive second flash unit:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "two_flash_cost = value(1e3 * sum(two_flash_totcost[idx] for idx in two_flash_totcost))\n", - "two_flash_prod = value(\n", - " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] * 365 * 24 * 3600\n", - ")\n", - "distillation_cost = value(\n", - " 1e3 * sum(distillation_totcost[idx] for idx in distillation_totcost)\n", - ")\n", - "distillation_prod = value(n.fs.D101.condenser.distillate.flow_mol[0] * 365 * 24 * 3600)\n", - "\n", - "print(\n", - " f\"Two flash case over one year: ${two_flash_cost/1e3:0.0f}K / {two_flash_prod/1e3:0.0f} kmol benzene = ${two_flash_cost/(two_flash_prod/1e3):0.2f} per kmol benzene produced\"\n", - ")\n", - "print(\n", - " f\"Distillation case over one year: ${distillation_cost/1e3:0.0f}K / {distillation_prod/1e3:0.0f} kmol benzene = ${distillation_cost/(distillation_prod/1e3):0.2f} per kmol benzene produced\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Summary\n", - "In this example, IDAES Process Costing Framework methods were applied to two HDA flowsheets for capital cost estimation. The costing blocks calls showcased multiple methods to define unit costing, demonstrating the flexibility and best practice of the costing framework. In the basic examples, the two-flash HDA did not include costing and the distillation HDA estimated a reactor capital cost comprising 3.3% of the total plant cost (TPC). With more rigorous costing, IDAES obtained total capital costs of 8.5% TPC (two flash HDA) and 9.6% (distillation HDA) and better modeled the impact of equipment cost on process economics. As printed above, the IDAES Process Costing Framework confirmed that replacing the second flash drum with a distillation column results in increased equipment costs, increased production and decreased cost per unit product." - ] - } - ], - "metadata": { - "celltoolbar": "Tags", - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "header", + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] }, - "nbformat": 4, - "nbformat_minor": 3 -} \ No newline at end of file + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# HDA Flowsheet Costing\n", + "\n", + "\n", + "## Note\n", + "\n", + "This example will demonstrate adding capital and operating costs to the two HDA examples, the basic [HDA with Flash](../tut/hda_flowsheet_solution_usr.ipynb) and a comparison with the HDA with Distillation.\n", + "\n", + "\n", + "## Learning outcomes\n", + "\n", + "- Import external pre-built steady-state flowsheets using the IDAES unit model library\n", + "- Define and add costing blocks using the IDAES Process Costing Framework\n", + "- Fomulate and solve a process economics optimization problem\n", + " - Defining an objective function\n", + " - Setting variable bounds\n", + " - Adding additional constraints \n", + "\n", + "\n", + "## Problem Statement\n", + "\n", + "Hydrodealkylation is a chemical reaction that often involves reacting\n", + "an aromatic hydrocarbon in the presence of hydrogen gas to form a\n", + "simpler aromatic hydrocarbon devoid of functional groups. In this\n", + "example, toluene will be reacted with hydrogen gas at high temperatures\n", + " to form benzene via the following reaction:\n", + "\n", + "**C6H5CH3 + H2 → C6H6 + CH4**\n", + "\n", + "\n", + "This reaction is often accompanied by an equilibrium side reaction\n", + "which forms diphenyl, which we will neglect for this example.\n", + "\n", + "This example is based on the 1967 AIChE Student Contest problem as\n", + "present by Douglas, J.M., Chemical Design of Chemical Processes, 1988,\n", + "McGraw-Hill.\n", + "\n", + "Users may refer to the prior examples linked at the top of this notebook for detailed process descriptions of the two HDA configurations. As before, the properties required for this module are defined in\n", + "\n", + "- `hda_ideal_VLE.py`\n", + "- `idaes.models.properties.activity_coeff_models.BTX_activity_coeff_VLE`\n", + "- `hda_reaction.py`\n", + "\n", + "Additionally, we will be importing externally-defined flowsheets for the two HDA configurations from\n", + "\n", + "- `hda_flowsheets_for_costing_notebook.py`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import and run HDA Flowsheets\n", + "First, we will generate solved flowsheets for each HDA model. The external scripts build and set inputs for the flowsheets, initialize unit models and streams, and solve the flowsheets before returning the model objects. Note that the HDA flowsheets contain all unit models and stream connections, and no costing equations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The flowsheet utilizes the Wegstein method to iteratively solve circular dependencies such as recycle streams, and is intended to approach a feasible solution. As such, the calls below will fail to converge after 3 iterations and pass to IPOPT to obtain an optimal solution as expected:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Source file for prebuilt flowsheets\n", + "from hda_flowsheets_for_costing_notebook import hda_with_flash\n", + "\n", + "# Build hda model with second flash unit and return model object\n", + "m = hda_with_flash(tee=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IDAES Process Costing Framework\n", + "IDAES provides a library of capital costing correlations based on those in the following source:\n", + "\n", + "*Process and Product Design Principles: Synthesis, Analysis, and Evaluation*. Seider, Seader, Lewin, Windagdo, 3rd Ed. John Wiley and Sons Chapter 22. Cost Accounting and Capital Cost Estimation 22.2 Cost Indexes and Capital Investment.\n", + "\n", + "Currently, IDAES supports calculation of capital costing for a wide array of unit operations, vesseel sizing and material properties, and specific unit options such as column tray types and heat exchanger configurations. Users may find further information on specific costing methods and options in the IDAES Process Costing Framework documentation (link pending).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Operating Cost Equations\n", + "Before adding capital costing blocks, we will add operating cost equations taken from the basic [HDA with Flash](../tut/hda_flowsheet_solution_usr.ipynb) and the HDA with Distillation examples. The examples assume constant cooling and heating coefficients over an annual cost basis. The IDAES Generic Costing Framework does not currently support variable cost calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Required imports\n", + "from pyomo.environ import Expression\n", + "\n", + "# Operating costs for HDA with second flash (model m)\n", + "m.fs.cooling_cost = Expression(\n", + " expr=0.212e-7 * (-m.fs.F101.heat_duty[0]) + 0.212e-7 * (-m.fs.R101.heat_duty[0])\n", + ")\n", + "m.fs.heating_cost = Expression(\n", + " expr=2.2e-7 * m.fs.H101.heat_duty[0] + 1.9e-7 * m.fs.F102.heat_duty[0]\n", + ")\n", + "m.fs.operating_cost = Expression(\n", + " expr=(3600 * 24 * 365 * (m.fs.heating_cost + m.fs.cooling_cost))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Capital Costing\n", + "Below, we will add add capital costing blocks to the imported flowsheets and evaluate the economic impact of replacing the second Flash with a Distillation column. First, let's import and define the main flowsheet costing block:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import costing methods - classes, heaters, vessels, compressors, columns\n", + "from idaes.models.costing.SSLW import (\n", + " SSLWCosting,\n", + " SSLWCostingData,\n", + ")\n", + "from idaes.core import UnitModelCostingBlock\n", + "\n", + "# Costing block\n", + "m.fs.costing = SSLWCosting()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will build the relevant costing blocks for the equipment we wish to cost. Note how the costing block, methods and flags are passed as arguments in the costing block call itself. Each unit model will have a single costing block, but each flowsheet model (m and n) will also have a single costing block for flowsheet-level properties.\n", + "\n", + "Users should note that IDAES costing methods support a wide array of heating sources (e.g. fired, steam boiler, hot water) and do not support direct capital costing of coolers. If users wish to cost Heater units acting as coolers, it is necessary to cost a \"dummy\" [0D shell and tube exchanger](https://idaes-pse.readthedocs.io/en/stable/reference_guides/model_libraries/generic/unit_models/heat_exchanger.html) with appropriate aliased hot stream properties and proper cooling water properties. This is not demonstrated here, as the HDA examples take advantage of Flash and Condenser operations to recover liquid product.\n", + "\n", + "Capital costing is independent of unit model connections, and building cost equations may be done piecewise in this fashion. Default options are passed explicitly to demonstrate proper syntax and usage. Now that all required properties are defined, let's cost our models connecting costing blocks, methods and unit models in each flowsheet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Flexibility of Costing Block Definitions\n", + "IDAES supports many ways to define batches of costing blocks, and several are shown in the example. Users may employ whichever method fits their modeling needs for explicit or concise code. In the code below, note how the unit model itself is never passed to the costing method; when the full model is executed, the costing block will automatically connect its parent block with child equation blocks.\n", + "\n", + "`Compressor` unit models with isothermal or adiabatic thermodynamics are too simple to cost and are therefore excluded from the economic analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define costing for the heater unit:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.costing.SSLW import (\n", + " HeaterMaterial,\n", + " HeaterSource,\n", + ")\n", + "\n", + "# Costing for heater - m.fs.H101\n", + "m.fs.H101.costing = UnitModelCostingBlock(\n", + " flowsheet_costing_block=m.fs.costing,\n", + " costing_method=SSLWCostingData.cost_fired_heater,\n", + " costing_method_arguments={\n", + " \"material_type\": HeaterMaterial.CarbonSteel,\n", + " \"heat_source\": HeaterSource.Fuel,\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The costing module provides a `unit_mapping` dictionary linking generic unit model classes with recommended costing methods. In this example, StoichiometricReactor and Flash vessels utilize different vessel costing methods with similar arguments. The diameter and length attributes need to exist in order to cost vessel sizing and material requirements, and we add them if they don't exist already. The `unit_mapping` method provides an opportunity to automatically select the correct vessel orientation (vertical or horizontal) based on the unit type; passing a `StoichiometricReactor` or `PFR` class object will call the `cost_horizontal_vessel` method, while passing a `Flash` or `CSTR` class object will call the `cost_vertical_vessel` method.\n", + "\n", + "All vessels are assigned costing succintly via a loop below - users may define each block individually if desired:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from idaes.models.costing.SSLW import (\n", + " VesselMaterial,\n", + " TrayType,\n", + " TrayMaterial,\n", + ")\n", + "\n", + "from idaes.core.util.constants import Constants\n", + "from pyomo.environ import Var, Constraint, units as pyunits, Param, value\n", + "from idaes.models.unit_models import StoichiometricReactor, Flash\n", + "\n", + "# Map unit models to unit classes\n", + "# Will pass to unit_mapping which calls costing methods based on unit class\n", + "unit_class_mapping = {\n", + " m.fs.R101: StoichiometricReactor,\n", + " m.fs.F101: Flash,\n", + " m.fs.F102: Flash,\n", + "}\n", + "\n", + "# Costing for vessels - m.fs.R101, m.fs.F101, m.fs.F102\n", + "\n", + "# Loop over units\n", + "for unit in [m.fs.R101, m.fs.F101, m.fs.F102]:\n", + " # Get correct unit class for unit model\n", + " unit_class = unit_class_mapping[unit]\n", + "\n", + " # Add dimension variables and constraint if they don't exist\n", + " if not hasattr(unit, \"diameter\"):\n", + " unit.diameter = Var(initialize=1, units=pyunits.m)\n", + " if not hasattr(unit, \"length\"):\n", + " unit.length = Var(initialize=1, units=pyunits.m)\n", + " if hasattr(unit, \"volume\"): # if volume exists, set diameter from volume\n", + " unit.volume_eq = Constraint(\n", + " expr=unit.volume[0]\n", + " == unit.length * unit.diameter**2 * 0.25 * Constants.pi\n", + " )\n", + " else: # fix diameter directly\n", + " unit.diameter.fix(0.2214 * pyunits.m)\n", + " # Either way, fix L/D to calculate L from D\n", + " unit.L_over_D = Constraint(expr=unit.length == 3 * unit.diameter)\n", + "\n", + " # Define vessel costing\n", + " unit.costing = UnitModelCostingBlock(\n", + " flowsheet_costing_block=unit.parent_block().costing, # e.g. m.fs.R101.costing\n", + " costing_method=SSLWCostingData.unit_mapping[\n", + " unit_class\n", + " ], # e.g. cost_vertical_vessel()\n", + " costing_method_arguments={\n", + " \"material_type\": VesselMaterial.CarbonSteel,\n", + " \"shell_thickness\": 1.25 * pyunits.inch,\n", + " },\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve Flowsheet Costing Blocks\n", + "Now, we may solve the full flowsheet for all costing blocks:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Eefine solver\n", + "from idaes.core.solvers import get_solver\n", + "\n", + "solver = get_solver()\n", + "\n", + "# Check that the degrees of freedom is zero\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "\n", + "assert degrees_of_freedom(m) == 0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Check physical units consistency, solve and check solver status\n", + "from pyomo.environ import TerminationCondition\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "\n", + "assert_units_consistent(m)\n", + "results = solver.solve(m, tee=True, symbolic_solver_labels=True)\n", + "assert results.solver.termination_condition == TerminationCondition.optimal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For comparison, we will call and build the HDA flowsheet replacing the second `Flash` with a `TrayColumn` distillation unit model. The flowsheet costing occurs in the external script `hda_flowsheets_for_costing_notebook.py` and is not shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from pyomo.common.log import LoggingIntercept\n", + "import logging\n", + "from io import StringIO\n", + "\n", + "stream = StringIO()\n", + "with LoggingIntercept(stream, \"idaes\", logging.WARNING):\n", + " # Source file for prebuilt flowsheets\n", + " from hda_flowsheets_for_costing_notebook import hda_with_distillation\n", + "\n", + " # Build hda model with distillation column and return model object\n", + " n = hda_with_distillation(tee=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results Comparison and Visualization\n", + "For the two flowsheets above, let's sum the total operating and capital costs of each scenario. We will display overall process economics results and compare the two flowsheets:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports and data gathering\n", + "from matplotlib import pyplot as plt\n", + "\n", + "plt.style.use(\"dark_background\") # if using browser in dark mode, uncomment this line\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Automatically get units that we costed - this will exclude C101 for both flowsheets\n", + "\n", + "two_flash_unitlist = [\n", + " getattr(m.fs, unit) for unit in dir(m.fs) if hasattr(getattr(m.fs, unit), \"costing\")\n", + "]\n", + "distillation_unitlist = [\n", + " getattr(n.fs, unit) for unit in dir(n.fs) if hasattr(getattr(n.fs, unit), \"costing\")\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare equipment purchase costs (actual capital costs)\n", + "\n", + "two_flash_capcost = {\n", + " unit.name: value(unit.costing.capital_cost / 1e3) for unit in two_flash_unitlist\n", + "}\n", + "distillation_capcost = {\n", + " unit.name: value(unit.costing.capital_cost / 1e3) for unit in distillation_unitlist\n", + "}\n", + "\n", + "two_flash_capdf = pd.DataFrame(\n", + " list(two_flash_capcost.items()), columns=[\"Equipment\", \"Two Flash\"]\n", + ").set_index(\"Equipment\")\n", + "distillation_capdf = pd.DataFrame(\n", + " list(distillation_capcost.items()), columns=[\"Equipment\", \"Distillation\"]\n", + ").set_index(\"Equipment\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "capcosts = two_flash_capdf.add(distillation_capdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "# Sort according to an easier order to view\n", + "capcosts = capcosts[[\"fs.H101\", \"fs.R101\", \"fs.F101\", \"fs.F102\", \"fs.D101\", \"fs.H102\"]]\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(capcosts) # view dataframe before plotting\n", + "\n", + "capplot = capcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Total Capital Costs\", ylabel=\"$1000\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare operating costs (per year)\n", + "\n", + "two_flash_opcost = {\n", + " \"cooling\": value(3600 * 24 * 365 * m.fs.cooling_cost / 1e3),\n", + " \"heating\": value(3600 * 24 * 365 * m.fs.heating_cost / 1e3),\n", + "}\n", + "distillation_opcost = {\n", + " \"cooling\": value(3600 * 24 * 365 * n.fs.cooling_cost / 1e3),\n", + " \"heating\": value(3600 * 24 * 365 * n.fs.heating_cost / 1e3),\n", + "}\n", + "\n", + "two_flash_opdf = pd.DataFrame(\n", + " list(two_flash_opcost.items()), columns=[\"Utilities\", \"Two Flash\"]\n", + ").set_index(\"Utilities\")\n", + "distillation_opdf = pd.DataFrame(\n", + " list(distillation_opcost.items()), columns=[\"Utilities\", \"Distillation\"]\n", + ").set_index(\"Utilities\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "opcosts = two_flash_opdf.add(distillation_opdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(opcosts) # view dataframe before plotting\n", + "\n", + "opplot = opcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Operating Costs\", ylabel=\"$1000/year\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare total costs (capital costs and operating costs)\n", + "\n", + "two_flash_totcost = {\n", + " \"capital\": sum(two_flash_capcost[idx] for idx in two_flash_capcost),\n", + " \"operating\": value(m.fs.operating_cost) / 1e3,\n", + "}\n", + "distillation_totcost = {\n", + " \"capital\": sum(distillation_capcost[idx] for idx in distillation_capcost),\n", + " \"operating\": value(n.fs.operating_cost) / 1e3,\n", + "}\n", + "\n", + "two_flash_totdf = pd.DataFrame(\n", + " list(two_flash_totcost.items()), columns=[\"Costs\", \"Two Flash\"]\n", + ").set_index(\"Costs\")\n", + "distillation_totdf = pd.DataFrame(\n", + " list(distillation_totcost.items()), columns=[\"Costs\", \"Distillation\"]\n", + ").set_index(\"Costs\")\n", + "\n", + "# Add dataframes, merge same indices, replace NaNs with 0s, and transpose\n", + "totcosts = two_flash_totdf.add(distillation_totdf, fill_value=0).fillna(0).transpose()\n", + "\n", + "print(\"Costs in $1000:\")\n", + "display(totcosts) # view dataframe before plotting\n", + "\n", + "totplot = totcosts.plot(\n", + " kind=\"bar\", stacked=True, title=\"HDA Total Plant Cost (TPC)\", ylabel=\"$1000/year\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's compare the total costs on a production basis. This will account for the greater efficiency provided by the distillation column relative to the less-expensive second flash unit:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "two_flash_cost = value(1e3 * sum(two_flash_totcost[idx] for idx in two_flash_totcost))\n", + "two_flash_prod = value(\n", + " m.fs.F102.vap_outlet.flow_mol_phase_comp[0, \"Vap\", \"benzene\"] * 365 * 24 * 3600\n", + ")\n", + "distillation_cost = value(\n", + " 1e3 * sum(distillation_totcost[idx] for idx in distillation_totcost)\n", + ")\n", + "distillation_prod = value(n.fs.D101.condenser.distillate.flow_mol[0] * 365 * 24 * 3600)\n", + "\n", + "print(\n", + " f\"Two flash case over one year: ${two_flash_cost/1e3:0.0f}K / {two_flash_prod/1e3:0.0f} kmol benzene = ${two_flash_cost/(two_flash_prod/1e3):0.2f} per kmol benzene produced\"\n", + ")\n", + "print(\n", + " f\"Distillation case over one year: ${distillation_cost/1e3:0.0f}K / {distillation_prod/1e3:0.0f} kmol benzene = ${distillation_cost/(distillation_prod/1e3):0.2f} per kmol benzene produced\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Summary\n", + "In this example, IDAES Process Costing Framework methods were applied to two HDA flowsheets for capital cost estimation. The costing blocks calls showcased multiple methods to define unit costing, demonstrating the flexibility and best practice of the costing framework. In the basic examples, the two-flash HDA did not include costing and the distillation HDA estimated a reactor capital cost comprising 3.3% of the total plant cost (TPC). With more rigorous costing, IDAES obtained total capital costs of 8.5% TPC (two flash HDA) and 9.6% (distillation HDA) and better modeled the impact of equipment cost on process economics. As printed above, the IDAES Process Costing Framework confirmed that replacing the second flash drum with a distillation column results in increased equipment costs, increased production and decreased cost per unit product." + ] + } + ], + "metadata": { + "celltoolbar": "Tags", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 3 +} diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb index b1250e16..a8ed1642 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb @@ -26,6 +26,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -82,6 +83,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -93,6 +95,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -131,6 +134,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -168,6 +172,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -214,6 +219,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -238,6 +244,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -261,6 +268,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -283,6 +291,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -310,6 +319,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -335,6 +345,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -383,6 +394,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -414,6 +426,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -423,6 +436,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -442,6 +456,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -478,6 +493,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -485,6 +501,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -553,6 +570,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -594,6 +612,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -612,6 +631,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -653,6 +673,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -674,6 +695,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -690,6 +712,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -723,6 +746,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -735,7 +759,15 @@ "cell_type": "code", "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -755,6 +787,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -780,6 +813,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -812,6 +846,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -831,6 +866,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -880,6 +916,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -900,6 +937,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -920,6 +958,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -940,6 +979,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -950,7 +990,42 @@ "cell_type": "code", "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" + ] + } + ], "source": [ "# Set scaling factors for heat duty, reaction extent and volume\n", "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", @@ -968,6 +1043,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1000,7 +1076,15 @@ "solution" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "# Todo: Check the degrees of freedom\n", "print(degrees_of_freedom(m))" @@ -1021,6 +1105,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1049,6 +1134,99 @@ ] }, { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " '_run_impl',\n", + " 'adj_lists',\n", + " 'all_cycles',\n", + " 'arc_to_edge',\n", + " 'cache',\n", + " 'cacher',\n", + " 'calculation_order',\n", + " 'check_tear_set',\n", + " 'check_value_fix',\n", + " 'combine_and_fix',\n", + " 'compute_err',\n", + " 'create_graph',\n", + " 'cycle_edge_matrix',\n", + " 'edge_to_idx',\n", + " 'fixed_inputs',\n", + " 'generate_first_x',\n", + " 'generate_gofx',\n", + " 'idx_to_edge',\n", + " 'idx_to_node',\n", + " 'indexes_to_arcs',\n", + " 'load_guesses',\n", + " 'load_values',\n", + " 'node_to_idx',\n", + " 'options',\n", + " 'pass_edges',\n", + " 'pass_single_value',\n", + " 'pass_tear_direct',\n", + " 'pass_tear_wegstein',\n", + " 'pass_values',\n", + " 'run',\n", + " 'run_order',\n", + " 'scc_calculation_order',\n", + " 'scc_collect',\n", + " 'select_tear_heuristic',\n", + " 'select_tear_mip',\n", + " 'select_tear_mip_model',\n", + " 'set_guesses_for',\n", + " 'set_tear_set',\n", + " 'solve_tear_direct',\n", + " 'solve_tear_wegstein',\n", + " 'source_dest_peer',\n", + " 'sub_graph_edges',\n", + " 'tear_diff_direct',\n", + " 'tear_set',\n", + " 'tear_set_arcs',\n", + " 'tear_upper_bound',\n", + " 'tree_order']" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(seq)" + ] + }, + { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1057,15 +1235,24 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.s03\n" + ] + } + ], "source": [ "for o in heuristic_tear_set:\n", " print(o.name)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1074,15 +1261,29 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.H101\n", + "fs.R101\n", + "fs.F101\n", + "fs.S101\n", + "fs.C101\n", + "fs.M101\n" + ] + } + ], "source": [ "for o in order:\n", " print(o[0].name)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1093,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1117,6 +1318,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1125,7 +1327,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1134,6 +1336,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1142,16 +1345,140 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-07-27 11:23:51 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-27 11:23:52 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-07-27 11:23:52 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:53 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:55 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-27 11:23:56 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:56 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:56 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:56 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:56 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:57 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:58 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-27 11:23:58 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:58 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:58 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:58 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:58 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-27 11:23:59 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:00 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-27 11:24:00 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:00 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:00 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:01 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-27 11:24:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:02 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:02 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-07-27 11:24:03 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:04 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-07-27 11:24:04 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:04 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-07-27 11:24:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", + "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" + ] + } + ], "source": [ "seq.run(m, function)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1163,9 +1490,125 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix\n", + "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", + "that contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 1097\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 877\n", + "\n", + "Total number of variables............................: 363\n", + " variables with only lower bounds: 8\n", + " variables with lower and upper bounds: 155\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 363\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", + " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", + " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.43e+03 - 1.00e+00 9.82e-01h 1\n", + " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 6.42e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 2.97e-05 2.81e+03 -3.8 2.13e-07 - 1.00e+00 1.00e+00H 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.7855284385533683e+04 1.7855284385533683e+04\n", + "Constraint violation....: 2.4734281289795490e-10 2.9668448405573148e-05\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.4734281289795490e-10 1.7855284385533683e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 12\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 12\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.025\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "# Create the solver object\n", "solver = get_solver()\n", @@ -1176,7 +1619,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": { "tags": [ "testing" @@ -1191,6 +1634,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1211,9 +1655,619 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" + ] + } + ], "source": [ "# Add distillation column to the flowsheet\n", "m.fs.D101 = TrayColumn(\n", @@ -1251,6 +2305,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1261,7 +2316,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1288,6 +2343,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1296,7 +2352,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": { "tags": [ "testing" @@ -1310,16 +2366,140 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix\n", + "'fs.D101.condenser.control_volume.properties_out[0.0].scaling_factor' that\n", + "contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", + "that contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4042\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2376\n", + "\n", + "Total number of variables............................: 1169\n", + " variables with only lower bounds: 112\n", + " variables with lower and upper bounds: 365\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", + " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", + " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", + " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 7.45e-09 5.93e-02 -3.8 3.58e-08 - 1.00e+00 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.5042542854672720e+04 1.5042542854672720e+04\n", + "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.8207660913467407e-11 1.5042542854672720e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.057\n", + "Total CPU secs in NLP function evaluations = 0.006\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.14362859725952148}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "solver.solve(m, tee=True)" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "metadata": { "tags": [ "testing" @@ -1334,6 +2514,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1344,9 +2525,28 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252124\n", + "operating cost = $ 427596.7305680538\n", + "capital cost = $ 14704.740184467468\n", + "\n", + "Distillate flowrate = 0.16196898920633476 mol/s\n", + "Benzene purity = 89.49161665800843 %\n", + "Residue flowrate = 0.10515007120697811 mol/s\n", + "Toluene purity = 43.32260291055274 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603166 %\n" + ] + } + ], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -1382,13 +2582,22 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "metadata": { "tags": [ "testing" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "427596.7305680538\n", + "14704.740184467468\n" + ] + } + ], "source": [ "import pytest\n", "\n", @@ -1399,6 +2608,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1407,14 +2617,48 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 0.0000 : watt : True : (None, None)\n", + " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", + " temperature kelvin 600.00 771.85\n", + " pressure pascal 3.5000e+05 3.5000e+05\n", + "====================================================================================\n" + ] + } + ], "source": [ "m.fs.R101.report()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1423,14 +2667,48 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.F101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -70343. : watt : False : (None, None)\n", + " Pressure Change : 0.0000 : pascal : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Vapor Outlet Liquid Outlet\n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", + " temperature kelvin 771.85 325.00 325.00 \n", + " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", + "====================================================================================\n" + ] + } + ], "source": [ "m.fs.F101.report()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1444,9 +2722,27 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Reactor Light Gases\n", + "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", + "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", + "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", + "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", + "temperature kelvin 771.85 325.00 \n", + "pressure pascal 3.5000e+05 3.5000e+05 \n" + ] + } + ], "source": [ "from idaes.core.util.tables import (\n", " create_stream_table_dataframe,\n", @@ -1458,6 +2754,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1470,6 +2767,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1494,6 +2792,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1502,7 +2801,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -1510,6 +2809,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1518,7 +2818,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -1531,6 +2831,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1545,7 +2846,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 63, "metadata": { "tags": [ "exercise" @@ -1558,7 +2859,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 64, "metadata": { "tags": [ "solution" @@ -1571,6 +2872,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1587,7 +2889,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -1616,6 +2918,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1629,7 +2932,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 66, "metadata": { "tags": [ "exercise" @@ -1645,7 +2948,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 67, "metadata": { "tags": [ "solution" @@ -1663,6 +2966,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1671,7 +2975,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -1683,6 +2987,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1696,7 +3001,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 69, "metadata": { "tags": [ "exercise" @@ -1709,7 +3014,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 70, "metadata": { "tags": [ "solution" @@ -1722,6 +3027,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1730,7 +3036,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -1740,6 +3046,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1751,16 +3058,155 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 72, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", + "that contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4073\n", + "Number of nonzeros in inequality constraint Jacobian.: 6\n", + "Number of nonzeros in Lagrangian Hessian.............: 2391\n", + "\n", + "Total number of variables............................: 1176\n", + " variables with only lower bounds: 113\n", + " variables with lower and upper bounds: 372\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 3\n", + " inequality constraints with only lower bounds: 2\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 1\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", + " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", + " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", + " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", + " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", + " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", + " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", + " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", + " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", + " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", + " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", + " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", + " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", + " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", + " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", + " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", + " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", + " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", + " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", + " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", + " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", + " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", + " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", + " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", + " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", + " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", + " 29 4.3884157e+05 6.56e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", + " 31 4.3883992e+05 3.05e-07 1.25e-05 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.46e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 1.49e-08 1.07e-07 -8.0 5.40e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842627057e+02 4.3883989842627058e+05\n", + "Dual infeasibility......: 1.0693122464843572e-07 1.0693122464843573e-04\n", + "Constraint violation....: 5.8207660913467407e-11 1.4901161193847656e-08\n", + "Complementarity.........: 9.0909948039747601e-09 9.0909948039747593e-06\n", + "Overall NLP error.......: 9.0909948039747601e-09 1.0693122464843573e-04\n", + "\n", + "\n", + "Number of objective function evaluations = 35\n", + "Number of objective gradient evaluations = 34\n", + "Number of equality constraint evaluations = 35\n", + "Number of inequality constraint evaluations = 35\n", + "Number of equality constraint Jacobian evaluations = 34\n", + "Number of inequality constraint Jacobian evaluations = 34\n", + "Number of Lagrangian Hessian evaluations = 33\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.310\n", + "Total CPU secs in NLP function evaluations = 0.050\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 73, "metadata": { "tags": [ "testing" @@ -1775,6 +3221,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1785,9 +3232,28 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 74, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.8984262706\n", + "operating cost = $ 408883.53148307273\n", + "capital cost = $ 29956.366943197827\n", + "\n", + "Distillate flowrate = 0.17999999002639896 mol/s\n", + "Benzene purity = 98.99999900049087 %\n", + "Residue flowrate = 0.10851616424263705 mol/s\n", + "Toluene purity = 15.67617808620809 %\n", + "\n", + "Conversion = 93.38705916369607 %\n", + "\n", + "Overhead benzene loss in F101 = 17.340617931156185 %\n" + ] + } + ], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -1823,13 +3289,22 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 75, "metadata": { "tags": [ "testing" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "408883.53148307273\n", + "29956.366943197827\n" + ] + } + ], "source": [ "import pytest\n", "\n", @@ -1841,6 +3316,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1849,9 +3325,25 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 76, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.9232042951996 K\n", + "\n", + "R101 outlet temperature = 790.3655425698917 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.74143399528367 K\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -1869,6 +3361,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1907,7 +3400,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb index 247e5621..5bc30ee9 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 71, "metadata": { "tags": [ "header", @@ -115,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 73, "metadata": { "tags": [ "exercise" @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 74, "metadata": { "tags": [ "solution" @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -195,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ @@ -222,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -271,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -291,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -320,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -352,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 82, "metadata": { "tags": [ "exercise" @@ -365,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 83, "metadata": { "tags": [ "solution" @@ -391,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -431,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 85, "metadata": {}, "outputs": [], "source": [ @@ -460,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -496,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 87, "metadata": { "tags": [ "exercise" @@ -509,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 88, "metadata": { "tags": [ "solution" @@ -525,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -564,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 90, "metadata": { "tags": [ "exercise" @@ -577,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 91, "metadata": { "tags": [ "solution" @@ -604,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -627,7 +627,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 93, "metadata": { "tags": [ "exercise" @@ -640,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 94, "metadata": { "tags": [ "solution" @@ -661,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -682,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ @@ -705,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ @@ -733,9 +733,17 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 98, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29\n" + ] + } + ], "source": [ "print(degrees_of_freedom(m))" ] @@ -749,7 +757,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ @@ -781,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -808,7 +816,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ @@ -834,7 +842,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 102, "metadata": { "tags": [ "exercise" @@ -850,7 +858,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 103, "metadata": { "tags": [ "solution" @@ -874,7 +882,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ @@ -894,7 +902,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -914,7 +922,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -934,9 +942,44 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq]\n", + "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap]\n" + ] + } + ], "source": [ "# Set scaling factors for heat duty, reaction extent and volume\n", "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", @@ -967,7 +1010,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 108, "metadata": { "tags": [ "exercise" @@ -980,13 +1023,21 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 109, "metadata": { "tags": [ "solution" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], "source": [ "# Todo: Check the degrees of freedom\n", "print(degrees_of_freedom(m))" @@ -1005,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 110, "metadata": {}, "outputs": [], "source": [ @@ -1029,9 +1080,17 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 111, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.s03\n" + ] + } + ], "source": [ "for o in heuristic_tear_set:\n", " print(o.name)" @@ -1046,9 +1105,22 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 112, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fs.H101\n", + "fs.R101\n", + "fs.F101\n", + "fs.S101\n", + "fs.C101\n", + "fs.M101\n" + ] + } + ], "source": [ "for o in order:\n", " print(o[0].name)" @@ -1065,7 +1137,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -1097,7 +1169,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ @@ -1114,11 +1186,134 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 115, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-06-26 12:37:47 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:47 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:48 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:49 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:50 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:51 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:53 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-06-26 12:37:54 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:54 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:54 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:54 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:55 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:56 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:56 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-06-26 12:37:56 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-06-26 12:37:57 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:57 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:57 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:57 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:57 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:58 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:58 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-06-26 12:37:58 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-06-26 12:37:58 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:58 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-06-26 12:37:58 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:37:59 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-06-26 12:37:59 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:00 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:00 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:00 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:00 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:01 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-06-26 12:38:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:02 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:02 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", + "2023-06-26 12:38:03 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:04 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", + "2023-06-26 12:38:04 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:04 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", + "2023-06-26 12:38:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", + "WARNING: Wegstein failed to converge in 3 iterations\n", + "2023-06-26 12:38:04 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:04 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", + "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" + ] + } + ], "source": [ "seq.run(m, function)" ] @@ -1135,9 +1330,125 @@ }, { "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix\n", + "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", + "that contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 1097\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 877\n", + "\n", + "Total number of variables............................: 363\n", + " variables with only lower bounds: 8\n", + " variables with lower and upper bounds: 155\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 363\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", + " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", + " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.43e+03 - 1.00e+00 9.82e-01h 1\n", + " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 6.42e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 2.97e-05 2.81e+03 -3.8 2.13e-07 - 1.00e+00 1.00e+00H 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.7855284385533683e+04 1.7855284385533683e+04\n", + "Constraint violation....: 2.4734281289795490e-10 2.9668448405573148e-05\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 2.4734281289795490e-10 1.7855284385533683e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 12\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 12\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.036\n", + "Total CPU secs in NLP function evaluations = 0.000\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "# Create the solver object\n", "solver = get_solver()\n", @@ -1167,9 +1478,619 @@ }, { "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", + "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101: Begin initialization.\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", + "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", + "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", + "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", + "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", + "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", + "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", + "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", + "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", + "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", + "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", + "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", + "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" + ] + } + ], "source": [ "# Add distillation column to the flowsheet\n", "m.fs.D101 = TrayColumn(\n", @@ -1217,7 +2138,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 118, "metadata": {}, "outputs": [], "source": [ @@ -1252,9 +2173,133 @@ }, { "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix\n", + "'fs.D101.condenser.control_volume.properties_out[0.0].scaling_factor' that\n", + "contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", + "that contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4042\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2376\n", + "\n", + "Total number of variables............................: 1169\n", + " variables with only lower bounds: 112\n", + " variables with lower and upper bounds: 365\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", + " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", + " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", + " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", + " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", + " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", + " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", + " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", + " 9 0.0000000e+00 7.45e-09 5.93e-02 -3.8 3.58e-08 - 1.00e+00 1.00e+00h 1\n", + "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", + "\n", + "Number of Iterations....: 9\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 1.5042542854672720e+04 1.5042542854672720e+04\n", + "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.8207660913467407e-11 1.5042542854672720e+04\n", + "\n", + "\n", + "Number of objective function evaluations = 11\n", + "Number of objective gradient evaluations = 10\n", + "Number of equality constraint evaluations = 11\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 10\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 9\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.068\n", + "Total CPU secs in NLP function evaluations = 0.009\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.16938281059265137}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "solver.solve(m, tee=True)" ] @@ -1270,9 +2315,28 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 120, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 442301.47075252124\n", + "operating cost = $ 427596.7305680538\n", + "capital cost = $ 14704.740184467468\n", + "\n", + "Distillate flowrate = 0.16196898920633476 mol/s\n", + "Benzene purity = 89.49161665800843 %\n", + "Residue flowrate = 0.10515007120697811 mol/s\n", + "Toluene purity = 43.32260291055274 %\n", + "\n", + "Conversion = 75.0 %\n", + "\n", + "Overhead benzene loss in F101 = 42.161938483603166 %\n" + ] + } + ], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -1315,9 +2379,42 @@ }, { "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.R101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 0.0000 : watt : True : (None, None)\n", + " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", + " temperature kelvin 600.00 771.85\n", + " pressure pascal 3.5000e+05 3.5000e+05\n", + "====================================================================================\n" + ] + } + ], "source": [ "m.fs.R101.report()" ] @@ -1331,9 +2428,42 @@ }, { "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "====================================================================================\n", + "Unit : fs.F101 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -70343. : watt : False : (None, None)\n", + " Pressure Change : 0.0000 : pascal : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Vapor Outlet Liquid Outlet\n", + " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", + " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", + " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", + " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", + " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", + " temperature kelvin 771.85 325.00 325.00 \n", + " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", + "====================================================================================\n" + ] + } + ], "source": [ "m.fs.F101.report()" ] @@ -1352,9 +2482,27 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 123, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Units Reactor Light Gases\n", + "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", + "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", + "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", + "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", + "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", + "temperature kelvin 771.85 325.00 \n", + "pressure pascal 3.5000e+05 3.5000e+05 \n" + ] + } + ], "source": [ "from idaes.core.util.tables import (\n", " create_stream_table_dataframe,\n", @@ -1410,7 +2558,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 124, "metadata": {}, "outputs": [], "source": [ @@ -1426,7 +2574,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ @@ -1453,7 +2601,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 126, "metadata": { "tags": [ "exercise" @@ -1466,7 +2614,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 127, "metadata": { "tags": [ "solution" @@ -1495,7 +2643,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 128, "metadata": {}, "outputs": [], "source": [ @@ -1537,7 +2685,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 129, "metadata": { "tags": [ "exercise" @@ -1553,7 +2701,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 130, "metadata": { "tags": [ "solution" @@ -1579,7 +2727,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 131, "metadata": {}, "outputs": [], "source": [ @@ -1604,7 +2752,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 132, "metadata": { "tags": [ "exercise" @@ -1617,7 +2765,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 133, "metadata": { "tags": [ "solution" @@ -1638,7 +2786,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 134, "metadata": {}, "outputs": [], "source": [ @@ -1659,9 +2807,142 @@ }, { "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", + "that contains 1 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", + "that contains 2 component keys that are not exported as part of the NL file.\n", + "Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "WARNING: model contains export suffix\n", + "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", + "component keys that are not exported as part of the NL file. Skipping.\n", + "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", + "tol=1e-06\n", + "max_iter=200\n", + "\n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 4073\n", + "Number of nonzeros in inequality constraint Jacobian.: 6\n", + "Number of nonzeros in Lagrangian Hessian.............: 2391\n", + "\n", + "Total number of variables............................: 1176\n", + " variables with only lower bounds: 113\n", + " variables with lower and upper bounds: 372\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 1169\n", + "Total number of inequality constraints...............: 3\n", + " inequality constraints with only lower bounds: 2\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 1\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", + " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", + " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", + " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", + " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", + " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", + " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", + " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", + " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", + " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", + " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", + " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", + " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", + " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", + " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", + " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", + " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", + " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", + " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", + " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", + " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", + " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", + " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", + " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", + " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", + " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", + " 29 4.3884157e+05 6.56e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", + " 31 4.3883992e+05 3.05e-07 1.25e-05 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", + " 32 4.3883990e+05 5.46e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", + " 33 4.3883990e+05 1.49e-08 1.07e-07 -8.0 5.40e-05 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 33\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 4.3883989842627057e+02 4.3883989842627058e+05\n", + "Dual infeasibility......: 1.0693122464843572e-07 1.0693122464843573e-04\n", + "Constraint violation....: 5.8207660913467407e-11 1.4901161193847656e-08\n", + "Complementarity.........: 9.0909948039747601e-09 9.0909948039747593e-06\n", + "Overall NLP error.......: 9.0909948039747601e-09 1.0693122464843573e-04\n", + "\n", + "\n", + "Number of objective function evaluations = 35\n", + "Number of objective gradient evaluations = 34\n", + "Number of equality constraint evaluations = 35\n", + "Number of inequality constraint evaluations = 35\n", + "Number of equality constraint Jacobian evaluations = 34\n", + "Number of inequality constraint Jacobian evaluations = 34\n", + "Number of Lagrangian Hessian evaluations = 33\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.522\n", + "Total CPU secs in NLP function evaluations = 0.078\n", + "\n", + "EXIT: Optimal Solution Found.\n" + ] + } + ], "source": [ "results = solver.solve(m, tee=True)" ] @@ -1677,9 +2958,28 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 136, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total cost = $ 438839.8984262706\n", + "operating cost = $ 408883.53148307273\n", + "capital cost = $ 29956.366943197827\n", + "\n", + "Distillate flowrate = 0.17999999002639896 mol/s\n", + "Benzene purity = 98.99999900049087 %\n", + "Residue flowrate = 0.10851616424263705 mol/s\n", + "Toluene purity = 15.67617808620809 %\n", + "\n", + "Conversion = 93.38705916369607 %\n", + "\n", + "Overhead benzene loss in F101 = 17.340617931156185 %\n" + ] + } + ], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -1722,9 +3022,25 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 137, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal Values\n", + "\n", + "H101 outlet temperature = 568.9232042951996 K\n", + "\n", + "R101 outlet temperature = 790.3655425698917 K\n", + "\n", + "F101 outlet temperature = 298.0 K\n", + "\n", + "H102 outlet temperature = 368.74143399528367 K\n" + ] + } + ], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -1780,7 +3096,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.10.6" } }, "nbformat": 4, From 7b4f9438d1866577e7ca85884cc79a4ffa04b0a5 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Fri, 25 Aug 2023 01:38:09 -0400 Subject: [PATCH 10/75] Changing HDA files to forked version --- .../hda_flowsheet_with_distillation.ipynb | 1597 +---------------- ...flowsheet_with_distillation_solution.ipynb | 1498 +--------------- 2 files changed, 143 insertions(+), 2952 deletions(-) diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb index a8ed1642..b1250e16 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation.ipynb @@ -26,7 +26,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -83,7 +82,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -95,7 +93,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -134,7 +131,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -172,7 +168,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -219,7 +214,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -244,7 +238,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -268,7 +261,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -291,7 +283,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -319,7 +310,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -345,7 +335,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -394,7 +383,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -426,7 +414,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -436,7 +423,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -456,7 +442,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -493,7 +478,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -501,7 +485,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -570,7 +553,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -612,7 +594,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -631,7 +612,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -673,7 +653,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -695,7 +674,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -712,7 +690,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -746,7 +723,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -759,15 +735,7 @@ "cell_type": "code", "execution_count": 28, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "29\n" - ] - } - ], + "outputs": [], "source": [ "print(degrees_of_freedom(m))" ] @@ -787,7 +755,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -813,7 +780,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -846,7 +812,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -866,7 +831,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -916,7 +880,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -937,7 +900,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -958,7 +920,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -979,7 +940,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -990,42 +950,7 @@ "cell_type": "code", "execution_count": 38, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2023-07-27 11:23:49 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n" - ] - } - ], + "outputs": [], "source": [ "# Set scaling factors for heat duty, reaction extent and volume\n", "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", @@ -1043,7 +968,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1076,15 +1000,7 @@ "solution" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], + "outputs": [], "source": [ "# Todo: Check the degrees of freedom\n", "print(degrees_of_freedom(m))" @@ -1105,7 +1021,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1134,99 +1049,6 @@ ] }, { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['__class__',\n", - " '__delattr__',\n", - " '__dict__',\n", - " '__dir__',\n", - " '__doc__',\n", - " '__eq__',\n", - " '__format__',\n", - " '__ge__',\n", - " '__getattribute__',\n", - " '__gt__',\n", - " '__hash__',\n", - " '__init__',\n", - " '__init_subclass__',\n", - " '__le__',\n", - " '__lt__',\n", - " '__module__',\n", - " '__ne__',\n", - " '__new__',\n", - " '__reduce__',\n", - " '__reduce_ex__',\n", - " '__repr__',\n", - " '__setattr__',\n", - " '__sizeof__',\n", - " '__str__',\n", - " '__subclasshook__',\n", - " '__weakref__',\n", - " '_run_impl',\n", - " 'adj_lists',\n", - " 'all_cycles',\n", - " 'arc_to_edge',\n", - " 'cache',\n", - " 'cacher',\n", - " 'calculation_order',\n", - " 'check_tear_set',\n", - " 'check_value_fix',\n", - " 'combine_and_fix',\n", - " 'compute_err',\n", - " 'create_graph',\n", - " 'cycle_edge_matrix',\n", - " 'edge_to_idx',\n", - " 'fixed_inputs',\n", - " 'generate_first_x',\n", - " 'generate_gofx',\n", - " 'idx_to_edge',\n", - " 'idx_to_node',\n", - " 'indexes_to_arcs',\n", - " 'load_guesses',\n", - " 'load_values',\n", - " 'node_to_idx',\n", - " 'options',\n", - " 'pass_edges',\n", - " 'pass_single_value',\n", - " 'pass_tear_direct',\n", - " 'pass_tear_wegstein',\n", - " 'pass_values',\n", - " 'run',\n", - " 'run_order',\n", - " 'scc_calculation_order',\n", - " 'scc_collect',\n", - " 'select_tear_heuristic',\n", - " 'select_tear_mip',\n", - " 'select_tear_mip_model',\n", - " 'set_guesses_for',\n", - " 'set_tear_set',\n", - " 'solve_tear_direct',\n", - " 'solve_tear_wegstein',\n", - " 'source_dest_peer',\n", - " 'sub_graph_edges',\n", - " 'tear_diff_direct',\n", - " 'tear_set',\n", - " 'tear_set_arcs',\n", - " 'tear_upper_bound',\n", - " 'tree_order']" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dir(seq)" - ] - }, - { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1235,24 +1057,15 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.s03\n" - ] - } - ], + "outputs": [], "source": [ "for o in heuristic_tear_set:\n", " print(o.name)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1261,29 +1074,15 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 44, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.H101\n", - "fs.R101\n", - "fs.F101\n", - "fs.S101\n", - "fs.C101\n", - "fs.M101\n" - ] - } - ], + "outputs": [], "source": [ "for o in order:\n", " print(o[0].name)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1294,7 +1093,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1318,7 +1117,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1327,7 +1125,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1336,7 +1134,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1345,140 +1142,16 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 47, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-07-27 11:23:51 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-27 11:23:52 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-07-27 11:23:52 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:53 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:54 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:55 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-27 11:23:56 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:56 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:56 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:56 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:56 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:57 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:58 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-27 11:23:58 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:58 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:58 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:58 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:58 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-27 11:23:59 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:00 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-27 11:24:00 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:00 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:00 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:01 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-27 11:24:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:02 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:02 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-07-27 11:24:03 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:04 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-07-27 11:24:04 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:04 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-07-27 11:24:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "WARNING: Wegstein failed to converge in 3 iterations\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", - "2023-07-27 11:24:06 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" - ] - } - ], + "outputs": [], "source": [ "seq.run(m, function)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1490,125 +1163,9 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 48, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 1097\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 877\n", - "\n", - "Total number of variables............................: 363\n", - " variables with only lower bounds: 8\n", - " variables with lower and upper bounds: 155\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 363\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", - " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", - " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.43e+03 - 1.00e+00 9.82e-01h 1\n", - " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 6.42e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 2.97e-05 2.81e+03 -3.8 2.13e-07 - 1.00e+00 1.00e+00H 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.7855284385533683e+04 1.7855284385533683e+04\n", - "Constraint violation....: 2.4734281289795490e-10 2.9668448405573148e-05\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.4734281289795490e-10 1.7855284385533683e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 12\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 12\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.025\n", - "Total CPU secs in NLP function evaluations = 0.001\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], + "outputs": [], "source": [ "# Create the solver object\n", "solver = get_solver()\n", @@ -1619,7 +1176,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 49, "metadata": { "tags": [ "testing" @@ -1634,7 +1191,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1655,619 +1211,9 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 50, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq], enthalpy_flow_terms\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap], enthalpy_flow_terms\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", - "2023-07-27 11:24:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", - "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101: Begin initialization.\n", - "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", - "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:08 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:10 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:11 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:12 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", - "2023-07-27 11:24:13 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:14 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", - "2023-07-27 11:24:15 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:16 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:17 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:18 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:19 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:20 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:21 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:22 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:23 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:24 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:25 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:26 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:27 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:28 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:29 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:30 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:31 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", - "2023-07-27 11:24:32 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", - "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", - "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", - "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", - "2023-07-27 11:24:33 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" - ] - } - ], + "outputs": [], "source": [ "# Add distillation column to the flowsheet\n", "m.fs.D101 = TrayColumn(\n", @@ -2305,7 +1251,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2316,7 +1261,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -2343,7 +1288,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2352,7 +1296,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 52, "metadata": { "tags": [ "testing" @@ -2366,140 +1310,16 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 53, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.D101.condenser.control_volume.properties_out[0.0].scaling_factor' that\n", - "contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4042\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 2376\n", - "\n", - "Total number of variables............................: 1169\n", - " variables with only lower bounds: 112\n", - " variables with lower and upper bounds: 365\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", - " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", - " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", - " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 7.45e-09 5.93e-02 -3.8 3.58e-08 - 1.00e+00 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042542854672720e+04 1.5042542854672720e+04\n", - "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 5.8207660913467407e-11 1.5042542854672720e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.057\n", - "Total CPU secs in NLP function evaluations = 0.006\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.14362859725952148}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "solver.solve(m, tee=True)" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 54, "metadata": { "tags": [ "testing" @@ -2514,7 +1334,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2525,28 +1344,9 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 55, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 442301.47075252124\n", - "operating cost = $ 427596.7305680538\n", - "capital cost = $ 14704.740184467468\n", - "\n", - "Distillate flowrate = 0.16196898920633476 mol/s\n", - "Benzene purity = 89.49161665800843 %\n", - "Residue flowrate = 0.10515007120697811 mol/s\n", - "Toluene purity = 43.32260291055274 %\n", - "\n", - "Conversion = 75.0 %\n", - "\n", - "Overhead benzene loss in F101 = 42.161938483603166 %\n" - ] - } - ], + "outputs": [], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -2582,22 +1382,13 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 56, "metadata": { "tags": [ "testing" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "427596.7305680538\n", - "14704.740184467468\n" - ] - } - ], + "outputs": [], "source": [ "import pytest\n", "\n", @@ -2608,7 +1399,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2617,48 +1407,14 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 57, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 0.0000 : watt : True : (None, None)\n", - " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", - " temperature kelvin 600.00 771.85\n", - " pressure pascal 3.5000e+05 3.5000e+05\n", - "====================================================================================\n" - ] - } - ], + "outputs": [], "source": [ "m.fs.R101.report()" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2667,48 +1423,14 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 58, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.F101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -70343. : watt : False : (None, None)\n", - " Pressure Change : 0.0000 : pascal : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Vapor Outlet Liquid Outlet\n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", - " temperature kelvin 771.85 325.00 325.00 \n", - " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", - "====================================================================================\n" - ] - } - ], + "outputs": [], "source": [ "m.fs.F101.report()" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2722,27 +1444,9 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 59, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Units Reactor Light Gases\n", - "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", - "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", - "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", - "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", - "temperature kelvin 771.85 325.00 \n", - "pressure pascal 3.5000e+05 3.5000e+05 \n" - ] - } - ], + "outputs": [], "source": [ "from idaes.core.util.tables import (\n", " create_stream_table_dataframe,\n", @@ -2754,7 +1458,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2767,7 +1470,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2792,7 +1494,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2801,7 +1502,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -2809,7 +1510,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2818,7 +1518,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -2831,7 +1531,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2846,7 +1545,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 62, "metadata": { "tags": [ "exercise" @@ -2859,7 +1558,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 63, "metadata": { "tags": [ "solution" @@ -2872,7 +1571,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2889,7 +1587,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -2918,7 +1616,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2932,7 +1629,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 65, "metadata": { "tags": [ "exercise" @@ -2948,7 +1645,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 66, "metadata": { "tags": [ "solution" @@ -2966,7 +1663,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2975,7 +1671,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -2987,7 +1683,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3001,7 +1696,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 68, "metadata": { "tags": [ "exercise" @@ -3014,7 +1709,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 69, "metadata": { "tags": [ "solution" @@ -3027,7 +1722,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3036,7 +1730,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -3046,7 +1740,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3058,155 +1751,16 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 71, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4073\n", - "Number of nonzeros in inequality constraint Jacobian.: 6\n", - "Number of nonzeros in Lagrangian Hessian.............: 2391\n", - "\n", - "Total number of variables............................: 1176\n", - " variables with only lower bounds: 113\n", - " variables with lower and upper bounds: 372\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 3\n", - " inequality constraints with only lower bounds: 2\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 1\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", - " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", - " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", - " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", - " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", - " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", - " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", - " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", - " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", - " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", - " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", - " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", - " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", - " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", - " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", - " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", - " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", - " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", - " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", - " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", - " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", - " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", - " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", - " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", - " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", - " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", - " 29 4.3884157e+05 6.56e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", - " 31 4.3883992e+05 3.05e-07 1.25e-05 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", - " 32 4.3883990e+05 5.46e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", - " 33 4.3883990e+05 1.49e-08 1.07e-07 -8.0 5.40e-05 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 33\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 4.3883989842627057e+02 4.3883989842627058e+05\n", - "Dual infeasibility......: 1.0693122464843572e-07 1.0693122464843573e-04\n", - "Constraint violation....: 5.8207660913467407e-11 1.4901161193847656e-08\n", - "Complementarity.........: 9.0909948039747601e-09 9.0909948039747593e-06\n", - "Overall NLP error.......: 9.0909948039747601e-09 1.0693122464843573e-04\n", - "\n", - "\n", - "Number of objective function evaluations = 35\n", - "Number of objective gradient evaluations = 34\n", - "Number of equality constraint evaluations = 35\n", - "Number of inequality constraint evaluations = 35\n", - "Number of equality constraint Jacobian evaluations = 34\n", - "Number of inequality constraint Jacobian evaluations = 34\n", - "Number of Lagrangian Hessian evaluations = 33\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.310\n", - "Total CPU secs in NLP function evaluations = 0.050\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], + "outputs": [], "source": [ "results = solver.solve(m, tee=True)" ] }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 72, "metadata": { "tags": [ "testing" @@ -3221,7 +1775,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3232,28 +1785,9 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 73, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 438839.8984262706\n", - "operating cost = $ 408883.53148307273\n", - "capital cost = $ 29956.366943197827\n", - "\n", - "Distillate flowrate = 0.17999999002639896 mol/s\n", - "Benzene purity = 98.99999900049087 %\n", - "Residue flowrate = 0.10851616424263705 mol/s\n", - "Toluene purity = 15.67617808620809 %\n", - "\n", - "Conversion = 93.38705916369607 %\n", - "\n", - "Overhead benzene loss in F101 = 17.340617931156185 %\n" - ] - } - ], + "outputs": [], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -3289,22 +1823,13 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 74, "metadata": { "tags": [ "testing" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "408883.53148307273\n", - "29956.366943197827\n" - ] - } - ], + "outputs": [], "source": [ "import pytest\n", "\n", @@ -3316,7 +1841,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3325,25 +1849,9 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 75, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "H101 outlet temperature = 568.9232042951996 K\n", - "\n", - "R101 outlet temperature = 790.3655425698917 K\n", - "\n", - "F101 outlet temperature = 298.0 K\n", - "\n", - "H102 outlet temperature = 368.74143399528367 K\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -3361,7 +1869,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3400,7 +1907,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.8.16" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb index 5bc30ee9..247e5621 100644 --- a/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb +++ b/idaes_examples/notebooks/docs/flowsheets/hda_flowsheet_with_distillation_solution.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 71, + "execution_count": 1, "metadata": { "tags": [ "header", @@ -115,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 3, "metadata": { "tags": [ "exercise" @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 4, "metadata": { "tags": [ "solution" @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -195,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -222,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -271,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -291,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -320,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -352,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 12, "metadata": { "tags": [ "exercise" @@ -365,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 13, "metadata": { "tags": [ "solution" @@ -391,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -431,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -460,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -496,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 17, "metadata": { "tags": [ "exercise" @@ -509,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 18, "metadata": { "tags": [ "solution" @@ -525,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -564,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 20, "metadata": { "tags": [ "exercise" @@ -577,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 21, "metadata": { "tags": [ "solution" @@ -604,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -627,7 +627,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 23, "metadata": { "tags": [ "exercise" @@ -640,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 24, "metadata": { "tags": [ "solution" @@ -661,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -682,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -705,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -733,17 +733,9 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 28, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "29\n" - ] - } - ], + "outputs": [], "source": [ "print(degrees_of_freedom(m))" ] @@ -757,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -789,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -816,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -842,7 +834,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 33, "metadata": { "tags": [ "exercise" @@ -858,7 +850,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 34, "metadata": { "tags": [ "solution" @@ -882,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -902,7 +894,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -922,7 +914,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -942,44 +934,9 @@ }, { "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Liq,toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].enth_mol_phase_comp[Vap,toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq]\n", - "2023-06-26 12:37:45 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.H102.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap]\n" - ] - } - ], + "execution_count": 38, + "metadata": {}, + "outputs": [], "source": [ "# Set scaling factors for heat duty, reaction extent and volume\n", "iscale.set_scaling_factor(m.fs.H101.control_volume.heat, 1e-2)\n", @@ -1010,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 39, "metadata": { "tags": [ "exercise" @@ -1023,21 +980,13 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 40, "metadata": { "tags": [ "solution" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n" - ] - } - ], + "outputs": [], "source": [ "# Todo: Check the degrees of freedom\n", "print(degrees_of_freedom(m))" @@ -1056,7 +1005,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1080,17 +1029,9 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 43, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.s03\n" - ] - } - ], + "outputs": [], "source": [ "for o in heuristic_tear_set:\n", " print(o.name)" @@ -1105,22 +1046,9 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 44, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fs.H101\n", - "fs.R101\n", - "fs.F101\n", - "fs.S101\n", - "fs.C101\n", - "fs.M101\n" - ] - } - ], + "outputs": [], "source": [ "for o in order:\n", " print(o[0].name)" @@ -1137,7 +1065,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1169,7 +1097,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1186,134 +1114,11 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 47, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-06-26 12:37:47 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:47 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:48 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:49 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:50 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:51 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:52 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:53 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-06-26 12:37:54 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:54 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:54 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:54 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:55 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:56 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:56 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-06-26 12:37:56 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-06-26 12:37:57 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:57 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:57 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:57 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:57 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:58 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:58 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-06-26 12:37:58 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-06-26 12:37:58 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:58 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-06-26 12:37:58 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:37:59 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-06-26 12:37:59 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:00 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:00 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:00 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:00 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:01 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:02 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-06-26 12:38:02 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:02 [INFO] idaes.init.fs.H101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:02 [INFO] idaes.init.fs.H101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.R101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.R101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.F101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.F101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.S101.purge_state: Initialization Complete\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.S101.recycle_state: Initialization Complete\n", - "2023-06-26 12:38:03 [INFO] idaes.init.fs.S101: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:04 [INFO] idaes.init.fs.C101.control_volume: Initialization Complete\n", - "2023-06-26 12:38:04 [INFO] idaes.init.fs.C101: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:04 [INFO] idaes.init.fs.M101.mixed_state: Initialization Complete\n", - "2023-06-26 12:38:04 [INFO] idaes.init.fs.M101: Initialization Complete: optimal - Optimal Solution Found\n", - "WARNING: Wegstein failed to converge in 3 iterations\n", - "2023-06-26 12:38:04 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:04 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: State Released.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.translator: Initialization Complete optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:05 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_out: State Released.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume: Initialization Complete\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102.control_volume.properties_in: State Released.\n", - "2023-06-26 12:38:06 [INFO] idaes.init.fs.H102: Initialization Complete: optimal - Optimal Solution Found\n" - ] - } - ], + "outputs": [], "source": [ "seq.run(m, function)" ] @@ -1330,125 +1135,9 @@ }, { "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 1097\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 877\n", - "\n", - "Total number of variables............................: 363\n", - " variables with only lower bounds: 8\n", - " variables with lower and upper bounds: 155\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 363\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.82e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.69e+03 1.44e+03 -1.0 2.00e+04 - 9.71e-01 4.67e-01H 1\n", - " 2 0.0000000e+00 1.29e+03 1.56e+03 -1.0 1.60e+04 - 9.79e-01 4.90e-01h 1\n", - " 3 0.0000000e+00 1.18e+03 1.55e+05 -1.0 1.40e+04 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 5.46e+02 2.32e+09 -1.0 8.43e+03 - 1.00e+00 9.82e-01h 1\n", - " 5 0.0000000e+00 5.46e+03 3.66e+10 -1.0 5.97e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.21e+03 8.01e+09 -1.0 5.75e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 6.42e+00 3.87e+07 -1.0 1.53e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.96e-04 9.36e+02 -1.0 7.28e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 2.97e-05 2.81e+03 -3.8 2.13e-07 - 1.00e+00 1.00e+00H 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.7855284385533683e+04 1.7855284385533683e+04\n", - "Constraint violation....: 2.4734281289795490e-10 2.9668448405573148e-05\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 2.4734281289795490e-10 1.7855284385533683e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 12\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 12\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.036\n", - "Total CPU secs in NLP function evaluations = 0.000\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], + "execution_count": 48, + "metadata": {}, + "outputs": [], "source": [ "# Create the solver object\n", "solver = get_solver()\n", @@ -1478,619 +1167,9 @@ }, { "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:07 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_feed[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_liq[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_in_vap[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].flow_mol_phase\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_comp[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_phase_equilibrium[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_out[0.0].eq_P_vap[toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Liq,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].material_flow_terms[Vap,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Liq]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.properties_in[0.0].enthalpy_flow_terms[Vap]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[2].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[3].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:08 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[7].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[8].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[9].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[4].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[6].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.feed_tray.e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_flow_vap_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.rectification_section[1].e_mole_frac_vap_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_flow_reflux[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.condenser.control_volume.e_mole_frac_reflux[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_flow_liq_out[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.stripping_section[10].e_mole_frac_liq_out[0.0,toluene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_flow_vapor_reboil[0.0]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,benzene]\n", - "2023-06-26 12:38:09 [WARNING] idaes.core.util.scaling: Missing scaling factor for fs.D101.reboiler.control_volume.e_mole_frac_vapor_reboil[0.0,toluene]\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101: Begin initialization.\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray: Begin initialization.\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:09 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:10 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: State Released.\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:11 [INFO] idaes.init.fs.D101.feed_tray: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray.properties_in_liq: State Released.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.feed_tray.properties_in_vap: State Released.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:12 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_out: State Released.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume: Initialization Complete\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser: Initialization Complete, optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.condenser.control_volume.properties_in: State Released.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:13 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: State Released.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler: Initialization Complete, optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.reboiler.control_volume.properties_in: State Released.\n", - "2023-06-26 12:38:14 [INFO] idaes.init.fs.D101.rectification_section[1]: Begin initialization.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:15 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: State Released.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_vap: State Released.\n", - "2023-06-26 12:38:16 [INFO] idaes.init.fs.D101.rectification_section[2]: Begin initialization.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:17 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: State Released.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:18 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_vap: State Released.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[2].properties_in_liq: State Released.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3]: Begin initialization.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:19 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:20 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: State Released.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_vap: State Released.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[3].properties_in_liq: State Released.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[4]: Begin initialization.\n", - "2023-06-26 12:38:21 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:22 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: State Released.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:23 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.rectification_section[4]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.rectification_section[4]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_liq: State Released.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6]: Begin initialization.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:24 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:25 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: State Released.\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:26 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[6]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[6]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_vap: State Released.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7]: Begin initialization.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:27 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: State Released.\n", - "2023-06-26 12:38:28 [INFO] idaes.init.fs.D101.stripping_section[7].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_vap: State Released.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[7].properties_in_liq: State Released.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8]: Begin initialization.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:29 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:30 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: State Released.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_vap: State Released.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[8].properties_in_liq: State Released.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[9]: Begin initialization.\n", - "2023-06-26 12:38:31 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:32 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: State Released.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_vap: State Released.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[9].properties_in_liq: State Released.\n", - "2023-06-26 12:38:33 [INFO] idaes.init.fs.D101.stripping_section[10]: Begin initialization.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:34 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 1 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 2 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 3 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 4 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Step 5 optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: State Released.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_out: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass and energy balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Mass, energy and pressure balance solve optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10]: Initialization complete, status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:35 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_liq: State Released.\n", - "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101: Rectification section initialization status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101: Stripping section initialization status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101.rectification_section[4].properties_in_vap: State Released.\n", - "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101.stripping_section[6].properties_in_liq: State Released.\n", - "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101: Column section initialization status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:36 [INFO] idaes.init.fs.D101.rectification_section[1].properties_in_liq: State Released.\n", - "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101: Column section + Condenser initialization status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101.stripping_section[10].properties_in_vap: State Released.\n", - "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101: Column section + Condenser + Reboiler initialization status optimal - Optimal Solution Found.\n", - "2023-06-26 12:38:37 [INFO] idaes.init.fs.D101.feed_tray.properties_in_feed: State Released.\n" - ] - } - ], + "execution_count": 50, + "metadata": {}, + "outputs": [], "source": [ "# Add distillation column to the flowsheet\n", "m.fs.D101 = TrayColumn(\n", @@ -2138,7 +1217,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -2173,133 +1252,9 @@ }, { "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix\n", - "'fs.D101.condenser.control_volume.properties_out[0.0].scaling_factor' that\n", - "contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H102.control_volume.properties_out[0.0].scaling_factor' that contains 1\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 26\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4042\n", - "Number of nonzeros in inequality constraint Jacobian.: 0\n", - "Number of nonzeros in Lagrangian Hessian.............: 2376\n", - "\n", - "Total number of variables............................: 1169\n", - " variables with only lower bounds: 112\n", - " variables with lower and upper bounds: 365\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 0\n", - " inequality constraints with only lower bounds: 0\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 0\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 0.0000000e+00 3.83e+04 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 0.0000000e+00 8.70e+03 1.50e+03 -1.0 3.69e+04 - 9.71e-01 4.62e-01H 1\n", - " 2 0.0000000e+00 1.53e+03 1.56e+03 -1.0 6.75e+03 - 9.77e-01 4.89e-01h 1\n", - " 3 0.0000000e+00 1.37e+03 1.55e+05 -1.0 9.37e+03 - 9.90e-01 4.99e-01h 1\n", - " 4 0.0000000e+00 6.14e+02 2.31e+09 -1.0 6.09e+03 - 1.00e+00 9.81e-01h 1\n", - " 5 0.0000000e+00 5.32e+03 3.62e+10 -1.0 5.56e+02 - 1.00e+00 9.90e-01h 1\n", - " 6 0.0000000e+00 1.16e+03 7.80e+09 -1.0 5.36e+00 - 1.00e+00 1.00e+00h 1\n", - " 7 0.0000000e+00 5.96e+00 3.64e+07 -1.0 1.47e-03 - 1.00e+00 1.00e+00f 1\n", - " 8 0.0000000e+00 1.69e-04 8.15e+02 -1.0 6.77e-06 - 1.00e+00 1.00e+00h 1\n", - " 9 0.0000000e+00 7.45e-09 5.93e-02 -3.8 3.58e-08 - 1.00e+00 1.00e+00h 1\n", - "Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator\n", - "\n", - "Number of Iterations....: 9\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Dual infeasibility......: 1.5042542854672720e+04 1.5042542854672720e+04\n", - "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", - "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Overall NLP error.......: 5.8207660913467407e-11 1.5042542854672720e+04\n", - "\n", - "\n", - "Number of objective function evaluations = 11\n", - "Number of objective gradient evaluations = 10\n", - "Number of equality constraint evaluations = 11\n", - "Number of inequality constraint evaluations = 0\n", - "Number of equality constraint Jacobian evaluations = 10\n", - "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 9\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.068\n", - "Total CPU secs in NLP function evaluations = 0.009\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - }, - { - "data": { - "text/plain": [ - "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 1169, 'Number of variables': 1169, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.16938281059265137}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 53, + "metadata": {}, + "outputs": [], "source": [ "solver.solve(m, tee=True)" ] @@ -2315,28 +1270,9 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 55, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 442301.47075252124\n", - "operating cost = $ 427596.7305680538\n", - "capital cost = $ 14704.740184467468\n", - "\n", - "Distillate flowrate = 0.16196898920633476 mol/s\n", - "Benzene purity = 89.49161665800843 %\n", - "Residue flowrate = 0.10515007120697811 mol/s\n", - "Toluene purity = 43.32260291055274 %\n", - "\n", - "Conversion = 75.0 %\n", - "\n", - "Overhead benzene loss in F101 = 42.161938483603166 %\n" - ] - } - ], + "outputs": [], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -2379,42 +1315,9 @@ }, { "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.R101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : 0.0000 : watt : True : (None, None)\n", - " Volume : 0.14705 : meter ** 3 : False : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Outlet \n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.2993e-07\n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 8.4147e-07\n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-12\n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.11936 0.35374\n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.31252 0.078129\n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.0377 1.2721\n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.56260 0.32821\n", - " temperature kelvin 600.00 771.85\n", - " pressure pascal 3.5000e+05 3.5000e+05\n", - "====================================================================================\n" - ] - } - ], + "execution_count": 57, + "metadata": {}, + "outputs": [], "source": [ "m.fs.R101.report()" ] @@ -2428,42 +1331,9 @@ }, { "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "====================================================================================\n", - "Unit : fs.F101 Time: 0.0\n", - "------------------------------------------------------------------------------------\n", - " Unit Performance\n", - "\n", - " Variables: \n", - "\n", - " Key : Value : Units : Fixed : Bounds\n", - " Heat Duty : -70343. : watt : False : (None, None)\n", - " Pressure Change : 0.0000 : pascal : True : (None, None)\n", - "\n", - "------------------------------------------------------------------------------------\n", - " Stream Table\n", - " Units Inlet Vapor Outlet Liquid Outlet\n", - " flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 0.20460 \n", - " flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 0.062520 \n", - " flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 2.6712e-07 \n", - " flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 1.0000e-08 \n", - " flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 1.0000e-08 \n", - " temperature kelvin 771.85 325.00 325.00 \n", - " pressure pascal 3.5000e+05 3.5000e+05 3.5000e+05 \n", - "====================================================================================\n" - ] - } - ], + "execution_count": 58, + "metadata": {}, + "outputs": [], "source": [ "m.fs.F101.report()" ] @@ -2482,27 +1352,9 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 59, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Units Reactor Light Gases\n", - "flow_mol_phase_comp ('Liq', 'benzene') mole / second 1.2993e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'toluene') mole / second 8.4147e-07 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'methane') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Liq', 'hydrogen') mole / second 1.0000e-12 1.0000e-08 \n", - "flow_mol_phase_comp ('Vap', 'benzene') mole / second 0.35374 0.14915 \n", - "flow_mol_phase_comp ('Vap', 'toluene') mole / second 0.078129 0.015610 \n", - "flow_mol_phase_comp ('Vap', 'methane') mole / second 1.2721 1.2721 \n", - "flow_mol_phase_comp ('Vap', 'hydrogen') mole / second 0.32821 0.32821 \n", - "temperature kelvin 771.85 325.00 \n", - "pressure pascal 3.5000e+05 3.5000e+05 \n" - ] - } - ], + "outputs": [], "source": [ "from idaes.core.util.tables import (\n", " create_stream_table_dataframe,\n", @@ -2558,7 +1410,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -2574,7 +1426,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -2601,7 +1453,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 62, "metadata": { "tags": [ "exercise" @@ -2614,7 +1466,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 63, "metadata": { "tags": [ "solution" @@ -2643,7 +1495,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -2685,7 +1537,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 65, "metadata": { "tags": [ "exercise" @@ -2701,7 +1553,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 66, "metadata": { "tags": [ "solution" @@ -2727,7 +1579,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -2752,7 +1604,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 68, "metadata": { "tags": [ "exercise" @@ -2765,7 +1617,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 69, "metadata": { "tags": [ "solution" @@ -2786,7 +1638,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -2807,142 +1659,9 @@ }, { "cell_type": "code", - "execution_count": 135, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: model contains export suffix 'fs.H102.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.F101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.F101.control_volume.scaling_factor'\n", - "that contains 1 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.R101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix 'fs.R101.control_volume.scaling_factor'\n", - "that contains 2 component keys that are not exported as part of the NL file.\n", - "Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_out[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "WARNING: model contains export suffix\n", - "'fs.H101.control_volume.properties_in[0.0].scaling_factor' that contains 25\n", - "component keys that are not exported as part of the NL file. Skipping.\n", - "Ipopt 3.13.2: nlp_scaling_method=gradient-based\n", - "tol=1e-06\n", - "max_iter=200\n", - "\n", - "\n", - "******************************************************************************\n", - "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", - " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", - " For more information visit http://projects.coin-or.org/Ipopt\n", - "\n", - "This version of Ipopt was compiled from source code available at\n", - " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", - " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", - " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", - "\n", - "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", - " for large-scale scientific computation. All technical papers, sales and\n", - " publicity material resulting from use of the HSL codes within IPOPT must\n", - " contain the following acknowledgement:\n", - " HSL, a collection of Fortran codes for large-scale scientific\n", - " computation. See http://www.hsl.rl.ac.uk.\n", - "******************************************************************************\n", - "\n", - "This is Ipopt version 3.13.2, running with linear solver ma27.\n", - "\n", - "Number of nonzeros in equality constraint Jacobian...: 4073\n", - "Number of nonzeros in inequality constraint Jacobian.: 6\n", - "Number of nonzeros in Lagrangian Hessian.............: 2391\n", - "\n", - "Total number of variables............................: 1176\n", - " variables with only lower bounds: 113\n", - " variables with lower and upper bounds: 372\n", - " variables with only upper bounds: 0\n", - "Total number of equality constraints.................: 1169\n", - "Total number of inequality constraints...............: 3\n", - " inequality constraints with only lower bounds: 2\n", - " inequality constraints with lower and upper bounds: 0\n", - " inequality constraints with only upper bounds: 1\n", - "\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 4.4230147e+05 2.99e+05 9.90e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 4.3753585e+05 2.91e+05 1.28e+02 -1.0 3.09e+06 - 3.58e-01 2.40e-02f 1\n", - " 2 4.3545100e+05 2.78e+05 1.55e+02 -1.0 1.78e+06 - 3.31e-01 4.74e-02h 1\n", - " 3 4.2822311e+05 2.20e+05 4.50e+02 -1.0 2.99e+06 - 2.95e-02 1.35e-01h 1\n", - " 4 4.2249096e+05 1.45e+05 1.43e+03 -1.0 7.01e+06 - 5.14e-01 2.03e-01h 1\n", - " 5 4.2194364e+05 8.17e+04 1.70e+04 -1.0 6.06e+06 - 5.97e-01 4.28e-01h 1\n", - " 6 4.2602765e+05 4.55e+04 1.10e+06 -1.0 4.32e+06 - 9.26e-01 5.07e-01h 1\n", - " 7 4.3776643e+05 2.03e+04 6.44e+09 -1.0 2.42e+06 - 9.90e-01 9.47e-01h 1\n", - " 8 4.3846260e+05 1.92e+04 6.05e+09 -1.0 4.42e+05 - 5.40e-01 5.74e-02h 1\n", - " 9 4.4529853e+05 4.05e+04 4.66e+10 -1.0 2.47e+05 - 9.96e-01 9.90e-01h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 4.4906283e+05 9.76e+03 1.10e+10 -1.0 1.12e+03 -4.0 1.26e-01 7.45e-01h 1\n", - " 11 4.5079086e+05 1.19e+03 1.54e+09 -1.0 5.63e+02 -4.5 3.77e-01 1.00e+00h 1\n", - " 12 4.5024224e+05 2.66e+00 3.67e+06 -1.0 6.61e+01 -5.0 1.00e+00 1.00e+00f 1\n", - " 13 4.4946170e+05 5.64e-01 9.29e+05 -1.0 1.81e+02 -5.4 1.00e+00 7.88e-01f 1\n", - " 14 4.4916780e+05 8.48e+00 1.62e+05 -1.0 2.83e+02 -5.9 1.00e+00 1.00e+00f 1\n", - " 15 4.4899127e+05 4.83e+00 9.07e+04 -1.0 1.01e+02 -6.4 1.00e+00 4.40e-01f 2\n", - " 16 4.4886718e+05 7.00e-01 4.61e+02 -1.0 2.35e+02 -6.9 1.00e+00 1.00e+00f 1\n", - " 17 4.4800159e+05 1.39e+02 4.52e+06 -3.8 1.17e+03 -7.3 9.79e-01 9.37e-01f 1\n", - " 18 4.4672196e+05 9.59e+02 1.22e+06 -3.8 4.55e+03 -7.8 1.00e+00 9.43e-01f 1\n", - " 19 4.4401667e+05 7.75e+03 1.55e+05 -3.8 1.08e+04 -8.3 1.00e+00 1.00e+00f 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 4.4185035e+05 1.91e+04 1.36e+04 -3.8 1.33e+04 -8.8 1.00e+00 1.00e+00h 1\n", - " 21 4.4241001e+05 3.52e+03 5.96e+03 -3.8 2.94e+03 -9.2 1.00e+00 1.00e+00h 1\n", - " 22 4.4185237e+05 7.82e+00 2.91e+02 -3.8 7.13e+03 -9.7 2.39e-01 1.00e+00h 1\n", - " 23 4.4124091e+05 1.53e+01 3.11e+02 -3.8 4.82e+04 -10.2 8.59e-01 1.36e-01f 1\n", - " 24 4.4137379e+05 1.80e+00 2.91e+02 -3.8 1.41e+04 - 1.95e-01 1.00e+00h 1\n", - " 25 4.3862833e+05 1.70e+03 9.48e+04 -3.8 1.57e+07 - 1.29e-03 9.10e-02f 1\n", - " 26 4.3883308e+05 1.49e+03 8.46e+04 -3.8 1.02e+06 - 1.00e+00 1.35e-01h 1\n", - " 27 4.3885472e+05 2.18e+01 3.40e+03 -3.8 1.38e+05 - 1.00e+00 1.00e+00h 1\n", - " 28 4.3884160e+05 5.90e-02 6.38e+01 -3.8 8.66e+03 - 1.00e+00 1.00e+00h 1\n", - " 29 4.3884157e+05 6.56e-07 4.63e-04 -3.8 2.89e+01 - 1.00e+00 1.00e+00h 1\n", - "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 30 4.3883990e+05 3.57e-01 2.38e+03 -5.7 8.19e+02 - 1.00e+00 1.00e+00f 1\n", - " 31 4.3883992e+05 3.05e-07 1.25e-05 -5.7 3.55e-01 - 1.00e+00 1.00e+00h 1\n", - " 32 4.3883990e+05 5.46e-05 3.63e-01 -8.0 1.01e+01 - 1.00e+00 1.00e+00h 1\n", - " 33 4.3883990e+05 1.49e-08 1.07e-07 -8.0 5.40e-05 - 1.00e+00 1.00e+00h 1\n", - "\n", - "Number of Iterations....: 33\n", - "\n", - " (scaled) (unscaled)\n", - "Objective...............: 4.3883989842627057e+02 4.3883989842627058e+05\n", - "Dual infeasibility......: 1.0693122464843572e-07 1.0693122464843573e-04\n", - "Constraint violation....: 5.8207660913467407e-11 1.4901161193847656e-08\n", - "Complementarity.........: 9.0909948039747601e-09 9.0909948039747593e-06\n", - "Overall NLP error.......: 9.0909948039747601e-09 1.0693122464843573e-04\n", - "\n", - "\n", - "Number of objective function evaluations = 35\n", - "Number of objective gradient evaluations = 34\n", - "Number of equality constraint evaluations = 35\n", - "Number of inequality constraint evaluations = 35\n", - "Number of equality constraint Jacobian evaluations = 34\n", - "Number of inequality constraint Jacobian evaluations = 34\n", - "Number of Lagrangian Hessian evaluations = 33\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.522\n", - "Total CPU secs in NLP function evaluations = 0.078\n", - "\n", - "EXIT: Optimal Solution Found.\n" - ] - } - ], + "execution_count": 71, + "metadata": {}, + "outputs": [], "source": [ "results = solver.solve(m, tee=True)" ] @@ -2958,28 +1677,9 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 73, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total cost = $ 438839.8984262706\n", - "operating cost = $ 408883.53148307273\n", - "capital cost = $ 29956.366943197827\n", - "\n", - "Distillate flowrate = 0.17999999002639896 mol/s\n", - "Benzene purity = 98.99999900049087 %\n", - "Residue flowrate = 0.10851616424263705 mol/s\n", - "Toluene purity = 15.67617808620809 %\n", - "\n", - "Conversion = 93.38705916369607 %\n", - "\n", - "Overhead benzene loss in F101 = 17.340617931156185 %\n" - ] - } - ], + "outputs": [], "source": [ "print(\"total cost = $\", value(m.fs.capital_cost) + value(m.fs.operating_cost))\n", "print(\"operating cost = $\", value(m.fs.operating_cost))\n", @@ -3022,25 +1722,9 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 75, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal Values\n", - "\n", - "H101 outlet temperature = 568.9232042951996 K\n", - "\n", - "R101 outlet temperature = 790.3655425698917 K\n", - "\n", - "F101 outlet temperature = 298.0 K\n", - "\n", - "H102 outlet temperature = 368.74143399528367 K\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Optimal Values\")\n", "print()\n", @@ -3096,7 +1780,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.8.16" } }, "nbformat": 4, From 9c40b5f4cb6ba7dc899dbf6e2668d39c134d3e0a Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 27 Sep 2023 14:21:13 -0400 Subject: [PATCH 11/75] New notebook SCO2_alamo_surrogate in directory docs/surrogates/alamo --- .../docs/surrogates/alamo/SCO2_alamo_surrogate_doc.ipynb | 6 ++++++ .../surrogates/alamo/SCO2_alamo_surrogate_exercise.ipynb | 6 ++++++ .../surrogates/alamo/SCO2_alamo_surrogate_solution.ipynb | 6 ++++++ .../docs/surrogates/alamo/SCO2_alamo_surrogate_src.ipynb | 6 ++++++ .../docs/surrogates/alamo/SCO2_alamo_surrogate_test.ipynb | 6 ++++++ .../docs/surrogates/alamo/SCO2_alamo_surrogate_usr.ipynb | 6 ++++++ 6 files changed, 36 insertions(+) create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_doc.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_exercise.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_solution.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_src.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_test.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_usr.ipynb diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_doc.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_doc.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_exercise.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_exercise.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_exercise.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_solution.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_solution.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_solution.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_src.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_src.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_src.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_test.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_test.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_usr.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_usr.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} From cc73e13e8b6066f335effdc45a2d40f075c76898 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 27 Sep 2023 14:25:28 -0400 Subject: [PATCH 12/75] New notebook supercritical_CO2_flowsheet in directory docs/surrogates/alamo --- .../surrogates/alamo/supercritical_CO2_flowsheet_doc.ipynb | 6 ++++++ .../surrogates/alamo/supercritical_CO2_flowsheet_src.ipynb | 6 ++++++ .../surrogates/alamo/supercritical_CO2_flowsheet_test.ipynb | 6 ++++++ .../surrogates/alamo/supercritical_CO2_flowsheet_usr.ipynb | 6 ++++++ 4 files changed, 24 insertions(+) create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_doc.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_src.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_test.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_usr.ipynb diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_doc.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_doc.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_src.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_src.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_src.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_test.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_test.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_test.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_usr.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_usr.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} From c7189fc447cf2f1a14bfbe7af492965722332fa3 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 27 Sep 2023 14:36:46 -0400 Subject: [PATCH 13/75] New notebook supercritical_CO2_surrogate in directory docs/surrogates/alamo --- .../surrogates/alamo/supercritical_CO2_surrogate_doc.ipynb | 6 ++++++ .../surrogates/alamo/supercritical_CO2_surrogate_src.ipynb | 6 ++++++ .../surrogates/alamo/supercritical_CO2_surrogate_test.ipynb | 6 ++++++ .../surrogates/alamo/supercritical_CO2_surrogate_usr.ipynb | 6 ++++++ 4 files changed, 24 insertions(+) create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_doc.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_src.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_test.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_usr.ipynb diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_doc.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_doc.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_src.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_src.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_src.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_test.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_test.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_usr.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_usr.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} From a32d719a805aca1d405cfe49651be311941bac27 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 27 Sep 2023 14:37:46 -0400 Subject: [PATCH 14/75] New notebook supercritical_CO2_surrogate in directory docs/surrogates/omlt --- .../surrogates/omlt/supercritical_CO2_surrogate_doc.ipynb | 6 ++++++ .../surrogates/omlt/supercritical_CO2_surrogate_src.ipynb | 6 ++++++ .../surrogates/omlt/supercritical_CO2_surrogate_test.ipynb | 6 ++++++ .../surrogates/omlt/supercritical_CO2_surrogate_usr.ipynb | 6 ++++++ 4 files changed, 24 insertions(+) create mode 100644 idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_doc.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_src.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_test.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_usr.ipynb diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_doc.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_doc.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_src.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_src.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_src.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_test.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_test.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_usr.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_usr.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} From f6cac7a3f95c96bfcf76afc70318b2c0c345d915 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 27 Sep 2023 14:38:07 -0400 Subject: [PATCH 15/75] New notebook supercritical_CO2_surrogate in directory docs/surrogates/pysmo --- .../surrogates/pysmo/supercritical_CO2_surrogate_doc.ipynb | 6 ++++++ .../surrogates/pysmo/supercritical_CO2_surrogate_src.ipynb | 6 ++++++ .../surrogates/pysmo/supercritical_CO2_surrogate_test.ipynb | 6 ++++++ .../surrogates/pysmo/supercritical_CO2_surrogate_usr.ipynb | 6 ++++++ 4 files changed, 24 insertions(+) create mode 100644 idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_doc.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_src.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_test.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_usr.ipynb diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_doc.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_doc.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_src.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_src.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_src.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_test.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_test.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_usr.ipynb new file mode 100644 index 00000000..363fcab7 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_usr.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} From 864306df4f5669a16107a0737e9f4291d49a4f0c Mon Sep 17 00:00:00 2001 From: Brandon Paul Date: Tue, 10 Oct 2023 15:23:56 -0700 Subject: [PATCH 16/75] Incorporate review suggestions --- .../surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb | 2 +- .../SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb | 2 +- .../ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb | 2 +- .../SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb | 2 +- .../surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb | 2 +- .../OMLT/SCO2_properties_keras_surrogate_embedding.ipynb | 2 +- .../SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb | 2 +- .../PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb | 2 +- .../surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb | 2 +- 9 files changed, 9 insertions(+), 9 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb index 57ceee63..928b9918 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb @@ -20,7 +20,7 @@ "\n", "### 1.2 Supercritical CO2 cycle process\n", "\n", - "The below flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", "\n", "In this example, we will train a model using AlamoTrainer for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb index 07f04c18..8688322a 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb @@ -86,7 +86,7 @@ "\n", "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", - "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to get the total work done. " + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb index 422fcdca..cb7f4fe1 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb @@ -446,7 +446,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, SCO2_flowsheet_alamo_surrogate.ipynb. To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_alamo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb index ea676f29..27c44415 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb @@ -86,7 +86,7 @@ "\n", "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", - "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to get the total work done. " + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb index fd6f6ce2..0bbaac34 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb @@ -20,7 +20,7 @@ "\n", "### 1.2 Supercritical CO2 cycle process\n", "\n", - "The below flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", "\n", "In this example, we will train a tanh model from our data and then demonstrate that we can solve an optimization problem with that surrogate model. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb index ddd415b2..bf9ece23 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb @@ -441,7 +441,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, SCO2_flowsheet_keras_surrogate.ipynb. To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_keras_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb index ffe09b65..bfe2f646 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb @@ -86,7 +86,7 @@ "\n", "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", - "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to get the total work done. " + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb index f7c263e1..79e6a24e 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb @@ -445,7 +445,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, SCO2_flowsheet_pysmo_surrogate.ipynb. To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_pysmo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb index fe6bd96f..ef5864fc 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb @@ -21,7 +21,7 @@ "\n", "### 1.2 Supercritical CO2 cycle process\n", "\n", - "The below flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", "\n", "In this example, we will train the model using polynomial regression for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " ] From a3b928b2bf76d23ef3e82445e18a33c946d3c950 Mon Sep 17 00:00:00 2001 From: Brandon Paul Date: Tue, 10 Oct 2023 15:38:52 -0700 Subject: [PATCH 17/75] Add new notebooks to TOC --- idaes_examples/notebooks/_toc.yml | 15 +++++++++++++++ .../surrogates/SCO2_example/ALAMO/__init__.py | 0 .../docs/surrogates/SCO2_example/ALAMO/index.md | 1 + .../docs/surrogates/SCO2_example/OMLT/__init__.py | 0 .../docs/surrogates/SCO2_example/OMLT/index.md | 1 + .../surrogates/SCO2_example/PySMO/__init__.py | 0 .../docs/surrogates/SCO2_example/PySMO/index.md | 1 + .../docs/surrogates/SCO2_example/__init__.py | 0 8 files changed, 18 insertions(+) create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/__init__.py create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/index.md create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/__init__.py create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/index.md create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/__init__.py create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/index.md create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/__init__.py diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index 270358a0..df661d8a 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -80,6 +80,21 @@ parts: - file: docs/surrogates/omlt/index sections: - file: docs/surrogates/omlt/keras_flowsheet_optimization_doc + - file: docs/surrogates/SCO2_example/ALAMO/index + sections: + - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate + - file: docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding + - file: docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate + - file: docs/surrogates/SCO2_example/OMLT/index + sections: + - file: docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate + - file: docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding + - file: docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate + - file: docs/surrogates/SCO2_example/PySMO/index + sections: + - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate + - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding + - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate # ----------------------------- # active (not documented) # ----------------------------- diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/__init__.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/index.md b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/index.md new file mode 100644 index 00000000..447c3e4a --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/index.md @@ -0,0 +1 @@ +# Supercritical CO2 ALAMO Surrogates diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/__init__.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/index.md b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/index.md new file mode 100644 index 00000000..ed2b49bc --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/index.md @@ -0,0 +1 @@ +# Supercritical CO2 Keras Surrogates diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/__init__.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/index.md b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/index.md new file mode 100644 index 00000000..47faaf28 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/index.md @@ -0,0 +1 @@ +# Supercritical CO2 PySMO Surrogates diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/__init__.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/__init__.py new file mode 100644 index 00000000..e69de29b From 31fcfabd552acbfb34b4ea40efb9d1e53b4e2f62 Mon Sep 17 00:00:00 2001 From: Brandon Paul Date: Tue, 10 Oct 2023 15:56:32 -0700 Subject: [PATCH 18/75] Missed some references, add those as links --- .../surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb | 2 +- .../surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb | 2 +- .../surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb index 928b9918..e2d4613a 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb @@ -541,7 +541,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the \"SCO2_properties_alamo_surrogate_embedding.ipynb\" file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_alamo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb index 0bbaac34..db85eabf 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb @@ -1049,7 +1049,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the \"SCO2_properties_keras_surrogate_embedding.ipynb\" file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_keras_surrogate_embedding.ipynb](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb index ef5864fc..ed79d580 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb @@ -603,7 +603,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the \"SCO2_properties_pysmo_surrogate_embedding.ipynb\" file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_pysmo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb) file." ] } ], From 55618a57dfc76727603be4de39580c2ecb0d34d1 Mon Sep 17 00:00:00 2001 From: Brandon Paul Date: Tue, 10 Oct 2023 15:59:14 -0700 Subject: [PATCH 19/75] Missed file endings in _toc file --- idaes_examples/notebooks/_toc.yml | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index df661d8a..bcbace89 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -82,19 +82,19 @@ parts: - file: docs/surrogates/omlt/keras_flowsheet_optimization_doc - file: docs/surrogates/SCO2_example/ALAMO/index sections: - - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate - - file: docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding - - file: docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate + - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc + - file: docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc + - file: docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc - file: docs/surrogates/SCO2_example/OMLT/index sections: - - file: docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate - - file: docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding - - file: docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate + - file: docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc + - file: docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc + - file: docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc - file: docs/surrogates/SCO2_example/PySMO/index sections: - - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate - - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding - - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate + - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc + - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc + - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc # ----------------------------- # active (not documented) # ----------------------------- From 729abba1c82f6fc155e5886485c30ddd02cc1c77 Mon Sep 17 00:00:00 2001 From: Brandon Paul Date: Tue, 10 Oct 2023 16:06:36 -0700 Subject: [PATCH 20/75] Add files generated by build and run tests --- .../ALAMO/SCO2_alamo_surrogate_doc.ipynb | 570 +++++++ .../ALAMO/SCO2_alamo_surrogate_test.ipynb | 570 +++++++ .../ALAMO/SCO2_alamo_surrogate_usr.ipynb | 570 +++++++ .../SCO2_flowsheet_alamo_surrogate_doc.ipynb | 667 ++++++++ .../SCO2_flowsheet_alamo_surrogate_test.ipynb | 667 ++++++++ .../SCO2_flowsheet_alamo_surrogate_usr.ipynb | 667 ++++++++ ...erties_alamo_surrogate_embedding_doc.ipynb | 461 ++++++ ...rties_alamo_surrogate_embedding_test.ipynb | 461 ++++++ ...erties_alamo_surrogate_embedding_usr.ipynb | 461 ++++++ .../SCO2_example/ALAMO/alamo_run.trc | 3 + .../surrogates/SCO2_example/OMLT/.mdl_co2.h5 | Bin 66976 -> 63872 bytes .../SCO2_flowsheet_keras_surrogate_doc.ipynb | 665 ++++++++ .../SCO2_flowsheet_keras_surrogate_test.ipynb | 665 ++++++++ .../SCO2_flowsheet_keras_surrogate_usr.ipynb | 665 ++++++++ .../OMLT/SCO2_keras_surrogate_doc.ipynb | 1078 +++++++++++++ .../OMLT/SCO2_keras_surrogate_test.ipynb | 1078 +++++++++++++ .../OMLT/SCO2_keras_surrogate_usr.ipynb | 1078 +++++++++++++ ...erties_keras_surrogate_embedding_doc.ipynb | 456 ++++++ ...rties_keras_surrogate_embedding_test.ipynb | 456 ++++++ ...erties_keras_surrogate_embedding_usr.ipynb | 456 ++++++ .../SCO2_flowsheet_pysmo_surrogate_doc.ipynb | 1426 +++++++++++++++++ .../SCO2_flowsheet_pysmo_surrogate_test.ipynb | 1426 +++++++++++++++++ .../SCO2_flowsheet_pysmo_surrogate_usr.ipynb | 1426 +++++++++++++++++ ...erties_pysmo_surrogate_embedding_doc.ipynb | 460 ++++++ ...rties_pysmo_surrogate_embedding_test.ipynb | 460 ++++++ ...erties_pysmo_surrogate_embedding_usr.ipynb | 460 ++++++ .../PySMO/SCO2_pysmo_surrogate_doc.ipynb | 632 ++++++++ .../PySMO/SCO2_pysmo_surrogate_test.ipynb | 632 ++++++++ .../PySMO/SCO2_pysmo_surrogate_usr.ipynb | 632 ++++++++ 29 files changed, 19248 insertions(+) create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_usr.ipynb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.ipynb 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b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc.ipynb new file mode 100644 index 00000000..1dbfb272 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc.ipynb @@ -0,0 +1,570 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part -1)\n", + "\n", + "## 1. Introduction\n", + "This notebook demonstrates leveraging of the ALAMO surrogate trainer and IDAES Python wrapper to produce an surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "### 1.1 Need for ML Surrogate\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebric, we will use the surrogates and relate enthalpy and entropy with the pressure and temperature as an algebric equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train a model using AlamoTrainer for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.alamopy import AlamoTrainer, AlamoSurrogate, alamo\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset to have cover different ranges of pressure and temperature. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", we change \".\" to \"_\" as ALAMO accepts alphanumerical characters or underscores as the labels for input/output. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=7, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path('500_Points_DataSet.csv')) \n", + "\n", + "### ALAMO only accepts alphanumerical characters (A-Z, a-z, 0-9) or underscores as input/output labels\n", + "cols=csv_data.columns\n", + "cols=[item.replace(\".\", \"_\") for item in cols]\n", + "csv_data.columns=cols\n", + "\n", + "data = csv_data.sample(n=500,random_state=0) \n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogate with ALAMO\n", + "\n", + "IDAES provides a Python wrapper for the ALAMO machine learning tool via an imported AlamoTrainer class. Regression settings can be directly set as config attributes, as shown below. In this example, allowed basis terms include constant and linear functions, monomial power order 2 and 3, variable product power order 1 and 2, and variable ratio power order 1 and 2. ALAMO seeks to minimize the number of basis terms; here, we restrict each surrogate expression to a maximum of 10 basis terms.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ***************************************************************************\n", + " ALAMO version 2023.2.13. Built: WIN-64 Mon Feb 13 21:30:56 EST 2023\n", + "\n", + " If you use this software, please cite:\n", + " Cozad, A., N. V. Sahinidis and D. C. Miller,\n", + " Automatic Learning of Algebraic Models for Optimization,\n", + " AIChE Journal, 60, 2211-2227, 2014.\n", + "\n", + " ALAMO is powered by the BARON software from http://www.minlp.com/\n", + " ***************************************************************************\n", + " Licensee: Javal Vyas at Carnegie Mellon University, jvyas@andrew.cmu.edu.\n", + " ***************************************************************************\n", + " Reading input data\n", + " Checking input consistency and initializing data structures\n", + " \n", + " Step 0: Initializing data set\n", + " User provided an initial data set of 400 data points\n", + " We will sample no more data points at this stage\n", + " ***************************************************************************\n", + " Iteration 1 (Approx. elapsed time 0.62E-01 s)\n", + " \n", + " Step 1: Model building using BIC\n", + " \n", + " Model building for variable CO2SM_CO2_Enthalpy\n", + " ----\n", + " BIC = 0.750E+04 with CO2SM_CO2_Enthalpy = - 0.38E+06\n", + " ----\n", + " BIC = 0.569E+04 with CO2SM_CO2_Enthalpy = 58. * CO2SM_Temperature - 0.42E+06\n", + " ----\n", + " BIC = 0.542E+04 with CO2SM_CO2_Enthalpy = 55. * CO2SM_Temperature - 0.61E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", + " ----\n", + " BIC = 0.516E+04 with CO2SM_CO2_Enthalpy = 49. * CO2SM_Temperature + 4.0 * CO2SM_Pressure^2 - 0.15E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", + " ----\n", + " BIC = 0.502E+04 with CO2SM_CO2_Enthalpy = 0.16E+03 * CO2SM_Temperature - 0.16 * CO2SM_Temperature^2 + 0.76E-04 * CO2SM_Temperature^3 - 0.56E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.44E+06\n", + " ----\n", + " BIC = 0.484E+04 with CO2SM_CO2_Enthalpy = 0.14E+03 * CO2SM_Temperature + 2.5 * CO2SM_Pressure^2 - 0.14 * CO2SM_Temperature^2 + 0.66E-04 * CO2SM_Temperature^3 - 0.11E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.43E+06\n", + " \n", + " Model building for variable CO2SM_CO2_Entropy\n", + " ----\n", + " BIC = 0.219E+04 with CO2SM_CO2_Entropy = - 0.48E+03 * CO2SM_Pressure/CO2SM_Temperature\n", + " ----\n", + " BIC = 0.147E+04 with CO2SM_CO2_Entropy = 1.9 * CO2SM_Pressure - 0.15E+04 * CO2SM_Pressure/CO2SM_Temperature\n", + " ----\n", + " BIC = 0.115E+04 with CO2SM_CO2_Entropy = 0.77E-01 * CO2SM_Temperature - 0.38E+03 * CO2SM_Pressure/CO2SM_Temperature - 50.\n", + " ----\n", + " BIC = 713. with CO2SM_CO2_Entropy = 0.20 * CO2SM_Temperature - 0.94E-04 * CO2SM_Temperature^2 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 89.\n", + " ----\n", + " BIC = 443. with CO2SM_CO2_Entropy = 0.52 * CO2SM_Temperature - 0.60E-03 * CO2SM_Temperature^2 + 0.26E-06 * CO2SM_Temperature^3 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", + " ----\n", + " BIC = 317. with CO2SM_CO2_Entropy = 0.54 * CO2SM_Temperature - 0.63E-03 * CO2SM_Temperature^2 + 0.27E-06 * CO2SM_Temperature^3 - 0.26E+03 * CO2SM_Pressure/CO2SM_Temperature + 0.79E-01 * CO2SM_Temperature/CO2SM_Pressure - 0.16E+03\n", + " ----\n", + " BIC = 259. with CO2SM_CO2_Entropy = 0.47 * CO2SM_Temperature + 0.15E-01 * CO2SM_Pressure^2 - 0.53E-03 * CO2SM_Temperature^2 + 0.23E-06 * CO2SM_Temperature^3 - 0.70E-03 * CO2SM_Pressure*CO2SM_Temperature - 0.46E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", + " ----\n", + " BIC = 240. with CO2SM_CO2_Entropy = - 2.1 * CO2SM_Pressure + 0.55 * CO2SM_Temperature + 0.76E-01 * CO2SM_Pressure^2 - 0.63E-03 * CO2SM_Temperature^2 - 0.94E-03 * CO2SM_Pressure^3 + 0.27E-06 * CO2SM_Temperature^3 - 0.23E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", + " ----\n", + " BIC = 224. with CO2SM_CO2_Entropy = - 1.9 * CO2SM_Pressure + 0.49 * CO2SM_Temperature + 0.83E-01 * CO2SM_Pressure^2 - 0.57E-03 * CO2SM_Temperature^2 - 0.10E-02 * CO2SM_Pressure^3 + 0.25E-06 * CO2SM_Temperature^3 - 0.73E-08 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", + " ----\n", + " BIC = 193. with CO2SM_CO2_Entropy = - 3.9 * CO2SM_Pressure + 0.52 * CO2SM_Temperature + 0.17 * CO2SM_Pressure^2 - 0.56E-03 * CO2SM_Temperature^2 - 0.21E-02 * CO2SM_Pressure^3 + 0.24E-06 * CO2SM_Temperature^3 - 0.10E-02 * CO2SM_Pressure*CO2SM_Temperature - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.20 * CO2SM_Temperature/CO2SM_Pressure - 0.12E+03\n", + " \n", + " Calculating quality metrics on observed data set.\n", + " \n", + " Quality metrics for output CO2SM_CO2_Enthalpy\n", + " ---------------------------------------------\n", + " SSE OLR: 0.515E+08\n", + " SSE: 0.659E+08\n", + " RMSE: 406.\n", + " R2: 0.999\n", + " R2 adjusted: 0.999\n", + " Model size: 6\n", + " BIC: 0.484E+04\n", + " Cp: 0.659E+08\n", + " AICc: 0.482E+04\n", + " HQC: 0.483E+04\n", + " MSE: 0.168E+06\n", + " SSEp: 0.659E+08\n", + " RIC: 0.659E+08\n", + " MADp: 0.594\n", + " \n", + " Quality metrics for output CO2SM_CO2_Entropy\n", + " --------------------------------------------\n", + " SSE OLR: 541.\n", + " SSE: 558.\n", + " RMSE: 1.18\n", + " R2: 0.997\n", + " R2 adjusted: 0.997\n", + " Model size: 10\n", + " BIC: 193.\n", + " Cp: 178.\n", + " AICc: 154.\n", + " HQC: 169.\n", + " MSE: 1.43\n", + " SSEp: 558.\n", + " RIC: 606.\n", + " MADp: 0.130E+04\n", + " \n", + " Total execution time 0.52 s\n", + " Times breakdown\n", + " OLR time: 0.30 s in 3863 ordinary linear regression problem(s)\n", + " MINLP time: 0.0 s in 0 optimization problem(s)\n", + " Simulation time: 0.0 s to simulate 0 point(s)\n", + " All other time: 0.22 s in 1 iteration(s)\n", + " \n", + " Normal termination\n", + " ***************************************************************************\n" + ] + } + ], + "source": [ + "# Create ALAMO trainer object\n", + "has_alamo=alamo.available()\n", + "if has_alamo:\n", + " trainer = AlamoTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + " )\n", + "\n", + " # Set ALAMO options\n", + " trainer.config.constant = True\n", + " trainer.config.linfcns = True\n", + " trainer.config.multi2power = [1, 2]\n", + " trainer.config.monomialpower = [2, 3]\n", + " trainer.config.ratiopower = [1]\n", + " trainer.config.maxterms = [10] * len(output_labels) # max terms for each surrogate\n", + " trainer.config.filename = os.path.join(os.getcwd(), \"alamo_run.alm\")\n", + " trainer.config.overwrite_files = True\n", + "\n", + " # Train surrogate (calls ALAMO through IDAES ALAMOPy wrapper)\n", + " success, alm_surr, msg = trainer.train_surrogate()\n", + "\n", + " # save model to JSON\n", + " model = alm_surr.save_to_file(\"alamo_surrogate.json\", overwrite=True)\n", + "\n", + " # create callable surrogate object\n", + " surrogate_expressions = trainer._results[\"Model\"]\n", + " input_labels = trainer._input_labels\n", + " output_labels = trainer._output_labels\n", + " xmin, xmax = [7,306], [40,1000]\n", + " input_bounds = {\n", + " input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))\n", + " }\n", + "\n", + " alm_surr = AlamoSurrogate(\n", + " surrogate_expressions, input_labels, output_labels, input_bounds\n", + " )\n", + "else:\n", + " print('Alamo not found.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing Surrogates\n", + "\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(alm_surr, data_training)\n", + "surrogate_parity(alm_surr, data_training)\n", + "surrogate_residual(alm_surr, data_training)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(alm_surr, data_validation)\n", + "surrogate_parity(alm_surr, data_validation)\n", + "surrogate_residual(alm_surr, data_validation)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_alamo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.md) file." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb new file mode 100644 index 00000000..5f61c8e8 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb @@ -0,0 +1,570 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part -1)\n", + "\n", + "## 1. Introduction\n", + "This notebook demonstrates leveraging of the ALAMO surrogate trainer and IDAES Python wrapper to produce an surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "### 1.1 Need for ML Surrogate\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebric, we will use the surrogates and relate enthalpy and entropy with the pressure and temperature as an algebric equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train a model using AlamoTrainer for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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sekxsQoLEJSdLXX4lhYdl5m2R/6zLkueWLZfjzhsoa9atk0eeeEIaN25s9wAAoHoprm98mL4R8BTzPl27Xp7N/EOOO/dc8z599KmneJ8CqHL0c23Mww/K+jVrZODxx8iq156STR+8KluW/273QLQQrHpMVnq6LBw82CwBr1m8ebO8um69vLpmrZxx6aWydsMGGf3QQ9KgQQO7BwAA1Yt/33g6fSPgSeZ9utb3Pj3tkktk3caNcs8jj/A+BVDl6efcg/eMlg3r1splZ54iGyZNkJz0CZKX+ZvdA5EiWPWYvIwMWTNxolkCXrFgU65MWLNO3t2YI3+9+hpZnZUlt6WlSWxsrN0DAIDqxb9vHGj7xtvpGwFPMe/T1WvlnQ3Zct6VV5r36R333MP7FEC1o597d99+m2StWSXXXnCebP74Ddn4zvOyefGvdg+Ei2DVYxJSUsw4qzocALC/zc3JkedXrZaPtm6TwSNHyrLVq2XkLbfYrQAAVD+ub5y8ZYtc/ve/m77x7/SNgKeY9+nK1fJh3hYZVPgd9o81a+TG22+3WwGgevvHyBGy+o9l8o8rB8ufX78vWW+Mk9yFP9utCBXBqsdosNpuzBhpmppqW4CKl7ExW55ZvlK+zN8tN9x1tyxatkyG3nCD3QoAQPWjfeO4Fatk2s5dcn1h37j4j+Vy7fXX260AvMD3Pl0pU//Ml+vuvFOWLF8uw0aMsFsBAP6GXXuNLFv0m4waOVxiZn4h6/79hOTM+8luRbAIVj1me2amGQYgPyfHtgAVZ9aGjfLkHytkZs1YueOhh2Te4sUy+Mor7VYAAKqfmRs27Okbby/sG39dskQup28EPEXfp08sW174Pq0ptz3woMz//XcZcvXVdisAoDRDBg+WRb/OlUfvuk3q/fq9rJn4mOT8/IPdirIQrHqMjq/6Y/fusmLsWNsClL/v12fJY0uXybz69eWBp56S2fPny4WXXGK3AgBQ/XyftUEez1wm8xvEyf2ub7z4YrsVgBfod9jH9TtsvQZy/xNPFL5PF8hFl15qtwIAQnHBBRfIvNk/yjMPPyDxS3+WVS88JJtmf2e3oiQEq0A1Nr3wpPHhJUtlaeMm8vSECTJjzs9yzrnn2q0AAFQ/0zdslIeW/C5LExvLUy9NkO8z5si59I2Ap3yzPsv3HbbwffrEiy/KDz//LOf99a92KwAgEgMGDJCMGd/JS08/IU3WLJLl4++V3J++sVsRKGb18hUFS5f/IX369LFNAMIVExMjz/TuZe9511eFJ43XLl5i1vM//1xOOOEEs14dfVn430z1KygwSwBAdFWWvvHrjRtl28XPm/Xr+hZU674R1U9leZ9+mbVBBv2+VOoXfm/LT0+XE/7yF7sFiL7Ue6aYZfrd/c0SqI50mMrpiYmyvVYtuaDJAdKkT39p2DPFbvWu2XdcIQUVdI5PxSpQTeQXfqhM3bBRRs1fIJsPOti2CieOAIBqy/SNG7Mlbf7Cwr6xjW2lbwS8RN+nU9ZvMN9h8w4+WOo3aGDa+/XrZ5YAgPIXV/jZ+97bb8rB27NlyT9vk83ffyEFu/Lt1uqNYNVjdGxVHWNVx1oFomH7rl3y2YaNcve8X2VX+w4y+fMv5MMpvl9fAQCojrRv/CI7x9c3tmsvH37+uXzwxRd2KwAvMN9hs/Q77PzC77Dt7XfYqRIbG2v3AABUpGOOOUa+mPyBfP7Rh9Kh5g5Z9PitkvftJ7J7x3a7R/VEsOoxWmadl5Eh2zMzbQsQnrz8fPl4w0a5a+6vUrdLV/nq2+/kvx99JL16ef8yLwAAyoP2jZ9m55i+sfZhXegbAQ8y32GzNsqdv8yTOocdVvg+/VYmffIJ71MA8Aj9PJ486V359qsvpWvDWrLgsZsl7+vJkr81z+5RvRCsekzT1FRpN2aMJKR4f8wKeFPOn3/Khxuy5c45v0jjI3vITxkZ8np6uhxxxBF2DwAAqhftGydv9PWNCd2P9PWNkybRNwIeou/TD9ZvMO/TxO7dZfacOfLGe+/xPgUAj9LP5/++/prM+ekn6dGskcx/5B+yedp7sjM32+5RPRCsekxccrK0HjGCYBUhy9qxQ97bkC13zJ4jB/3f/8lvixbJxDfflEMPPdTuAQBA9eLrGzeavrG16xvfeIO+EfAQ8z7N2rDX+/Tlt9/mfQoAlYR+Xr8+8SVZ9NtvckzbljL34X9I3pRJsmPjertH1Uaw6jE6DICOr6pLIBhrtm2T/xaeNN4/b74c2r+/rFq1Sp59+WVJSkqyewAAUL1o3zhpY7bcN/dXOfSEE3x940T6RsBLzHfY9VnmfdrxeN932OdffZX3KQBUUvr5PeG58ebz/IQu7WXRU6Nk82dvy/b1a+weVRPBqsdkpafLwsGDzRIozcqtW+Wtwi+jYxb/Lt1PPU1WrV4tTzz3nLRo0cLuAQBA9bJC+8asDfLP3xZL99NOl9Vr1sgTz9I3Al5i3qfrsuSfiwrfp6efYd6nT/7rX7xPAaCK0M/z8U+MldWrVsmZRyVL5gsPyeaP35Btq5fbPaoWglWPqZuUZIYDiE1IsC3A3jLztsjr67NkfOYfcuy558nqtWvl0aeekiZNmtg9AACoXnx94wZ51vaNa9avl0eeeIK+EfAQ8z5dt968T/uec46sKVznOywAVF36+T7m4Ydk3erVcl7K/8mq156STR+8KltXLLV7VA0Eqx7TfNAg6TF7thlnFfC3ePNmeXXtOnl59Ro59eJLZO2GDXLvI49IXFyc3QMAgOrF9I3r1ssra9YW9o0X+/rGhx+mbwQ8xLxP16yTVwq/w55y4UXmfXr/Y4/xPgWAakI/7x+8Z7RkrV0jl515iqx/9wXJSZ8geZmL7B6VG8Eq4HELNuXKhMITxnc2ZMt5V11tvozeMXq01KpVy+4BAED14usb1+3pG9dkZdE3Ah5j3qer18jbWRvl3CuvlDWF32HvvO8+3qcAUE3p5//dt98mG9aukWvOP0c2f/S6ZL/7L9m85Fe7R+VEsOoxmWlpMj0xUVaMHWtbUF3NzcmRf61aI5O3bJVBI0bKH2vWyI233Wa3IhKzP/9MXhhxg9zW7xj55uCDzE3XtU23AQC8yfSNq7Vv3CKDR9q+8dZb7VYAXmDepytXy+S8LXLZDSNk+dq1ctMdd9itAACI3Pz3v8vq5cvkxiGXyY5p70nWG+Mkd+HPdmvlQrDqQfmFX0b0huopY2O2jFuxUqbt3CXDCr+ELv7jDxnG0BBRsX75chlz2aXy/r33yO45s+XoBg2kd7fDzU3Xd83+Ud4bnSaPX3Sh2RcA4A3aN45fscr0jdfdeVdh37hcht5wg90KwAvMd9jlK2Tan/ky9LbbZHHhd6nhhSfOAACUZPjQa+WPJYtk1MjhIjO/kHX/fkJy5v1kt1YOBKseo2Osdps61SxRvczasFGeWLZcfqhZU2574EH5dckSueLqq+1WROq7Sf+VtDNOk7iNG6Rf40Q5NCFBEurUkVqF/9560/VOjRtLStMmErchS9JOP1Wmv/WmfTQAYH/QvvHJP1bIzJqxcttDD5m+8fIrr7RbAXiB731a+B22Rk259f4H5Nfff5crhw61WwEAKNuQwYNl8a9z5dG7bpO6876TtS8/Ljk//2C3ehvBqsfUTUqShJQUs0T18P36LHl86TKZV7+BPPDkk5Ixf4FcdOmldiuiYfq778h7jz4iKS2aS8fYmra1ZJ3q1ZWUli3k/ccfk+nvvG1bAQAV5fusDfLPzD9kXoPCvvHpp2X2/Ply4cUX260AvMB8h/09U+bVqy/3jRkjGQsWyMUUhwAAInDBBRfIrxk/ydMP3S/xv8+RVS88JLkZ39mt3kSw6jFZ6emyZORIyZk2zbagqppe+GX04SVLJbNxE3nqpZdkxpw5cu7AgXYroiUvO1vevO9e6dEoXg6sX9+2lk337ZHQSN68/z5zDABA+ft240Z5+Hdf3/ik9o0Zc+Scc86xWwF4wTfr1stDi3+XpQmJ8sQLL8iMn3+W8/52vt0KAEDkBgwYIBk/fC8vPf2ENF69SJaPv1c2//SN3eotBKsek5eRYSauIlitur7K2iD3/bZY1jZvIS++/rp8NXOmnH7GGXYrou31u++STi1byQF169qW4Gm42qFpU/nPHUwaBgDl6auNG+W+RYtlzYHN5cX/FPaNP/wgp59+ut0KwAu+XLfe9x22WXN5qfA77Nc//ihnnn223QoAQPTp98Efvv5SXp/wgjTPWSnLnh4lm2d5Ky8jWPWYuORkaZqaylAAVcyuggKZmrVB0hb8JrkHHSxv/u9/8vn06XLiiSfaPRCqhYMHy/bMTHuveAu+/14WFZ6cH1Y71raE7vC4BuYYeiwAQPRo3zgtO8fXN7Yu7BvTC/vGb76hbwQ8RN+nX6xbL6PmL5Tcg9uY77BffPednHjSSXYPAADKn34//PrzTyX9zdfl4O3Z8vuY22XLD1OkYNcuu8f+Q7DqMRqqdpk0icmrqogdu3bLZ1kb5K5582Vnu/bywaefyuSpU+XYY4+1eyBcWtU9o21bmTdgQIkB689ffC5JjRMkxt4Phz724Lg4mf3hB74GAEBEtG/8IjtH7v51gew8pJ2vb5wyhb4R8BDzHXZ9ltxd+B12d4eO8uFnn8lHhd+9eJ8CAPYn7Ye+mPyBfPrh+9IuZrssevwW2fLdZ7L7zx12j4pHsOox+Tk5JiTSJSqvLfn58vGGjXLX3HlS+7Au8uX06ZL+ySdy1FFH2T0QLToucUkB69yvvpQDdxfYe+FrUaeOzPt2ur0HAAiH9o2fZueYvrFW58Nk2jffyKSPPqJvBDzEfIddn7XnO+y0wu+wk/gOCwDwGO2XJk96V6Z/OU0Oa1BDFjx6s2z55iPJ35pn96g4MauXryhYuvwP6dOnj23C/pSZlibLRo+WNqNGSVLhOiqXmJgYOeWg1jJl9Rq55Pzz5R933CGdOnWyW73ly8LXqvT/a5XRyieeKPYHCK36bjdmjBlOY0SvHnJq8wOlXmwtuzU8W/N3yieF/03HzpptWwAAwfLvGy+7+GK58ZZbPNs3pt4zxSzT7+5vlkB1oe/Tk1u1lGlr18nFHv8OOz0x0XwH7JudLbEJCbYViD76BMBX/Kefu/p5q5+7XrVgwQJ5+LHH5d+vvCzNjzlZVkz9UAoKIi+yCgbBqscQrFZu+qV02NVXm5PGtm3b2lZvcl9KqyoNV9PGPS0XdzksoqEA1O7C22vzfpUXflvsawAABK0y9Y2cRKO60vfpNZdfLjffeWel+Q5LsIryRp8AVJ5g1Vm6dKk8+vg/ZfwzTxOsVmcu7OKLAspTXkaGuYy+siqpYjUhJcWEqjoRnFasnta8mdSNDX/yKrUtf6d8TMUqAFR5nEQD3kewiopCnwBUvmB1fyBYBVAp6biq/mOq+geqzt2nnCRHFC4PqF/P1xCm9Vu3SUZ+vtz3xVTbAgCoijiJBryPYBUVhT4BIFgNBpNXeYxWEOokPGsmTrQtAEqjgWqP2bOl29Spe4Wqqsuxx8m6mEgHAhBZvWO7dOnzf/YeAAAAAAAAwarnuMuzA2c3B7C30gJVp9uJJ0lmdrZEMrKKPvaPzVuk+5ln+RoAAAAAAAAK1fDFBvAKDYt04ipdAijZoRMmlBioOp2OPlo6HtVb5m7fYVtC90vuZmnfs6c5FgAAAAAAgEPFqsdooJqUlkawCkTJ+ffcK4vWrpV1W7faluDpY37bsEEueuhh2wIAAAAAAOBDsOoxDAUARFdcYqL89Y475afcvJDCVd33p+xNcv6dd5ljAAAAAAAA+KshEvnELogeJq8Coq/vuefJWTfeKFNXrZYF27fb1pLNz8uTqStXyek33CB9zxtoWwEAAAAAAIpQseoxsQkJ5gYguv7vvIEyevLHsqXJATJtfZYs2LhRcnbskJ27dpmbri8sbJu6Zq1sOeBAGf3RJ3LsRRfbRwMAAAAAAOyNYNVjWo8YIX2zs804qwCi64CDDpK///s1OTtttNTo1l2+25wnX/8yV2Zk/GzWYw4/Qv5y731y43/eMPsCAAAAAACUhGAVQLXT/cST5Monn5KHvp4u5yQdIqc2aWrWr3x6nNkGAAAAAABQFoJVj1kxdqzMaNvWLAGUr5xp08zNTRoHAAAAAAAQLIJVj8nPyZHtmZlmCaB8LRs92q7tvQ4AAAAAAFAWglWPaZqaKl0mTZLmgwbZFgDlwVWrOlStAgAAAACAUNQQKbCr8IK45GQTrtZNSrItAMpDcRWqVK0CAAAAAIBgUbHqMVpBp+OravUcgPIRWK3qULUKAAAAAACCRbDqMRr2LBk5knAHKEelVaZStQoAAAAAAIJBsOoxOgRAQkoKQwEA5USrUnWCOH2PFXfTieOKq2YFAAAAAADwR7DqMTppVbepU5m8CignOo5x76VL99zajRljbv5t+uMGAAAAAABAaQhWPUar5fQGoGLMGzDA3AAAAAAAAEJBsOoxOnHV9MREyUxLsy0AylNsQoK5AQAAAAAAhIJgFUC11jc729wAAAAAAABCQbDqMa1HjJAes2czxioAAAAAAADgYQSrHqOXJOvkOjo7OYDyN6NtW3MDAAAAAAAIBcGqx2Slp8vCwYPNEkD5256ZaW4AAAAAAAChIFj1mLyMDFkzcaJZAih/XSZNMjcAAAAAAIBQEKx6TEJKihlnVYcDAFD+mqammhsAAAAAAEAoCFY9RoPVdmPGEPQAFWTF2LHmBgAAAAAAEAqCVY/RsR51GID8nBzbAqA8LRk50twAAAAAAABCQbDqMTq+6o/du1NBB1QQrRLXGwAAAAAAQCgIVgFUa92mTjU3AAAAAACAUBCsekxSWpr0KygwSwDlT4fdYOgNAAAAAAAQKoJVANXa9MREcwMAAAAAAAgFwarH6NiqOsaqjrUKAAAAAAAAwJsIVj1GL0nOy8iQ7ZmZtgVAeeoxe7a5AQAAAAAAhIJg1WOapqZKuzFjmKUcqCBxycnmBgAAAAAAEAqCVY/RgKf1iBEEq0AFWTh4sLkBAAAAAACEgmDVY3QYAB1fVZcAyp++3xjTGAAAAAAAhIpg1WOy0tNN9ZwuAZQ/rRDXGwAAAAAAQCgIVj2mblKSGQ4gNiHBtgAoTzqmsd4AAAAAAABCQbDqMc0HDTIzlFNBB1QMHXaDoTcAAAAAAECoCFYBVGs/du9ubgAAAAAAAKEgWPWYzLQ0mZ6YKCvGjrUtAAAAAAAAALyGYNWD8nNyzA1A+etXUGBuAAAAAAAAoSBY9RgdY7Xb1KlmCQAAAAAo4ib5pRAFAOAFBKseUzcpSRJSUswSQPljjFX8P3vvASBZVab9P7dy7K4OkzMzwOAwzICkIQtIRkBRZEGFVTEtK6j/NbAK+n2gu58Kyuq6uizoigooQaJIlCxpCMPk2JM7d+V4/+c5996ZmpoKPT2pe+b9Dafr3nNPrjqHuk+99z2CIAiCIAiCIAiCMBREWB1mdN1/P5Zfey36nnnGjhEEYXeSmD9fB0EQBEEQBEEQBEEQhB1BhNVhBgUeblwlwqog7Bmm33yzDoIgCIIgCIIgCIIgCDuCCKvDjMjcuWi/8EJxBSAIe4iJ11yjgyAIgiAIgiAIgiAIwo4gwuowg6LqrPvuk82rBGEPsfGOO3QQBEEQBEEQBEEQBEHYEURYHWZwd8vMqlWyy6Ug7CEWX3mlDoIgCIIgCIIgCIIgCDuCCKvDDPpXfWXaNP0qCMLuh+43GARBEARBEARBEARBEHYEEVYFQdivef+bb+ogCIIgCIIgCIIgCIKwI4iwOsyYesMNOL63VzbTEQRBEARBEARBEARBEIRhjAirwxBPLKaDIAi7nxdaWnQQBEEQBEEQBEEQBEHYEURYHWZ03X8/Flx0kexSLgh7CG4UJ5vFCYIgCIIgCIIgCIKwo4iwOsxIzJ+vxdXMqlV2jCAIu5M5Tz+tgyAIgiAIgiAIgiAIwo4gwuowI3bKKZhy/fX6VRCE3Q/nmsw3QRAEYSRjGIaEfSQIwkii2md4XwoPXH+aDtWu7UtBEISdw9jQ0WGu7OjAvHnz7ChBEIT9h+XXXqtfp998s34VBEEQ9l8u/N5T+vX+75yqX0cKvDHeGBd/4SOdsdFemKZpnwm1eGXaNP103zErVyIwdaodK+wNZO0Z+ci6IzSCbvO4Jwn3AeJG68L2iMXqMENcAQjCnmXtLbfoIAiCIAiCIAiCIAiCsCOIsDrMkM2rBGHP0n7hhToIgiAIgiAIww9aS731gQ9sCc6mo7xncuL6nnlGxwmCIAjCnkaE1WEGzasZBEHYM8y67z4dBEEQBEEQhOGHc39E8ZTBEVb5pB/P+aRfZO5cHScIgiAIexoRVocZE6+5RvutmHrDDXaMIAi7E34ZF9cbgiAIgiAIw5d6vvAnfPnLYpgiCIIg7DVEWBUEYb+GGyAwCIIgCIIgCMMTblJVzXUT48decYV9JgiCIAh7HhFWhxncRIcij2ymIwiCIAiCIAiCYFHNalWsVQVBEIS9jQirwwz6DOJjyY7vIEEQdi90vcEgCIIgCIIgDF8qrVbFWlUQBEEYDoiwOszglwVupCNfEgRhz+BsiCAIgiAIgiAMb8qtVsVaVRAEQRgOiLA6zOCOlhRX+QusIAi7nwUXXaSDIAiCIAiCMLxxrFbFWlUQBEEYLoiwOszoe+YZ7V81MX++HSMIwu6k6/77dRAEQRAEQRCGP7RaFWtVQRAEYbggwuowg8Lq8muvFaFHEPYQU66/XgdBEARBEARhePL7Nzbjkl+/hyk3vIS2n3XgiOUH6eNLfr1AXxMEQRCEvYUIq8MMPtYSO+UUcQUgCHuIqTfcoIMgCIIg7Ku8+FzBPqpNozS//FnWPqpOf7+Jd98u2mfVefShvH1UnUZtGEwdjcq4686cfVSdXVHHruiHYPFaRxzH/Ph1fPfBZViydBMONNP4QKSAo9uD+njJ0k59jWmYVhheDGa+dawp2WfV2RPrRqM27OyawPJ3to5GY8n8jepo1E5BEIaGCKvDDPoKmvP00+IzSBD2EOIKQBAEQdjX+dXPM3XFCV5jmno89nCu7k07b9jrlUFx40ffT9tn1WEd9W78B1PHlz+ftM+q89hDO9cP8p1vpOyj6tx1Z7auCDKYOgTge4+twnE/eRO+VBozjQym+ExE3IDHMHXg8RRfSV/zqjRMe/0jK+3cwnCg0Xy767e5hnP6O1+vP9+Yf2fmG+s4/bgB+6w6V16aqNuPRm149ME8fnhT7fWPbfjIOfV/GGg0lrzOemoxmHVeEIShIcLqMKPQ16eDIAh7Btm8ShAEQdgXqHfDTUsm3nTXYsHbhbqCJvMvUKHeTfvdd2ZVHbWvs3yWU68elk9RshaDqYPjUGssKF5QXLjrt0Ovg31gqCeiLHiH/axdxkvP5+vWIQA3/XUN/t9THTg5UkBbqfZYO7SrNEz7o2fW6rzCnqPefOOcrCco8scUiqu14LUBVU6t+cY6WH+9+cY5XW/dceqv1Q/Od7ah1vrHNlh9rd0G9rPenG/UBsZz7ao/lvWFU647HAe2VRCEXYsIq8MMblz1QksLVsmjyYKwR+DGB7L5gSAIgjDSqSUW8mb87PO9dYUF3pCfdZ63pnjBvJdc7qt50+4ICyyjljBAYfeSy3w1hVO287gTPTXbOZg6KKDoOmqMBcWLq77kV/2ofp3ls+xG/fjaNwM1RRSnHxRXa8E+1uvrvkoyM7j+8pH+Gx5bhWNDeYR34G6VaeepPDf8ZZW4BdhJBvtekVpiH+fbZ7/or/k55xxrbjbqzjeKgbf8IlxzvrHss8711pxvzrrBNZBzsxoUPb/6zWDNdYNz/nv/FtLpqsE2HHqYu2YbKMw6/aw1Fk4baq2xFHWtdad6ftYxabJLt6PaWHIMmJfruPyoIwi7HhFWBUHYrzm+t1cHQRAEQRiOvLuqF0/N32Cf1aaWWEhhYt4J3ppCnnND/tkv1hYLedPP65OmuKretDvCwlnn1RY1KTpQnKglDNBqlvnZzqHU4YiirKPeWLAftYQc1sH6WUctgYMi9Ge/FKgporAfznhXE3IYx/H+2GX+uta5+yIU6y783lO47fGldkx1rvr9Ihzd4tohUdWBeY5sMlQZi+0YYSis3BjHZf/+Nzz4cocdU5tac4Hz7ZLL/TXXHoqFs2Zbc7qaOLtFFFXztVYd/DHFWTeq/TBEcfe4E9ScPpeC4vbXuQ5Q9LR+OKo+H50fnjpWV7eG121Q5ddqA/vONUGvXVXmfHkbalnFcg12xrJaG9g3rimso1oZztpG8bbaOAiCsHOIsDrMmHjNNXj/m2+Kj1VBEIR9gHVXh5F+8177TBAEYcehGPXTPy/UIkc9gXUwYmG1m3rnhryWxRWFDT4GSzHwEpWu2k17ubBQTZxwrDgpHtQUBh620rCd1UTNRnU4/axlAeeINOzHcSd6q/ajXIR2Hs0txxGhnX5UE1GcfrCcagIGhVeON9NUE5v2ByjW1RJYucN/IpnTj/YPldHIqzKyuixh6HDt4XvUSAyv9lnm/HPmW821xxYLawmnjihKqs03Z05z7eIPJtXmW7m4W60OR9yttW6Ur11f+1Zwu3XDaQPT1BIt2QZazNbqZ3kbWE5lGxyLV2csq4nQW9dPb1Vf1s76yXJYHtssCMKuQ4TVYQYfSY7MnYvA1Kl2jCAIu5NXpk3TQRB2F31/uBrJ535lnwmCIAyNRgJrNbGQYke5EFhNyHNuyAlfK8ULiht81Jbwpv3u320rkPAGnUIA87KeauKEY8VJqgkD2wmWFaLmYOqgcODUUWssHJGmlvig09SpwxGhCUWKSgvf8n5QRKk33k5fK+vYn6gmsP7h9Y2IlrYXvXeUJjOvytpknwk7Sz0xnHOhUjjl/HPmWzVBsVwsJJwLlfPFEV5JNYv68jnNcirnkiN6ltdRub6V11HtRx3nxxZSbf0rbwP7U9kG9pOUt6HSkr2yDZXrI+tw2lBrLJ11h6Fy7SpfPwndpYg7AEHYtRgbOjrMlR0dmDdvnh0l7E24O3n3Aw+g7YIL0H7hhXasIAi7i2cNQ7+ebMovt8Kuhxar7R/7Ifqe+ClCR1+K6Lnfsa8IgjAcoXBAPn3Ggfp1OLByc6KqkBoOeHQ7T507Dob6f9nitTEcPatfvzrwBn3WYR59M064Yz6trpybfN6QU2C8/fcRfc4bfoqHP/lFWJ8T7obNx+udPEepOu59NLrlnHkoPnzvByF9TuGiv88EfZk6nH78AP70SFTf9PMmf0fbOdg6nnihSR9Xq4P9oDjjiAuV/WIdFGedsWjUD+fcqZNU6wetfJ06q413eb8+9+M/6te9CT9Xu5N6vjvPP3YSbnihEwcjo3f83xkSRXVfZXjx4wtn2DEW9ep3SGQai06p3PaWh9UYTH17Mg1JDqJ/LKteeXyvuP5w7dkYb9luLnz4nLheR5z59Z1vpLQ46MwNzpWmZteW+VW59lAI/M7XU1vmCqmso3IOs45Zsz1aOCS//FkWA/0lvZYQlsl6nToq5yPXjdOPG8CrC5r1OWlUZ+W6UtmGyjWBIilF6FptIFxjy9swmLEsr6Ny7apcZ9jPa9Ta5NQ5NtoLU+6DhDpwc3XuA0QjQHGhVx0RVocZ3LRq9Xe/iynXX4+psoGVIOx2+GMGkR8yhN0BhdWxX7gHxWQv+p68Ff5px6D5kp/YVwVBGG44wupIYnQsgDu+fiY6Or3b3YBXCoG8wab1qCM0VN6Qk3IhgTfgFA7uVWU4UKwgzk175U1+pahZTTioFE4r20lhoGN1cUs7G9VRKRyQ8rGo1o9K8aFyLAbTD7bLcU9Aqo13uVjUqI7hIKzuTQ6d2oL/XjCAI0MleIydE3oKprHTZQjVofjOH3TOP3oSxrQEtbBaLihWmyuVgmKlQEnK155KUZSU11FtTjOuXDBsVAfnY7m4S8rXpmrrSvm6MZg2cB3isbMmkMG0wflBZjBjWbnuVK4rbGO5+EvKfyATYVVohAirjRFhdZjR98wz2mK1+eSTRegRBEEY4TjCqqZUQN+T/wEjMgqtn77TihMEYVjBDVueenujfTY82NyXwSuLOu2zrVDcOO/oiTht7vgt4kb5TX+1G/LKG+5KIZaUi4WVAiepFA4qb+pJuaBRTbwtL3cw7RxqHc5YVBNIWEe5dVq1OspFlmp1lAuntfqxI3UMB4FjsFaPQ2FzXxrX/vJV+2wrFFQ/fcYMTBsbRej/+xtOixbhxs4Lq5uLBi4/cowdYzEYi9yQr7G5bCSw9XNQj8HUtyfTkPAg2s618Pt3v2OfWbB8R1DlDzrEsVjl55+P0XMuVP744sDPPwXFanOFlP+AUm1tKl97qgmvxJlj9AvNuVUuepJycbaa8Fo+p6sJkuVzmmnLf6hyoGj5xItNerOrWv0cbBuqrTvEGctqaxtx1pUmtdawjspxKH+PRFgVGiHCamNEWBUEYb9m7S236FduHCcIu5pthFWb/r/9N0r5NNquugeGf9sv24IgCJVQVC0XOChuOIJqpbjBm/6PnBPXN9y1bsidG26KjtVu+iuFBW7Iws1hynGEA1pOlT8+71AualYTE9lORzht1E6KMOVWnw6DqcMZi2oCCXGstmqNRXkd1YQe4jy2O9TxLheZ93WBgz8SXPXTF+2zbQVVh0nXv4SDkd4lrgAWI4CO7x5nxwg7wrurevGvv3lTH1cTVB2ctYc4Yl+tueIIivy8V5srjjjLuVptrhBnrlNgrRQkiVMH51s14dURZ7muNaqjmiBJnDlNVwXV1keuBZOmuHe6n4MZywXvWP5XK+tw1hXWX0385Tg4/RNhVWiECKuN2XaGCnudzKpVSMyfrz+8giDsfpZfe60OgrCnaD7pM/A0jUbXzaeh2L3KjhUEQagPxY1LTpqKm686GpeecsB2AgehsEhBgb4EyzelKsfZ2IliJW/IK+ENOgVT3ngzVIoGhDtcc/MTZ4f7Srh5CoUDCggUBMoFT8JzZ4OVF58vVK3DaSfr4IYulQymDo4Fr5dv3FIOxQZnLBr1g+VVihuE5dYbb7b9rt9mG9Sx7YY4+zoUVG++6ij8308evo2oSg6fEEF3cdv3cih0F126LGHocM05ZuYo/T7Rn2q1Naccfv4p+NWaKx/7B79aW/J6zlebK1wHuDFTrbWJMB83t+Ocq1aHszN/+YZQ5bBter7WmI+EdVDwrLYuEa5NnNO11sfPfilgt6H6mlDez3ptoEDbcCzVWl0pqhJnXeEPX2xvJXocVpf0WAiCsPOIsDrM2HjHHXj98MO3WNEJgrB7iZ1yig6CsCeJHnUJ/FOPROctZyC3+nU7VhAEYXsGI6iWQysobTFVRWwkvOGmaEBxgwJpNRzxwtntupKt4kV14UCLAVNcWpSsLRx46woHTjt3pg6OBYWeavkJ66AA06gO7gReS+ihcEoLtlrjzXIpcNQSkJmH7dgfBA5+lmsJqg6XHjkG/Ub1sd4R+g2PKmusfSbsKKNjQf0+ffNjs2u+V5VwvnHdqDUf+fmnEMjPfLW5QjhfaG1Za23i2sM5fda51a+z3Ho/hBCnjprrwrk+/ag8X6vB+frD72dqro9OG2qtCWQwbeC60Wgsa+VnvWwnxd9aaZwflgRB2HlEWBUEYb9mztNP6yAIe5rwYecicsRF6L71HGTe+4sdKwiCsC207huMoOpwyeU+fcNe64acN9y80a4nPFAstATJ+uJFPeGAm6/QqrWmcKBu+tnOataohOXOOsy9U3VwLCg+1BJFWS6tthrVQZGlngjNOuqNN/vK11rj7Vjn7utQWG0k0l16xGhEI35sKFV/TwfDuqIbTaoMliUMDa43gxVUHfj55g8R1X5AcOA8qjVXCMVZ5q81V5x5VM0a1YFl1JrzxHl8v9ac55ymFWitdYX56B+1URsG0896beBY1moD4VjWEn8J19Za4i/R61Ksev2CIOwYIqwOM6becANONk39KgjC7oduN8T1hrC3CB58MppP/RJ6fvlxJF/+rR0rCIIwdHijzhv2euIGb7hnza59nTfzzN/opr6ecMCbduavJRwwvp54QShq7mwd7Ee1R2UdKJA0qoP56wk9LKPeeFM4rSUgExE4tuW2f5iJN+NAcghGvMzzVsLAf6syhD0PN1GqNVcIBcV6c555KXzWgz5J69XBH1Rq/RBCOGcb1UH/rfVgPxq1YWf7ybGstbaRRmPJa/XEX2ftEgRh55HNqwRB2K951rC+sPAHDUHY1VTbvKoa+U2L0ffETxH5wNWInP4VO1YQBGFwlG8gQ2hNWk/o4+Oh3DW7njDQqAw+us4dp+vd+A+mjHptYDvJztTBMurlH8xY0LK2ngAxmH40qoPIJjJb+b+Pr8b3VTguUkC4/rBtgaLqi0kPvnH6FHz7zCl2rLA7qVx7hJGHrDtCI2TzqsYM8n9Twp6CvlXpY5W+VgVBEIT9A++YgxE75zqkXvo1Bu7/lh0rCIIwNOoJjYRCYyORr1EZzF9PsCSDKaMeLH9n62iUfzBj0ciqazD9aJRG2JZ/PWMKvnzyBDwb9+hH+xuxNu/Saf/5pAkiqgqCIAh7FPk//DCDvwYk5s9HZpXsFC0Ie4L3v/mmDoKwt/G2jEfLudcht/Rv6Lvz83asIAiCIOyf3HT+dLx4zeEwo2G8lfNiVdaFRFHdL5mGDjxelTX0NTRHdNrvqzyCIAiCsCcRYXWY0X7hhZh+882yS7kg7CEic+fqIAjDAVcggpZzv4Vibwe6/+tiQB7NEgRBEPZjjpwUxWtfOxI3ffhgTJ3WisUI4Mm4WwceT53Wrq8xDdMKgiAIwp5GfKwKgrBfs/jKK/Xrwbffrl8FYVcyWB+r1Yi/+Bvk45vR+tm74Y6027GCIAjbI34O9w3E16Ew0pC1Z+Qj647QCPGx2hixWB1m0A0A/avyVRCE3Q/nm/g0FoYj0eM+CW/7NHTf8kEUNi2xYwVBEARBEARBEIThggirw4yu++/XFnR8FQRh9zPxmmt0EIThSPT9H0Hw4JPQ9ZMzkFv+oh0rCIKwY7z4XME+qk2jNL/8WdY+qg53vucO/fV49KG8fVSdRm0YTB2NyuAO//XYFXXsin4Iwr7AYOZCx5qSfVadPbFuNGrDzq4Jg+lno+uN1i62sVEZjdopCMLQEGF1mBGYOlX7e6SZtSAIux/6NGYQhOFKaNaZiB5zGTp/ejbS8+VHN0EQdpxf/TxTV5zgNaapx2MP5+retPOGvV4ZFBZ+9P20fVYd1lHvxn8wdXz580n7rDqPPbRz/SDf+UbKPqrOXXdm64ogg6lDEPYF+DmvN9/u+m1Ohdo/2nBOf+fr9ecb69jZdePDZ8fts+pceWlCp6sF1656a+xg+tmoDY3WLl5/9MHabaDwyrVJEIRdjwirw4yxV1yhdygXCzpB2DPQ7Ya43hCGO4EDj0fLOd9A72+vQuqF2+xYQRCErdS74eYNNW+6a7Hg7UJdYYL5F6hQ76b9bnXD/lgdYYHls5x69bD8ejf+g6mD41BrLCheUPyoJ3A0qoN9sASKOuP5DvtZu4yXns/XrUMQRhL15hvnZL35xrnwq5/Xvs78A6oczrlaWPNx59YN1lGrHyyf1+qVwbWr3ho72H42Gst6azDXHdZTC7ZP1h1B2D2IsCoIwn7N64cfroMgDHf8k+ag7fzvIP6Xf8PAo9+3YwVBECxqiRcUEs8+36tvymvx2MN5nHWet6ZYyLyXXO6rafXFm34GllFLGOBN/SWX+WoKIGzncSd6arZzMHVQQNF11BgLWo1d9SV/TYGD5bPsRv342jcDqp3VBQqnHxQ5asE+1uurIIwkaol9W9eN2vNtUHO6ztrTaH3bkTpq9YNz/ie/COvXalB4PfQw925tgzOWtaz+HeGZr6yrGs46zzETBGHXIsLqMGPVDTfoHdfW3nKLHSMIgiAIFp5RB6Dl3OuQefNP6P/jV+1YQRAEPg5bXbygBdO8E7w1hTzeiE+a7MJnv1hbLORjrrw+aYqrqjDAciksnHVebVGTQuP3/i1UU3yg1Szzs51DqYNiAvOxjnpjwX7UEjhYB+tnHbWEHIoTn/1SoKZwyn44411NwGAcx/tjl/nrWtkJwkihlthHsfCsc33159sJHjVXvFUFRUeQ/Oo3gzXXjfL1bajrhlPH3b+rvXbVKp9QcOV8rteGwfaz1rrDsfzYP/jrjiXHgT8sVbNKddZ5jkMtgVgQhKEjwuowpNDXp4MgCLufk01TB0EYjvTFk3j21Xfw6/ufwKp1m3ScOzoKLed+E7k1b6Dv11fqOEEQhMGIhdWEPEcUoPhQTSzkDT8fUeVN+SUqXTVhwBFQWE81UdOx4mxuNmqKDxQsnXZWExca1eH0k3VUGwtHvGA/agkc5SINrVsrccQJpx/VLHydfrCcagIGhVdHhGGbBWGkw89yrfnGa7XmG+c0r3G+VpvznB9cl+qtG+XrW7UyOAedOV1r3XDq6O/b/lF8xxLUWjfqz/m6a5e6xn5WE6HL21DtxytnLJmm1ljyxy9a7vJHn2rrjrPOn63aUM+aXhCEoSHC6jCDPlbnPP20fhUEQRD2bz593c047Ypv6NcZZ/yjFlmJyxtE61n/H8xUD7p/9iGY+eoWDoIg7D9Uu+HmDXu5EMjzShxRgFQTDigwnnWuVx9TGKi06uINP4UA5mU91URNx4qTUGD44U3bigvbCZYVouZg6qB44dRRayxoNUbqCRz16nDECUKRt9LCt7wfFDDqjbfT18o6BGGkwTldbb5RCCScS5WCoyMWOnOhmqBYPqdZR6XFaaN1g1BE5FysNaedH2wIrdmr9cNpAy1G6815tqEyv9PPegJxeRuq/XjVaCxZnvPjF+vgOess58XnrbWN8LXa2iQIwtARYXWYEZg6FbFTTtGvgiDsfip9rG684w4dHKvxxPz5+pyvhPFOGoeu++/fJk1m1Sp9zngHJ09luX3PPKPPiZPGgdd4XllueRqnbl4j9cqVPu35Pk38jxQ8s84dcnjgyZfw5U9egD/99F8Ri4bxm/uf0HU4NJ38ObiDzei65XQU+9bZsYIg7I9UEwspAjhCYLWb+nJRgFBAqBQOaMV5yeVby6i06uINunPDTqqJmpXibeWjquWCJeuoFEAGUwfTU0Ah1caC4gXzkWpj4Tyi74zFYPpRaflV3g/CNOUCRuV417JwE4SRBOdb5Q8u5aIoP++Vwmm5WEiqCYrlc5pzqdLitNG64VjKO1TO6XJxl9CHKde7chxLUML21pvz1daVyn5WitCVbWAfWGc55cJrtbFkec6PX4Rlla+xXHeYj4HwfdmX3ZCsuzqM9Jv32meCsGcQYXWYwRv85ddeu81NvCAIu4/E/PlbRDSy+MordWA82fTrX+vzdT/5iT5nWieNk4/XeO4IdMzLc85lh9Xf/a6Oc+Y20/Kc8aS8XEdQdOrmK2G8k8ahstxqdTt5pE97r087w7e/eBkuOG0ePnXR6VpopXuAcqLHXgrf+Fno+skZyK+zLFoFQdj/4E3zLHUDX37DXS4Ekkqrr0ohsPIxUeemn2KgQ6VVl/P4vEOlqFkpJvK1UgCpbCcFkPJ2NqqjUkCpHItK8YJUChzOI/oOjfpBWF65cFp1vMsEjGrCazUrO0EYSXBONKlQPqd57IiipFI4LRcLSaWgWG1OD2bdqJzT9daNStGTdXCec60grMuxBHVgfWybQ7klKKlcY6v1s1yErtaGjtWlumtX5ViW//hFuEZzrXHgMdvlwLLK1619kb4/XI3kc7+yzwRh9yPC6jAjoW70uXGVc1MvCMLu5eDbb0f7hRfaZ5Y7DgZPLKbPw3Pm6HO+EsY7aRzaLrhAn0fmztXntDjneXm5Yz71KR3nlMu0PG8++WR9TnjO4MBrPHfqdsotT+PU7Vi5O+XS8t3BySN92vN9WvtPIRQWPDykcNuNluDbN5DQr6vWbcaUCWO05WolkcMvQOjQs9B1yxnILpb/fwjC/kr5DXctIbDc6ouiQPlNPSm/6abVk/P4vEOlVRfTlt/0sz6eO8JApZhIyoXTau2k+LBNOxvUUSmgkPKxqBQviFXHVmvRSpGGdZQLOdX6UW75NRThtbIOQRiplM+3SlGU8HNeLpzyM185F8oFxWpzulw4rbVulAun1eZb+Q8ulT/YkPLNn1hXuSUoYXpHtGQbWGZ5G1ifs3ZREK3Wz3Kr/1ptaLR2OWuwI7yWi788dsonleIv28Dz8jT7Gq3n/SsST/wY8Ye/Z8cIwu7F2NDRYa7s6MC8efPsKGFvQusoWj05AoAgCIIwcuHjSGO/cI99tmPQMrX92I/p4zkzD8Bbi1bgO1/8B3znS5fpuGpkVryMvsdvRusnf4XgkR+3YwVB2NcxDAMb4y36Bvsj58TxxAtN2o/prMM821iNkS9/PomvfSuob+4pQtz++4h9xYKiCIWDn/wijCsvTeidqitv7I+a1Y8nXmzSN/20yKosg35aWf5VX/Lj9OMH8KdHotuID2zn0aqMxWtjDdtJ8cJpTzmDqcMZC/aDVlzl4gJhP+59NFpzLMrr+LAqi20oFzAIy3h1QfOQx5t1dKwu6jRjo70wZUNNYQRRvvZcoz7r/HzXmgvOfOOcpjD4vR+E7CsWzNfU7Nol68Zg5jTr4PpQTnk/uG58799C2815J98vf5ZFc8zQQmg55WtXtX4yHxlMG77zjZQWXuuNJUVo1lcO882a7VHBXXOdd/Lta+uO8927mOxF35O3wj/tGDRfYj3RJgwNPrH3QkuLNjw5vrfXjhXKEYvVYQYtp2bdd5+IqoIgCPs5tEx98o4faFF19bpN2h3AP39yq3VtNQIHHIvWD30Hffd8DYln/sOOFQRhf6HcGqrSWsvBsfqiWFlpKUV4A0/BlDf3DJWiKqHPQVp18ca80oqT0KKK1qC86a+0KiM8dyw1q1nNEqedrKP8MVaHwdTBseB11lNtLCgqOGPRqB8sr1JgIY7lV63xdh4Nrl/HVutcQRiJcH44c63WXHAsMatZaRLuaM9r9ea0Y3FaaYXpUL6+1ZpvtJytZlVLWAfLd9a/anOeYiXXSJbTqA3V+ulY/Q+mDayn3lhqVwPV1sdzrc31mJ/trYRl7uvrjjvcgrbzvoXCxoXoua22UYIg7ApEWB1m8NcA+udzfAIKgiAI+y8nHzUbr//pVnS9fPeWDawaQX+rred/G8ln/xPxhyzfsIIg7D/QMpPWXNWECUJhgRZMFCacTVkq4U03fX9WugFwoBUrRYNaAgrr5QYrFCWriRuE4gPbqdNWES+cdu5MHRwLWm5Vy08ckaVRHfSJWE0gIRQ1aKFWa7xZLgWMWgIy87AdFFIEYSRDsa/e2uMIp7XEQuahqMr5VmtO0+WAs25Uq4NziWtCvTlNlwO1RE/CfLQYrbX+cc5zXWFZtdYutoH+qistTYnTz1oCM2EbvvN1Wp26q/aTY8m1q9aPX8zP+llHuf9VB5a5X6w7Lg9iH7wGyKfRdevZMLOWey1B2NWIsDrMoH/VV6ZN06+CIAiCMBQ8rZPQeu51yCx4DL1/uNqOFQRhf4DWUHzctZYwwRtq3nTXEgWIIxxQ/KyGIwzUElAIBRDto7WKuEF4U892VrO2IiyX1mk7UwfHgiJOLfGC5VJkaVQHH92tJ0Kzjnrjzb7ytdZ4OxZugjCSodjXaO1ptG5wnukfdXZi3eCPIfXqsDZ3qr1u8DqtSWvVwXxcN6pZghKnDbXKJxSh6/WTP1416ifbUEv8Jbqda6z1rRr707rTfNJn4Gkaja6bT0Ox29p8VhB2JSKsCoIgCMI+iCsUQ8u530Rp8xL0/PeldqwgCPs6vOGmBVM1KyYH3qzXEgUIb8iZv54wQAGkloBCKIAwfy1xg/G05qpXB0XNna2D/ahmNeZAgaNRHcxfS5xgHSyj3nhTwKglkBAtvMaq90EQRgrOfNuZdYOCYqM5zbm4M+sGf3CpVwfL55yvNaeZj3O2miWoA9tQ6wcdQhF6MGtXo7Gs9eMXYf2Vm2+Vs7+tO9GjLoF/6pHovOUM5Fa/bscKwq5BNq8ahjhuAJxdqQVBEISRyc5sXrUr6X/uNpTSCbR97h4YwW03SRAEYeTjbCDjQKuwekIfHx8dqOE/0KFRGbSEalI3/7WEATKYMuq1ge0kO1MHy6iXfzBjQcsxiqe1GEw/GtVBZPMqYaRRufbUeszfYVfM6T21vtXLvyfWrkZjORjYjnptIPvq5lW1SC9+FvEX7kDLP/4GgfedaccK9ZDNqxojwqogCIIg7CaGi7BK4q/ehey699B21V3wtB9gxwqCsC9QKW4IIxMRVoWRhqw9I5/9TVgl2VWvoe/xm9H88VsRPvZyO1aohQirjan/s6mwx+m6/34suOgibLzjDjtGEARBEHYePgIVmn4sum45E7lVr9qxgiAIgiAIgrD/QJcArRd8B/EHv43EEz+2YwVh6IiwOsxIzJ+vxdXMKnGqLAiCIOxaQrPPRuT9H0bXrecg8+5jdqwgCIIgCIIg7D94xxyM2DnXIfXSrzFw/7fsWEEYGiKsDjNip5yCKddfr18FQRAEYVcTPPhkxE7/Z/TcdhlSL//GjhUEQRAEQRCE/Qdvy3i0nHsdckv/hr47P2/HCsKOI8LqMIOC6tQbbhBhVRAEQdht+Ke8H60f+jb6H7wBib/+yI4VBEEQBEEQhP0HVyCClnO/hWJvB7r/62JA/FwLQ0CE1WGGuAIQBEEQ9gTeMQeh9bzrkHzlf+URKEEQBEEQBGG/JXbql+B2e9B5y2koJrrsWEEYHCKsDjNk8ypBEARhT+FpHofWc69Ddtnz6PvtVXasIAiCIAw/Fr38Mu7+/k34zhmn4dqj3o/PHDRDv/Kc8bwuCIIwVKLHfRLe9mnovuWDKGxaYscKQmNEWB1meGIxHQRBEARhT+Dyh9F6zjdQ6l+Prl98GGapaF8RBEEQhL1PorcX//O1r+J//vmfsOnRR3CY4cIZY0fjslnvw5nqleeM/5+r/wn/fe01Or0wPHjxuYJ9VJ3+fhMda0r2WXUefShvH1VnMHW8+3b97zaN2tAo/67oZ6Prd92Zs4+qwzY2KqNROwUg+v6PIHjwSej6yRnILX/RjhWE+oiwOsyYeM01OF59GaCfVUEQBEHYUzR/4Itwe3z6V/pSotOOFQRhX2AwN9ON0vzyZ1n7qDoUDhqVsScEkkZ1NBInBlNHo3YOph+N0ggWz9/1B1x32geQXvAuzpkwHoe1tWJUKIiQx6tvZIPqleeMP2fieGTeW4BvnXoKnlP5hL3Pr36eqSv23fXbnAq11xbOle98PWWfVYd11JtPvMY0tWAdHz47bp9V58pLEzpdLR57OFd37RlMPxu14bGHcnXHktcffbB2G7iu3XVn/XVcsAjNOhPRYy5D50/PRnr+/XasINRGhFVBEARBEDRNfARq9HR03nwaChsX2bGCIIx0KCrUExQpCNQTHsjdv8vWvamneFHvpn2wAkm9dg5GIGlUB9vYqB8N6/hGqq7IMph+iMDRmJfuvw8P33IzThjVjkMDfhh2fC14fXYoiBNHj8Kjt9yi8wu7n1rziXNEf9brCIovPc+1p/Z15h9Q5dT7saORYHi3uvZYHdHTqaNWP1g+r9Urg4Imhc1aDLafjcaynnC64J2irqcWbF+9PgjbEjjweLSc8w30/vYqpF64zY4VhOqIsDrMWKu+BLwybZp+FQRBEIQ9TeSIixCaeSo6bzlD+17dUbgJoyAIe55awgNvyHmz/uJztW+oKTxQXK0lFmphYXWprkDCMnjjXwtHOKglOA62nYOpo9ZYMH6BCj+8KW3HbM9g6mA5tQQKpx/1RJZGdQhAZ0cH/vfb/4ojmqIYHQrZsYOD6Q9viuj8LEfYvdQS+/gZv+RyX01BkXOF4azzvDUFRc4Vq4zqP3Zw3Tr7fG/N+bQjddTqB+fyT34RrjmnuR4ceph7t7bBGcsffb/62uWseXxlXdV47OG8bkMjq/5K9ufvdf5Jc9B2/ncQ/8u/YeDR79uxgrA9IqwOMwp9fcisWqVfBUEQBGFvEJp1BqLzLkf3recg89bgHoHqe+YZvfkigyAIe55awgNvyM8616utmarBm3De7H/tmwH9uGo1KCh8799CDQUSChy1hFMKByyjlnDKdh53oqduOwdTx1e/Gaw5FuwHrzcSQBrVcfvvIzVFFme8OaYsq5LB1FGL/Ung+PXXvoq5kybtsKjqwHxzJkzAr7/6FTumOhxT2TR456gl9nGunHWur6agqOf8CR41771VBUVnrtSbs7TQnHeCV68dteqg6HnWeb6qPwyV10Gr/GpwTapVPuFa8LHL/HXbMNh+1lq7OJYf+wd/3bHkOFxymU+1Z/s6KLhOmuzS41Br7aqE3+ve+sAH9vvvdZ5RB6Dl3OuQefNP6P/jV+1YQdgWEVaHGe0XXohZ992HsVdcYccIgiAIwtD46f8+gFnnfQ6tx3wU533+eixauda+0pjggSeg5Zxvofe3n0fyuV/asdvjCKr88t11v/ihEoS9RS1BkjfkvKHnTX81IU/f9Ktrn/1SQPsJrIZj6VTrpp6CLIUD3vhXE04d4YA3/fXaSRGmVjsHW8dVX/LXrIPxFDQZqlltOQJIvTrY/7PVOFCoqDYW5eNdTeBw6vjsF9V474DAsT/9cPXmE39Fcv06HOz32TFDY2YwgOS6tbq8ShLz52P5tdfq/3et+8lP7FhhKFQTFJ35yGu1BEVnrnBdqSYocq5QFG1uNqrWQZz1i4JhtTI4xxzhtdoPQ+V19PdZ87scxxKUoiTLqLY2cX2s1wa9tqlr7Gc1Ebq8DZOmuGqOJdPUGkuu3VzX9DpeZV1xxF+uXbXWRwdHUGXg8f7GM39/Gz/8nz/hzgefRi5vCfru6Ci0nPtN5Na8gb5fX6njBKEcEVaHGZG5c7W4Gpg61Y4RBEEQhB2HoupXfvBLLF+zAbNmTMFjz72Gi7703S1fEgeDb9JhaD3/24j/9UeIP3KTHWshgqogDC94Y18pFpaLG7SGqnbD7QiavKnn4/7VhAWKCrxOcaDaY/S0Grvkcr++8a920+6IiaSaOFHezlqCY6M6HOGVVBuLcoGE/a01Fg37ocombGelwDHY8aY4wnZwrJm+FvvrOvv3e+/FeL/fPts5xvt9eFWV58AnAx1Bla7X+JSgPCm4c3BdqJwLjlhIqgmn5XOllqDIuUJRlLCOSovT8rWJ5VSzuNc/pqj6rfVr+x+GnPWPVJvT7IfThmo/uFS2odaawLFw0tRrwyWX+Xd4LFkeXaA47ai2rrz4/Na1i68ss5L9XVAlX/23X+L0K7+Jb/zof/Cpb/wQcy74whajBJc3iNaz/j+YqR50/+xDMPPbi+jC/ouxoaPDXNnRgXnz5tlRwt6Ei1hi/nzETjlFi6yCIAjCyGXd1WGM/cI99tmehZaqFFVXPnkHxo1q1b++84vizvDFc4/AjV/7kbbuqXeT74nF7KO9w96uf2cZqe0fyeM+0j8zx6r5+JtHouAGLvQF6EBxccHbBXztW0F9fvrxA/iTSsebb8Kbb+52fa+KIxQ8abVFq08HCqmzDvNocYLpTz9uAK8uaLavWjf1LOOJF5r0OTd2mjXbo61THXidbgB4488yrvl8Uj9O79ConYOp48PnxHXfWQdFg8qxKO8HYR1OeaRyLGr1g+ILhYnK9KSyH0fN6se9j0Z1m0ijOsZGe2Gapr4fqLbO0vDimJUr7bN9l2+efAKODYcR2wXial82i5eTSVz/69/qMeVj/5VCqjOuzjXnPoz3ZHwveJ2GL2TVDTfoVz5dyHhed9IwjvlXf/e7Os2U66/XawsF3Ozq1Wi74AJdNst1rGQPvv12/Uqxl3knfPnLum6+95t+/WuE58zBVLtOx2J5+s036/rYlv5nn8WYT31K181yWQ6Z8/TT+pV5KCazLewD+8i6WYdT9+uHH67r5lOTjGe5rJvpWRfzMw3hOLFPLJf9vkXlu3FtDB9R8698PvFz7lhIkvL5SThX+GPJ935guXqotvaUz1HOnaPVfFqs6nKonNNfVusK595g66icjzyvXJsq2125buyONlSuKzx31h1S2aZf/iyLgf7SlnWncl2h+EtLWadfbBN/9HHWx7PUunOH+lzy/ayGMz/42eJn2XnClp8L7g1Dju/t3fK54GeXnzd+bnnMOF5jGsI8zMsyWBbLZNmcG/zc8rP4QkuLTvv+N9/Un8nFV16pP7sTr7lGfyb5WXc+kyerNZPwnPHtJ/ow8xc7vnEdLVUpqrY0RXDnD7+Otxev1N+bzzrxSDz0C2tOO8Rf/j3y3SvR+tm74I5NsGP3XZz3pPx9FLZFhNVhBv9Hxv8ZO4uRIAiCMHLZm8IqH/+npepzd/5Qnz/+whs456pv6+Oh8vmTJ+Hrh03HpgdX6S/FgiAMD85TYVm8Zbubft6QO4ImqbzhrhQCeVNfKZBUipwUDmhV5dzkV97UUwTlzvzOTXw1oYCC4xMvNm0pk9dp4elYZVW2s1odFDQcYaBSeCWVY1HZj8HUMZh+lAunlePNMokjolQb73IhhwLHby+8cFj/cFXO7mrLHQEfPnLgDHjd1udhZ8gXi3j3tTdwVDqjxYFqOMKRI8xQuKGAQyGHgg6FHQo8hOICy6EARCFopIhNu6tP9E77A7X2NBIgKwVFzpVysbByflUKkqRStKyc05V1VIqerKNcnK1WR/naVG1d4bpBC9ZywZjz12lD5Ryv1s/yNbZWG5x1pdq6U20sy9edyrWL4zBpinvLWscyy38gu1itOz9Uc6DW97qRJqz+hzr+o44ZGheePg9//Mm/6uMTL/saFixbjZ5Xtv8un3jzAWSWPY/Wz/wB3gmz7dh9E+c9KX8fhW0RYXWYwQWDvxA6vzwKgiAII5e9KazSpyof///BV/8Rhx08DZd97d/QO6BuEG7/Pk45+jA71eAxC1n0P/kfcLVMRMsVv9ZflvlFuPKLOL+AOzdrewt+ARzJjNT2j+RxH+mfmVHqxnVjvGUbsbDaDTnjyoW8ypt+QrGC13mTzpt+PqbqpCeMK7d2qhRUSLngUSlYkh1tZ606HHGiUR2VFluEcXyk1ulHpThByvtRKZiQcoGjVj/K46rVUS7kNBI49hf+cPCBuHjmQfAYW8dpqFBYvXflKnzr05/VlprVxtYRjijwVFqW8r7M3dy8xeCl0mKV/y9kOoo/FIm4lvB+jlAEIrQEdNKwXLaBcRQpmIewHObldZbLNMzDNIzjNceikOeMZxoGHrNsUp6G8DrzMg3LJSyXOHmYhjANA9MzECePc87rDoxraWnRP+qUz4VqYmHlXCifvw7lgmKlKErK66g2p1lHuXBaPn8dyteSSpGUlIuQ1daV8vWvURt4XPlDFSnvZ602NDW76o6lsz5WjqtD+fhWWz/LRWrHUr7e9zrOj5GCYWx9v4fCCe+fhWd+8+/Y0NmDaaddgemTx2HBQ/9lX92W1MInkXj5d2j9zO/hP9iac/sinOsirNZHhFVBEARB2E3sTWGVPqHoU3Xp6vV2jPoi/ckL8KOvX2WfDZ5Cogt9T9yKwIEnovmjP7ZjLSq/iI+0L+CCsC/AG0kKq+WWnNVEAeLc1Dc1G1VvyMvFi2riBnFu2qsJC6Rc1KwmJpYLA/XaScGRfl8b1VFNOCgfi0b9qGaZRir7UW5VS9gPx/JrqONdLqIMRuDY2z9cleOIbbua6y+7FMc3N6N5JzevIo4rgO8/+7w+p3haKbDK/7eGjrP2lM/pWvPNmQtcNyrFQlI+F6qJoqzDES1r1eEIhlzDqq0b5etbufjoUN6PamsXcfJxzjfHDL0+lOO0oVY/mY8Mpg3VhFdSPpaVP/gQZ+2aNdtddRzKfyhy1h2Hkf69bqjfvbkHAX2q8nvzvLmH4NV3lqBQLOLH37gK//yJC+xU25NZ8TL6Hr8ZrZ/8FYJHftyO3bcQYbUxO/8zoLBL4Yd2d31JEQRBEPYfZk6biLce+E/8+gdf01artFQdkqjatRK9D92I4NyLthNVCa1t+IWbj3Txy7cgCHsPCgDOpkvcJZobMVVCC1VujkI/pM5mT+WUb47i7HZdCeMcyy1nY5dynI2bKFgyVAoTFEsoCPAahYdq7aRYwV31G9XBclheZR3bjkXtflBo4XicdW7tOih0MJSLqoT1Mq5ePyhcsPzyzbXK0bt4q/eqnHrrqmNVOBwC27Y7wgGHzcGm3PY7uA+FTek0pr5vln1mWZFybOlflHUJu4byOV1rvnFOcy5wrlSb05wLvMZyOH/LRVXC81n2fCvfjKkcZ9d8zlv6eK2E6xvnG9evavlZB8t35nzlukIoVnL9ZDmN2lCtn5dc7tP9HEwb9DpdZyz1xlfnbf8DBDfD4kZbzM/2VsIyf/Xz6nNsf/1e5/N6cN/Prtc+Vfn4Py1VG4mqJHDAsWj90HfQd8/XkHiGjgiE/RERVocZ/BWVvwY4j5kIgiAIwlDhl8TLzv8AvvaPHxnS4/+5jrfR8+D/QfSDX0XTOd+yY6vjfBF3NsMQBGHvwBtmWnM5u0RXwpt6CgK8Ia8mBPKmnmIhrapYVqW4QZxd9fWO2+dvLxwwD8UR7uJdTbAkFHh5nQJCtXZqwXEQddz9u607h1fC9tNyq5pIQ9gG1lFLhHbq+NXPMlVFUUJRg0J0rX5QyGEbawmvrINWucxfyf4qcBz9kY9gfXb7Hd6HwoZcXpV3sX22FT7KLwLrroViH9eemvPNFk5riYXlc7qaKEro35l1MG21OpwfhmqJu858qyV6EuajxWi9Oc91hWXVmvNsg1671HElTj9rCcxEr11fT2lRtFo/nR9kqv3gQ5if9ddbd9jOauuOw/74vY5GCdyoij5V+fh/I1HVwTd+FlrP/zaSz/4n4g9tu9GVsH8gwqogCIIgCNuRXvo8eh+5CS2X/yfCJw7e0tXx6yYIwt6BVpY//H6mpqDpCAu1rLGIIxzUu+mnOEJqlUGRhRZR1W7qCQXeRu2k+EDq1UGrrWrCK+FYUCCuJdKwXNbBuobaD4oTrKNeP+gGoJbwShyr1lrsbwLH4ad/EKFx47EoY1lOD5VFqTRC4yfo8mpBgZUb5gg7D8U+WoDXmm/OnOY84HE1OJe1dXcVUZRwvrGOalaaRM/lKa66dVg/qFQXXom26lfXa9XBfFxDq1mCEqcNtconXFfq9ZNrV6N+sg21xF+i21ln3XEsaxsh3+sGh6d1ElrPvQ6ZBY+h9w9X27HC/kL1WSbsNfh4Cn0nycZVgiAIwt4iteBxxF/6LdqufgSBORfZsYIgjAT0Tb26ka4lBBIKefVuyClesIxq1lYOFEBqCSiEIgtv7Gvd1A+2nY3qoLVWvTp4vZ7AwX7UEpAJ63CEmmqwDook9fpBK7tawivheNNXYyP2J4Hjih/9GG+tXYfNqZQds2Mw39vr1+OKH99sxwi7m8HOt3pzmoIi87OsajjrRr06ON/q1cEfderVwfK59lWzBCXMxznbaM43Wlca9XMwY0lxtBasf1esO8LgcYViaDn3myhtXoKe/77UjhX2B2TzKkEQBEHYTezNzauGSuKN+5BZ+QraPns3PGNn2rGCIAxnnA1kHGo9autAa9VargIcaFlWS1ggtISiJWYtYYDQ6queOFvPmoqwnaReHYNpZ6M6Go0FLccontZiV9RBKjeR2d956f778MD3b8L7Y80YHdp2A6B6UFR9va8fF3zzW5h3ofw4uDsZytpDdmZON7q+q9a3evn3xNrVaCwHA9tRrw1kX1t3hst37/7nbkMpnUDb5+6BEdx2g7KRhmxe1RgRVocZ3IWv+4EH0HbBBfqxH0EQBGHkMtKE1YEXf4PiwGa0XnUXXJFRdqwgCMOdSnFDGJmIsLo9f/vdnfjj//t3HDh6FGaHQqgnEXHk3k2msKSzExd/7f/DSZddbl0Qdhuy9ox8RFjdfcRfvQvZde+hTX2v9rQfYMeOPERYbUz9n02FPU5i/nxsvOMO/SoIgiAIe4r+p3+OYiGHtmv+KqKqIAiCMCw46R8uw01PPYPg+w7FI+vW4+3uHnSm0kgX8uC2Oyn1ynPGP7J2PYKHztbpRVQVBGFvEz3qEoSmH4uuW85EbtWrdqywLyLC6jCDvpPoZzUyd64dIwiCIAi7j1I2iZ5HfgBX83i0f/5eGK7aj6UJgiAIwp4m0tKCz9x8C/7xp/+BMWefg7fNEl5fuhx/e+VVPL5xsz5n/D/e+h/49I9v1ukFQRCGA6HZZyPy/g+j69ZzkHn3MTtW2NcQVwCCIAiCsJsY7q4ACv0b0PfkrQgeeg6aLrzJjhUEYaQhj+PuG4grgMHz1gc+oB9P5aa/wt5D1p6Rj7gC2DNkV7+OvsdvQeySmxE69pN27MhAXAE0RixWhxmZVau0GwB+eAVBEARhd5HftAQ9D92I8DGfEFFVEARBGDH0PfOMDrxn4v4UgiAIwx3/lPej9UPfRv+DNyDx1x/ZscK+ggirwwz6V3398MOx9pZb7BhBEARB2LXwV/OeP/8fNJ9/AyIf/KodKwiCIAjDn9Xf/a59tO2xIAjCcMY75iC0nncdkq/8Lwbu/5YdK+wLiLAqCIIgCPsR6cXPou+Jn6L103eOuEeRBEEQhP0bx1rVQaxWBUEYSXiax6H13OuQXfY8+n57lR0rjHREWB1mTL3hBpxsmvpVEARBEHYlqXceReL1P6H96kcQOPQsO1YQBEEQRgbVLFTFalUQhJGEyx9G6znfQKl/Pbp+8WGYpaJ9RRipiLAqCIIgCPsB8VfvQnrFK2i/5nH4ph5lxwqCIAjCyKDSWtVBrFYFQRiJNH/gi3B7fOi+5YMoJTrtWGEkIsLqMIO+Veljlb5WBUEQBGFX0P/cbSj2bcKoa56Ap/0AO1YQBEEQRg71LFPFalUQhJFI03GfhHf0dHTefBoKGxfZscJIQ4TVYUahr0//6ppZtcqOEQRBEIShYZYK6HviJ4Dbh7YvPwYj2GRfEQRBEISRg3N/FJg6VQcH55z3UNWsWQVBEIY7kSMuQmjmqei85Qzte1UYeRgbOjrMlR0dmDdvnh0l7E34pYFfCiJz5yJ2yil2rCAIgjASWXd1GGO/cI99NjT64km8tWgFVq3bhJOPmo2pE8bYV+pTSqmbzCdvhWfKkWj5+K12rCAI+yKGYdhHwkjHNE37SKjHs/ZnnntTCHsPWXv2DfaldWdXfPfeW6SXPo+Bp/4Drf/4GwTmXGjHNoYaEvWj3QV/uHqhpQWeWAzH9/basUI5IqwKgiAIwm5iV3y5+8g//1888ORL9hnw5B0/0AJrPQo9Heh78qcIHfExRM+/3o4VBEEQhH0D5+m+cutVQRCE4SCspjM5BAM++2zHyHW8jb4nbkHTh76H8IlX2bHVoUHeup/8RAurx6xcacfuekRYbYwIq8MMTgrnFwcGfoidLw7OrxC8TpxzXmc6ftCdLxe10vA609UrtzLNYMqtl6ZWuaQyzY6U66QZTLmVaQZTbr00tcollWl2pFwnzWDKrUwzmHLrpalVLqlMsyPlOmkGU25lmsGUWy9NrXJJZZodKddJM5hyK9MMptx6aWqVSyrT7Ei5TprBlFuZZjDl1ktTq1xSmWZHynXSDKbcyjSDKbdeGue8/7bjMfE/Uvp4Z/jyJy/ASUfOxqevuxkXnDYPt914rX1le3LrF6Dvrz9B9OxvIHLKP9mxgiAIgiAIgrBvszeF1Y1dvbj+1v/FHff9FYdMn4wrLzpDf4ffUQpdK9H7xE8RPvZTiJ7zLTt2K46g6mzax/sREVb3MhRWX3zxRVMYHqy8/nrzGcBcds01+rz36af1+fOxmD4nr82dq+M23H67Pu+4+WZ9zngHnjPE33xTn7M8ni+64gp9zngnTb63V8fNP+UUfc7ySOd99+nzl6dO1eeE7WAcrxGnve9eeKE+Z1k8Z0ivXKnjeI3n0ifpE5E+SZ94zrCv94n51v5TiM9W7XToevlus7DgYVN9OTNj0fCW88qQePDb5tqrI2bq1d/rdgiCIAjCvgj/3+78/10QBMGB372rfUfeE+HTF59pGoZhXn35h8xTj52rv8MvevRXVdM2CplXfm1uvPFIs+/ua+2eWfdIzn1TeSi/b9kdOPc65fdmwrbI5lXDjLFXXKF9q7qbm/U5fxWgBZRjBUUqz/kLBc8dyylSmcY/ZYo+Z3mkXrmVaeqVO5i6w3Pm6HPpk/SJSJ+kT5Vp9tU+OdfVl6MhB8cytW8goV9XrduMKRPGIBYN6/NyUgufxMCzv0L7lx5E8MiP27GCIAiCsO+x8Y47dBAE8vzzz4P+XmuFnb1eLfz7v/+7DtWulYcZM2bg4x//uK5D2Hfh4/+0VP2ny87Hzd/8HB6/7UYE/D7MPPuz8Mw6d4dD4JhPYex1r+EbP/sjOn98PhZcdBHe+sAHtliplsOn6WhRurvCK9Om2TUJtRBXAIIgCIKwm9jZx5G4cVX7sR/Tx3NmHqA3sfrOF/8B3/nSZTrOIfHmA8gsfR6tn/0DvBPq+18VBEHYE5x11ln4y1/+Yp9ty7/927/hX/7lX+wzCwoUX//61+2zwWGaphYuGnHmmWfi1FNPxac//Wm0tbXZscJIZvm11g+P02++Wb8KArnuuutw00032Wfqc7J8OQ444AD7DPjDH/6ASy+91D4Dfv/732vRk3At+cUvfoGLL75YrxOVaxLXG0KB9Fr1+fvoRz+6ZR076qij8Nprr+ljrjePPfaYPmZ9X/ziF9FrPz5dXp+we9ibrgDmXvQljG6N4U8//Vfc/ejf8Lnrf2pfGTqfP3kSvnvOLKz8z0VbXJrtLWgEePDtt9tnQjlisSoIgiAIwxRapnKzKoqqq9dt0v5V//mT2+4SGn/5d9qvavs1j4uoKgjCsIHCQldXl31m8YUvfEGLE5WiqsORRx6J+fPn6zSOiOFAMZZxLJPHDox7+OGH7TOL5557TsdTVLnkkku0wEuBhGJvd3e3nUoYyVBQFVFVqKTZfvrJoVxUJRMnTrSPLMrPuT597nOfa/jjywknnICbKz57tfJQRP35z39un0GLrLIG7bvQp+pTL8/HmOM/rkXVpkgIiTfuq/pUWqOQeeXX2HjjkfjBly7G6H95VPtQnXXffds8MefAOPo+3d1BRNXaiLAqCIIgCMOYk4+ajdf/dCu6Xr5b/wJe7gag/9n/QjE9gPZrnoA7NsGOFQRBGB5Uig1Tq9wQlnPXXXdhzpw59ll1WCaFWVqFOTQ1NdlH20JRhRZj06dP1+e0KLvtttv0sTCy4eYtDIKwqygXQBtBcbXWD0SVlIu3tFxduHChfSbsa3Cjqr/f/RN8+uIzceu/fhHr/3andgewo+jNqx66EcG5F6H5oz+2Y4H2Cy+sKbDSndnuDkJtRFgVBEEQhBFGKZ9Gz2P/D65QK9q+9GcY3oB9RRAEYWRCkaLSuqwezqO2g4E+Dh2eeuop+0gYydDXIIMgjDSi0ah9JOyLHDFrhhZVv3DpuUMSVXMdb6Pnwf+D6Ae/iqZzvmXHbks9gVXYO4iwKgiCIAgjiGK8E70PfR++yUcg9il5JEcQBGFHiInVjSAIe5C1a9faR5b/1UZW+cL+S3rp8+h95Ca0XP6fCJ94lR1bG0dglUf09z4irAqCIAjCCKHQuQK9D9+IwBEfQfPFP7JjBUEQhHosW7bMPgL+6Z/+yT4SRjInm6YOglCP8t35GU488UT7yu6HvlSdzasIfbjeeeed+lgQKkkteBzxl36LtqsfQWDORXbs4Iidcop9JOwthp2wyv89amf1KvBfSZ87F6wTJ15fs18ZrBPL2b2d1PlTESx4VFJ/Syjqv4IgCIIwXMl2vIXuB7+H6Jn/gqazv2nHCoIgCLV466239OYx3MSKfla5yRV9IwqCsH+gdYGywI3tdjfcLI8ibnt7Oy699FLtV5Wb8tGHa6ONsYT9E25wlVr0FEZd8zj8M+T/USOR4Wuxapa4EvIAMKwoLYOq+FKJMqiJggrd+SIW9aexNJFBf0ldM5w0TGELplxIVW7+dRZVnqlU+p8+t6sSBEEQhOFGZukL6H3kB2i5/JcIHf8ZO1YQBEGoBq3SKGzMnTtXb4h1ySWXaKvVc845x04hjHReP/xwHQRhuMHH/fljTktLix0DfOYz8t1NqM7Ai79BfvNyjLr2SXjGzrRjhZHGsBNWDVsMLRpsmgGXaemqpvpjqiNqnyV1KaEOFicLeGxVL+55Zz3ue289/rahD6tzJWTh1umLFFkNlZCZdAFWmYxgHSzNreLc8MBgoYIgCIIwzEgt+Avir9yJUf/8KIJzL7RjBUEQhFrQKu1b39q66QfF1UceecQ+E/YFEvPn6yAIwxFuxPfb3/7WPgNee+01XHfddfaZIFj0P/1zFAs5tF3zV7gio+xYYSQy7NREiqclw7AEUfucUqu2VjXUDabhwuqsicfXZ3D7u914byCHww8ah5lj2vDumj488N56vNQTx6YCpVOPyqfKozrLkhyBVXXbUP9cJXVVW7OaKKo0JbtOQRAEQRgOxF//E9KL/4b2Lz8O3/Tj7FhBEIR9D8cP4a7ixhtvxJFHHmmfAZdffjlWrFhhnwkjHW7WIhu2CMMZWsiX/8Bz0003yQ88gqaUTaLnkR/A1Twe7Z+/F4bLbV8RRirDTlilHalRMuApqSMzD9MsqLgSMjCxoVDCC105/G5BAvcsyWJhxg9/LIqZLT6cP6UZH507GS0BL15YvBaPLdmE9/py6C+6kDfduoySUUDRKKKgQlHVZRoUWS0R1zRUXTpWEARBEPY+cT4a1LVS/4rtGXOQHSsIgrDv4WzysquhparzOC79HNIlgLBvMPaKK3QQhOGM/MAjVFLo34Ceh2/UvlRjl//SjhVGOsNMWDVVgwrqtaT+8nF+ugQoordg4p3ePO5ZMoD/XRjHC3EXeoIR5D1RvLcujzc2JJFWuWaEPPjYIeNxwSHTUMyU8MiCDfjLij6sTOSQNFWphirfLMAoFVAyi6oOAwVax9J6Vf+j0CoIgiAIe5e+p36GYrGAUdc8CXek3Y4VBEHYN7nttttw9NFH22e7Dnkcd99l4x136CAI5fT399tHFpUi5tq1a+0ji8rzclatWmUfWfAHoFqUX6tMV+0HHm6sJ+x/5DctQc9DNyJ8zCfQdOFNdqywLzDMhFXrMX1CdwADph8LUy48vroPjy7ajPldeXR6QsiF/IC7BJfLhXTRh66UgWTRRMk04S+ZmN0awIePmIgjD2jDqr5u/HnRajy/fgDrMgZyhg9uww2PSucyi9palfW6Sh4YpphgC4IgCHuPUiaB3odvgrtlEto+90f1vyf5wU8QhJFLpcDwxhtv2EcWFD3+/d//HV//+tftmK1UCiKVgkk5AwMD9pFF+Xm1x3FZpzCyWXzllToIAnn++ef1hnWc3+VMnz5dxzvXuUt/OTx3rjtwfWDcf/7nf9oxFtzl/6yzzrLPLJy0/NHGgceMc9aZaj/wcGO98jqFfZ/s6tfR8+f/g+bzb0Dkg1+1Y4V9BWNDR4e5sqMD8+bNs6P2LJasuRXu9J9RkZ2ZAhZsTuG9DQPwevM4eFI7liXdeGpdEQkjBI9Le0ZFKJ/G+RO8+Mi0MJpdJRRNywKV//F1Y76I1zu6sHBjH4KhIGZPaMXBLSGMdpfgM0souSw/rC7TbolaBC0qWyYIgiAIO8a6q8MY+4V77LP65HvXY+CpWxGYfa78ii0IwoiHAsRf/vIX+6wxX/jCF/Dzn/9cH1OUqAU3pjrhhBPss/ppTf393uKoo47aRvzgzt2PPfaYfSaMNF4//HD9+v4339SvgiAIZEe+e+8p0oufxcDz/4PWK/8XgUO3FeeFfYNdL6za31/0JvzWEQzQXyq/9NhffIySTlCCS6dx8br6l1Ghs1jE0t4k3l3bh2yqgEPGNWPW+AhChqni8/jL2hzeS3qQd/tVMRkcEMziogOiOLYtCF9JlavQm/+z3pKqz+XS5a5J5vDK6o1Y2Z/G2JZmHK7KnR7xolldt2xki6qNqiWGW31BU8f0t6otWN2qJNVKVT/buKUPgiAIgtCAwX65y29ajL4nforIB65G5PSv2LGCIAiCIAiCIAyW4Saspt55FEkVWj/ze/imHmXHCvsau94VgH60niIlxUnTElQtpVNB4bMI/njMI74yeVGd9ZTyeHsgg4eXdOP5pZvREvLhQ3Mm4IRxfjSl4gjkizhiTATnT/PhpNYk5oT7cHwsgzNHGZjuyQO5jK7BNOgpVQUtkqqyVSVuFaaHfbjofZNx1sxJyKezeHzBejy6Jo6F2SIGVBuLWkC1G8W8pkfFUfJlrAosjME+FQRBEIRdQXbVa+h54HuInv9/RFQVBEEQBEEQhH2A+Kt3Ib3iFbRf87iIqvs4u9xiVVt30gJVC5C07mTgi4mSUYKp4rhFFY+p6xaKwJpMAa9u7MWKTQMYFQrjqKltmB5WBSTiupxIJAyvz4uCOu5PpZE1XMipvB51MVQqopRIwOfxIRRpgtvv1YIuoc9VQltTvd+/bpQLcVX1K+sG8GTHAAy/C8dNjOKIWBBjvS742VzTpdMbKr1Lt1P1ynSrnHZfSNmhIAiCIFSj0a/mfDQo/sIdaPnH3yDwvjPtWEEQBEEQ6vGs7QLiZPt+TxAEgQwXi9X+526DmUmi9aq7YQSb7FhhX2WXW6xaIqYbJRW0+KgCDVZN/T8/SpMGr6BolrAhk8Wz6+O4d8EmLO5KY+bkMTj7fWNxkCcNd38PwoEQYq3t8Hh8qgATmXwBpYKJVpcHowsFtKm4ADei8oVQNHwYSGVQKNLG1KqYOiotZtlJj4p1qXO2rFVFjIkFYQajeLvfh98vTuC3S3rxSm8OnUUDeZVdN9coqdSqTgrFFFi1MMt464UwxgmCIAiCMFiSbz+MxBv3oe3qR0RUFQRBEARBEIQRjlkqoO+JnwBuH9q+/JiIqvsJu1xYtSRNS2qkdWpJHXJDqaJ6NUzas5rYXCzh1e4cHlrYhTfWdGF0SwinHTIJs2IRIJFSeV1oam2DPxhQxbAsNtMFM5uF3zDg8XmRR1FbpPp9QXXNrT63LhjuEhKJBIqqMmajM3urPSq3+uNWR3QLkEQBCzozWDVQRDHYjE7vWPytx4tfL+zFfSv78HYii17TdgJgutXkcNP81WpLmahKnNOKaEEQBEGoCR8NoguAUXw0aMr77VhBEARBEAbDnKef1kEQBGG4UEr1offh78M1+iDtU1XYf9gNwio9k+bADaD0dk+mCXcpj5JZQm/Jhbf783hg6Wb8Zfl6BENufPjwyfjglBaMLRXgTWcRcBvwB4MwXT6VhxtcqQ+oy0CeYmmhAJ+7BI8q26XKo9Dp9qg6VCgVsgj7PSqugGQyri5RwqVTAhOlUglFlTefy6CQyyKVyiCRzqFouqCqg9ul0vhj2IBmvLQxgYcXd+Dpjl6sTJnImlqOVeXS1tVxMrAtIqoKgiAIg6X/b/+NwsBmtF/7JNxtU+1YQRAEQRAGS+yUU3QQBEHY1Tz76jv43s/uxK/vf8KOaUyhpwM9D9+IwPvOQsvHb7Vjhf0F99e+8pUb+gYGMGnSJDtq56HQSFEUhgumYSJl0o9qES9t6MeLa/rQVzQwc9IoHD2xBeO8Kl0ug2Ihh4DLQNTngc/n1u5ZuXUU5UyXKieTyaBUKiIQDMLlciNHtwBuN9xeH7zqNZfN65qDgSCSyRSyuaxOl83mkMmmVfosMuk88gUTBZVycyaHNakS0iU3PKYJV7GIFncBp04L4eCWIJati2NJZxpp1Y6g342AV9WlLWAtadWyhbVx1NayKEEQBEGIP3oTIkd9zDrRjwb9FEawGW2fvxcG3dwIgiAIgrDDLL/2WvT+5S9oPessfb7xjjvQ98wzcAUC8I0di8yqVTpu4OWX0XTssduk4XVPLKaPu+6/H7mNGxGaOVOnWXvLLToPz1mWk6ak7kUDU6fWLbey7tSiRYjMnavT1Cq3Wt2NypU+SZ+kT7X7VFx579bv3kPgI//8f/Gtm+/AW4tW4O5H/4bv/fx3mDPzAMw8oLZellu/AL2P/jsip1+L6Jn/YscK+xXcvOrFF180d5SSDvxbtM8siuqQoVQqmslSwVyaLZgPrOs3f/DaKvNf/77avHFJn3nj0pR53dtJ87tv95v/s6zffHZT3OxI58yMylgoFlReK5ilnCoxp0ovmn39fWb/wICuSV01e+Nxs3cgbuZyeTOXzZudnV3mmjVrzN6ebrNz82Zz2bLlZkfHOp0nlUmZ2XzWLBWLZjqTMzv7+81FPf3mrxYPmJ99rtv8xLNd5j8+t8H80bubzaWJrJlXdazIlMzfLVxv3vDce+bP3lprvtiZMjfnS6o17DF7bvVe/3GCIAiCIJSx9p9CZmHBw2b27781N33/GLPvD/9sXxEEQRAEYag8A+iQXrlSn7974YX6fNk11+jz3qef1ufPx2L6nLw2d66O23D77fq84+ab9TnjHXjOEH/zTX3O8ni+6Ior9DnjnTT53l4dN/+UU/Q5yyOd992nz1+eOlWfE7aDcbxGVl5/vT5nuwnL4jmD9En6RKRPQ+sTv3tDHe9MOPyQ6WbXy3ebr//pVnPqhDFmLBrW3+erhcSD3zbXXh0xU6/+XrdF2D8ZksUqXY1yQye9y7/6Z5RKepMoZ/f9ggH0F0t4pz+FZ1duxqquBMaOaUGwJYZFXXkVSujI+LEy7cbSngx6MlmMiXoxLuSFlyWo8ujbtGC4kVchkckhncvB7fbq/aMK+SwS8Tgy6RRMlbZQyMFwqbqLJlxuN1pamhEKR2CqvKbXj4zHj4TpQrZQQDqVRMDlxoRYBJNjAUwMG5gSKuLYcSEc2eJFq6rA7fEg4gHGhj0Y1RxGdzKHt9b2ojNThMvnRtALeFQfOQ5sq9V36A2vrI2zLDcF2q6VpzwkPBYEQRD2G2ixGjjgGPQ9+u8IHXUJmi680b4iCIIgCMJQoRsAb0uLtnKjtRyt1vzjxiE8Z84Wqzbut9F88slbXAbQSi58yCE6D63lmMcdDOo8Tppif7++znOWyzwsN3rMMbpc5uG+H04anpPgtGn6nOUyD8tlnvK6o4cfrstgGsL2s32M43WnXOmT9En6NPQ+mRsewY//zqeZh86Rsw/EFRd9EGPbW7RbgNXrNuFfPvNR++pWUgufRPyFX6Pt839C4LDz7Fhhf8SgxerKjg7MmzfPjhoEWjQs6Mf1TZN+TRlpoOgyMVAysTKRx1vre7CxdwCjAkHMmTgaaZXsoZVxLBjwo+iPwHS5UNJCZAmeTC+OjxVw6YwWTA+6UcwVUDDd6FPlrkwWsLanF1FXCQfHmtDiMeFR9eRyebhUGU1NTfB4VOGKdDql4tUED4dVnB+dmTxe25TC/L4ccqqm2U0eHDcmhLFBF8xSUftxhaHqU3nZhWwuh3gyg5LLq7tomEWEIgEkC0Us7VJt70wiqdp28JgYDh3TjHEBN0JqgaH/WCq+brgopepjXaJ2hyBqqiAIwv7KuqvDcIViaDrvBoRP/KwdKwiCIAiCIAjCrobfvcd+4R77bMfoiyfRfuzHMHXCGHzygtOwev1m7Wf1UxeejttuvNZOZZF48wFklj6P1s/+Ad4Js+1YYX9laBartlZomC6YcKHgMpBECR3pIv6+oR+vru5CNpvH0ZNH4fSDxmBUwINX1w7glR4g640ALpXPxZxE5Te8yOfzGO0pod1tIpdKI10q4O+b4rh3eQIv9QIbMiWMaw5gxqgmhP1+vSEVxU+fz6derQ2qXIaBdDqNoior4Pdgjcrz4NoUXugpYU3KQLGQx4xYAKODPuQp6pY88JhFuI08skWgP6Py593IFF3IlrgFlxvpTE5b0R6o6j1wbIvqr4GFG+JY1p1GTpXh87vh9RjwajGVPaKVarmYqo7LTwVBEIT9BlqstnziVwgdc7kdIwiCIAiCIAjC7mCb/Q12kIDfhwtOm4dNXb346f8+oOM+97Gz8Z0vXaavOcRf/h3ym5ei7Ut/hmf0DDtW2J8ZgsWqdgKAkukC/3EjqE35IhZ2JfDeul7kzSIOm9SGGWE/2j1AcyiInmIRdy8fwEMbPMh5w4DbhOky4CmoEkygYJqIlAZw/rgSzhrtR6yUw4DbjXs6Mnh0kxtZXwyBXBxnjcnhokkBxAygP57QYmwkEtFCJq1GXapNyWQauWwO0aYIFmeAe9bnsLAQUc124UBvHJcfFMYx7U263UaJj/QXkYeJ3pSBnrQBU5XlUnF0clAy3KpcEz7VyLagGzG/qkflW6vK/3tHN1ZuHkB7OIrZE1sxPeZDzO2Cl8arHCZ2zLFaFXFVEARBEARBEARBEARht7EzFquDof/Z/4JZMtF61d0wvJbrAkGwjEZ3CAM0FeWO/S51uCqZxUNLNuG1lZvRFvTi7EMm4sQxUYwppeEr5S0B0+XSPksDBh+6pwsAPn7vQlHVbhq0AS3Ab5QQdBvwqEINlxt+txttIS9ingIC+QG0efMYF/EhFgohFA5rQTUajerQ3NyMWHMMTbEmjBk3GrG2dphuP8aFAzgs6sZEVxpjjBQObfFicjSgO+2BCS+FT1V7RjUrUSih4Cqh5FYtduXgduVV/wpQTQLUNbongJpAPtXy6X4vLpwxDmcfOkWP4GOL1+KZNV3oLxRV21V6BX3P8p91xL+CIAiCIAiCIAiCIAjCSKKUT6Pnsf8HV6hVW6qKqCqUMwRhFdpC1MXH79Xx0u4BdKbyOPF9k3H++8ZinJlDumcA9Djq9/vhUemaVfpJYRdafFmYxQyNXnXevGEiR7G1mMVYfwnjte/TPFIlE/SaeniLBx8YBRwVjeOsScC8MSGE3G4qtXCrVwb6WWWwzl3wetV1Vw4D8W7EfCWcM7MNnzwoiEunenDmxAjG++k6gLXTmpQbT1EvNbTVKuPYL9N0oajaz1RsKa8WiwyqrbRAVRcCKhzWFMAls8fj0LERdPT0oTOR1jlUEdRgLViN/U8YOdC9BIMgCIIgCIIgCIIgCPsnxXgneh/6PnyTj0DsU7fbsYKwlSEJq7TypPRp2Z+aGB3yYnLAi5jLjaDHh0Qig2yhBMPrQ6mQR36gHxO9Jby/zY1xrgQC+SR8+Sy8xQwChRQmepI4epQLU0MGAoYBvyrDKBoIJxM4PmriUwe34INjQmhV9dFylIKX3iSqArNoIplIal+q0ZAPftW70R4XThoVwUltPoxGXiXiQ/4u0JVByaUSuAzdB4+pDimkMd7woWj4UaJdK3VU1a+MupZIZZBJZ7QLgmK+oPqWQ7Mqc0rIj5A3iLztfJZ/KclZwqzD9u0Vhj+irQqCIAiCIAiCIAjC/kehcwV6H74RgSM+guaLf2THCsK2DMHHKqEAaehd/f+6uhMr+ws4c/poTAu7kckV0N3VqWVEPpqfzWVhFExEmpvQa7jx7No+vLE5i96iV7sBoJXqYa0+HB5zY4zPQC6bh5kvIeDzIZNPw+Nxw+fz602k8kVTux8I+Lwo5IsomkWEwyH41fVCsYhUIqlfo81RLehmkmkEwhH4Az4kU2nkc/TJGoLH7dF+MaiDFs08EukMBjJAGj4U3G4VT72ZVrkluFR5HtXOoBcI07q2VEQun4fH5QZUWq/Pg7e6MninJ4Pjp8RwWCyoFdWSy4Sh/b5afltZl6uGuEqhmNawjoWkY4FbDq+VivRtu61c2xirTtUEy9JYtZvHW2Gd1ds1WLhxmLbmtTG0RbHq7bYVNaSynF2BY9VcD9aZU5+NbDarRfNcLodMJqviS/D7fQgGA3qTNJ/63NEKu/K9EQRBEARBEARBEARh77IrfaxmO95C319vRuyimxA6/jN2rCBszxCEVQpxlhhXUOGJNZuxoj+PM6aPwYyQB4lEUotTmUwaqVQKrW1tiDU3WyKbaaIrmUJXvoSU26MtOkeHfNqqNBcfQC5fRCQa1ZajnZ2d6jyHUe3tCIcjlvanqqU4ms0XkEqkkM2lEQoG4fUGkM5k4FHlxJqb4PGqsksm4qpM1tvU1KTFslQqjUDAj4DfjwKF12wW6WwO0CKpF/GCAVWKqoiuAOjMoAR3MY+w14WWSABhVT7hhlalUhFZisAuF17ZlMDC3gxOntqG2bGAymnCoOqs8psqC21iqwmYFBIp+vX29mLpshVIxJN6jCZMGI/pMw7QorIDhb7ly1dg0+bNtmipytRvhSqT/gzKUWUwxqX6rsdd/ceyQqEgmumLVo0HxUKKhlZ6FVQ6S9g1VZvYzlpipPOIvNUXlr9u3VqsWLFKjznF31hLDAceOF37waXbBd3WGvA6y6Pg29GxDqtWrkZRjQv98tIuWrdtCOgy3QamTZuGiRPHq3Mdq+PZdkP1sVgooKenB2vWWO1ft24d0hTgbXGVwirFVAr7waAfEyaOw4wZMzBlyhS0trboNlvl8X1k+fxjjYsgCIIgCIIgCIIgCHuOXSWsZpa+gL6n/gOtV/4awbkX2rGCUJ1dIqyuGsjjg9PHYnrAwObOLmSzOTQ1RbUlIIVDCmwej0cLrflsSluNur0hXVpJ/ytQCUMqzsfsCzpvOp3R6b1enxYFA4GALsNwW+JVvpBX6Sni5pDMpLW+2BSOwq/S06KQghjFMVohhkIhbXHY3z+gLVV9Xo8WfllWKByBz+9XZWQxkM7rx/kLWqx0aUtVVymPoNeF5nAAXgqwrJ1iZRkvbOzHW5sTOHFyK2bHgpTvGgqrliBnlbV48WL8/vd3Y8WqNTBU/KmnnoKPfvQjWgh16O3tw91334OXXn5ZW02yLBqvaj2voj120VvgZbfH0AJhOBxGW1sLZsyYjlmzZmHc+AlaaLbao9qt0lr927bMrVCctI6YjuN7//3344knnlTvtWVJPGbsaFxyyUcxZ85sK2EdLOHVqu+vf30S9/7pAf3eutV7rSuq7MwgKRYL+jNw8Ucv1uNJKGQTfibj8QTmvzUfL7/0Mlav6UAuk9cDpVvCDdTsY6sJliBLr8IU5inWnnDCcTj00EMRDKr3W/eBJVv9EARBEARBEARBEARhz7IrhNXUgr8g+eYDaP307+CbfpwdKwi10brcjmOJcA604KSVYW9/HxLJhBZS9U79sZgWpBKJhBZZaQloer0oen06vckn600DRdWMEtyIhqMI+Pzo7u5BJpNBS0uLCjFdRzwe10JrIZ+DoTK6Vb5sno/lmxjXPgYHTJyIpgjF2pIWTfv7++1HujMYGBjQFp+0WO0fiCNfKCLa1IyWWKt2OZDLZpFNpxH2uhE0ivDkk2jxuzA6GkR7JAhPMY+8uk7xzJLNKLJtK/rVMcosG6nq0KLTq8YlaFvTetyVj+szjQGvh4+iB+HzMfi1xSlFYYrGFKCdYFlZMqhjVS5FStNwI5PNoau7G4sWL8GDDz2K/77tDjzxxFPo7umx+qYrpeVqReXbYIuOdgNXrlyJJYuXwu3yqrYHtWuGvp5eLFZ1ZDM5nWawUDj3+dlH9svqg1cHnrNf7J8TpwKPy4L1uL4VPDqdJbITiqpsM0XV/r4B/OWxx3H33X/CkmXLdX99auz9QT+C6jMUaYpqcZ+WvZFIWIunFPb9voD63AILFy1Wef+I5557Xn++LItchnrjJgiCIAiCIAiCIAjCcCX++p+QXvw3tH/5cRFVhUHj/tpXvnJD38AAJk2aZEcNBkqFFEMNrOhPojOZx1i/F8F8SltEtrZymylbDPR6tVUjhVG3y4VgKAS32wNXiT5MaRVIRc+lSuM/QwtoyVRSi6iOpaljrZrNZLVYSvkqp4774wmEI2E0RSNa3HV7fNqak+lZL0mn09qtQDKZ0gIar0ebouq6R5dDwZfCr9/nhV/F5VR6n9uNSMiHoNcNr8cNs1jQlrEejxd8/JviLR+/t9puYG08g03JHCY3hTCGzlgVhhYn1TjxxfpjpbdxhEm+9vT04r2Fi7RFLWOnTp2CQw6ZuaUPJJ3OYtHCJVi/YT2rRDAYwvjx4zF69Gi0tMb0o+kUovna1taq3wMet6rjpuZmhNT7QjHW8jdqaMGRfmeXLV2GuKp37LhxWkx0cNpXSbmlLa2LX3zxJbzz7ntwqfeU0P0ALU75mP2ECRPQ1s7PAvNUL8/Qbgx4zcCqVau0SFssqfdStS+m+jN6zBjEYs1otgPdDFCwZ6Do7hxbgWI+QxOam5v0uBx88EEYp/pm1UUL2xyeePIJPPXUU+q9N9WYWJ+tUaPaVdqDcdjsQ3WgNe/MQw7GAdOnY9z4cepzG9S+dfn54+eXIv2GDRt0vZMmTdTlC4IgCIIgCIIgCIKwd4g/ehMiR33MPtsx4i/+Bvm+dWj70oPwtO6IPibs7wzNFQCfQYeBguHCE6s3Y2lPGqdOG43JnjxchluLq+VQWKUIxceox4wZrXLSryZlRhfVLhalSqW4ZupH9Sl00hKQ4hrFVQqlFMWo6ZuTmTQAAExdSURBVA3Ek+jq3azOS/AEoohGo4gF/XpTJ4P+UVUi+j+1/LxmtBBIMc2y5LTcAdBHKS0rKQJmVRqXx60tbFPJFLiJUcDerIjiKykVixgY6NfiIa0Y2X9rEymXFiqfX9+Lt7qSOGkyfazy8X3V0DJXAEyn/YXqPm6FbWO/li5dhnv++CesXt2h23/SSSfiwx++QFtKOvT19uPee+/H3197DfT9ecghh+AjF12ICRPGIV+obxlKK8tMOoPevj50qPf6vfcWahGT40O/qKbq31FHHYXzP3QuRo1qs3NVp1xYXbJkGf74x3uxavVqy6LTr96HUkmL2RyXM888E2effQY8arydvm6P9VlieObpZ/HAAw9q6+JAKIhzzz0XJ554nBa1We12uasVZzXPuqYyWe+jV9fPNi1Y8J52qbBx4yb9mfCrdh9++FyceNIJmDRxot4crRpZ9blYsmSpauMzWLJ0qa6H4v7sObPx0Y9+WH2ux9gpBUEQBEEQBEEQBEHY0wzVFUDfUz8DvEG0XaXyVtUtBKE2WvYbCvbD8BoKVxQO6R/US9+YFVDUokDKR6bTqSxKFEANN0wXc7EUyqpFFUrI5ij2lbQlIIXFZDKp/a3SjyXFUm6O1NzUinyoFe/EC5jfn8OmAlAy3SgWijrtwIDlNoCiGi0329raQD+tfMS8paVZC79+fwAJVfZAIq6tVukuIJlOo6DqtjaV2jqZXG7u/u/X9XOTLLoSoNCWzmSRUCGdK9jCX/UJuEumJQuxC+J4Uhz2B/nIu1uPU70QCQfR3t6CA2dMw6kfOAlXXPEJnH76qXqjL/2+qfflzTfn45VX/q6tUBtBgbSgxmDRwsXYsGGjKsPQAvdRRx2JyZMn6/GjZeeSJYuxft36LXkaovtopeMLN4yiJXFEvV/RSFg/lr9NUPHbhfJrkYj+DDiiKlm9eg26u3rgcXthqnbOnXMYLrjgPEyZZImqemMyHdQ4qz/sC/OzHbMPfR8+9KFzMXXKJPWZKWihvWNNBzo61uqyBUEQBEEQBEEQBEEYGZQyCfQ+fBPcLZPQ9rk/btEjBGFHGLKwSjHN+chRfMpkcjDVh5C+LcuhKEXLSAqrseZmLXymUxn7Iv9Q2mMzrJDPWsIeH89mHoqg6XRKW5omEgNg8Ua0CW+k3fjTeuA3S5J4aEUP1vQOYCA+gGzO2qyKYirzsn6Kqpwf2SwFWlWLFtlMLTqOHTcebe1tWiikGEghl4/mJxNJ1c6Utr5MqNciSkhns9i8ebN2a0ArWAqtdG/g83ng0n42dyeWAL01qL/sjMIS/7YNKpYpdDBN1fpS0bqm0kbCIZzxwdNxyiknqbEKaBGZfZ//5ltYsWIli6yJI5BSUFy0aLHKl2UNmDJ1Ck479QOYefBB+r3jGFNwXErrThu212lzNcov8Zj9ss+GFNhfWuQ6babgvmnTJv1es+ympghmzToE0UhEp2VgSloya8mf7h7svE5bpkyZgtmzZ+s+koT6nHRu7tLHgiAIgiAIgiAIgiAMf/K969H78I3wHXgSYpf9wo4VhB1nSGogZStLWqUIZT1m3t+fQCZbQK5YQl4FLUSZlqjK9B6vXz92HQz5kcqmkM6mVf4i5SsUVQLarBZo+Vkq2kKlyqTyBwN8HN5AZ2cXn8BHJBLFplQOL2/KYLkZxSpXDK90ZrG0s1+nD4eC2rqQJoe0PFR/QR01EAyqNvGxeNW+Qh6JgQF43R7dJvr09LgNxKJR7QOWj9on4wls3LgRGzZtQE93NzKpjHZlELA3jNKbczU3waPqGigZSJVcug/EGp/dixYo7YrYP1qdlgfrrWV71PtUfk0lZl6KqUcfdRRmzXqfGhc15qpfGzZuwOIlS/QYEUcI5b9yKFa+t3Ax1q5bp8o00KTG7X0zZ6KtrQUHHDAV48aO0e8FhWlulLVp82Y7J9tpjVEtrNqc+ramrSfI1oJ1lVen3UOoz6olRJv6feR7Sqw6rTpYFfNSLHfay1enDRMnTtA+ibVfV26upq4VCuWWvlvLEgRBEARBEARBEARh+JDftBh9j9yI0LxPoenCm+xYQRgaQxJWy9UqCnSFPB+LdsPj9iART2JgIKktWPlYeTaThtttwOP3omiWtDVp0B9EOplGLldQZXETLMu+ktamLNrv82tdihsgxeMDur72UaNB/60U60pFE0HDg3CpgGAhh9ZACBPGjtUWqAPxuLZspXjqSFv0h6p3x3e5VR059PX1I5vPaZExlU6jp6cP/b2WFSr3UuJj5KNGj8akyZMxfsJEtLa1IeALwqvaUVL54n10NZDR/VmWzOLVzhR68gYMNQa7X1Dj2NuCn974iWx9P6pDgXHbNBQJm5qbcOihs9DU0oyCGsu8Gm/6eaWIbaey69g279qOddoNQDqb0Y/MTz9gGg46aLq+xo2cDjxwhrbk5b9VK1di2fIV+hrZXiB1BGAbXV1ley1x2Eq3Y4HlOGXRZ69laUpXCh69odnmzk5dJT9blvjMtKYWVx0qx27K1Kn46EcvxhVXfgKfuuITeP/7j9AWultx6hcEQRAEQRAEQRAEYbiQXfUaeh74HqLn/x9ETv+KHSsIQ2cIwioFI5d+7J9iKDehoqDq9boRDnjQ3hxBwO9BKptBZ3cnUqkEPEYJrhJ9q5ZQVOkprtIvazyZRiZX0JsrZfMmMgUTpscHw+NFoVBCPJ7QO7FHomE0N0fhcruwYXMnPJl+HD/KjaPCBRzmSePYVgMToh69c3usqRkFVWBfPx/nH0A+V0QuW1R1JZEr5bC5tw8ru1NYXQxiUa6EDarOvNuLSNsoxNpaEY2G0ByLwuPzaBEuoNoTUe1tagoh2hRFU3OrtZlVTze6Eim8uSmBhT15FFz0M8sHyEmleLir2bnyy4XCiRMnYcK48do/Ld/b7u5u9Pb0WhfLKK9x0eLFWLN2jbbo5NjMnDkTo0eP1te4idUhh9B6tY3ypH78fvGiRer96LfrrRQcq/fFshq1T3YSR8yl79X2Ue2WT2BVdiKZwt///jreevsdLbA7LgLYzmp1sxxeD6s+T506GQcffCAOOnCG6nu7/oFBEARBEARBEARBEIThSXrxs+h/6mdoveoPCB97uR0rCDvHkCxWjZIKpvXgOzf44WP2wUgAmQI3dsogHPKjrTkKn5v+Vj3IZotI9A+gyMewtYBnIhiOoOjyoiueRncih854BvECkFXpk/kC+pIJmKqCaDSi8qjz3l79yHosFkXM78JR7V5ceWirCi04ssUDd44WsFn9SLbLo+p1G+jq7MS6teuRUHUUVJv7VHdXGhH83RyF+zt9+N2qHP64JoNXBoCkz6eF34IqgxsT0UkBH3nXfgrUK6U50+VCULUn1h7TIh39yQ7kTKRKPrgMrx6TcrY9G1444iofZ6d1rlv7RTWQVOPe39+vr+k0uhNbe7Ju3Xq899572vcs4ydPsQRG4giYU6dOxfQZ022B0sDy5SuwcuUqfW17wZJ5ykZKXXeETbpkIHyEfyiBm5I5QimhUD5t2lTEWmMo0v2B16fatRq/v/MPuPvuP+Lll/+ud/7fvLkTiURCu7Hg58kpg1apjmUq+2r5cKXvWn6mBUEQBEEQBEEQBEEYjiTffhiJN+5D29WPIPC+M+1YQdh53F/7yldu6BsY0D4jdwRKYQXDwIq+FOL5Eg4aE0MAJSRyJkxvAKlsHm4YaGlugS8Y1o/jU6ji4/bcIijPx/pLLqTyQLZoIgcXCipHrlhUabLwutzwe73IpFNaMKUlZDgcQYg+MU03MokMgvkUWnwuZNMZDPT1wWOa2oqSomgwFEBrSww+T1D7Zs16PZjfX8TjG4p4KxtCR8GLTaqtq5IlrOlPw+MxMSrshq+QV8duuGjVqP65VDBdKlDw4x9VmKlamkpmVR+L2JA3sSyRR3PAg7ltfoz20x2AndZQY8EX9c/Ced0KBbuenh68995CvUEX4QZJtPqkZaUDx27hwkVYt36DFnzHjBm9ZeMli+3LHgwUGzds3Ihly1ZoAZGWq5MmTdCP81tlWuKmI06+/sYbePXVV0E3D3xPTjh+HubOnaOvOWn8fp9+zyimZjJZpDNpRNV7N336dNUna9OnrfCTxHwGVq1ajcWLl+p2uNT7zx39E8mkdk/QsXat3gyrY439Wi2oNHRTwJ3/16tx8vv9aGqK6locotEmLZquVWkp3Hrc6rOkxpabcb3z7rt4990FWLhoIZYsXYrNmzajT32uEomULaRaYavAagnC7LbVd6v/giAIgiAIgiAIgiDsWeKP3oTIUR+zz7YSf/Uu5Na+jfYv/Rnece+zYwVh1zA0YVULhiUUaQHal0J/toRpbREYbi/e6MriVRU25kw0h7xoCdF3pWFtFBT0w6BVZLaAgXQB6ZJbW62WXG5VngrqGkVXa/f6Ik0WEfB5EQoFtS9PinS5TA65nGX1msskUSgWEI40wa/qptAXaYogqOqiP1VaYHJzKopn73X24y9r0liSjyIfCMPwuOBy+1D0BZEwXehPZtHmMzAp7EPA49HXWYdRMlBQ7aH1Y0G1O5NJIZOnC4MiSgV1XaXrzOagWo/D2kPbCKvaMQBFty2C2/bC294WVsm6dRv0Dv+01KUAPmP6dBx88EH6mqWVqn4aLmzcuAnPPPOstlqlsfMB06bhpJNORHNzk06jU7LLKpPfH9DiJkVbWjXzfZo4YTza29u1OMk0VlrmYyWWsMp2WNeBtWs78NZbb2HBggV49933VHh3i/i5fXhXp2OYr/IsXboMk/QmUxN1uwjLpVg7duw4bYna092lLVtZt8tNi12Xdj3R29uHDRs2YPmy5XjnnXe1he6CBQuxbNkydHf3aJcBbo9bvz/022pBK9uhvweCIAiCIAiCIAiCIAydasJq/9/+G8V0P9r/6SG4opYLQ0HYlQzJFQAVMe6BT0mJWhJFs/5cHm/3pvDI2jj+uCaN+1an8FZ/HimKdfks8pksCpk83IYbQX9AP65fNFwosQAGtkSdmy6Piveoc68Wu3KqXG4URb+mFLECoTDaYk0Y296MCWNHIxr0wY88/D4PsoWiFsaowlLiokhL68d8MYd4yY1+dxgljweqBrhVH0xVH8UwlzeAzQUflg8UkTbdemOjUjGPfC6rfW/GEwm9KVYql4HhcSMUjqCttQWeUhFjkMXclgBa3YYWEPnPERlHCvRdS1FRC4Oq6Rwzip4OjmC4ZOkSrFrFR/oNBIIBzJo1S4uXFlYaOyna29vwvvfNRCQSUWW7sXHDJu2blYLmVlHVSluOJUJbF5jPoz4nbjeDRwePx6fjtg++inRe9XFyRM+tUCBvbY3hggs+hI9+7GIcNudQjBrVrjc+c6s8zOv1+uDzBdSrX7chnkhiTUcH3nzzLTz40MO47X/uwG9+cydeevFlvRGabnW1zgiCIAiCIAiCIAiCsOcpFdD311sAbxDtVz8Kw+8YpQnCrmWHhVUtG5ZrSKoEw2UiVyhiXV8GG3IeZEMx9Jb8WN2TRWdfEslkEvF4CgPxNPr7E0imkqBvSlp0sigt0ppaBrXKd3lQKJooqkDLx2AgAJ/Pr60NoeqyNsFSKb0qLtSEdC6PfDGv49OZjCpAlasb6UYynUY+n0PB40XScCPncqNgqPLVMd2nuvjYuao7p5J3pjLoTeaRyeQxMDCg/Y0W1WRkG2LNMbQ2NyHkD6KYLSKTSsHn98Cv2hN1FRDxubUlaTWqxw4fKFprK2GOm2H5Et1WJ3Tp3fMXvPceEomkFkXHjxuHAw+cri2QKZbSipMbjlmvBZ1r2rRp2mKUZRfUe0D/pXwEn7B81rc9fNzecj/Q3NyM8ePHY5yqa9y4sXYYUyfw+jidZ8yYMfpzU85WS1l+rnx6N/9PfeoT+MdPX4kLL7xAW9/OmTMHkydPRmtrqxbQvbbAyuDx+uBxe7Xl9ML3FuHue/6I++//s7bk3Rb2a7i/64IgCIIgCIIgCIKw71FM9qL7oZvgGXsIWj99px0rCLsHY0NHh7myowPz5s2zoxpDq0wDRdCL6l87urGyK41jJrVjY6aAP3dksSlrYLQnh3Mmh/GBiVFEjJJ+pN4wXaAxaipfQFcqi0TRgGn44GI8ZVHDVMEFd7GEEAoIuUvwq0C/psUCNwoytbDKR/v5aDp1OVoUUojLZtLIqnJNjx/haBj+gF/7cy2kMmiO+PFaErh9WQ4r80EYXo+WvVRRKrA3BYQLSRzljeO8cSEc2BqF21eEV2/oRJ+glH6BQjaLZDKlex8KqfIzWQwUing9UcLqRBGnTIxhdsyvR8goUXG2hGJthVnFotER+vjY+j1//JP2JcpOUeD78Icv0FaUDvT1ee+99+Pvr76mxcvZsw/Fxz52McaNHaOuWrUMFkfQdKwsH3/8CTz40COq3LwWVc879xycfbblzJlJmeyll17B/fc9gHg8zkHHzJmH4Kgj3w+/36s3gtqK1Ra+L8VCAa+9/gYWLHhP1+nzeXH+eefgtNNO1ems/jMP/xh4+uln8MCfH9bvGy1iz/jg6Zh37NHIq3btCGyzSxUcDofVGG4rrjYiny8ioTfwGkB3V5cWlHt7etDT06sD+5/NZnTaQrGo+l7CB045Beeffw7CodB2YysIgiAIgiAIgiAIwu5n3dVhtH/sh+j7608QOu5TiJ71TfuKIOw+huRj1ZKMaDnqwvL+NAZyJRw2pgnTYwH4TROjPHkcM96PI0eH0Gr7KqV0SrGL1qOpVAq5Eu1L6ZmU1ylcUmQ1tOWql4/ql/LqmK4DTHi8HgQCIQR18MPr88Ljtnax11avqkFsEzfMWt6XwZt9OSweyKA3nkHE7UbU70JelbkxmUG3CqAwa7rgof9UlTGn6prkzuOEUW7MUW1uivi1eEu/ryXT0P5ds+m0tmDlZk/Rpmakcxlk8nkYoWasThbRl8nhgFgQYwLW5ky0mNVGszxm62oIbRTgdszH6notMFs+Vt+3Uz5WWTfLfXP+W1i5coUWBcOhIA4/fO6WzwObTVH3b88+hxXLV6mx92vXAYyz/Jm+jbfffndLeEuFd95+B2/Nfxvvquv0Scp6nLoo3E6ZPAmRSFTFWZapVtvpY3UVFi1agkK+oEXYI46Yq61iKVjuUAiHEFKv3CjLEm8tK1WOm2OZ6wigzivTELfqGy1dYzFay47DgTOmaxH7sMNmY+bMg9HW1qY/vwPxAZXHpa1zU6k0JkwYj9GjR6k4XcyW8gRBEARBEARBEARB2P3Qx2pm5d8RPftbiJz2ZTtWEHYvO+wKYCuUCw0ticKgeFVELJ/ESTHgEzPbccb4GMZ7DbiLBUtkVCmz+SwSiT54VPr2SBgRj1v7KaX1KzcxcpdK6ryAkMoX9ntUOgO0Vs1m04jH+5FIxvVmQ5SsKHBSAKNVYliVFWtpQdv4cVjrDuCRDQX8cVUWz27OocugR1UTY9wlHNfuwSGRHAKFpKonD6+ZR8DMYIzZj7nNRRzY5IYrn0E+m0MmV0IubyKbKyLeH0c6mdCbaEWbmlR7ssjkC8h7vEiVaO9qjQSHohy2c7jLaxR1N27YqN0umOp9ampq1o/Bl7N06QosW75SC6p8S0pFNTZ0v6DeC4qghZwKfFWhqI6LauwK+aIaRyuOG2IR+shdvXo1li9foc+3//hRgOUwWsInfb1W4oiitYKDc+wInCxr7dp1mD//bR244RX77viWrZa/PI4bo1E8PeWUk3DxxR/B1KnTtKhK8bu/vw+bNm3W6Yb/Oy4IgiAIgiAIgiAI+yaxj9+K8Imftc8EYfczJGGVUpNlgwp4SurIdCNbdCOdKyBomGh2A/5SgU/CqzQe9ceFTDaDRHxAWxFGmyIIqdfWoB/NATd8RgE+M4tAKYeo10BLyIu2aAjRcEhX5lL5g6GQtmLUj9/3x5EYiCOTTqOQz28R4FJFExsKHnR6m9EXGouOQhDdBROBYBgt4QiOGteCc6a24OjmIg7x9OMgbxzHRnL46AQ3jo+VEKAVaq6EZLaIZCKFns2dWL9uPQYScbj9HrCWgb4BHegRtuD2IENRzkW3BBRXa7BVrxsmUMC0WtvRsQ4bN22G22NZd1Kgbo4162tkQI3ze+++h96eXv14P62FW9tatXUmd/hn4OZPo/hqn7e1t22Jb29vRay5Sb/v3Ek/EU9oq1TuvG+JkGWjpg+3tq3auPFaveBQfkz4GXn99ddx++2343/+53/wm9/8r3ZRUE61/NbLtiLrlCmTtbUwxX3GcUOshPqM0EUDqaxbEARBEARBEARBEITdy4Rbkwge/mH7TBD2DEO0WKUFKu1MiQmXYSKVzaLg8sITCmvRsWTyUXqPfhw+m0whnUggEAgiEmkGH6E2TBNBL9DsB6LuIsLIIWimEXYVEeAu9apkbjDEDYy4U3sqmYRZKuld5puiUS1q8RFsPj6fTCT1Dv5eo4R2VWaLmUWkmEC7r4AWPwVgE9lsHvmBJGb4DJwzJYyPHhjBp2c147Pvi+H8qaPQptq9puDGQgSwwetH3udSdRTR3hbB6DGj4PUHkM3lVX39qsuq9y43svT7yk2wthhWbiuoWQK0Yi/rbI4oWC4OEoqbFBf5WL9b9YfC9bSpUzB61Gg7BbB8+XIsXbFMvY90/WBi5iGH4LJLL8UVn7wcn/zEP+CTn7wMnygLn/zU1sDzT33qk7j8sssw65CZVDf1WCxbvgwrVjhWq2Xopm1t49aW7jy0LI2qz42L7h3UBzcRT2Lzpk5teUvKhmULlkDqhK3jSGgt7fV4t7Z1VzZWEARBEARBEARBEARBGPbsuLBqgk/+642f9KkqIV/IoJjLwO8G6FKVYpOhXwtIJQeQTcURCYcQpj9Qww392LzLEquMUhF+PpIP9ari6FPVpPLFy6zL5UYkGkVzc1SLp3zsmpsG0YcmH1lvaorqcvrjcZSSSRwe8+OUVuCEUApnTwpgRpNXi2i0lPSqRuddHqzsziOVKWJiyMBYtwl6Ml2WAe5bV8R/vduP3767GcsSJTS3j0KsOYag14eQL6D9rtKac/TYUdqPp1eVpa1yVVuHu5GiIwBSLGRbc7kCXv3763hvwUItqnLjKu6kf+BBB2o/oySpxpPCa1dXtz6PxZrw/iPm4uCZMzB12hRMn36ACtPqhmkq3cyZB+HwIw633ivTQE9vHxYuWqw3ArM+glsHj83cavFpf8h2EfSbGggGrfJVWL1mNdatW6ev0V+vM0ZWvU7YFual9Wtff/+WTbXoTiASCcPjURNAEARBEARBEARBEARB2C+wFLQdgaqqfije2naKCmo8lUE6m4ZZzCGXzsAsFFHM5zEQjyOTyyHa3IRAKIAShStbqzJt4YrCKnfmd7tc8Pi8KBQLyOWyVhqDtqbqVV33eb1oiTXrXd65idRA3BK2PCq+qakJrS2tiASDmBgATmgBzh7nw/sCBZT6e8BNiygK0tp17UAazy7djHfW9mMgU1ClGxgoFbCwN4MlmTA2esZhadyDTWkTRbeP3l+11S2tY+lTNBC0Nm/iRlw+uOEtAm7dLas/e47yR9+3CpPVYDqKf4SPrdPK929/ew7PPPMsUhQ3VXZadM6dOwfTDzhApyPLl6/EkiXL9PixjAMPmoFpB0y1rzqwz/w8VA/cLIoccMA0HDBdlU1NU/1j2atWr9HXtowbr/E9d0639G+oWG1z2jBu3FiMHTtaf8ZobUp/ry+//Ire7Z9Y9ZW/h1vrLx/DpUuXYuHChdrHKj+bFIzp+oBsFWcFQRAEQRAEQRAEQRCEfZkdFlYpG/GxcAqsOnPJhVC4GbHWNsDjRzJXQG/fANZv2IxEKodAuAkGBUruyG7lZi4Nj7KmgSw8KLo9MPlotYs78VtpuKEVxS3tdoDimAFVVwSRlhbkVO098QRS6bQqm5tfGQj4/Ii6XWguJBHK9MOTy6uSvXCpuvP5AjL5HJo8JRw+JohD28KIebiDvypXtcHrMuB35+D2pOAPFLSbAm7MRWktk8nrEPAH4fP7YRZLMHM5lPJZuFS7nEHc2rNqVLm6RTgsu1a/ENUeCqrWbvSJhOp/KoV4PF430E9qT28v1qzpwN///ir+8Ie78dCDD1suADxu7WJh9uxZOOboo7QvVMId/BctXISuzi4tIjc3N+GQmTMRa6b/VTbSaSj7wBGoFSxL0FgspvIfgqao+jyo8jZv7sTSpcuQ135J7XGwi3SsR+l2obu7R28MtWnTJmzcWB42lx1vrAhW/IYNG7WITGGYjB8/ATNVG7gRFWMY/9JLL+P++/+MZcuWq/FMatcAhUIJRfUeU4QuqvZxrLPZnC7rjTfeUmP3CFavWq034zLNorbKnTRpoq6DaHFVBFZBEARBEARBEARBEIR9GmNDR4e5sqMD8+bNs6MaQ2+b3Om/aBj46+pOrIjnccb0sZgRdCGXz6N/YEBvKkUhEhRUTRNev1dbRXo8BrgZFQXUzkIR73WnkMmbmBIxMDHsA/JFGKUSmiIhbbFK0ZO6GMVcSlX9JWBNMoe+dAF+dT7KXUSrt4SA24NiAXpDK8MoobklBr8/oHenp1iWzmQQ7x+AmxanoQi31EJL0AufakeqCLy0eQAvbUqjp2hiesyDcye2YGrYi0yxiIGBFPxuN6JNIapxyCdVWemkKicEdziC59cOYFlvCqdMbsFhMdUH1VKjpPqohWGKs5bVqz7WBxSL9YuWFGkBec8992LN6rV6rE466UR8+MMXIBgKMJWmt68P9933AP7+99e0oEe3CmNGj1Jj7FPjY70fDpYcrMrmuKny6Bs1XyhoNwq0zqTIzDIYaIU58+ADcf5552DS5Ek6PYXP995biHvuvhfr168H3TYcccRcfOTDF2rLTN32waKHQJWpyqBAyn6+8+4CXQ+tWD/20Y+oV8sK9qmnn8EDf35QvWdsnwdR1cdQOGyVsbV7W1Hx1khWtojCLF1U5HHkUUfgtNNO1T5RyaZNnbj/gQfx1vy3tAsEPT7qPW5uacbUqVMxZcoUtLS06Mf6KShTfGV7urq6sHzFcqxevUZb+brVZ9lU+caOacOHLjwfc+YerltB21gX54ducLVGC4IgCIIgCIIgCIIgCPsCQxBWLYHTElaBx9dsxoqBHM44YCymeYropy9Tn0+LYhToSoUScvkcsrksigU+el9S193ocwfw5Losnl2X09aBx45y4UMzWtFsZrWQFYs2AW5VARUyCmWqrL6Sib9t6Mdf1yTQmTXQHvDgpLEhzPFn4Uv26jr9wbAWvUKhgCVtqewsgpabuWwOfn8QGdWWgWQ/ItEgmkJhZFMFxHMF5ClS+v1wFYsY41Nl+DzoTcS1+NYcDWsRMplIa5E2EvKpOsKqBjee29iLtzcncOKkdsxuoYBnVhFWLZFNR9kHtMylyLxkyVLcc/cftUUp0594MoXVixAKBvU5+09h9U9/uh+vvvoqfD4/6OeTflFL9Eera9C9VcH5a8G2O9CFAcVU57F2bgI2Z+4cnHLySRg1ul1brhoqnuP05wcewlNPPa3riURDuODCC3DiiSeo8igaqjJ1f7bWWR1LVCe0siVPPPEkHnr4YaTSGXg8Xpx37tk4++wzVSkGnnjqKVXvg9pilO2j4FnizmD22PGN3NobhYov05O3gfXms1mc+oGTcPHFH9E+eWldahhuPc4PPfQoFi1aZJWpCi2oz2ZR9ZViKut20V+qyxJWdTvUNdbuiNGUT8e0j8GZZ56OI48+Ah7byrak2sPx4ZZp9cdGEARBEARBEARBEARBGMlYatdOYGmHBjLZHBLJJAJ+P6KRqL5GMcpwG/AH/Fv8oDZFYvB4QljXn8Mb3TmsNaLY4I7gnVQJa9I5ld4S1NLqOJ8vIpPPIKsCha/VfUm8sCGFRbkIugOjsSgfwrPqvN/wYfLkCQgFA+jv7dOWqWZRNUy1i3JYItGPYjGL5tYoos1hjGqJIdYcRU6VuWFzJwYSAwiaRYxCAdNDPrQZQC6bRV8qBRRMNAeCcKuSEgP9up8h1ZcAhTrWodAPvG/R0KqLaVtlNubZ6veTaKGTflvVWHG8eO5g1UBswc9FYc/QFpV8pJ3iLkXDYCioLVytwOOg3qgpFA4h0hRBc3Ozfoyf1piTJ0/GccfNwz9c9g+46MMXaFHVEg4t6Pt0ydKlWnCk0Dpt2jQceOAM+yoj7deGQaHSsuSSGl9y0EEH6sfm3aqPxUIey5Yv1Y/vE4qrVv8oALu0QO9X76lf9ZPWx/pVB3/Fq3PshIAeG/rDpZW0A4VRtmPy5Em45JKP4PTTT0X7qFHaty/ropsHt8er+2wJ1xRUVT9UW91et7rOND71GYpi7uGzcMmlH8KRxxyuPwBF9dkxDO2RV/1znEgIgiAIgiAIgiAIgiAI+yrG+o4Oc9VQLFbVP0plf+3oxKLONE4Y34zDWv3wefmAvqGS0XqPaS2JiekpwlKLLBkG3u1N4q6Vcbyb8MNdMnFgNI+LDohgVqCEZM8AvN4QfEEfcmYKrgItKQN4oz+HP64HVqAZLo8L+byJiUYK/zjdg5PHRrSW193Zpf2Ocvd+iorZbFZbLoYjYXj9Pp2G1pOJVAb9ff3w+1Sbw0HE40k+O46IykODxHhyQFuUtre1w+/1oTcxgGIxj1ikCR51ToFO2yWqvry0sR9vdSZwwsQ2HNbC/m9vsUpxjnVTZuQBLS15xaX+dW7uwltvvat9nnK8Dph2AGYfNgs+n0dn4filVHvfeftdrF27VltN6qIULNt5Ryzs+nik2k/Bln0MBkOIRi2BtbWtBS0tTToNYT+dPGzmkiXL8S4f12dT1flBB8/ArFmHaGtOllku/A4WJx/dELz99jtYsXyljg+psT/88DkYP348lixdgQW2mwBuZrZ1szOnvm37ufW4OvSResD0qTh09vv05mdWeVY7GNi/9es3YfHixVizZg26uruRTCT15lZM6vzqQDcGHMOWlhjGT5iAgw6cgenTp8AXoHVyyRo/9V5bMj4leLfOu+OjJAiCIAiCIAiCIAiCIIwUdl5YXbMZS3uz+OD0cZgZ8ehHt3mNT34znanFJvW3aCKfK2i3APlCFvES8HbSjbd7SvAWi5jb6sX7xzehLehGOp6Cy+VFIOxXufNwqbyFogtv92Vw/8o03k57UfC64M6XcGjEhUsPDGBWs09bGOYyGT7Brd0OJPrj4KPfLW2tCIT8KPJRcNUml4rr74ujv6cXbe0t8DdFEI+n4TMMeD0mksk4NqzfAL8viDETxiOl2m0YHu0OwOdRHStRVDVAxwYUAF/c2I/5nUmcOKkVc2J+3d+awioVO3VI0ZnQn6y+7nLr8y2YLL0C1W6deadRg69VU4Zy7PdW12NtYmXB9JbLAQur7YNri5NWQTWZWbbpB6+rvtIy1MU6HTlzV6HK1W1nPWVtsRqi/rP6WSqqzyQ3Q0ul9MZdFGXZJgqwfr/6LIaC2hKbriasNnJMnPJYljWefLfpHsJxfSAIgiAIgiAIgiAIgiDsmwxdWDUtH6sUVpf35/DBA8bioDB3mGcaWlGWwMfduYlVNqcC/asabni5C30hj6ILKIabkIYbAdOEP5eGV+UJhyOID8RVOSYiTWHkCjlkUxkUVGX5YAjvxgt4fl0cXekCWtwGjp/cjHnjQ4ipvJlsEfFEBqGQX+/y39vVpeUufyQCXzAIn9cNj2Egm8lr8ZbuAYJNQf0YeLIvqTc4CkYC6E8MIJMuIpfMIJlJwB8KojXaAj9dAngM7U+T5remy0RKtf/Jjn4s60ngg1PaMTtGv6gldZmPg1tiniWs6kMrqGM9TOovUxXSCWR7NsLMpqDNZdV4aMmOeTTqjFaR6oj67Jay9CvT28fEOdZCri7Fto7lgfPHCU4m50gnUKgW2xXpWnU5qk0sqBzn1MlWjfI0LEed66K3KYuCJL2S8p+VxsEeiRo4hZHyfHrEVXVsszVu25XijI96I9hXuldwaf+zajy3lEOYk61Qn+hSSYWi/mzqcVcJ9RX1ufbH2lUYpeIo1LItVn8EQRAEQRAEQRAEQRCEfZMhCKvEkqkKMPDEms1Y3JXGB6ePxfua/dpilZsA5fM57ae0VCzA7fHBGwqh6HJhcyKLzqy67nYDPhfafG5M8HtgpBMwKKaGo0gmE8hmMtqvJcvz+vza16bH7UFa1btZ5e/PqNoLOYwJqDLCfmRNA13pPOKZHKIqX7BURFSVHfT7EE8kkc4XEPKH4FFtoFga8ge1xW28WNSP9hfTWRXnB3d0z+Vy8KnrA919gLuAllEtKGYLyKp4w3TD51b9CRooqvJf7crjviX9iKi+XDqzFbOi9OlpamHVGicGQ/1HCY6ndB/AWPVPtZE6as/aFVj14l+Q27RG+/is1C91RgqAPNCPnBMnEV8rM1Sia7OPnbR2u3RgmdZ1CpGMKk9FKDjqVNqK047UOCmqY/VlawYeGVrwZF0sV8Uw0i5mm7LV8dZ216I8QyXMy/aqtuv6nLEj1hH/6hrUH123CnoMVMSW94Gn+iLzO4k4HkxbRN7lw6T3n4QpKsBFlwN0XcFU/AwIgiAIgiAIgiAIgiAI+yJDFFYpSQJ5uPFURzfmb0jhmPExHN7qhVHMaWGSvkP9fp/eQMil0iULRSzrS+K1zgIWJYDufEE/Dj824MGx48OYHSmizWUi6AsiGY8jEeeu/VG0tLSCmwlpUUurYwb0s/6KVDaNXCYNVyiCd/pL+FtHDxL5PA5uCeH4CU2YFlJ1a+tCE6VCAQMDSfSqsoPNITQ3NWMgXUJvDqodblVkEV7Vr4BZQMBtiWdukxalBfgCftWPMErqeiFTRCaTV33PYVMJuL8jj2c3AYeO8uOKGUHMjvp0W3U7bYGOopzlFoCnWnGzj0vqUgmF5ACy3etgZhPq3LlYhl0MRVWNFgjL01nHlgipjvV/Zde3tKNS6FNpdJGW/ayFnVbHq3brLE59tYRCO48tPjp5iW6T05SyQwdaqloHzGRd3ZqcGXRhNuqKTmbH8bJ1VB19UWdQf3li/dVo1VQFZ7x1/UzBV5XO7osVy96rI36W2F59jXnVPKCvXcMLT+t4+FrGqWOPTmsVa5ctCIIgCIIgCIIgCIIg7HMMyRUAxSRKcQXDg6fX9eHR5UlMCntwSruJyTEvosEg/G6fFkQLpRLSqRTWZYp4YnMRf+sy0OcKwXR5Ve2qrEIO44wsTmotYl6rC2P8LnjchraMDKpyuLu7Fr22CGCWrEtdzK3KTqbiWJ0u4c9r83i2B8ireg/yZvGJgyI4dpS1cz+zcmOh/ngSqXQWbq8XedX2lOlF1qAQCrhUn+g11meWEFBpmyM+RAI+JAf6QHebkeYmuG2RkfIcW7OwP4PfLMvg1V5gbruBT80IYXbEr9tOQdJli6t0GUCqWTBqK0qnb/sEVl/Z212FUyIZriNF1xeWOGttaCYIgiAIgiAIgiAIgiDs22yv9DXEsK3yPDpzyWWg0yyhx+2CNxBEyBOCzx1QaVza52lfbwpplXZ1ycD8uIkeVxQFfxAFr4GSx0BRHa9HCK92F7A+byDa0oxYczO8Xi8KBe7Obqoqy4QqderWgYKkC0FvGLk8MFDIweVzw+XxIquuZQt5LcIZLkrAJhLxFMxCCaNGtcEfiSJTcqOoeuAyTB1oBWu63MiqMgteNwyPtZmU10+Zle4BMvqcW0oVqZ+pOsaG/Jja5EXYyMIwWR8vKLY01zrX7dgauQ2OCJdIJjEwEEdJW0Vui9brKuC4cId9btjFcWLedJqbLpV0nG6iHbhrPdOzHB5b+ayx1Qa9LNBOTb+4Oqg+8zWdSWNz52b09/epcy1p69Rb0249d+L4KDxfWQc3gcpmc+jrG0Amk7VSqqRWUOm3lGeVUA7z9vR0Y/26tXozsQ0bNqK3r09vgKZ9nW6p26LynDAunkjoUPX6Ni2wYDIrKf+UBwern7TM7urqVG3qATdn46ZfIqoKgiAIgiAIgiAIgiDsH+ywsEp5qUBhSf3jNj1GyYC3ZGJU0I0xzWGU8kUkk2mkcnn0JxPwBTwIhoPoTBTQk3Xp3f49RVbs1sFtulBy+zHgDiDn9sCtH1c3UCpaguF26MslmAzqn9vrwahIGAeGfZhmpDChNICZTT6MC/pglpjGg1QqjWw+g1DUB7fbhUy+hLzhRsnlVjXR/lSVZKg61XnR40ZG5ctk8yioPnBTIwqraVVGQeWjz9eCWdRWrM0eF44eF8AhbWoMTIqr24pv7IfltqCx2LZxw0asXbsW+XxeC4oU7bg7vTMGFEuz2eyWOKZh+vXr16s4Cnxd2jft5s2bsHr1KmQz9G9rtZeiLjde4nhk0hmsWbMGnZ1dWgSkn1f6s02ns7oOq60qmCqXei82bdyMBe++p8rv1X2mSJpOp7U4y3QsN8fNyVS81VZDddml0pq6no0bNyIeH1DHq9DT06P7R/+7jlBZUOWwPPaN7SuHdaxYsQrvvbdI9XUd1nasxaKFi7BmdYctQBtb2sPxsgRRU7eH48RrjIv3D6C/r0+XyTZSgGZ9bIs1RkV9zDgGiraWPuq8d4bqjxo7PU7qc6DGiWPDMXznnXexYf0mXe9WeFx+LgiCIAiCIAiCIAiCIOxruL/6la/c0DcwgEmTJtlRg8EEn26nMLcplca6eArRoA/jWgLwukro7+9HIZtBS7Mf4ZAfhWIJ724u4L2kF0WPT2uNJW0QasBNLc0w4SlmcEgUODDmh+Wl0rKudLtccHOjKxtLvyqhxDJUOgqfnlIB4WJe5Q3imMnNOGZsEG3FlPa/WiyUkM+mEQwF9a7/qilI5V3IqNeSar8lrCooMqoj/qVbAHc+i3wyiVQqoQW1lOpjMZ8HN7fSwqdqSE+ugM2FAjqTOTSpfLNaw2j1edQRhVpqwLpkdtNmy8F29PX1q7+8bmD9+o1atKOYSCvPcDiMzZs7sXLlKv2qBcGSieXLV+jzUCikxkjL3Fi3bj3WrOlAIBDUAiJFxnA4pMvvV+8zX5cvX66vRaMRLXYuW7YCGzdu0HGsi9bCfG+Tqv/Lli3T8WPHjlVjkVJ5V2oxl8ehUBi9vf1a/Ozs7FTvhUuV2aTb0d3dg4ULF+v6fD6/LiMep/DbqfL06TZRrFy5ciVWrVqjhWHmj0Qium7CfvKzNGHCRMyadYh6Ha/bxvHxeDw6/9Kly/U4JRJJPQ4crxUrVupx6OvrU3Xbrh5cbp2HYu+qVat1uyj4UhBlHevWrVNt26yFYNbBdjhQSOV1jvf69Rv0mHK8Oc4DakzHjh2DWCym278ttd9vQRAEQRAEQRAEQRAEYWQzRGEVWljlhkzBkA8BvxfruuNY3tmPJEraSrPJ70MsGKJMiZzpwqpkCYsTJWRcHphul964iiKllqKKRcSQw6yIC+O8JRiFvBa8KGhSVKXQZVkEbhWqLNHS0AJrIZ9GMZNGS9SPWDRoPe7u8yGZyyPV3Y1IwItIU4tqsFu1B8gWisgUaM1Kq0y7VL6qfIZZQkC1rSUUQEtTRIuHUfpX9XjgUiEYjaDg9mNNysTza3vw+qpNaFXxJ0wZhSlNAXh1eaoPqiL6VLU2f7LrsP9Wo6enV79StKWI19bWipaWGHp7e7WQRwGUwl17e5seFwqGtL6kH9q2tjZ0q35yszAKzRQmx40bp4VFCrCxWLMWOilsRiJhna+1tUXVVdCiZEtLC5qamrFp0yadl+eEYmR//4Cug/XxOts0YcIELULSypVWsr29PRg/fpxuB8VLRxil6BiJRFVdrVp4ZTkTJ07Q7aI1KgVWCpwTJ05S59Zj9RR2mY5Q0GQa9ot9IBQ0E4m4tizlmPGR/IkTJ2phltakFIPj8TjGjBmj+86mWBa2dEfQp9s0ZcpkXQ7b5PX6dDytmqdNm6byp7RIy36yL2Tdug3o6FinBdTRo0frcWDb/D4/XOrzOW7cWC3qOv223ufa77UgCIIgCIIgCIIgCIIw8qk0sRsUfGCcWz3RfLTN5cIpY6K4eNZETIlGsHBtHK91F7EwaWDVQAG9yTzSqSQmN5Uwo6kIbyGjN3ei61ODj3MXs4iU4pgRKWJKzA2/G/qx7kQioa0iKZRRSKQYqP1q8nFxipamAT7Iz0fR4+p1oy+Ip7qK+K93unHrO124fUkCL/X7EA+2IGt4kOSj/EXrsfVSLgs/8vCaBccEVouqtFT1qeBV9aiCtdWp2+OG2+WBLxxGxuPFykwJz3dn8JdlXehOFnHCgZNx8ZzJmN0SgJYDKc5qXc0S1lg6LVgHCwVkCpETJozD6NGjtNDoUW2gRWShkNeWrHzEnZt6UfRtbm7SlqeE6Rjf3NyMpqYmLYw6Wh/HibA85mlujuq4nBoLS7QuIRj06zTWuVVeMMjyoqALhUDAj/Hjx6K9vVULs9lsRr8nFHEpVlIQdfJSaGRdTU0RXS7bOm7cGC0MUyTle8r3mLAdHjW2Pl9AHW87VrzmtJ2wTsePLC1aCV/9fr8W4B1htqurW4u+FJopSFPIpdUsx3bMmNFaCG5vb9fXWO+YMWO1MMxxpihaKGytM6U+v6FQAJMmTcTYsaMRU+NXVO+Fz+fRY8Px3yqqCoIgCIIgCIIgCIIgCPsDQxJWXdrWk2KkCy7ThL9UwvQAcO6B7Tj3sCkIe114Zc1GPLhsA94ayFBlw6HtUXxwXACzfGk0F/oRLGQQUCFWSmB2NI+TR7swNWAiHPBrYXDUqFEIqnwUzWhxSCtEbkCUTGeQz+VRKOZVK0rI5opYEy/i6Y0FPLy2hJf7Q3g7HcYL3Sb+vCaNFxJu9HgjGEglsXHTBgwMJBHx+9AaDsDvtqxmKQbSUpXbWYV8bu3WIJNLoy+ZQMEE6BF0wHDjvXgejyxch/mrNmJyewgXzpmIE8ZGMUrl9aqEHAtVmJZRy2W2baXC2jiipOX6wPLraYmCfPS/pK0waTHJsaAlK0VDPvrubAplYcVZvkJLWywwafFKwZq+QSky0kKTQmQoFNHCIIMlhlqWl05b9GZUqhyKqpZVaZcWRGk1Ggh4bStZ9Ymw028VGHlOq+OsrpfCrDMqFE99Kh9F0FAwhOamGCLhqH78nkJuOSyXAjsf16dVLh+/Z3v4+aCYagnFzbrtfn9Ajxnjpk6drPM6rhPYforOtMDlRl+04KU1LPvndluWy1Z9/Mtzqz+EY8K+08KVFq8J9bnwB1S/1ftEYblc+BUEQRAEQRAEQRAEQRD2D3bYFQD1J0siU0c80c+8qwOKi0YRo3weTB1FkSyEDYkcVvZnkSy5EPJ70O4zMDGqXgNAqyuPaYESjhntxwljwpiEHEKlEuiPEy63LpN72/t8XsSiTTqeG0wVCwVksymkcznk8iV0ZXJ4fmMKL/d40euJweTj2S4DpteNpGppVyKPZlcJE4MmzGIOfr8XTYGALtft52ZaJvyq3JDHQDjgQpNqZ1il8Xq8SGby6CuUsCZTxItrOrF4Qx9GR8P4wIHjcOyYCNrctNvlY/90TGA9gs+hsMRFfaBfiB6vOlD8Y5soAFJYpdBJwZKP64dCQW2xS2Evk0lrIZEWlyyegilfmYaCIoVOCpBsA4XKvr5eLSDy0Xdaa9JalWIrhcq2tnb9KPyGDfRH2qPFSQq3FFwJy6UIyzQUMsnGjZuxadNmbRU6adIU/bi816vGTNVdDuundSnrp+UsXRBY7aNAm0dI5Wd7aJG8YcMG3a+W1hbthmCrOEuR2PLNSjGXfeXYTJkyRbWzXaVzqbhu7UKAedhGtp3jZPlX9erH9NlWCrYUVim00pqV/ae1LttA4dQRadk2j4e+YqO6b4TxdNHAdrJsXps8eYquk4Ixx7ncD7AgCIIgCIIgCIIgCIKw72Os7+gwV3V0YN68eXbUTmDyv6L2n1qEB91F4K31fXhnQ48WRWeOasJhY6NoDvjAh7gpRVGOo6Xn+o2b4XaZGDe6TT/izwf903nu1J5ByOcHfXxysyltUmgW9CZU8YEk3u4ewJ97PFiQi8H0BnTOkotyZ1E/3m9kMjg5lsMnD4piQtCL1EAPipkcAtEYPEFubMTyaINL61Va4jKvC7QBXZ3I4M113Vij6mgOh3HE5FE4pDUIvT1TiRtoqUYYFFVVoIGjowcOAVpXllt9OsdsHwVES6i0rFApAlJ05TGFQF5zxFDmowhLGEf3ATSodDZxYrG00mQaioF83J8WpU65Vho1FrYVqtMM1sHhz+XU2BeLWsClgM2y2UYrWOkcmJ+Wnmy/075ymJb1WhatlkBbjtVWq1ynHbQuJTxnVfl8Ufdxq+Ws5T7AKtOt+u1RcVbb6UZh9eoO3WemoYA6depU7TaB507bWafV363vA485/jznOFltZ7pt0wuCIAiCIAiCIAiCIAj7B7tWWCVmSQXamtK/p4GcCquyBby2thcdmwfQHvLj0IktmBoLotVtwqtSZktubB7IIl0ooLXJi2avoeI9yOYKiKfTCAb82kVAqVTQG2ZRyiwVTWSyebyt8v1uvYEFqRACtjCXM9zaPyof70c2i2ObsvjU9CAOjPq1IpdIJZHK5FSZAYRUoA1uyVBtVnkKpgud+QLe60ni3Y092mJ0zrhROGJMDG2egiqTga1WdVFUs+vRkpoxJM8KW6BAR/GOUAgtF+qcY+txfktIdQS9Wmx/3VRjqHrr2hqnkqg09sl2UFS0LtJylWNBK1BaoFJUzOUyqr1We1gXhU1ah9LSs7Luam1t1H5S2T7mcWDddDXAx/E5HrRItTbPshMoyutgVrpFoLUq20pfr7T0rdZWsjXftueVNLouCIIgCIIgCIIgCIIg7HvssCuARtAylRafNGSkhakBE60eYEZrBK2xCNb1p/HO+l5szhTg9vsQ8HqRyZtI5lwoFF3IF62NjPwuNzwqf7GYBy1PfV66CGBptHA1kEomtauAjDeIBf0FrFf5+fh/idqWysc0tIQ1SnlMDpRwWLMbrW4KgIYWB91uE6lMGqVCER5adqr6aGG7qDeJF1dsxPLOfkwc3YJTD5yIObEgmgzatJagWo2iSktB1apHvahDHttHQ4aWoIsWLcLatWv1I/G0jKRm5/g55WPxvEYrVT6O7sBrFPf4Wihwk69trS9ZLgPho+u8zjIY54isTMe8TjonP32WEj5uv3HjRgQCQd0upl26dClWrlypH9PnI/J8rJ/uBChwMh/LckRHUlm+1TYrHXHawFcGtlPFbmmLdU5h2KX97q5YsUKNRwd6enr1Tv2M52ZZzvvg5OeLVS436Apo61aOJceCfWE6XuerZfW6tX6rbVZ5TtustvOc42pZBzOf005BEARBEATh/2/vzH4bubI7/GNVcWlSOyVKYsvuRUonSJxBMvEYM0AeggxmnvOeP3Ke8pDHIC9OAjiBHRh2DzojtZqSWiu1keJWlfudIt20ppexp+NG2ueTuNStuufeW9LTh1PnOo7jOI7jvP+89YxV9GP+gwQtiEfkYyxUOBpFkdrh25fHHX22faLuYKRHd+f14eKMyuHaeDRSPEy1UI60VC2qGEsXF6FHGml2bkGjgrlVZb0bXV5fKrlT1U25pt88OdU/HQx1XlxQIUoUUfM0DBkNe1rILvXL9Vi/apS0EI7tsfRSpFKxoKGiEKenm0JRx4VEXxycqnVyoeb8jD6+t6rNmZLusJY0De8RNtCEMauhbAAvU8eIttCGVvtj1Br1P588eWLScHNz03baJysTaYm85HF5Nq5C4iHC8535e1ZPNN/Iinqk4b4kie1yTxvHZLkSk3qi7ITP9YhIJCF1SZeW6nYd4/NYPVK30VgJ/QZ2HRmhjI9o3NraskfnGQsJjER9+PChCUjmRwYpgpV5sZs+9WCp20obtVmJubS0YLvvkz16cXEe+hVUq82M+3QsW5c1MB79mc/l5YVdTz+yYvf29vT06VO7D2zq9exZy8ZDOHMNn9RkZZ4hjM2ff8Pl5bqN8fjx17ZG5s5mVmdnpzbm+vp6+EsUbJMqRCrrmZubtYxYNgzLr1mzDF7qzQ4G+XnmMCml4DiO4ziO4ziO4ziO47z/vPWM1fyx+Cx84YH9cdYothEvqZEqStWslvWwMW8Zfl/tn+p3Z9casMHRnZKWKAPQ74bLByqWirYrfzYKbXGiOIkIo+51xzJGKzM13YkiVRLprHOj9s3AJGyUFpSkqeaGV/rZ4ki/vDenD2fuaJgU9TzNdD0cajQY6mqQ6Eglffb8Qp9tH4X5SZ88XNff3l/Rh6VYRcbG0IZ55ht25TI1tpzcsK5winNvQ6ySucmO92yUhExkwyY+9/cPTCIi7xCLCEIkJps9scHVRJJ2wvqRp9VqTcfHpyZTyUolJvITOcq1SEUEKoIQWUi8bvcmjPPcsi+Rg2Sm0sbO+dfXVzYOm0SRoTkRiMwhz1LtmLhst89tHcxxe/t3yuu3JrZxFsKUmMwJKYpgpe3w8LkJTUQtbfRdXFywzbeOj4+/WStCuNXas8xUxOikDAI7/NPGXMmUXVxcsvtFbGQs4pVxWTPzQTQjXxG2CFfkK8KXa9iAiu9IVsZF1LJOxtre3gn9uiZs2fiKNbfbF3Zv2PgKCYsMvr2Bl+M4juM4juM4juM4jvP+8vZLAZhajCyzFLHKj+V4FjgTXmlqj8/PxAXdn6+ouTKn9s1Ijw/aOu8iu0qqlhMNB30lyFQV1e8NxGPbSZG6qz11en2VS5XwKoVYqRYqJdVrJZWzvkqDjuZ0o/XijX6+WtSv789rq0oc6Yujjv55+0S9pKS5uXl9ddbTv24f6LLf11/fW9ffba7q0UxJMynTzdjTKswYQRwIb9RhDQvIRbG95Vmsdjh+fV+QiUhQZCiZktQBJSJSEEm4ufnA5B/ylJ39OUc2K1IRqbdcXzahiHBEECJTySBFZG5tbY775rIUkfno0Z9YJuns7Lxt6nR+3rZ2ZCJxJnVIHzy4Z1mcUZRYtilysVJBrKbWj/GQvsCYkxqw9+/f08pK3eaCgCXDE/nJC6mJMCV7mKzcepg7sZrNZvg/3AjryePPzuabSrHmfN3zlnGLyKWdsgSIZGK3Ws9sfsTnHnI9cyGrl5qwzIM/GWvkXhAfiYsk7Xa6JmWpG0tc1o903dx8aPcaIY3QZU3MnfvOPcizYZlHZXxfqNfrOI7jOI7jOI7jOI7j/Bh4+xmrY8VYYMd8ZVZnFQFpOZ/hO5mmJl+zWEmWWobqg3pN9fkZHbav9d+tY7XTSElcsozRarmoESbNxGqsi86V0ijWbLVm2bEUHYjDdSuVRI/qVf3Z0h19tFTSzzeq+lmjrLViGDZl3Ei/Pe3o0/2OWn1p+7St/bNzba3O6VePmlZHdSHMmQxb5si0EcFUi83zUclZpY5mbGux3FVbW7gu9LFv4+PvCtmkT58+M0nJY+YIRcQjO+oj7hCVCD6yPxGjPL6P2EPEMuTa6qqJSbIt19ebGg2Zcz4Xsn7r9SWThZeX1yYU6YOMRUCSgYmEJLsUgUvWJXKVTMx+v2eZnkjGgwOyYrth7GU7j/zlUXtEJcJxZqZmfRCVnENaAnPON70qmxglFlmgEwHK5lHIX9bFHNhQijkhdrkX4ZZoZ2fHrr97txniFG3+rda+Dg8PtbbWCO3rti7kKfcGobq7+8zWzZj8+zBPxCuSF7ivcVxUlvK3LWi1sRr+54phHbN276jHSn/+DqyTtSFPJ+UFWAeClvns7++bhK7X6xbbcRzHcRzHcRzHcRzHef9562IVn5f7Rd5QVEjW8aEd5YIVNQkIrFL4XK0k2mrMqVYq6cnzth6f3Kg9GCquxOpEiU6HmXppatKwVi6rXIxVyEZikyrbsCrEu5NlWilFWg99lpKCKqENv8uD+1dhuKNw7eEg1fVNV/cXy/r1n67rF415rSBtEWxMKcrnaxsRTb04ttfYuoZ3wz7H13wXkKhATIQoNUQRh83mumWAsskSmZHlclE89r+zs2vlARCdZE8iCqn5iQjd+GBD152Orq4u1ev3TWSm6TAsJ9Pc7JzFGwwHJgTr9QWThTs72zo6OlSSUK/1Q2vjkXjEIWKULFVEJ4/EU2qA+SFBEauIUSDLFaF7cnJmj+AzP9aDtEU6IhuRnYuL82EdpRB/P8SnpMDA5s8tQ9IidMk6ZSzi8UJ8Nptrdg3jLy8vaW1t1e4bYxSLicnX3d2WiU1qxFKmYGOjGdov1G6fWfYrGa2t1q5lnXJPuF8rKw1dhGuurq+0OK5Te3B4ENraqtaqNlfGRfJOMoDpNykdwDnkLeUKkM/cD7J/WbPjOI7jOI7jOI7jOI7z4+Ctb171h4NYzGUm2Z8Yylw1SnujTJ/unenr1r4Ul3QTzavbT1UvDfWLu7P6ZHVW1QLykE6JhjG74qcqjfK6rrhPXmkUqZdl2r8Z6PPDc311eKFqMdHHG8v6qF7TfLgm/CpF1o2FnVmzH4BpsYqkQySS/YiM5BTnyehEbiI6Ly6uTELyuDm1QhGsk8fu6UfGJrVBqYOKAORu8h0ByYv4xCEesRGNfJKFmY+RmpxkzPn5WRuLeSBL2aAJ0ZskRRs3nyPz65v85TqOOYdk5DwxgXiUcaANEcqcySKdZMzymD7zQ5BSS5b19Xo3YjMrSgMcHuY1UtnMCxnLOoB4yGDuC8IYqTk7W7P5PnnyPzYPSiAgSRGn1EEFYrA2sm/zfrM2d0QsWbVz82GMEJcsYmKwNtY5WRtzBsYnJrGJSUat4ziO4ziO4ziO4ziO8+Ph3YnV3CuOP3n8nh33OaCOqdRTolZ3oE932/q35yPtDYsaDHv66Uqsf3y0oEfVWIOUR/PJfu2FnuEzLYVQIU4s9UOMs/5QX51d6b9aJ+r1R/qLjYZ+2lzQ3ShcPaJMAf1HoWcYuUAVVsTsDwMyb4IJ3QBtk3baJu3TcJ7m/DLO/36cvO1F3zwm8SbfX1w7GXN6vLyNa6Zjvug7YfrcNJN4Eyb9ptsQpNPHSE4yU5GvloW7cddqwpJFy+P9PGY/HXfy/fY4ZM4iPJGyZOoyDhJ0mum223ObPp4eDyZznm5zHMdxHMdxHMdxHMdxfpz84GJ17K2Mb/RUaCTDNP+aSz00J4/l9woFfXkx1L+0rvSfx10VleofHs7q7+/OqBQ62ab9Go5lV6JhOD7LUj25uNbnz451fN7VZmNRn9xr6INyrGKaKs2GGkXI1ERJFtlmWhk1A5Bm+cg/CNPibiL03kR+OW/5fbKjb+5d/vmH8G1pSMfp78TMj6dj3haN+fcX8/i+TOJOx77Nq+/PpD2fCxti5TAvzr06JjDmdOzpNU7PZ/qaSfvtOb1u/o7jOI7jOI7jOI7jOM77xbsTqwX7xX/lYEg5yNgqihfwbWiZpSdZrM+Puvps91Sj4UA/aS7oo8aMGsVYxdCXbNdO6PS0M9DnByfaOTxXo1bTJ/fXtDVfVpVYGXVHCxpZhddUkW1WhUiNTawyH6sB+46Ylnq/z/j+vJRJn1f1fRNviv26uK/rC2/q/8fwsrFvj/Xdxn7938BxHMdxHMdxHMdxHMdxct5JKQDk6rfcFW5sIlbzA3uNwvso/NCSKBbqk/qr/7F7rsd7Z5qpxPrJxqI2F6vqptIXR+f6eu9YlSjWxx+s6i8bM5oLoZCuhI+szEBoyNj4iq/U62QUxovzc47jOI7jOI7jOI7jOI7jOG/gHYhVNCnclpg8up1vTDQ5h/xEwlJ/FelJHusofPbCuaedof5950h7Z5eaq1XVH2W66Q/0581F/U1zQasJZQLSsTbNlGTkoo7raka5rOWxf/O5ogwAn7zdnpfjOI7jOI7jOI7jOI7jOM63eQelAHKxyuPW469TiaJZ+EGuRipQdJXz4aIMHxoOLYc1pT3SKC6Ifd5/277WlzsnqpSK+qv7y3pwp6hylpqkJQQ61WKFXxsoxMxswyqCxyEiJxiRIgGQvzuO4ziO4ziO4ziO4ziO47yKd1IK4PtjpvVFFqvYz18a2DfKBUhRlloWaiGaCNLwSbdX+NLXnHIcx3Ecx3Ecx3Ecx3Ecx3kp726npu8FWa55+mr+CD9Zppkq4VvJWnkL72PpOm755uNlvOaU4ziO4ziO4ziO4ziO4zjOS/l/JlbHznSMZaaGTwoI8M7j/FFo8V3dHcdxHMdxHMdxHMdxHMf5v0P6X8UIufTATV4kAAAAAElFTkSuQmCC", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.alamopy import AlamoTrainer, AlamoSurrogate, alamo\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset to have cover different ranges of pressure and temperature. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", we change \".\" to \"_\" as ALAMO accepts alphanumerical characters or underscores as the labels for input/output. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=7, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path('500_Points_DataSet.csv')) \n", + "\n", + "### ALAMO only accepts alphanumerical characters (A-Z, a-z, 0-9) or underscores as input/output labels\n", + "cols=csv_data.columns\n", + "cols=[item.replace(\".\", \"_\") for item in cols]\n", + "csv_data.columns=cols\n", + "\n", + "data = csv_data.sample(n=500,random_state=0) \n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogate with ALAMO\n", + "\n", + "IDAES provides a Python wrapper for the ALAMO machine learning tool via an imported AlamoTrainer class. Regression settings can be directly set as config attributes, as shown below. In this example, allowed basis terms include constant and linear functions, monomial power order 2 and 3, variable product power order 1 and 2, and variable ratio power order 1 and 2. ALAMO seeks to minimize the number of basis terms; here, we restrict each surrogate expression to a maximum of 10 basis terms.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ***************************************************************************\n", + " ALAMO version 2023.2.13. Built: WIN-64 Mon Feb 13 21:30:56 EST 2023\n", + "\n", + " If you use this software, please cite:\n", + " Cozad, A., N. V. Sahinidis and D. C. Miller,\n", + " Automatic Learning of Algebraic Models for Optimization,\n", + " AIChE Journal, 60, 2211-2227, 2014.\n", + "\n", + " ALAMO is powered by the BARON software from http://www.minlp.com/\n", + " ***************************************************************************\n", + " Licensee: Javal Vyas at Carnegie Mellon University, jvyas@andrew.cmu.edu.\n", + " ***************************************************************************\n", + " Reading input data\n", + " Checking input consistency and initializing data structures\n", + " \n", + " Step 0: Initializing data set\n", + " User provided an initial data set of 400 data points\n", + " We will sample no more data points at this stage\n", + " ***************************************************************************\n", + " Iteration 1 (Approx. elapsed time 0.62E-01 s)\n", + " \n", + " Step 1: Model building using BIC\n", + " \n", + " Model building for variable CO2SM_CO2_Enthalpy\n", + " ----\n", + " BIC = 0.750E+04 with CO2SM_CO2_Enthalpy = - 0.38E+06\n", + " ----\n", + " BIC = 0.569E+04 with CO2SM_CO2_Enthalpy = 58. * CO2SM_Temperature - 0.42E+06\n", + " ----\n", + " BIC = 0.542E+04 with CO2SM_CO2_Enthalpy = 55. * CO2SM_Temperature - 0.61E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", + " ----\n", + " BIC = 0.516E+04 with CO2SM_CO2_Enthalpy = 49. * CO2SM_Temperature + 4.0 * CO2SM_Pressure^2 - 0.15E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", + " ----\n", + " BIC = 0.502E+04 with CO2SM_CO2_Enthalpy = 0.16E+03 * CO2SM_Temperature - 0.16 * CO2SM_Temperature^2 + 0.76E-04 * CO2SM_Temperature^3 - 0.56E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.44E+06\n", + " ----\n", + " BIC = 0.484E+04 with CO2SM_CO2_Enthalpy = 0.14E+03 * CO2SM_Temperature + 2.5 * CO2SM_Pressure^2 - 0.14 * CO2SM_Temperature^2 + 0.66E-04 * CO2SM_Temperature^3 - 0.11E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.43E+06\n", + " \n", + " Model building for variable CO2SM_CO2_Entropy\n", + " ----\n", + " BIC = 0.219E+04 with CO2SM_CO2_Entropy = - 0.48E+03 * CO2SM_Pressure/CO2SM_Temperature\n", + " ----\n", + " BIC = 0.147E+04 with CO2SM_CO2_Entropy = 1.9 * CO2SM_Pressure - 0.15E+04 * CO2SM_Pressure/CO2SM_Temperature\n", + " ----\n", + " BIC = 0.115E+04 with CO2SM_CO2_Entropy = 0.77E-01 * CO2SM_Temperature - 0.38E+03 * CO2SM_Pressure/CO2SM_Temperature - 50.\n", + " ----\n", + " BIC = 713. with CO2SM_CO2_Entropy = 0.20 * CO2SM_Temperature - 0.94E-04 * CO2SM_Temperature^2 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 89.\n", + " ----\n", + " BIC = 443. with CO2SM_CO2_Entropy = 0.52 * CO2SM_Temperature - 0.60E-03 * CO2SM_Temperature^2 + 0.26E-06 * CO2SM_Temperature^3 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", + " ----\n", + " BIC = 317. with CO2SM_CO2_Entropy = 0.54 * CO2SM_Temperature - 0.63E-03 * CO2SM_Temperature^2 + 0.27E-06 * CO2SM_Temperature^3 - 0.26E+03 * CO2SM_Pressure/CO2SM_Temperature + 0.79E-01 * CO2SM_Temperature/CO2SM_Pressure - 0.16E+03\n", + " ----\n", + " BIC = 259. with CO2SM_CO2_Entropy = 0.47 * CO2SM_Temperature + 0.15E-01 * CO2SM_Pressure^2 - 0.53E-03 * CO2SM_Temperature^2 + 0.23E-06 * CO2SM_Temperature^3 - 0.70E-03 * CO2SM_Pressure*CO2SM_Temperature - 0.46E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", + " ----\n", + " BIC = 240. with CO2SM_CO2_Entropy = - 2.1 * CO2SM_Pressure + 0.55 * CO2SM_Temperature + 0.76E-01 * CO2SM_Pressure^2 - 0.63E-03 * CO2SM_Temperature^2 - 0.94E-03 * CO2SM_Pressure^3 + 0.27E-06 * CO2SM_Temperature^3 - 0.23E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", + " ----\n", + " BIC = 224. with CO2SM_CO2_Entropy = - 1.9 * CO2SM_Pressure + 0.49 * CO2SM_Temperature + 0.83E-01 * CO2SM_Pressure^2 - 0.57E-03 * CO2SM_Temperature^2 - 0.10E-02 * CO2SM_Pressure^3 + 0.25E-06 * CO2SM_Temperature^3 - 0.73E-08 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", + " ----\n", + " BIC = 193. with CO2SM_CO2_Entropy = - 3.9 * CO2SM_Pressure + 0.52 * CO2SM_Temperature + 0.17 * CO2SM_Pressure^2 - 0.56E-03 * CO2SM_Temperature^2 - 0.21E-02 * CO2SM_Pressure^3 + 0.24E-06 * CO2SM_Temperature^3 - 0.10E-02 * CO2SM_Pressure*CO2SM_Temperature - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.20 * CO2SM_Temperature/CO2SM_Pressure - 0.12E+03\n", + " \n", + " Calculating quality metrics on observed data set.\n", + " \n", + " Quality metrics for output CO2SM_CO2_Enthalpy\n", + " ---------------------------------------------\n", + " SSE OLR: 0.515E+08\n", + " SSE: 0.659E+08\n", + " RMSE: 406.\n", + " R2: 0.999\n", + " R2 adjusted: 0.999\n", + " Model size: 6\n", + " BIC: 0.484E+04\n", + " Cp: 0.659E+08\n", + " AICc: 0.482E+04\n", + " HQC: 0.483E+04\n", + " MSE: 0.168E+06\n", + " SSEp: 0.659E+08\n", + " RIC: 0.659E+08\n", + " MADp: 0.594\n", + " \n", + " Quality metrics for output CO2SM_CO2_Entropy\n", + " --------------------------------------------\n", + " SSE OLR: 541.\n", + " SSE: 558.\n", + " RMSE: 1.18\n", + " R2: 0.997\n", + " R2 adjusted: 0.997\n", + " Model size: 10\n", + " BIC: 193.\n", + " Cp: 178.\n", + " AICc: 154.\n", + " HQC: 169.\n", + " MSE: 1.43\n", + " SSEp: 558.\n", + " RIC: 606.\n", + " MADp: 0.130E+04\n", + " \n", + " Total execution time 0.52 s\n", + " Times breakdown\n", + " OLR time: 0.30 s in 3863 ordinary linear regression problem(s)\n", + " MINLP time: 0.0 s in 0 optimization problem(s)\n", + " Simulation time: 0.0 s to simulate 0 point(s)\n", + " All other time: 0.22 s in 1 iteration(s)\n", + " \n", + " Normal termination\n", + " ***************************************************************************\n" + ] + } + ], + "source": [ + "# Create ALAMO trainer object\n", + "has_alamo=alamo.available()\n", + "if has_alamo:\n", + " trainer = AlamoTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + " )\n", + "\n", + " # Set ALAMO options\n", + " trainer.config.constant = True\n", + " trainer.config.linfcns = True\n", + " trainer.config.multi2power = [1, 2]\n", + " trainer.config.monomialpower = [2, 3]\n", + " trainer.config.ratiopower = [1]\n", + " trainer.config.maxterms = [10] * len(output_labels) # max terms for each surrogate\n", + " trainer.config.filename = os.path.join(os.getcwd(), \"alamo_run.alm\")\n", + " trainer.config.overwrite_files = True\n", + "\n", + " # Train surrogate (calls ALAMO through IDAES ALAMOPy wrapper)\n", + " success, alm_surr, msg = trainer.train_surrogate()\n", + "\n", + " # save model to JSON\n", + " model = alm_surr.save_to_file(\"alamo_surrogate.json\", overwrite=True)\n", + "\n", + " # create callable surrogate object\n", + " surrogate_expressions = trainer._results[\"Model\"]\n", + " input_labels = trainer._input_labels\n", + " output_labels = trainer._output_labels\n", + " xmin, xmax = [7,306], [40,1000]\n", + " input_bounds = {\n", + " input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))\n", + " }\n", + "\n", + " alm_surr = AlamoSurrogate(\n", + " surrogate_expressions, input_labels, output_labels, input_bounds\n", + " )\n", + "else:\n", + " print('Alamo not found.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing Surrogates\n", + "\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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RsGFDUVBQYLIsJydHFBQUiLp164oBAwaYLF++fLkAIBYsWCC7/Q8++EDExMTo/n7ooYfEsGHDzJZp8uTJolWrVuL+++8X7777ru71rVu3ipCQEPH888+L7t27W7F3Qjz//PPCz89PXLx40WTZjRs3xK1bt0R5eblo3ry5aNeunSgrKzNY58CBA0Kj0Yi0tDTZz1i0aJHw8vISt27dsliejIwMAUDs379fdh3t/v/f//2fiIqKEv7+/mLQoEEiPz9fCFFx/wRg8C8jI0Ns2LBBABArVqwQbdq0ETVr1hQbNmwQN2/eFC+88IIIDQ0V3t7eonPnzmLXrl26z9O+75dffhGJiYnC29tbdOzYURw+fFgIIURBQYGoU6eO+OGHHwzKuXTpUuHr66srlz5zvwkl92+XNv18/vnneOSRRzBmzBg0a9YML7/8Mp599llMnTrVlcVCRnYhyoXha2VC4Gx2kfQbiIgcqDKbna9fv45Vq1Zh7Nix8PPzM1keGBiINWvW4K+//sLLL79ssvyBBx5AXFwc5s+fL/sZeXl5CA4O1v3doEED7NixA+fOnbNYvhEjRmDu3Lm6v+fMmYMhQ4ZY3Q28vLwcCxYswJAhQxAeHm6yvHbt2qhRowYOHDiAo0eP4qWXXjKp6W/VqhV69eplcR/9/f0dWnNx5swZLFu2DL/88gt++eUXbNy4EWlpaQCATz/9FElJSRg9ejSysrKQlZWFiIgI3XtTUlKQlpaGY8eOoWXLlpg4cSJ+/PFHfPPNN9i3bx8aN26MPn364Pr16waf+corr+A///kPdu/ejdDQUDzwwAO4desW/Pz8MHjwYKSnpxusn56ejkceeQR16tRx2H4bc2mgUqdOHXzyySc4d+4ciouLcebMGbz77rsuH4cgJsQPHka9qjw1GkSHcBAnInKuhbsz0TltPZ6YvROd09Zj4e5Mp37e6dOnIYRAfHy87DraISSaNWsmuTw+Pl63jtT2P//8czz77LO61yZPnozAwEBER0ejadOmGDZsGBYtWiTZ7H///fcjPz8fmzZtQmFhIRYtWoQRI0ZYvX/Z2dnIyckxu3+A5X1s1qyZ7D5mZ2dj6tSpeOaZZ6wuF1AxREft2rUN/ukrLy/H3LlzkZCQgK5du+Kpp57S5QwFBATAy8sLvr6+aNCgARo0aGAwVsk777yDe++9F7GxsfD29sbMmTMxffp0JCcno3nz5pg9ezZ8fHzw9ddfG3zm5MmTce+99yIxMRHffPMNrly5gqVLlwIARo0ahdWrVyMrKwtAxWBuK1asUPR92IJjPEsIC/BB6oBEeP7dB9xTo8H7AxIQFuDj4pIRUVWWlVesy40DgHIBvLbkiFNrVoQQlleyYV0AuHjxIvr27YtHH30Uo0eP1r0eFhaG7du34/Dhw3jxxRdx+/ZtDB06FH379jUJVmrWrIknn3wS6enp+OGHHxAXF4eWLVs6rcxK18/Pz0e/fv3QvHlzTJkyRdF7Fy5ciAMHDhj80xcdHW1QUxEWFqYblt6Sdu3a6f7/zJkzuHXrFjp37qx7rWbNmujQoQOOHTtm8L6kpCTd/wcHB6Np06a6dTp06IAWLVroeuZ+9913iIqKQrdu3azbYRu5cXaNcw1qH4lucaE4m12E6BBfBilE5HTmmp2ddQ1q0qSJLglWTlxcHADg2LFj6NSpk8nyY8eOoXnz5gavXbp0CT179kSnTp3wv//9T3K7CQkJSEhIwJgxY/Dcc8+ha9eu2LhxI3r27Gmw3ogRI9CxY0ccOXJE8dN7aGgoAgMDze4fYLiPrVu3Nll+7Ngx3TpaN27cQN++fVGnTh0sXboUNWvWVFS2iIgING7cWHa58fY0Go3VnU2kmvEcYdSoUZgxYwZSUlKQnp6O4cOHO32QQ9aomBEW4IOk2LoMUoioUrii2Tk4OBh9+vTBjBkzUFhYaLI8NzcXvXv3RnBwMP7zn/+YLF++fDlOnTqFxx9/XPfaxYsX0aNHD7Rt2xbp6elWTdCoDXSkytCiRQu0aNECR44cwRNPPKFk9+Dh4YHBgwfj+++/x6VLl0yWFxQU4Pbt27jzzjsRHx+Pjz/+2CQYOHjwIH777TeDfczPz0fv3r3h5eWF5cuXo1atWorK5QheXl4GXcPlxMbGwsvLC1u3btW9duvWLezevdskwNTvHp2Tk4OTJ08aNIc9+eSTOHfuHD777DMcPXoUQ4cOdcCemMdAhYhIJVzV7DxjxgyUlZWhQ4cO+PHHH3Hq1CkcO3YMn332GZKSkuDn54dZs2bhp59+wjPPPINDhw7h7Nmz+PrrrzFs2DA88sgjeOyxxwD8E6RERkbiww8/xLVr13D58mVcvnxZ93nPP/88pk6diq1bt+LcuXPYsWMHnn76aYSGhho0Pehbv349srKyEBgYqHj/3nvvPURERKBjx474v//7Pxw9ehSnTp3CnDlz0Lp1axQUFECj0eDrr7/G0aNHMXDgQOzatQuZmZn44Ycf8MADDyApKUk3Zok2SCksLMTXX3+N/Px83T5aEzho/fXXX7r3af/dvHnT6vdHR0dj586dOHv2LLKzs2VrW/z8/PD888/jlVdewapVq3D06FGMHj0aRUVFGDlypMG677zzDtatW4cjR45g2LBhCAkJQf/+/XXLg4KCMGDAALzyyivo3bu3Qbdxp7HYL0jFnNU9mYhIKUd2T76UWyS2nc6u1CERLl26JMaOHSuioqKEl5eXaNiwoXjwwQfFhg0bdOts2rRJ9OnTR/j7+wsvLy/RokUL8eGHH4rbt2/r1klPTzfpNqv9p7V48WJx3333ibCwMOHl5SXCw8PFwIEDxaFDh3TraLvnynnxxRet7p4shBC5ubkiJSVFNGnSRHh5eYn69euLXr16iaVLl4ry8nLdeocOHRIDBw4UwcHBombNmiI2Nla88cYborCwULeOtiuv1L+MjAyLZdF2T5b6N3/+fNn9//jjj0VUVJTu7xMnToi77rpL+Pj4mHRPzsnJMXhvcXGxeOGFF0RISIjZ7sk///yzaNGihfDy8hIdOnQQBw8eNCn/unXrBACxaNEis/vpqO7JGiEUZg6pSH5+PgICAnTdwoiIXOXmzZvIyMhATEyMS5oBiOzx+++/o2fPnsjJybFYa/Xtt9/iX//6Fy5dumS2l66534SS+zeTaYmIiMiioqIiZGVlIS0tDc8++2ylDSXCHBUiInJrxmOR6P/bvHlzpZfnueeeky3Pc889V+nlcZQPPvgA8fHxaNCgASZNmlRpn8umHyIiB2DTj+ucPn1adlnDhg3h41O5PTevXr2K/Px8yWX+/v6oV69epZbHVdj0Q0REBJgdi8QV6tWrV22CkcrAph8iIiJSLQYqREQO5Mat6UQOZe0oupaw6YeIyAFq1qwJjUaDa9euITQ01OnDihOplRACpaWluHbtGjw8POzuHcRAhYjIATw9PXHHHXfgwoULOHv2rKuLQ+Ryvr6+iIyMtGoKBXMYqBAROUjt2rXRpEkT3Lp1y9VFIXIpT09P1KhRwyE1iwxUiIgcyNPTE56enq4uBlGVwWRaIiIiUi0GKkRERKRaDFSIiIhItRioEBERkWoxUCEiIiLVYqBCREREqsVAhYiIiFSLgQoRERGpFgMVIiIiUi0GKkRERKRaDFSIiIhItRioEBERkWoxUCEiIiLVYqBCREREqsVAhYiIiFTLpYFKdHQ0NBqNyb+xY8e6slhERESkEjVc+eG7d+9GWVmZ7u8jR47g3nvvxaOPPurCUhEREZFauDRQCQ0NNfg7LS0NsbGx6N69u4tKRERERGqimhyV0tJSfPfddxgxYgQ0Go2ri0NEREQq4NIaFX3Lli1Dbm4uhg0bJrtOSUkJSkpKdH/n5+dXQsmIiIjIVVRTo/L1118jOTkZ4eHhsuukpqYiICBA9y8iIqISS0hERESVTSOEEK4uxLlz59CoUSMsWbIEDz30kOx6UjUqERERyMvLg7+/f2UUlYiIiOyUn5+PgIAAq+7fqmj6SU9PR7169dCvXz+z63l7e8Pb27uSSkVERESu5vKmn/LycqSnp2Po0KGoUUMVcRMRERGphMsDld9++w2ZmZkYMWKEq4tCREREKuPyKozevXtDBWkyREREpEIur1EhIiIiksNAhYiIiFSLgQoRERGpFgMVIiIiUi0GKkRERKRaDFSIiIhItRioEBERkWoxUCEiIiLVYqBCREREqsVAhYiIiFSLgQoRERGpFgMVC7LyirHtTDay8opdXRQiIqJqx+WTEqrZwt2ZmLTkMMoF4KEBUgckYlD7SFcXi4iIqNpgjYqMrLxiXZACAOUCeG3JEdasEBERVSIGKjIysgt1QYpWmRA4m13kmgIRERFVQwxUZMSE+MFDY/iap0aD6BBf1xSIiIioGmKgIiMswAepAxLhqamIVjw1Grw/IAFhAT4uLhkREVH1wWRaMwa1j0S3uFCczS5CdIgvgxQiIqJKxkDFgrAAHwYoRERELsKmHyIiIlItBipERESkWgxUiIiISLUYqNiJQ+wTERE5D5Np7cAh9omIiJyLNSo24hD7REREzsdAxUYcYp+IiMj5GKjYiEPsExEROR8DFRtxiH0iIiLnYzKtHTjEPhERkXMxULETh9gnIiJyHjb9EBERkWoxUCEiIiLVcnmgcvHiRTz55JOoW7cufHx8kJiYiD179ri6WERERKQCLs1RycnJQefOndGzZ0+sXLkSoaGhOHXqFIKCglxZLCIiIlIJlwYq06ZNQ0REBNLT03WvxcTEuLBEREREpCYubfpZvnw52rVrh0cffRT16tVD69atMXv2bNn1S0pKkJ+fb/CPiIiIqi6XBip//vknZs6ciSZNmmD16tV4/vnnMX78eHzzzTeS66empiIgIED3LyIiopJLTERERJVJI4QQlldzDi8vL7Rr1w7btm3TvTZ+/Hjs3r0b27dvN1m/pKQEJSUlur/z8/MRERGBvLw8+Pv7V0qZiYiIyD75+fkICAiw6v7t0hqVsLAwNG/e3OC1Zs2aITMzU3J9b29v+Pv7G/wjIiKiqsulgUrnzp1x4sQJg9dOnjyJqKgoF5WIiIiI1MSlgcq//vUv7NixA++//z5Onz6NefPm4X//+x/Gjh3rymIRERGRSrg0UGnfvj2WLl2K+fPnIyEhAVOnTsUnn3yCIUOGuLJYREREpBIuTaa1l5JkHCIiIlIHt0mmJSIiIjKHgQoRERGpFgMVIiIiUi0GKkRERKRaDFSIiIhItRioEBERkWoxUCEiIiLVYqBCREREqsVAhYiIiFTLpkClsLDQ0eUgIiIiMmFToFK/fn2MGDECW7ZscXR5iIiIiHRsClS+++47XL9+HXfffTfi4uKQlpaGS5cuObpsREREVM3ZFKj0798fy5Ytw8WLF/Hcc89h3rx5iIqKwv33348lS5bg9u3bji4nERERVUMOmz35888/xyuvvILS0lKEhITgueeeQ0pKCnx9fR2xeUmcPZmIiMj9KLl/17Dng65cuYJvvvkGc+fOxblz5/DII49g5MiRuHDhAqZNm4YdO3ZgzZo19nwEERERVWM2BSpLlixBeno6Vq9ejebNm2PMmDF48sknERgYqFunU6dOaNasmaPKSURERNWQTYHK8OHDMXjwYGzduhXt27eXXCc8PByvv/66XYUjIiKi6s2mHJWioiKn5p5YizkqRERE7sfpOSq+vr4oKyvD0qVLcezYMQBAs2bN0L9/f9SoYVfaCxEREZGOTVHFH3/8gQceeABXrlxB06ZNAQDTpk1DaGgofv75ZyQkJDi0kERERFQ92TSOyqhRo5CQkIALFy5g37592LdvH86fP4+WLVvimWeecXQZiYiIqJqyqUblwIED2LNnD4KCgnSvBQUF4b333pNNriUiIiJSyqYalbi4OFy5csXk9atXr6Jx48Z2F4qIiIgIsDFQSU1Nxfjx47F48WJcuHABFy5cwOLFizFhwgRMmzYN+fn5un9EREREtrKpe7KHxz/xjUajAQBoN6P/t0ajQVlZmSPKKYndk4mIiNyP07snb9iwwaaCERERESlhU6DSvXt3R5eDiIiIyITNo7Pl5ubi66+/1g341qJFC4wYMQIBAQEOKxwRERFVbzYl0+7ZswexsbH4+OOPcf36dVy/fh0fffQRYmNjsW/fPkeXkYiIiKopm5Jpu3btisaNG2P27Nm6IfNv376NUaNG4c8//8SmTZscXlApTKYlIiJyP0ru3zYFKj4+Pti/fz/i4+MNXj969CjatWuHoqIipZu0CQMVIiIi96Pk/m1T04+/vz8yMzNNXj9//jzq1KljyyaJiIiITNgUqAwaNAgjR47EwoULcf78eZw/fx4LFizAqFGj8Pjjj1u9nSlTpkCj0Rj8M66lISIiourLpl4/H374ITQaDZ5++mncvn0bAFCzZk08//zzSEtLU7StFi1a4LfffvunQDVs7ohEREREVYziqKCsrAw7duzAlClTkJqaijNnzgAAYmNj4evrq7wANWqgQYMGit9HREREVZ/iph9PT0/07t0bubm58PX1RWJiIhITE20KUgDg1KlTCA8PR6NGjTBkyBDJ3BetkpISg3mEOJcQERFR1WZTjkpCQgL+/PNPuz+8Y8eOmDt3LlatWoWZM2ciIyMDXbt2xY0bNyTXT01NRUBAgO5fRESE3WUgIiIi9bKpe/KqVaswadIkTJ06FW3btoWfn5/Bclu7Cufm5iIqKgofffQRRo4cabK8pKQEJSUlur/z8/MRERHB7slERERuxOmTEt53330AgAcffFA3WzJg/4zJgYGBiIuLw+nTpyWXe3t7w9vb26ZtExERkftR1ezJBQUFOHPmDJ566imnbJ+IiIjci02BSkxMDCIiIgxqU4CKGpXz589bvZ2XX34ZDzzwAKKionDp0iVMnjwZnp6eisZiISIioqrL5kAlKysL9erVM3j9+vXriImJsbrp58KFC3j88cfx119/ITQ0FF26dMGOHTsQGhpqS7GIiIioirEpUNHmohgrKChArVq1rN7OggULbPl4IiIiqiYUBSovvfQSAECj0eDNN980GDulrKwMO3fuxJ133unQArpKVl4xMrILERPih7AAH1cXh4iIqFpSFKjs378fQEWNyuHDh+Hl5aVb5uXlhVatWuHll192bAldYOHuTExachjlAvDQAKkDEjGofaSri0VERFTtKApUtL19hg8fjk8//bRKjl2SlVesC1IAoFwAry05gm5xoaxZISIiqmQ2jUybnp5eJYMUAMjILtQFKVplQuBsdpFrCkRERFSN2ZRMW1hYiLS0NKxbtw5Xr15FeXm5wXJHDK/vKjEhfvDQwCBY8dRoEB1i21xGREREZDubApVRo0Zh48aNeOqppxAWFibZA8hdhQX4IHVAIl5bcgRlQsBTo8H7AxLY7ENEROQCNs31ExgYiF9//RWdO3d2RpmspmSuAKWy8opxNrsI0SG+DFKIiIgcyOlz/QQFBSE4ONimwrmLsAAfBihEREQuZlMy7dSpU/HWW2+hqIgJpkREROQ8NtWo/Oc//8GZM2dQv359REdHo2bNmgbL9+3b55DCERERUfVmU6DSv39/BxeDiIiIyJRNybRq4cxkWiIiInIOJfdvRTkqu3btMjszcklJCRYtWqRkk0RERESyFAUqSUlJ+Ouvv3R/+/v7Gwzulpubi8cff9xxpSMiIqJqTVGgYtxKJNVq5MYtSURERKQyNnVPNqcqjVJLREREruXwQIWIiIjIURR3Tz569CguX74MoKKZ5/jx4ygoKAAAZGdnO7Z0REREVK0p6p7s4eEBjUYjmYeifV2j0ZjtGeRI7J5MRETkfpw2109GRoZdBSMiIiJSQlGgEhUVpWjjY8aMwTvvvIOQkBBF7yMiIiICnJxM+9133yE/P9+ZH0FERERVmFMDFY6pQkRERPZg92QiIiJSLQYqREREpFoMVIiIiEi1GKgQERGRajk1UHnyySc5EBsRERHZzKZApby8XPb1zMxM3d8zZ87kGCpERERkM0WBSn5+Ph577DH4+fmhfv36eOuttwyGy7927RpiYmIcXkgiIiKqnhSNTPvmm2/i4MGD+Pbbb5Gbm4t3330X+/btw5IlS+Dl5QWAY6cQERGR4yiqUVm2bBlmzZqFRx55BKNGjcKePXtw7do1PPDAAygpKQFQMTmhLdLS0qDRaDBhwgSb3q8WWXnF2HYmG1l5xa4uChERkdtTFKhcu3bNYL6fkJAQ/Pbbb7hx4wbuu+8+FBUV2VSI3bt3Y9asWWjZsqVN71eLhbsz0TltPZ6YvROd09Zj4e5My28iIiIiWYoClcjISBw7dszgtTp16mDNmjUoLi7Gww8/rLgABQUFGDJkCGbPno2goCDF71eLrLxiTFpyGOV/t3yVC+C1JUdYs0JERGQHRYFK7969kZ6ebvJ67dq1sXr1atSqVUtxAcaOHYt+/fqhV69eit+rJhnZhbogRatMCJzNtq2WiYiIiBQm07799tu4dOmS5LI6depg7dq12Ldvn9XbW7BgAfbt24fdu3dbtX5JSYkuFwaAqmZm9vPylHzd14tj6hEREdlK0V00KCgILVq0kF1ep04ddO/e3aptnT9/Hi+++CK+//57q2tiUlNTERAQoPsXERFh1fsqQ2FpmeTrRaXSY84QERGRZYof92/fvo3p06ejTZs2qF27NmrXro02bdrgww8/xK1bt6zezt69e3H16lW0adMGNWrUQI0aNbBx40Z89tlnqFGjhsH4LFqTJk1CXl6e7t/58+eVFt9pYkL84GHU4clTo0F0iK9rCkRERFQFaISCgU+Ki4tx7733Yvv27ejVqxeaNWsGADh27Bh+++03dO7cGWvWrLGqhuTGjRs4d+6cwWvDhw9HfHw8Xn31VSQkJFjcRn5+PgICApCXl6eKofoX7s7Ea0uOoEwIeGo0eH9AAga1j3R1sYiIiFRFyf1bUY5KWloazp8/j/3795t0JT548CAefPBBpKWlYcqUKRa3VadOHZNgxM/PD3Xr1rUqSFGjQe0j0S0uFGezixAd4ouwAB9XF4mIiMitKWr6WbBgAT766CPJ8U5atWqFDz/8EPPmzXNY4dxRWIAPkmLrMkghIiJyAEU1KufOnUOHDh1kl991110GkxIq9fvvv9v8XiIiIqp6FNWo+Pv74+rVq7LLL1++jDp16thdKCIiIiJAYaDSs2dPvP/++7LL09LS0LNnT7sLRURERAQobPqZPHkyOnbsiLvuugsvvfQS4uPjIYTAsWPH8PHHH+Po0aPYsWOHs8pKRERE1YyiQKV58+ZYu3YtRo4cicGDB+tmShZCID4+HmvWrDE7IBwRERGREooCFaAiYfaPP/7AgQMHcPLkSQBAXFwc7rzzTkeXjYiIiKo5xYFKfn4+ateujTvvvNMgOCkvL0dBQYEqBl4jIiKiqkFRMu3SpUvRrl073Lx502RZcXEx2rdvj59//tlhhSMiIqLqTVGgMnPmTEycOBG+vqbz1/j5+eHVV1/Ff//7X4cVjoiIiKo3RYHKkSNH0KNHD9nl3bp1w+HDh+0tExEREREAhYFKTk4Obt++Lbv81q1byMnJsbtQRERERIDCQCU6Ohp79uyRXb5nzx5ERUXZXSgiIiIiQGGgMmDAALz++uu4cuWKybLLly/jjTfewMCBAx1WOCIiIqreNEIIYe3KN27cQFJSEjIzM/Hkk0+iadOmAIDjx4/j+++/R0REBHbs2FFp8/3k5+cjICAAeXl57BZNRETkJpTcvxWNo1KnTh1s3boVkyZNwsKFC3X5KIGBgXjyySfx3nvvcVJCIiIichhFNSr6hBDIzs6GEAKhoaG64fT1bd26Fe3atYO3t7fdBZXCGhUiIiL3o+T+rShHRZ9Go0FoaCjq1asnGaQAQHJyMi5evGjrRxAREVE1Z3OgYg0bK2vcVlZeMbadyUZWXrGri0JERFQlKJ7rh6Qt3J2JSUsOo1wAHhogdUAiBrWPdHWxiIiI3JpTa1Sqi6y8Yl2QAgDlAnhtyRHWrBAREdmJgYoDZGQX6oIUrTIhcDa7yDUFIiIiqiKcGqjIJdlWNTEhfvAw2lVPjQbRIaaTNxIREZH1mEzrAGEBPkgdkAjPvwMzT40G7w9IQFiAj4tLRkRE5N6cmkx748YNZ25eVQa1j0S3uFCczS7S1aRsO5ONmBA/BixEREQ2UhSo3H333Vatt379epsK4+7CAnwQFuDDHkBEREQOoihQ+f333xEVFYV+/fqhZs2aziqTW5PrAdQtLpQ1K0RERAopClSmTZuG9PR0/PDDDxgyZAhGjBiBhIQEZ5XNLZnrAcRAhYiISBlFybSvvPIKjh49imXLluHGjRvo3LkzOnTogC+//BL5+fnOKqNbYQ8gIiIix7Gp109SUhJmz56NrKwsjB07FnPmzEF4eDiDFbAHEBERkSPZ1etn37592LhxI44dO4aEhATmrfzNuAcQgxQiIiLbKA5ULl26hLlz52Lu3LnIz8/Hk08+iZ07d6J58+bOKJ/b0vYAIiIiItspClTuu+8+bNiwAb1798b06dPRr18/1KjBeQ2JiIjIOTRCwfCxHh4eCAsLQ7169cwOj79v3z6HFM6S/Px8BAQEIC8vD/7+/pXymURERGQfJfdvRdUhkydPtqtgxmbOnImZM2fi7NmzAIAWLVrgrbfeQnJyskM/h4iIiNyTohoVR/v555/h6emJJk2aQAiBb775BtOnT8f+/fvRokULi+9njQoREZH7UXL/dkigsnHjRhQWFiIpKQlBQUF2bSs4OBjTp0/HyJEjLa7LQIWIiMj9OK3pZ9q0aSgoKMDUqVMBVMyOnJycjDVr1gAA6tWrh3Xr1llVG2KsrKwMP/zwgy7gkVJSUoKSkhLd3xy3hYiIqGpTNODbwoULDYbMX7x4MTZt2oTNmzcjOzsb7dq1w9tvv62oAIcPH0bt2rXh7e2N5557DkuXLpXt6pyamoqAgADdv4iICEWfRURERO5FUdNPUFAQtm3bhmbNmgEAhg8fjrKyMvzf//0fAGDHjh149NFHcf78easLUFpaiszMTOTl5WHx4sX46quvsHHjRslgRapGJSIigk0/REREbsRpTT+3b9+Gt7e37u/t27djwoQJur/Dw8ORnZ2tqLBeXl5o3LgxAKBt27bYvXs3Pv30U8yaNctkXW9vb4PPJyIioqpNUdNPbGwsNm3aBADIzMzEyZMn0a1bN93yCxcuoG7dunYVqLy83KDWhIiIiKovRTUqY8eOxbhx47B582bs2LEDSUlJBk0069evR+vWra3e3qRJk5CcnIzIyEjcuHED8+bNw++//47Vq1crKRYRERFVUYoCldGjR8PT0xM///wzunXrZjIA3KVLlzBixAirt3f16lU8/fTTyMrKQkBAAFq2bInVq1fj3nvvVVIsIiIiqqJcOuCbvTiOChERkftRcv9WlKNCREREVJkUBSq3bt3CxIkT0bhxY3To0AFz5swxWH7lyhV4eno6tIBERERUfSkKVN577z383//9H5577jn07t0bL730Ep599lmDddy4JYmIiIhURlEy7ffff4+vvvoK999/PwBg2LBhSE5OxvDhw3W1KxqNxvGlJCIiompJUY3KxYsXDYbQb9y4MX7//Xds27YNTz31FMrKyhxeQCIiIqq+FAUqDRo0wJkzZwxea9iwITZs2IDdu3dj2LBhjiwbERERVXOKApW7774b8+bNM3k9PDwc69evR0ZGhsMKRo6XlVeMbWeykZVXbNc6RERElUVRjsqbb76J48ePSy5r2LAhNm7ciLVr1zqkYORYC3dnYtKSwygXgIcGSB2QiEHtIxWvQ0REVJk44Fs1kJVXjM5p61Gu9017ajTYktITYQE+Vq9DRETkCE4f8O2HH37AgAEDkJCQgISEBAwYMACLFy+2qbDkfBnZhQYBCACUCYGz2UWK1iEiIqpsigKV8vJyDBo0CIMGDcLRo0fRuHFjNG7cGH/88QcGDRqEwYMHcxwVFYoJ8YOHUa9xT40G0SG+itYhIiKqbIoClU8//RS//fYbli9fjuPHj2PZsmVYtmwZTpw4gaVLl2Lt2rX49NNPnVVWVVNzEmpYgA9SByTC8+8xbjw1Grw/IMGgSceadYiIiCqbohyVli1bYsKECbIzJH/99df49NNPcejQIYcV0By15Ki4SxJqVl4xzmYXITrEVzYAsWYdIiIieyi5fysKVHx8fHDixAlERkrfhM+dO4f4+HgUF1dOrYIaAhVHJqFm5RUjI7sQMSF+DBKIiKjKUnL/VtQ92cfHB7m5ubKBSn5+PmrVqqVkk27PXBKqkmDDXWpliIiIKpOiHJWkpCTMnDlTdvmMGTOQlJRkd6HciSOSULPyinVBCgCUC+C1JUdUme9CRERUmRQFKq+//jq+/vprPPbYY9i1axfy8/ORl5eHHTt24NFHH8WcOXPw+uuvO6usquSIJFR2DSYiIpKmqOmnU6dOWLhwIZ555hn8+OOPBsuCgoIwf/58dO7c2aEFdAeD2keiW1yozUmo2loZ4zwXqVoZteSxqKUcRERUtdk0Mm1RURFWr16NU6dOAQDi4uLQu3dv+PpW7pgbakimdZSFuzPx2pIjKBNCVyuj1iHu1VIOIiJyT07r9bN+/XqMGzcOO3bsMNlwXl4eOnXqhC+//BJdu3a1reQKuSJQcXRNgv72AMjWyqhliHu1lIOIiNyX03r9fPLJJxg9erTkRgMCAvDss8/io48+qrRApbI5uiZByfYc1bvIXmopBxERVQ+KkmkPHjyIvn37yi7v3bs39u7da3eh1MjRPXOUbk8tQ9yrpRxERFQ9KApUrly5gpo1a8our1GjBq5du2Z3odTI0T1zlG7PXO+iyhy+Xy3lICKi6kFR00/Dhg1x5MgRNG7cWHL5oUOHEBYW5pCCqY2SnjnO2p5U7yJXJLYOah+J+AZ1sPtsDtpHB6FVRBATbImIyCkU1ajcd999ePPNN3Hz5k2TZcXFxZg8eTLuv/9+hxVOTRw9aZ+t2wsL8EFSbF1dDYYrBopbuDsTD3+xDe/+egwPf7ENszad4YB1RETkFIp6/Vy5cgVt2rSBp6cnxo0bh6ZNmwIAjh8/jhkzZqCsrAz79u1D/fr1nVZgfa7q9ePISfvs2d62M9l4YvZOk9f/+3hr3N8q3O6ySZHq9aPRAFJn0fzRdyEptq5TykFERO7Lab1+6tevj23btuH555/HpEmToI1xNBoN+vTpgxkzZlRakOIqYQE+Du3dYs/2pJqPAGD8gv0oLL3tlKYXqdwaqSCFCbZEROQIigIVAIiKisKKFSuQk5OD06dPQwiBJk2aICgoyBnlI8iP3aJtPpr042GU662vbXrpFhfq8C7DcsGRPnubxYiIiLQUBypaQUFBaN++vSPLQhIsJakOah8JP+8aGDdvv8H7nDW2iS440stJ0fdmv2a4r2UYgxQiInIIRcm0VLmsTZZtGxVkMraJhwbw9XLO1zuofSSWjukEo4+Ep0bDIIWIiByKgYqKWTvWinEPIqAiqHn4i21YuDvTKWVrFRGEtIGO6wVFREQkxeamH3I+JWOtaMc26T9jG7SrOzNXRfuZ9swaTUREZIlLa1RSU1PRvn171KlTB/Xq1UP//v1x4sQJVxZJVZSOtVJYWgbjtBFrRs+1Z0RZ/XFdiIiIHM2lNSobN27E2LFj0b59e9y+fRuvvfYaevfujaNHj8LPz8+VRVMNS7UW+j2CbBntliPKEhGRmika8M3Zrl27hnr16mHjxo3o1q2bxfVdMeCbmkgFGUBFc0+ZEPDQAK8mx+PZbrGS75cavM1To8GWlJ5OryGR63JNRERVn5L7t6qSafPy8gAAwcHBkstLSkqQn59v8K+6kusR1C0uFBP7NoXm79emrTwum1Dr6IkW5cpp3Ky0cHcmOqetxxOzd6Jz2nqnJfwSEZH7U02gUl5ejgkTJqBz585ISEiQXCc1NRUBAQG6fxEREZVcSvWQCzL2ncvBtFXHTRJqtYGCfuCgbSrS58gRZaUCElfNT0RERO5JNYHK2LFjceTIESxYsEB2nUmTJiEvL0/37/z585VYQnWRCzKuF5XK1pIYBw6bTl5z6ESL+uQCkr3ncpxei0NERFWHKronjxs3Dr/88gs2bdqEO+64Q3Y9b29veHt7V2LJ1EvbI0ibj+Kp0aB/63C8tewPk3U9NRr4enlIBg5bUnpiS0pPh3cxlqvxwd/5NEoSfomIqPpyaaAihMALL7yApUuX4vfff0dMTIwri1MpHJlEqt8jyNfLAw9/sc2ke7KHBnh/QAIKS8tkazKc0b1YrgdS2+ggkwCLA8UREZEclwYqY8eOxbx58/DTTz+hTp06uHz5MgAgICAAPj5V78bljK7A2tmXt53Jlpx757PBrXF/q3Bk5RVXak2GVI2PNiDhQHFERGQtl3ZP1miMZ4upkJ6ejmHDhll8vzt1T3Z2V2Brtr9wd6ZJ4KAkULKlNigrr9hsQMJuykRE6uPsa7OS+7fLm36qC3NdgR1xEpirwdCecN3iQm3OR7G1Nkhb4+PIbRIRkfOo7dqsimTa6sCWUWOVkmpSccQJZ27MFluDLGdsk5yLtV9EVZ8ar82q6Z5c1Smdt8eez9Emx9ozZon+eCvOGBiuMgabI8fhIH1E1YMar82sUalE1iaROurJ1dbmJuNamFf7xju8NqgyapjIMdT4hEVEzqHGazNrVCqZpdmGHfnkasvIs1I3pQ9WncCYHrG6bTmiNqiyapjIfmp8wiIi5zC+NntogBFdol1aJlVNSqiUO/X6sYYzegZJ9fTpFhcqW2Oz7Uw2npi902Q7GgDi7/+mJMfj2e7SEx0qZalXELmeKyevJCLXyMorRvrWDMzelAEBxyfVuk2vHzLkjJ5Bxs1Nm05e0910pE48qWo/ALqB5AQqalgevDPcYb2VLG2HSZyuZa5HGRFVXV9tzjCZN84VTb4MVFTEWW2D2mDAmlyDsAAfPNy6IX7cd1F2e47sVm2J2rrJVVccpI+oenH2kBpKMEdFRZydt2FNrkFWXjGW7pcPUrTlqozEKs60rC6W8quIqOqwJcfRWVijojLO7BlkTY2NVDAD/DORYGVW+6spoiciqk7U1OTLQEWFLOVt2DNKrKUTTy6YWTImCUWl5ZVa7a/GbnJERNZy9/w6tTT5stePm3FEDwxLPW3snRPIkdRUFkdy9wuYNarDPhLJYX6deez14yRquPA6ojnEUo2NWqJotZXFUarDBaw67CORHA6S6FhMprWSWoYQl0pw8tAA2QU3HZpkqpbESW1wWFWClOqQIFwd9pHIHHccJFF/2hS1YaBiBTVdeI17Bmk0gBDAC/MPuM0cLNb+INQSHDqS2i9gjrhYqX0fiZxNTT1mrKH2ay0DFSuo7cI7qH0ktqT0xIwnWgMCJgPy2HKTqaxo2tofRFZeMVJ+NAwOU5YcVmW0r4SaL2COulipeR+JKoM7TREi9yB+8HyOampYmKNiBbX1PtE2hwj8E6Ro2dJ9t7LyCZS02+49l2Oyb0IA+87loF9LH932XJ0zpJSauvzpc2Sbulr3kagyuUt+ndyDeP8Z25wydL4tGKhYQU0XXv2gQoN/5uDRUhpAVWbSl5JEYLnOaNqXpWZ4TrwjwC2CFjVewBw9Zo0j99EdA1IiwLopQlzN0rQpakgEZqBiJXMX3sq6kBoHFdpJAu0ZjK0yB1VTUjPVLjrYJAjTAGgbHSQZXKWuPA5AHdG/NdR2AXNGraEt+2j8W2LvIXVgsFh1GT+ISwUtrh5ok4GKAlIX3sq8kEoFFQLA54Nbo25tb5ueXCuzWUtJzVRYgA/SBiZi0o+HUY6KZKrUgYkIC/DBtjPZkqPnAuqI/t2RGmoNpWrJpq06zi6eLsZgseoxDjy1D+L7zuXgelEp3lr2h1019Y7GQMUOld1XXi6oaBsdZPPnVfYNSkmTgNy6clWVWq6O/t2V3PGujKdpqd/StJXHUW60Hr/byqW28UBYs2M/ucBz08lrkmkFasgxY6Bih8qei8ZZQUVl50woaRKQWtf4OBjzAODrxQ5ttjA+3pX1NC31WyrHP93vtVz9ZFfdqGm+Ldbs2E8u8IxvUMckrcBDU1Fbb8+DsKMwULGDK3oDOSuoUFvOhCX6x+HQxVx8sPKELmgpB/DwF9uq7YXM3FOnkifSynyalvstTUxuqvtu1fBkV92opcej2mp23JVc4Ln7bI7pg4IA6tb2VsXxZaBiB1e167tbUAE4p8pWexySYuvirphg9P9im+7pu7pdyLTH9/CFPF1eh/FTp9InUlufpm35ruV+S4PaR+LBVuGq6iFVnaghdwlQV82OO5MLPNtHB6kiIJXDQMVOauxqqjaVUWVbWFoG41ag6nIh0z+++vSDNQCKn0j9vDwlXzfXrGbPdy33W3LHwLwqUcM1zlU1O1UtJ0aqh8+ILtGo519LFQGpHAYqNjA+eXkhlVdZVbZqqaKubMbH15g2WBMQip9IC0vLJF8vKjVOcZUui7nvWu4GoP3/jOxCg7/NvYecz9XXOFfU7FTVnBht4Jm+NQOzN2Vg9uYMfL0lA6kDErElpacqH7oZqChUVU9eZ6ms5gO1VFFXNqnjq08/WFMayCkN/qz9rg0GLdQAKcnxeLZbrMky/d8Xf3dUmTU71SEn5qvNGSaDum1J6Ymk2LouLZcUBioKVIeT19Fsqemw9aakhipqoHKf/M111TYO1oyrfCcmNzVbPqXBnzXftcmghQJIXXEcEMCDd4Zb1SOBv7vqy9E1O3K/1aqWE2O8n+62fwxUFHC3L9cR7L3pKr3Z2RsMurqKurKf/MMCfCoGRvt7zBFtT5mWDQNNgrVB7SORW3QLaSuP68YpCfSpabZ8SoI/a75ruRqgaSuPo2GQj9U9Eqr6746cz9xvtSo1JUvtZ7e4ULfaPwYqCth78rpbG7ujbrpKbnbuHAw6osYtK68Ye85eh0ajQdso+fELdL18Lv7dywcVzSgTk5vqmlGk3jNt1XHFc3goCf4sfdcxIX4mY6MAFV3KIaSbp9TeI4Hcj6XfalVpSpbbzy0pPd1q/xioKGDPyetubeyObuay9mbnzk8y9gZZC3dnIuXHw7pAQgMgbaDpeSLXy0cI4IOVJ/Bgq3DJz3NGEGgcfFsKxsMCfJCSHF/R3KNHO8Ky1O+rVYT062q9qJL6WfNbUEtTsj3M7ac77R8DFYVs+XKlbvqTlhyGr5cn2kUHq/IEcVXNhjs/ydgTZGXlFRsEKUDF6JCTlhw2CA6t7eUjdbwcHQQaB98Pt26IpfsvWgzGn+0WC+gNke8BYGLfpgCAiGBfLBmThKLScoPfl7W/O3ertSTXsDafytK5pPbzTWo/PQD8VViCrLxilzeVW8ulgcqmTZswffp07N27F1lZWVi6dCn69+/vyiJZRemXKzk8uABemH9AtbUrrqzZcFak7+yLij1BVkZ2IaRij3IBg8BDSS8fR5bPmFTw/eO+iwblNlcD92z3WEADXb5M2srjSFt5XDd0d+qARJPeB5Z+d+5Wa0mOo/1t+3l5orC0zOJv3NJvwZpzyR3ON+P91M7fM27efnhogJFdYjCiS4zqgxWXBiqFhYVo1aoVRowYgQEDBriyKE5lrmeGWnswuLpmw9GRfmVdVGwNsmJC/HQXEX0eGhgEHkp6+TiyfMYsBUyA+dqdrLxiTFt5XJeror8pW3N72DOoepJqCrXmN25uEk5L55I7nW/a/dx7NgcvLNhvMHr37M0Z+GpzhkETsxpriVwaqCQnJyM5OdmVRagUxjd9Y2pNFtU/waEB2kYFubpINqnsi4qlIEvqQhAW4IO0gYmGOSp/X2yNB0QzDiAn9m2KlneY9vKxpnxKc0y0LM1gDSgfc0Wf0t+EuaZK7XJL+6TGCzSZJ9cUak+iuDXN3u6W9B8W4INDFzJMktiBiocE7bHSn0FZTbVEbpWjUlJSgpKSEt3f+fn5LiyNMtqb/r5zORg3b7/BE6Sak0XVeuIqoaaLirmaHf3AUKMB2sj0+nFUrYitOSaAdMDUv3U4lu2/ZPOYK/o8AMnfhFwwIddUeehCLoZ8tcPiPrlDNX5lUFOwZk1ZzAW8SgNVLWuavd0t6T8rrxj/25whu7xMCOw9m6PaWiK3ClRSU1Px9ttvu7oYNgsL8EG/lj4oKLnt0mRRay9G7lS9aY6Si4ozL9TWHM+wAB/c38r548XYm2MCSAdML/dpqmjMFbnE4FHdTNvNzQUTkjVNyU0rEnYlktgjg311uQyA8nmQqqLKCtas+Y1ZU5asvGJcLyyVbDIFlAWq+qxp9nZ107i1tMf69NUbZtfz1GgAiQcHtdQSuVWgMmnSJLz00ku6v/Pz8xEREeHCEtnGUU/EttxUlVyM1FQTYQ9rLyrOvlCr6XjuOXvdrhwTLeOASUkANah9JOIb1EH/GdsMbjQeGmB45xiDda0J8ox/V+aS2PU/a1SXGNV8L65SWQ8l1gYglspiMA0DYBKsyAWq1u6TNddotXfvNT5GcrTXw7ZR6h2vyK0CFW9vb3h7e7u6GA5h7xOxLTdVpRcjd6veNMfSRaUyLtSHL+SZvGZNzQ5gWnVtT82P9tyxpDK+61YRQUgbaDmItDbI0/6uLD1ta5WLijlPqsp5bitnBdHG57E1vzFLZTGZhgEV18DPB7dGRLCPrmu7vftkzTVard17pY6RMQ2Azx9vjbbR/zQxq7WWyK0CFapg603V2h+u/sVFrSeuLcxdVJxd26EdFdaY1Hw7Uk9C+t12AVgdpEoly0o1t3gAeLhNQ6tzTBzJmidTuZyWQxdzTboxW3raNlYO4JkujfD1lowqcZ7bQulDiS3NN3I1V3vP5hg0d1oqi1xNWd3a3mgVYZjwX10DULncHe1vQXvduL9VuMFytdYSuTRQKSgowOnTp3V/Z2Rk4MCBAwgODkZkZPVLZLOWrTdVay5GUjU1rpr629WT+znyoiZ34WjZMNDgb3NPQuUCmPTjYYO2ZHNBqtR3GRHsK1mOz59ojX4tw63OMXE0S0+mYQEVcxqlrjQM9oxH4pV72p76UAu8uewP2VyG4V2iMbxLtOou0JVB+zt7NTkeH6w8YTFYs7X5RqrmCgBeWLAfBy/k6sbzsNRUq2SwNmv3qaqRO0ZSgykaU2MtkUsDlT179qBnz566v7X5J0OHDsXcuXNdVCrruDI73p7kUHMXALkRdJeO6eSwqb+tPW6umNzPmbVH1n5nlrrtaufE0SdXKyZV67ZkTJJkOdr83fVcjRcprcQ7Akxes6bbaLkAYkPrGDQxaRl/z45oWnMnxr+zV/vGm+3qbm1truT3AOCJ9pGYvyvTcARmYTqeh7kne0u/VYMaNQ0wpkcsujQONdlOVfuOrbnWG9c4uQuXBio9evSAkOrYrXKu7spob3Ko3AVA7iLff8Y23QXEEbkRlo6bq3obObPa07iXi4cGkt+ZNd12jbPzrQ14yoRAUWm52zbn2dttNCm2ru779fXykHyyzMorRvqWDMzenGFQRV4VuypL/c4+WHUCW1J66poJjX/rcufVvnM56NfSfPONBsCC3ZmyzXACphMDWupxZjzGk0mNmgBmbDgD/1o1DR62XH0NdzSl13p3wxwVhdTSZdfe5FAleQDaC0hu0a2KmXpt+HErOW7WJNM560nI0TUKxsmEQu8CKvf5qQMSkbLksMk62qACgMVAw9obtrtcwHRV+X3j8cEq+ap8S0G8ue9XaoTTqtxV2dzvTG78JLlrxLh5+1FQclu2u7gHKq4jjuhppiVVRrmmzWkrj+PBO8Ml87T0v2PtcXGnWhZbrvXuhoGKQmrqYuro5FDjp37j92rnYgGUX8CVlMfcTdadnoSMyyrEP602xk+P+rrFhZo073gAWDImSVd1aynQsOeGbY3KrDY3aZ5IjkfLhvLNE46YOFRfVe2q7OflKdmt19fLw+yNT+oaIVCRP+XnXQNt/x6oUP97+KuwBOPm7bdYJiWTeMo1bWo0pg8C5fhnziy5a9F7vx7Fr4cuu1VNWlZeMX45dEk19yRnYaCikDt02ZXrmmlNOc2NbWHPj0HJcZO7yQLuMzCX1IXUmNzxk5qgsBxAUWm57m9rAg1nVf1WZrAo2Tyx8p/mCTlKAzFzuUFq+33bQxtgHr6Qh2mrjptcH94fkIDC0jKzv/VB7SPh6+VpMB4NUHGOaie7054T2u8hK6/YqikXlEziKde0mZIcj9QVhknX+t+hXK3QL4cu/7MvKr62aEnVAGpVpXMWYKCimLOTLu0l1zVTSTmlxrYwHjwJUDa6q9LjJnWT3XYm222eHKyZtE/u+DkyGLa15kSuxqSymz5trcFUUuOTlVeMvwpKJG9eHjDNJXLXJExzNzYPzT81dlJBhfH51y462OxEq8a1K+Zqaz1Q0etMO2WENcfXUtMmREVzTzmkE6bNzb2mpdZrC2C+BtBDYzjsgbuer/oYqNjAkU+qjjyJzA2EpD+ojzWk9jHQp6Zdo7sqPW7GN1k/L0+Tal21PjlIJhNqAI2A5MVTn63BsKPOJXPfob1Nn0rLKHdD8vXywLYz2ZLbMe71kZIcj2e7xVrcVw2gO78qxv1ohOFdog22705Nj/rM3diAiuOrrbGz5vwLC5DuLq7bHgxrV7rFhSIi2BdTHmyOt346arJusJ83wgJ8rD6+lsr4bPdYPHhnuOy1Rnst+vVQFt799ZjkPqj12gKYfxAqF0DaiuOAAAJ9a7rl+WpMI9yx283f8vPzERAQgLy8PPj7+7u6OIo5+qK37Uw2npi90+T1+aPvcmj3YnMJvJ3T1pvcVCxV01tD6mlQe3FS6w9v4e5MkwupkkDN3LGW+ixHnEuWvkN7vmMlZdQPaDadvGYy+aHc5IlS5QOAScnxeLZ7rMlnGK9r7ukegOn6GuDTwXeiXXSwqp9W5a4NWlLfoaXzz9I2tTR/V+tqH5z0c7X0PxswPb6Wzi39MgLKE2HlzhcPAKkDDc8rW2YZdxa5chuTav53xPXYEZTcv1mj4iIHz+cY9OxwRBV6ZeTPODqB1xpST4PGyaVqJFeDZO2xkDrWUhdIRzbHmPsOtcuf7xGLmb+fQbmwvklRSRnNDTro6+WBh7/YJrsduSdN414fGdmF+KugxGRdc0/3UiOrlouKuYPU/rRqrtu73HdoqdnQUld6Lf1HYW0tl4dEzaItTbvaMtoaqJv0TpKoSTOudevSJARbTmW7NOlWqtxS34PxS2puzjKHgYoLzNp0xiTZC7D/JHJ1/oyzAiW5waP0k0utVdlPQvb2rtEndzF2ZIAo9x0euvjPLLRaGgAT+za16iKtZPoGc4MOWrqZxYT4me31YdylVS7hXMnIqtrlak6+DAvwwavJ8bo8M23embneU9ZsM3VAIib9eBhKfolCAP99ojWC/bwNPtvW64e9gbq5Jmmp5vTNp7J1y2393h1xHdIvt6+Xh0kHCClqbs4yx8PVBahuZm2UDlIAx5xEg9pHYktKT8wffRe2pPR0yUB0npqKGWpsCZSy8oqx7Uw2svKKda9pL2D6bDlWC3dnonPaejwxeyc6p63Hwt2ZistiK3u3JXcxzsordtjxAaS/w4l9TROpgYqL9gerTuDg+RyL+2ZtGc0NOrhwd6bF7YQF+CAlOd7k8+W63ULzz0VQ/3yVC45HdWmkOzbG9Gue1Gbh7kzdd6gNMJ/tFouk2LqSv09rz9dB7SOxddLdeKZrI933os31Af4JBvVpR0KW+uxRXWIkvw9zLNUCWiMswEeyPNYkxSv9LKXXIXO05W4VESR53utTW8cPJVijUomy8oqRJpN8JjdSqS0c+RSvlD2JxnI1Bo6oKVL61OXI/CFHbMvcxTgptq5Da9KMv0NzF+syIdD/i226BFRbkx+1LA06uCWlp8XtPNstVrLXh1S3W6VP99o5gfady8G4efsVd/93BalagQ9WndA1hRmz5XztER+Kfi0b6Eb7BSpqsA5dyDW45mlkrnPGn/mMRCKzHEfX5OrXdljTvOWhAbILbiIrr9ihTaBKPds9FueuF2LezvMmy97s1wz3tQxzyyAFYKDiENZW40mNjwFUPHEsHdNJ1fkWStgSKFn6Advb00pJ84gjLyaO2pali7Gjx0wx/g7NXaytzbOypoy65gSZQQfPZhdZtR2pXh9y3W61ibNS5ZALiPq19EFByW3VDlOgz5nnvlRQo5+4P+SrHQbXPI2AbgRY7eftOXvd5DO/3pKB4V2iAVTk8+06ex0dooMlr5GObPKW2p/UAYlI+fGw/ND/CvKUnJHHp3//eeHuJliw67zJOe7OQQrAQMVuSp4+5KLzlPviLQYprs4ydzZrfsD21BRJHXu5JyFHXkwctS1rLsZKjo+S88n4s7U8AJPcBGuTH82RG3TQuInHmnIbHx9bx/Lx9fJAYWmZwbkiFTCp8XeqpIu3I4MaueYz7bbMjeui/cwPV5/Aj/su6l4f2KYh/vPYnSbrKwnUlY4RNDG5qWkyE/55SftyuagYPya+QR3Z67mja3/kAit3CKCVYKBiB6VzRphkaqNiOHC5MR603HXsBiWclYirf1HSP/bahEupJyFry2LvwFRKOarWxJbzyfimXVRabtIDB3Bc84fUoIOOuODaMpaP3Lw32uVSvUO0MxEn3hEgeX5UZkAjFaD1bx2u++4szeWjn2CsX2ZLQY2lbZkb18VTo0FR6S2DIAUAftx3EU8nRcnWrFg6lraMEZS28rjk3FxSRS8H0P+LbUizsgnUAxX5QracA3L3ny0pPXW95NxlLi9LOI6KHeTGEXimayN8teVP2RuBkvExnDk2iTPYO7uy8Y3JnoBM6qLULS5UNr9A/5haKouSm72j98sejj6fnLlv2mYBD5nmmcpg7fEyN66F8fmhZBZxRwYz2uuOXICp3Sep7xSA5G/J0rGROz/MjcGi7ZGU+VchvjeTb+GIMVMsjRFkK0u/qVmbzugCIFsfPitj3Cxn4jgqlUSyOQHQBSmAdBuvkip6NU2CaInSJ3XthdjPyxOFpWXoFhfqsCcBc08bQX5eFscXUNJl0RG5Geb2w5E3K6Xnk6XPrwrzCZlj7fGyNFKofk2ruXPHeC4eR+6/9rpjqYu38XcKGA7Epv9bstTMIHd+yF07P3+iNS7kFEv2MtO6XliqK4+SY2PpuwwLqOjCLdcrUwlLv6lperU0zspbq0oYqNhBqkp1ZJdo/G9zhsF69gQW7nIy2pOEp+XIG5K5i5K1x1QuoJTb9t6zObi/le25GcaccbNWcj4pGc7cmppBJXPvWHMuKQ3ibAn6rD1elnqHaM89ASF7Xi4/eEmymcERPUMs9WQx3if979RcYGNtgrS1ycptooLwwvz9ssfx7qah+OL3MzbN4h4T4mdxotbEhgGS79UeLw8NkJwQhl8PZ5n9LHPX6MrMW6sqGKjYSerp46stGQ4LLNzlZLQnCU/LkV31zF2MrTmm5m5qfl6eUrl1eGH+flzMLTYZqt0WcgOfmUvUM7ct/X2x5nyypbeS3DFT2tQhNWKs8bmkNIhz1Mil5kZxNTfRnf41QKomYcGuTPx08JJsOYxHB1YSbNmbcCmXiK4ksVmKVLLynrPXzTa9rD9xzeQ1cw8J+ufkh6tPmPxm9SfvA+SvG0vGJBl0vV55JMtgHe3YMeXC8nglasxbUzsGKg5g/ENVGli4qnrdkZT8+CyNy+GIZi1LNxhzx9TcTU27TKr4AqiYpE0DiwnSlpgb+CxtoPU1K3L7Yul8UvrUJ/c51gY8xu839+QrF8TJBVHOHLlUbr1DF3PxwcoTkueeQVL33/tpLkjR7r/+6MDWBluOSLjU/pb0p/wQAth08prduTXGycrSQ+lZNn7BfhSW3pbNI5OTuuI4An1q6s7VPWevY3D7iIouvvhnwMPC0jKD/ZG6tlh7jbb34VPq+AqLY9K6NwYqTqAksDB3UzQ+IdUYoGgp+fFZmnfEUc1alr4HqWNqqSeXpQsf8Pe8Mq2kB9OylqWBz6y5yVq6QZt7v5LA09znWBPwSL1fbj4YQD6IS9+agdfua25SPkdUtVv7+9OulxRbFw+2kp69V3te7j2bgxcW7JfsUaJPanRga2vYLA0UaG1eUre4UIPIUaCiK66lgNPc9Uz7mvFgdLaQyvWx5rcKVLwvt+hWRbPb369pUNEpom4dL91x198fe+fxsvXh0/j4Pty6oewEnVUJAxUnsbbdXu4Cb65LpFpZ++OTqyZ3RrOW0gDP3IVdKr9ASrmA3bVC2mNkbuAzS9u3N3HW2sDT3nwgqffLjRgLyAdxX23KwPDOMSZldFWel7lzLyzAB4cuZJgNUsb1jEXnxqGyowNbU8OmZAwVwPx8UsZFLQeQvuUsXuvXDIBt1zNrhqi3lv65rWS7ZUJU1ITqEajoFKE/07NUoG/vb1zJ+6WOr373bUfnMqnpwZiBigvJXeD3nctx2jDLzqb98WnnCrGmOUs7LkdlNGtZ+iFauqlJ5aYYc9RN0NLAZ/buiz5bm4gsfY41AY/c++W6JIcF+GBklxjMNkpa1x9MzHh9V+Z5ZeUVY++5HAgh0C46WPf7MC6/vic6RuDlPoZzt9hSwya173JjqJgLNKQSUYGKm7l2qHtbrmdS371xvkf/1uFYtv+SrqkMGtNJJwHDc9vamZ3ljqu2nMbM5d3J/RZtvflbM26NufIp/Vy19LSTwkDFheQu0OVCvmeA2gMVwLG9RSq7XHI3NQCYs8X0xiKVS+HIm6DcwGfW1Lg5KnHWmjwD48+ZmNwUGdmFuJp/ExHBvgbJiI4IJEZ0icFXmzMszrWjvVg7suu7Egt3ZxoMv64BkDYwERHBvrIBr4cGeOHuJgav2VPDZvxQoD+GirXNdEmxdTG6a4xJj0b92kOpYKYi4JC/nsnNU2UcIL/cp6lBhwXtPEIfrJLOA5I7p+Ib1MHszX/i10OXIfDPmC1pK46bfB8efwdEls4xa3LalN785caAshR8actny1ARan44ZqDiQnI/pnbRwW7RJVlKZZ/w1j41KCmXcU3CppPX0Cl1vWwCrZYHgCVjkhw+Z5O141pYsy+OSJzV0h8HRz8YOXQh12QcDKl5YJSWU19YgI/FkWtd/YSoPef0D602t2Pp2E6SNx0PVJRTP4dDe35bM7WAHG3Aaa6rsaUauOFdYpT3aBRAZLCvTfNU6X+XxgGzLg/oTuk8IHPb/e8TbfF6P8NBNwN9ahoGlH+fLwDMnmMHz+cYvE//t3g1/6ZBArI2r8jXy1NXsyYlK69YcptS49bo1zZJDc5nXCa5z1T7eF0MVFxM7sfkDl2SpVTmCa/kRqS0XPpNWHK9fIyVAygqNZ79xjH0L9SWBuwy914ptuRwyI2D82pyvG6gMn3WXCyV1rApHZTP1u7d1rK2ql57nhgMpa4BRhnNGCx3ftsztYAtzXQAdM24xsO/j/x74kBAetJVIbGv9s5Tpc/S++SWG7+un+Cs0cCg2dFc78AUiWtDmRBI35qB2ZsyTPN6hOUJDD9fd0p2QEqpc16/tslcMPrroSz0k5mcUO3jdTFQUQGpH5M7dEmWUlknvJIakqy8YlwvLJXt8mquVkZJUl5l/bCVzMdiDaVNL+bGwTE3oqgzAlbttjKyC03+tiX51BZZecWYsyUDX/9d26BfVa+dU0qfByrGIEmKrWvT6Mf2XBvCAnzwat/4iu8Jpk2VUrWJxqPAbknpifQtZ/HVlj/xv80Z+GpLhmzThPa8NLevahEW4CM5FovU9Vn3ACNxrntoIBmk6CsX0hMYztp4BvN2mU4boD1npMpj/Ldcfs67vx7D+yuO2dVM7CoMVFSssnM4HKGyTnhra0j0n0o1+CenRCrXQwNgdNcYDO/yT88RuR+9tmugcbWr0v10ZGBhT08xJTc/S8PFyyUcOyOQk6t1cET3bkuy8oqRviVDMm/jtSVHMLFvU8kZd1MH/tO0I/cbNzf6cXDtivPF18sDvxy6hA7RwVbXEi3cnVlR44WK5o2JyU0lb1r6tYnGwdKSMUmS04RYGlLfHa9ncuR+AxW1TKaJ3lK0EximJMcjsWEA/Lw8kbZSevj+wR0jTIJxOcbXB4PPtLOZ2FU4KSE5hZKJF21x8HyOZFu9/kRgB8/noP8X2wyeejw0wGeDW6NtdMWFXWoSMqmEOP3q7sEdI9ApNgRtoyq2Yet+2ptDoX+MpfbFQwN8OvhOs+3htjA3eZt2zA9tkqP+646ejNHSJHPmBvyyd+I2awYTM0ksBbBsbCerggqpfdPvDWNsYJuG+M9jdyreprnJ8+QmvXujXzO8++sxk9e1x9TZv301kDqWHhpg6ZhOqOdfS/K7A8z3GJQL8PXfq9EAKcnxVg0omZVXjF8PZZn9rlxJyf3bo5LKRAppu/dm5RW7uig20Sa7KblQWbvPC3dn4uEvTIMU/ae3hbszKwIZier/urW9zecQ/P3UoS3HoPaR2JLSE/NH34VX74vHgl3nMW7efnROW49NJ68p3k/tvko9rSr5vvWPsVxTxwvzD6Bz2nos3J2pqHyWPjd1QCI8NYbjiGq/g2e7x+qO109jO2H+6LuwJaWnwxNZzdU6ABXf29IxnUxGO7W3ZseawcQ8NKY3HW2+hjXCAiomyPP4u/DaC7XcZ/647yIOns8xu01ztZCA6e9PWyulz1OjQfvoIMnX9ZsmbPlNuBPj34CnRoPUAYloFREkuSxtYCLSBiaaveHKnU5Cb5kQFaPpztp4xqoy9msZZva7chds+rFCZQ+C4+reCq5g7T6vO3bZICMeqFhfv7eNuQRY/bbewxfzZMtjXM2u/d61Q5gDlhNElea+WJvDIbVdc+NGOKPnlaVxcCqjml9un1+Y/89w6nLdu+0pmzV5S2N6xOKL38/YnKu1cHemQb5Pz/hQrDtuOs+Nvj1nc8zW1pjLbZL7/Uk15bSKCFJ1PkNlMddUIjWPUUyIH5aO7WRSy2vO3fH1sP74VZPXp608jgfvtDz6tdpzT6zFQMWCyg4a1N6f3Rms3ed/LzpgMBKjVrkwfFI1dyMRqJijpFtcKKbJtAcDFdWt4xfsN/jeI4J9rQ4uLJ03chO9ZRfcRFZesex3LbfdTSevmb34OSuR1ZXnpLbWIXWF6aii+nP/OLrtXW7wM33NwvxtvkFI1dhYClIAoF20+SYlc716jLvDpvx9/OSOnZrzGSqTud9AWICPZN5YmpnJK/V5AHjxnsbYcOKqac0wrB/9uip8V2z6McMR1fNKWaqerYqs2eeD53MkgxTA9ElVqspaS5tMaW6WVuNqdm3X1uLS25LbPXQh1+Bva84b4+phbe8Qc001cts9eD7HYhdqd6zutUZiwwDJ18sFDM4fe5sj9JtFNp20HDRcLyzVjS2jtOnLmhob49Owa5MQ1POvZXHb+s2Y2jLtPZdj2kwlgH3nKpqS5I5ddWjisYe5B7AtKT3xTNdGJt+j9m9PjQapAyuaklKS42FMAyj6PVv6rtSeasAaFTMqexCcrLxinL56w+zMsVWRNV2ad529Lvlebc8F/Yx4c1nvAP4et0JjWqMB4PMnWkMAGDdvv8F7ygUw6pu9SE5ogBVHLhss+2DVCYNqWLnzxngcA+2Tzr5zORg3b7/snCJactvdfTZHugeC3jDk7ljdaw252g0PjbILuTnGPccg8XnG3vrpj4rkR1SMRGucuGiuWdDS8O9v9muG+1qG4Wr+Tcze/Cd+OXQZm09lo3PaeqtqfI1rAeT6U7hvNwt1MHf/iA7xrZhLSG+ZNhnXuBn1wVbhpiPnyjyI2cIdUg1UUaMyY8YMREdHo1atWujYsSN27drl6iIBkE8mc0bQsHB3JjqlrsdbPx116pDsaiSVfGa8zx2igyXf+3THKExbeRxPzN6Jzmnr8d6vR5GVV6x7cvzv460lv8M2UUGmyXADE9GvZTjaRpkmCwIVN6dVRkEKYFr7I1ej8+6vx0xqS8ICfBDk5yU7wJM+pcmNS8c4L5FVLcICKkao1d997aii5n4zUk+Qcq8Zz+5rrmeGcSAjUNGsor/Nhbsz0Tltve6c1T8ftAHMq33jZS/Ot8sFwgJ8UM+/FlYc/ud8LBcVn/XfDafwy6FLyMorRlZeMf5vewY+WntCNtm2XXSw5JN9WwtNSWSe3O9V261cKvm9qLTcpOZDciA9oxpDW7mi1cAWLq9RWbhwIV566SV8+eWX6NixIz755BP06dMHJ06cQL169VxatspKRDIeMlnLOEm0KrPUjtoqIggD2zQ0aP65L7EBvt15zuBHNntzBr7anKEb1Ov+Vj4oLL0t+R2aG7pbbl4V7fgT+k+bxsGrNndCavAzqdoSuXwV44BY7nyUS26sDucNYH5UUSlST5AADF4b2SUGI7rEWNUM46nR6KYPOH3tBt5c9ofBcgFg79kc3N9KfmwSqRmGX70vHueyizBvl2EzoLYGT3LWaQAfrj4pWc7P1p2W7MasDfYm/XgY5fh7GP+B5gM9skzq96o/IaQxuYdgZw6iqfah87VcPo5Kx44d0b59e/z3v/8FAJSXlyMiIgIvvPACUlJSzL63ssZRcfa4AHLjFQDq6O+uJgfP52DP2Ry0iw5CYWmZ7HEzHh/Clu9QbqyWiclN8cFKw8nQ9GssrBljw/h71Q3H/fd7tE0GUjUhcvtSHcavsJfk+BcAINHUogGQcp90wKll/P3/cuiSSbMhAMx4ojX6tQyX/a3/9/HWuuRt/W1/MrgVXph/wGT9+aPvwqGLuSaJxNb4SWYsF54/zqE9rsYTQuqzNM6Q/lhOjhyTSOnYOo6k5P7t0hqV0tJS7N27F5MmTdK95uHhgV69emH79u0m65eUlKCkpET3d35+fqWU09m9Gyqjnb2qaBURZNANWa4t3/ipwJbvUK5r66D2kXiwlfRkaNaMsSH1NNQtLtTgBNAm/Ur19pLbF1f3wnEHkuPNAJLtOQLABytPGAw5r0+qxrNtVJDkDMJt/h4cUO7pWCpQksul0jYfmOu1Zo5cN2aeP86hPa5Sc/AA/+QcmTv2zuq54y7dl12ao5KdnY2ysjLUr1/f4PX69evj8mXTXIDU1FQEBATo/kVERFRWUZ3K1nb26k77I5PKB3FU1ahULwntZ0tl0csOra35p1xSFwKpduiq3tvLFaTyBjwAyXMIqPgOWt4RiM+eaG2yzLhbPPDPb1k3UJsGSBtgOGS+VD6WVF6UXC7V+wMSUFhaZrFJSo6lbszkHHI5K5aCFC1n9bKSu8apictzVJSYNGkSXnrpJd3f+fn5VSZYUdrOThW0xy19awa+2pQhOdGavZQ8aco9MWtzGOSehtQ+e2lVYW4sEamaMO13EB3ia/X3Y+npV+mM6VLrm6tNNGdgm4bVJndJbdRce6H22jSX5qiUlpbC19cXixcvRv/+/XWvDx06FLm5ufjpp5/Mvp9z/ZA+tbSx29qe7Kx2aDIlda5k5RVLBrxScz456/tRcg4bl2diclPcEeiD64WlCPbz0jU3/Xb0CrILSnB3fD0GKSqgluuUqym5f6simbZDhw74/PPPAVQk00ZGRmLcuHGqSaYlUsrWixEvYq5n7jtQ2/ejtvIQWcttkmkB4KWXXsLQoUPRrl07dOjQAZ988gkKCwsxfPhwVxeNyGa2VqWqvQq2OrA0LLqavh+1lYfIGVweqAwaNAjXrl3DW2+9hcuXL+POO+/EqlWrTBJsiYiIqPpxedOPPdj0Q0RE5H6U3L9VMYQ+ERERkRQGKkRERKRaDFSIiIhItRioEBERkWoxUCEiIiLVYqBCREREqsVAhYiIiFSLgQoRERGpFgMVIiIiUi2XD6FvD+2guvn5+S4uCREREVlLe9+2ZnB8tw5Ubty4AQCIiIhwcUmIiIhIqRs3biAgIMDsOm491095eTkuXbqEOnXqQKPR2LWt/Px8RERE4Pz589V23iAeAx4DgMcA4DEAeAwAHgPAecdACIEbN24gPDwcHh7ms1DcukbFw8MDd9xxh0O36e/vX21PSC0eAx4DgMcA4DEAeAwAHgPAOcfAUk2KFpNpiYiISLUYqBAREZFqMVD5m7e3NyZPngxvb29XF8VleAx4DAAeA4DHAOAxAHgMAHUcA7dOpiUiIqKqjTUqREREpFoMVIiIiEi1GKgQERGRajFQISIiItWq0oHKzJkz0bJlS91ANUlJSVi5cqVu+c2bNzF27FjUrVsXtWvXxsCBA3HlyhWDbWRmZqJfv37w9fVFvXr18Morr+D27duVvSsOkZaWBo1GgwkTJuheqw7HYMqUKdBoNAb/4uPjdcurwzG4ePEinnzySdStWxc+Pj5ITEzEnj17dMuFEHjrrbcQFhYGHx8f9OrVC6dOnTLYxvXr1zFkyBD4+/sjMDAQI0eOREFBQWXvis2io6NNzgONRoOxY8cCqPrnQVlZGd58803ExMTAx8cHsbGxmDp1qsFcK9XhPLhx4wYmTJiAqKgo+Pj4oFOnTti9e7dueVU8Bps2bcIDDzyA8PBwaDQaLFu2zGC5o/b50KFD6Nq1K2rVqoWIiAh88MEHjtkBUYUtX75c/Prrr+LkyZPixIkT4rXXXhM1a9YUR44cEUII8dxzz4mIiAixbt06sWfPHnHXXXeJTp066d5/+/ZtkZCQIHr16iX2798vVqxYIUJCQsSkSZNctUs227Vrl4iOjhYtW7YUL774ou716nAMJk+eLFq0aCGysrJ0/65du6ZbXtWPwfXr10VUVJQYNmyY2Llzp/jzzz/F6tWrxenTp3XrpKWliYCAALFs2TJx8OBB8eCDD4qYmBhRXFysW6dv376iVatWYseOHWLz5s2icePG4vHHH3fFLtnk6tWrBufA2rVrBQCxYcMGIUTVPw/ee+89UbduXfHLL7+IjIwM8cMPP4jatWuLTz/9VLdOdTgPHnvsMdG8eXOxceNGcerUKTF58mTh7+8vLly4IISomsdgxYoV4vXXXxdLliwRAMTSpUsNljtin/Py8kT9+vXFkCFDxJEjR8T8+fOFj4+PmDVrlt3lr9KBipSgoCDx1VdfidzcXFGzZk3xww8/6JYdO3ZMABDbt28XQlR8uR4eHuLy5cu6dWbOnCn8/f1FSUlJpZfdVjdu3BBNmjQRa9euFd27d9cFKtXlGEyePFm0atVKcll1OAavvvqq6NKli+zy8vJy0aBBAzF9+nTda7m5ucLb21vMnz9fCCHE0aNHBQCxe/du3TorV64UGo1GXLx40XmFd6IXX3xRxMbGivLy8mpxHvTr10+MGDHC4LUBAwaIIUOGCCGqx3lQVFQkPD09xS+//GLweps2bcTrr79eLY6BcaDiqH3+4osvRFBQkMFv4dVXXxVNmza1u8xVuulHX1lZGRYsWIDCwkIkJSVh7969uHXrFnr16qVbJz4+HpGRkdi+fTsAYPv27UhMTET9+vV16/Tp0wf5+fn4448/Kn0fbDV27Fj069fPYF8BVKtjcOrUKYSHh6NRo0YYMmQIMjMzAVSPY7B8+XK0a9cOjz76KOrVq4fWrVtj9uzZuuUZGRm4fPmywTEICAhAx44dDY5BYGAg2rVrp1unV69e8PDwwM6dOytvZxyktLQU3333HUaMGAGNRlMtzoNOnTph3bp1OHnyJADg4MGD2LJlC5KTkwFUj/Pg9u3bKCsrQ61atQxe9/HxwZYtW6rFMTDmqH3evn07unXrBi8vL906ffr0wYkTJ5CTk2NXGd16UkJrHD58GElJSbh58yZq166NpUuXonnz5jhw4AC8vLwQGBhosH79+vVx+fJlAMDly5cNLkra5dpl7mDBggXYt2+fQRus1uXLl6vFMejYsSPmzp2Lpk2bIisrC2+//Ta6du2KI0eOVItj8Oeff2LmzJl46aWX8Nprr2H37t0YP348vLy8MHToUN0+SO2j/jGoV6+ewfIaNWogODjYLY6BsWXLliE3NxfDhg0DUD1+CykpKcjPz0d8fDw8PT1RVlaG9957D0OGDAGAanEe1KlTB0lJSZg6dSqaNWuG+vXrY/78+di+fTsaN25cLY6BMUft8+XLlxETE2OyDe2yoKAgm8tY5QOVpk2b4sCBA8jLy8PixYsxdOhQbNy40dXFqhTnz5/Hiy++iLVr15o8QVQn2idGAGjZsiU6duyIqKgoLFq0CD4+Pi4sWeUoLy9Hu3bt8P777wMAWrdujSNHjuDLL7/E0KFDXVw61/j666+RnJyM8PBwVxel0ixatAjff/895s2bhxYtWuDAgQOYMGECwsPDq9V58O2332LEiBFo2LAhPD090aZNGzz++OPYu3evq4tGMqp804+XlxcaN26Mtm3bIjU1Fa1atcKnn36KBg0aoLS0FLm5uQbrX7lyBQ0aNAAANGjQwCTrX/u3dh0127t3L65evYo2bdqgRo0aqFGjBjZu3IjPPvsMNWrUQP369av8MZASGBiIuLg4nD59ulqcB2FhYWjevLnBa82aNdM1f2n3QWof9Y/B1atXDZbfvn0b169fd4tjoO/cuXP47bffMGrUKN1r1eE8eOWVV5CSkoLBgwcjMTERTz31FP71r38hNTUVQPU5D2JjY7Fx40YUFBTg/Pnz2LVrF27duoVGjRpVm2Ogz1H77MzfR5UPVIyVl5ejpKQEbdu2Rc2aNbFu3TrdshMnTiAzMxNJSUkAgKSkJBw+fNjgC1q7di38/f1NLvxqdM899+Dw4cM4cOCA7l+7du0wZMgQ3f9X9WMgpaCgAGfOnEFYWFi1OA86d+6MEydOGLx28uRJREVFAQBiYmLQoEEDg2OQn5+PnTt3GhyD3Nxcg6fO9evXo7y8HB07dqyEvXCc9PR01KtXD/369dO9Vh3Og6KiInh4GF7yPT09UV5eDqD6nQd+fn4ICwtDTk4OVq9ejYceeqjaHQPAcd97UlISNm3ahFu3bunWWbt2LZo2bWpXsw+Aqt09OSUlRWzcuFFkZGSIQ4cOiZSUFKHRaMSaNWuEEBXdESMjI8X69evFnj17RFJSkkhKStK9X9sdsXfv3uLAgQNi1apVIjQ01G26I0rR7/UjRPU4Bv/+97/F77//LjIyMsTWrVtFr169REhIiLh69aoQouofg127dokaNWqI9957T5w6dUp8//33wtfXV3z33Xe6ddLS0kRgYKD46aefxKFDh8RDDz0k2T2xdevWYufOnWLLli2iSZMmqu6SKaWsrExERkaKV1991WRZVT8Phg4dKho2bKjrnrxkyRIREhIiJk6cqFunOpwHq1atEitXrhR//vmnWLNmjWjVqpXo2LGjKC0tFUJUzWNw48YNsX//frF//34BQHz00Udi//794ty5c0IIx+xzbm6uqF+/vnjqqafEkSNHxIIFC4Svry+7J1syYsQIERUVJby8vERoaKi45557dEGKEEIUFxeLMWPGiKCgIOHr6ysefvhhkZWVZbCNs2fPiuTkZOHj4yNCQkLEv//9b3Hr1q3K3hWHMQ5UqsMxGDRokAgLCxNeXl6iYcOGYtCgQQZjiFSHY/Dzzz+LhIQE4e3tLeLj48X//vc/g+Xl5eXizTffFPXr1xfe3t7innvuESdOnDBY56+//hKPP/64qF27tvD39xfDhw8XN27cqMzdsNvq1asFAJN9E6Lqnwf5+fnixRdfFJGRkaJWrVqiUaNG4vXXXzfoTlodzoOFCxeKRo0aCS8vL9GgQQMxduxYkZubq1teFY/Bhg0bBACTf0OHDhVCOG6fDx48KLp06SK8vb1Fw4YNRVpamkPKrxFCb1hCIiIiIhWpdjkqRERE5D4YqBAREZFqMVAhIiIi1WKgQkRERKrFQIWIiIhUi4EKERERqRYDFSIiIlItBipERESkWgxUiFTg8uXLeOGFF9CoUSN4e3sjIiICDzzwgMH8G9u2bcN9992HoKAg1KpVC4mJifjoo49QVlamW+fs2bMYOXIkYmJi4OPjg9jYWEyePBmlpaUGnzd79my0atUKtWvXRmBgIFq3bq2bnA4ApkyZAo1Gg759+5qUdfr06dBoNOjRo4fF/YqOjoZGo5H9N2zYMOUHS+V69OiBCRMmuLoYRFVGDVcXgKi6O3v2LDp37ozAwEBMnz4diYmJuHXrFlavXo2xY8fi+PHjWLp0KR577DEMHz4cGzZsQGBgIH777TdMnDgR27dvx6JFi6DRaHD8+HGUl5dj1qxZaNy4MY4cOYLRo0ejsLAQH374IQBgzpw5mDBhAj777DN0794dJSUlOHToEI4cOWJQrrCwMGzYsAEXLlzAHXfcoXt9zpw5iIyMtGrfdu/erQuktm3bhoEDB+LEiRPw9/cHAPj4+DjiEFaKW7duoWbNmpX2eaWlpfDy8qq0zyNSLYcMxE9ENktOThYNGzYUBQUFJstycnJEQUGBqFu3rhgwYIDJ8uXLlwsAYsGCBbLb/+CDD0RMTIzu74ceekgMGzbMbJkmT54sWrVqJe6//37x7rvv6l7funWrCAkJEc8//7zo3r27FXv3D+18Izk5ObrXli1bJlq3bi28vb1FTEyMmDJlisHcOQDEl19+Kfr16yd8fHxEfHy82LZtmzh16pTo3r278PX1FUlJSQZzN2nL/uWXX4o77rhD+Pj4iEcffdRgPhchhJg9e7aIj48X3t7eomnTpmLGjBm6ZRkZGbrj2q1bN+Ht7S3S09NFdna2GDx4sAgPDxc+Pj4iISFBzJs3T/e+oUOHmsynkpGRIdLT00VAQIDB5y9dulToX4K15Z49e7aIjo4WGo1GCFFxDowcOVKEhISIOnXqiJ49e4oDBw4oOvZE7oxNP0QudP36daxatQpjx46Fn5+fyfLAwECsWbMGf/31F15++WWT5Q888ADi4uIwf/582c/Iy8tDcHCw7u8GDRpgx44dOHfunMXyjRgxAnPnztX9PWfOHAwZMsQhT/qbN2/G008/jRdffBFHjx7FrFmzMHfuXLz33nsG602dOhVPP/00Dhw4gPj4eDzxxBN49tlnMWnSJOzZswdCCIwbN87gPadPn8aiRYvw888/Y9WqVdi/fz/GjBmjW/7999/jrbfewnvvvYdjx47h/fffx5tvvolvvvnGYDspKSl48cUXcezYMfTp0wc3b95E27Zt8euvv+LIkSN45pln8NRTT2HXrl0AgE8//RRJSUkYPXo0srKykJWVhYiICKuPyenTp/Hjjz9iyZIlOHDgAADg0UcfxdWrV7Fy5Urs3bsXbdq0wT333IPr168rOdxE7svVkRJRdbZz504BQCxZskR2nbS0NJOaCH0PPvigaNasmeSyU6dOCX9/f4PZki9duiTuuusuAUDExcWJoUOHioULF4qysjLdOtqn+9LSUlGvXj2xceNGUVBQIOrUqSMOHjwoXnzxRbtrVO655x7x/vvvG6zz7bffirCwMN3fAMQbb7yh+3v79u0CgPj66691r82fP1/UqlXLoOyenp7iwoULutdWrlwpPDw8dLMhx8bGGtSECCHE1KlTRVJSkhDinxqVTz75xOJ+9evXT/z73//W/W08Q7kQwuoalZo1a4qrV6/qXtu8ebPw9/cXN2/eNHhvbGysmDVrlsWyEVUFzFEhciGhYPJyJesCwMWLF9G3b188+uijGD16tO71sLAwbN++HUeOHMGmTZuwbds2DB06FF999RVWrVoFD49/Klpr1qyJJ598Eunp6fjzzz8RFxeHli1bKiqHnIMHD2Lr1q0GNShlZWW4efMmioqK4OvrCwAGn1e/fn0AQGJiosFrN2/eRH5+vi73JTIyEg0bNtStk5SUhPLycpw4cQJ16tTBmTNnMHLkSIPjcvv2bQQEBBiUsV27dgZ/l5WV4f3338eiRYtw8eJFlJaWoqSkRFdWe0VFRSE0NFT398GDB1FQUIC6desarFdcXIwzZ8445DOJ1I6BCpELNWnSRJcEKycuLg4AcOzYMXTq1Mlk+bFjx9C8eXOD1y5duoSePXuiU6dO+N///ie53YSEBCQkJGDMmDF47rnn0LVrV2zcuBE9e/Y0WG/EiBHo2LEjjhw5ghEjRijdRVkFBQV4++23MWDAAJNltWrV0v2/fgKrRqORfa28vNzqzwUqej517NjRYJmnp6fB38bNcdOnT8enn36KTz75BImJifDz88OECRNMelUZ8/DwMAk0b926ZbKe8ecVFBQgLCwMv//+u8m6gYGBZj+TqKpgoELkQsHBwejTpw9mzJiB8ePHm9yocnNz0bt3bwQHB+M///mPSaCyfPlynDp1ClOnTtW9dvHiRfTs2RNt27ZFenq6QQ2JHG2gU1hYaLKsRYsWaNGiBQ4dOoQnnnjClt2U1KZNG5w4cQKNGzd22Da1MjMzcenSJYSHhwMAduzYAQ8PDzRt2hT169dHeHg4/vzzTwwZMkTRdrdu3YqHHnoITz75JICK4OjkyZMGgaKXl5dBl3EACA0NxY0bN1BYWKj7jrU5KOa0adMGly9fRo0aNRAdHa2orERVBQMVIhebMWMGOnfujA4dOuCdd95By5Ytcfv2baxduxYzZ87EsWPHMGvWLAwePBjPPPMMxo0bB39/f6xbtw6vvPIKHnnkETz22GMAKoKUHj16ICoqCh9++CGuXbum+5wGDRoAAJ5//nmEh4fj7rvvxh133IGsrCy8++67CA0NRVJSkmQZ169fj1u3bjn0Kf6tt97C/fffj8jISDzyyCPw8PDAwYMHceTIEbz77rt2bbtWrVoYOnQoPvzwQ+Tn52P8+PF47LHHdMfg7bffxvjx4xEQEIC+ffuipKQEe/bsQU5ODl566SXZ7TZp0gSLFy/Gtm3bEBQUhI8++ghXrlwxCFSio6Oxc+dOnD17FrVr10ZwcDA6duwIX19fvPbaaxg/fjx27txpkKQsp1evXkhKSkL//v3xwQcfIC4uDpcuXcKvv/6Khx9+2KRpiqgqYq8fIhdr1KgR9u3bh549e+Lf//43EhIScO+992LdunWYOXMmAOCRRx7Bhg0bkJmZia5du6Jp06b4+OOP8frrr2PBggW65o+1a9fi9OnTWLduHe644w6EhYXp/mn16tULO3bswKOPPoq4uDgMHDgQtWrVwrp160xyIbT8/Pwc3tTQp08f/PLLL1izZg3at2+Pu+66Cx9//DGioqLs3nbjxo0xYMAA3HfffejduzdatmyJL774Qrd81KhR+Oqrr5Ceno7ExER0794dc+fORUxMjNntvvHGG2jTpg369OmDHj16oEGDBujfv7/BOi+//DI8PT3RvHlzhIaGIjMzE8HBwfjuu++wYsUKJCYmYv78+ZgyZYrF/dBoNFixYgW6deuG4cOHIy4uDoMHD8a5c+d0+TpEVZ1GKM3QIyJSsSlTpmDZsmVWNa0QkfqxRoWIiIhUi4EKEdmldu3asv82b97s6uIRkZtj0w8R2eX06dOyyxo2bOhW8/kQkfowUCEiIiLVYtMPERERqRYDFSIiIlItBipERESkWgxUiIiISLUYqBAREZFqMVAhIiIi1WKgQkRERKrFQIWIiIhU6/8Bjp8cpm7PK3YAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(alm_surr, data_training)\n", + "surrogate_parity(alm_surr, data_training)\n", + "surrogate_residual(alm_surr, data_training)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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QoUOiXbt2Qi6Xi8aNG4t169apvN/SpUuFk5OTkMvlokOHDuLYsWNK2x8/fixGjx4tateuLUxNTcU777wjbt68qVQnJSVFdOvWTZiYmAhra2sxYcIEkZeXV67jLmlK9OPHj8X58+fF48ePy7W/6mDo0KGid+/eKuUxMTECgPj++++FEEIsWrRItG7dWpiamooGDRqIUaNGiQcPHgghis4hAKVHaGioEEKI9evXC3d3d+nfwMCBA8WtW7eq6vAqnTafeyKi6mTnTiEUiqI/K5qmljSRCfF/Mw2o2snKyoKlpSUyMzNhYWEhlT958gTJyclwdnaGsbExgKLFaTW1zIWhoWGZZ2X6+/sjIyMDO3bsUNnWrl07ODg4YM+ePfj666/Rtm1bODs74/Llyxg9ejTefPNNrFixArm5uVi5ciWmT58uTYIxNzeHubk51q5dC3t7ezRv3hy3b99GUFAQrKyssGfPnoo8ZI1Rd+6JiKj8vL3v4cQJA7RsaYmn7mNQIUr6/q5sWnNHCSpdXl4e5s6dq5H3DgkJKfHax/Jo0aIFzpw5AwAYP368VN6oUSN8+eWXCAwMxIoVKyCXy2FpaQmZTKYy8WTYsGHS3xs3bowlS5bg1VdfxcOHD2Fubv7SbSQiIu1WWFiIefPmoWPHXHh4mKBDh0kAdGO5KK24po5qBiGE1ON34MABvPXWW6hfvz5q1aqFwYMH4969e3j06FGp+zh58iR69eoFJycn1KpVC506dQJQNAOaiIhqtlu3bmHWrFnIzc0FAFhYCPTurRuBDmBPnc4wNDRESEiIxt67IiQkJMDZ2RkpKSno2bMnRo0ahdmzZ6NOnTr466+/EBAQgNzc3BLvpJGdnQ0fHx/4+Phgw4YNsLGxwdWrV+Hj4yP9ABMRUc0UExODv/76S3ru5OT03GXVtA1DnY6QyWQVMgSqKQcPHsS///6Lzz77DCdPnkRhYSEWLVoEPb2izuTIyEil+nK5HAUFBUpliYmJuHfvHsLDw6W7cZw4caJqDoCIiKql/Px8zJ49W6nsvffe08klxhjqqMrl5OQgLS0NBQUFuHXrFvbu3Yu5c+eiZ8+eGDJkCM6ePYu8vDwsXboUvXr1wpEjR7Bq1SqlfTRq1AgPHz5ETEwM2rZtC1NTUzg5OUEul2Pp0qUIDAzE2bNnMWvWLA0dJRERadr169exZs0apbJJkybp7L3TeU0dVbm9e/fC3t4ejRo1gq+vLw4dOoQlS5Zg586d0NfXR9u2bfHVV19h3rx5aN26NTZs2KAyCcTLywuBgYHo378/bGxsMH/+fNjY2CAiIgJbt25Fy5YtER4ejoULF2roKImISJN2796tFOiaN2+O0NBQnQ10AMAlTaqx8ixpQjUDzz0RUelyc3NVOgIGDRqEpk2bVlkbuKQJERER0UtITk7G+vXrlcqCg4NrzN2cGOqIiIhI661bt05p+ap27dqhd+/eGmxR1WOoIyIiIq2VkZGBb775RqnM398fDRs21FCLNIehjoiIiLTSr7/+in/++UepbP/+zxEaWjHrp2obhjoiIiLSOmFhYSpl0dGhCA7WQGOqCYY6IiIi0hrnzp3Dtm3bninrichId4SGaqhR1QRDHREREWkFdb1zMTFTMHEil3gCGOqIiIiomhNCYObMmSrlbm6hNb537mkMdURERFRtRUdH49ixY0plWVl26NRpJPz8NNSoaoqhjnSSv78/MjIysGPHDgBA586d0a5dO3z99dcvvM+K2AcREZWduuHWmrSYcHnx3q9Upfz9/SGTySCTySCXy9G0aVPMnDkT+fn5lfq+v/zyC2bNmlWmuocPH4ZMJkNGRsYL74OIiF5cXl6e2kAXGhrKQFcK9tRRlfP19cW6deuQk5ODPXv2YMyYMTA0NERISIhSvdzcXMjl8gp5zzp16lSLfRARUelmzZqFwsJCpbIrV1pj7dq+GmqR9mBPHVU5IyMj2NnZoWHDhhg1ahS8vb0RFRUFf39/9OnTB7Nnz4aDgwOaN28OALh27Rref/99WFlZoU6dOujduzdSUlKk/RUUFCAoKAhWVlaoW7cuJk+eDCGE0nt27twZ48ePl57n5ORgypQpcHR0hJGREZo2bYo1a9YgJSUFXbp0AQDUrl0bMpkM/v7+avdx//59DBkyBLVr14apqSm6deuGixcvStsjIiJgZWWF6OhouLq6wtzcHL6+vrh586ZU5/Dhw+jQoQPMzMxgZWWF1157DVeuXKmgT5qISLuEhYWpBLpZs6bh6FEGurJgqCONMzExQW5uLgAgJiYGSUlJ2L9/P3bt2oW8vDz4+PigVq1a+PPPP3HkyBEpHBW/ZtGiRYiIiMDatWvx119/IT09Hdu3by/1PYcMGYJNmzZhyZIlSEhIwLfffgtzc3M4Ojri559/BgAkJSXh5s2bKrefKebv748TJ04gKioKsbGxEEKge/fuyMvLk+o8evQICxcuxI8//og//vgDV69excSJEwEA+fn56NOnDzp16oQzZ84gNjYWH3/8MWQy2Ut/pkRE2iQzM1PtcKubWyg6dNDH/PkaaJQW4vAraYwQAjExMYiOjsa4ceNw584dmJmZYfXq1dKw608//YTCwkKsXr1aCjvr1q2DlZUVDh8+jK5du+Lrr79GSEgI3n33XQDAqlWrEB0dXeL7XrhwAZGRkdi/fz+8vb0BAI0bN5a2Fw+z1qtXD1ZWVmr3cfHiRURFReHIkSPw8vICAGzYsAGOjo7YsWMH3nvvPQBF14WsWrUKTZo0AQCMHTtWmpaflZWFzMxM9OzZU9ru6upa/g+SiEiLqQtzt2+7YPnygQDAGa7lwJ46QlQU4OVV9GdV2LVrF8zNzWFsbIxu3bqhf//+mDFjBgCgTZs2StfRnT59Gv/99x9q1aoFc3NzmJubo06dOnjy5AkuXbqEzMxM3Lx5Ex4eHtJrDAwM0L59+xLf/9SpU9DX10enTp1e+BgSEhJgYGCg9L5169ZF8+bNkZCQIJWZmppKgQ0A7O3tcfv2bQBF4dHf3x8+Pj7o1asXvvnmG6WhWSIiXacu0C1cOB0+PgM10Brtx546Qng4EBtb9GdV/I+oS5cuWLlyJeRyORwcHGBg8L9/hmZmZkp1Hz58CHd3d2zYsEFlPzY2Ni/0/iYmJi/0uhdhaKh8U2mZTKZ0vd+6devwySefYO/evdiyZQumTZuG/fv3w9PTs8raSERU1W7cuIHVq1erlEdHh2LDBvbOvSj21BGCgwGFAlV2E2QzMzM0bdoUTk5OSoFOnVdeeQUXL15EvXr10LRpU6WHpaUlLC0tYW9vj7i4OOk1+fn5OHnyZIn7bNOmDQoLC/H777+r3V7cU1hQUFDiPlxdXZGfn6/0vvfu3UNSUhJatmxZ6jE9y83NDSEhITh69Chat26NjRs3luv1RETaJCwsTCXQtW3bFqGhoTh6lIHuZTDUEfz8UG1/kAYNGgRra2v07t0bf/75J5KTk3H48GF88sknuH79OgDg008/RXh4OHbs2IHExESMHj1aZY25pzVq1AhDhw7FsGHDsGPHDmmfkZGRAICGDRtCJpNh165duHPnDh4+fKiyj2bNmqF3794YMWIE/vrrL5w+fRoffvgh6tevj969e5fp2JKTkxESEoLY2FhcuXIF+/btw8WLF3ldHRHprJLWnuvTp0/VN0YHMdRRtWZqaoo//vgDTk5OePfdd+Hq6oqAgAA8efIEFhYWAIAJEyZg8ODBGDp0KBQKBWrVqoV33nmn1P2uXLkS/fr1w+jRo9GiRQuMGDEC2dnZAID69esjLCwMwcHBsLW1xdixY9XuY926dXB3d0fPnj2hUCgghMCePXtUhlxLO7bExET07dsXLi4u+PjjjzFmzBiMHDmyHJ8QEVH1t27dqRJnt1LFkYlnF/SiaiMrKwuWlpbIzMyUAgwAPHnyBMnJyXB2doaxsbEGW0hVjeeeiLSNujCXlPQ2Nm3ygkJRNFKka0r6/q5snChBRERElaKk3jk3NyAlpequ5a4pGOqIiIioQm3evBlJSUkq5TNmhEq9c9XxOm5tx1BHREREFUZd79y///ZB8+Ztq3SlhZqIoY6IiIgqREnDraGcD1ElGOq0GOe41Dw850RUHYWHhyMnJ0elvHi4lUOtVYOhTgsVL5nx6NGjKr07Amneo0ePAKjeqYKISFPU9c75+/vj9OmGHG6tYgx1WkhfXx9WVlbSPURNTU2lm92TbhJC4NGjR7h9+zasrKygr6+v6SYRUQ1XWFiIWbNmqZSH/t9Ya8OG7KGragx1WsrOzg4ApGBHNYOVlZV07omINEVd7xzwv0BHmsFQp6VkMhns7e1Rr1495OXlabo5VAUMDQ3ZQ0dEGqcu0H366aewsrKq+saQEoY6Laevr88veiIiqnRPnjzBvHnzVMrZO1d9MNQRERFRqTjcqh0Y6oiIiKhE6gJdSEgI5HK5BlpDpWGoIyIiIhX37t3DsmXLVMrZO1d9MdQRERGREg63aieGOiIiIpKoC3TTp0/neqhagKGOiIiIkJSUhM2bN6uUs3dOezDUERER1XDqeufkcjlCQkI00Bp6UXqabgARERFpjrpA5+YWil9/DUFUlAYaRC+MPXVEREQ10IEDB3DkyBGVcje3UISHA7GxQHg479+qTRjqiIiIahh1vXPOzs4YMmSI9Dw8HAgOrspW0ctiqCMiIqpB1AW6ZydD+Pmxh04bMdQRERHVAF999RUePHigUs7ZrbqDoY6IiEjHqeudS0z0xMCBPhpoDVUWhjoiIiIdpi7QzZgRCoWCQ6y6hqGOiIhIB5V0qy83t6JAx0kQuoehjoiISMeoC3R9+/ZF69atAbCHTldx8WEiIiIdUVhYqDbQRUeH4vLl1hpoEVUl9tQRERHpgJKGW6OjQ7mQcA3BUEdERKTl1AW6kSNHws7ODm5uXEi4pmCoIyIi0lKPHj3CggULVMqfXnuOCwnXHAx1REREWqik4VYuJlxzMdQRERFpGXWBbvLkyTAxMdFAa6i6YKgjIiLSEjdv3sR3332nUu7mFgrmOWKoIyIi0gIlDbfy7hBUjKGOiIiomlMX6KZPn45ff5Xx7hAkYagjIiKqpk6cOIHdu3erlBdPhuDMVnoaQx0REVE1xNmtVF4MdURERNWMukDn5hbKXjkqFUMdERFRNbFt2zacO3dOpZyTIags9DTdgLLy8/ODk5MTjI2NYW9vj8GDByM1NVXaPmPGDMhkMpWHmZmZ0n62bt2KFi1awNjYGG3atMGePXuUtgshMH36dNjb28PExATe3t64ePGiUp309HQMGjQIFhYWsLKyQkBAAB4+fKhU58yZM+jYsSOMjY3h6OiI+fPnV/AnQkREuiQsLEwl0Onr68PNLZSTIahMtCbUdenSBZGRkUhKSsLPP/+MS5cuoV+/ftL2iRMn4ubNm0qPli1b4r333pPqHD16FAMHDkRAQADi4+PRp08f9OnTB2fPnpXqzJ8/H0uWLMGqVasQFxcHMzMz+Pj44MmTJ1KdQYMG4dy5c9i/fz927dqFP/74Ax9//LG0PSsrC127dkXDhg1x8uRJLFiwADNmzFC7thAREZG64dbQ0FBMmzYNfn7A0aPspaPnkwkhhKYb8SKioqLQp08f5OTkwNDQUGX76dOn0a5dO/zxxx/o2LEjAKB///7Izs7Grl27pHqenp5o164dVq1aBSEEHBwcMGHCBEycOBEAkJmZCVtbW0RERGDAgAFISEhAy5Ytcfz4cbRv3x4AsHfvXnTv3h3Xr1+Hg4MDVq5cialTpyItLQ1yuRwAEBwcjB07diAxMbHMx5iVlQVLS0tkZmbCwsLihT8rIiKqnjgZQjdp6vtba3rqnpaeno4NGzbAy8tLbaADgNWrV8PFxUUKdAAQGxsLb29vpXo+Pj6IjY0FACQnJyMtLU2pjqWlJTw8PKQ6sbGxsLKykgIdAHh7e0NPTw9xcXFSnTfeeEMKdMXvk5SUhPv377/k0RMRkS5QF+hatWrFQEcvTKtC3ZQpU2BmZoa6devi6tWr2Llzp9p6T548wYYNGxAQEKBUnpaWBltbW6UyW1tbpKWlSduLy0qrU69ePaXtBgYGqFOnjlIddft4+j3UycnJQVZWltKDiIh0j7pAFx0dqnRZEVF5aTTUBQcHq53c8PTj6eHKSZMmIT4+Hvv27YO+vj6GDBkCdaPH27dvx4MHDzB06NCqPJyXNnfuXFhaWkoPR0dHTTeJiIgqUFhYWImBjhMh6GVpdEmTCRMmwN/fv9Q6jRs3lv5ubW0Na2truLi4wNXVFY6Ojjh27BgUCoXSa1avXo2ePXuq9JbZ2dnh1q1bSmW3bt2CnZ2dtL24zN7eXqlOu3btpDq3b99W2kd+fj7S09OV9qPufZ5+D3VCQkIQFBQkPc/KymKwIyLSEerCXIMGvlizxgPBwZwIQS9Po6HOxsYGNjY2L/TawsJCAEVDlk9LTk7GoUOHEBUVpfIahUKBmJgYjB8/Xirbv3+/FAqdnZ1hZ2eHmJgYKcRlZWUhLi4Oo0aNkvaRkZGBkydPwt3dHQBw8OBBFBYWwsPDQ6ozdepU5OXlSdf87d+/H82bN0ft2rVLPCYjIyMYGRm9wKdBRETVlRACM2fOVCkPDQ2FlxcQGwuEhzPU0cvTisWH4+LicPz4cbz++uuoXbs2Ll26hC+++AJNmjRR6aVbu3Yt7O3t0a1bN5X9fPrpp+jUqRMWLVqEHj16YPPmzThx4oS01IhMJsP48ePx5ZdfolmzZnB2dsYXX3wBBwcH9OnTBwDg6uoKX19fjBgxAqtWrUJeXh7Gjh2LAQMGwMHBAQDwwQcfICwsDAEBAZgyZQrOnj2Lb775BosXL67cD4qIiKqV581uDQ4uCnQceqWKoBVLmvz777/49NNPcfr0aWRnZ8Pe3h6+vr6YNm0a6tevL9UrLCxEw4YNMWTIEMyePVvtvrZu3Ypp06YhJSUFzZo1w/z589G9e3dpuxACoaGh+O6775CRkYHXX38dK1asgIuLi1QnPT0dY8eOxa+//go9PT307dsXS5Ysgbm5uVTnzJkzGDNmDI4fPw5ra2uMGzcOU6ZMKddxc0kTIiLtpS7QBQQEoEGDBhpoDVUlTX1/a0Woq6kY6oiItM+jR4+wYMEClXIuVVJzaOr7WyuGX4mIiLQBFxMmTWKoIyIiqgDqAt3EiRNV7kFOVFm0avFhIiKi6ubGjRtqA52bWyjeftsMahZjIKoU7KkjIiJ6QaUNt3K5EqpqDHVEREQvQF2g++KLL6CnVzQIxuVKqKox1BEREZVDXFwc9u7dq1Lu5hYKvacuavLzYw8dVS2GOiIiojIqabh1xoxQKBQMcaRZDHVERERloC7QhYaGIioKUCg4zEqax1BHRERUisjISCQkJKiUF689x2FWqi4Y6oiIiErAxYRJm3CdOiIiIjXUBbro6FC4uTHQUfXEnjoiIqKnlNQ7Fx0dynXnqFpjqCMiIvo/6gJdkyZN8OGHH8LNjevOUfXGUEdERISSh1uPHi36OydEUHXHUEdERDVaacOt7JUjbcJQR0RENZa6QHfhwlvo3/91cIIraRuGOiIiqnGEEJg5c6ZKOZcqIW3GUEdERDUK154jXcV16oiIqMZQF+h++GEIoqMZ6Ej7MdQREZHOe/TokdpA5+YWCjs7Z06IIJ3A4VciItJpzxtu5TIlpCsY6oiISGepC3RBQUGoVauWBlpDVLkY6oiISOdcu3YNa9euVSnnZAjSZQx1RESkUzi7lWoqhjoiItIZ6gLdvn1f4MgRzgsk3cdQR0REWu/PP//EwYMHVcp5qy+qSRjqiIhIq5U23MoRV6pJ2B9NRERaS12gmzEjlIsJU43EnjoiItI633//PVJTU1XK3dxCoVCAQ65UIzHUERGRVuFiwkTqMdQREZHWUBfouFQJURGGOiIiqvZK6p2LjuZkCKJiDHVERFStqQt0BgZm2L17Iq+dI3oKQx0REVVbpQ23Tp1a1a0hqt4Y6oiIqNrhcCtR+THUERFRtaIu0CUleSAlxZfDrUSlYKgjIqJqQQiBmTNnqpQX3+qLS5UQlY6hjoiINI63+iJ6eQx1RESkEVFRQHg44OOjGuj69++PFi1aaKBVRNqLoY6IiDRi0aJH8PFZoFLOxYSJXgxDHRERVbmwsDC8+aZqOQMd0YtjqCMioiql7vq5Tz75BLVr19ZAa4h0B0MdERFViStXriAiIkKlnL1zRBWj3KFOX18fN2/eRL169ZTK7927h3r16qGgoKDCGkdERLqhpNmtbm4MdEQVpdyhTgihtjwnJwdyufylG0RERLpFXaCbNm0a9PX1NdAaIt1V5lC3ZMkSAIBMJsPq1athbm4ubSsoKMAff/zB6edERCQ5fPgwfv/9d5VyDrcSVY4yh7rFixcDKOqpW7VqldL/sORyORo1aoRVq1ZVfAuJiKjaK15zrvjOD6UtJkxElaPMoS45ORkA0KVLF/zyyy+cpURERJLwcCA2FpgyBYiPVw10DHNEla/c19QdOnSoMtpBRERaLDgYOHLkG5iaZqhsY6AjqhrlDnXDhg0rdfvatWtfuDFERKSd4uPDYGqqWs5AR1R1yh3q7t+/r/Q8Ly8PZ8+eRUZGBt5Utzw4ERHpNHXXzzHMEVW9coe67du3q5QVFhZi1KhRaNKkSYU0ioiIqj+uPUdUvchESQvPlVNSUhI6d+6MmzdvVsTuCEBWVhYsLS2RmZkJCwsLTTeHiEia5erjw9mtRCXR1Pd3hd0m7NKlS8jPz6+o3RERUTVUUqBjmCPSvHKHuqCgIKXnQgjcvHkTu3fvxtChQyusYUREVL2EhYXBx0e1nIGOqHood6iLj49Xeq6npwcbGxssWrTouTNjiYhIO6m7fq5p06YYNGiQBlpDROpwnToiIiqREAIzZ85UKWfvHFH188LX1N2+fRtJSUkAgObNm6NevXoV1igiItI83uqLSLuUO9RlZWVhzJgx2LRpEwoLCwEA+vr66N+/P5YvXw5LS8sKbyQREVUtdYHOz88Pbm5uGmgNEZWFXnlfMGLECMTFxWH37t3IyMhARkYGdu3ahRMnTmDkyJGV0UYiIqoi2dnZJS4mzEBHVL2Ve506MzMzREdH4/XXX1cq//PPP+Hr64vs7OwKbWBNxnXqiKgqcbiVqGJozTp1devWVTvEamlpidq1a1dIo4iIqGqpC3SjR4+GjY2NBlpDRC+i3MOv06ZNQ1BQENLS0qSytLQ0TJo0CV988UWFNo6IiCpWVBTg5VX0JwAkJyeXONzKQEekXco9/Orm5ob//vsPOTk5cHJyAgBcvXoVRkZGaNasmVLdf/75p+JaWgNx+JWIKpqXFxAbCygUvNUXUWXRmuHX3r17QyaTVUZbiIiokgUHl3yrr6lTp8LAoMLuHklEVazcPXVUddhTR0QVISqqKMgFBwOmpgdw5MgRlTrsnSOqOJr6/i73NXWNGzfGvXv3VMozMjLQuHHjCmkUERFVnPDwoiHX+PgwBjoiHVbufvaUlBQUFBSolOfk5OD69esV0igiIqoYUVFAejowY4b6yRBEpDvK3FMXFRWFqP+bLhUdHS09j4qKwvbt2zFr1iw4OztXWkP9/Pzg5OQEY2Nj2NvbY/DgwUhNTVWqEx0dDU9PT9SqVQs2Njbo27cvUlJSlOocPnwYr7zyCoyMjNC0aVNERESovNfy5cvRqFEjGBsbw8PDA3///bfS9idPnmDMmDGoW7cuzM3N0bdvX9y6dUupztWrV9GjRw+YmpqiXr16mDRpEvLz8yvksyAiKouoKCA2dh4GDmSgI6oJynxNnZ5eUf6TyWR49iWGhoZo1KgRFi1ahJ49e1Z8KwEsXrwYCoUC9vb2uHHjBiZOnAgAOHr0KICiafmurq4ICgpCQEAAMjMz8dlnn+HBgwfSLNzk5GS0bt0agYGBGD58OGJiYjB+/Hjs3r0bPj4+AIAtW7ZgyJAhWLVqFTw8PPD1119j69atSEpKku5vO2rUKOzevRsRERGwtLTE2LFjoaenJw1rFBQUoF27drCzs8OCBQtw8+ZNDBkyBCNGjMCcOXPKfMy8po6IXgYXEybSDE19f5d7ooSzszOOHz8Oa2vrympTmURFRaFPnz7IycmBoaEhtm3bhoEDByInJ0cKoL/++it69+4t1ZkyZQp2796Ns2fPSvsZMGAAMjIysHfvXgCAh4cHXn31VSxbtgwAUFhYCEdHR4wbNw7BwcHIzMyEjY0NNm7ciH79+gEAEhMT4erqitjYWHh6euK3335Dz549kZqaCltbWwDAqlWrMGXKFNy5cwdyubxMx8hQR0QvqqS154io8mnNRInk5GSNB7r09HRs2LABXl5eMDQ0BAC4u7tDT08P69atQ0FBATIzM/Hjjz/C29tbqhMbGwtvb2+lffn4+CA2NhYAkJubi5MnTyrV0dPTg7e3t1Tn5MmTyMvLU6rTokULODk5SXViY2PRpk0bKdAVv09WVhbOnTtX4nHl5OQgKytL6UFEVB5hYWEMdEQ1VLknSsycObPU7dOnT3/hxjzPlClTsGzZMjx69Aienp7YtWuXtM3Z2Rn79u3D+++/j5EjR6KgoAAKhQJ79uyR6qSlpSkFLQCwtbVFVlYWHj9+jPv376OgoEBtncTERGkfcrkcVlZWKnWK77JR0vsUbyvJ3LlzSxwuISJ61tNLlfj5cbiVqKYrd0/d9u3blR6RkZGYN28eFi1ahB07dpRrX8HBwZDJZKU+isMUAEyaNAnx8fHYt28f9PX1MWTIEOn6vrS0NIwYMQJDhw7F8ePH8fvvv0Mul6Nfv34q1wBWVyEhIcjMzJQe165d03STiKgaK16qJDy85OFWBjqimqPcPXXx8fEqZVlZWfD398c777xTrn1NmDAB/v7+pdZ5eu07a2trWFtbw8XFBa6urnB0dMSxY8egUCiwfPlyWFpaYv78+VL9n376CY6OjoiLi4Onpyfs7OxUZqneunULFhYWMDExgb6+PvT19dXWsbOzAwDY2dkhNzcXGRkZSr11z9Z5dsZs8T6L66hjZGQEIyOjUj8PIqJiwcFFa8+pwzBHVPOUu6dOHQsLC4SFheGLL74o1+tsbGzQokWLUh8lTSooLCwEUHQdGgA8evRImiBRTF9fX6muQqFATEyMUp39+/dDoVAAAORyOdzd3ZXqFBYWIiYmRqrj7u4OQ0NDpTpJSUm4evWqVEehUODff//F7du3ld7HwsICLVu2LNdnRESkTlSU+kDn6OjIQEdUQ1XYTf6KhwwrQ1xcHI4fP47XX38dtWvXxqVLl/DFF1+gSZMmUpDq0aMHFi9ejJkzZ2LgwIF48OABPv/8czRs2BBubm4AgMDAQCxbtgyTJ0/GsGHDcPDgQURGRmL37t3SewUFBWHo0KFo3749OnTogK+//hrZ2dn46KOPAACWlpYICAhAUFAQ6tSpAwsLC4wbNw4KhQKenp4AgK5du6Jly5YYPHgw5s+fj7S0NEybNg1jxoxhTxwRvTQhBOLjVa9vZpgjqtnKHeqWLFmi9FwIgZs3b+LHH39Et27dKqxhTzM1NcUvv/yC0NBQZGdnw97eHr6+vpg2bZoUkt58801s3LgR8+fPx/z582FqagqFQoG9e/fCxMQEQNFkit27d+Ozzz7DN998gwYNGmD16tXSGnUA0L9/f9y5cwfTp09HWloa2rVrh7179ypNfFi8eDH09PTQt29f5OTkwMfHBytWrJC26+vrY9euXRg1ahQUCgXMzMwwdOjQ504yISIqTUm9cwADHRG94Dp1T9PT04ONjQ3efPNNhISEoFatWhXawJqM69QR0dPUTYbo1q0bOnTooIHWEFFJNPX9Xe6euuTk5MpoBxERleDRo0dYsGCBSjl754joaS90TV1GRgb+++8/AEDTpk1V1mwjIqKKwbXniKisyjX7NSUlBT169IC1tTU8PDzg4eEBa2tr9OzZEykpKZXURCKimiEqCvDyKvoTUB/oAgMDGeiISK0yX1N37do1vPrqqzA0NMTo0aPh6uoKADh//jxWrlyJ/Px8HD9+HA0aNKjUBtckvKaOqGbx8ipaTLh79xR06PCDynaGOSLtoKnv7zKHuoCAAPz333+Ijo6GsbGx0rbHjx/D19cXzZo1w+rVqyuloTURQx1RzcLZrUS6odpPlNi7dy+2bNmiEugAwMTEBLNmzcKAAQMqtHFERDWJukD3+eefw9DQUAOtISJtU+ZQd/fuXTRq1KjE7Y0bN0Z6enpFtImIqEaJiYnBX3/9pVLO3jkiKo8yhzp7e3ucP3++xGvmzp49W+p9TYmISBVntxJRRSlzqOvTpw8mTpyImJgY2NjYKG27ffs2pkyZgj59+lR0+4iIdJa6QMcwR0QvqswTJe7fvw8PDw+kpaXhww8/RIsWLSCEQEJCAjZu3Ag7OzscO3YMderUqew21xicKEGkm2bPXoj8/GyVcgY6It1Q7SdK1K5dG3Fxcfj888+xefNmZGRkAACsrKzwwQcfYM6cOQx0RETPweFWIqos5b73KwAIIXDnzh0AgI2NDWQyWYU3jNhTR6RrONxKVDNU+566p8lkMtSrV6+i20JEpJPYO0dEVaFctwkjIqLyYaAjoqryQj11RERUsqgoIDwc8PHhcCsRVR321BERVZCoqKL7t8bHhzHQEVGVY08dEVEFKal3zszMERMnDtNAi4ioJilTqFuyZEmZd/jJJ5+8cGOIiLRN8VDrlCkCPj4zVbazd46IqkqZljRxdnYu285kMly+fPmlG0VFuKQJUfXn5aW+dw5goCOqqar1kibJycmV3Q4iIq2kLtD5+vrCw8NDA60hoprsha+py83NRXJyMpo0aQIDA16aR0Q1y5MnTzBv3jyVcvbOEZGmlHv266NHjxAQEABTU1O0atUKV69eBQCMGzcO4eHhFd5AIqLqJiwsjIGOiKqdcoe6kJAQnD59GocPH4axsbFU7u3tjS1btlRo44iIqht1iwkHBgYy0BGRxpV73HTHjh3YsmULPD09le752qpVK1y6dKlCG0dEVF3cuHEDq1evVilnmCOi6qLcoe7OnTtq7/uanZ2tFPKIiHQFb/VFRNqg3KGuffv22L17N8aNGwcAUpBbvXo1FApFxbaOiEjD1AW6zz//HIaGhhpoDRFRycod6ubMmYNu3brh/PnzyM/PxzfffIPz58/j6NGj+P333yujjUREVS4uLg579+5VKWfvHBFVV+WeKPH666/j1KlTyM/PR5s2bbBv3z7Uq1cPsbGxcHd3r4w2EhFVqbCwMAY6ItI6ZbqjBGkG7yhBVLWiooD4eNXhVoY5IiqPan1HiaysrDLvkOGDiLTRmjVrcP36dZVyBjoi0hZlCnVWVlZlntlaUFDwUg0iIqpqnN1KRLqgTKHu0KFD0t9TUlIQHBwMf39/abZrbGwsfvjhB8ydO7dyWklEVEnUBTqGOSLSRuW+pu6tt97C8OHDMXDgQKXyjRs34rvvvsPhw4crsn01Gq+pI6o87J0josqiqe/vcs9+jY2NRfv27VXK27dvj7///rtCGkVEVJkY6IhIF5V7nTpHR0d8//33mD9/vlL56tWr4ejoWGENIyKqDBxuJSJdVe5Qt3jxYvTt2xe//fYbPDw8AAB///03Ll68iJ9//rnCG0hEVBHYO0dEuq7cw6/du3fHxYsX0atXL6SnpyM9PR29evXChQsX0L1798poIxHRS1EX6MzNnRnoiEincPHhaowTJYhejhACM2fOVClnmCOiylStFx9+VkZGBtasWYOEhAQAQKtWrTBs2DBYWlpWaOOIiF4Uh1uJqKYpd0/diRMn4OPjAxMTE3To0AEAcPz4cTx+/Bj79u3DK6+8UikNrYnYU0f0YtQFum7dukm/s4iIKpOmvr/LHeo6duyIpk2b4vvvv4eBQVFHX35+PoYPH47Lly/jjz/+qJSG1kQMdUTls317Ds6cCVcpZ+8cEVUlrQl1JiYmiI+PR4sWLZTKz58/j/bt2+PRo0cV2sCajKGOqOw43EpE1YXWXFNnYWGBq1evqoS6a9euoVatWhXWMCKislIX6D7++GPY29troDVERJpR7lDXv39/BAQEYOHChfDy8gIAHDlyBJMmTVK5dRgRUWWaNu0mDA2/Uyln7xwR1UTlDnULFy6ETCbDkCFDkJ+fDwAwNDTEqFGjEB6uei0LEVFlCAsLg6GharmbGwMdEdVML7xO3aNHj3Dp0iUAQJMmTWBqalqhDSNeU0dUEnXDrSEhIZDL5RpoDRGRMq25pq6Yqakp2rRpU5FtISIq1YkTJ7B7926Vcg63EhGVI9QNGzasTPXWrl37wo0hIioJZ7cSEZWuzKEuIiICDRs2hJubG3hnMSKqSuoCHcMcEZGyMoe6UaNGYdOmTUhOTsZHH32EDz/8EHXq1KnMthFRDRcZGSndjvBpubkMdEREzyrXRImcnBz88ssvWLt2LY4ePYoePXogICAAXbt2hUwmq8x21kicKEE1GYdbiUhbac0dJYpduXIFERERWL9+PfLz83Hu3DmYm5tXdPtqNIY6qqnUBTo3t1D4+WmgMURE5aR1s1/19PQgk8kghEBBQUFFtomIaij2zhERvTi98lTOycnBpk2b8Pbbb8PFxQX//vsvli1bhqtXr7KXjoheCgMdEdHLKXNP3ejRo7F582Y4Ojpi2LBh2LRpE6ytrSuzbURUQ3B2KxHRyyvzNXV6enpwcnKCm5tbqZMifvnllwprXE3Ha+pI15XUOxcdHYqjR6u4MUREFaTaX1M3ZMgQznAlogqjLtBZWbXEli3vIThYAw0iItJyLzz7lSofe+pIV3G4lYh0WbXvqSMielmlDbcy0xERvRyGOiKqEuoCXX5+H8TEtOVwKxFRBWCoI6JKlZeXhzlz5qiUFw+3zppV1S0iItJNDHVEVGm49hwRUdVhqCOiSqEu0AUGBsLW1lYDrSEi0n0MdURUoe7du4dly5aplLu5hYJ5joio8jDUEVGFKWm4dcaMUCgUgJ9fFTeIiKgGYagjogqhLtCFhIRg7145FApwhisRUSVjqCOil3Lu3Dls27ZNpbx4MoSfH3voiIiqgp6mG1BWfn5+cHJygrGxMezt7TF48GCkpqYq1YmMjES7du1gamqKhg0bYsGCBSr7OXz4MF555RUYGRmhadOmiIiIUKmzfPlyNGrUCMbGxvDw8MDff/+ttP3JkycYM2YM6tatC3Nzc/Tt2xe3bt1SqnP16lX06NEDpqamqFevHiZNmoT8/PyX/yCIqpGwsLBSAx0REVUdrQl1Xbp0QWRkJJKSkvDzzz/j0qVL6Nevn7T9t99+w6BBgxAYGIizZ89ixYoVWLx4sdIF28nJyejRowe6dOmCU6dOYfz48Rg+fDiio6OlOlu2bEFQUBBCQ0Pxzz//oG3btvDx8cHt27elOp999hl+/fVXbN26Fb///jtSU1Px7rvvStsLCgrQo0cP5Obm4ujRo/jhhx8QERGB6dOnV/KnRFR11A23Tp8+nYGOiEhDtPber1FRUejTpw9ycnJgaGiIDz74AHl5edi6datUZ+nSpZg/fz6uXr0KmUyGKVOmYPfu3Th79qxUZ8CAAcjIyMDevXsBAB4eHnj11VelMFhYWAhHR0eMGzcOwcHByMzMhI2NDTZu3CiFysTERLi6uiI2Nhaenp747bff0LNnT6SmpkrLN6xatQpTpkzBnTt3IJfLy3SMvPcrVUe//fabSu81wN45IqJimvr+1pqeuqelp6djw4YN8PLygqGhIQAgJycHxsbGSvVMTExw/fp1XLlyBQAQGxsLb29vpTo+Pj6IjY0FAOTm5uLkyZNKdfT09ODt7S3VOXnyJPLy8pTqtGjRAk5OTlKd2NhYtGnTRmk9Lh8fH2RlZeHcuXMlHldOTg6ysrKUHkTVSVhYGAMdEVE1pVWhbsqUKTAzM0PdunVx9epV7Ny5U9rm4+ODX375BTExMSgsLMSFCxewaNEiAMDNmzcBAGlpaSoLn9ra2iIrKwuPHz/G3bt3UVBQoLZOWlqatA+5XA4rK6tS66jbR/G2ksydOxeWlpbSw9HRsawfDVGlUzfcGhoaykBHRFRNaDTUBQcHQyaTlfpITEyU6k+aNAnx8fHYt28f9PX1MWTIEBSPHo8YMQJjx45Fz549IZfL4enpiQEDBgAo6m3TBiEhIcjMzJQe165d03STiPDtt9+WGOiiogAvLyAqSgMNIyIiJRpd0mTChAnw9/cvtU7jxo2lv1tbW8Pa2houLi5wdXWFo6Mjjh07BoVCAZlMhnnz5mHOnDlIS0uDjY0NYmJilPZhZ2enMkv11q1bsLCwgImJCfT19aGvr6+2jp2dnbSP3NxcZGRkKPXWPVvn2SGq4n0W11HHyMgIRkZGpX4eRFVJXZjLzjbFX39NQmgoEB4OxMYW/cllS4iINEujoc7GxgY2NjYv9NrCwkIARdehPU1fXx/169cHAGzatAkKhUJ6D4VCgT179ijV379/PxQKBQBALpfD3d0dMTEx6NOnj/Q+MTExGDt2LADA3d0dhoaGiImJQd++fQEASUlJuHr1qrQfhUKB2bNn4/bt26hXr570PhYWFmjZsuULHS9RVVMX6DZvDkXt2v9bSDg4uCjQcWFhIiLN04rFh+Pi4nD8+HG8/vrrqF27Ni5duoQvvvgCTZo0kYLU3bt3sW3bNnTu3BlPnjzBunXrpCVHigUGBmLZsmWYPHkyhg0bhoMHDyIyMhK7d++W6gQFBWHo0KFo3749OnTogK+//hrZ2dn46KOPAACWlpYICAhAUFAQ6tSpAwsLC4wbNw4KhQKenp4AgK5du6Jly5YYPHgw5s+fj7S0NEybNg1jxoxhTxxVeyXd6is6OhTz5in3yHFhYSKi6kMrQp2pqSl++eUXhIaGIjs7G/b29vD19cW0adOUQtIPP/yAiRMnQggBhUKBw4cPo0OHDtJ2Z2dn7N69G5999hm++eYbNGjQAKtXr4aPj49Up3///rhz5w6mT5+OtLQ0tGvXDnv37lWa+LB48WLo6emhb9++yMnJgY+PD1asWCFt19fXx65duzBq1CgoFAqYmZlh6NChmDlzZiV/UkQvR12g69ChA7p16wY3t6JeOYBBjoioOtLadepqAq5TR1VJXaCLjg5FcHBRiPPyKrp+TqEAjh7VQAOJiLSEpr6/taKnjogqT2nDrU9PguD1c0RE1RtDHVENpi7Q9e/fHy1atJCGW4tDHK+fIyKq3hjqiGqg/Px8zJ49W6X86YWEGeKIiLQLQx1RDVPScCvvDEFEpN0Y6ohqEHWBbuzYsahbt64GWkNERBVJO+6fRUQvJSsrq8TZrUeOMNAREekC9tQR6biyzm4lIiLtxlBHpMPUBbqQkBDI5XKV2a1ERKTdGOqIdNCVK1cQERGhUs7ZrUREuovX1BHpmLCwsOcGuqdFRRXdLSIqqpIbRkRElYqhjkiHqBtunTFjOqKjS16uJDwc0rV1RESkvTj8SqQDjh07hujoaJVyN7dQKBSlXzfH238REekGmRBCaLoRpJ6mbghM2oWLCRMRVS+a+v5mTx2RFlMX6BjmiIhqJoY6Ii20efNmJCUlqZQz0BER1VwMdURaRl3vnIODA0aMGKGB1hARUXXBUEekRTjcSkREJWGoI9ICERERuHLlikp5bi4DHRERFWGoI6rm1PXO7dnTC3///QqMjYHZszXQKCIiqna4+DBRNVbScOuNG68AAOrWreoWERFRdcWeOqJq6Hlrz61YwQWDiYhIGUMdUTWjLtB99NFHcHJykp77+RU9iIiIijHUEVUThYWFmDVrlko5Z7cSEVFZMNQRVQPludVXVNT/hl7ZW0dERMU4UYJIw9QFusOHP4Obm/oeuvBwIDa26E8iIqJi7Kkj0pBHjx5hwYIFKuXR0aGIjQVyctT3xAUHc5IEERGpYqgj0oDShlvd3EoPbZwkQURE6jDUEVUxdYHu888/h6GhIQCGNiIiejG8po6oiqSlpakNdNHRofjtN0MNtIiIiHQJe+qIqkBJw60LF4bi4cOi4Vb2zhER0ctgqCOqZOoC3fTp0/HaazI8fAiYm3PSAxERvTwOvxJVkrNnz5Z471aZTIbgYEChADZsYC8dERG9PPbUEVUCdWHOyMgIwU91yXFCBBERVSSGOqIKVlLvHBERUWViqCOqIPv27UNsbKxKOQMdERFVBYY6ogqgrneuZcuWeO+99zTQGiIiqokY6oheEodbiYioOmCoI3pB69evR3Jysko5Ax0REWkCQx3RC1DXO9e1a1coFAoNtIaIiIihjqjc1AU6N7dQMM8REZEmMdQRldHy5ctx9+5dlfIZM4oCHdecIyIiTWKoIyoDdb1zsbEj4O7uAIWCt/kiIiLNY6gjKkVhYSFmzZqlUr55cygSE4ErV4CEBA00jIiI6BkMdUQlUNc7BxTNbt20qejvQlRhg4iIiErBUEekhrpAFxQUhFq1agEA5s8HwsM57EpERNUHQx3RU3JychAeHq5S/uzac35+nBhBRETVC0Md0f8pbbiViIioumOoI4L6QDd16lQYGPBHhIiItIOephtApEkZGRklLib8xhsGiIrSQKOIiIheALshqMZSF+Zq1aqFoKAgeHkBsbFFkyF47RwREWkDhjqqkdQFuunTp0MmkwEomtXK2a1ERKRNGOqoRklNTcX333+vUs7ZrUREpO0Y6qjGUNc717JlS7z33nsaaA0REVHFYqijGkFdoONSJUREpEsY6kinJSQkIDIyUqU8OjoUbm4cYiUiIt3BUEc6S13vnK+vLz77zIMzW4mISOcw1JFOKm24lTNbiYhIFzHUkU75888/cfDgQZXyp6+f48xWIiLSRQx1pDPU9c4NGjQITZs21UBriIiIqhZvE0Y6oaTh1uJAFxUFeHmBt/0iIiKdxZ460mrbtm3DuXPnVMqfXa4kPJy3/SIiIt3GUEdaS13v3KhRo1CvXj2Vck6OICIiXcdQR1pHCIGZM2eqlJe2mDAnRxARka7jNXVULZT1mreIiIhyBzoiIqKagD11VC2U5Zo3dcOtrVtPRN++ZpXcOiIiouqPoY6qhdKuecvLy8OcOXNUytk7R0RE9D8MdVQtlHTN2+zZs5Gfn69UZmZmhokTJ1ZRy4iIiLQDr6mjUmlyfbewsDCVQDd16lQGOiIiIjW0LtTl5OSgXbt2kMlkOHXqlNK2M2fOoGPHjjA2NoajoyPmz5+v8vqtW7eiRYsWMDY2Rps2bbBnzx6l7UIITJ8+Hfb29jAxMYG3tzcuXryoVCc9PR2DBg2ChYUFrKysEBAQgIcPH5a7Ldrg6WvdqsqjR49KXEzYwICdy0REROpoXaibPHkyHBwcVMqzsrLQtWtXNGzYECdPnsSCBQswY8YMfPfdd1Kdo0ePYuDAgQgICEB8fDz69OmDPn364OzZs1Kd+fPnY8mSJVi1ahXi4uJgZmYGHx8fPHnyRKozaNAgnDt3Dvv378euXbvwxx9/4OOPPy5XW7RFcDCgUFTd+m5hYWFYsGCBUlnTpk15/RwREdFzyIQQQtONKKvffvsNQUFB+Pnnn9GqVSvEx8ejXbt2AICVK1di6tSpSEtLg1wuBwAEBwdjx44dSExMBAD0798f2dnZ2LVrl7RPT09PtGvXDqtWrYIQAg4ODpgwYYI0xJeZmQlbW1tERERgwIABSEhIQMuWLXH8+HG0b98eALB37150794d169fh4ODQ5naUhZZWVmwtLREZmYmLCwsXvrzq+7U9c5Nnz4dMplMA60hIiJ6MZr6/taanrpbt25hxIgR+PHHH2FqaqqyPTY2Fm+88YYUogDAx8cHSUlJuH//vlTH29tb6XU+Pj6IjY0FACQnJyMtLU2pjqWlJTw8PKQ6sbGxsLKykgIdAHh7e0NPTw9xcXFlbos6OTk5yMrKUnrUBBkZGWoDXXR0KH79lYGOiIioLLQi1Akh4O/vj8DAQKUw9bS0tDTY2toqlRU/T0tLK7XO09uffl1JdZ69DZWBgQHq1Knz3Pd5+j3UmTt3LiwtLaWHo6NjiXV1RVhYGL755hulMm9vb0RHh1b5tXxERETaTKOhLjg4GDKZrNRHYmIili5digcPHiAkJESTza10ISEhyMzMlB7Xrl3TdJMqVUmTIV577bUqv5aPiIhI22l0KuGECRPg7+9fap3GjRvj4MGDiI2NhZGRkdK29u3bY9CgQfjhhx9gZ2eHW7duKW0vfm5nZyf9qa7O09uLy+zt7ZXqFF+7Z2dnh9u3byvtIz8/H+np6c99n6ffQx0jIyOVY9RFqamp+P7771XKn54MwXu1EhERlY9GQ52NjQ1sbGyeW2/JkiX48ssvpeepqanw8fHBli1b4OHhAQBQKBSYOnUq8vLyYGhoCADYv38/mjdvjtq1a0t1YmJiMH78eGlf+/fvh0KhAAA4OzvDzs4OMTExUojLyspCXFwcRo0aJe0jIyMDJ0+ehLu7OwDg4MGDKCwsLFdbaip1vXMffvghmjRpooHWEBER6Q6tmv1aLCUlBc7OzkqzXzMzM9G8eXN07doVU6ZMwdmzZzFs2DAsXrxYWm7k6NGj6NSpE8LDw9GjRw9s3rwZc+bMwT///IPWrVsDAObNm4fw8HD88MMPcHZ2xhdffIEzZ87g/PnzMDY2BgB069YNt27dwqpVq5CXl4ePPvoI7du3x8aNG8vclrLQtdmvJQ23EhER6RJNfX/rzEqulpaW2LdvH8aMGQN3d3dYW1tj+vTpSiHKy8sLGzduxLRp0/D555+jWbNm2LFjhxTogKJ18LKzs/Hxxx8jIyMDr7/+Ovbu3SsFOgDYsGEDxo4di7feegt6enro27cvlixZUq621CQXL16UAu/TGOiIiIgqjlb21NUUutBTp653buTIkaVeW0hERKTN2FNHOofDrURERFWHoY4q3IkTJ7B7926VcgY6IiKiysNQRxVKXe/cZ599prXDx0RERNqCoY4qhBACM2fOVCln7xwREVHVYKijl3bgwAEcOXJEqaxevXrS2n5ERERU+Rjq6KWoG26dMmWK0hIwREREVPkY6uiFFBYWYtasWSrlHG4lIiLSDIY6Krfjx49jz549SmWvvfYavL29NdQiIiIiYqgjREUB4eFAcDDg51d6XXXDrdOmTYO+vn4ltY6IiIjKQk/TDSDNCw8HYmOL/ixJXl5eiYsJM9ARERFpHkMdITgYUCiK/lTn0KFDmDNnjlJZ//794eYWCi+vop4+IiIi0ize+7Uaqw73flXXOzd9+nTIZDJ4eRX18CkUwNGjGmgcERFRNaSp72/21JFaubm5JQ63ymQyAM/v4SMiIqKqw4kSpELd7NaPPvoITk5OSmV+fs+fWEFERERVg6GOlJTUO0dERETVG4dfCQDw6NEjlUD36quvlinQRUWBEyaIiIg0jD11hL/++gsxMTFKZePHj4elpWWZXv/0kigcjiUiItIM9tTVcNu3b1cJdKGhoWUOdAAnTBAREVUH7KmrgaKigIULn+Ctt+YplXfu3BmdOnUq9/44YYKIiEjzGOpqoNWrL+KttzYqlX3++ecwNDTUUIuIiIjoZTHU1UDu7v8LdB4eHvD19dVga4iIiKgiMNTVQG+88Qbi4uIwZMgQODg4aLo5REREVAF4m7BqrDrcJoyIiIjKh7cJIyIiIqIXxlBHREREpAMY6oiIiIh0AEMdERERkQ5gqCMiIiLSAQx1RERERDqAoY6IiIhIBzDUEREREekAhjoiIiIiHcBQR0RERKQDGOqIiIiIdABDHREREZEOYKgjIiIi0gEMdUREREQ6wEDTDaCSCSEAAFlZWRpuCREREZVV8fd28fd4VWGoq8YePHgAAHB0dNRwS4iIiKi8Hjx4AEtLyyp7P5mo6hhJZVZYWIjU1FTUqlULMplM082pdFlZWXB0dMS1a9dgYWGh6eZQCXieqj+eI+3A86QdXuQ8CSHw4MEDODg4QE+v6q50Y09dNaanp4cGDRpouhlVzsLCgr/gtADPU/XHc6QdeJ60Q3nPU1X20BXjRAkiIiIiHcBQR0RERKQDGOqo2jAyMkJoaCiMjIw03RQqBc9T9cdzpB14nrSDNp0nTpQgIiIi0gHsqSMiIiLSAQx1RERERDqAoY6IiIhIBzDUEREREekAhjqqEDk5OWjXrh1kMhlOnTqltO3MmTPo2LEjjI2N4ejoiPnz56u8fuvWrWjRogWMjY3Rpk0b7NmzR2m7EALTp0+Hvb09TExM4O3tjYsXLyrVSU9Px6BBg2BhYQErKysEBATg4cOH5W6LLvLz84OTkxOMjY1hb2+PwYMHIzU1ValOZGQk2rVrB1NTUzRs2BALFixQ2c/hw4fxyiuvwMjICE2bNkVERIRKneXLl6NRo0YwNjaGh4cH/v77b6XtT548wZgxY1C3bl2Ym5ujb9++uHXrllKdq1evokePHjA1NUW9evUwadIk5Ofnv/wHUc2V5TxFR0fD09MTtWrVgo2NDfr27YuUlBSlOjxPlet552nGjBmQyWQqDzMzM6X98Pde5SrLz5MQAgsXLoSLiwuMjIxQv359zJ49W6mOVv08CaIK8Mknn4hu3boJACI+Pl4qz8zMFLa2tmLQoEHi7NmzYtOmTcLExER8++23Up0jR44IfX19MX/+fHH+/Hkxbdo0YWhoKP7991+pTnh4uLC0tBQ7duwQp0+fFn5+fsLZ2Vk8fvxYquPr6yvatm0rjh07Jv7880/RtGlTMXDgwHK1RVd99dVXIjY2VqSkpIgjR44IhUIhFAqFtH3Pnj3CwMBArFy5Uly6dEns2rVL2Nvbi6VLl0p1Ll++LExNTUVQUJA4f/68WLp0qdDX1xd79+6V6mzevFnI5XKxdu1ace7cOTFixAhhZWUlbt26JdUJDAwUjo6OIiYmRpw4cUJ4enoKLy8vaXt+fr5o3bq18Pb2FvHx8WLPnj3C2tpahISEVPKnpHnPO0+XL18WRkZGIiQkRPz333/i5MmT4o033hBubm5KdXieKtfzztODBw/EzZs3lR4tW7YUQ4cOlerw917le955EkKIcePGiebNm4udO3eKy5cvixMnToh9+/ZJ27Xt54mhjl7anj17RIsWLcS5c+dUQt2KFStE7dq1RU5OjlQ2ZcoU0bx5c+n5+++/L3r06KG0Tw8PDzFy5EghhBCFhYXCzs5OLFiwQNqekZEhjIyMxKZNm4QQQpw/f14AEMePH5fq/Pbbb0Imk4kbN26UuS01xc6dO4VMJhO5ublCCCEGDhwo+vXrp1RnyZIlokGDBqKwsFAIIcTkyZNFq1atlOr0799f+Pj4SM87dOggxowZIz0vKCgQDg4OYu7cuUKIovNmaGgotm7dKtVJSEgQAERsbKwQoujfk56enkhLS5PqrFy5UlhYWCidu5rg2fO0detWYWBgIAoKCqQ6UVFRSnV4nqres+fpWadOnRIAxB9//CGV8fde1Xv2PJ0/f14YGBiIxMTEEl+jbT9PHH6ll3Lr1i2MGDECP/74I0xNTVW2x8bG4o033oBcLpfKfHx8kJSUhPv370t1vL29lV7n4+OD2NhYAEBycjLS0tKU6lhaWsLDw0OqExsbCysrK7Rv316q4+3tDT09PcTFxZW5LTVBeno6NmzYAC8vLxgaGgIoGj43NjZWqmdiYoLr16/jypUrAJ5/nnJzc3Hy5EmlOnp6evD29pbqnDx5Enl5eUp1WrRoAScnJ6Vz2aZNG9ja2iq9T1ZWFs6dO1dRH0O1p+48ubu7Q09PD+vWrUNBQQEyMzPx448/wtvbW6rD81S11J2nZ61evRouLi7o2LGjVMbfe1VL3Xn69ddf0bhxY+zatQvOzs5o1KgRhg8fjvT0dOl12vbzxFBHL0wIAX9/fwQGBir9UnlaWlqa0j9SANLztLS0Uus8vf3p15VUp169ekrbDQwMUKdOnee+z9PvocumTJkCMzMz1K1bF1evXsXOnTulbT4+Pvjll18QExODwsJCXLhwAYsWLQIA3Lx5E0DJn19WVhYeP36Mu3fvoqCg4LnnSS6Xw8rKqtQ6PE/qz5OzszP27duHzz//HEZGRrCyssL169cRGRkp1eF5qhqlnaenPXnyBBs2bEBAQIBSOX/vVY3SztPly5dx5coVbN26FevXr0dERAROnjyJfv36SXW07eeJoY5UBAcHq73I9+lHYmIili5digcPHiAkJETTTa6Rynqeik2aNAnx8fHYt28f9PX1MWTIEIj/u6HMiBEjMHbsWPTs2RNyuRyenp4YMGAAgKL/ddKLq8jzlJaWhhEjRmDo0KE4fvw4fv/9d8jlcvTr10+qQy+mIs/T07Zv344HDx5g6NChVXk4Oqsiz1NhYSFycnKwfv16dOzYEZ07d8aaNWtw6NAhJCUlaeoQX4qBphtA1c+ECRPg7+9fap3GjRvj4MGDiI2NVbkfXvv27TFo0CD88MMPsLOzU5nhU/zczs5O+lNdnae3F5fZ29sr1WnXrp1U5/bt20r7yM/PR3p6+nPf5+n30CZlPU/FrK2tYW1tDRcXF7i6usLR0RHHjh2DQqGATCbDvHnzMGfOHKSlpcHGxgYxMTFK+yjp87OwsICJiQn09fWhr6//3HOZm5uLjIwMpf+1Plvn2ZljPE9F52n58uWwtLRUmr34008/wdHREXFxcfD09OR5ekEVeZ6etnr1avTs2VOlF4a/915MRZ4ne3t7GBgYwMXFRarv6uoKoGgmavPmzbXv56nMV98RPePKlSvi33//lR7R0dECgNi2bZu4du2aEOJ/F+k+fQFxSEiIykSJnj17Ku1boVCoXDC8cOFCaXtmZqbaC4ZPnDgh1YmOjlZ7wXBpbakprly5IgCIQ4cOlVhn8ODBSjPFJk+eLFq3bq1UZ+DAgSoXDI8dO1Z6XlBQIOrXr69ywfC2bdukOomJiWovGH565ti3334rLCwsxJMnT17sgLXUs+cpKChIdOjQQalOamqqACCOHDkihOB50oSSfp4uX74sZDKZ+PXXX1Vew997Ve/Z81T8nfXff/9JdYontSQlJQkhtO/niaGOKkxycrLK7NeMjAxha2srBg8eLM6ePSs2b94sTE1NVZY0MTAwEAsXLhQJCQkiNDRU7dR+KysrsXPnTnHmzBnRu3dvtVP73dzcRFxcnPjrr79Es2bNlKb2l6UtuujYsWNi6dKlIj4+XqSkpIiYmBjh5eUlmjRpIv2yuHPnjli5cqVISEgQ8fHx4pNPPhHGxsYiLi5O2k/x1P5JkyaJhIQEsXz5crVT+42MjERERIQ4f/68+Pjjj4WVlZXSjK7AwEDh5OQkDh48KE6cOKGyzEDx1P6uXbuKU6dOib179wobGxudXyqjLOcpJiZGyGQyERYWJi5cuCBOnjwpfHx8RMOGDcWjR4+EEDxPla0s56nYtGnThIODg8jPz1fZD3/vVa6ynKeCggLxyiuviDfeeEP8888/4sSJE8LDw0O8/fbb0n607eeJoY4qjLpQJ4QQp0+fFq+//rowMjIS9evXF+Hh4SqvjYyMFC4uLkIul4tWrVqJ3bt3K20vLCwUX3zxhbC1tRVGRkbirbfekv4nVezevXti4MCBwtzcXFhYWIiPPvpIPHjwoNxt0TVnzpwRXbp0EXXq1BFGRkaiUaNGIjAwUFy/fl2qc+fOHeHp6SnMzMyEqampeOutt8SxY8dU9nXo0CHRrl07IZfLRePGjcW6detU6ixdulQ4OTkJuVwuOnTooLKfx48fi9GjR4vatWsLU1NT8c4774ibN28q1UlJSRHdunUTJiYmwtraWkyYMEHk5eVVzAdSTZXlPAkhxKZNm4Sbm5swMzMTNjY2ws/PTyQkJCjV4XmqPGU9TwUFBaJBgwbi888/L3Ff/L1Xecp6nm7cuCHeffddYW5uLmxtbYW/v7+4d++eUh1t+nmSCcGra4mIiIi0Hae1EREREekAhjoiIiIiHcBQR0RERKQDGOqIiIiIdABDHREREZEOYKgjIiIi0gEMdUREREQ6gKGOiIhUHD58GDKZDBkZGS+1n0aNGuHrr7+ukDYRUekY6oioQqWlpWHcuHFo3LgxjIyM4OjoiF69eiEmJkaqc/ToUXTv3h21a9eGsbEx2rRpg6+++goFBQVSnZSUFAQEBMDZ2RkmJiZo0qQJQkNDkZubq/R+33//Pdq2bQtzc3NYWVnBzc0Nc+fOlbbPmDEDMpkMvr6+Km1dsGABZDIZOnfuXObjy8rKwtSpU9GiRQsYGxvDzs4O3t7e+OWXX/D0Wu7nzp3D+++/DxsbGxgZGcHFxQXTp0/Ho0ePpDrp6ekYN24cmjdvDhMTEzg5OeGTTz5BZmZmmdqSkpICmUym9nHs2LEyH1Pnzp0xfvz4MtcnourJQNMNICLdkZKSgtdeew1WVlZYsGAB2rRpg7y8PERHR2PMmDFITEzE9u3b8f777+Ojjz7CoUOHYGVlhQMHDmDy5MmIjY1FZGQkZDIZEhMTUVhYiG+//RZNmzbF2bNnMWLECGRnZ2PhwoUAgLVr12L8+PFYsmQJOnXqhJycHJw5cwZnz55Vape9vT0OHTqE69evo0GDBlL52rVr4eTkVObjy8jIwOuvv47MzEx8+eWXePXVV2FgYIDff/8dkydPxptvvgkrKyscO3YM3t7e8Pb2xu7du2Fra4u///4bEyZMQExMDA4dOgS5XI7U1FSkpqZi4cKFaNmyJa5cuYLAwECkpqZi27ZtZW7XgQMH0KpVK6WyunXrlvn1RKQjynVTMSKiUnTr1k3Ur19fPHz4UGXb/fv3xcOHD0XdunXFu+++q7I9KipKABCbN28ucf/z588Xzs7O0vPevXsLf3//UtsUGhoq2rZtK3r27Cm+/PJLqfzIkSPC2tpajBo1SnTq1KkMRyfEqFGjhJmZmbhx44bKtgcPHoi8vDxRWFgoWrZsKdq3by8KCgqU6pw6dUrIZLJS770ZGRkp5HJ5me75WNL9lp9WfPzr168XDRs2FBYWFqJ///4iKytLCCHE0KFDBQClR3Jysjh06JAAIA4cOCDc3d2FiYmJUCgUIjExUdr3f//9J/z8/ES9evWEmZmZaN++vdi/f7/S+zds2FAsXrxYeg5ArFixQvj6+gpjY2Ph7Owstm7dKm3v0qWLGDNmjNI+bt++LQwNDcWBAwee+5kQ1WQcfiWiCpGeno69e/dizJgxMDMzU9luZWWFffv24d69e5g4caLK9l69esHFxQWbNm0q8T0yMzNRp04d6bmdnR2OHTuGK1euPLd9w4YNQ0REhPR87dq1GDRoEORy+XNfCwCFhYXYvHkzBg0aBAcHB5Xt5ubmMDAwwKlTp3D+/HkEBQVBT0/5V2zbtm3h7e393GO0sLCAgUHFDaRcunQJO3bswK5du7Br1y78/vvvCA8PBwB88803UCgUGDFiBG7evImbN2/C0dFReu3UqVOxaNEinDhxAgYGBhg2bJi07eHDh+jevTtiYmIQHx8PX19f9OrVC1evXi21PV988QX69u2L06dPY9CgQRgwYAASEhIAAMOHD8fGjRuRk5Mj1f/pp59Qv359vPnmmxX2mRDpIoY6IqoQ//33H4QQaNGiRYl1Lly4AABwdXVVu71FixZSHXX7X7p0KUaOHCmVhYaGwsrKCo0aNULz5s3h7++PyMhIFBYWqry+Z8+eyMrKwh9//IHs7GxERkYqBZTnuXv3Lu7fv1/q8QHPP0ZXV9cSj/Hu3buYNWsWPv744zK3CwC8vLxgbm6u9HhaYWEhIiIi0Lp1a3Ts2BGDBw+WrnG0tLSEXC6Hqakp7OzsYGdnB319fem1s2fPRqdOndCyZUsEBwfj6NGjePLkCYCikDpy5Ei0bt0azZo1w6xZs9CkSRNERUWV2t733nsPw4cPh4uLC2bNmoX27dtj6dKlAIB3330XALBz506pfkREBPz9/SGTycr1uRDVNAx1RFQhxFOTBCqyLgDcuHEDvr6+eO+99zBixAip3N7eHrGxsfj333/x6aefIj8/H0OHDoWvr69KsDM0NMSHH36IdevWYevWrXBxccH/+3//r9LaXN76WVlZ6NGjB1q2bIkZM2aU67VbtmzBqVOnlB5Pa9SoEWrVqiU9t7e3x+3bt8u076c/I3t7ewCQXvvw4UNMnDgRrq6usLKygrm5ORISEp7bU6dQKFSeF/fUGRsbY/DgwVi7di0A4J9//sHZs2fh7+9fpvYS1WScKEFEFaJZs2bSBIeSuLi4AAASEhLg5eWlsj0hIQEtW7ZUKktNTUWXLl3g5eWF7777Tu1+W7dujdatW2P06NEIDAxEx44d8fvvv6NLly5K9YYNGwYPDw+cPXu2XL10AGBjYwMrK6tSjw9QPkY3NzeV7QkJCVKdYg8ePICvry9q1aqF7du3w9DQsFxtc3R0RNOmTUvc/uz+ZDKZ2t7M5722uKes+LUTJ07E/v37sXDhQjRt2hQmJibo16+fygzl8ho+fDjatWuH69evY926dXjzzTfRsGHDl9onUU3AnjoiqhB16tSBj48Pli9fjuzsbJXtGRkZ6Nq1K+rUqYNFixapbI+KisLFixcxcOBAqezGjRvo3Lkz3N3dsW7dOpVr1NQpDoXq2tCqVSu0atUKZ8+exQcffFCew4Oenh4GDBiADRs2IDU1VWX7w4cPkZ+fj3bt2qFFixZYvHixSnA6ffo0Dhw4oHSMWVlZ6Nq1K+RyOaKiomBsbFyudlUEuVyutJxMWR05cgT+/v5455130KZNG9jZ2SElJeW5r3t2uZVjx44pDVe3adMG7du3x/fff4+NGzeWO4AT1VQMdURUYZYvX46CggJ06NABP//8My5evIiEhAQsWbIECoUCZmZm+Pbbb7Fz5058/PHHOHPmDFJSUrBmzRr4+/ujX79+eP/99wH8L9A5OTlh4cKFuHPnDtLS0pCWlia936hRozBr1iwcOXIEV65cwbFjxzBkyBDY2NioDPEVO3jwIG7evAkrK6tyH9/s2bPh6OgIDw8PrF+/HufPn8fFixexdu1auLm54eHDh5DJZFizZg3Onz+Pvn374u+//8bVq1exdetW9OrVCwqFQloTrjjQZWdnY82aNcjKypKOsTwh6969e9Lrih/F172VRaNGjRAXF4eUlBTcvXu3zL14zZo1wy+//IJTp07h9OnT+OCDD8r02q1bt2Lt2rW4cOECQkND8ffff2Ps2LFKdYYPH47w8HAIIfDOO++U+ViIajRNTr0lIt2TmpoqxowZIxo2bCjkcrmoX7++8PPzE4cOHZLq/PHHH8LHx0dYWFgIuVwuWrVqJRYuXCjy8/OlOuvWrVNZaqP4UWzbtm2ie/fuwt7eXsjlcuHg4CD69u0rzpw5I9UpXtKjJJ9++mmZlzQRQoiMjAwRHBwsmjVrJuRyubC1tRXe3t5i+/btorCwUKp35swZ0bdvX1GnTh1haGgomjRpIqZNmyays7OlOsXLhqh7JCcnP7ctxUuaqHts2rSpxONfvHixaNiwofQ8KSlJeHp6ChMTE5UlTe7fvy/Vi4+PV2pbcnKy6NKlizAxMRGOjo5i2bJlolOnTuLTTz+VXqNuSZPly5eLt99+WxgZGYlGjRqJLVu2qBzbgwcPhKmpqRg9evRzPwciKiITopxX8xIREb0gmUyG7du3o0+fPqXWS0lJQZMmTXD8+HG88sorVdM4Ii3HiRJERFRt5OXl4d69e5g2bRo8PT0Z6IjKgdfUERH9n2fXenv68eeff1Z5ewIDA0tsT2BgYJW3pyocOXIE9vb2OH78OFatWqXp5hBpFQ6/EhH9n//++6/EbfXr14eJiUkVtqZoPbisrCy12ywsLFCvXr0qbQ8RVW8MdUREREQ6gMOvRERERDqAoY6IiIhIBzDUEREREekAhjoiIiIiHcBQR0RERKQDGOqIiIiIdABDHREREZEOYKgjIiIi0gH/H1vGzWntUJnfAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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GnZ1dmXJnZ2fs3r0b//zzDz744IMy5d26dUNoaCjWrl2r8j0yMzPh6uqqeO7l5YVjx47h1q1blcY3ePBgrFq1SvF85cqV6N+/PywtLSt9LQAUFRVh3bp16N+/P3x8fMqU29vbo1atWjh79iwuXryICRMmwMxM+fIZFRWFmJiYSvfR0dFR0hqRGzduYOvWrdi+fTu2b9+OAwcOID4+HgDw+eefIzo6GsOGDUNqaipSU1Ph5+eneG1cXBzi4+Nx6dIlNGrUCJMmTcKPP/6I7777DqdPn0ZISAg6duyIhw8fKr3nxIkT8e9//xsnT56Eu7s7unXrhvz8fNjZ2aFv375ISEhQWj8hIQE9e/aEg4ODZPutCpMaDQW52cGs1Ag0c5kMgW6ce4KIasb6k7fRKn4f3lpxHK3i92H9ydvV+n7Xr1+HEAJhYWEq17l69SoAoEGDBuWWh4WFKdYpb/tffvkl3n33XcWy6dOnw9nZGYGBgahfvz4GDhyIDRs2oKioqMzru3btCrlcjoMHDyI7OxsbNmzA4MGD1d6/9PR0PHr0qML9AyrfxwYNGqjcx/T0dMyaNQvDhw9XOy4AaNmyJezt7ZUeJRUVFWHVqlWIiIjAiy++iLffflvRx8nJyQmWlpawtbWFl5cXvLy8lCa4++STT/DKK68gODgYVlZWWLp0KebPn4/OnTsjPDwcK1asgI2NDb799lul95w+fTpeeeUVREZG4rvvvsO9e/ewZcsWAMDQoUOxa9cupKamAng2sd6OHTs0+jyqgkmNhrydbDC3RyTM/39svblMhk97RMDbyUbHkRGRKUjNzFX06QOAIgF8uDmxWmtshBCVr6TFugBw9+5ddOrUCb169cKwYcMUy729vXH06FFcuHAB48aNQ0FBAWJjY9GpU6cyiY2FhQUGDBiAhIQEbNy4EaGhoWjUqFG1xazp+nK5HF26dEF4eDhmzJih0WvXr1+Ps2fPKj1KCgwMVKoB8fb2Vtx6oDJNmzZV/H3jxg3k5+ejVatWimUWFhZo3rw5Ll26pPS66Ohoxd+urq6oX7++Yp3mzZujYcOG+O677wAAq1evRkBAANq0aaPeDleREfQKqnl9mvmjTag7bqbnINDNlgkNEdWYiprAq+u3qF69eooOvqqEhoYCAC5duoSWLVuWKb906RLCw8OVlqWkpKB9+/Zo2bIlvv7663K3GxERgYiICIwaNQojRozAiy++iAMHDqB9+/ZK6w0ePBgtWrRAYmKixrUC7u7ucHZ2rnD/AOV9bNy4cZnyS5cuKdYp9vjxY3Tq1AkODg7YsmULLCwsNIrNz88PISEhKstLb08mk5Vbm1We8poSpTB06FAsWbIEcXFxSEhIwKBBg2ps0knW1GjJ28kG0cG1mdAQUY3SRRO4q6srOnbsiCVLliA7O7tMeUZGBjp06ABXV1f8+9//LlO+bds2XLt2Df369VMsu3v3Ltq1a4fnn38eCQkJZfqolKc4KSovhoYNG6Jhw4ZITEzEW2+9pcnuwczMDH379sUPP/yAlJSUMuVZWVkoKCjAc889h7CwMCxatKhM4nDu3Dn89ttvSvsol8vRoUMHWFpaYtu2bbC2ttYoLilYWloqDadXJTg4GJaWljh8+LBiWX5+Pk6ePFkmGS05pPzRo0e4evWqUpPcgAEDcOvWLXzxxRe4ePEiYmNjJdgT9TCpISIyILpqAl+yZAkKCwvRvHlz/Pjjj7h27RouXbqEL774AtHR0bCzs8Py5cvx008/Yfjw4Th//jxu3ryJb7/9FgMHDkTPnj3Ru3dvAP9LaPz9/bFgwQI8ePAAaWlpSEtLU7zfyJEjMWvWLBw+fBi3bt3CsWPH8M4778Dd3V2p+aOkffv2ITU1Fc7Ozhrv35w5c+Dn54cWLVrgv//9Ly5evIhr165h5cqVaNy4MbKysiCTyfDtt9/i4sWLePPNN3HixAncvn0bGzduRLdu3RAdHa2YE6Y4ocnOzsa3334LuVyu2Ed1koxi//zzj+J1xY8nT56o/frAwEAcP34cN2/eRHp6uspaHDs7O4wcORITJ07Ezp07cfHiRQwbNgw5OTkYMmSI0rqffPIJ9u7di8TERAwcOBBubm7o3r27otzFxQU9evTAxIkT0aFDB6Wh9tVO7XFSBkrqId1ERNqQekh3SkaOOHI9vUank0hJSRGjR48WAQEBwtLSUvj6+orXXntN7N+/X7HOwYMHRceOHYWjo6OwtLQUDRs2FAsWLBAFBQWKdRISEsoMNS5+FNu0aZN49dVXhbe3t7C0tBQ+Pj7izTffFOfPn1esUzykWZVx48apPaRbCCEyMjJEXFycqFevnrC0tBSenp4iJiZGbNmyRRQVFSnWO3/+vHjzzTeFq6ursLCwEMHBweKjjz4S2dnZinWKhz+X90hKSqo0luIh3eU91q5dq3L/Fy1aJAICAhTPr1y5Il544QVhY2NTZkj3o0ePlF6bm5sr3nvvPeHm5lbhkO6ff/5ZNGzYUFhaWormzZuLc+fOlYl/7969AoDYsGFDhfsp9ZBumRAa9ngyMHK5HE5OToqhdEREuvDkyRMkJSUhKChIJ80QRFX1+++/o3379nj06FGltWHff/89/vWvfyElJaXCofUVfS+0uX6zozARERFJIicnB6mpqYiPj8e7776r9lxBUmGfGiIiMnql53op+Th06FCNxzNixAiV8YwYMaLG45HKZ599hrCwMHh5eWHKlCk1/v5sfiIiqgFsftKt69evqyzz9fWFjU3NjmS9f/8+5HJ5uWWOjo7w8PCo0Xh0hc1PREREGqporhdd8PDwMJnEpSax+YmIiIiMApMaIqIapO5sr0SmQOrvA5ufiIhqgKWlJczMzJCSkgJ3d3dYWlrW2NTxRPpGCIGnT5/iwYMHMDMzk2yUFJMaIqIaYGZmhqCgIKSmppY7FT+RKbK1tYW/v79at8lQB5MaIqIaYmlpCX9/fxQUFGg0VT6RMTI3N0etWrUkrbFkUkNEVINkMhksLCw0vlszEVWOHYWJiIjIKDCpISIiIqPApIaIiIiMApMaIiIiMgpMaoiIiMgoMKkhIiIio8CkhoiIiIwCkxoiIiIyCkxqiIiIyCgwqSEiIiKjoNOk5uDBg+jWrRt8fHwgk8mwdetWRVl+fj4mT56MyMhI2NnZwcfHB++88w5vBEdERETl0mlSk52djaioKCxZsqRMWU5ODk6fPo1p06bh9OnT2Lx5M65cuYLXXntNB5ESERGRvpMJIYSugwCe3eRty5Yt6N69u8p1Tp48iebNm+PWrVvw9/dXa7tyuRxOTk7IzMyEo6OjRNESERFRddLm+m1Qd+nOzMyETCaDs7OzynXy8vKQl5eneC6Xy2sgMiIiItI1g+ko/OTJE0yePBn9+vWrMGObO3cunJycFA8/P78ajJKIiIh0xSCSmvz8fPTu3RtCCCxdurTCdadMmYLMzEzFIzk5uYaiJCIiIl3S++an4oTm1q1b2LdvX6XtalZWVrCysqqh6IiIiEhf6HVSU5zQXLt2Dfv370ft2rV1HRIRERHpKZ0mNVlZWbh+/brieVJSEs6ePQtXV1d4e3ujZ8+eOH36NLZv347CwkKkpaUBAFxdXWFpaamrsImIiEgP6XRI9++//4727duXWR4bG4sZM2YgKCio3Nft378f7dq1U+s9OKSbiIjI8BjckO527dqhopxKT6bQISIiIgNgEKOfiIiIiCrDpIaIiIiMApMaIiIiMgpMaoiIiMgoMKkhIiIio8CkhoiIiIwCkxoiIiIyCkxqiIiIyCgwqSEiIiKjwKSGiIiIjAKTGiIiIjIKTGqIiIjIKDCpISIiIqPApIaIiIiMApMaIiIiMgpMaoiIiMgoMKkhIiIio8CkhoiIiIwCkxoiIiIyCkxqiIiIyCgwqSEiIiKjwKSGiIiIjAKTGiIiIjIKTGqIiIjIKDCpISIiIqPApIaIiIiMApMaIiIiMgpMaoiIiMgoMKkhIiIio8CkhoiIiIwCkxoiIiIyCkxqiIiIyCgwqSEiIiKjwKSGiIiIjAKTGiIiIjIKTGqIiIjIKDCpISIiIqPApIaIiIiMApMaIiIiMgo6TWoOHjyIbt26wcfHBzKZDFu3blUqF0Lg448/hre3N2xsbBATE4Nr167pJlgiIiLSazpNarKzsxEVFYUlS5aUW/7ZZ5/hiy++wLJly3D8+HHY2dmhY8eOePLkSQ1HSkRERPquli7fvHPnzujcuXO5ZUIILF68GB999BFef/11AMB///tfeHp6YuvWrejbt29NhkpERER6Tm/71CQlJSEtLQ0xMTGKZU5OTmjRogWOHj2qw8iIiIhIH+m0pqYiaWlpAABPT0+l5Z6enoqy8uTl5SEvL0/xXC6XV0+AREREpFf0tqZGW3PnzoWTk5Pi4efnp+uQiIiIqAbobVLj5eUFALh3757S8nv37inKyjNlyhRkZmYqHsnJydUaJxEREekHvU1qgoKC4OXlhb179yqWyeVyHD9+HNHR0SpfZ2VlBUdHR6UHERERGT+d9qnJysrC9evXFc+TkpJw9uxZuLq6wt/fH+PHj8fs2bNRr149BAUFYdq0afDx8UH37t11FzQRERHpJZ0mNadOnUL79u0VzydMmAAAiI2NxapVqzBp0iRkZ2dj+PDhyMjIQOvWrbFz505YW1vrKmQiIiLSUzIhhNB1ENVJLpfDyckJmZmZbIoiIiIyENpcv/W2Tw0RERGRJpjUEBERkVFgUkNERERGgUkNERERGQUmNURERGQUmNQQERGRUWBSQ0REREZBq6QmOztb6jiIiIiIqkSrpMbT0xODBw/GH3/8IXU8RERERFrRKqlZvXo1Hj58iJdeegmhoaGIj49HSkqK1LERERERqU2rpKZ79+7YunUr7t69ixEjRmDNmjUICAhA165dsXnzZhQUFEgdJxEREVGFJLv305dffomJEyfi6dOncHNzw4gRIxAXFwdbW1spNq813vuJiIjI8Ghz/a7SXbrv3buH7777DqtWrcKtW7fQs2dPDBkyBHfu3MG8efNw7Ngx7N69uypvQURERKQWrZKazZs3IyEhAbt27UJ4eDhGjRqFAQMGwNnZWbFOy5Yt0aBBA6niJCIiIqqQVknNoEGD0LdvXxw+fBjNmjUrdx0fHx9MnTq1SsERERERqUurPjU5OTk67yujLvapISIiMjw11qfG1tYWhYWF2LJlCy5dugQAaNCgAbp3745atarUTYeIiIhIK1plIH/99Re6deuGe/fuoX79+gCAefPmwd3dHT///DMiIiIkDZKIiIioMlrNUzN06FBERETgzp07OH36NE6fPo3k5GQ0atQIw4cPlzpGIiIiokppVVNz9uxZnDp1Ci4uLoplLi4umDNnjsqOw0RERETVSauamtDQUNy7d6/M8vv37yMkJKTKQRERERFpSqukZu7cuRg7diw2bdqEO3fu4M6dO9i0aRPGjx+PefPmQS6XKx5ERERENUGrId1mZv/LhWQyGQCgeDMln8tkMhQWFkoRp9Y4pJuIiMjw1NiQ7v3792vzMiIiIqJqo1VS07ZtW6njICIiIqoSrWfKy8jIwLfffquYfK9hw4YYPHgwnJycJAuOiIiISF1adRQ+deoUgoODsWjRIjx8+BAPHz7EwoULERwcjNOnT0sdIxEREVGltOoo/OKLLyIkJAQrVqxQ3BahoKAAQ4cOxd9//42DBw9KHqi22FGYiIjI8Ghz/dYqqbGxscGZM2cQFhamtPzixYto2rQpcnJyNN1ktWFSQ0REZHi0uX5r1fzk6OiI27dvl1menJwMBwcHbTZJREREVCVaJTV9+vTBkCFDsH79eiQnJyM5ORnr1q3D0KFD0a9fP6ljJCIiIqqUVqOfFixYAJlMhnfeeQcFBQUAAAsLC4wcORLx8fGSBkhERESkDo371BQWFuLw4cOIjIyElZUVbty4AQAIDg6Gra1ttQRZFexTQ0REZHhqZEZhc3NzdOjQAZcuXUJQUBAiIyM1DpSIiIhIalr1qYmIiMDff/8tdSxEREREWtMqqZk9ezY++OADbN++HampqUp35eaduYmIiEgXJLtLN6A/d+YuiX1qiIiIDA/v0k1EREQmS6ukJigoCH5+fkq1NMCzmprk5GRJAiMiIiLShFZ9aoKCgvDgwYMyyx8+fIigoKAqB0VERESkKa2SmuK+M6VlZWXB2tq6ykEVKywsxLRp0xAUFAQbGxsEBwdj1qxZ0KIbEBERERk5jZqfJkyYAOBZ5+Bp06YpTbZXWFiI48eP47nnnpMsuHnz5mHp0qX47rvv0LBhQ5w6dQqDBg2Ck5MTxo4dK9n7EBERkeHTKKk5c+YMgGc1NRcuXIClpaWizNLSElFRUfjggw8kC+7IkSN4/fXX0aVLFwBAYGAg1q5dixMnTkj2HkRERGQcNEpqikc9DRo0CJ9//nm1D5Fu2bIlvv76a1y9ehWhoaE4d+4c/vjjDyxcuFDla/Ly8pCXl6d4znlziIiITINWo58SEhKkjqNccXFxkMvlCAsLg7m5OQoLCzFnzhz0799f5Wvmzp2LmTNn1kh8REREpD+0Smqys7MRHx+PvXv34v79+ygqKlIql+oWChs2bMAPP/yANWvWoGHDhjh79izGjx8PHx8fxMbGlvuaKVOmKPr+AM9qavz8/CSJh4iIiPSXVknN0KFDceDAAbz99tvw9vYudySUFCZOnIi4uDj07dsXABAZGYlbt25h7ty5KpMaKysrWFlZVUs8REREpL+0Smp+/fVX/PLLL2jVqpXU8SjJyclRuiUD8Owu4aVrhoiIiIi0SmpcXFzg6uoqdSxldOvWDXPmzIG/vz8aNmyIM2fOYOHChRg8eHC1vzcREREZFq1uaLl69Wr89NNP+O6775TmqpHa48ePMW3aNGzZsgX379+Hj48P+vXrh48//lhpOHlFeENLIiIiw6PN9VurpKZx48a4ceMGhBAIDAyEhYWFUvnp06c13WS1YVJDRERkeGrsLt3du3fX5mVERERE1UarmhpDwpoaIiIiw6PN9VujG1qeOHEChYWFKsvz8vKwYcMGTTZJREREJAmNkpro6Gj8888/iueOjo5KE+1lZGSgX79+0kVHREREpCaNkprSLVXltVwZeWsWERER6SmNkhp1VNfswkREREQVkTypISIiItIFjYd0X7x4EWlpaQCeNTVdvnwZWVlZAID09HRpoyMiIiJSk0ZDus3MzCCTycrtN1O8XCaTVThCqqZxSDcREZHhqfbJ95KSkrQKjIiIiKi6aZTUBAQEaLTxUaNG4ZNPPoGbm5tGryMiIiLSVLV2FF69ejXkcnl1vgURERERgGpOajhnDREREdUUDukmIiIio8CkhoiIiIwCkxoiIiIyCkxqiIiIyChUa1IzYMAATnhHRERENUKrpKaoqEjl8tu3byueL126lHPUEBERUY3QKKmRy+Xo3bs37Ozs4OnpiY8//ljplggPHjxAUFCQ5EESERERVUajGYWnTZuGc+fO4fvvv0dGRgZmz56N06dPY/PmzbC0tATAuWmIiIhINzSqqdm6dSuWL1+Onj17YujQoTh16hQePHiAbt26IS8vD8CzG1sSERER1TSNkpoHDx4o3f/Jzc0Nv/32Gx4/foxXX30VOTk5kgdIREREpA6Nkhp/f39cunRJaZmDgwN2796N3NxcvPHGG5IGR0RERKQujZKaDh06ICEhocxye3t77Nq1C9bW1pIFRkRERKQJjToKz5w5EykpKeWWOTg4YM+ePTh9+rQkgRERERFpQqOkxsXFBS4uLirLHRwc0LZt2yoHRURERKQpjSffKygowPz589GkSRPY29vD3t4eTZo0wYIFC5Cfn18dMRIRERFVSqOamtzcXLzyyis4evQoYmJi0KZNGwDApUuXMHnyZGzbtg27d+9m3xoiIiKqcRolNfHx8UhOTsaZM2fQqFEjpbJz587htddeQ3x8PGbMmCFljERERESV0qj5ad26dVi4cGGZhAYAoqKisGDBAqxZs0ay4IiIiIjUpVFSc+vWLTRv3lxl+QsvvKB0Q0siIiKimqJRUuPo6Ij79++rLE9LS4ODg0OVgyIiIiLSlEZJTfv27fHpp5+qLI+Pj0f79u2rHBQRERGRpjTqKDx9+nS0aNECL7zwAiZMmICwsDAIIXDp0iUsWrQIFy9exLFjx6orViIiIiKVNEpqwsPDsWfPHgwZMgR9+/ZV3JFbCIGwsDDs3r0bDRs2rJZAiYiIiCqiUVIDPOsM/Ndff+Hs2bO4evUqACA0NBTPPfec1LERERERqU3jpEYul8Pe3h7PPfecUiJTVFSErKwsODo6ShkfERERkVo06ii8ZcsWNG3aFE+ePClTlpubi2bNmuHnn3+WLDgiIiIidWmU1CxduhSTJk2Cra1tmTI7OztMnjwZ//nPfyQLjoiIiEhdGiU1iYmJaNeuncryNm3a4MKFC1WNScndu3cxYMAA1K5dGzY2NoiMjMSpU6ckfQ8iIiIyfBr1qXn06BEKCgpUlufn5+PRo0dVDqrk+7Vq1Qrt27fHr7/+Cnd3d1y7dg0uLi6SvQcREREZB42SmsDAQJw6dQphYWHllp86dQoBAQGSBAYA8+bNg5+fHxISEhTLgoKCJNs+ERERGQ+Nmp969OiBqVOn4t69e2XK0tLS8NFHH+HNN9+ULLht27ahadOm6NWrFzw8PNC4cWOsWLGiwtfk5eVBLpcrPYiIiMj4yYQQQt2VHz9+jOjoaNy+fRsDBgxA/fr1AQCXL1/GDz/8AD8/Pxw7dkyy+z9ZW1sDACZMmIBevXrh5MmTGDduHJYtW4bY2NhyXzNjxgzMnDmzzPLMzEwONyciIjIQcrkcTk5OGl2/NUpqgGfJwZQpU7B+/XpF/xlnZ2f07dsXc+bMkbS/i6WlJZo2bYojR44olo0dOxYnT57E0aNHy31NXl4e8vLyFM/lcjn8/PyY1BARERkQbZIajSffc3JywldffYUlS5YgPT0dQgi4u7srbplQ0uHDh9G0aVNYWVlp+jYAAG9vb4SHhysta9CgAX788UeVr7GystL6/YiIiMhwadSnpiSZTAZ3d3d4eHiUm9AAQOfOnXH37l2tg2vVqhWuXLmitOzq1auSdkYmIiIi46B1UqMODVu2yvjXv/6FY8eO4dNPP8X169exZs0afP311xg9erREERIREZGxqNakpqqaNWuGLVu2YO3atYiIiMCsWbOwePFi9O/fX9ehERERkZ7RuE9NTevatSu6du2q6zCIiIhIz+l1TQ0RERGRuqo1qVHVgZiIiIhIanrdUZiIiIhIXdXap+bx48fVuXkiIiIiBY2Smpdeekmt9fbt26dVMERERETa0iip+f333xEQEIAuXbrAwsKiumIiIiIi0phGSc28efOQkJCAjRs3on///hg8eDAiIiKqKzYiIiIitWnUUXjixIm4ePEitm7disePH6NVq1Zo3rw5li1bBrlcXl0xEhEREVVK47t0l5STk4ONGzdiyZIluHjxIlJSUvTuTtja3OWTiIiIdEub63eVhnSfPn0aBw4cwKVLlxAREcF+NkRERKQzGic1KSkp+PTTTxEaGoqePXvC1dUVx48fx7Fjx2BjY1MdMRIRERFVSqOOwq+++ir279+PDh06YP78+ejSpQtq1dL720cRERGRCdCoT42ZmRm8vb3h4eFR4S0QTp8+LUlwUmCfGiIiIsOjzfVbo2qW6dOnaxUYERERUXWr0ugnQ8CaGiIiIsNT7TU1qhw4cADZ2dmIjo6Gi4uLFJskIiIi0ojGMwpnZWVh1qxZAJ7dhbtz587YvXs3AMDDwwN79+5Fw4YNpY+UiIiIqAIaDelev3690m0RNm3ahIMHD+LQoUNIT09H06ZNMXPmTMmDJCIiIqqMRklNUlISGjVqpHi+Y8cO9OzZE61atYKrqys++ugjHD16VPIgiYiIiCqjUVJTUFAAKysrxfOjR4+iZcuWiuc+Pj5IT0+XLjoiIiIiNWmU1AQHB+PgwYMAgNu3b+Pq1ato06aNovzOnTuoXbu2tBESERERqUGjjsKjR4/GmDFjcOjQIRw7dgzR0dEIDw9XlO/btw+NGzeWPEgiIiKiymiU1AwbNgzm5ub4+eef0aZNmzKT8aWkpGDw4MGSBkhERESkDk6+R0RERHpHm+u3xnfpJiIiItJHGiU1+fn5mDRpEkJCQtC8eXOsXLlSqfzevXswNzeXNEAiIiIidWiU1MyZMwf//e9/MWLECHTo0AETJkzAu+++q7SOkbdmERERkZ7SqKPwDz/8gG+++QZdu3YFAAwcOBCdO3fGoEGDFLU2MplM+iiJiIiIKqFRTc3du3eVbpMQEhKC33//HUeOHMHbb7+NwsJCyQMkIiIiUodGSY2Xlxdu3LihtMzX1xf79+/HyZMnMXDgQCljIyIiIlKbRknNSy+9hDVr1pRZ7uPjg3379iEpKUmywIiIiIg0oVGfmmnTpuHy5cvllvn6+uLAgQPYs2ePJIERERERaYKT7xEREZHeqbHJ9zZu3IgePXogIiICERER6NGjBzZt2qTNpoiIiIgkoVFSU1RUhD59+qBPnz64ePEiQkJCEBISgr/++gt9+vRB3759OU8NERER6YRGfWo+//xz/Pbbb9i2bZtirppi27Ztw6BBg/D5559j/PjxUsZIREREVCmNamoSEhIwf/78MgkNALz22mv47LPPytw6gYiIiKgmaJTUXLt2DTExMSrLY2JicO3atSoHRURERKQpjZIaGxsbZGRkqCyXy+WwtrauakxEREREGtMoqYmOjsbSpUtVli9ZsgTR0dFVDoqIiIhIUxolNVOnTsW3336L3r1748SJE5DL5cjMzMSxY8fQq1cvrFy5ElOnTq2uWBEfHw+ZTMaOyERERFSGRqOfWrZsifXr12P48OH48ccflcpcXFywdu1atGrVStIAi508eRLLly9Ho0aNqmX7REREZNg0SmoA4I033kDHjh2xa9cuRafg0NBQdOjQAba2tpIHCABZWVno378/VqxYgdmzZ1fLexAREZFh06j5ad++fQgPD0dBQQHeeOMNTJo0CZMmTUL37t2Rn5+Phg0b4tChQ5IHOXr0aHTp0qXCkVfF8vLyIJfLlR5ERERk/DRKahYvXoxhw4aVew8GJycnvPvuu1i4cKFkwQHAunXrcPr0acydO1et9efOnQsnJyfFw8/PT9J4iIiISD9plNScO3cOnTp1UlneoUMH/Pnnn1UOqlhycjLGjRuHH374Qe2h4lOmTEFmZqbikZycLFk8REREpL806lNz7949WFhYqN5YrVp48OBBlYMq9ueff+L+/fto0qSJYllhYSEOHjyI//znP8jLy4O5ubnSa6ysrGBlZSVZDERERGQYNEpqfH19kZiYiJCQkHLLz58/D29vb0kCA4CXX34ZFy5cUFo2aNAghIWFYfLkyWUSGiIiIjJdGiU1r776KqZNm4ZOnTqVaQ7Kzc3F9OnTy70vlLYcHBwQERGhtMzOzg61a9cus5yIiIhMm0ZJzUcffYTNmzcjNDQUY8aMQf369QEAly9fxpIlS1BYWFitk+8RERERqSITQghNXnDr1i2MHDkSu3btQvFLZTIZOnbsiCVLliAoKKhaAtWWXC6Hk5MTMjMzyx21RURERPpHm+u3xpPvBQQEYMeOHXj06BGuX78OIQTq1asHFxcXjQMmIiIikorGSU0xFxcXNGvWTMpYiIiIiLSm0Tw1RERERPqKSQ0REREZBSY1eiQ1MxdHbqQjNTNX16EQEREZHK371JC01p+8jSmbL6BIAGYyYG6PSPRp5q/rsIiIiAwGa2r0QGpmriKhAYAiAXy4OZE1NkRERBpgUqMHktKzFQlNsUIhcDM9RzcBERERGSAmNXogyM0OZjLlZeYyGQLdbHUTEBERkQFiUqMHvJ1sMLdHJMxlzzIbc5kMn/aIgLeTjY4jIyIiMhzsKKwn+jTzR5tQd9xMz0Ggmy0TGiIiIg0xqdEj3k42TGaIiIi0xOYnIiIiMgpMaoiIiMgoMKkhIiIio8CkhoiIiIwCkxoiIiIyCkxqJMabUhIREekGh3RLiDelJCIi0h3W1EiEN6UkIiLSLSY1EuFNKYmIiHSLSY1EeFNKIiIi3WJSIxHelJKIiEi32FFYQrwpJRERke4wqZGYNjelTM3MRVJ6NoLc7JgIERERaYlJjY5xGDgREZE02KdGhzgMnIiISDpManSIw8CJiIikw6RGhzgMnIiISDpManSIw8CJiIikw47COsZh4ERERNJgUqMHtBkGTkRERMrY/EQKqZm5OHIjnaOviIjIILGmhgBwvhwiIjJ8rKkhnEt+hDjOl0NERAaOSY2JW3/yNrovOQLB+XKIiMjAMakxYcUzGotyyjhfDhGZOvYzNDzsU2PCypvRGHjWp4bz5RCRKWM/Q8PEmhoTVt6MxmYAtoxqyS8vEZks3pfPcOl1UjN37lw0a9YMDg4O8PDwQPfu3XHlyhVdh2U0ypvReO6bkYjyc9FxZEREusP78hkuvW5+OnDgAEaPHo1mzZqhoKAAH374ITp06ICLFy/Czs5O1+EZBc5oTESkrLgWu2Riw36GhkEmROlxL/rrwYMH8PDwwIEDB9CmTRu1XiOXy+Hk5ITMzEw4OjpWc4RERGQM1p+8jQ83J6JQCMV9+dgsX7O0uX7rdU1NaZmZmQAAV1dXHUeiv1Izc5GUno0gNzvWuhARaYm12IbJYJKaoqIijB8/Hq1atUJERITK9fLy8pCXl6d4LpfLayI8vcDe+kRE0uF9+QyPXncULmn06NFITEzEunXrKlxv7ty5cHJyUjz8/PxqKELdYm99IiIydQaR1IwZMwbbt2/H/v37UadOnQrXnTJlCjIzMxWP5OTkGopSt9hbn4iITJ1eNz8JIfDee+9hy5Yt+P333xEUFFTpa6ysrGBlZVUD0ekX9tYnIiJTp9c1NaNHj8bq1auxZs0aODg4IC0tDWlpacjNZZNKaeXNOcNZgYmIyJTo9ZBumUxW7vKEhAQMHDhQrW2Y2pDu1Mxck+mtz5FeRETGy+iGdOtxvqW3TKW3Pkd6GQYmnkRUk/Q6qSEqj6qRXm1C3Xnh1CNMPImopul1nxqi8nCkl/7jFANEpAtMasjglHd3cY700i9MPIlIF5jUkMHhSC/dSc3MxZEb6ZXWuDDxJCJdYJ8aMijFHU/bhLrjj7j2JjPSSx9o0kemOPEsfUNAfk5EVJ30eki3FExtSLcxY8dT3UnNzEWr+H1lJnf8I659hYmKKU0xQETS0ub6zeYnMgjseKpb2vaR8XayQXRwbY0TGnWbuYiISmLzExmEii6qrAGofjV5Gw7WyBGRtlhTQwaBHU91q3TnbDMZMLh1oOTvwxo5orJYc6k+JjWkEV19uTjiSff6NPPHH3HtMbxNEIQAVhxKQqv4fVh/8rZk78Gh4ETK1p+8jVbx+/DWiuOSf9+koG8JF5ufSG3aNgtINVV+n2b+aBPqzo6nOvbNoSQU5x1Sz+bMu80T/Y++z56uj03FrKkhtWjbLCD1fxnadjwlaVR3TQpr5Ij+R59rLvW1qZg1NRIx9hv3adNRV9//yyDN1URNSnGN3J83HwEy4PkAF8m2TWRI9LnmUl8Hb7CmRgL63uYpBW066urzfxnGrrrauWuqJuXg1QcYu+4Mxqw5Y7TfKaLK6HPNpb4O3mBNTRWZUm3E0NZB+OZQEopQ+ZcrNTMX/2Tl6e1/Gcasutu5VfVtkqq20pS+U0SV0de+hPo6aziTmirS1yo4KZW+SA5vXReDWgeq3L+S68sAyGSAEPr1X4YmDKlpsaYSAm8nG6XtSZlImcJ3ikgTpb9v+kIfEy4mNVWkz22eUjiX/Ahxmy9AlLhIfvtHEgapmKOk9EVVADATwH/eaowmAS56cdJrQh9791dEFwmB1ImUsX+niIyJviVc7FNTRfrc5llV60/eRvclR1D67mAV9Ysp76JaBMDVzsrgjom+9u6vSE20c5furyN13ylj/k4RUfViTY0E9LEKrqqKL+jl3e20ooukMf2Xrataj6o0dVV3O3d5NVdtQt0l/8yN8TtFRNWPSY1E9K0KrqrKu6AXm9S5vsp91faiqqt+KxW9b00naFI1dVVXQqCq5uqPuPbVkkjV1HfKkPpMEVHFmNRQuYLc7CADyq2paeTrXOFrNb2o6qrfSmXvqypBA4AjN9IlvQhK3S+lOhKCimquDLVmxdD6TJH0mNQaFyY1VC5vJxvEdQ7D3F8vKy1Xt6ZC3Yuqrobvqvu+pS/WB68+QKv4fZJfBGuqqasqP+CV1VwZWm2l1OceL47SqaljWdWklp+5/mFSQyq92zYYkAHzfr2Momoakq2r4buavG/xxbo6EzB1mrqq+gNa1R9wfZ2XQltSnnus8amcuudvTR3Lqn6f+ZnrJyY1VKF32wTjtSgfSZoVUjNz8eetRxBCoGmgK7ydbMq9mJsB1d6xWJv+MlInYKV/5CtKGKT4j1KKhMxQm5nKI1WfKU4WWDl1z9+aPJZV+T7zM9dfTGqoUlI0K6w/eRtxP/5vNJUMQPybz37Y5vaIVCoTeDZNfnX+16NNrYOUHYdV/cirmqm3qj+gUiZk1dnMVJPV+VLVPKk6tqdvPUKXRrzAaXL+1mTNbVW+z5wgUn8xqaFqV97wcAFgyo8X0CbUHW1C3RWzDheX1cR/PZrWOkh1EazsR746fugNYai9Lqrzpah5Ku/YAsCYNWeQlVdg8k0Smpy/NXmelvd9ntSpPpLSsxXlqhjC98lUMamhaqdqeHgRgJvpORAQOvuvR9Nah4ougurWMmiapEjxA6rv/WF0WZ1f1Zqn4mNbMn6g5pJzfafJ+VuTIw4B5e/z+bsZiv6DlSXV+v59MmVMaqjaqfpPtmTfGUP6r6e8i2DpWobJncIQWcdJkvlvpPoB1ef+MIZend+nmT9sLc3x3tqzSssNaR+qi6bnrzYjDqvSbFm8fv9vjmmUVOvz98mUMakxcIYwpLD4R63kPaRkAOa+GamIueSPnpkMGKzi3lL6qLxahuKh8JrMf1PR5yfVD6i+Drs2hur8poGuBr8P1UWbpl51RxxK0WypbVKtr98nU8akRseqkpQY0pDC4h+107ceQQjg+UDlm1sWlyccTsKKg0lYcSgJ3/6RVGaf9DGJq2j2ZXXnv1FnX6r7B1SXx9YYqvOrax/08ZzXhjbnb2XJhrrNlpUdQ2NIqtVRfBzsLM2R/bTQ4M+p8jCp0aGqJCWGOKTQ28mm0tEg3xxKUnQoLr1P+prEqWpeK1bZ/Df6QB+OrTFU50u9D/rwuWhDqkSssmRDnRoWdY5hRQmpsSSVJY9DsZLHw1j2k0mNjlQ1KTH0Pgjlqexuz/qaxJX+QSxN3//j06cEWVWipy8/uOrEIVWyqk+fS3lUHQspE7HKar8qS3o0OYblJaSGmlSWVvo4FCs+Hhk5+Zi3U71O0vqOSY2OVDUpMYbq0tI/ihXtk74ncaVHUXz26xWDaUbR92OrLxeWmo5Dnz8XVceiOhKximq/Kkt6ND2GJRNSfU8qNVFRE3mhEIj/9bLKGnJDw6RGR6qalBh6HwRVP4oV7ZO+J3HFP4jRwbUlm4W5JuhzgqwvFxZdxKFqtu1/svOQmpmrs/OqomNRXYlYRbVfFSU9hjbBntQ1kiX70KhqIi9vub4kz9pgUqMjUiQlpb/MQPXM5SC1in4UVf1AGVoSp0/9ZSpT+tjWxOiz6prTp7roIo7Sn4sMz+a+GbPmTLk1RVJcENXZRkXHQlcJsqrvW1V+N2p6XyqrCdT08y29vTca+2LrmZQyTeRFAopzq5i+/FOjDSY1OiRFp8LiL7O+VNGro7ILhKofKGPoSKqvVI0+G9I6CINbB0l6rDU5V1XVVthamkkWjzrKjUNW/XH0aeaPjJx8zC3RPABUTyd6dbdR0cVeH//50PZ3oyb3pbKaQE0/3/K2t/VMCjaPisadR7kYveZMmdcUf6b68JlVhUyIcno2GhG5XA4nJydkZmbC0dFR1+FUi9TMXMXkVMXMZTL8EddeL09MQ4vXVJT3uQDK9+mqjveo7LNff/J2mU7Y2l64qzqFglRxqEvVZ1Js7bAXEOhmW+Xvk6afS8ljUXwRLF2rYCz/fNTEvhy5kY63Vhwvs1zbz7ei7QmIcsv+068xattb6dVnps31mzU1RkBfqujVpY//zWmjqtX9+jKip5iqzoQC0vUf0eZc7dPMH2FeDuj+1RHF5I3a9Gmpam2GIo4lRzTqVFmVz7miDp5SdqLXdBuV1X4YUvNrZWpiX6QaJFFRH5qSTUrllZWeO8xQManRE1X54dPnjp6qGHpTUlUvkPrYXFjRfDtSJcnanqvZTwtRuk5Zk5ik6uib/bQQpQ9PRXFU9XNWeYsRGSTtRK/N52JMiYuuFf+jV/Jc0fTzXX7ghmIUU+k+NKX/cTSGfypVqdmGaSrX+pO30Sp+H95acRyt4vdh/cnbGr2++AthLpMBMJw20eKRQrocxXHkRjpSM3M1WkfVBbKi7ZTeZlVeX12KzyMzWdkyqZJkbc/V4ouutjFVNgeSujSJQ4rPufTxMgMwvE0QDse9pEiOqvL9Lz6/ARjkb4ixKU7cSybw6ny+yw/eUOp3VbIPzdphL+CPuPZKyXSfZv74I659uWWGziBqapYsWYL58+cjLS0NUVFR+PLLL9G8eXNdhyUJqf6DNPSaj5pW8j9oGYC4zmF4t22wynVK/pdd1ep+fW4uLNlh+JuDSSiC9Bc4bW8RUZX/LqWqzdQkDik+59TMXPi52mLzqGjkPC1Seby0Oablnd9/xLXnb4gOFF8Hik8XAWDKjxcQ5uWAKD+XCj/f1MxcxP//veZKKhQCdx7loksjn3Lf01hr2vQ+qVm/fj0mTJiAZcuWoUWLFli8eDE6duyIK1euwMPDQ9fhVZmUFzhjPUmlVjqRFPj/G1DKgHfbBJe7Tslks6oXSG1eX7J5EkC19sXxdrLBh6+GY1CroGq7wGlzrlYlcZeyH5e6cVT1PCkv6YgOrq1yfVXHtLymbVXn9+ZR0RBlGtioupV3HSgC0P2rI4j//3+mVH2+SenZZZpmi41ZcwZZeQVGVRNTGb1PahYuXIhhw4Zh0KBBAIBly5bhl19+wcqVKxEXF6fj6KrOEPvDGDpVnS/n/XoZr0X5wNvJpsJkMzq4dpUukJpeYEvXKgFQtJtXZ18cfUySqxKTlLWZ6sRRlURKqhpcTWsbiztj60s/L1Ohqu+UUONzr6gvnJSd/A2FXic1T58+xZ9//okpU6YolpmZmSEmJgZHjx4t9zV5eXnIy8tTPJfL5dUeZ1UYy0ggQxLkZldmsing2Y9CcQ1ZZclmVS+Q6r6+vFqlkvGa2g9WVdV0oqbteSJV05UmtY0AqjS6jLSn6Cj84wUUlSqr7HMvfQ0pTV+atmuKXncUTk9PR2FhITw9PZWWe3p6Ii0trdzXzJ07F05OToqHn59fTYRaJcbcaUsfeTvZIK5zWJnlJZMWdTrnVbWjszqvr2hIL6BdZ1eqWdqcJ1XtGA1UnhiV7oBcGs+tmtWnmT+2jG4JmRafe/E1ZMlbjVG6n7+p1fzrdU2NNqZMmYIJEyYonsvlcoNIbPSxqt+Yvds2GJA9a3JSNYumPnS+rqhqGTC9HyxTIUUNria1jbaWZnjjqyNsBtexKD8XxGv5uXs72aBLIxtk5RWYdM2/Xs8o/PTpU9ja2mLTpk3o3r27YnlsbCwyMjLw008/VboNU5hRmLRnCDOflpy9VQYAsmfNBOXN5ErGparnZ2Uz/2q7LlWvqn7uhvC7pg5trt96ndQAQIsWLdC8eXN8+eWXAICioiL4+/tjzJgxanUUZlJDxqDkjxQAo/jBopqhyQXOWC6GZByM8jYJEyZMQGxsLJo2bYrmzZtj8eLFyM7OVoyGIjIFpZsnecEhdWnStM1mcDJ0ep/U9OnTBw8ePMDHH3+MtLQ0PPfcc9i5c2eZzsNERERk2vS++amq2PxERERkeLS5fuv1kG4iIiIidTGpISIiIqPApIaIiIiMApMaIiIiMgpMaoiIiMgoMKkhIiIio8CkhoiIiIwCkxoiIiIyCkxqiIiIyCjo/W0Sqqp4wmS5XK7jSIiIiEhdxddtTW58YPRJzePHjwEAfn5+Oo6EiIiINPX48WM4OTmpta7R3/upqKgIKSkpcHBwgEwm03o7crkcfn5+SE5ONul7SPE4PMPj8AyPw//wWDzD4/AMj8MzVTkOQgg8fvwYPj4+MDNTr7eM0dfUmJmZoU6dOpJtz9HR0aRP0GI8Ds/wODzD4/A/PBbP8Dg8w+PwjLbHQd0ammLsKExERERGgUkNERERGQUmNWqysrLC9OnTYWVlpetQdIrH4Rkeh2d4HP6Hx+IZHodneByeqenjYPQdhYmIiMg0sKaGiIiIjAKTGiIiIjIKTGqIiIjIKDCpISIiIqNg0knN0qVL0ahRI8WkQNHR0fj1118V5U+ePMHo0aNRu3Zt2Nvb480338S9e/eUtnH79m106dIFtra28PDwwMSJE1FQUFDTuyKp+Ph4yGQyjB8/XrHMFI7FjBkzIJPJlB5hYWGKclM4BsXu3r2LAQMGoHbt2rCxsUFkZCROnTqlKBdC4OOPP4a3tzdsbGwQExODa9euKW3j4cOH6N+/PxwdHeHs7IwhQ4YgKyurpnelSgIDA8ucEzKZDKNHjwZgOudEYWEhpk2bhqCgINjY2CA4OBizZs1SuiePqZwTjx8/xvjx4xEQEAAbGxu0bNkSJ0+eVJQb43E4ePAgunXrBh8fH8hkMmzdulWpXKp9Pn/+PF588UVYW1vDz88Pn332mebBChO2bds28csvv4irV6+KK1euiA8//FBYWFiIxMREIYQQI0aMEH5+fmLv3r3i1KlT4oUXXhAtW7ZUvL6goEBERESImJgYcebMGbFjxw7h5uYmpkyZoqtdqrITJ06IwMBA0ahRIzFu3DjFclM4FtOnTxcNGzYUqampiseDBw8U5aZwDIQQ4uHDhyIgIEAMHDhQHD9+XPz9999i165d4vr164p14uPjhZOTk9i6das4d+6ceO2110RQUJDIzc1VrNOpUycRFRUljh07Jg4dOiRCQkJEv379dLFLWrt//77S+bBnzx4BQOzfv18IYTrnxJw5c0Tt2rXF9u3bRVJSkti4caOwt7cXn3/+uWIdUzknevfuLcLDw8WBAwfEtWvXxPTp04Wjo6O4c+eOEMI4j8OOHTvE1KlTxebNmwUAsWXLFqVyKfY5MzNTeHp6iv79+4vExESxdu1aYWNjI5YvX65RrCad1JTHxcVFfPPNNyIjI0NYWFiIjRs3KsouXbokAIijR48KIZ590GZmZiItLU2xztKlS4Wjo6PIy8ur8dir6vHjx6JevXpiz549om3btoqkxlSOxfTp00VUVFS5ZaZyDIQQYvLkyaJ169Yqy4uKioSXl5eYP3++YllGRoawsrISa9euFUIIcfHiRQFAnDx5UrHOr7/+KmQymbh79271BV/Nxo0bJ4KDg0VRUZFJnRNdunQRgwcPVlrWo0cP0b9/fyGE6ZwTOTk5wtzcXGzfvl1peZMmTcTUqVNN4jiUTmqk2uevvvpKuLi4KH0vJk+eLOrXr69RfCbd/FRSYWEh1q1bh+zsbERHR+PPP/9Efn4+YmJiFOuEhYXB398fR48eBQAcPXoUkZGR8PT0VKzTsWNHyOVy/PXXXzW+D1U1evRodOnSRWmfAZjUsbh27Rp8fHxQt25d9O/fH7dv3wZgWsdg27ZtaNq0KXr16gUPDw80btwYK1asUJQnJSUhLS1N6Vg4OTmhRYsWSsfC2dkZTZs2VawTExMDMzMzHD9+vOZ2RkJPnz7F6tWrMXjwYMhkMpM6J1q2bIm9e/fi6tWrAIBz587hjz/+QOfOnQGYzjlRUFCAwsJCWFtbKy23sbHBH3/8YTLHoSSp9vno0aNo06YNLC0tFet07NgRV65cwaNHj9SOx+hvaFmZCxcuIDo6Gk+ePIG9vT22bNmC8PBwnD17FpaWlnB2dlZa39PTE2lpaQCAtLQ0pR+r4vLiMkOybt06nD59WqltuFhaWppJHIsWLVpg1apVqF+/PlJTUzFz5ky8+OKLSExMNJljAAB///03li5digkTJuDDDz/EyZMnMXbsWFhaWiI2NlaxL+Xta8lj4eHhoVReq1YtuLq6GtSxKGnr1q3IyMjAwIEDAZjO9wIA4uLiIJfLERYWBnNzcxQWFmLOnDno378/AJjMOeHg4IDo6GjMmjULDRo0gKenJ9auXYujR48iJCTEZI5DSVLtc1paGoKCgspso7jMxcVFrXhMPqmpX78+zp49i8zMTGzatAmxsbE4cOCArsOqUcnJyRg3bhz27NlT5j8QU1L8XycANGrUCC1atEBAQAA2bNgAGxsbHUZWs4qKitC0aVN8+umnAIDGjRsjMTERy5YtQ2xsrI6j051vv/0WnTt3ho+Pj65DqXEbNmzADz/8gDVr1qBhw4Y4e/Ysxo8fDx8fH5M7J77//nsMHjwYvr6+MDc3R5MmTdCvXz/8+eefug6NYOKjnwDA0tISISEheP755zF37lxERUXh888/h5eXF54+fYqMjAyl9e/duwcvLy8AgJeXV5mRDsXPi9cxBH/++Sfu37+PJk2aoFatWqhVqxYOHDiAL774ArVq1YKnp6fJHIuSnJ2dERoaiuvXr5vU+eDt7Y3w8HClZQ0aNFA0xRXvS3n7WvJY3L9/X6m8oKAADx8+NKhjUezWrVv47bffMHToUMUyUzonJk6ciLi4OPTt2xeRkZF4++238a9//Qtz584FYFrnRHBwMA4cOICsrCwkJyfjxIkTyM/PR926dU3qOBSTap+l+q6YfFJTWlFREfLy8vD888/DwsICe/fuVZRduXIFt2/fRnR0NAAgOjoaFy5cUPqw9uzZA0dHxzIXBX328ssv48KFCzh79qzi0bRpU/Tv31/xt6kci5KysrJw48YNeHt7m9T50KpVK1y5ckVp2dWrVxEQEAAACAoKgpeXl9KxkMvlOH78uNKxyMjIUPrvdd++fSgqKkKLFi1qYC+klZCQAA8PD3Tp0kWxzJTOiZycHJiZKV8uzM3NUVRUBMA0zwk7Ozt4e3vj0aNH2LVrF15//XWTPA5S7XN0dDQOHjyI/Px8xTp79uxB/fr11W56AmDaQ7rj4uLEgQMHRFJSkjh//ryIi4sTMplM7N69WwjxbLimv7+/2Ldvnzh16pSIjo4W0dHRitcXD9fs0KGDOHv2rNi5c6dwd3c3uOGa5Sk5+kkI0zgW77//vvj9999FUlKSOHz4sIiJiRFubm7i/v37QgjTOAZCPBvWX6tWLTFnzhxx7do18cMPPwhbW1uxevVqxTrx8fHC2dlZ/PTTT+L8+fPi9ddfL3cIZ+PGjcXx48fFH3/8IerVq6fXw1ZVKSwsFP7+/mLy5MllykzlnIiNjRW+vr6KId2bN28Wbm5uYtKkSYp1TOWc2Llzp/j111/F33//LXbv3i2ioqJEixYtxNOnT4UQxnkcHj9+LM6cOSPOnDkjAIiFCxeKM2fOiFu3bgkhpNnnjIwM4enpKd5++22RmJgo1q1bJ2xtbTmkWxODBw8WAQEBwtLSUri7u4uXX35ZkdAIIURubq4YNWqUcHFxEba2tuKNN94QqampStu4efOm6Ny5s7CxsRFubm7i/fffF/n5+TW9K5IrndSYwrHo06eP8Pb2FpaWlsLX11f06dNHaW4WUzgGxX7++WcREREhrKysRFhYmPj666+VyouKisS0adOEp6ensLKyEi+//LK4cuWK0jr//POP6Nevn7C3txeOjo5i0KBB4vHjxzW5G5LYtWuXAFBm/4QwnXNCLpeLcePGCX9/f2FtbS3q1q0rpk6dqjT81lTOifXr14u6desKS0tL4eXlJUaPHi0yMjIU5cZ4HPbv3y8AlHnExsYKIaTb53PnzonWrVsLKysr4evrK+Lj4zWOVSZEiSkhiYiIiAwU+9QQERGRUWBSQ0REREaBSQ0REREZBSY1REREZBSY1BAREZFRYFJDRERERoFJDRERERkFJjVERERkFJjUEBmItLQ0vPfee6hbty6srKzg5+eHbt26Kd1z5ciRI3j11Vfh4uICa2trREZGYuHChSgsLFSsc/PmTQwZMgRBQUGwsbFBcHAwpk+fjqdPnyq934oVKxAVFQV7e3s4OzujcePGihsYAsCMGTMgk8nQqVOnMrHOnz8fMpkM7dq1q3S/AgMDIZPJVD4GDhyo+cHSc+3atcP48eN1HQaR0aml6wCIqHI3b95Eq1at4OzsjPnz5yMyMhL5+fnYtWsXRo8ejcuXL2PLli3o3bs3Bg0ahP3798PZ2Rm//fYbJk2ahKNHj2LDhg2QyWS4fPkyioqKsHz5coSEhCAxMRHDhg1DdnY2FixYAABYuXIlxo8fjy+++AJt27ZFXl4ezp8/j8TERKW4vL29sX//fty5cwd16tRRLF+5ciX8/f3V2reTJ08qkq4jR47gzTffxJUrV+Do6AgAsLGxkeIQ1oj8/HxYWFjU2Ps9ffoUlpaWNfZ+RHpPy1tBEFEN6ty5s/D19RVZWVllyh49eiSysrJE7dq1RY8ePcqUb9u2TQAQ69atU7n9zz77TAQFBSmev/7662LgwIEVxjR9+nQRFRUlunbtKmbPnq1YfvjwYeHm5iZGjhwp2rZtq8be/U/xPWYePXqkWLZ161bRuHFjYWVlJYKCgsSMGTOU7p0EQCxbtkx06dJF2NjYiLCwMHHkyBFx7do10bZtW2Frayuio6OV7uNVHPuyZctEnTp1hI2NjejVq5fSPXyEEGLFihUiLCxMWFlZifr164slS5YoypKSkhTHtU2bNsLKykokJCSI9PR00bdvX+Hj4yNsbGxERESEWLNmjeJ1sbGxZe6hk5SUJBISEoSTk5PS+2/ZskWU/JkujnvFihUiMDBQyGQyIcSzc2DIkCHCzc1NODg4iPbt24uzZ89qdOyJjAGbn4j03MOHD7Fz506MHj0adnZ2ZcqdnZ2xe/du/PPPP/jggw/KlHfr1g2hoaFYu3atyvfIzMyEq6ur4rmXlxeOHTuGW7duVRrf4MGDsWrVKsXzlStXon///pLUIBw6dAjvvPMOxo0bh4sXL2L58uVYtWoV5syZo7TerFmz8M477+Ds2bMICwvDW2+9hXfffRdTpkzBqVOnIITAmDFjlF5z/fp1bNiwAT///DN27tyJM2fOYNSoUYryH374AR9//DHmzJmDS5cu4dNPP8W0adPw3XffKW0nLi4O48aNw6VLl9CxY0c8efIEzz//PH755RckJiZi+PDhePvtt3HixAkAwOeff47o6GgMGzYMqampSE1NhZ+fn9rH5Pr16/jxxx+xefNmnD17FgDQq1cv3L9/H7/++iv+/PNPNGnSBC+//DIePnyoyeEmMny6zqqIqGLHjx8XAMTmzZtVrhMfH1+mhqOk1157TTRo0KDcsmvXrglHR0elO3GnpKSIF154QQAQoaGhIjY2Vqxfv14UFhYq1imuNXj69Knw8PAQBw4cEFlZWcLBwUGcO3dOjBs3rso1NS+//LL49NNPldb5/vvvhbe3t+I5APHRRx8pnh89elQAEN9++61i2dq1a4W1tbVS7Obm5uLOnTuKZb/++qswMzNT3GU7ODhYqYZFCCFmzZoloqOjhRD/q6lZvHhxpfvVpUsX8f777yuet23bVowbN05pHXVraiwsLMT9+/cVyw4dOiQcHR3FkydPlF4bHBwsli9fXmlsRMaEfWqI9JwQolrWBYC7d++iU6dO6NWrF4YNG6ZY7u3tjaNHjyIxMREHDx7EkSNHEBsbi2+++QY7d+6Emdn/KnktLCwwYMAAJCQk4O+//0ZoaCgaNWqkURyqnDt3DocPH1aqmSksLMSTJ0+Qk5MDW1tbAFB6P09PTwBAZGSk0rInT55ALpcr+ur4+/vD19dXsU50dDSKiopw5coVODg44MaNGxgyZIjScSkoKICTk5NSjE2bNlV6XlhYiE8//RQbNmzA3bt38fTpU+Tl5SliraqAgAC4u7srnp87dw5ZWVmoXbu20nq5ubm4ceOGJO9JZCiY1BDpuXr16ik6+KoSGhoKALh06RJatmxZpvzSpUsIDw9XWpaSkoL27dujZcuW+Prrr8vdbkREBCIiIjBq1CiMGDECL774Ig4cOID27dsrrTd48GC0aNECiYmJGDx4sKa7qFJWVhZmzpyJHj16lCmztrZW/F2yc65MJlO5rKioSO33BZ6NAGvRooVSmbm5udLz0k2C8+fPx+eff47FixcjMjISdnZ2GD9+fJnRZaWZmZmVSUrz8/PLrFf6/bKysuDt7Y3ff/+9zLrOzs4VvieRsWFSQ6TnXF1d0bFjRyxZsgRjx44tc1HLyMhAhw4d4Orqin//+99lkppt27bh2rVrmDVrlmLZ3bt30b59ezz//PNISEhQqnlRpTgpys7OLlPWsGFDNGzYEOfPn8dbb72lzW6Wq0mTJrhy5QpCQkIk22ax27dvIyUlBT4+PgCAY8eOwczMDPXr14enpyd8fHzw999/o3///hpt9/Dhw3j99dcxYMAAAM8SqatXryollZaWlkrD7AHA3d0djx8/RnZ2tuIzLu4zU5EmTZogLS0NtWrVQmBgoEaxEhkbJjVEBmDJkiVo1aoVmjdvjk8++QSNGjVCQUEB9uzZg6VLl+LSpUtYvnw5+vbti+HDh2PMmDFwdHTE3r17MXHiRPTs2RO9e/cG8CyhadeuHQICArBgwQI8ePBA8T5eXl4AgJEjR8LHxwcvvfQS6tSpg9TUVMyePRvu7u6Ijo4uN8Z9+/YhPz9f0tqBjz/+GF27doW/vz969uwJMzMznDt3DomJiZg9e3aVtm1tbY3Y2FgsWLAAcrkcY8eORe/evRXHYObMmRg7diycnJzQqVMn5OXl4dSpU3j06BEmTJigcrv16tXDpk2bcOTIEbi4uGDhwoW4d++eUlITGBiI48eP4+bNm7C3t4erqytatGgBW1tbfPjhhxg7diyOHz+u1AFblZiYGERHR6N79+747LPPEBoaipSUFPzyyy944403yjSPERkzjn4iMgB169bF6dOn0b59e7z//vuIiIjAK6+8gr1792Lp0qUAgJ49e2L//v24ffs2XnzxRdSvXx+LFi3C1KlTsW7dOkUTzJ49e3D9+nXs3bsXderUgbe3t+JRLCYmBseOHUOvXr0QGhqKN998E9bW1ti7d2+ZvhvF7OzsJG/u6NixI7Zv347du3ejWbNmeOGFF7Bo0SIEBARUedshISHo0aMHXn31VXTo0AGNGjXCV199pSgfOnQovvnmGyQkJCAyMhJt27bFqlWrEBQUVOF2P/roIzRp0gQdO3ZEu3bt4OXlhe7duyut88EHH8Dc3Bzh4eFwd3fH7du34erqitWrV2PHjh2IjIzE2rVrMWPGjEr3QyaTYceOHWjTpg0GDRqE0NBQ9O3bF7du3VL0LyIyFTKhac9CIiIDN2PGDGzdulWt5h0iMhysqSEiIiKjwKSGiKqdvb29ysehQ4d0HR4RGQk2PxFRtbt+/brKMl9fX4O6vxMR6S8mNURERGQU2PxERERERoFJDRERERkFJjVERERkFJjUEBERkVFgUkNERERGgUkNERERGQUmNURERGQUmNQQERGRUfg/m8X4cdn2MvMAAAAASUVORK5CYII=", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(alm_surr, data_validation)\n", + "surrogate_parity(alm_surr, data_validation)\n", + "surrogate_residual(alm_surr, data_validation)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_alamo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb) file." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_usr.ipynb new file mode 100644 index 00000000..ca5490e3 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_usr.ipynb @@ -0,0 +1,570 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part -1)\n", + "\n", + "## 1. Introduction\n", + "This notebook demonstrates leveraging of the ALAMO surrogate trainer and IDAES Python wrapper to produce an surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "### 1.1 Need for ML Surrogate\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebric, we will use the surrogates and relate enthalpy and entropy with the pressure and temperature as an algebric equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train a model using AlamoTrainer for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.alamopy import AlamoTrainer, AlamoSurrogate, alamo\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset to have cover different ranges of pressure and temperature. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", we change \".\" to \"_\" as ALAMO accepts alphanumerical characters or underscores as the labels for input/output. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=7, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path('500_Points_DataSet.csv')) \n", + "\n", + "### ALAMO only accepts alphanumerical characters (A-Z, a-z, 0-9) or underscores as input/output labels\n", + "cols=csv_data.columns\n", + "cols=[item.replace(\".\", \"_\") for item in cols]\n", + "csv_data.columns=cols\n", + "\n", + "data = csv_data.sample(n=500,random_state=0) \n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns\n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogate with ALAMO\n", + "\n", + "IDAES provides a Python wrapper for the ALAMO machine learning tool via an imported AlamoTrainer class. Regression settings can be directly set as config attributes, as shown below. In this example, allowed basis terms include constant and linear functions, monomial power order 2 and 3, variable product power order 1 and 2, and variable ratio power order 1 and 2. ALAMO seeks to minimize the number of basis terms; here, we restrict each surrogate expression to a maximum of 10 basis terms.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ***************************************************************************\n", + " ALAMO version 2023.2.13. Built: WIN-64 Mon Feb 13 21:30:56 EST 2023\n", + "\n", + " If you use this software, please cite:\n", + " Cozad, A., N. V. Sahinidis and D. C. Miller,\n", + " Automatic Learning of Algebraic Models for Optimization,\n", + " AIChE Journal, 60, 2211-2227, 2014.\n", + "\n", + " ALAMO is powered by the BARON software from http://www.minlp.com/\n", + " ***************************************************************************\n", + " Licensee: Javal Vyas at Carnegie Mellon University, jvyas@andrew.cmu.edu.\n", + " ***************************************************************************\n", + " Reading input data\n", + " Checking input consistency and initializing data structures\n", + " \n", + " Step 0: Initializing data set\n", + " User provided an initial data set of 400 data points\n", + " We will sample no more data points at this stage\n", + " ***************************************************************************\n", + " Iteration 1 (Approx. elapsed time 0.62E-01 s)\n", + " \n", + " Step 1: Model building using BIC\n", + " \n", + " Model building for variable CO2SM_CO2_Enthalpy\n", + " ----\n", + " BIC = 0.750E+04 with CO2SM_CO2_Enthalpy = - 0.38E+06\n", + " ----\n", + " BIC = 0.569E+04 with CO2SM_CO2_Enthalpy = 58. * CO2SM_Temperature - 0.42E+06\n", + " ----\n", + " BIC = 0.542E+04 with CO2SM_CO2_Enthalpy = 55. * CO2SM_Temperature - 0.61E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", + " ----\n", + " BIC = 0.516E+04 with CO2SM_CO2_Enthalpy = 49. * CO2SM_Temperature + 4.0 * CO2SM_Pressure^2 - 0.15E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.41E+06\n", + " ----\n", + " BIC = 0.502E+04 with CO2SM_CO2_Enthalpy = 0.16E+03 * CO2SM_Temperature - 0.16 * CO2SM_Temperature^2 + 0.76E-04 * CO2SM_Temperature^3 - 0.56E+05 * CO2SM_Pressure/CO2SM_Temperature - 0.44E+06\n", + " ----\n", + " BIC = 0.484E+04 with CO2SM_CO2_Enthalpy = 0.14E+03 * CO2SM_Temperature + 2.5 * CO2SM_Pressure^2 - 0.14 * CO2SM_Temperature^2 + 0.66E-04 * CO2SM_Temperature^3 - 0.11E+06 * CO2SM_Pressure/CO2SM_Temperature - 0.43E+06\n", + " \n", + " Model building for variable CO2SM_CO2_Entropy\n", + " ----\n", + " BIC = 0.219E+04 with CO2SM_CO2_Entropy = - 0.48E+03 * CO2SM_Pressure/CO2SM_Temperature\n", + " ----\n", + " BIC = 0.147E+04 with CO2SM_CO2_Entropy = 1.9 * CO2SM_Pressure - 0.15E+04 * CO2SM_Pressure/CO2SM_Temperature\n", + " ----\n", + " BIC = 0.115E+04 with CO2SM_CO2_Entropy = 0.77E-01 * CO2SM_Temperature - 0.38E+03 * CO2SM_Pressure/CO2SM_Temperature - 50.\n", + " ----\n", + " BIC = 713. with CO2SM_CO2_Entropy = 0.20 * CO2SM_Temperature - 0.94E-04 * CO2SM_Temperature^2 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 89.\n", + " ----\n", + " BIC = 443. with CO2SM_CO2_Entropy = 0.52 * CO2SM_Temperature - 0.60E-03 * CO2SM_Temperature^2 + 0.26E-06 * CO2SM_Temperature^3 - 0.34E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", + " ----\n", + " BIC = 317. with CO2SM_CO2_Entropy = 0.54 * CO2SM_Temperature - 0.63E-03 * CO2SM_Temperature^2 + 0.27E-06 * CO2SM_Temperature^3 - 0.26E+03 * CO2SM_Pressure/CO2SM_Temperature + 0.79E-01 * CO2SM_Temperature/CO2SM_Pressure - 0.16E+03\n", + " ----\n", + " BIC = 259. with CO2SM_CO2_Entropy = 0.47 * CO2SM_Temperature + 0.15E-01 * CO2SM_Pressure^2 - 0.53E-03 * CO2SM_Temperature^2 + 0.23E-06 * CO2SM_Temperature^3 - 0.70E-03 * CO2SM_Pressure*CO2SM_Temperature - 0.46E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", + " ----\n", + " BIC = 240. with CO2SM_CO2_Entropy = - 2.1 * CO2SM_Pressure + 0.55 * CO2SM_Temperature + 0.76E-01 * CO2SM_Pressure^2 - 0.63E-03 * CO2SM_Temperature^2 - 0.94E-03 * CO2SM_Pressure^3 + 0.27E-06 * CO2SM_Temperature^3 - 0.23E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.15E+03\n", + " ----\n", + " BIC = 224. with CO2SM_CO2_Entropy = - 1.9 * CO2SM_Pressure + 0.49 * CO2SM_Temperature + 0.83E-01 * CO2SM_Pressure^2 - 0.57E-03 * CO2SM_Temperature^2 - 0.10E-02 * CO2SM_Pressure^3 + 0.25E-06 * CO2SM_Temperature^3 - 0.73E-08 * (CO2SM_Pressure*CO2SM_Temperature)^2 - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.13E+03\n", + " ----\n", + " BIC = 193. with CO2SM_CO2_Entropy = - 3.9 * CO2SM_Pressure + 0.52 * CO2SM_Temperature + 0.17 * CO2SM_Pressure^2 - 0.56E-03 * CO2SM_Temperature^2 - 0.21E-02 * CO2SM_Pressure^3 + 0.24E-06 * CO2SM_Temperature^3 - 0.10E-02 * CO2SM_Pressure*CO2SM_Temperature - 0.36E+03 * CO2SM_Pressure/CO2SM_Temperature - 0.20 * CO2SM_Temperature/CO2SM_Pressure - 0.12E+03\n", + " \n", + " Calculating quality metrics on observed data set.\n", + " \n", + " Quality metrics for output CO2SM_CO2_Enthalpy\n", + " ---------------------------------------------\n", + " SSE OLR: 0.515E+08\n", + " SSE: 0.659E+08\n", + " RMSE: 406.\n", + " R2: 0.999\n", + " R2 adjusted: 0.999\n", + " Model size: 6\n", + " BIC: 0.484E+04\n", + " Cp: 0.659E+08\n", + " AICc: 0.482E+04\n", + " HQC: 0.483E+04\n", + " MSE: 0.168E+06\n", + " SSEp: 0.659E+08\n", + " RIC: 0.659E+08\n", + " MADp: 0.594\n", + " \n", + " Quality metrics for output CO2SM_CO2_Entropy\n", + " --------------------------------------------\n", + " SSE OLR: 541.\n", + " SSE: 558.\n", + " RMSE: 1.18\n", + " R2: 0.997\n", + " R2 adjusted: 0.997\n", + " Model size: 10\n", + " BIC: 193.\n", + " Cp: 178.\n", + " AICc: 154.\n", + " HQC: 169.\n", + " MSE: 1.43\n", + " SSEp: 558.\n", + " RIC: 606.\n", + " MADp: 0.130E+04\n", + " \n", + " Total execution time 0.52 s\n", + " Times breakdown\n", + " OLR time: 0.30 s in 3863 ordinary linear regression problem(s)\n", + " MINLP time: 0.0 s in 0 optimization problem(s)\n", + " Simulation time: 0.0 s to simulate 0 point(s)\n", + " All other time: 0.22 s in 1 iteration(s)\n", + " \n", + " Normal termination\n", + " ***************************************************************************\n" + ] + } + ], + "source": [ + "# Create ALAMO trainer object\n", + "has_alamo=alamo.available()\n", + "if has_alamo:\n", + " trainer = AlamoTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + " )\n", + "\n", + " # Set ALAMO options\n", + " trainer.config.constant = True\n", + " trainer.config.linfcns = True\n", + " trainer.config.multi2power = [1, 2]\n", + " trainer.config.monomialpower = [2, 3]\n", + " trainer.config.ratiopower = [1]\n", + " trainer.config.maxterms = [10] * len(output_labels) # max terms for each surrogate\n", + " trainer.config.filename = os.path.join(os.getcwd(), \"alamo_run.alm\")\n", + " trainer.config.overwrite_files = True\n", + "\n", + " # Train surrogate (calls ALAMO through IDAES ALAMOPy wrapper)\n", + " success, alm_surr, msg = trainer.train_surrogate()\n", + "\n", + " # save model to JSON\n", + " model = alm_surr.save_to_file(\"alamo_surrogate.json\", overwrite=True)\n", + "\n", + " # create callable surrogate object\n", + " surrogate_expressions = trainer._results[\"Model\"]\n", + " input_labels = trainer._input_labels\n", + " output_labels = trainer._output_labels\n", + " xmin, xmax = [7,306], [40,1000]\n", + " input_bounds = {\n", + " input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))\n", + " }\n", + "\n", + " alm_surr = AlamoSurrogate(\n", + " surrogate_expressions, input_labels, output_labels, input_bounds\n", + " )\n", + "else:\n", + " print('Alamo not found.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing Surrogates\n", + "\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(alm_surr, data_training)\n", + "surrogate_parity(alm_surr, data_training)\n", + "surrogate_residual(alm_surr, data_training)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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T5OCEEOrlrT179uDpp59GzZo14eXlhZdeegkymQwPHjzQeY3jx4+jZ8+eqFWrFry8vNC+fXsAwJUrV8w+fiIiRyKXe6mDEwAQwgnbt/cwyYyQaiVC33vY05IbgyAHd/bsWURHRyMjIwM9evRA06ZNsXXrVhw/fhwff/wxAN2/8Pfv30dCQgK8vb2xceNGHDt2DN9++63e1xERkfFycvzVwYmKEE7IyfEDANy+fbvSS1b63sOecDnMge3btw9//PEH3njjDRw/fhwKhQLvv/8+nJyUv/xbtmzRON/NzQ3FxcUaz507dw4ymQwLFixAREQEAOD333+vmg9ARGRBbm5uJj3PEH5+MkgkCo0gRSJRwM8vBwDUOZ6VWbLS9x72hEGQg8jPz0d2drbGFvn58+ejR48eePnll5GWlobCwkJ8+OGH6NmzJw4dOoSVK1dqXCMqKgr37t3D3r170axZM3h6eqJWrVpwc3PDhx9+iFdffRVpaWmYO3euhT4lEZF5lS510r9/f40ehy4uLhr11Exd+kQqvYuePXeUWK5SID4+tcx5lZmJL/0eqpwgqfRuJUZunRgEOYiffvoJoaGhcHFxga+vL5o1a4YPPvgAiYmJcHJyQrNmzbBkyRIsXLgQU6dORbt27TB//ny8/PLL6mu0adMGr776Kvr37w+ZTKbeIr9u3Tq89dZb+OCDDxAbG4v33nsPvXr1suCnJSIyPX2lTuRyL+Tk+GPChF5o0sTXpO9dcjYpNvYkYmIu4ujRVkhNjcfhw08iNTXeZAnSJd8jJ8cPfn45GgHQ7du31WOy9QRpiRBCWHoQ1iQvLw9SqRRyuRze3t7q5x8+fIj09HRER0ejWrVqRl/X0nWCbE1l7zcRkallZWVh1apV5R4ruZvKyUlg1SoJkpJM+/4ymQzXr19HSkoK5HIvLFs2rsyS1bhxyyCV3kVycjJCQ0ONur6uz6cK8Pz8lPlGqr+/8ko3+Pj4WEVApO37WxfOBFURf39/jB49mo32iIjsTOndVAqFBCNGAAkJQHi46d7H399f/R2iK3lZ37KVtu4Fqhme0koGeIACgASABBKJApcu2fbWeQZBVcjWfjmIiEi/8gKS4mLg4kXTBkElVTR52dBVCZXSAV7JTeWqrfMxMRchld41KA8pMxO4cAGoW9d898YY3CJPRERUAaqqza6u+ZBINJtROzsDdeqY771Vycuq9zU0ebl0oKKt8nTHjh0BlB/glWTM1vnVq4HISKBTJ+Wfq1cb9DKz4kwQERGRkUpXVG7a9AzOnGkKIZzg7Czw6acSs8906EpeNoSuqtC+vsrE7vJmnEoydOt8ZiaQnAwo/hcrKhQwy5KhsTgTREREZITyKiqfOdMUSUmfIzFxHY4evWnypGiV0jWHpNK7iI6+XCYA0lebyNDK06VnnACF0bNPMpkMR47I1AGQSnExcPSozKL9yDgTREREZARtScmFhW6Ijr6MsDCFlldWnq5NNnK5HIWFhXBxcUFBQQGysrI0jpcMjIxJrC4946R8vWGzT6ocJLncCxJJ2d1shw6tR1raXYslVTMIIiIiMoAqiNCXlGzKCtHlKS9YkMlk2Lx5s97X9u/fH4DxidVS6V2NgMfQpTdVsKavAKOl2iwxCCIiIjJAyVmYmjXzMHmyFMXFEjg7CyxcmIeBA1+0WKkTQ4MIVXVrfUGJi4vpw4PK5jCZA4MgIiIiA6kCnAkTgP79ldvg69SRIDzcB4CPJYemoWRxQ23Bhq6gxMfHRx3wyeVyg2aZDJkBKz2jZGk2FQT98ssvWLx4MY4fP46srCx8++236N27t/q4EAKzZs3CZ599htzcXDz55JNYsWIF6tata7lBO4Cff/4ZHTt2xJ07dzR65ugSFRWFcePGYdy4cWYdGxGRuYSHW0etm9J07foqPcOjLSgpOaMVGhpqt8V+bSoIun//Ppo1a4Zhw4ahT58+ZY4vWrQIH3zwAdavX4/o6GjMmDEDCQkJ+Ouvvxy69cKQIUOwfv16jBgxokxT1FGjRuGTTz5BYmIi1q1bZ5kBEhGRSWjb9aUqaFhyhkeb8gIaWwxwDGFTQVC3bt3QrVu3co8JIbBs2TJMnz4dzz33HADgv//9L4KDg/Hdd99hwIABVTlUqxMREYGvvvoKS5cuhYeHBwBlf65NmzahVq1aFh4dERGZgiG7vuw1oKkIu6kTlJ6ejuzsbHTu3Fn9nFQqRatWrZCamqr1dfn5+cjLy9P4sUexsbGIiIhASkqK+rmUlBTUqlULLVq0UD+Xn5+PMWPGICgoCNWqVcNTTz2FY8eOaVzrhx9+QL169eDh4YGOHTsiIyOjzPv9+uuvaNu2LTw8PBAREYExY8bg/v37Zvt8RET0aNdXSYYWNHREdhMEZWdnAwCCg4M1ng8ODlYfK8/8+fMhlUrVPxEREWYdp0pmJrB/v/LPqjJs2DCsXbtW/XjNmjUYOnSoxjmTJk3C1q1bsX79epw4cQJ16tRBQkICcnKU/wJdvXoVffr0Qc+ePXHq1CkMHz4cU6ZM0bjGpUuX0LVrVzz//PM4c+YMNm/ejF9//RWjR482/4ckInJgFW2nYS6Glgswd1kBbWxqOcwcpk6divHjx6sf5+XlmT0QWr36UflwJydg1SqYrbpoSYMHD8bUqVNx+fJlAMChQ4fw1Vdf4eeffwagzLlasWIF1q1bp152/Oyzz7B7926sXr0aEydOxIoVKxATE4P3338fAFC/fn388ccfWLhwofp95s+fj0GDBqmTnuvWrYsPPvgA7du3x4oVKxw6P4uIyBxKBhG6dn0ZEmyU7DJ//boT0tNdEB1dpC4CaUwStK7ijiXHZKklOrsJgkJCQgAAN27cQGhoqPr5GzduoHnz5lpf5+7uDnd3d3MPT82S/VMCAwPRvXt3rFu3DkIIdO/eHQEBAerjly5dQmFhIZ588kn1c66urnjiiSdw9uxZAMDZs2fRqlUrjevGx8drPD59+jTOnDmDjRs3qp8TQkChUCA9PR2PPfaYOT4eEZHDMlWwUbLLvK5dZsZUeLbmHCS7CYKio6MREhKCvXv3qoOevLw8HD16FCNHjrTs4Eq4cAHl9k+5eLFqtloOGzZMvSz18ccfm+U97t27hxEjRmDMmDFljjEJm4jIPEwRbKiCKH27zCxV4dnUbCoIunfvHi5evKh+nJ6ejlOnTsHPzw+1atXCuHHj8M4776Bu3brqLfJhYWEatYQsrW5d5RJYyUDI2RmoU6dq3r9r164oKCiARCJBQkKCxrGYmBi4ubnh0KFDiIyMBKCsLnrs2DH10tZjjz2Gbdu2abzuyJEjGo9jY2Px119/oU5VfSgiIjIpY3qL2TKbCoJ+//13dOzYUf1YlcujqnEzadIk3L9/H8nJycjNzcVTTz2Fn376yapyUMLDlTlAI0YoZ4CcnYFPP626glvOzs7qpS1nZ2eNY9WrV8fIkSMxceJEdWC5aNEiPHjwAEn/S1p69dVX8f7772PixIkYPnw4jh8/Xqa+0OTJk9G6dWuMHj0aw4cPR/Xq1fHXX39h9+7d6mlWIiJbUDI/pjxVnc9i6vGUzv85depfyOVeRvcWs1U2FQR16NABQgitxyUSCd5++228/fbbVTgq4yUlKXOAlOXWq77iqLe3t9ZjCxYsgEKhwEsvvYS7d++iZcuW2LlzJ3x9fQEol7O2bt2KN954Ax9++CGeeOIJvPvuuxg2bJj6Gk2bNsWBAwcwbdo0tG3bFkIIxMTEqBv3ERHZgpL5MbpUVQd0U4+n/PyfYEgk49Cz5w6dvcXshUToiiocUF5eHqRSKeRyuUaw8PDhQ6SnpyM6OtqqZpbsFe83EVlaVlYWVq1apfe85ORkjQ051jqe0rNIt2/fRkpKCuRyLyxbNq7MrM+4ccsAoNxdZlX1mY2h7ftbF5uaCSIiIrIl5lxOM6RJqsqlS5fwxRdflHtMV/5PdPRlu5v9KYlBEBERkRmYczlN1/b18sZRMgCSy71w9aqyHl5ExFWHyf8pD4MgIiIiMzB0G7mx2831bV/Xdf0TJ1pg27aeACT/e0aBXr2Mz/+xVIVnU2MQREREZABjlp/MqaLb11XB06MACACcsG1bD7zxxjKMG7es3PyfPn36aBTWtWSFZ1NjEGQk5pFXDd5nIrImxiw/mVtFl6/KC56UdOf/BAQEWF0StKnYTQNVc3N1dQUAPHjwwMIjcQyq+6y670RElqJt+Uku9zL6OunpUUa/rrSKNkktr8O8kmPk/5SHM0EGcnZ2ho+PD27evAkA8PT0hEQi0fMqMpYQAg8ePMDNmzfh4+NTpqAjEVFVUeW96Ft+MiQ/xhQzSZVtkqoKnkoGdKqcIEfI/ykPgyAjqJq0qgIhMh8fHx/1/SYisgRVU9KMjCJs2CCgUDz6H19nZ4HXX++GqCgXvfkxxiYy6xtPZbbcq4Knq1eVVXojIjIdJv+nPAyCjCCRSBAaGoqgoCAUFhZaejh2y9XVlTNARGQV/P394e9fXrsjCeLigg26hin7cJkiIJFK70IqPVvusbCwMLsOekpjEFQBzs7O/JImInIgFWl3pFpG0pfIbO7lJkOvP3jwYIcKgAC2zSijImW3iYiIyqOqGL1pkwcmT5aiuFgCZ2eBhQvlGDjwX5MsNxlSlRrQXY/IHpa9KvL9zSCoFAZBRESPWFsXdVuWmWn6xtnW1uTVktg7jIiITIZfsKYVHm664EfFXFWpHQXrBBERUblKf3Fqq3PDL1iyVZwJIiIivaypYjKRqXAmiIiIdDJVxWQia8OZICIi0smUdW7sEZPHbReDICIi0klfnZvbt28DcMwve2tLHreWTve2gkEQERHpVLrnVOmGnSkpKepzHW2nmDXtzmLelvEYBBERkV66GnaWxJ1iVUtVCFFffzJ7boJaGQyCiIioXKW/OJU9p5TBjyWXXaw5B6eq74uqqer+/cDSpWXztp58MhEdOpim55g9YhBERETlKtm1/Pbt2+plL0suu1hbDk5Jlrov/v7+aN0acHICFIpHzzs7A61aKRvAUvm4RZ6IiLTy9/dHaGgoAgICAFh+u7w15eCUZOn7Eh6u7HSv6u2t7HRv+grV9oYzQURERsjMBC5cAOrWdcwvGHvcLm+K5TVruC8V6XTv6BgEEREZaPVqIDlZueTg5KT8P++kJEuPqmrp2y5fGYZ2QzclUy2vmfO+GMMc/cnsGYMgIiI9ZDIZMjKKkJwcBIVCAkAZCI0YIdC8+U1ERbk4TOKpvu3yFWVoMNK/f3+Nx5VNRK7s8poqMNN3X7g7yzoxCCIi0kH15ZyeHgWFIlHjWHGxBB9++COioy/bfX2ckl/iurbLV/TL3tBgpLCwUP13a6iLUzJ5HABmzryFjAwXREUVISzscQCPO2QRSVvBIIiISAfVl5u+5Q57r49T+su+PFX5Za+vLk5lrmvszFLJzxwaCsTFVfjtqYoxCCIiMoC5loFsiTXNZhibiKwt30jV8gOwjpklqlp2GQR9/PHHWLx4MbKzs9GsWTN8+OGHeOKJJyw9LCKycYZWTabK0zYj4+Ki/NrSNzNXclnOkHwjc80skXWzuyBo8+bNGD9+PFauXIlWrVph2bJlSEhIwPnz5xEUFGTp4RGRjStZNZnMQ9eMjI+Pj3pZrmbNPEyeLEVxsQTOzgILF+Zh4MAXyyzLGbJUaQ1b3Knq2V0QtGTJErzyyisYOnQoAGDlypX4/vvvsWbNGkyZMsXCoyMi0uSIdYfKW5pSLUsZMiOjCnAmTAD691fVxZEgPNwHgE+FxmQtW9ypatlVEFRQUIDjx49j6tSp6uecnJzQuXNnpKamWnBkRESPqIKATZs8MGmSFAqFBE5OAosWyTFw4L92vZtI39KUsTMyqro4MpkMWVnlz/iUzPsByl9q4xZ3x2RXQdDt27dRXFyM4OBgjeeDg4Nx7ty5cl+Tn5+P/Px89eO8vDyzjpGIHJsqCJDLvbBs2TgIoao7JMHEid64dm0NpNK7Nr/l3pBE5PIYk+tT8r0MqTEE6F5qe+edaG5xdzB2FQRVxPz58zFnzhxLD4OIKsGcS0qGzgAYep4qMNA342HLW+6NCUpKU83I7NjREwqF7lwfFUPvlb6ltoCAAISGBnOLuwOxqyAoICAAzs7OuHHjhsbzN27cQEhISLmvmTp1KsaPH69+nJeXh4iICLOOk4hMx9ytLMxVH8eec1CMCeDKW5qKjT2JmTNb4e7d4Ern+pTE5Gcqza6CIDc3N8TFxWHv3r3o3bs3AEChUGDv3r0YPXp0ua9xd3eHu7t7FY6SiEwlM/NRAASoWlkom0iackbIHEshrDuke2kqLEyB0NCKXVfb9np7DjypYuwqCAKA8ePHIzExES1btsQTTzyBZcuW4f79++rdYkRkPy5ceBQAqRQXK3cL2cJOK0epO1ReUGKuujy6AismP1NpdhcE9e/fH7du3cLMmTORnZ2N5s2b46effiqTLE1Etk0mk8HbuwhOTo+amgKAs7OAl9dNyGS20dTU3usOaQtKzLE0pS+w6tOnD5KTA5j8TGp2FwQBwOjRo7UufxGR7SuZeNujh+aXbPfuO7Bjh/L//G19h5Wt0xWUmGNpSl9gpUx8DmV/L1KzyyCIiOxbycRbXUtKtrzDyh7oCkqioy+bfGmKOT9kLAZBRGTzbGlJydRb7q2ZvqDEVHV5VPeKOT9kLAZBRERVyFxb7lW0FSkEgNzcXEgkEkilUpO/b+nrAPqDkrCwMPj7+1d6aar0PWXODxmKQRARURUz15dxZYoUqpgij8oSQUnJazHnhwzFIIiIyE6YIgfKVHlUDErIFjAIIiKyU9qKBuo6Vl5vLy4lkb1iEEREZId0FQ3UdSwlJaXc67HcANkjBkFEZHMcaYdVReiqzwNAZ0FBbTNEqmUyXYnXAGeNyLYwCCIim2PuHVZVyRxBha76PIBE67FLl+ponSFSjdWQxGvOGpGtYBBERFYjM1PZD6xuXf29v+zhS9ZcQYW++jzlHXN1LdDby6t0sKZv1ojI2jnpP4WIyPxWrwYiI4FOnZR/rl5t6RGZX3lBRXp6FORyL53n6aOqzyORKLvLlqzPo+1YYaG7jtmjsk6caIFly8Zh/fpELFs2DidOtDBqjETWgDNBRGRxmZlAcvKjjvAKBTBiBJCQYBvd4E1BV7KyoUrmQOlqJ1LeMbncy+CWE+bqAE9U1RgEEZHFXbjwKABSKS4GLl40PAgyZinN2pgqqNCXK1Vexejbt28jJSVFb3XnkszRAZ7IEhgEEZHF1a0LODlpBkLOzkCdOoa9fvXqRzNJTk7AqlVAUpJ5xmoOpggqDEmwfuyxx3ReQ9fsUUlsVEr2gkEQEVmUTCaDs3MBFi3ywOTJUhQXS+DsLLBwoRzOzv9CJtO+O0omkyEjowjJyUFQKCQAVEtpAs2b30RUlItNJFBXNqgonWCtLWG5vATr0mUEtDWjdXNzUwdZxswaEVkzBkFEZDGlv7zHjPFSz0Lcu3cXq1Ypny/vy1v12vT0KCgUiRrHiosl+PDDHxEdfdkmtmtXNqgoOQOkK7eovJkiY8oNZGVlqZ8zdNaIyJoxCCIiiyn9xattFqK8L2jVc/pmUWxlu7YpgoqK5hYZGiQaM2tEZAsYBBGRTTPl0kxVV0M2dVBh7oRleypSSQQwCCIiO2CKWZTK5NVUlKmDiqpIWGaAQ/aEQRAR2QVtsyiGqkxeTWWYIqiQy+UAlPegc+c92L27MwAmLBPpwyCIiKgEU9XsqaqlNZlMhs2bNwNQBm979jwKgDp33mN0wUUiR8IgiIjMwlaLF5qqZk9VLa2pAq3ygrc9ezqjceM0zgQRacEgiIhMzpaLF5oir8YSS2uGBG/ctUWkiQ1UiciktPUBy8wse66hX8rlnVeZ1+qiq/mosbQtrZVukFoZubm5AB4FbyWVDN769evHpGaiUjgTREQmZUwfsMrsjir52tzcXBQVFQEAsrNdcOWKO2rVykdEhAQFBQWQyWRGBQCmKgRo7i3rMpkMW7ZsAaC/VICPj0+l34/I3jAIIiKTkclk8PYugpPTozYWAODsLODldRMyWdk2FpWZnfD399cIBHQtPRmbf1PZ3WaA+beslw4eY2Iu4vnntwIQiIjIZC4QkR4MgojIJEomA/fooRmMdO++Azt2VCwY0UdXYnDJXV368m/MsbxWlT22dAWARFS+CgVB9+/fR/Xq1U09FiKyYSWDDF3LSeZqY1HZpSdzVUOuih5bptrWT+RoKhQEBQcHo1+/fhg2bBieeuopU4+JiOyAKZaTjGGKpSdzJQ6Xdy/kci8cOuSG1q0rX0KAO8OIKqZCu8O++OIL5OTkoFOnTqhXrx4WLFiA69evm3psREQGM+WursrSF3CcONECy5aNQ9++/oiMVJYUqAx9O8P69+/PnWFE5ajQTFDv3r3Ru3dv3Lp1Cxs2bMC6deswY8YMJCQkYNiwYejVqxdcXJhuRERVqyqWngyhbWlNLpfj6lWBt99uACGUiePKEgICzZvfRFRU2cRxQ+jLPZJKpZX/UER2qFJ1ggIDAzF+/HicOXMGS5YswZ49e/DCCy8gLCwMM2fOxIMHD0w1TsybNw9t2rSBp6en1q2eV65cQffu3eHp6YmgoCBMnDhRvW2WiByDVHoX0dGXLZ4L4+/vj9DQUPWPm5sbNm/ejI0bf9PYOQcAxcUSfPjhj/joo48gk8kq9H6xsScxbtwyJCauw7hxy5gUTWSASk3X3LhxA+vXr8e6detw+fJlvPDCC0hKSkJmZiYWLlyII0eOYNeuXSYZaEFBAfr27Yv4+HisLmfuuLi4GN27d0dISAgOHz6MrKwsvPzyy3B1dcW7775rkjEQke26ffs2ANP17DKWalZIX+6SMYnjpZfdtOVhMR+IqHwVCoJSUlKwdu1a7Ny5Ew0bNsRrr72GwYMHa8zQtGnTBo899pipxok5c+YAANatW1fu8V27duGvv/7Cnj17EBwcjObNm2Pu3LmYPHkyZs+ezf8IENkpXf9ul+zZlZKSon7e1Nv0jWHKbfPm2tFG5CgqFAQNHToUAwYMwKFDh/D444+Xe05YWBimTZtWqcEZIzU1FU2aNEFwcLD6uYSEBIwcORJ//vknWrRoUe7r8vPzkZ+fr36cl5dn9rES2SNztbHQp2QgcPv2bXWwU1U9uyrClLlLDHCIKq5CQVBWVhY8PT11nuPh4YFZs2ZVaFAVkZ2drREAAVA/zs7O1vq6+fPnq2eZiKjiLDkrUfqatlA3p6pLCBBRWRUKgjw9PVFcXIxvv/0WZ8+eBQA89thj6N27t1G7wqZMmYKFCxfqPOfs2bNo0KBBRYZpkKlTp2L8+PHqx3l5eYiIiDDb+xHZM2uZlTB3zy4isg8VCoL+/PNP9OzZEzdu3ED9+vUBAAsXLkRgYCC2b9+Oxo0bG3SdCRMmYMiQITrPqV27tkHXCgkJwW+//abx3I0bN9THtHF3d4e7u7tB70FEtsHcPbuIyD5UKAgaPnw4GjdujOPHj8PX1xcAcOfOHQwZMgTJyck4fPiwQdcJDAxEYGBgRYZQRnx8PObNm4ebN28iKCgIALB79254e3ujYcOGJnkPInuUmans/F63buUrF1uLquzZVZpMJlMvCV6/7oT0dBdERxfBze222d+biIxToSDo1KlT+P3339UBEAD4+vpi3rx5WhOlK+vKlSvIycnBlStXUFxcjFOnTgEA6tSpgxo1aqBLly5o2LAhXnrpJSxatAjZ2dmYPn06Ro0axZkeIi1WrwaSk5UF+5ycgFWrgKQkS49KqWQwUR59+UWWKJxYsols2cTso4iN1X8N7mQlqjoVCoLq1auHGzduoFGjRhrP37x5E3Xq1DHJwEqbOXMm1q9fr36s2u21f/9+dOjQAc7OztixYwdGjhyJ+Ph4VK9eHYmJiXj77bfNMh4iW5eZ+SgAAlSVi4GEBMvPCJUMJgDNre4lgxl9W92rOvnY0I72ffr0QUBAQJnXczs7UdWqUBA0f/58jBkzBrNnz0br1q0BAEeOHMHbb7+NhQsXamwz9/b2NslA161bp7VGkEpkZCR++OEHk7wfkT2TyWQ4cgRQKDS/cIuLgaNHZfDwsGySc8kZIGO2ultqm35p+hKzAwICEBoaatYxEJF+FQqCevToAQDo168fJBJl+XchBACgZ8+e6scSiQTFxcWmGCcRmYhqlkUu94JEMq5M8vChQ+uRlnbXogUFVYzd6m4txQOZmE1kGyoUBO3fv9/U4yCiKqIKEPQlD1u6oCCgf0bl+nUnnDunmdRt6cANsGxiNhEZrkJBUPv27U09DiKyAGvpuq6NrhmVEyda4O23g/6X1C2waJEcAwf+azV5NdZ+b4moEg1Uc3NzsXr1anWxxEaNGmHYsGGQSqUmGxwRmZ81Vy7WNqMC4H/PKZfjFQoJJk70xrVrayCVWsdSHmDd95aIKhgE/f7770hISICHhweeeOIJAMCSJUswb9487Nq1C7GG7AMlIjJAeTMq6elROpfJrGEpj4isX4WCoDfeeAO9evXCZ599pm6TUVRUhOHDh2PcuHH45ZdfTDpIInJspWdUrDXx2Fp2pxGRYSo8E1QyAAIAFxcXTJo0CS1btjTZ4IjIMekLEqw18dhadqcRkWEqFAR5e3vjypUrZRqbXr16FV5eXiYZGBE5Lm3BxO3bt5GSkgLAehOPGeAQ2Y4KBUH9+/dHUlIS3nvvPbRp0wYAcOjQIUycOBEvvviiSQdIRKZlK0s2hgQTTDwmosqoUBD03nvvQSKR4OWXX0ZRUREAwNXVFSNHjsSCBQtMOkAiMi0u2RARKRkdBBUXF+PIkSOYPXs25s+fj0uXLgEAYmJi4OnpafIBElkTe+m4zgCHiAhw0n+KJmdnZ3Tp0gW5ubnw9PREkyZN0KRJEwZAZPdWrwYiI4FOnZR/rl5d/nmZmcD+/co/ybRsZSmPiGxDhZbDGjdujH/++QfR0dGmHg+R1ZHJZMjIKEJychAUClVxPmDECIHmzW8iKspFPbOyevWjzuxOTsCqVUBSkiVHbz4ymazKl9S4lEdEpiQRqs6nRvjpp58wdepUzJ07F3FxcahevbrGcVN1jreEvLw8SKVSyOVym/4cZBqqZqPp6VFYvz6xzPHExHWIjr6M0aNH499//REZqQyAVJydgYwM2146K4/qvqjI5V7IyfGHn59MI1HZWio3E5H9q8j3d4Vmgp599lkAQK9evdRd5AF2jif7o5px0Fec7+bNm/jzT0Ch0PzCLy4Gjh6VwcPDvvJwbt26pf77iRMtytTriY09CUB7E1Z7ya0iItvGLvJEBtBXnG/Lli2Qy70gkYwrEygdOrQeaWnW08+qsmQyGTZv3gxAOQO0bVsPqNILhXDC9u09EBNzUevWdUdaMiQi61ahICg6OhoREREas0CAcibo6tWrJhkYkbXRV5xPX6BkL/2sSn6Oo0dbofT+ipI9vErLzHwUAAGq3CogIYEzQkRU9SocBGVlZSEoKEjj+ZycHERHR3M5jOyWvuJ81lrF2Bzkci+kpsaXc6T8Hl4ymQxHjjjOkiERWb8KBUGq3J/S7t27h2rVqlV6UES2zNJVjKtq11ZOjn+ZTu4A0KZNapnPr0qkdpQlQyKyDUYFQePHjwcASCQSzJgxQ6M2UHFxMY4ePYrmzZubdIBEZLjSu7a0qWiwIZPJcPv2bQCAq2s+AAVKLodJJAq0anW0zOtUQZmjLBkSkW0wKgg6eVK540MIgT/++EOjIJmbmxuaNWuGN99807QjJCKDlQ4itG1dr0iwUTLAUu0IUwZAAoDE4E7ujrRkSETWzaggSLUrbOjQoVi+fDnr6JDds+XKw7q2rleEKnCSy73U11VSBkBJSZ8jPDxL4zXa7p+llwyJiIAK5gStXbvW1OMgskqGVCiWy+XqLePWonSgYsjWdUOVlwskhBMKCzUDnn79+jG/h4isWoWCoPv372PBggXYu3cvbt68CUXJErkA/vnnH5MMjsga6Psit8Z+VtoCFW1b142hr3Ckimr3aGYmcOSIG+RyL87+EJFVqVAQNHz4cBw4cAAvvfQSQkNDy90pRuQorLGflaGBSkXoS27u06cPwsLC4O/vX6Iwoj8kknGVXpIjIjKlCgVBP/74I77//ns8+eSTph4PkU2ytmUffYFKZelKbg4ICIC/v3+ZwoimXJIjIjKFCgVBvr6+8PPzM/VYiMiEzL0LS19y84ULms1kAf1LcraciE5EtqdCQdDcuXMxc+ZMrF+/XqNWEBFZVukgQlugYkiwUZkmpzKZDN7eRXByCoJC8Wi53MlJYNCgVggPbwUfH58yY7K2GTUism8VCoLef/99XLp0CcHBwYiKioKrq6vG8RMnTphkcERknMrmJ6mqTW/a5IFJk6RQKCRwchJYtEiO7t3lBo3hwYMHWLVqFQCgRw/Nbfo9euzA4cPKnCBWhyYiS6tQENS7d28TD4OITKWigUXJ1hbLlo2DEMoZHIVCgokTvXHt2hpIpcqt76VncVTc3Nw0ArDY2JMICsrGlSu1UKvWFY06QqwOTUSWVqEgaNasWaYeh04ZGRmYO3cu9u3bh+zsbISFhWHw4MGYNm2axrT+mTNnMGrUKBw7dgyBgYF4/fXXMWnSpCodK5GtUgUl+rbX+/j4IDQ0VOt1srIeBTqmLthIRGRKZbsf6vDbb7/p7BCfn5+PLVu2VHpQpZ07dw4KhQKffvop/vzzTyxduhQrV67EW2+9pT4nLy8PXbp0QWRkJI4fP47Fixdj9uzZ6ml5IjKMant9SRXZXq+tYKNc7mWysRIRVYZRQVB8fDxkMpn6sbe3t0ZhxNzcXLz44oumG93/dO3aFWvXrkWXLl1Qu3Zt9OrVC2+++SZSUlLU52zcuBEFBQVYs2YNGjVqhAEDBmDMmDFYsmSJycdDZM9U2+tVgZCx2+vlcmXukK4ZpZLnERFZilHLYUIInY+1PWcOcrlcY5t+amoq2rVrp7E8lpCQgIULF+LOnTvw9fUt9zr5+fnIz89XP87LyzPfoInMRJXQrI2xO68qs72+sLAQgP6CjarziIgspUI5QbpURfXoixcv4sMPP8R7772nfi47OxvR0dEa5wUHB6uPaQuC5s+fjzlz5phvsERmVrK7O6C9c7yxu7Eq2+TU3AUbiYgqy+RBkDGmTJmChQsX6jzn7NmzaNCggfrxtWvX0LVrV/Tt2xevvPJKpccwdepUjB8/Xv04Ly8PERERlb4uUVUpOQOkKxHZEruxzF2wkYioMowOgv766y9kZ2cDUC59nTt3Dvfu3QMA3L5926hrTZgwAUOGDNF5Tu3atdV/v379Ojp27Ig2bdqUSXgOCQnBjRs3NJ5TPQ4JCdF6fXd3d7i7uxs1biJrZM7O8cZwcdH8z4q2GaXS5xERVTWj/yv09NNPa+T99OjRA4ByGUwIYdRyWGBgIAIDAw0699q1a+jYsSPi4uKwdu1aODlpJlzGx8dj2rRpKCwsVBdv3L17N+rXr691KYzInlS2c7yhLSv0naethlBFzyMiMhejgqD09HRzjUOna9euoUOHDoiMjMR7772HW7duqY+pZnkGDhyIOXPmICkpCZMnT0ZaWhqWL1+OpUuXWmTMZBqmTvi1Z5XtHF/ZatNERLbGqCAoMjLSqIu/9tprePvttxEQEGDU60rbvXs3Ll68iIsXLyK8VBMj1ayUVCrFrl27MGrUKMTFxSEgIAAzZ85EcnJypd6bLKd0wq82bL+gZIpEZN5HInIkZl2U/+KLL/Dmm29WOggaMmSI3twhAGjatCkOHjxYqfci62FoIq89t18wZCasJGtIRDbVshoRkbmZNQiqqppB5Bi0bf22V4bOhPXr10/jcWW3tlcWl9WIyFZwewbZBHvsQaVvlsfQisqGbkaoypkXBjhEZAsYBJHVs5at36Zk6CxPSdpmwqRSKWdeiIgqgEEQWb3Kbv22RsbmMembCWOAQ0RkPKMaqBJZgqm6mtsqdmMnIjIPswZBgwcPhre3tznfguyQTCZDVlaWugJ5Zbua2zp93diJiKhiKrQcplAoylRsVj2fmZmJWrVqAQBWrFhRudGRw9GWK6Nr67c9bLXWtfOtskUQiYiofEYFQXl5eRg+fDi2b98Ob29vjBgxArNmzYKzszMA4NatW4iOjkZxcbFZBkv2T1euTMmt33369EFAQIBVJvwaW9tHX74Pu7ETEZmHUUHQjBkzcPr0aWzYsAG5ubl45513cOLECaSkpKj/w87aQFQVAgICEBoaaulhlGHorq/+/fsDMHznm7aZMLncC0ePeqJlS6BUMXUiItLDqCDou+++w/r169GhQwcAQO/evdG9e3f07NkT27ZtA2B4zRIie2Torq/CwkIA+ne+9evXT2uj0U2bPPD221IsXSqBkxOwahWQlFSp4RMRORSjgqBbt25p9A8LCAjAnj17kJCQgGeffRaff/65yQdItseUTU/ttUq0i4vyXz19+T5BQUFl7pVMJkNGRhEmTZJCoVD+T4dCAYwYIdC8+U1ERbnA398fmZnAhQtA3bqcJSIiKo9RQVCtWrVw9uxZREdHq5/z8vLCrl270KVLF/znP/8x+QDJtpiy6ak9VolW8fHxURc4rFkzD5MnS1FcLIGzs8DChXkYOPDFcoNF1f1NT4+CQpGocay4WIIPP/wR0dGX4eycjFmzQqBQSODkJLBokRwDB/5rlTlURESWYtQW+S5dumDt2rVlnq9RowZ27tyJatWqmWxgZJtM1fTUXmrjyOVeSE+PKnfc/v7+CA0NxYQJPsjIkGD/fiAjQ4IJE3wQGhpabrCium+6aifJ5V6YOTO4xCyRBBMnemPx4i/x0UcfQSaTmeGTEhHZHqNmgubMmYPr16+Xe8zLywu7d+/GiRMnTDIwcmz2UCXamJms8HDjlqx07RhLT4/See+MrVZNRGSvjAqCfH194evrq/W4l5cX2rdvX+lBkeNS7TLUlytj7bWBqqLfmbYdY6wrRERkGKOLJRYVFWHp0qX48ssv8ffffwMA6tWrh4EDB2Ls2LFwdXU1+SDJcfj7+1coV8baVNVMVsnaSSWfY10hIiL9jAqC/v33XzzzzDNITU1F586d0a5dOwDA2bNnMXnyZGzbtg27du1ibhBViirAmTAB6N8fuHgRqFNHgvBwHwA+lhyaXtYyk6WrwjYRESkZFQQtWLAAV69excmTJ9G0aVONY6dPn0avXr2wYMECzJ4925RjJBtW2S3uxubKWFrJmSw/v2zMmhWq3qE1c+Z19OnzDFxdXVFQUACZTGbWGa3yZomIiOgRo4Kgr776CkuWLCkTAAFAs2bN8N5772HatGkMggiAfW9x18Xf3x8ymQzFxR9h7Fgv9WwMcBcpKZrnGlIqAIC65o+3t1l7HhMRORSj/ot6+fJlPPHEE1qPt27dGleuXKn0oMh2qZZ59G1xt/bE5spS7cCSSu8iOvoyAJS7VV7XTi2ZTIasrCy8/34uIiMFOnUCnngiCCdOtDDfwImIHIhRM0He3t64efMmIiIiyj2enZ0NLy/bquNCpqVaDtq/H1i6tGxi8JNPJqJDB1h9YrMpVWRGTFUUUS73wrJl4yDEo5o/JXeZlWyrkZubiy1btugdj70HoEREhjIqCOrYsSPeffddbN26tdzjCxYsQMeOHU0yMLJd/v7+aN0acHJStnNQcXYGWrXyhwPFPxXeKq+aIdK3y8zHx0fdSDY0NFSdj6SNLeysIyKqKkYFQbNmzUKrVq3QunVrjB8/Hg0aNIAQAmfPnsXSpUvx119/4ciRI+YaK9mQ8HBlQ88RI4DiYmUA9OmntpXkbAqV3SpvbM0fBjhERIYzKghq2LAhdu/ejaSkJAwYMEDdMV4IgQYNGmDXrl1o1KiRWQZKticpCUhIUG1xd7wACKh84ULW/CEiMh+jiyW2bt0af/75J06dOqVRLLF58+amHhvZAVvb4l4RMpmszBLU7du3AWgPYgBlovT1607432qWVqz5Q0RkHkYHQXl5eahRowaaN2+uEfgoFArcu3cP3t7ephwfkVVTJTDrUjqIuXSpzv+SnZ2wYYPAokW5eju8s+YPEZHpGbVF/ttvv0XLli3x8OHDMsf+/fdfPP7449i+fbvJBkdk7QxtRlpyq3zJRGl2eCcishyjgqAVK1Zg0qRJ8PT0LHOsevXqmDx5st7/KyayZ3K5V7n1gFS7JnUlSgOGB1VERFR5Ri2HpaWl4ZNPPtF6vF27dpg+fXqlB0Vki3TVA/L19QVgeKK0obV8WPOHiKjijAqC7ty5g6KiIq3HCwsLcefOnUoPisjWGFoPyNDdXiV7kGnDmj9ERJVjVBAUFRWF33//HQ0aNCj3+O+//47IyEiTDKy0Xr164dSpU7h58yZ8fX3RuXNnLFy4EGFhYepzzpw5g1GjRuHYsWMIDAzE66+/jkmTJpllPEQlGVMPyNDdXgxwiIjMy6icoD59+mDatGm4ceNGmWPZ2dmYPn06nn/+eZMNrqSOHTtiy5YtOH/+PLZu3YpLly7hhRdeUB/Py8tDly5dEBkZiePHj2Px4sWYPXs2Vq1aZZbxEJWkWuYqSVc9IFWiNHd8ERFZjlEzQVOmTMH//d//oW7duhg8eDDq168PADh37hw2btyIiIgITJkyxSwDfeONN9R/j4yMxJQpU9C7d28UFhbC1dUVGzduREFBAdasWQM3Nzc0atQIp06dwpIlS5CcnGyWMZH1K6+GT0mmWlJiUUMiIttjVBDk5eWFQ4cOYerUqdi8ebM6/8fHxweDBw/GvHnzqqSBak5ODjZu3Ig2bdrA1dUVAJCamop27dppJIomJCRg4cKFuHPnjjoxlRyHITV8AGD06NEVDoRK/r7pWuZycTG6JBcREZmZ0f9llkql+OSTT/Dxxx/j9u3bEEIgMDBQ3UKjpEOHDqFly5Zwd3c3yWBVW/AfPHiA1q1bY8eOHepj2dnZiI6O1jg/ODhYfUxbEJSfn4/8/Hz147y8PJOMlSzP0O3mldmWbmgCs6G424uIqOpU+H9PJRIJAgMDdZ7TrVs3nDp1CrVr1y73+JQpU7Bw4UKd1zh79qw6EXvixIlISkrC5cuXMWfOHLz88svYsWNHuQGYoebPn485c+ZU+PVEhs4icbcXEZF1MescvRBC5/EJEyZgyJAhOs8pGUAFBAQgICAA9erVw2OPPYaIiAgcOXIE8fHxCAkJKZOwrXocEhKi9fpTp07F+PHj1Y/z8vIQERGhc0zkGEydT8QAh4jIulg0USEwMFDvbJI2CoVyJ45qKSs+Ph7Tpk1TJ0oDwO7du1G/fn2d+UDu7u4mW64j+1EV+URERGRZRm2Rt5SjR4/io48+wqlTp3D58mXs27cPL774ImJiYhAfHw8AGDhwINzc3JCUlIQ///wTmzdvxvLlyzVmeYgMVXoGSFs7DLa5ICKyXTaxZcXT0xMpKSmYNWsW7t+/j9DQUHTt2hXTp09Xz+JIpVLs2rULo0aNQlxcHAICAjBz5kxuj6dK09UOg4iIbJdZg6DKJCyX1KRJE+zbt0/veU2bNsXBgwdN8p5EgOHtMIiIyPZYNDGabE9VFR+sqJLjy83N1Tgml3shJ8cffn4yjQBG17Z0Y9phEBGRbTFrEHT3Lr8k7Im1JwvrGl/JJS0nJ4FFi+QYOPBfvUGboV3fiYjI9hgVBHXq1Mmg8wxZuiLbUxXFBytD2/uWXtJSKCSYPNkH/fv7QF+spq8dxu3bty0++0VERBVjVBD0888/IzIyEt27d1dvQyeyduUtaRUXAxcvAuHh+l+vqx1GSkoKAG6VJyKyRUYFQQsXLsTatWvx9ddfY9CgQRg2bBgaN25srrERmUR5S1rOzkCdOtpfUzpPSCq9C6n0rnqrfOm8Im6VJyKyPUbVCZo4cSL++usvfPfdd7h79y6efPJJPPHEE1i5ciV7bpHVUi1pSSTKApvOzgKffqp7FkjVE6xPnz7q506caIFly8Zh/fpELFs2DidOtDD30ImIyIwqVCwxPj4en332GbKysjBq1CisWbMGYWFhDITIasXGnsS4ccuQmLgOR4/eRFKS/tf4+/sjICAAgPat8qWLJxIRke2o1O6wEydO4MCBAzh79iwaN27MPCGyaqolrbAwhdGv5VZ5IiL7Y/RM0PXr1/Huu++iXr16eOGFF+Dn54ejR4/iyJEj8PDwMMcYiSpEW6uLilDlFZXErfJERLbNqJmgZ599Fvv370eXLl2wePFidO/eHS4uNtF5g0xAV1HBipxnaiXfV1eri4qMT99WeSIisj0SYURZZycnJ4SGhiIoKEhnS4wTJ06YZHCWkJeXB6lUCrlcDm9vb0sPx+rYQsXojIwiPPFEEBSKR7+jzs4CR4/eRFSUi1Hjy8rKwqpVq9SPlVWny26VT05ORmhoqGk+BBERGa0i399GTePMmjWrQgMj+2HttXD8/f1x5gygKJX2U1wswd27wXqLI5ambau8vvOIiMj6GTUT5Ag4E2S4zEzgwgWgbl3Dig5WlT/+uIPmzX3KnQkKC1MYPVtl7bNfRERUBTNB2hw4cAD3799HfHw8fH19TXFJsnKrVwPJycoZFycnYNUqGLTt3NxkMhlSUj5Cjx6aOUHdu+/Ajh0n1ecZU+GZAQ4RkX0yumL0vXv3MHfuXADKLvHdunXDrl27AABBQUHYu3cvGjVqZPqRktXIzHwUAAHKP0eMABISLD8jpJqx0dXqouR5RETkuIzaIr9582aNNhnffPMNfvnlFxw8eBC3b99Gy5YtMWfOHJMPkqzLhQvl5dwoe3FZE6n0LqKjL3MHFxERlcuoICg9PR1NmzZVP/7hhx/wwgsv4Mknn4Sfnx+mT5+O1NRUkw+SrEvdusolsJL09eKyFFPWCiIiIvti1HJYUVER3N3d1Y9TU1Mxbtw49eOwsDDcvn3bZIMj6xQerswBGjFCOQPk7Ay9vbgsQVetICIiIqNmgmJiYvDLL78AAK5cuYK///4b7dq1Ux/PzMxkEqmDSEoCMjKA/fuVf1pDUnRJ7PVFRET6GDUTNGrUKIwePRoHDx7EkSNHEB8fj4YNG6qP79u3Dy1asLO2owgPt77ZHxX2+iIiIn2MCoJeeeUVODs7Y/v27WjXrl2Z4onXr1/HsGHDTDpAIhVD6vWoqHp9lQyE2OuLiIhKMrpO0LBhw7QGOp988kmlB0RUHplMho8++kjveb169QKgv9cXKzwTERErRpfCitHWqXQPL12effZZ1KhRAz4+Prh+3QkZGS6IiipCWJhyXz8rPBMR2Z+KfH8blRhdWFiISZMmoU6dOnjiiSewZs0ajeM3btyAs7OzMZckMrkffvgBW7ZsgZubG+LigvH88/6IiwtGaGgoQkNDGQAREREAI4OgefPm4b///S9effVVdOnSBePHj8eIESM0zuHEElkLVoUmIiJdjMoJ2rhxIz7//HP06NEDADBkyBB069YNQ4cOVc8KSSQSXZcgIiIisgpGzQRdu3ZNo21GnTp18PPPP+Pw4cN46aWXUFxcbPIBEpVHLvdCWlpDpKU1RGZmKKtCExGR0YyaCQoJCcGlS5cQFRWlfq5mzZrYv38/OnbsiCFDhph4eOQIjNn6DigrQW/b1gOPYngBQMKq0EREZBSjgqBOnTph06ZNePrppzWeDwsLw759+9ChQwdTjo0cgKFb3/v37w9AOQOkGQABgHIJVlUVOibmIgsiEhGRXkYFQTNmzMC5c+fKPVazZk0cOHAAu3fvNsnAyDEYmrysSrjPyfGHrlVcVoUmIiJDGZUTFBkZiYSEBK3Hw8LCkJiYWOlBEZXm4+ODwYMHw89PBkCh9TxWhSYiIkMZFQSpfP311+jTpw8aN26Mxo0bo0+fPvjmm29MPbZy5efno3nz5pBIJDh16pTGsTNnzqBt27aoVq0aIiIisGjRoioZE1WNmJgYTJuWiLlzs6EZCClniVgVmoiIjGHUcphCocCLL76Ir7/+GvXq1UODBg0AAH/++Sf69++Pvn374ssvvzTrNvlJkyYhLCwMp0+f1ng+Ly8PXbp0QefOnbFy5Ur88ccfGDZsGHx8fJCcnGy28VDFyWQy3L5926jX+Pv7Y/p04Lnn7iA1VblMFhFRjH//dfpfVejHATzOqtBERKSXUUHQ8uXLsWfPHmzbtk1dK0hl27ZtGDp0KJYvX45x48aZcoxqP/74I3bt2oWtW7fixx9/1Di2ceNGFBQUYM2aNXBzc0OjRo1w6tQpLFmyhEGQFTI0IVqbJk180aSJCQdEREQOx6jlsLVr12Lx4sVlAiBA2bhy0aJFZVppmMqNGzfwyiuvYMOGDfD09CxzPDU1Fe3atdNYAklISMD58+dx584drdfNz89HXl6exg+Zn7aEaLncizV/iIioShgVBF24cAGdO3fWerxz5864cOFCpQdVmhACQ4YMwauvvoqWLVuWe052djaCg4M1nlM9zs7O1nrt+fPnQyqVqn8iIiJMN3AyyokTLbBs2TisX5+IZcvG4cSJFpYeEhER2TGjgiAPDw/k5uZqPZ6Xl4dq1aoZfL0pU6ZAIpHo/Dl37hw+/PBD3L17F1OnTjVmuAaZOnUq5HK5+ufq1asmfw/STy73wvbtPSCE8ldSVfNHNSPEJGciIjI1o3KC4uPjsWLFCqxYsaLc4x9//DHi4+MNvt6ECRP0VpmuXbs29u3bh9TUVLi7u2sca9myJQYNGoT169cjJCQEN27c0DiuehwSEqL1+u7u7mWua+8MqdBc1UnFOTn+6gBIRQgnNG7cG889J2WSMxERmZxRQdC0adPQoUMHyGQyvPnmm2jQoAGEEDh79izef/99/N///R/2799v8PUCAwMRGBio97wPPvgA77zzjvrx9evXkZCQgM2bN6NVq1YAlAHatGnTUFhYCFdXVwDA7t27Ub9+ffj6+hrzMe2aoQnJo0ePrtLAw89PBolEoREISSQKRETko6CgAFlZWdzxRUREJmVUENSmTRts3rwZycnJ2Lp1q8YxX19ffPnll3jyySdNOkAAqFWrlsbjGjVqAFDWjQkPDwcADBw4EHPmzEFSUhImT56MtLQ0LF++HEuXLjX5eGyZoRWaDT3PlOLjU5GaGg8hnNQ1fw4fPonDhx+dU9XBGRER2S+jgiAA+M9//oOEhATs3LlTnQRdr149dOnSpdxdW1VFKpVi165dGDVqFOLi4hAQEICZM2dye7wNOHGiRYl8IAXatDmEVq2Oltv6whLBGRER2SejgqB9+/Zh9OjROHLkCP7zn/9oHJPL5WjUqBFWrlyJtm3bmnSQpUVFRal7SZXUtGlTHDx40KzvTaahSnQunRANOCE1NR6tWh213OCIiMghGLU7bNmyZXjllVfg7e1d5phUKsWIESOwZMkSkw2O7Je/vz9Gjx6NNm0Sy02Izsnxs9DIiIjIURgVBJ0+fRpdu3bVerxLly44fvx4pQdFjsHf3x+tW/vDqdRvoUSigKtrAYsmEhGRWRm1HHbjxg31zqtyL+biglu3blV6UOQ4wsOBVauAESMEioslkEgUaNr0DFavHq6RIB0be9LSQyUiIjtj1ExQzZo1kZaWpvX4mTNnEBoaWulBkWNJSgKOHr2JxMR1SEr6HGfONNVaNJGIiMhUjAqCnn32WcyYMQMPHz4sc+zff//FrFmzyu0rRtbD0MrLVV2hOSxMgejoyygsdGeOEBERVQmjlsOmT5+OlJQU1KtXD6NHj0b9+vUBAOfOncPHH3+M4uJiTJs2zSwDJdNQJSSrtppfv+6E9HQXREcXISxMAaBiFaMrW4VaFXRpK5ro55ejcR4REVFlSUR5e811uHz5MkaOHImdO3eqt6lLJBIkJCTg448/RnR0tFkGWlXy8vIglUohl8vL3QVnT1avBpKTAYUCcHJS5uYkJRl/HVNVoVYFUps2eWDyZCmKiyVwdhZYuFCOgQP/ZcVoIiLSqiLf30YHQSp37tzBxYsXIYRA3bp17aY1haMEQZmZQGSkMgBScXYGMjKUycrGyMrKwqpVq/Sel5ycbHDOWGYmcPEiUKeO8eMhIiLHU5Hvb6MrRqv4+vri8ccfr+jLycIuXNAMgACguFgZeFhD0BEebh3jICIi+1XhIIiqlqk7v9etq1wCKz0TVKeO8WPLzc01+DzuHiQiImvBIMgGmKPz+6P6PMoZIGdn4NNPKzb7UlRUZNLziIiIqgKDIBtQegZILvdCTo4//PxkGk1GjW0umpQEJCQw94aIiBwTgyAbU7LjuimqKZsj90ZbkEZERGRNGATZkNId11XVlGNiLlpNsGHqII2IiMhcjKoYTZaVk+NvldWUVf3ktAVpqpYXuvrOERERVTUGQTZEVU25pJLVlC1FKpUC0B+kqc4jIiKyBgyCbIhUehc9e+5QB0Kq5SZrWQpzdc0HULr2poCrq3EJ20RERFWBOUE2Jjb2JGJiLiInxw9+fjlWEQCp+nkVFroDkJQ6KkFhoZvGeURERNaAQVAVycxUVmmuW9f43Vilgwep9G65wY+lggxVU9aMjCJs2CCgUDwKhJydBV5/vRuiolzY94uIiKxKhXuH2Stz9A4zRaNSU1eMNpfVq8sWYKxIU1YiIiJjVGkDVXtl6iDIlI1KrVF5wdn1607IyHBBVFQRZ4CIiKhKVGkDVTKMtTcqrQx97TzS0pR/GtPOg4iIqKpwd5iZqRqVllTRRqXWxtA2Hca28yAiIqoKDILMTNWo1NlZ+bgyjUqJiIjIdLgcVgXYqJSIiMj6MAiqIuZoVEpEREQVx+UwMhm53Avp6VHqXmFERETWjDNBZBLsHk9ERLaGM0FUafq6xxMREVkjBkFUYao2Hfq6x7NnGBERWSObCYKioqIgkUg0fhYsWKBxzpkzZ9C2bVtUq1YNERERWLRokYVG6xhUPcNef70rnJw0C4+reoaxUCIREVkrm8oJevvtt/HKK6+oH3t5PVpuycvLQ5cuXdC5c2esXLkSf/zxB4YNGwYfHx8kJydbYrgOwd/fH/7+ylpImj3DJIiLC7b08IiIiLSyqSDIy8sLISEh5R7buHEjCgoKsGbNGri5uaFRo0Y4deoUlixZwiCoCrAWEhER2RqbWQ4DgAULFsDf3x8tWrTA4sWLUVRUpD6WmpqKdu3aaeSfJCQk4Pz587hz547Wa+bn5yMvL0/jhyomPBzo0IEBEBER2QabmQkaM2YMYmNj4efnh8OHD2Pq1KnIysrCkiVLAADZ2dmIjo7WeE1wcLD6mK+vb7nXnT9/PubMmWPewRMREZHVsWgQNGXKFCxcuFDnOWfPnkWDBg0wfvx49XNNmzaFm5sbRowYgfnz58Pd3b3CY5g6darGtfPy8hAREVHh69kjmUymswmqm5sbk5+JiMjmWDQImjBhAoYMGaLznNq1a5f7fKtWrVBUVISMjAzUr18fISEhuHHjhsY5qsfa8ogAwN3dvVJBlL2TyWT46KOP9J7HXWBERGRrLBoEBQYGIjAwsEKvPXXqFJycnBAUFAQAiI+Px7Rp01BYWAhXV1cAwO7du1G/fn2tS2G2KjMTuHABqFvX/Pk3umaAKnIeERGRtbCJxOjU1FQsW7YMp0+fxj///IONGzfijTfewODBg9UBzsCBA+Hm5oakpCT8+eef2Lx5M5YvX66x1GUPVq8GIiOBTp2Uf65ebekRERER2SabSIx2d3fHV199hdmzZyM/Px/R0dF44403NAIcqVSKXbt2YdSoUYiLi0NAQABmzpxpV9vjMzOB5GRAoVA+ViiUtXkSErgji4iIyFg2EQTFxsbiyJEjes9r2rQpDh48WAUjsowLFx4FQCrFxcraPIYEQUxwJiIiesQmgiBSqlsXcHLSDIScnZXFCfVhgjMREZEmm8gJIqXwcGV7Cmdn5WNlewrDZoGY4ExERKSJM0E2xlTtKeRyL+Tk+MPPTwap9K5pB0lERGQDGATZoPDwyiVCnzjRAtu394AQTpBIFOjZcwdiY0+We27JNiS6GHoeERGRtWAQ5GDkci91AAQAQjhh+/YeiIm5WO6MkL+/P0aPHs2EaiIisjsMgmyEqXZ25eT4qwMgFSGckJPjp3VZjAEOERHZIwZBNsCUO7v8/GSQSBQagZBEooCfX45BY6nKatVERETmxN1hNsCUO7uk0rvo2XMHJBLlPntVTpAhydGsVk1ERPaEM0EOomTicmzsScTEXEROjh/8/HI0AiBtCc6sVk1ERPaGQZCDqGyCc2WrVRMREVkbBkEOpDIJzpWpVk1ERGSNGATZOVPtKlNVqx4xQjkDZEy1aiIiImvEIMiOmbpfmKmqVRMREVkDBkF2zBz9wipbrZqIiMhacIu8DWDrCiIiItPjTJANMFXrCjZNJSIieoRBkI2obOsKY5qmEhEROQIuhzkAbU1T5XIvC4+MiIjIchgEOQBdTVOJiIgcFYMgB6BqmlqSMU1TiYiI7BGDIDum2i2mr2kqd5UREZEjkgghhKUHYU3y8vIglUohl8vh7e1t6eFUWsmK0devOyEjwwVRUUUIC1MGRIZWjCYiIrJmFfn+5u4wO1cywAkNBeLiLDgYIiIiK8LlMCIiInJIDIKIiIjIITEIIiIiIofEIIiIiIgcEoMgIiIickgMgoiIiMghMQgiIiIih2RTQdD333+PVq1awcPDA76+vujdu7fG8StXrqB79+7w9PREUFAQJk6ciKKiIssMFspChVlZWVp/ZDKZxcZGRETk6GymWOLWrVvxyiuv4N1330WnTp1QVFSEtLQ09fHi4mJ0794dISEhOHz4MLKysvDyyy/D1dUV7777bpWPVyaT4aOPPtJ73ujRo1mxmYiIyAJsIggqKirC2LFjsXjxYiQlJamfb9iwofrvu3btwl9//YU9e/YgODgYzZs3x9y5czF58mTMnj27yvtjqVpVmOo8IiIiMi2bWA47ceIErl27BicnJ7Ro0QKhoaHo1q2bxkxQamoqmjRpguDgYPVzCQkJyMvLw59//mmJYRMREZEVs4kg6J9//gEAzJ49G9OnT8eOHTvg6+uLDh06ICcnBwCQnZ2tEQABUD/Ozs7Weu38/Hzk5eVp/BAREZH9s2gQNGXKFEgkEp0/586dg0Kh7Hg+bdo0PP/884iLi8PatWshkUjw9ddfV2oM8+fPh1QqVf9ERESY4qMRERGRlbNoTtCECRMwZMgQnefUrl0bWVlZADRzgNzd3VG7dm1cuXIFABASEoLffvtN47U3btxQH9Nm6tSpGD9+vPpxXl6e1QVCMplMZ+6Qm5sbk6uJiIiMZNEgKDAwEIGBgXrPi4uLg7u7O86fP4+nnnoKAFBYWIiMjAxERkYCAOLj4zFv3jzcvHkTQUFBAIDdu3fD29tbI3gqzd3dHe7u7ib4NMaTy71w6JAbWrcGwsPLP4e7zIiIiMzDJnKCvL298eqrr2LWrFnYtWsXzp8/j5EjRwIA+vbtCwDo0qULGjZsiJdeegmnT5/Gzp07MX36dIwaNcpiQY4uJ060wLJl49C3rz8iI4HVq8s/z9DdY9evX2ftISIiIiPYxBZ5AFi8eDFcXFzw0ksv4d9//0WrVq2wb98++Pr6AgCcnZ2xY8cOjBw5EvHx8ahevToSExPx9ttvW2S8urbky+Ve2L69B4RQxqAKBTBiBJCQoH1GSJ+UlBT13zkrREREpJ9ECCEsPQhrkpeXB6lUCrlcDm9v70pdS1suz6FDbujbt2yQsn8/0KGD5nNZWVlYtWqV+rFc7oWcHH/4+ckgld4t932Tk5MRGhpaqbETERHZkop8f9vMTJAt0jYb07o14OSknAFScXYG6tTRfb0TJ1qoZ5AkEgV69tyB2NiTJhwxERGR47CJnCB7Ex4OrFqlDHwA5Z+ffqp7Kaz0EpoQTti+vQfkcq8qGDEREZH94UyQhSQlKXOALl5UzgDpywXKyfFXB0AqQjghJ8dP67IYERERaccgyILCww1PhPbzk0EiUWgEQhKJAn5+OWYaHRERkX3jcpiVU+0yk0rvomfPHZBIlIlEqpwgzgIRERFVDGeCrJy/vz9Gjx6NgoIC3L59GzExy5CT4wc/vxwGQERERJXAIKiKVaQFhuqxm5sbpNK7eoMfXTWKiIiISIlBUBWqbAuMkrNC2rCPGBERkWEYBFUhQ1tg6DqPAQ4REZFpMDGaiIiIHBKDICIiInJIDIIsRC73Qnp6FCs+ExERWQhzgiyAPcCIiIgsjzNBVcyUPcAyM5Wd5zMzTT1KIiIi+8cgqArl5ubq7AFmjNWrgchIoFMn5Z+rV5typERERPaPQVAVkclk2LJli7oHWEmle4DpK3aYmQkkJwOK/11GoQBGjOCMEBERkTEYBFURVe0ffT3A+vfvr7cW0IULjwIgleJiZUd6IiIiMgwToy0gNvYkYmIultsDTCqV6n193bqAk5NmIOTsDNSpY47REhER2SfOBFmIVHoX0dGXK9QENTwcWLVKGfgAyj8//VT5PBERERmGM0E2KikJSEhQLoHVqcMAiIiIyFgMgmxYeDiDHyIioorichgRERE5JAZBRERE5JAYBFURfbV/jD2PiIiIKoc5QVXE398fo0ePVtcLKo+bm5veGkFERERkGgyCqhADHCIiIuvB5TAiIiJySAyCiIiIyCExCCIiIiKHxCCIiIiIHBKDICIiInJINhEE/fzzz5BIJOX+HDt2TH3emTNn0LZtW1SrVg0RERFYtGiRBUdNRERE1swmtsi3adMGWVlZGs/NmDEDe/fuRcuWLQEAeXl56NKlCzp37oyVK1fijz/+wLBhw+Dj44Pk5GRLDJuIiIismE0EQW5ubggJCVE/LiwsxP/93//h9ddfh0QiAQBs3LgRBQUFWLNmDdzc3NCoUSOcOnUKS5YsYRBEREREZdjEclhp27Ztg0wmw9ChQ9XPpaamol27dhptJxISEnD+/HncuXNH67Xy8/ORl5en8UNERET2zyZmgkpbvXo1EhISEB4ern4uOzsb0dHRGucFBwerj/n6+pZ7rfnz52POnDllnmcwREREZDtU39tCCINfY9EgaMqUKVi4cKHOc86ePYsGDRqoH2dmZmLnzp3YsmWLScYwdepUjB8/Xv342rVraNiwISIiIkxyfSIiIqo6d+/ehVQqNehciwZBEyZMwJAhQ3SeU7t2bY3Ha9euhb+/P3r16qXxfEhICG7cuKHxnOpxyXyi0tzd3eHu7q5+XKNGDVy9ehVeXl7qfKOKyMvLQ0REBK5evQpvb+8KX8fW8T4o8T4o8T4o8T4o8T48wnuhVJn7IITA3bt3ERYWZvBrLBoEBQYGIjAw0ODzhRBYu3YtXn75Zbi6umoci4+Px7Rp01BYWKg+tnv3btSvX1/rUlh5nJycNJbZKsvb29uhf6FVeB+UeB+UeB+UeB+UeB8e4b1Qquh9MHQGSMWmEqP37duH9PR0DB8+vMyxgQMHws3NDUlJSfjzzz+xefNmLF++XGOpi4iIiEjFphKjV69ejTZt2mjkCKlIpVLs2rULo0aNQlxcHAICAjBz5kxujyciIqJy2VQQtGnTJp3HmzZtioMHD1bRaHRzd3fHrFmzNPKNHBHvgxLvgxLvgxLvgxLvwyO8F0pVfR8kwpi9ZERERER2wqZygoiIiIhMhUEQEREROSQGQUREROSQGAQRERGRQ2IQZIQVK1agadOm6iJO8fHx+PHHH9XHHz58iFGjRsHf3x81atTA888/X6aK9ZUrV9C9e3d4enoiKCgIEydORFFRUVV/FJNZsGABJBIJxo0bp37OUe7D7NmzIZFINH5Klm9wlPsAKNvNDB48GP7+/vDw8ECTJk3w+++/q48LITBz5kyEhobCw8MDnTt3xoULFzSukZOTg0GDBsHb2xs+Pj5ISkrCvXv3qvqjVFhUVFSZ3weJRIJRo0YBcJzfh+LiYsyYMQPR0dHw8PBATEwM5s6dq9HPyRF+HwBl+4Zx48YhMjISHh4eaNOmDY4dO6Y+bq/34ZdffkHPnj0RFhYGiUSC7777TuO4qT73mTNn0LZtW1SrVg0RERFYtGiR8YMVZLBt27aJ77//Xvz999/i/Pnz4q233hKurq4iLS1NCCHEq6++KiIiIsTevXvF77//Llq3bi3atGmjfn1RUZFo3Lix6Ny5szh58qT44YcfREBAgJg6daqlPlKl/PbbbyIqKko0bdpUjB07Vv28o9yHWbNmiUaNGomsrCz1z61bt9THHeU+5OTkiMjISDFkyBBx9OhR8c8//4idO3eKixcvqs9ZsGCBkEql4rvvvhOnT58WvXr1EtHR0eLff/9Vn9O1a1fRrFkzceTIEXHw4EFRp04d8eKLL1riI1XIzZs3NX4Xdu/eLQCI/fv3CyEc5/dh3rx5wt/fX+zYsUOkp6eLr7/+WtSoUUMsX75cfY4j/D4IIUS/fv1Ew4YNxYEDB8SFCxfErFmzhLe3t8jMzBRC2O99+OGHH8S0adNESkqKACC+/fZbjeOm+NxyuVwEBweLQYMGibS0NPHll18KDw8P8emnnxo1VgZBleTr6ys+//xzkZubK1xdXcXXX3+tPnb27FkBQKSmpgohlL8YTk5OIjs7W33OihUrhLe3t8jPz6/ysVfG3bt3Rd26dcXu3btF+/bt1UGQI92HWbNmiWbNmpV7zJHuw+TJk8VTTz2l9bhCoRAhISFi8eLF6udyc3OFu7u7+PLLL4UQQvz1118CgDh27Jj6nB9//FFIJBJx7do18w3ejMaOHStiYmKEQqFwqN+H7t27i2HDhmk816dPHzFo0CAhhOP8Pjx48EA4OzuLHTt2aDwfGxsrpk2b5jD3oXQQZKrP/cknnwhfX1+NfzcmT54s6tevb9T4uBxWQcXFxfjqq69w//59xMfH4/jx4ygsLETnzp3V5zRo0AC1atVCamoqACA1NRVNmjRBcHCw+pyEhATk5eXhzz//rPLPUBmjRo1C9+7dNT4vAIe7DxcuXEBYWBhq166NQYMG4cqVKwAc6z5s27YNLVu2RN++fREUFIQWLVrgs88+Ux9PT09Hdna2xr2QSqVo1aqVxr3w8fFBy5Yt1ed07twZTk5OOHr0aNV9GBMpKCjAF198gWHDhkEikTjU70ObNm2wd+9e/P333wCA06dP49dff0W3bt0AOM7vQ1FREYqLi1GtWjWN5z08PPDrr786zH0ozVSfOzU1Fe3atYObm5v6nISEBJw/fx537twxeDw2VTHaGvzxxx+Ij4/Hw4cPUaNGDXz77bdo2LAhTp06BTc3N/j4+GicHxwcjOzsbABAdna2xn/gVMdVx2zFV199hRMnTmisbatkZ2c7zH1o1aoV1q1bh/r16yMrKwtz5sxB27ZtkZaW5lD34Z9//sGKFSswfvx4vPXWWzh27BjGjBkDNzc3JCYmqj9LeZ+15L0ICgrSOO7i4gI/Pz+buhcq3333HXJzczFkyBAAjvXvxZQpU5CXl4cGDRrA2dkZxcXFmDdvHgYNGgQADvP74OXlhfj4eMydOxePPfYYgoOD8eWXXyI1NRV16tRxmPtQmqk+d3Z2NqKjo8tcQ3XM0MbpDIKMVL9+fZw6dQpyuRzffPMNEhMTceDAAUsPq8pcvXoVY8eOxe7du8v8H46jUf2fLaBs2dKqVStERkZiy5Yt8PDwsODIqpZCoUDLli3x7rvvAgBatGiBtLQ0rFy5EomJiRYenWWsXr0a3bp1Q1hYmKWHUuW2bNmCjRs3YtOmTWjUqBFOnTqFcePGISwszOF+HzZs2IBhw4ahZs2acHZ2RmxsLF588UUcP37c0kOj/+FymJHc3NxQp04dxMXFYf78+WjWrBmWL1+OkJAQFBQUIDc3V+P8GzduICQkBAAQEhJSZjeI6rHqHGt3/Phx3Lx5E7GxsXBxcYGLiwsOHDiADz74AC4uLggODnaI+1AeHx8f1KtXDxcvXnSY3wcACA0NRcOGDTWee+yxx9RLg6rPUt5nLXkvbt68qXG8qKgIOTk5NnUvAODy5cvYs2cPhg8frn7OkX4fJk6ciClTpmDAgAFo0qQJXnrpJbzxxhuYP38+AMf6fYiJicGBAwdw7949XL16Fb/99hsKCwtRu3Zth7oPJZnqc5vq3xcGQZWkUCiQn5+PuLg4uLq6Yu/evepj58+fx5UrVxAfHw8AiI+Pxx9//KHxD3f37t3w9vYu8yVirZ5++mn88ccfOHXqlPqnZcuWGDRokPrvjnAfynPv3j1cunQJoaGhDvP7AABPPvkkzp8/r/Hc33//jcjISABAdHQ0QkJCNO5FXl4ejh49qnEvcnNzNf4Ped++fVAoFGjVqlUVfArTWbt2LYKCgtC9e3f1c470+/DgwQM4OWl+tTg7O0OhUABwvN8HAKhevTpCQ0Nx584d7Ny5E88995xD3gfAdP/84+Pj8csvv6CwsFB9zu7du1G/fn2Dl8IAcIu8MaZMmSIOHDgg0tPTxZkzZ8SUKVOERCIRu3btEkIot8DWqlVL7Nu3T/z+++8iPj5exMfHq1+v2gLbpUsXcerUKfHTTz+JwMBAm9sCW1rJ3WFCOM59mDBhgvj5559Fenq6OHTokOjcubMICAgQN2/eFEI4zn347bffhIuLi5g3b564cOGC2Lhxo/D09BRffPGF+pwFCxYIHx8f8X//93/izJkz4rnnnit3S2yLFi3E0aNHxa+//irq1q1r9VuBSysuLha1atUSkydPLnPMUX4fEhMTRc2aNdVb5FNSUkRAQICYNGmS+hxH+X346aefxI8//ij++ecfsWvXLtGsWTPRqlUrUVBQIISw3/tw9+5dcfLkSXHy5EkBQCxZskScPHlSXL58WQhhms+dm5srgoODxUsvvSTS0tLEV199JTw9PblF3pyGDRsmIiMjhZubmwgMDBRPP/20OgASQoh///1XvPbaa8LX11d4enqK//znPyIrK0vjGhkZGaJbt27Cw8NDBAQEiAkTJojCwsKq/igmVToIcpT70L9/fxEaGirc3NxEzZo1Rf/+/TVq4zjKfRBCiO3bt4vGjRsLd3d30aBBA7Fq1SqN4wqFQsyYMUMEBwcLd3d38fTTT4vz589rnCOTycSLL74oatSoIby9vcXQoUPF3bt3q/JjVNrOnTsFgDKfTQjH+X3Iy8sTY8eOFbVq1RLVqlUTtWvXFtOmTdPYyuwovw+bN28WtWvXFm5ubiIkJESMGjVK5Obmqo/b633Yv3+/AFDmJzExUQhhus99+vRp8dRTTwl3d3dRs2ZNsWDBAqPHKhGiRBlPIiIiIgfBnCAiIiJySAyCiIiIyCExCCIiIiKHxCCIiIiIHBKDICIiInJIDIKIiIjIITEIIiIiIofEIIiIiIgcEoMgIjuVnZ2N119/HbVr14a7uzsiIiLQs2dPjZ49hw8fxrPPPgtfX19Uq1YNTZo0wZIlS1BcXKw+JyMjA0lJSYiOjoaHhwdiYmIwa9YsFBQUaLzfZ599hmbNmqFGjRrw8fFBixYt1E0zAWD27NmQSCTo2rVrmbEuXrwYEokEHTp00Pu5oqKiIJFItP4MGTLE+Jtl5Tp06IBx48ZZehhEdsfF0gMgItPLyMjAk08+CR8fHyxevBhNmjRBYWEhdu7ciVGjRuHcuXP49ttv0a9fPwwdOhT79++Hj48P9uzZg0mTJiE1NRVbtmyBRCLBuXPnoFAo8Omnn6JOnTpIS0vDK6+8gvv37+O9994DAKxZswbjxo3DBx98gPbt2yM/Px9nzpxBWlqaxrhCQ0Oxf/9+ZGZmIjw8XP38mjVrUKtWLYM+27Fjx9RB2uHDh/H888/j/Pnz8Pb2BgB4eHiY4hZWicLCQri6ulbZ+xUUFMDNza3K3o/I6lWwNQgRWbFu3bqJmjVrinv37pU5dufOHXHv3j3h7+8v+vTpU+b4tm3bBADx1Vdfab3+okWLRHR0tPrxc889J4YMGaJzTLNmzRLNmjUTPXr0EO+88476+UOHDomAgAAxcuRI0b59ewM+3SOqHkV37txRP/fdd9+JFi1aCHd3dxEdHS1mz56t0X8LgFi5cqXo3r278PDwEA0aNBCHDx8WFy5cEO3btxeenp4iPj5eow+cauwrV64U4eHhwsPDQ/Tt21ejD5QQQnz22WeiQYMGwt3dXdSvX198/PHH6mPp6enq+9quXTvh7u4u1q5dK27fvi0GDBggwsLChIeHh2jcuLHYtGmT+nWJiYllejClp6eLtWvXCqlUqvH+3377rSj5n3XVuD/77DMRFRUlJBKJEEL5O5CUlCQCAgKEl5eX6Nixozh16pRR957IHnA5jMjO5OTk4KeffsKoUaNQvXr1Msd9fHywa9cuyGQyvPnmm2WO9+zZE/Xq1cOXX36p9T3kcjn8/PzUj0NCQnDkyBFcvnxZ7/iGDRuGdevWqR+vWbMGgwYNMskMxcGDB/Hyyy9j7Nix+Ouvv/Dpp59i3bp1mDdvnsZ5c+fOxcsvv4xTp06hQYMGGDhwIEaMGIGpU6fi999/hxACo0eP1njNxYsXsWXLFmzfvh0//fQTTp48iddee019fOPGjZg5cybmzZuHs2fP4t1338WMGTOwfv16jetMmTIFY8eOxdmzZ5GQkICHDx8iLi4O33//PdLS0pCcnIyXXnoJv/32GwBg+fLliI+PxyuvvIKsrCxkZWUhIiLC4Hty8eJFbN26FSkpKTh16hQAoG/fvrh58yZ+/PFHHD9+HLGxsXj66aeRk5NjzO0msn2WjsKIyLSOHj0qAIiUlBSt5yxYsKDMDEpJvXr1Eo899li5xy5cuCC8vb01OsVfv35dtG7dWgAQ9erVE4mJiWLz5s2iuLhYfY5qVqKgoEAEBQWJAwcOiHv37gkvLy9x+vRpMXbs2ErPBD399NPi3Xff1Thnw4YNIjQ0VP0YgJg+fbr6cWpqqgAgVq9erX7uyy+/FNWqVdMYu7Ozs8jMzFQ/9+OPPwonJyd1J/iYmBiNGRwhhJg7d66Ij48XQjyaCVq2bJnez9W9e3cxYcIE9eP27duLsWPHapxj6EyQq6uruHnzpvq5gwcPCm9vb/Hw4UON18bExIhPP/1U79iI7AlzgojsjBDCLOcCwLVr19C1a1f07dsXr7zyivr50NBQpKamIi0tDb/88gsOHz6MxMREfP755/jpp5/g5PRo0tnV1RWDBw/G2rVr8c8//6BevXpo2rSpUePQ5vTp0zh06JDGzE9xcTEePnyIBw8ewNPTEwA03i84OBgA0KRJE43nHj58iLy8PHWuUa1atVCzZk31OfHx8VAoFDh//jy8vLxw6dIlJCUladyXoqIiSKVSjTG2bNlS43FxcTHeffddbNmyBdeuXUNBQQHy8/PVY62syMhIBAYGqh+fPn0a9+7dg7+/v8Z5//77Ly5dumSS9ySyFQyCiOxM3bp11QnN2tSrVw8AcPbsWbRp06bM8bNnz6Jhw4Yaz12/fh0dO3ZEmzZtsGrVqnKv27hxYzRu3BivvfYaXn31VbRt2xYHDhxAx44dNc4bNmwYWrVqhbS0NAwbNszYj6jVvXv3MGfOHPTp06fMsWrVqqn/XjIZWSKRaH1OoVAY/L6Acodcq1atNI45OztrPC69RLl48WIsX74cy5YtQ5MmTVC9enWMGzeuzO670pycnMoEsYWFhWXOK/1+9+7dQ2hoKH7++ecy5/r4+Oh8TyJ7wyCIyM74+fkhISEBH3/8McaMGVPmSzA3NxddunSBn58f3n///TJB0LZt23DhwgXMnTtX/dy1a9fQsWNHxMXFYe3atRozO9qogqj79++XOdaoUSM0atQIZ86cwcCBAyvyMcsVGxuL8+fPo06dOia7psqVK1dw/fp1hIWFAQCOHDkCJycn1K9fH8HBwQgLC8M///yDQYMGGXXdQ4cO4bnnnsPgwYMBKAOvv//+WyMIdXNz0yhbAACBgYG4e/cu7t+/r/5nrMr50SU2NhbZ2dlwcXFBVFSUUWMlsjcMgojs0Mcff4wnn3wSTzzxBN5++200bdoURUVF2L17N1asWIGzZ8/i008/xYABA5CcnIzRo0fD29sbe/fuxcSJE/HCCy+gX79+AJQBUIcOHRAZGYn33nsPt27dUr9PSEgIAGDkyJEICwtDp06dEB4ejqysLLzzzjsIDAxEfHx8uWPct28fCgsLTTr7MHPmTPTo0QO1atXCCy+8ACcnJ5w+fRppaWl45513KnXtatWqITExEe+99x7y8vIwZswY9OvXT30P5syZgzFjxkAqlaJr167Iz8/H77//jjt37mD8+PFar1u3bl188803OHz4MHx9fbFkyRLcuHFDIwiKiorC0aNHkZGRgRo1asDPzw+tWrWCp6cn3nrrLYwZMwZHjx7VSDjXpnPnzoiPj0fv3r2xaNEi1KtXD9evX8f333+P//znP2WW64jsGXeHEdmh2rVr48SJE+jYsSMmTJiAxo0b45lnnsHevXuxYsUKAMALL7yA/fv348qVK2jbti3q16+PpUuXYtq0afjqq6/US0K7d+/GxYsXsXfvXoSHhyM0NFT9o9K5c2ccOXIEffv2Rb169fD888+jWrVq2Lt3b5ncE5Xq1aubfPklISEBO3bswK5du/D444+jdevWWLp0KSIjIyt97Tp16qBPnz549tln0aVLFzRt2hSffPKJ+vjw4cPx+eefY+3atWjSpAnat2+PdevWITo6Wud1p0+fjtjYWCQkJKBDhw4ICQlB7969Nc5588034ezsjIYNGyIwMBBXrlyBn58fvvjiC/zwww9o0qQJvvzyS8yePVvv55BIJPjhhx/Qrl07DB06FPXq1cOAAQNw+fJldX4UkaOQCGMzI4mIHMzs2bPx3XffGbTcRES2gzNBRERE5JAYBBGR1alRo4bWn4MHD1p6eERkJ7gcRkRW5+LFi1qP1axZ06b6gxGR9WIQRERERA6Jy2FERETkkBgEERERkUNiEEREREQOiUEQEREROSQGQUREROSQGAQRERGRQ2IQRERERA6JQRARERE5pP8HSi3/nfWVEUYAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(alm_surr, data_validation)\n", + "surrogate_parity(alm_surr, data_validation)\n", + "surrogate_residual(alm_surr, data_validation)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_alamo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb) file." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.ipynb new file mode 100644 index 00000000..b7ba95b5 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.ipynb @@ -0,0 +1,667 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - SCO2_flowsheet_optimization_alamo_surrogate (Part 3)\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory, TerminationCondition,\n", + " value, Expression, minimize, units)\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (Mixer, MomentumMixingType,\n", + " PressureChanger, Heater,\n", + " Separator, HeatExchanger)\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from SCO2_properties_alamo_surrogate import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 452\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 118\n", + "\n", + "Total number of variables............................: 178\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 59\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 178\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 9.79e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.43e-01 1.25e-02 -1.0 2.50e+01 - 9.88e-01 1.00e+00h 1\n", + " 2 0.0000000e+00 8.54e-06 1.06e-06 -1.0 2.50e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 7.45e-09 2.83e-08 -2.5 1.79e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.3897e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -1.1759e+06 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 692.18\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.2825e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 692.18 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.2825e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 560.75 747.89\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.1004e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.1004e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 416.53 598.89\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -3.4109e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 3.7116e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 416.53\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.4569e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 473.64\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 416.53 416.53 416.53\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 21707. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 416.53 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 598.89 473.64 560.75\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "659.042605510511 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory('ipopt')\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True)\n", + "\n", + " m.fs.turbine = PressureChanger(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.splitter_1 = Separator(property_package= m.fs.properties,\n", + " outlet_list= [\"bypass\", \"to_cooler\"])\n", + "\n", + " m.fs.co2_cooler = Heater(dynamic= False,\n", + " property_package=m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.main_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.splitter_2 = Separator(property_package= m.fs.properties,\n", + " ideal_separation= False,\n", + " outlet_list= [\"to_FG_cooler\",\n", + " \"to_LTR\"])\n", + "\n", + " m.fs.FG_cooler = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.mixer = Mixer(property_package= m.fs.properties,\n", + " inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"])\n", + "\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet,\n", + " destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet,\n", + " destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet,\n", + " destination=m.fs.LTR_pseudo_shell.inlet)\n", + " m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet,\n", + " destination=m.fs.splitter_1.inlet)\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler,\n", + " destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(source=m.fs.splitter_1.bypass,\n", + " destination=m.fs.bypass_compressor.inlet)\n", + " m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet,\n", + " destination=m.fs.main_compressor.inlet)\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet,\n", + " destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(source=m.fs.main_compressor.outlet,\n", + " destination=m.fs.splitter_2.inlet)\n", + " m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler,\n", + " destination=m.fs.FG_cooler.inlet)\n", + " m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR,\n", + " destination=m.fs.LTR_pseudo_tube.inlet)\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet,\n", + " destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", + " destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline \n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1/3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0) \n", + " m.fs.LTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.LTR_pseudo_tube.heat_duty[0])\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.HTR_pseudo_tube.heat_duty[0])\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW)\n", + " return m\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb new file mode 100644 index 00000000..b7ba95b5 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb @@ -0,0 +1,667 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - SCO2_flowsheet_optimization_alamo_surrogate (Part 3)\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory, TerminationCondition,\n", + " value, Expression, minimize, units)\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (Mixer, MomentumMixingType,\n", + " PressureChanger, Heater,\n", + " Separator, HeatExchanger)\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from SCO2_properties_alamo_surrogate import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 452\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 118\n", + "\n", + "Total number of variables............................: 178\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 59\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 178\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 9.79e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.43e-01 1.25e-02 -1.0 2.50e+01 - 9.88e-01 1.00e+00h 1\n", + " 2 0.0000000e+00 8.54e-06 1.06e-06 -1.0 2.50e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 7.45e-09 2.83e-08 -2.5 1.79e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.3897e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -1.1759e+06 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 692.18\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.2825e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 692.18 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.2825e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 560.75 747.89\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.1004e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.1004e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 416.53 598.89\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -3.4109e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 3.7116e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 416.53\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.4569e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 473.64\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 416.53 416.53 416.53\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 21707. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 416.53 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 598.89 473.64 560.75\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "659.042605510511 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory('ipopt')\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True)\n", + "\n", + " m.fs.turbine = PressureChanger(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.splitter_1 = Separator(property_package= m.fs.properties,\n", + " outlet_list= [\"bypass\", \"to_cooler\"])\n", + "\n", + " m.fs.co2_cooler = Heater(dynamic= False,\n", + " property_package=m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.main_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.splitter_2 = Separator(property_package= m.fs.properties,\n", + " ideal_separation= False,\n", + " outlet_list= [\"to_FG_cooler\",\n", + " \"to_LTR\"])\n", + "\n", + " m.fs.FG_cooler = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.mixer = Mixer(property_package= m.fs.properties,\n", + " inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"])\n", + "\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet,\n", + " destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet,\n", + " destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet,\n", + " destination=m.fs.LTR_pseudo_shell.inlet)\n", + " m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet,\n", + " destination=m.fs.splitter_1.inlet)\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler,\n", + " destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(source=m.fs.splitter_1.bypass,\n", + " destination=m.fs.bypass_compressor.inlet)\n", + " m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet,\n", + " destination=m.fs.main_compressor.inlet)\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet,\n", + " destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(source=m.fs.main_compressor.outlet,\n", + " destination=m.fs.splitter_2.inlet)\n", + " m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler,\n", + " destination=m.fs.FG_cooler.inlet)\n", + " m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR,\n", + " destination=m.fs.LTR_pseudo_tube.inlet)\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet,\n", + " destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", + " destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline \n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1/3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0) \n", + " m.fs.LTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.LTR_pseudo_tube.heat_duty[0])\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.HTR_pseudo_tube.heat_duty[0])\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW)\n", + " return m\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_usr.ipynb new file mode 100644 index 00000000..b7ba95b5 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_usr.ipynb @@ -0,0 +1,667 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - SCO2_flowsheet_optimization_alamo_surrogate (Part 3)\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory, TerminationCondition,\n", + " value, Expression, minimize, units)\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (Mixer, MomentumMixingType,\n", + " PressureChanger, Heater,\n", + " Separator, HeatExchanger)\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from SCO2_properties_alamo_surrogate import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:01 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:02 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:43:03 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 452\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 118\n", + "\n", + "Total number of variables............................: 178\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 59\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 178\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 9.79e+01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 1.43e-01 1.25e-02 -1.0 2.50e+01 - 9.88e-01 1.00e+00h 1\n", + " 2 0.0000000e+00 8.54e-06 1.06e-06 -1.0 2.50e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 7.45e-09 2.83e-08 -2.5 1.79e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 5.8207660913467407e-11 7.4505805969238281e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.003\n", + "Total CPU secs in NLP function evaluations = 0.001\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.3897e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -1.1759e+06 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 692.18\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.2825e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 692.18 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.2825e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 560.75 747.89\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.1004e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.1004e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 416.53 598.89\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -3.4109e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 3.7116e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 416.53\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.4569e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 473.64\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 416.53 416.53 416.53\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 21707. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 416.53 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 598.89 473.64 560.75\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "659.042605510511 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory('ipopt')\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True)\n", + "\n", + " m.fs.turbine = PressureChanger(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.splitter_1 = Separator(property_package= m.fs.properties,\n", + " outlet_list= [\"bypass\", \"to_cooler\"])\n", + "\n", + " m.fs.co2_cooler = Heater(dynamic= False,\n", + " property_package=m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.main_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.splitter_2 = Separator(property_package= m.fs.properties,\n", + " ideal_separation= False,\n", + " outlet_list= [\"to_FG_cooler\",\n", + " \"to_LTR\"])\n", + "\n", + " m.fs.FG_cooler = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.mixer = Mixer(property_package= m.fs.properties,\n", + " inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"])\n", + "\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet,\n", + " destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet,\n", + " destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet,\n", + " destination=m.fs.LTR_pseudo_shell.inlet)\n", + " m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet,\n", + " destination=m.fs.splitter_1.inlet)\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler,\n", + " destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(source=m.fs.splitter_1.bypass,\n", + " destination=m.fs.bypass_compressor.inlet)\n", + " m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet,\n", + " destination=m.fs.main_compressor.inlet)\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet,\n", + " destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(source=m.fs.main_compressor.outlet,\n", + " destination=m.fs.splitter_2.inlet)\n", + " m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler,\n", + " destination=m.fs.FG_cooler.inlet)\n", + " m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR,\n", + " destination=m.fs.LTR_pseudo_tube.inlet)\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet,\n", + " destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", + " destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline \n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1/3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0) \n", + " m.fs.LTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.LTR_pseudo_tube.heat_duty[0])\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.HTR_pseudo_tube.heat_duty[0])\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW)\n", + " return m\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb new file mode 100644 index 00000000..3ec37222 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_alamo_surrogate.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Changes the divide behavior to not do integer division\n", + "from __future__ import division\n", + "\n", + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, Param, \\\n", + " Reals, Set, value, Var, NonNegativeReals, units\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component)\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occuring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + " def build(self):\n", + " '''\n", + " Callable method for Block construction.\n", + " '''\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties({\n", + " 'flow_mol': {'method': None, 'units': 'kmol/s'},\n", + " 'pressure': {'method': None, 'units': 'MPa'},\n", + " 'temperature': {'method': None, 'units': 'K'},\n", + " 'enth_mol': {'method': None, 'units': 'kJ/kmol'},\n", + " 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}})\n", + "\n", + " obj.add_default_units({'time': units.s,\n", + " 'length': units.m,\n", + " 'mass': units.kg,\n", + " 'amount': units.mol,\n", + " 'temperature': units.K})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the SCO2_alamo_surrogate_doc.md file) using the Alamopy Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\",\n", + " block_class=_StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " # Create state variables\n", + "\n", + " self.flow_mol = Var(domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol/units.s,\n", + " doc='Total molar flowrate [kmol/s]')\n", + " \n", + " self.pressure = Var(domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc='State pressure [MPa]')\n", + "\n", + " self.temperature = Var(domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760+273.15),\n", + " units=units.K,\n", + " doc='State temperature [K]')\n", + "\n", + " self.entr_mol = Var(domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ/units.kmol/units.K,\n", + " doc='Entropy [kJ/ kmol / K]')\n", + " \n", + " self.enth_mol = Var(domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ/units.kmol,\n", + " doc='Enthalpy [kJ/ kmol]')\n", + " \n", + " inputs=[self.pressure,self.temperature]\n", + " outputs=[self.enth_mol,self.entr_mol]\n", + " self.alamo_surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.alamo_surrogate,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol*self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure}\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error('{} Temperature set below lower bound.'\n", + " .format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error('{} Temperature set above upper bound.'\n", + " .format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error('{} Pressure set below lower bound.'.format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error('{} Pressure set above upper bound.'.format(blk.name))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would interm fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the varible with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + " def initialize(blk, state_args=None, hold_state=False, outlvl=1,\n", + " state_vars_fixed=False, solver='ipopt',\n", + " optarg={'tol': 1e-8}):\n", + "\n", + " '''\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output infomation (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating whcih solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states varaibles are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " relase_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " '''\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag,\n", + " \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\")\n", + "\n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " '''\n", + " Method to relase state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " '''\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags['Fcflag'][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags['Pflag'][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags['Tflag'][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info('{} State Released.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_alamo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.md). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb new file mode 100644 index 00000000..413e0aa2 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_alamo_surrogate.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Changes the divide behavior to not do integer division\n", + "from __future__ import division\n", + "\n", + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, Param, \\\n", + " Reals, Set, value, Var, NonNegativeReals, units\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component)\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occuring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + " def build(self):\n", + " '''\n", + " Callable method for Block construction.\n", + " '''\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties({\n", + " 'flow_mol': {'method': None, 'units': 'kmol/s'},\n", + " 'pressure': {'method': None, 'units': 'MPa'},\n", + " 'temperature': {'method': None, 'units': 'K'},\n", + " 'enth_mol': {'method': None, 'units': 'kJ/kmol'},\n", + " 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}})\n", + "\n", + " obj.add_default_units({'time': units.s,\n", + " 'length': units.m,\n", + " 'mass': units.kg,\n", + " 'amount': units.mol,\n", + " 'temperature': units.K})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the SCO2_alamo_surrogate_test.ipynb file) using the Alamopy Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\",\n", + " block_class=_StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " # Create state variables\n", + "\n", + " self.flow_mol = Var(domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol/units.s,\n", + " doc='Total molar flowrate [kmol/s]')\n", + " \n", + " self.pressure = Var(domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc='State pressure [MPa]')\n", + "\n", + " self.temperature = Var(domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760+273.15),\n", + " units=units.K,\n", + " doc='State temperature [K]')\n", + "\n", + " self.entr_mol = Var(domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ/units.kmol/units.K,\n", + " doc='Entropy [kJ/ kmol / K]')\n", + " \n", + " self.enth_mol = Var(domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ/units.kmol,\n", + " doc='Enthalpy [kJ/ kmol]')\n", + " \n", + " inputs=[self.pressure,self.temperature]\n", + " outputs=[self.enth_mol,self.entr_mol]\n", + " self.alamo_surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.alamo_surrogate,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol*self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure}\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error('{} Temperature set below lower bound.'\n", + " .format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error('{} Temperature set above upper bound.'\n", + " .format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error('{} Pressure set below lower bound.'.format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error('{} Pressure set above upper bound.'.format(blk.name))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would interm fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the varible with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + " def initialize(blk, state_args=None, hold_state=False, outlvl=1,\n", + " state_vars_fixed=False, solver='ipopt',\n", + " optarg={'tol': 1e-8}):\n", + "\n", + " '''\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output infomation (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating whcih solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states varaibles are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " relase_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " '''\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag,\n", + " \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\")\n", + "\n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " '''\n", + " Method to relase state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " '''\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags['Fcflag'][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags['Pflag'][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags['Tflag'][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info('{} State Released.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_alamo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb new file mode 100644 index 00000000..9bc2c853 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_alamo_surrogate.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Changes the divide behavior to not do integer division\n", + "from __future__ import division\n", + "\n", + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, Param, \\\n", + " Reals, Set, value, Var, NonNegativeReals, units\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component)\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.alamopy import AlamoSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occuring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + " def build(self):\n", + " '''\n", + " Callable method for Block construction.\n", + " '''\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties({\n", + " 'flow_mol': {'method': None, 'units': 'kmol/s'},\n", + " 'pressure': {'method': None, 'units': 'MPa'},\n", + " 'temperature': {'method': None, 'units': 'K'},\n", + " 'enth_mol': {'method': None, 'units': 'kJ/kmol'},\n", + " 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}})\n", + "\n", + " obj.add_default_units({'time': units.s,\n", + " 'length': units.m,\n", + " 'mass': units.kg,\n", + " 'amount': units.mol,\n", + " 'temperature': units.K})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the SCO2_alamo_surrogate_usr.ipynb file) using the Alamopy Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\",\n", + " block_class=_StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " # Create state variables\n", + "\n", + " self.flow_mol = Var(domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol/units.s,\n", + " doc='Total molar flowrate [kmol/s]')\n", + " \n", + " self.pressure = Var(domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc='State pressure [MPa]')\n", + "\n", + " self.temperature = Var(domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760+273.15),\n", + " units=units.K,\n", + " doc='State temperature [K]')\n", + "\n", + " self.entr_mol = Var(domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ/units.kmol/units.K,\n", + " doc='Entropy [kJ/ kmol / K]')\n", + " \n", + " self.enth_mol = Var(domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ/units.kmol,\n", + " doc='Enthalpy [kJ/ kmol]')\n", + " \n", + " inputs=[self.pressure,self.temperature]\n", + " outputs=[self.enth_mol,self.entr_mol]\n", + " self.alamo_surrogate = AlamoSurrogate.load_from_file(\"alamo_surrogate.json\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.alamo_surrogate,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol*self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure}\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error('{} Temperature set below lower bound.'\n", + " .format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error('{} Temperature set above upper bound.'\n", + " .format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error('{} Pressure set below lower bound.'.format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error('{} Pressure set above upper bound.'.format(blk.name))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would interm fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the varible with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + " def initialize(blk, state_args=None, hold_state=False, outlvl=1,\n", + " state_vars_fixed=False, solver='ipopt',\n", + " optarg={'tol': 1e-8}):\n", + "\n", + " '''\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output infomation (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating whcih solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states varaibles are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " relase_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " '''\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag,\n", + " \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\")\n", + "\n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " '''\n", + " Method to relase state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " '''\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags['Fcflag'][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags['Pflag'][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags['Tflag'][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info('{} State Released.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_alamo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc index 4e4089e4..66926a21 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc @@ -89,3 +89,6 @@ c:\Users\javal\Desktop\Internship\IDAES-examples\idaes_examples\notebooks\docs\s #filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model c:\Users\javal\Desktop\Internship\IDAES-examples\idaes_examples\notebooks\docs\surrogates\SCO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.10937500, 1816, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.21875000, 1, 0, 0.0000000, 0.0000000, 0.42187500, 0.0000000, 0.20312500, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 c:\Users\javal\Desktop\Internship\IDAES-examples\idaes_examples\notebooks\docs\surrogates\SCO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.18750000, 2047, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.21875000, 1, 0, 0.0000000, 0.0000000, 0.42187500, 0.0000000, 0.21875000, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure^2 - 0.55969916790357242958320E-003 * CO2SM_Temperature^2 - 0.21077870265129327632947E-002 * CO2SM_Pressure^3 + 0.24061231665087056461711E-006 * CO2SM_Temperature^3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045 +#filename, NINPUTS, NOUTPUTS, INITIALPOINTS, OUTPUT, SET, INITIALIZER, SAMPLER, MODELER, BUILDER, GREEDYBUILD, BACKSTEPPER, GREEDYBACK, REGULARIZER, SOLVEMIP, SSEOLR, SSE, RMSE, R2, ModelSize, BIC, RIC, Cp, AICc, HQC, MSE, SSEp, MADp, OLRTime, numOLRs, OLRoneCalls, OLRoneFails, OLRgsiCalls, OLRgsiFails, OLRdgelCalls, OLRdgelFails, OLRclrCalls, OLRclrFails, OLRgmsCalls, OLRgmsFails, CLRTime, numCLRs, MIPTime, NumMIPs, LassoTime, Metric1Lasso, Metric2Lasso, LassoSuccess, LassoRed, nBasInitAct, nBas, SimTime, SimData, TotData, NdataConv, OtherTime, NumIters, IterConv, TimeConv, Step0Time, Step1Time, Step2Time, TotalTime, AlamoStatus, AlamoVersion, Model +C:\Users\Brandon\GitHub\IDAES\examples\idaes_examples\notebooks\docs\surrogates\SCO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 1, 0, 3, 1, 1, 1, T, 0, T, 0, F, 0.515E+08, 0.659E+08, 406., 0.999, 6, 0.484E+04, 0.659E+08, 0.659E+08, 0.482E+04, 0.483E+04, 0.168E+06, 0.659E+08, 0.594, 0.31250000E-01, 1816, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.10937500, 0.0000000, 0.46875000E-01, 0, 2023.2.13, CO2SM_CO2_Enthalpy = 142.00232605043254352495 * CO2SM_Temperature + 2.5280649736985938424993 * CO2SM_Pressure^2 - 0.13788428202598035365867 * CO2SM_Temperature^2 + 0.66186633129257225506559E-004 * CO2SM_Temperature^3 - 114667.63130721540073864 * CO2SM_Pressure/CO2SM_Temperature - 428949.09007398976245895 +C:\Users\Brandon\GitHub\IDAES\examples\idaes_examples\notebooks\docs\surrogates\SCO2_example\ALAMO\alamo_run.alm, 2, 2, 400, 2, 0, 3, 1, 1, 1, T, 0, T, 0, F, 541., 558., 1.18, 0.997, 10, 193., 606., 178., 154., 169., 1.43, 558., 0.130E+04, 0.62500000E-01, 2047, 22, 0, 0, 0, 3841, 0, 0, 0, 0, 0, 0.0000000, 0, 0.0000000, 0, 0.0000000, 0.17976931+309, 0.17976931+309, F, 0.0000000, 11, 11, 0.0000000, 0, 400, 0, 0.31250000E-01, 1, 0, 0.0000000, 0.0000000, 0.10937500, 0.0000000, 0.62500000E-01, 0, 2023.2.13, CO2SM_CO2_Entropy = - 3.9179528198356607937569 * CO2SM_Pressure + 0.51570723686001085361852 * CO2SM_Temperature + 0.17222545182333473534619 * CO2SM_Pressure^2 - 0.55969916790357242958320E-003 * CO2SM_Temperature^2 - 0.21077870265129327632947E-002 * CO2SM_Pressure^3 + 0.24061231665087056461711E-006 * CO2SM_Temperature^3 - 0.10420555302271612869991E-002 * CO2SM_Pressure*CO2SM_Temperature - 363.27314562306145262482 * CO2SM_Pressure/CO2SM_Temperature - 0.20456756625658267800816 * CO2SM_Temperature/CO2SM_Pressure - 116.67325766759245198045 diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/.mdl_co2.h5 b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/.mdl_co2.h5 index 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--git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb new file mode 100644 index 00000000..c4a1dbf3 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb @@ -0,0 +1,665 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - SCO2_flowsheet_optimization_keras_surrogate (Part 3)\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory, TerminationCondition,\n", + " value, Expression, minimize, units)\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (Mixer, MomentumMixingType,\n", + " PressureChanger, Heater,\n", + " Separator, HeatExchanger)\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from SCO2_properties_keras_surrogate import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-19 22:20:40 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 22:20:40 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:43 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 51411\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2674\n", + "\n", + "Total number of variables............................: 5920\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 5669\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 5920\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 9.10e-01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 7.86e-09 7.53e-01 -1.0 9.10e-01 - 9.89e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.119\n", + "Total CPU secs in NLP function evaluations = 0.003\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.3854e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -1.0221e+06 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 719.28\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.5254e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 719.28 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.5254e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 543.23 750.68\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.0875e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.0875e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 396.40 579.39\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -3.1174e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 2.7059e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 396.40\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.0998e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 452.96\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 396.40 396.40 396.40\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 25836. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 396.40 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 579.39 452.96 543.23\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "641.5293430698576 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory('ipopt')\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True)\n", + "\n", + " m.fs.turbine = PressureChanger(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.splitter_1 = Separator(property_package= m.fs.properties,\n", + " outlet_list= [\"bypass\", \"to_cooler\"])\n", + "\n", + " m.fs.co2_cooler = Heater(dynamic= False,\n", + " property_package=m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.main_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.splitter_2 = Separator(property_package= m.fs.properties,\n", + " ideal_separation= False,\n", + " outlet_list= [\"to_FG_cooler\",\n", + " \"to_LTR\"])\n", + "\n", + " m.fs.FG_cooler = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.mixer = Mixer(property_package= m.fs.properties,\n", + " inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"])\n", + "\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet,\n", + " destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet,\n", + " destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet,\n", + " destination=m.fs.LTR_pseudo_shell.inlet)\n", + " m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet,\n", + " destination=m.fs.splitter_1.inlet)\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler,\n", + " destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(source=m.fs.splitter_1.bypass,\n", + " destination=m.fs.bypass_compressor.inlet)\n", + " m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet,\n", + " destination=m.fs.main_compressor.inlet)\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet,\n", + " destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(source=m.fs.main_compressor.outlet,\n", + " destination=m.fs.splitter_2.inlet)\n", + " m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler,\n", + " destination=m.fs.FG_cooler.inlet)\n", + " m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR,\n", + " destination=m.fs.LTR_pseudo_tube.inlet)\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet,\n", + " destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", + " destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline \n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1/3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0) \n", + " m.fs.LTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.LTR_pseudo_tube.heat_duty[0])\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.HTR_pseudo_tube.heat_duty[0])\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW)\n", + " return m\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb new file mode 100644 index 00000000..c4a1dbf3 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb @@ -0,0 +1,665 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - SCO2_flowsheet_optimization_keras_surrogate (Part 3)\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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sekxsQoLEJSdLXX4lhYdl5m2R/6zLkueWLZfjzhsoa9atk0eeeEIaN25s9wAAoHoprm98mL4R8BTzPl27Xp7N/EOOO/dc8z599KmneJ8CqHL0c23Mww/K+jVrZODxx8iq156STR+8KluW/273QLQQrHpMVnq6LBw82CwBr1m8ebO8um69vLpmrZxx6aWydsMGGf3QQ9KgQQO7BwAA1Yt/33g6fSPgSeZ9utb3Pj3tkktk3caNcs8jj/A+BVDl6efcg/eMlg3r1splZ54iGyZNkJz0CZKX+ZvdA5EiWPWYvIwMWTNxolkCXrFgU65MWLNO3t2YI3+9+hpZnZUlt6WlSWxsrN0DAIDqxb9vHGj7xtvpGwFPMe/T1WvlnQ3Zct6VV5r36R333MP7FEC1o597d99+m2StWSXXXnCebP74Ddn4zvOyefGvdg+Ei2DVYxJSUsw4qzocALC/zc3JkedXrZaPtm6TwSNHyrLVq2XkLbfYrQAAVD+ub5y8ZYtc/ve/m77x7/SNgKeY9+nK1fJh3hYZVPgd9o81a+TG22+3WwGgevvHyBGy+o9l8o8rB8ufX78vWW+Mk9yFP9utCBXBqsdosNpuzBhpmppqW4CKl7ExW55ZvlK+zN8tN9x1tyxatkyG3nCD3QoAQPWjfeO4Fatk2s5dcn1h37j4j+Vy7fXX260AvMD3Pl0pU//Ml+vuvFOWLF8uw0aMsFsBAP6GXXuNLFv0m4waOVxiZn4h6/79hOTM+8luRbAIVj1me2amGQYgPyfHtgAVZ9aGjfLkHytkZs1YueOhh2Te4sUy+Mor7VYAAKqfmRs27Okbby/sG39dskQup28EPEXfp08sW174Pq0ptz3woMz//XcZcvXVdisAoDRDBg+WRb/OlUfvuk3q/fq9rJn4mOT8/IPdirIQrHqMjq/6Y/fusmLsWNsClL/v12fJY0uXybz69eWBp56S2fPny4WXXGK3AgBQ/XyftUEez1wm8xvEyf2ub7z4YrsVgBfod9jH9TtsvQZy/xNPFL5PF8hFl15qtwIAQnHBBRfIvNk/yjMPPyDxS3+WVS88JJtmf2e3oiQEq0A1Nr3wpPHhJUtlaeMm8vSECTJjzs9yzrnn2q0AAFQ/0zdslIeW/C5LExvLUy9NkO8z5si59I2Ap3yzPsv3HbbwffrEiy/KDz//LOf99a92KwAgEgMGDJCMGd/JS08/IU3WLJLl4++V3J++sVsRKGb18hUFS5f/IX369LFNAMIVExMjz/TuZe9511eFJ43XLl5i1vM//1xOOOEEs14dfVn430z1KygwSwBAdFWWvvHrjRtl28XPm/Xr+hZU674R1U9leZ9+mbVBBv2+VOoXfm/LT0+XE/7yF7sFiL7Ue6aYZfrd/c0SqI50mMrpiYmyvVYtuaDJAdKkT39p2DPFbvWu2XdcIQUVdI5PxSpQTeQXfqhM3bBRRs1fIJsPOti2CieOAIBqy/SNG7Mlbf7Cwr6xjW2lbwS8RN+nU9ZvMN9h8w4+WOo3aGDa+/XrZ5YAgPIXV/jZ+97bb8rB27NlyT9vk83ffyEFu/Lt1uqNYNVjdGxVHWNVx1oFomH7rl3y2YaNcve8X2VX+w4y+fMv5MMpvl9fAQCojrRv/CI7x9c3tmsvH37+uXzwxRd2KwAvMN9hs/Q77PzC77Dt7XfYqRIbG2v3AABUpGOOOUa+mPyBfP7Rh9Kh5g5Z9PitkvftJ7J7x3a7R/VEsOoxWmadl5Eh2zMzbQsQnrz8fPl4w0a5a+6vUrdLV/nq2+/kvx99JL16ef8yLwAAyoP2jZ9m55i+sfZhXegbAQ8y32GzNsqdv8yTOocdVvg+/VYmffIJ71MA8Aj9PJ486V359qsvpWvDWrLgsZsl7+vJkr81z+5RvRCsekzT1FRpN2aMJKR4f8wKeFPOn3/Khxuy5c45v0jjI3vITxkZ8np6uhxxxBF2DwAAqhftGydv9PWNCd2P9PWNkybRNwIeou/TD9ZvMO/TxO7dZfacOfLGe+/xPgUAj9LP5/++/prM+ekn6dGskcx/5B+yedp7sjM32+5RPRCsekxccrK0HjGCYBUhy9qxQ97bkC13zJ4jB/3f/8lvixbJxDfflEMPPdTuAQBA9eLrGzeavrG16xvfeIO+EfAQ8z7N2rDX+/Tlt9/mfQoAlYR+Xr8+8SVZ9NtvckzbljL34X9I3pRJsmPjertH1Uaw6jE6DICOr6pLIBhrtm2T/xaeNN4/b74c2r+/rFq1Sp59+WVJSkqyewAAUL1o3zhpY7bcN/dXOfSEE3x940T6RsBLzHfY9VnmfdrxeN932OdffZX3KQBUUvr5PeG58ebz/IQu7WXRU6Nk82dvy/b1a+weVRPBqsdkpafLwsGDzRIozcqtW+Wtwi+jYxb/Lt1PPU1WrV4tTzz3nLRo0cLuAQBA9bJC+8asDfLP3xZL99NOl9Vr1sgTz9I3Al5i3qfrsuSfiwrfp6efYd6nT/7rX7xPAaCK0M/z8U+MldWrVsmZRyVL5gsPyeaP35Btq5fbPaoWglWPqZuUZIYDiE1IsC3A3jLztsjr67NkfOYfcuy558nqtWvl0aeekiZNmtg9AACoXnx94wZ51vaNa9avl0eeeIK+EfAQ8z5dt968T/uec46sKVznOywAVF36+T7m4Ydk3erVcl7K/8mq156STR+8KltXLLV7VA0Eqx7TfNAg6TF7thlnFfC3ePNmeXXtOnl59Ro59eJLZO2GDXLvI49IXFyc3QMAgOrF9I3r1ssra9YW9o0X+/rGhx+mbwQ8xLxP16yTVwq/w55y4UXmfXr/Y4/xPgWAakI/7x+8Z7RkrV0jl515iqx/9wXJSZ8geZmL7B6VG8Eq4HELNuXKhMITxnc2ZMt5V11tvozeMXq01KpVy+4BAED14usb1+3pG9dkZdE3Ah5j3qer18jbWRvl3CuvlDWF32HvvO8+3qcAUE3p5//dt98mG9aukWvOP0c2f/S6ZL/7L9m85Fe7R+VEsOoxmWlpMj0xUVaMHWtbUF3NzcmRf61aI5O3bJVBI0bKH2vWyI233Wa3IhKzP/9MXhhxg9zW7xj55uCDzE3XtU23AQC8yfSNq7Vv3CKDR9q+8dZb7VYAXmDepytXy+S8LXLZDSNk+dq1ctMdd9itAACI3Pz3v8vq5cvkxiGXyY5p70nWG+Mkd+HPdmvlQrDqQfmFX0b0huopY2O2jFuxUqbt3CXDCr+ELv7jDxnG0BBRsX75chlz2aXy/r33yO45s+XoBg2kd7fDzU3Xd83+Ud4bnSaPX3Sh2RcA4A3aN45fscr0jdfdeVdh37hcht5wg90KwAvMd9jlK2Tan/ky9LbbZHHhd6nhhSfOAACUZPjQa+WPJYtk1MjhIjO/kHX/fkJy5v1kt1YOBKseo2Osdps61SxRvczasFGeWLZcfqhZU2574EH5dckSueLqq+1WROq7Sf+VtDNOk7iNG6Rf40Q5NCFBEurUkVqF/9560/VOjRtLStMmErchS9JOP1Wmv/WmfTQAYH/QvvHJP1bIzJqxcttDD5m+8fIrr7RbAXiB731a+B22Rk259f4H5Nfff5crhw61WwEAKNuQwYNl8a9z5dG7bpO6876TtS8/Ljk//2C3ehvBqsfUTUqShJQUs0T18P36LHl86TKZV7+BPPDkk5Ixf4FcdOmldiuiYfq778h7jz4iKS2aS8fYmra1ZJ3q1ZWUli3k/ccfk+nvvG1bAQAV5fusDfLPzD9kXoPCvvHpp2X2/Ply4cUX260AvMB8h/09U+bVqy/3jRkjGQsWyMUUhwAAInDBBRfIrxk/ydMP3S/xv8+RVS88JLkZ39mt3kSw6jFZ6emyZORIyZk2zbagqppe+GX04SVLJbNxE3nqpZdkxpw5cu7AgXYroiUvO1vevO9e6dEoXg6sX9+2lk337ZHQSN68/z5zDABA+ft240Z5+Hdf3/ik9o0Zc+Scc86xWwF4wTfr1stDi3+XpQmJ8sQLL8iMn3+W8/52vt0KAEDkBgwYIBk/fC8vPf2ENF69SJaPv1c2//SN3eotBKsek5eRYSauIlitur7K2iD3/bZY1jZvIS++/rp8NXOmnH7GGXYrou31u++STi1byQF169qW4Gm42qFpU/nPHUwaBgDl6auNG+W+RYtlzYHN5cX/FPaNP/wgp59+ut0KwAu+XLfe9x22WXN5qfA77Nc//ihnnn223QoAQPTp98Efvv5SXp/wgjTPWSnLnh4lm2d5Ky8jWPWYuORkaZqaylAAVcyuggKZmrVB0hb8JrkHHSxv/u9/8vn06XLiiSfaPRCqhYMHy/bMTHuveAu+/14WFZ6cH1Y71raE7vC4BuYYeiwAQPRo3zgtO8fXN7Yu7BvTC/vGb76hbwQ8RN+nX6xbL6PmL5Tcg9uY77BffPednHjSSXYPAADKn34//PrzTyX9zdfl4O3Z8vuY22XLD1OkYNcuu8f+Q7DqMRqqdpk0icmrqogdu3bLZ1kb5K5582Vnu/bywaefyuSpU+XYY4+1eyBcWtU9o21bmTdgQIkB689ffC5JjRMkxt4Phz724Lg4mf3hB74GAEBEtG/8IjtH7v51gew8pJ2vb5wyhb4R8BDzHXZ9ltxd+B12d4eO8uFnn8lHhd+9eJ8CAPYn7Ye+mPyBfPrh+9IuZrssevwW2fLdZ7L7zx12j4pHsOox+Tk5JiTSJSqvLfn58vGGjXLX3HlS+7Au8uX06ZL+ySdy1FFH2T0QLToucUkB69yvvpQDdxfYe+FrUaeOzPt2ur0HAAiH9o2fZueYvrFW58Nk2jffyKSPPqJvBDzEfIddn7XnO+y0wu+wk/gOCwDwGO2XJk96V6Z/OU0Oa1BDFjx6s2z55iPJ35pn96g4MauXryhYuvwP6dOnj23C/pSZlibLRo+WNqNGSVLhOiqXmJgYOeWg1jJl9Rq55Pzz5R933CGdOnWyW73ly8LXqvT/a5XRyieeKPYHCK36bjdmjBlOY0SvHnJq8wOlXmwtuzU8W/N3yieF/03HzpptWwAAwfLvGy+7+GK58ZZbPNs3pt4zxSzT7+5vlkB1oe/Tk1u1lGlr18nFHv8OOz0x0XwH7JudLbEJCbYViD76BMBX/Kefu/p5q5+7XrVgwQJ5+LHH5d+vvCzNjzlZVkz9UAoKIi+yCgbBqscQrFZu+qV02NVXm5PGtm3b2lZvcl9KqyoNV9PGPS0XdzksoqEA1O7C22vzfpUXflvsawAABK0y9Y2cRKO60vfpNZdfLjffeWel+Q5LsIryRp8AVJ5g1Vm6dKk8+vg/ZfwzTxOsVmcu7OKLAspTXkaGuYy+siqpYjUhJcWEqjoRnFasnta8mdSNDX/yKrUtf6d8TMUqAFR5nEQD3kewiopCnwBUvmB1fyBYBVAp6biq/mOq+geqzt2nnCRHFC4PqF/P1xCm9Vu3SUZ+vtz3xVTbAgCoijiJBryPYBUVhT4BIFgNBpNXeYxWEOokPGsmTrQtAEqjgWqP2bOl29Spe4Wqqsuxx8m6mEgHAhBZvWO7dOnzf/YeAAAAAAAAwarnuMuzA2c3B7C30gJVp9uJJ0lmdrZEMrKKPvaPzVuk+5ln+RoAAAAAAAAK1fDFBvAKDYt04ipdAijZoRMmlBioOp2OPlo6HtVb5m7fYVtC90vuZmnfs6c5FgAAAAAAgEPFqsdooJqUlkawCkTJ+ffcK4vWrpV1W7faluDpY37bsEEueuhh2wIAAAAAAOBDsOoxDAUARFdcYqL89Y475afcvJDCVd33p+xNcv6dd5ljAAAAAAAA+KshEvnELogeJq8Coq/vuefJWTfeKFNXrZYF27fb1pLNz8uTqStXyek33CB9zxtoWwEAAAAAAIpQseoxsQkJ5gYguv7vvIEyevLHsqXJATJtfZYs2LhRcnbskJ27dpmbri8sbJu6Zq1sOeBAGf3RJ3LsRRfbRwMAAAAAAOyNYNVjWo8YIX2zs804qwCi64CDDpK///s1OTtttNTo1l2+25wnX/8yV2Zk/GzWYw4/Qv5y731y43/eMPsCAAAAAACUhGAVQLXT/cST5Monn5KHvp4u5yQdIqc2aWrWr3x6nNkGAAAAAABQFoJVj1kxdqzMaNvWLAGUr5xp08zNTRoHAAAAAAAQLIJVj8nPyZHtmZlmCaB8LRs92q7tvQ4AAAAAAFAWglWPaZqaKl0mTZLmgwbZFgDlwVWrOlStAgAAAACAUNQQKbCr8IK45GQTrtZNSrItAMpDcRWqVK0CAAAAAIBgUbHqMVpBp+OravUcgPIRWK3qULUKAAAAAACCRbDqMRr2LBk5knAHKEelVaZStQoAAAAAAIJBsOoxOgRAQkoKQwEA5USrUnWCOH2PFXfTieOKq2YFAAAAAADwR7DqMTppVbepU5m8CignOo5x76VL99zajRljbv5t+uMGAAAAAABAaQhWPUar5fQGoGLMGzDA3AAAAAAAAEJBsOoxOnHV9MREyUxLsy0AylNsQoK5AQAAAAAAhIJgFUC11jc729wAAAAAAABCQbDqMa1HjJAes2czxioAAAAAAADgYQSrHqOXJOvkOjo7OYDyN6NtW3MDAAAAAAAIBcGqx2Slp8vCwYPNEkD5256ZaW4AAAAAAAChIFj1mLyMDFkzcaJZAih/XSZNMjcAAAAAAIBQEKx6TEJKihlnVYcDAFD+mqammhsAAAAAAEAoCFY9RoPVdmPGEPQAFWTF2LHmBgAAAAAAEAqCVY/RsR51GID8nBzbAqA8LRk50twAAAAAAABCQbDqMTq+6o/du1NBB1QQrRLXGwAAAAAAQCgIVgFUa92mTjU3AAAAAACAUBCsekxSWpr0KygwSwDlT4fdYOgNAAAAAAAQKoJVANXa9MREcwMAAAAAAAgFwarH6NiqOsaqjrUKAAAAAAAAwJsIVj1GL0nOy8iQ7ZmZtgVAeeoxe7a5AQAAAAAAhIJg1WOapqZKuzFjmKUcqCBxycnmBgAAAAAAEAqCVY/RgKf1iBEEq0AFWTh4sLkBAAAAAACEgmDVY3QYAB1fVZcAyp++3xjTGAAAAAAAhIpg1WOy0tNN9ZwuAZQ/rRDXGwAAAAAAQCgIVj2mblKSGQ4gNiHBtgAoTzqmsd4AAAAAAABCQbDqMc0HDTIzlFNBB1QMHXaDoTcAAAAAAECoCFYBVGs/du9ubgAAAAAAAKEgWPWYzLQ0mZ6YKCvGjrUtAAAAAAAAALyGYNWD8nNyzA1A+etXUGBuAAAAAAAAoSBY9RgdY7Xb1KlmCQAAAAAo4ib5pRAFAOAFBKseUzcpSRJSUswSQPljjFX8P3vvASBZVab9P7dy7K4OkzMzwOAwzICkIQtIRkBRZEGFVTEtK6j/NbAK+n2gu58Kyuq6uizoigooQaJIlCxpCMPk2JM7d+V4/+c5996ZmpoKPT2pe+b9Dafr3nNPrjqHuk+99z2CIAiCIAiCIAiCMBREWB1mdN1/P5Zfey36nnnGjhEEYXeSmD9fB0EQBEEQBEEQBEEQhB1BhNVhBgUeblwlwqog7Bmm33yzDoIgCIIgCIIgCIIgCDuCCKvDjMjcuWi/8EJxBSAIe4iJ11yjgyAIgiAIgiAIgiAIwo4gwuowg6LqrPvuk82rBGEPsfGOO3QQBEEQBEEQBEEQBEHYEURYHWZwd8vMqlWyy6Ug7CEWX3mlDoIgCIIgCIIgCIIgCDuCCKvDDPpXfWXaNP0qCMLuh+43GARBEARBEARBEARBEHYEEVYFQdivef+bb+ogCIIgCIIgCIIgCIKwI4iwOsyYesMNOL63VzbTEQRBEARBEARBEARBEIRhjAirwxBPLKaDIAi7nxdaWnQQBEEQBEEQBEEQBEHYEURYHWZ03X8/Flx0kexSLgh7CG4UJ5vFCYIgCIIgCIIgCIKwo4iwOsxIzJ+vxdXMqlV2jCAIu5M5Tz+tgyAIgiAIgiAIgiAIwo4gwuowI3bKKZhy/fX6VRCE3Q/nmsw3QRAEYSRjGIaEfSQIwkii2md4XwoPXH+aDtWu7UtBEISdw9jQ0WGu7OjAvHnz7ChBEIT9h+XXXqtfp998s34VBEEQ9l8u/N5T+vX+75yqX0cKvDHeGBd/4SOdsdFemKZpnwm1eGXaNP103zErVyIwdaodK+wNZO0Z+ci6IzSCbvO4Jwn3AeJG68L2iMXqMENcAQjCnmXtLbfoIAiCIAiCIAiCIAiCsCOIsDrMkM2rBGHP0n7hhToIgiAIgiAIww9aS731gQ9sCc6mo7xncuL6nnlGxwmCIAjCnkaE1WEGzasZBEHYM8y67z4dBEEQBEEQhOGHc39E8ZTBEVb5pB/P+aRfZO5cHScIgiAIexoRVocZE6+5RvutmHrDDXaMIAi7E34ZF9cbgiAIgiAIw5d6vvAnfPnLYpgiCIIg7DVEWBUEYb+GGyAwCIIgCIIgCMMTblJVzXUT48decYV9JgiCIAh7HhFWhxncRIcij2ymIwiCIAiCIAiCYFHNalWsVQVBEIS9jQirwwz6DOJjyY7vIEEQdi90vcEgCIIgCIIgDF8qrVbFWlUQBEEYDoiwOszglwVupCNfEgRhz+BsiCAIgiAIgiAMb8qtVsVaVRAEQRgOiLA6zOCOlhRX+QusIAi7nwUXXaSDIAiCIAiCMLxxrFbFWlUQBEEYLoiwOszoe+YZ7V81MX++HSMIwu6k6/77dRAEQRAEQRCGP7RaFWtVQRAEYbggwuowg8Lq8muvFaFHEPYQU66/XgdBEARBEARhePL7Nzbjkl+/hyk3vIS2n3XgiOUH6eNLfr1AXxMEQRCEvYUIq8MMPtYSO+UUcQUgCHuIqTfcoIMgCIIg7Ku8+FzBPqpNozS//FnWPqpOf7+Jd98u2mfVefShvH1UnUZtGEwdjcq4686cfVSdXVHHruiHYPFaRxzH/Ph1fPfBZViydBMONNP4QKSAo9uD+njJ0k59jWmYVhheDGa+dawp2WfV2RPrRqM27OyawPJ3to5GY8n8jepo1E5BEIaGCKvDDPoKmvP00+IzSBD2EOIKQBAEQdjX+dXPM3XFCV5jmno89nCu7k07b9jrlUFx40ffT9tn1WEd9W78B1PHlz+ftM+q89hDO9cP8p1vpOyj6tx1Z7auCDKYOgTge4+twnE/eRO+VBozjQym+ExE3IDHMHXg8RRfSV/zqjRMe/0jK+3cwnCg0Xy767e5hnP6O1+vP9+Yf2fmG+s4/bgB+6w6V16aqNuPRm149ME8fnhT7fWPbfjIOfV/GGg0lrzOemoxmHVeEIShIcLqMKPQ16eDIAh7Btm8ShAEQdgXqHfDTUsm3nTXYsHbhbqCJvMvUKHeTfvdd2ZVHbWvs3yWU68elk9RshaDqYPjUGssKF5QXLjrt0Ovg31gqCeiLHiH/axdxkvP5+vWIQA3/XUN/t9THTg5UkBbqfZYO7SrNEz7o2fW6rzCnqPefOOcrCco8scUiqu14LUBVU6t+cY6WH+9+cY5XW/dceqv1Q/Od7ah1vrHNlh9rd0G9rPenG/UBsZz7ao/lvWFU647HAe2VRCEXYsIq8MMblz1QksLVsmjyYKwR+DGB7L5gSAIgjDSqSUW8mb87PO9dYUF3pCfdZ63pnjBvJdc7qt50+4ICyyjljBAYfeSy3w1hVO287gTPTXbOZg6KKDoOmqMBcWLq77kV/2ofp3ls+xG/fjaNwM1RRSnHxRXa8E+1uvrvkoyM7j+8pH+Gx5bhWNDeYR34G6VaeepPDf8ZZW4BdhJBvtekVpiH+fbZ7/or/k55xxrbjbqzjeKgbf8IlxzvrHss8711pxvzrrBNZBzsxoUPb/6zWDNdYNz/nv/FtLpqsE2HHqYu2YbKMw6/aw1Fk4baq2xFHWtdad6ftYxabJLt6PaWHIMmJfruPyoIwi7HhFWBUHYrzm+t1cHQRAEQRiOvLuqF0/N32Cf1aaWWEhhYt4J3ppCnnND/tkv1hYLedPP65OmuKretDvCwlnn1RY1KTpQnKglDNBqlvnZzqHU4YiirKPeWLAftYQc1sH6WUctgYMi9Ge/FKgporAfznhXE3IYx/H+2GX+uta5+yIU6y783lO47fGldkx1rvr9Ihzd4tohUdWBeY5sMlQZi+0YYSis3BjHZf/+Nzz4cocdU5tac4Hz7ZLL/TXXHoqFs2Zbc7qaOLtFFFXztVYd/DHFWTeq/TBEcfe4E9ScPpeC4vbXuQ5Q9LR+OKo+H50fnjpWV7eG121Q5ddqA/vONUGvXVXmfHkbalnFcg12xrJaG9g3rimso1oZztpG8bbaOAiCsHOIsDrMmHjNNXj/m2+Kj1VBEIR9gHVXh5F+8177TBAEYcehGPXTPy/UIkc9gXUwYmG1m3rnhryWxRWFDT4GSzHwEpWu2k17ubBQTZxwrDgpHtQUBh620rCd1UTNRnU4/axlAeeINOzHcSd6q/ajXIR2Hs0txxGhnX5UE1GcfrCcagIGhVeON9NUE5v2ByjW1RJYucN/IpnTj/YPldHIqzKyuixh6HDt4XvUSAyv9lnm/HPmW821xxYLawmnjihKqs03Z05z7eIPJtXmW7m4W60OR9yttW6Ur11f+1Zwu3XDaQPT1BIt2QZazNbqZ3kbWE5lGxyLV2csq4nQW9dPb1Vf1s76yXJYHtssCMKuQ4TVYQYfSY7MnYvA1Kl2jCAIu5NXpk3TQRB2F31/uBrJ535lnwmCIAyNRgJrNbGQYke5EFhNyHNuyAlfK8ULiht81Jbwpv3u320rkPAGnUIA87KeauKEY8VJqgkD2wmWFaLmYOqgcODUUWssHJGmlvig09SpwxGhCUWKSgvf8n5QRKk33k5fK+vYn6gmsP7h9Y2IlrYXvXeUJjOvytpknwk7Sz0xnHOhUjjl/HPmWzVBsVwsJJwLlfPFEV5JNYv68jnNcirnkiN6ltdRub6V11HtRx3nxxZSbf0rbwP7U9kG9pOUt6HSkr2yDZXrI+tw2lBrLJ11h6Fy7SpfPwndpYg7AEHYtRgbOjrMlR0dmDdvnh0l7E24O3n3Aw+g7YIL0H7hhXasIAi7i2cNQ7+ebMovt8Kuhxar7R/7Ifqe+ClCR1+K6Lnfsa8IgjAcoXBAPn3Ggfp1OLByc6KqkBoOeHQ7T507Dob6f9nitTEcPatfvzrwBn3WYR59M064Yz6trpybfN6QU2C8/fcRfc4bfoqHP/lFWJ8T7obNx+udPEepOu59NLrlnHkoPnzvByF9TuGiv88EfZk6nH78AP70SFTf9PMmf0fbOdg6nnihSR9Xq4P9oDjjiAuV/WIdFGedsWjUD+fcqZNU6wetfJ06q413eb8+9+M/6te9CT9Xu5N6vjvPP3YSbnihEwcjo3f83xkSRXVfZXjx4wtn2DEW9ep3SGQai06p3PaWh9UYTH17Mg1JDqJ/LKteeXyvuP5w7dkYb9luLnz4nLheR5z59Z1vpLQ46MwNzpWmZteW+VW59lAI/M7XU1vmCqmso3IOs45Zsz1aOCS//FkWA/0lvZYQlsl6nToq5yPXjdOPG8CrC5r1OWlUZ+W6UtmGyjWBIilF6FptIFxjy9swmLEsr6Ny7apcZ9jPa9Ta5NQ5NtoLU+6DhDpwc3XuA0QjQHGhVx0RVocZ3LRq9Xe/iynXX4+psoGVIOx2+GMGkR8yhN0BhdWxX7gHxWQv+p68Ff5px6D5kp/YVwVBGG44wupIYnQsgDu+fiY6Or3b3YBXCoG8wab1qCM0VN6Qk3IhgTfgFA7uVWU4UKwgzk175U1+pahZTTioFE4r20lhoGN1cUs7G9VRKRyQ8rGo1o9K8aFyLAbTD7bLcU9Aqo13uVjUqI7hIKzuTQ6d2oL/XjCAI0MleIydE3oKprHTZQjVofjOH3TOP3oSxrQEtbBaLihWmyuVgmKlQEnK155KUZSU11FtTjOuXDBsVAfnY7m4S8rXpmrrSvm6MZg2cB3isbMmkMG0wflBZjBjWbnuVK4rbGO5+EvKfyATYVVohAirjRFhdZjR98wz2mK1+eSTRegRBEEY4TjCqqZUQN+T/wEjMgqtn77TihMEYVjBDVueenujfTY82NyXwSuLOu2zrVDcOO/oiTht7vgt4kb5TX+1G/LKG+5KIZaUi4WVAiepFA4qb+pJuaBRTbwtL3cw7RxqHc5YVBNIWEe5dVq1OspFlmp1lAuntfqxI3UMB4FjsFaPQ2FzXxrX/vJV+2wrFFQ/fcYMTBsbRej/+xtOixbhxs4Lq5uLBi4/cowdYzEYi9yQr7G5bCSw9XNQj8HUtyfTkPAg2s618Pt3v2OfWbB8R1DlDzrEsVjl55+P0XMuVP744sDPPwXFanOFlP+AUm1tKl97qgmvxJlj9AvNuVUuepJycbaa8Fo+p6sJkuVzmmnLf6hyoGj5xItNerOrWv0cbBuqrTvEGctqaxtx1pUmtdawjspxKH+PRFgVGiHCamNEWBUEYb9m7S236FduHCcIu5pthFWb/r/9N0r5NNquugeGf9sv24IgCJVQVC0XOChuOIJqpbjBm/6PnBPXN9y1bsidG26KjtVu+iuFBW7Iws1hynGEA1pOlT8+71AualYTE9lORzht1E6KMOVWnw6DqcMZi2oCCXGstmqNRXkd1YQe4jy2O9TxLheZ93WBgz8SXPXTF+2zbQVVh0nXv4SDkd4lrgAWI4CO7x5nxwg7wrurevGvv3lTH1cTVB2ctYc4Yl+tueIIivy8V5srjjjLuVptrhBnrlNgrRQkiVMH51s14dURZ7muNaqjmiBJnDlNVwXV1keuBZOmuHe6n4MZywXvWP5XK+tw1hXWX0385Tg4/RNhVWiECKuN2XaGCnudzKpVSMyfrz+8giDsfpZfe60OgrCnaD7pM/A0jUbXzaeh2L3KjhUEQagPxY1LTpqKm686GpeecsB2AgehsEhBgb4EyzelKsfZ2IliJW/IK+ENOgVT3ngzVIoGhDtcc/MTZ4f7Srh5CoUDCggUBMoFT8JzZ4OVF58vVK3DaSfr4IYulQymDo4Fr5dv3FIOxQZnLBr1g+VVihuE5dYbb7b9rt9mG9Sx7YY4+zoUVG++6ij8308evo2oSg6fEEF3cdv3cih0F126LGHocM05ZuYo/T7Rn2q1Naccfv4p+NWaKx/7B79aW/J6zlebK1wHuDFTrbWJMB83t+Ocq1aHszN/+YZQ5bBter7WmI+EdVDwrLYuEa5NnNO11sfPfilgt6H6mlDez3ptoEDbcCzVWl0pqhJnXeEPX2xvJXocVpf0WAiCsPOIsDrM2HjHHXj98MO3WNEJgrB7iZ1yig6CsCeJHnUJ/FOPROctZyC3+nU7VhAEYXsGI6iWQysobTFVRWwkvOGmaEBxgwJpNRzxwtntupKt4kV14UCLAVNcWpSsLRx46woHTjt3pg6OBYWeavkJ66AA06gO7gReS+ihcEoLtlrjzXIpcNQSkJmH7dgfBA5+lmsJqg6XHjkG/Ub1sd4R+g2PKmusfSbsKKNjQf0+ffNjs2u+V5VwvnHdqDUf+fmnEMjPfLW5QjhfaG1Za23i2sM5fda51a+z3Ho/hBCnjprrwrk+/ag8X6vB+frD72dqro9OG2qtCWQwbeC60Wgsa+VnvWwnxd9aaZwflgRB2HlEWBUEYb9mztNP6yAIe5rwYecicsRF6L71HGTe+4sdKwiCsC207huMoOpwyeU+fcNe64acN9y80a4nPFAstATJ+uJFPeGAm6/QqrWmcKBu+tnOataohOXOOsy9U3VwLCg+1BJFWS6tthrVQZGlngjNOuqNN/vK11rj7Vjn7utQWG0k0l16xGhEI35sKFV/TwfDuqIbTaoMliUMDa43gxVUHfj55g8R1X5AcOA8qjVXCMVZ5q81V5x5VM0a1YFl1JrzxHl8v9ac55ymFWitdYX56B+1URsG0896beBY1moD4VjWEn8J19Za4i/R61Ksev2CIOwYIqwOM6becANONk39KgjC7oduN8T1hrC3CB58MppP/RJ6fvlxJF/+rR0rCIIwdHijzhv2euIGb7hnza59nTfzzN/opr6ecMCbduavJRwwvp54QShq7mwd7Ee1R2UdKJA0qoP56wk9LKPeeFM4rSUgExE4tuW2f5iJN+NAcghGvMzzVsLAf6syhD0PN1GqNVcIBcV6c555KXzWgz5J69XBH1Rq/RBCOGcb1UH/rfVgPxq1YWf7ybGstbaRRmPJa/XEX2ftEgRh55HNqwRB2K951rC+sPAHDUHY1VTbvKoa+U2L0ffETxH5wNWInP4VO1YQBGFwlG8gQ2hNWk/o4+Oh3DW7njDQqAw+us4dp+vd+A+mjHptYDvJztTBMurlH8xY0LK2ngAxmH40qoPIJjJb+b+Pr8b3VTguUkC4/rBtgaLqi0kPvnH6FHz7zCl2rLA7qVx7hJGHrDtCI2TzqsYM8n9Twp6CvlXpY5W+VgVBEIT9A++YgxE75zqkXvo1Bu7/lh0rCIIwNOoJjYRCYyORr1EZzF9PsCSDKaMeLH9n62iUfzBj0ciqazD9aJRG2JZ/PWMKvnzyBDwb9+hH+xuxNu/Saf/5pAkiqgqCIAh7FPk//DCDvwYk5s9HZpXsFC0Ie4L3v/mmDoKwt/G2jEfLudcht/Rv6Lvz83asIAiCIOyf3HT+dLx4zeEwo2G8lfNiVdaFRFHdL5mGDjxelTX0NTRHdNrvqzyCIAiCsCcRYXWY0X7hhZh+882yS7kg7CEic+fqIAjDAVcggpZzv4Vibwe6/+tiQB7NEgRBEPZjjpwUxWtfOxI3ffhgTJ3WisUI4Mm4WwceT53Wrq8xDdMKgiAIwp5GfKwKgrBfs/jKK/Xrwbffrl8FYVcyWB+r1Yi/+Bvk45vR+tm74Y6027GCIAjbI34O9w3E16Ew0pC1Z+Qj647QCPGx2hixWB1m0A0A/avyVRCE3Q/nm/g0FoYj0eM+CW/7NHTf8kEUNi2xYwVBEARBEARBEIThggirw4yu++/XFnR8FQRh9zPxmmt0EIThSPT9H0Hw4JPQ9ZMzkFv+oh0rCIKwY7z4XME+qk2jNL/8WdY+qg53vucO/fV49KG8fVSdRm0YTB2NyuAO//XYFXXsin4Iwr7AYOZCx5qSfVadPbFuNGrDzq4Jg+lno+uN1i62sVEZjdopCMLQEGF1mBGYOlX7e6SZtSAIux/6NGYQhOFKaNaZiB5zGTp/ejbS8+VHN0EQdpxf/TxTV5zgNaapx2MP5+retPOGvV4ZFBZ+9P20fVYd1lHvxn8wdXz580n7rDqPPbRz/SDf+UbKPqrOXXdm64ogg6lDEPYF+DmvN9/u+m1Ohdo/2nBOf+fr9ecb69jZdePDZ8fts+pceWlCp6sF1656a+xg+tmoDY3WLl5/9MHabaDwyrVJEIRdjwirw4yxV1yhdygXCzpB2DPQ7Ya43hCGO4EDj0fLOd9A72+vQuqF2+xYQRCErdS74eYNNW+6a7Hg7UJdYYL5F6hQ76b9bnXD/lgdYYHls5x69bD8ejf+g6mD41BrLCheUPyoJ3A0qoN9sASKOuP5DvtZu4yXns/XrUMQRhL15hvnZL35xrnwq5/Xvs78A6oczrlaWPNx59YN1lGrHyyf1+qVwbWr3ho72H42Gst6azDXHdZTC7ZP1h1B2D2IsCoIwn7N64cfroMgDHf8k+ag7fzvIP6Xf8PAo9+3YwVBECxqiRcUEs8+36tvymvx2MN5nHWet6ZYyLyXXO6rafXFm34GllFLGOBN/SWX+WoKIGzncSd6arZzMHVQQNF11BgLWo1d9SV/TYGD5bPsRv342jcDqp3VBQqnHxQ5asE+1uurIIwkaol9W9eN2vNtUHO6ztrTaH3bkTpq9YNz/ie/COvXalB4PfQw925tgzOWtaz+HeGZr6yrGs46zzETBGHXIsLqMGPVDTfoHdfW3nKLHSMIgiAIFp5RB6Dl3OuQefNP6P/jV+1YQRAEPg5bXbygBdO8E7w1hTzeiE+a7MJnv1hbLORjrrw+aYqrqjDAciksnHVebVGTQuP3/i1UU3yg1Szzs51DqYNiAvOxjnpjwX7UEjhYB+tnHbWEHIoTn/1SoKZwyn44411NwGAcx/tjl/nrWtkJwkihlthHsfCsc33159sJHjVXvFUFRUeQ/Oo3gzXXjfL1bajrhlPH3b+rvXbVKp9QcOV8rteGwfaz1rrDsfzYP/jrjiXHgT8sVbNKddZ5jkMtgVgQhKEjwuowpNDXp4MgCLufk01TB0EYjvTFk3j21Xfw6/ufwKp1m3ScOzoKLed+E7k1b6Dv11fqOEEQhMGIhdWEPEcUoPhQTSzkDT8fUeVN+SUqXTVhwBFQWE81UdOx4mxuNmqKDxQsnXZWExca1eH0k3VUGwtHvGA/agkc5SINrVsrccQJpx/VLHydfrCcagIGhVdHhGGbBWGkw89yrfnGa7XmG+c0r3G+VpvznB9cl+qtG+XrW7UyOAedOV1r3XDq6O/b/lF8xxLUWjfqz/m6a5e6xn5WE6HL21DtxytnLJmm1ljyxy9a7vJHn2rrjrPOn63aUM+aXhCEoSHC6jCDPlbnPP20fhUEQRD2bz593c047Ypv6NcZZ/yjFlmJyxtE61n/H8xUD7p/9iGY+eoWDoIg7D9Uu+HmDXu5EMjzShxRgFQTDigwnnWuVx9TGKi06uINP4UA5mU91URNx4qTUGD44U3bigvbCZYVouZg6qB44dRRayxoNUbqCRz16nDECUKRt9LCt7wfFDDqjbfT18o6BGGkwTldbb5RCCScS5WCoyMWOnOhmqBYPqdZR6XFaaN1g1BE5FysNaedH2wIrdmr9cNpAy1G6815tqEyv9PPegJxeRuq/XjVaCxZnvPjF+vgOess58XnrbWN8LXa2iQIwtARYXWYEZg6FbFTTtGvgiDsfip9rG684w4dHKvxxPz5+pyvhPFOGoeu++/fJk1m1Sp9zngHJ09luX3PPKPPiZPGgdd4XllueRqnbl4j9cqVPu35Pk38jxQ8s84dcnjgyZfw5U9egD/99F8Ri4bxm/uf0HU4NJ38ObiDzei65XQU+9bZsYIg7I9UEwspAjhCYLWb+nJRgFBAqBQOaMV5yeVby6i06uINunPDTqqJmpXibeWjquWCJeuoFEAGUwfTU0Ah1caC4gXzkWpj4Tyi74zFYPpRaflV3g/CNOUCRuV417JwE4SRBOdb5Q8u5aIoP++Vwmm5WEiqCYrlc5pzqdLitNG64VjKO1TO6XJxl9CHKde7chxLUML21pvz1daVyn5WitCVbWAfWGc55cJrtbFkec6PX4Rlla+xXHeYj4HwfdmX3ZCsuzqM9Jv32meCsGcQYXWYwRv85ddeu81NvCAIu4/E/PlbRDSy+MordWA82fTrX+vzdT/5iT5nWieNk4/XeO4IdMzLc85lh9Xf/a6Oc+Y20/Kc8aS8XEdQdOrmK2G8k8ahstxqdTt5pE97r087w7e/eBkuOG0ePnXR6VpopXuAcqLHXgrf+Fno+skZyK+zLFoFQdj/4E3zLHUDX37DXS4Ekkqrr0ohsPIxUeemn2KgQ6VVl/P4vEOlqFkpJvK1UgCpbCcFkPJ2NqqjUkCpHItK8YJUChzOI/oOjfpBWF65cFp1vMsEjGrCazUrO0EYSXBONKlQPqd57IiipFI4LRcLSaWgWG1OD2bdqJzT9daNStGTdXCec60grMuxBHVgfWybQ7klKKlcY6v1s1yErtaGjtWlumtX5ViW//hFuEZzrXHgMdvlwLLK1619kb4/XI3kc7+yzwRh9yPC6jAjoW70uXGVc1MvCMLu5eDbb0f7hRfaZ5Y7DgZPLKbPw3Pm6HO+EsY7aRzaLrhAn0fmztXntDjneXm5Yz71KR3nlMu0PG8++WR9TnjO4MBrPHfqdsotT+PU7Vi5O+XS8t3BySN92vN9WvtPIRQWPDykcNuNluDbN5DQr6vWbcaUCWO05WolkcMvQOjQs9B1yxnILpb/fwjC/kr5DXctIbDc6ouiQPlNPSm/6abVk/P4vEOlVRfTlt/0sz6eO8JApZhIyoXTau2k+LBNOxvUUSmgkPKxqBQviFXHVmvRSpGGdZQLOdX6UW75NRThtbIOQRiplM+3SlGU8HNeLpzyM185F8oFxWpzulw4rbVulAun1eZb+Q8ulT/YkPLNn1hXuSUoYXpHtGQbWGZ5G1ifs3ZREK3Wz3Kr/1ptaLR2OWuwI7yWi788dsonleIv28Dz8jT7Gq3n/SsST/wY8Ye/Z8cIwu7F2NDRYa7s6MC8efPsKGFvQusoWj05AoAgCIIwcuHjSGO/cI99tmPQMrX92I/p4zkzD8Bbi1bgO1/8B3znS5fpuGpkVryMvsdvRusnf4XgkR+3YwVB2NcxDAMb4y36Bvsj58TxxAtN2o/prMM821iNkS9/PomvfSuob+4pQtz++4h9xYKiCIWDn/wijCsvTeidqitv7I+a1Y8nXmzSN/20yKosg35aWf5VX/Lj9OMH8KdHotuID2zn0aqMxWtjDdtJ8cJpTzmDqcMZC/aDVlzl4gJhP+59NFpzLMrr+LAqi20oFzAIy3h1QfOQx5t1dKwu6jRjo70wZUNNYQRRvvZcoz7r/HzXmgvOfOOcpjD4vR+E7CsWzNfU7Nol68Zg5jTr4PpQTnk/uG58799C2815J98vf5ZFc8zQQmg55WtXtX4yHxlMG77zjZQWXuuNJUVo1lcO882a7VHBXXOdd/Lta+uO8927mOxF35O3wj/tGDRfYj3RJgwNPrH3QkuLNjw5vrfXjhXKEYvVYQYtp2bdd5+IqoIgCPs5tEx98o4faFF19bpN2h3AP39yq3VtNQIHHIvWD30Hffd8DYln/sOOFQRhf6HcGqrSWsvBsfqiWFlpKUV4A0/BlDf3DJWiKqHPQVp18ca80oqT0KKK1qC86a+0KiM8dyw1q1nNEqedrKP8MVaHwdTBseB11lNtLCgqOGPRqB8sr1JgIY7lV63xdh4Nrl/HVutcQRiJcH44c63WXHAsMatZaRLuaM9r9ea0Y3FaaYXpUL6+1ZpvtJytZlVLWAfLd9a/anOeYiXXSJbTqA3V+ulY/Q+mDayn3lhqVwPV1sdzrc31mJ/trYRl7uvrjjvcgrbzvoXCxoXoua22UYIg7ApEWB1m8NcA+udzfAIKgiAI+y8nHzUbr//pVnS9fPeWDawaQX+rred/G8ln/xPxhyzfsIIg7D/QMpPWXNWECUJhgRZMFCacTVkq4U03fX9WugFwoBUrRYNaAgrr5QYrFCWriRuE4gPbqdNWES+cdu5MHRwLWm5Vy08ckaVRHfSJWE0gIRQ1aKFWa7xZLgWMWgIy87AdFFIEYSRDsa/e2uMIp7XEQuahqMr5VmtO0+WAs25Uq4NziWtCvTlNlwO1RE/CfLQYrbX+cc5zXWFZtdYutoH+qistTYnTz1oCM2EbvvN1Wp26q/aTY8m1q9aPX8zP+llHuf9VB5a5X6w7Lg9iH7wGyKfRdevZMLOWey1B2NWIsDrMoH/VV6ZN06+CIAiCMBQ8rZPQeu51yCx4DL1/uNqOFQRhf4DWUHzctZYwwRtq3nTXEgWIIxxQ/KyGIwzUElAIBRDto7WKuEF4U892VrO2IiyX1mk7UwfHgiJOLfGC5VJkaVQHH92tJ0Kzjnrjzb7ytdZ4OxZugjCSodjXaO1ptG5wnukfdXZi3eCPIfXqsDZ3qr1u8DqtSWvVwXxcN6pZghKnDbXKJxSh6/WTP1416ifbUEv8Jbqda6z1rRr707rTfNJn4Gkaja6bT0Ox29p8VhB2JSKsCoIgCMI+iCsUQ8u530Rp8xL0/PeldqwgCPs6vOGmBVM1KyYH3qzXEgUIb8iZv54wQAGkloBCKIAwfy1xg/G05qpXB0XNna2D/ahmNeZAgaNRHcxfS5xgHSyj3nhTwKglkBAtvMaq90EQRgrOfNuZdYOCYqM5zbm4M+sGf3CpVwfL55yvNaeZj3O2miWoA9tQ6wcdQhF6MGtXo7Gs9eMXYf2Vm2+Vs7+tO9GjLoF/6pHovOUM5Fa/bscKwq5BNq8ahjhuAJxdqQVBEISRyc5sXrUr6X/uNpTSCbR97h4YwW03SRAEYeTjbCDjQKuwekIfHx8dqOE/0KFRGbSEalI3/7WEATKYMuq1ge0kO1MHy6iXfzBjQcsxiqe1GEw/GtVBZPMqYaRRufbUeszfYVfM6T21vtXLvyfWrkZjORjYjnptIPvq5lW1SC9+FvEX7kDLP/4GgfedaccK9ZDNqxojwqogCIIg7CaGi7BK4q/ehey699B21V3wtB9gxwqCsC9QKW4IIxMRVoWRhqw9I5/9TVgl2VWvoe/xm9H88VsRPvZyO1aohQirjan/s6mwx+m6/34suOgibLzjDjtGEARBEHYePgIVmn4sum45E7lVr9qxgiAIgiAIgrD/QJcArRd8B/EHv43EEz+2YwVh6IiwOsxIzJ+vxdXMKnGqLAiCIOxaQrPPRuT9H0bXrecg8+5jdqwgCIIgCIIg7D94xxyM2DnXIfXSrzFw/7fsWEEYGiKsDjNip5yCKddfr18FQRAEYVcTPPhkxE7/Z/TcdhlSL//GjhUEQRAEQRCE/Qdvy3i0nHsdckv/hr47P2/HCsKOI8LqMIOC6tQbbhBhVRAEQdht+Ke8H60f+jb6H7wBib/+yI4VBEEQBEEQhP0HVyCClnO/hWJvB7r/62JA/FwLQ0CE1WGGuAIQBEEQ9gTeMQeh9bzrkHzlf+URKEEQBEEQBGG/JXbql+B2e9B5y2koJrrsWEEYHCKsDjNk8ypBEARhT+FpHofWc69Ddtnz6PvtVXasIAiCIAw/Fr38Mu7+/k34zhmn4dqj3o/PHDRDv/Kc8bwuCIIwVKLHfRLe9mnovuWDKGxaYscKQmNEWB1meGIxHQRBEARhT+Dyh9F6zjdQ6l+Prl98GGapaF8RBEEQhL1PorcX//O1r+J//vmfsOnRR3CY4cIZY0fjslnvw5nqleeM/5+r/wn/fe01Or0wPHjxuYJ9VJ3+fhMda0r2WXUefShvH1VnMHW8+3b97zaN2tAo/67oZ6Prd92Zs4+qwzY2KqNROwUg+v6PIHjwSej6yRnILX/RjhWE+oiwOsyYeM01OF59GaCfVUEQBEHYUzR/4Itwe3z6V/pSotOOFQRhX2AwN9ON0vzyZ1n7qDoUDhqVsScEkkZ1NBInBlNHo3YOph+N0ggWz9/1B1x32geQXvAuzpkwHoe1tWJUKIiQx6tvZIPqleeMP2fieGTeW4BvnXoKnlP5hL3Pr36eqSv23fXbnAq11xbOle98PWWfVYd11JtPvMY0tWAdHz47bp9V58pLEzpdLR57OFd37RlMPxu14bGHcnXHktcffbB2G7iu3XVn/XVcsAjNOhPRYy5D50/PRnr+/XasINRGhFVBEARBEDRNfARq9HR03nwaChsX2bGCIIx0KCrUExQpCNQTHsjdv8vWvamneFHvpn2wAkm9dg5GIGlUB9vYqB8N6/hGqq7IMph+iMDRmJfuvw8P33IzThjVjkMDfhh2fC14fXYoiBNHj8Kjt9yi8wu7n1rziXNEf9brCIovPc+1p/Z15h9Q5dT7saORYHi3uvZYHdHTqaNWP1g+r9Urg4Imhc1aDLafjcaynnC64J2irqcWbF+9PgjbEjjweLSc8w30/vYqpF64zY4VhOqIsDrMWKu+BLwybZp+FQRBEIQ9TeSIixCaeSo6bzlD+17dUbgJoyAIe55awgNvyHmz/uJztW+oKTxQXK0lFmphYXWprkDCMnjjXwtHOKglOA62nYOpo9ZYMH6BCj+8KW3HbM9g6mA5tQQKpx/1RJZGdQhAZ0cH/vfb/4ojmqIYHQrZsYOD6Q9viuj8LEfYvdQS+/gZv+RyX01BkXOF4azzvDUFRc4Vq4zqP3Zw3Tr7fG/N+bQjddTqB+fyT34RrjmnuR4ceph7t7bBGcsffb/62uWseXxlXdV47OG8bkMjq/5K9ufvdf5Jc9B2/ncQ/8u/YeDR79uxgrA9IqwOMwp9fcisWqVfBUEQBGFvEJp1BqLzLkf3recg89bgHoHqe+YZvfkigyAIe55awgNvyM8616utmarBm3De7H/tmwH9uGo1KCh8799CDQUSChy1hFMKByyjlnDKdh53oqduOwdTx1e/Gaw5FuwHrzcSQBrVcfvvIzVFFme8OaYsq5LB1FGL/Ung+PXXvoq5kybtsKjqwHxzJkzAr7/6FTumOhxT2TR456gl9nGunHWur6agqOf8CR41771VBUVnrtSbs7TQnHeCV68dteqg6HnWeb6qPwyV10Gr/GpwTapVPuFa8LHL/HXbMNh+1lq7OJYf+wd/3bHkOFxymU+1Z/s6KLhOmuzS41Br7aqE3+ve+sAH9vvvdZ5RB6Dl3OuQefNP6P/jV+1YQdgWEVaHGe0XXohZ992HsVdcYccIgiAIwtD46f8+gFnnfQ6tx3wU533+eixauda+0pjggSeg5Zxvofe3n0fyuV/asdvjCKr88t11v/ihEoS9RS1BkjfkvKHnTX81IU/f9Ktrn/1SQPsJrIZj6VTrpp6CLIUD3vhXE04d4YA3/fXaSRGmVjsHW8dVX/LXrIPxFDQZqlltOQJIvTrY/7PVOFCoqDYW5eNdTeBw6vjsF9V474DAsT/9cPXmE39Fcv06HOz32TFDY2YwgOS6tbq8ShLz52P5tdfq/3et+8lP7FhhKFQTFJ35yGu1BEVnrnBdqSYocq5QFG1uNqrWQZz1i4JhtTI4xxzhtdoPQ+V19PdZ87scxxKUoiTLqLY2cX2s1wa9tqlr7Gc1Ebq8DZOmuGqOJdPUGkuu3VzX9DpeZV1xxF+uXbXWRwdHUGXg8f7GM39/Gz/8nz/hzgefRi5vCfru6Ci0nPtN5Na8gb5fX6njBKEcEVaHGZG5c7W4Gpg61Y4RBEEQhB2HoupXfvBLLF+zAbNmTMFjz72Gi7703S1fEgeDb9JhaD3/24j/9UeIP3KTHWshgqogDC94Y18pFpaLG7SGqnbD7QiavKnn4/7VhAWKCrxOcaDaY/S0Grvkcr++8a920+6IiaSaOFHezlqCY6M6HOGVVBuLcoGE/a01Fg37ocombGelwDHY8aY4wnZwrJm+FvvrOvv3e+/FeL/fPts5xvt9eFWV58AnAx1Bla7X+JSgPCm4c3BdqJwLjlhIqgmn5XOllqDIuUJRlLCOSovT8rWJ5VSzuNc/pqj6rfVr+x+GnPWPVJvT7IfThmo/uFS2odaawLFw0tRrwyWX+Xd4LFkeXaA47ai2rrz4/Na1i68ss5L9XVAlX/23X+L0K7+Jb/zof/Cpb/wQcy74whajBJc3iNaz/j+YqR50/+xDMPPbi+jC/ouxoaPDXNnRgXnz5tlRwt6Ei1hi/nzETjlFi6yCIAjCyGXd1WGM/cI99tmehZaqFFVXPnkHxo1q1b++84vizvDFc4/AjV/7kbbuqXeT74nF7KO9w96uf2cZqe0fyeM+0j8zx6r5+JtHouAGLvQF6EBxccHbBXztW0F9fvrxA/iTSsebb8Kbb+52fa+KIxQ8abVFq08HCqmzDvNocYLpTz9uAK8uaLavWjf1LOOJF5r0OTd2mjXbo61THXidbgB4488yrvl8Uj9O79ConYOp48PnxHXfWQdFg8qxKO8HYR1OeaRyLGr1g+ILhYnK9KSyH0fN6se9j0Z1m0ijOsZGe2Gapr4fqLbO0vDimJUr7bN9l2+efAKODYcR2wXial82i5eTSVz/69/qMeVj/5VCqjOuzjXnPoz3ZHwveJ2GL2TVDTfoVz5dyHhed9IwjvlXf/e7Os2U66/XawsF3Ozq1Wi74AJdNst1rGQPvv12/Uqxl3knfPnLum6+95t+/WuE58zBVLtOx2J5+s036/rYlv5nn8WYT31K181yWQ6Z8/TT+pV5KCazLewD+8i6WYdT9+uHH67r5lOTjGe5rJvpWRfzMw3hOLFPLJf9vkXlu3FtDB9R8698PvFz7lhIkvL5SThX+GPJ935guXqotvaUz1HOnaPVfFqs6nKonNNfVusK595g66icjzyvXJsq2125buyONlSuKzx31h1S2aZf/iyLgf7SlnWncl2h+EtLWadfbBN/9HHWx7PUunOH+lzy/ayGMz/42eJn2XnClp8L7g1Dju/t3fK54GeXnzd+bnnMOF5jGsI8zMsyWBbLZNmcG/zc8rP4QkuLTvv+N9/Un8nFV16pP7sTr7lGfyb5WXc+kyerNZPwnPHtJ/ow8xc7vnEdLVUpqrY0RXDnD7+Otxev1N+bzzrxSDz0C2tOO8Rf/j3y3SvR+tm74I5NsGP3XZz3pPx9FLZFhNVhBv9Hxv8ZO4uRIAiCMHLZm8IqH/+npepzd/5Qnz/+whs456pv6+Oh8vmTJ+Hrh03HpgdX6S/FgiAMD85TYVm8Zbubft6QO4ImqbzhrhQCeVNfKZBUipwUDmhV5dzkV97UUwTlzvzOTXw1oYCC4xMvNm0pk9dp4elYZVW2s1odFDQcYaBSeCWVY1HZj8HUMZh+lAunlePNMokjolQb73IhhwLHby+8cFj/cFXO7mrLHQEfPnLgDHjd1udhZ8gXi3j3tTdwVDqjxYFqOMKRI8xQuKGAQyGHgg6FHQo8hOICy6EARCFopIhNu6tP9E77A7X2NBIgKwVFzpVysbByflUKkqRStKyc05V1VIqerKNcnK1WR/naVG1d4bpBC9ZywZjz12lD5Ryv1s/yNbZWG5x1pdq6U20sy9edyrWL4zBpinvLWscyy38gu1itOz9Uc6DW97qRJqz+hzr+o44ZGheePg9//Mm/6uMTL/saFixbjZ5Xtv8un3jzAWSWPY/Wz/wB3gmz7dh9E+c9KX8fhW0RYXWYwQWDvxA6vzwKgiAII5e9KazSpyof///BV/8Rhx08DZd97d/QO6BuEG7/Pk45+jA71eAxC1n0P/kfcLVMRMsVv9ZflvlFuPKLOL+AOzdrewt+ARzJjNT2j+RxH+mfmVHqxnVjvGUbsbDaDTnjyoW8ypt+QrGC13mTzpt+PqbqpCeMK7d2qhRUSLngUSlYkh1tZ606HHGiUR2VFluEcXyk1ulHpThByvtRKZiQcoGjVj/K46rVUS7kNBI49hf+cPCBuHjmQfAYW8dpqFBYvXflKnzr05/VlprVxtYRjijwVFqW8r7M3dy8xeCl0mKV/y9kOoo/FIm4lvB+jlAEIrQEdNKwXLaBcRQpmIewHObldZbLNMzDNIzjNceikOeMZxoGHrNsUp6G8DrzMg3LJSyXOHmYhjANA9MzECePc87rDoxraWnRP+qUz4VqYmHlXCifvw7lgmKlKErK66g2p1lHuXBaPn8dyteSSpGUlIuQ1daV8vWvURt4XPlDFSnvZ602NDW76o6lsz5WjqtD+fhWWz/LRWrHUr7e9zrOj5GCYWx9v4fCCe+fhWd+8+/Y0NmDaaddgemTx2HBQ/9lX92W1MInkXj5d2j9zO/hP9iac/sinOsirNZHhFVBEARB2E3sTWGVPqHoU3Xp6vV2jPoi/ckL8KOvX2WfDZ5Cogt9T9yKwIEnovmjP7ZjLSq/iI+0L+CCsC/AG0kKq+WWnNVEAeLc1Dc1G1VvyMvFi2riBnFu2qsJC6Rc1KwmJpYLA/XaScGRfl8b1VFNOCgfi0b9qGaZRir7UW5VS9gPx/JrqONdLqIMRuDY2z9cleOIbbua6y+7FMc3N6N5JzevIo4rgO8/+7w+p3haKbDK/7eGjrP2lM/pWvPNmQtcNyrFQlI+F6qJoqzDES1r1eEIhlzDqq0b5etbufjoUN6PamsXcfJxzjfHDL0+lOO0oVY/mY8Mpg3VhFdSPpaVP/gQZ+2aNdtddRzKfyhy1h2Hkf69bqjfvbkHAX2q8nvzvLmH4NV3lqBQLOLH37gK//yJC+xU25NZ8TL6Hr8ZrZ/8FYJHftyO3bcQYbUxO/8zoLBL4Yd2d31JEQRBEPYfZk6biLce+E/8+gdf01artFQdkqjatRK9D92I4NyLthNVCa1t+IWbj3Txy7cgCHsPCgDOpkvcJZobMVVCC1VujkI/pM5mT+WUb47i7HZdCeMcyy1nY5dynI2bKFgyVAoTFEsoCPAahYdq7aRYwV31G9XBclheZR3bjkXtflBo4XicdW7tOih0MJSLqoT1Mq5ePyhcsPzyzbXK0bt4q/eqnHrrqmNVOBwC27Y7wgGHzcGm3PY7uA+FTek0pr5vln1mWZFybOlflHUJu4byOV1rvnFOcy5wrlSb05wLvMZyOH/LRVXC81n2fCvfjKkcZ9d8zlv6eK2E6xvnG9evavlZB8t35nzlukIoVnL9ZDmN2lCtn5dc7tP9HEwb9DpdZyz1xlfnbf8DBDfD4kZbzM/2VsIyf/Xz6nNsf/1e5/N6cN/Prtc+Vfn4Py1VG4mqJHDAsWj90HfQd8/XkHiGjgiE/RERVocZ/BWVvwY4j5kIgiAIwlDhl8TLzv8AvvaPHxnS4/+5jrfR8+D/QfSDX0XTOd+yY6vjfBF3NsMQBGHvwBtmWnM5u0RXwpt6CgK8Ia8mBPKmnmIhrapYVqW4QZxd9fWO2+dvLxwwD8UR7uJdTbAkFHh5nQJCtXZqwXEQddz9u607h1fC9tNyq5pIQ9gG1lFLhHbq+NXPMlVFUUJRg0J0rX5QyGEbawmvrINWucxfyf4qcBz9kY9gfXb7Hd6HwoZcXpV3sX22FT7KLwLrroViH9eemvPNFk5riYXlc7qaKEro35l1MG21OpwfhmqJu858qyV6EuajxWi9Oc91hWXVmvNsg1671HElTj9rCcxEr11fT2lRtFo/nR9kqv3gQ5if9ddbd9jOauuOw/74vY5GCdyoij5V+fh/I1HVwTd+FlrP/zaSz/4n4g9tu9GVsH8gwqogCIIgCNuRXvo8eh+5CS2X/yfCJw7e0tXx6yYIwt6BVpY//H6mpqDpCAu1rLGIIxzUu+mnOEJqlUGRhRZR1W7qCQXeRu2k+EDq1UGrrWrCK+FYUCCuJdKwXNbBuobaD4oTrKNeP+gGoJbwShyr1lrsbwLH4ad/EKFx47EoY1lOD5VFqTRC4yfo8mpBgZUb5gg7D8U+WoDXmm/OnOY84HE1OJe1dXcVUZRwvrGOalaaRM/lKa66dVg/qFQXXom26lfXa9XBfFxDq1mCEqcNtconXFfq9ZNrV6N+sg21xF+i21ln3XEsaxsh3+sGh6d1ElrPvQ6ZBY+h9w9X27HC/kL1WSbsNfh4Cn0nycZVgiAIwt4iteBxxF/6LdqufgSBORfZsYIgjAT0Tb26ka4lBBIKefVuyClesIxq1lYOFEBqCSiEIgtv7Gvd1A+2nY3qoLVWvTp4vZ7AwX7UEpAJ63CEmmqwDook9fpBK7tawivheNNXYyP2J4Hjih/9GG+tXYfNqZQds2Mw39vr1+OKH99sxwi7m8HOt3pzmoIi87OsajjrRr06ON/q1cEfderVwfK59lWzBCXMxznbaM43Wlca9XMwY0lxtBasf1esO8LgcYViaDn3myhtXoKe/77UjhX2B2TzKkEQBEHYTezNzauGSuKN+5BZ+QraPns3PGNn2rGCIAxnnA1kHGo9autAa9VargIcaFlWS1ggtISiJWYtYYDQ6queOFvPmoqwnaReHYNpZ6M6Go0FLccontZiV9RBKjeR2d956f778MD3b8L7Y80YHdp2A6B6UFR9va8fF3zzW5h3ofw4uDsZytpDdmZON7q+q9a3evn3xNrVaCwHA9tRrw1kX1t3hst37/7nbkMpnUDb5+6BEdx2g7KRhmxe1RgRVocZ3IWv+4EH0HbBBfqxH0EQBGHkMtKE1YEXf4PiwGa0XnUXXJFRdqwgCMOdSnFDGJmIsLo9f/vdnfjj//t3HDh6FGaHQqgnEXHk3k2msKSzExd/7f/DSZddbl0Qdhuy9ox8RFjdfcRfvQvZde+hTX2v9rQfYMeOPERYbUz9n02FPU5i/nxsvOMO/SoIgiAIe4r+p3+OYiGHtmv+KqKqIAiCMCw46R8uw01PPYPg+w7FI+vW4+3uHnSm0kgX8uC2Oyn1ynPGP7J2PYKHztbpRVQVBGFvEz3qEoSmH4uuW85EbtWrdqywLyLC6jCDvpPoZzUyd64dIwiCIAi7j1I2iZ5HfgBX83i0f/5eGK7aj6UJgiAIwp4m0tKCz9x8C/7xp/+BMWefg7fNEl5fuhx/e+VVPL5xsz5n/D/e+h/49I9v1ukFQRCGA6HZZyPy/g+j69ZzkHn3MTtW2NcQVwCCIAiCsJsY7q4ACv0b0PfkrQgeeg6aLrzJjhUEYaQhj+PuG4grgMHz1gc+oB9P5aa/wt5D1p6Rj7gC2DNkV7+OvsdvQeySmxE69pN27MhAXAE0RixWhxmZVau0GwB+eAVBEARhd5HftAQ9D92I8DGfEFFVEARBGDH0PfOMDrxn4v4UgiAIwx3/lPej9UPfRv+DNyDx1x/ZscK+ggirwwz6V3398MOx9pZb7BhBEARB2LXwV/OeP/8fNJ9/AyIf/KodKwiCIAjDn9Xf/a59tO2xIAjCcMY75iC0nncdkq/8Lwbu/5YdK+wLiLAqCIIgCPsR6cXPou+Jn6L103eOuEeRBEEQhP0bx1rVQaxWBUEYSXiax6H13OuQXfY8+n57lR0rjHREWB1mTL3hBpxsmvpVEARBEHYlqXceReL1P6H96kcQOPQsO1YQBEEQRgbVLFTFalUQhJGEyx9G6znfQKl/Pbp+8WGYpaJ9RRipiLAqCIIgCPsB8VfvQnrFK2i/5nH4ph5lxwqCIAjCyKDSWtVBrFYFQRiJNH/gi3B7fOi+5YMoJTrtWGEkIsLqMIO+Veljlb5WBUEQBGFX0P/cbSj2bcKoa56Ap/0AO1YQBEEQRg71LFPFalUQhJFI03GfhHf0dHTefBoKGxfZscJIQ4TVYUahr0//6ppZtcqOEQRBEIShYZYK6HviJ4Dbh7YvPwYj2GRfEQRBEISRg3N/FJg6VQcH55z3UNWsWQVBEIY7kSMuQmjmqei85Qzte1UYeRgbOjrMlR0dmDdvnh0l7E34pYFfCiJz5yJ2yil2rCAIgjASWXd1GGO/cI99NjT64km8tWgFVq3bhJOPmo2pE8bYV+pTSqmbzCdvhWfKkWj5+K12rCAI+yKGYdhHwkjHNE37SKjHs/ZnnntTCHsPWXv2DfaldWdXfPfeW6SXPo+Bp/4Drf/4GwTmXGjHNoYaEvWj3QV/uHqhpQWeWAzH9/basUI5IqwKgiAIwm5iV3y5+8g//1888ORL9hnw5B0/0AJrPQo9Heh78qcIHfExRM+/3o4VBEEQhH0D5+m+cutVQRCE4SCspjM5BAM++2zHyHW8jb4nbkHTh76H8IlX2bHVoUHeup/8RAurx6xcacfuekRYbYwIq8MMTgrnFwcGfoidLw7OrxC8TpxzXmc6ftCdLxe10vA609UrtzLNYMqtl6ZWuaQyzY6U66QZTLmVaQZTbr00tcollWl2pFwnzWDKrUwzmHLrpalVLqlMsyPlOmkGU25lmsGUWy9NrXJJZZodKddJM5hyK9MMptx6aWqVSyrT7Ei5TprBlFuZZjDl1ktTq1xSmWZHynXSDKbcyjSDKbdeGue8/7bjMfE/Uvp4Z/jyJy/ASUfOxqevuxkXnDYPt914rX1le3LrF6Dvrz9B9OxvIHLKP9mxgiAIgiAIgrBvszeF1Y1dvbj+1v/FHff9FYdMn4wrLzpDf4ffUQpdK9H7xE8RPvZTiJ7zLTt2K46g6mzax/sREVb3MhRWX3zxRVMYHqy8/nrzGcBcds01+rz36af1+fOxmD4nr82dq+M23H67Pu+4+WZ9zngHnjPE33xTn7M8ni+64gp9zngnTb63V8fNP+UUfc7ySOd99+nzl6dO1eeE7WAcrxGnve9eeKE+Z1k8Z0ivXKnjeI3n0ifpE5E+SZ94zrCv94n51v5TiM9W7XToevlus7DgYVN9OTNj0fCW88qQePDb5tqrI2bq1d/rdgiCIAjCvgj/3+78/10QBMGB372rfUfeE+HTF59pGoZhXn35h8xTj52rv8MvevRXVdM2CplXfm1uvPFIs+/ua+2eWfdIzn1TeSi/b9kdOPc65fdmwrbI5lXDjLFXXKF9q7qbm/U5fxWgBZRjBUUqz/kLBc8dyylSmcY/ZYo+Z3mkXrmVaeqVO5i6w3Pm6HPpk/SJSJ+kT5Vp9tU+OdfVl6MhB8cytW8goV9XrduMKRPGIBYN6/NyUgufxMCzv0L7lx5E8MiP27GCIAiCsO+x8Y47dBAE8vzzz4P+XmuFnb1eLfz7v/+7DtWulYcZM2bg4x//uK5D2Hfh4/+0VP2ny87Hzd/8HB6/7UYE/D7MPPuz8Mw6d4dD4JhPYex1r+EbP/sjOn98PhZcdBHe+sAHtliplsOn6WhRurvCK9Om2TUJtRBXAIIgCIKwm9jZx5G4cVX7sR/Tx3NmHqA3sfrOF/8B3/nSZTrOIfHmA8gsfR6tn/0DvBPq+18VBEHYE5x11ln4y1/+Yp9ty7/927/hX/7lX+wzCwoUX//61+2zwWGaphYuGnHmmWfi1FNPxac//Wm0tbXZscJIZvm11g+P02++Wb8KArnuuutw00032Wfqc7J8OQ444AD7DPjDH/6ASy+91D4Dfv/732vRk3At+cUvfoGLL75YrxOVaxLXG0KB9Fr1+fvoRz+6ZR076qij8Nprr+ljrjePPfaYPmZ9X/ziF9FrPz5dXp+we9ibrgDmXvQljG6N4U8//Vfc/ejf8Lnrf2pfGTqfP3kSvnvOLKz8z0VbXJrtLWgEePDtt9tnQjlisSoIgiAIwxRapnKzKoqqq9dt0v5V//mT2+4SGn/5d9qvavs1j4uoKgjCsIHCQldXl31m8YUvfEGLE5WiqsORRx6J+fPn6zSOiOFAMZZxLJPHDox7+OGH7TOL5557TsdTVLnkkku0wEuBhGJvd3e3nUoYyVBQFVFVqKTZfvrJoVxUJRMnTrSPLMrPuT597nOfa/jjywknnICbKz57tfJQRP35z39un0GLrLIG7bvQp+pTL8/HmOM/rkXVpkgIiTfuq/pUWqOQeeXX2HjjkfjBly7G6H95VPtQnXXffds8MefAOPo+3d1BRNXaiLAqCIIgCMOYk4+ajdf/dCu6Xr5b/wJe7gag/9n/QjE9gPZrnoA7NsGOFQRBGB5Uig1Tq9wQlnPXXXdhzpw59ll1WCaFWVqFOTQ1NdlH20JRhRZj06dP1+e0KLvtttv0sTCy4eYtDIKwqygXQBtBcbXWD0SVlIu3tFxduHChfSbsa3Cjqr/f/RN8+uIzceu/fhHr/3andgewo+jNqx66EcG5F6H5oz+2Y4H2Cy+sKbDSndnuDkJtRFgVBEEQhBFGKZ9Gz2P/D65QK9q+9GcY3oB9RRAEYWRCkaLSuqwezqO2g4E+Dh2eeuop+0gYydDXIIMgjDSi0ah9JOyLHDFrhhZVv3DpuUMSVXMdb6Pnwf+D6Ae/iqZzvmXHbks9gVXYO4iwKgiCIAgjiGK8E70PfR++yUcg9il5JEcQBGFHiInVjSAIe5C1a9faR5b/1UZW+cL+S3rp8+h95Ca0XP6fCJ94lR1bG0dglUf09z4irAqCIAjCCKHQuQK9D9+IwBEfQfPFP7JjBUEQhHosW7bMPgL+6Z/+yT4SRjInm6YOglCP8t35GU488UT7yu6HvlSdzasIfbjeeeed+lgQKkkteBzxl36LtqsfQWDORXbs4Iidcop9JOwthp2wyv89amf1KvBfSZ87F6wTJ15fs18ZrBPL2b2d1PlTESx4VFJ/Syjqv4IgCIIwXMl2vIXuB7+H6Jn/gqazv2nHCoIgCLV466239OYx3MSKfla5yRV9IwqCsH+gdYGywI3tdjfcLI8ibnt7Oy699FLtV5Wb8tGHa6ONsYT9E25wlVr0FEZd8zj8M+T/USOR4Wuxapa4EvIAMKwoLYOq+FKJMqiJggrd+SIW9aexNJFBf0ldM5w0TGELplxIVW7+dRZVnqlU+p8+t6sSBEEQhOFGZukL6H3kB2i5/JcIHf8ZO1YQBEGoBq3SKGzMnTtXb4h1ySWXaKvVc845x04hjHReP/xwHQRhuMHH/fljTktLix0DfOYz8t1NqM7Ai79BfvNyjLr2SXjGzrRjhZHGsBNWDVsMLRpsmgGXaemqpvpjqiNqnyV1KaEOFicLeGxVL+55Zz3ue289/rahD6tzJWTh1umLFFkNlZCZdAFWmYxgHSzNreLc8MBgoYIgCIIwzEgt+Avir9yJUf/8KIJzL7RjBUEQhFrQKu1b39q66QfF1UceecQ+E/YFEvPn6yAIwxFuxPfb3/7WPgNee+01XHfddfaZIFj0P/1zFAs5tF3zV7gio+xYYSQy7NREiqclw7AEUfucUqu2VjXUDabhwuqsicfXZ3D7u914byCHww8ah5lj2vDumj488N56vNQTx6YCpVOPyqfKozrLkhyBVXXbUP9cJXVVW7OaKKo0JbtOQRAEQRgOxF//E9KL/4b2Lz8O3/Tj7FhBEIR9D8cP4a7ixhtvxJFHHmmfAZdffjlWrFhhnwkjHW7WIhu2CMMZWsiX/8Bz0003yQ88gqaUTaLnkR/A1Twe7Z+/F4bLbV8RRirDTlilHalRMuApqSMzD9MsqLgSMjCxoVDCC105/G5BAvcsyWJhxg9/LIqZLT6cP6UZH507GS0BL15YvBaPLdmE9/py6C+6kDfduoySUUDRKKKgQlHVZRoUWS0R1zRUXTpWEARBEPY+cT4a1LVS/4rtGXOQHSsIgrDv4WzysquhparzOC79HNIlgLBvMPaKK3QQhOGM/MAjVFLo34Ceh2/UvlRjl//SjhVGOsNMWDVVgwrqtaT+8nF+ugQoordg4p3ePO5ZMoD/XRjHC3EXeoIR5D1RvLcujzc2JJFWuWaEPPjYIeNxwSHTUMyU8MiCDfjLij6sTOSQNFWphirfLMAoFVAyi6oOAwVax9J6Vf+j0CoIgiAIe5e+p36GYrGAUdc8CXek3Y4VBEHYN7nttttw9NFH22e7Dnkcd99l4x136CAI5fT399tHFpUi5tq1a+0ji8rzclatWmUfWfAHoFqUX6tMV+0HHm6sJ+x/5DctQc9DNyJ8zCfQdOFNdqywLzDMhFXrMX1CdwADph8LUy48vroPjy7ajPldeXR6QsiF/IC7BJfLhXTRh66UgWTRRMk04S+ZmN0awIePmIgjD2jDqr5u/HnRajy/fgDrMgZyhg9uww2PSucyi9palfW6Sh4YpphgC4IgCHuPUiaB3odvgrtlEto+90f1vyf5wU8QhJFLpcDwxhtv2EcWFD3+/d//HV//+tftmK1UCiKVgkk5AwMD9pFF+Xm1x3FZpzCyWXzllToIAnn++ef1hnWc3+VMnz5dxzvXuUt/OTx3rjtwfWDcf/7nf9oxFtzl/6yzzrLPLJy0/NHGgceMc9aZaj/wcGO98jqFfZ/s6tfR8+f/g+bzb0Dkg1+1Y4V9BWNDR4e5sqMD8+bNs6P2LJasuRXu9J9RkZ2ZAhZsTuG9DQPwevM4eFI7liXdeGpdEQkjBI9Le0ZFKJ/G+RO8+Mi0MJpdJRRNywKV//F1Y76I1zu6sHBjH4KhIGZPaMXBLSGMdpfgM0souSw/rC7TbolaBC0qWyYIgiAIO8a6q8MY+4V77LP65HvXY+CpWxGYfa78ii0IwoiHAsRf/vIX+6wxX/jCF/Dzn/9cH1OUqAU3pjrhhBPss/ppTf393uKoo47aRvzgzt2PPfaYfSaMNF4//HD9+v4339SvgiAIZEe+e+8p0oufxcDz/4PWK/8XgUO3FeeFfYNdL6za31/0JvzWEQzQXyq/9NhffIySTlCCS6dx8br6l1Ghs1jE0t4k3l3bh2yqgEPGNWPW+AhChqni8/jL2hzeS3qQd/tVMRkcEMziogOiOLYtCF9JlavQm/+z3pKqz+XS5a5J5vDK6o1Y2Z/G2JZmHK7KnR7xolldt2xki6qNqiWGW31BU8f0t6otWN2qJNVKVT/buKUPgiAIgtCAwX65y29ajL4nforIB65G5PSv2LGCIAiCIAiCIAyW4Saspt55FEkVWj/ze/imHmXHCvsau94VgH60niIlxUnTElQtpVNB4bMI/njMI74yeVGd9ZTyeHsgg4eXdOP5pZvREvLhQ3Mm4IRxfjSl4gjkizhiTATnT/PhpNYk5oT7cHwsgzNHGZjuyQO5jK7BNOgpVQUtkqqyVSVuFaaHfbjofZNx1sxJyKezeHzBejy6Jo6F2SIGVBuLWkC1G8W8pkfFUfJlrAosjME+FQRBEIRdQXbVa+h54HuInv9/RFQVBEEQBEEQhH2A+Kt3Ib3iFbRf87iIqvs4u9xiVVt30gJVC5C07mTgi4mSUYKp4rhFFY+p6xaKwJpMAa9u7MWKTQMYFQrjqKltmB5WBSTiupxIJAyvz4uCOu5PpZE1XMipvB51MVQqopRIwOfxIRRpgtvv1YIuoc9VQltTvd+/bpQLcVX1K+sG8GTHAAy/C8dNjOKIWBBjvS742VzTpdMbKr1Lt1P1ynSrnHZfSNmhIAiCIFSj0a/mfDQo/sIdaPnH3yDwvjPtWEEQBEEQ6vGs7QLiZPt+TxAEgQwXi9X+526DmUmi9aq7YQSb7FhhX2WXW6xaIqYbJRW0+KgCDVZN/T8/SpMGr6BolrAhk8Wz6+O4d8EmLO5KY+bkMTj7fWNxkCcNd38PwoEQYq3t8Hh8qgATmXwBpYKJVpcHowsFtKm4ADei8oVQNHwYSGVQKNLG1KqYOiotZtlJj4p1qXO2rFVFjIkFYQajeLvfh98vTuC3S3rxSm8OnUUDeZVdN9coqdSqTgrFFFi1MMt464UwxgmCIAiCMFiSbz+MxBv3oe3qR0RUFQRBEARBEIQRjlkqoO+JnwBuH9q+/JiIqvsJu1xYtSRNS2qkdWpJHXJDqaJ6NUzas5rYXCzh1e4cHlrYhTfWdGF0SwinHTIJs2IRIJFSeV1oam2DPxhQxbAsNtMFM5uF3zDg8XmRR1FbpPp9QXXNrT63LhjuEhKJBIqqMmajM3urPSq3+uNWR3QLkEQBCzozWDVQRDHYjE7vWPytx4tfL+zFfSv78HYii17TdgJgutXkcNP81WpLmahKnNOKaEEQBEGoCR8NoguAUXw0aMr77VhBEARBEAbDnKef1kEQBGG4UEr1offh78M1+iDtU1XYf9gNwio9k+bADaD0dk+mCXcpj5JZQm/Jhbf783hg6Wb8Zfl6BENufPjwyfjglBaMLRXgTWcRcBvwB4MwXT6VhxtcqQ+oy0CeYmmhAJ+7BI8q26XKo9Dp9qg6VCgVsgj7PSqugGQyri5RwqVTAhOlUglFlTefy6CQyyKVyiCRzqFouqCqg9ul0vhj2IBmvLQxgYcXd+Dpjl6sTJnImlqOVeXS1tVxMrAtIqoKgiAIg6X/b/+NwsBmtF/7JNxtU+1YQRAEQRAGS+yUU3QQBEHY1Tz76jv43s/uxK/vf8KOaUyhpwM9D9+IwPvOQsvHb7Vjhf0F99e+8pUb+gYGMGnSJDtq56HQSFEUhgumYSJl0o9qES9t6MeLa/rQVzQwc9IoHD2xBeO8Kl0ug2Ihh4DLQNTngc/n1u5ZuXUU5UyXKieTyaBUKiIQDMLlciNHtwBuN9xeH7zqNZfN65qDgSCSyRSyuaxOl83mkMmmVfosMuk88gUTBZVycyaHNakS0iU3PKYJV7GIFncBp04L4eCWIJati2NJZxpp1Y6g342AV9WlLWAtadWyhbVx1NayKEEQBEGIP3oTIkd9zDrRjwb9FEawGW2fvxcG3dwIgiAIgrDDLL/2WvT+5S9oPessfb7xjjvQ98wzcAUC8I0di8yqVTpu4OWX0XTssduk4XVPLKaPu+6/H7mNGxGaOVOnWXvLLToPz1mWk6ak7kUDU6fWLbey7tSiRYjMnavT1Cq3Wt2NypU+SZ+kT7X7VFx579bv3kPgI//8f/Gtm+/AW4tW4O5H/4bv/fx3mDPzAMw8oLZellu/AL2P/jsip1+L6Jn/YscK+xXcvOrFF180d5SSDvxbtM8siuqQoVQqmslSwVyaLZgPrOs3f/DaKvNf/77avHFJn3nj0pR53dtJ87tv95v/s6zffHZT3OxI58yMylgoFlReK5ilnCoxp0ovmn39fWb/wICuSV01e+Nxs3cgbuZyeTOXzZudnV3mmjVrzN6ebrNz82Zz2bLlZkfHOp0nlUmZ2XzWLBWLZjqTMzv7+81FPf3mrxYPmJ99rtv8xLNd5j8+t8H80bubzaWJrJlXdazIlMzfLVxv3vDce+bP3lprvtiZMjfnS6o17DF7bvVe/3GCIAiCIJSx9p9CZmHBw2b27781N33/GLPvD/9sXxEEQRAEYag8A+iQXrlSn7974YX6fNk11+jz3qef1ufPx2L6nLw2d66O23D77fq84+ab9TnjHXjOEH/zTX3O8ni+6Ior9DnjnTT53l4dN/+UU/Q5yyOd992nz1+eOlWfE7aDcbxGVl5/vT5nuwnL4jmD9En6RKRPQ+sTv3tDHe9MOPyQ6WbXy3ebr//pVnPqhDFmLBrW3+erhcSD3zbXXh0xU6/+XrdF2D8ZksUqXY1yQye9y7/6Z5RKepMoZ/f9ggH0F0t4pz+FZ1duxqquBMaOaUGwJYZFXXkVSujI+LEy7cbSngx6MlmMiXoxLuSFlyWo8ujbtGC4kVchkckhncvB7fbq/aMK+SwS8Tgy6RRMlbZQyMFwqbqLJlxuN1pamhEKR2CqvKbXj4zHj4TpQrZQQDqVRMDlxoRYBJNjAUwMG5gSKuLYcSEc2eJFq6rA7fEg4gHGhj0Y1RxGdzKHt9b2ojNThMvnRtALeFQfOQ5sq9V36A2vrI2zLDcF2q6VpzwkPBYEQRD2G2ixGjjgGPQ9+u8IHXUJmi680b4iCIIgCMJQoRsAb0uLtnKjtRyt1vzjxiE8Z84Wqzbut9F88slbXAbQSi58yCE6D63lmMcdDOo8Tppif7++znOWyzwsN3rMMbpc5uG+H04anpPgtGn6nOUyD8tlnvK6o4cfrstgGsL2s32M43WnXOmT9En6NPQ+mRsewY//zqeZh86Rsw/EFRd9EGPbW7RbgNXrNuFfPvNR++pWUgufRPyFX6Pt839C4LDz7Fhhf8SgxerKjg7MmzfPjhoEWjQs6Mf1TZN+TRlpoOgyMVAysTKRx1vre7CxdwCjAkHMmTgaaZXsoZVxLBjwo+iPwHS5UNJCZAmeTC+OjxVw6YwWTA+6UcwVUDDd6FPlrkwWsLanF1FXCQfHmtDiMeFR9eRyebhUGU1NTfB4VOGKdDql4tUED4dVnB+dmTxe25TC/L4ccqqm2U0eHDcmhLFBF8xSUftxhaHqU3nZhWwuh3gyg5LLq7tomEWEIgEkC0Us7VJt70wiqdp28JgYDh3TjHEBN0JqgaH/WCq+brgopepjXaJ2hyBqqiAIwv7KuqvDcIViaDrvBoRP/KwdKwiCIAiCIAjCrobfvcd+4R77bMfoiyfRfuzHMHXCGHzygtOwev1m7Wf1UxeejttuvNZOZZF48wFklj6P1s/+Ad4Js+1YYX9laBartlZomC6YcKHgMpBECR3pIv6+oR+vru5CNpvH0ZNH4fSDxmBUwINX1w7glR4g640ALpXPxZxE5Te8yOfzGO0pod1tIpdKI10q4O+b4rh3eQIv9QIbMiWMaw5gxqgmhP1+vSEVxU+fz6derQ2qXIaBdDqNoior4Pdgjcrz4NoUXugpYU3KQLGQx4xYAKODPuQp6pY88JhFuI08skWgP6Py593IFF3IlrgFlxvpTE5b0R6o6j1wbIvqr4GFG+JY1p1GTpXh87vh9RjwajGVPaKVarmYqo7LTwVBEIT9BlqstnziVwgdc7kdIwiCIAiCIAjC7mCb/Q12kIDfhwtOm4dNXb346f8+oOM+97Gz8Z0vXaavOcRf/h3ym5ei7Ut/hmf0DDtW2J8ZgsWqdgKAkukC/3EjqE35IhZ2JfDeul7kzSIOm9SGGWE/2j1AcyiInmIRdy8fwEMbPMh5w4DbhOky4CmoEkygYJqIlAZw/rgSzhrtR6yUw4DbjXs6Mnh0kxtZXwyBXBxnjcnhokkBxAygP57QYmwkEtFCJq1GXapNyWQauWwO0aYIFmeAe9bnsLAQUc124UBvHJcfFMYx7U263UaJj/QXkYeJ3pSBnrQBU5XlUnF0clAy3KpcEz7VyLagGzG/qkflW6vK/3tHN1ZuHkB7OIrZE1sxPeZDzO2Cl8arHCZ2zLFaFXFVEARBEARBEARBEARht7EzFquDof/Z/4JZMtF61d0wvJbrAkGwjEZ3CAM0FeWO/S51uCqZxUNLNuG1lZvRFvTi7EMm4sQxUYwppeEr5S0B0+XSPksDBh+6pwsAPn7vQlHVbhq0AS3Ab5QQdBvwqEINlxt+txttIS9ingIC+QG0efMYF/EhFgohFA5rQTUajerQ3NyMWHMMTbEmjBk3GrG2dphuP8aFAzgs6sZEVxpjjBQObfFicjSgO+2BCS+FT1V7RjUrUSih4Cqh5FYtduXgduVV/wpQTQLUNbongJpAPtXy6X4vLpwxDmcfOkWP4GOL1+KZNV3oLxRV21V6BX3P8p91xL+CIAiCIAiCIAiCIAjCSKKUT6Pnsf8HV6hVW6qKqCqUMwRhFdpC1MXH79Xx0u4BdKbyOPF9k3H++8ZinJlDumcA9Djq9/vhUemaVfpJYRdafFmYxQyNXnXevGEiR7G1mMVYfwnjte/TPFIlE/SaeniLBx8YBRwVjeOsScC8MSGE3G4qtXCrVwb6WWWwzl3wetV1Vw4D8W7EfCWcM7MNnzwoiEunenDmxAjG++k6gLXTmpQbT1EvNbTVKuPYL9N0oajaz1RsKa8WiwyqrbRAVRcCKhzWFMAls8fj0LERdPT0oTOR1jlUEdRgLViN/U8YOdC9BIMgCIIgCIIgCIIgCPsnxXgneh/6PnyTj0DsU7fbsYKwlSEJq7TypPRp2Z+aGB3yYnLAi5jLjaDHh0Qig2yhBMPrQ6mQR36gHxO9Jby/zY1xrgQC+SR8+Sy8xQwChRQmepI4epQLU0MGAoYBvyrDKBoIJxM4PmriUwe34INjQmhV9dFylIKX3iSqArNoIplIal+q0ZAPftW70R4XThoVwUltPoxGXiXiQ/4u0JVByaUSuAzdB4+pDimkMd7woWj4UaJdK3VU1a+MupZIZZBJZ7QLgmK+oPqWQ7Mqc0rIj5A3iLztfJZ/KclZwqzD9u0Vhj+irQqCIAiCIAiCIAjC/kehcwV6H74RgSM+guaLf2THCsK2DMHHKqEAaehd/f+6uhMr+ws4c/poTAu7kckV0N3VqWVEPpqfzWVhFExEmpvQa7jx7No+vLE5i96iV7sBoJXqYa0+HB5zY4zPQC6bh5kvIeDzIZNPw+Nxw+fz602k8kVTux8I+Lwo5IsomkWEwyH41fVCsYhUIqlfo81RLehmkmkEwhH4Az4kU2nkc/TJGoLH7dF+MaiDFs08EukMBjJAGj4U3G4VT72ZVrkluFR5HtXOoBcI07q2VEQun4fH5QZUWq/Pg7e6MninJ4Pjp8RwWCyoFdWSy4Sh/b5afltZl6uGuEqhmNawjoWkY4FbDq+VivRtu61c2xirTtUEy9JYtZvHW2Gd1ds1WLhxmLbmtTG0RbHq7bYVNaSynF2BY9VcD9aZU5+NbDarRfNcLodMJqviS/D7fQgGA3qTNJ/63NEKu/K9EQRBEARBEARBEARh77IrfaxmO95C319vRuyimxA6/jN2rCBszxCEVQpxlhhXUOGJNZuxoj+PM6aPwYyQB4lEUotTmUwaqVQKrW1tiDU3WyKbaaIrmUJXvoSU26MtOkeHfNqqNBcfQC5fRCQa1ZajnZ2d6jyHUe3tCIcjlvanqqU4ms0XkEqkkM2lEQoG4fUGkM5k4FHlxJqb4PGqsksm4qpM1tvU1KTFslQqjUDAj4DfjwKF12wW6WwO0CKpF/GCAVWKqoiuAOjMoAR3MY+w14WWSABhVT7hhlalUhFZisAuF17ZlMDC3gxOntqG2bGAymnCoOqs8psqC21iqwmYFBIp+vX29mLpshVIxJN6jCZMGI/pMw7QorIDhb7ly1dg0+bNtmipytRvhSqT/gzKUWUwxqX6rsdd/ceyQqEgmumLVo0HxUKKhlZ6FVQ6S9g1VZvYzlpipPOIvNUXlr9u3VqsWLFKjznF31hLDAceOF37waXbBd3WGvA6y6Pg29GxDqtWrkZRjQv98tIuWrdtCOgy3QamTZuGiRPHq3Mdq+PZdkP1sVgooKenB2vWWO1ft24d0hTgbXGVwirFVAr7waAfEyaOw4wZMzBlyhS0trboNlvl8X1k+fxjjYsgCIIgCIIgCIIgCHuOXSWsZpa+gL6n/gOtV/4awbkX2rGCUJ1dIqyuGsjjg9PHYnrAwObOLmSzOTQ1RbUlIIVDCmwej0cLrflsSluNur0hXVpJ/ytQCUMqzsfsCzpvOp3R6b1enxYFA4GALsNwW+JVvpBX6Sni5pDMpLW+2BSOwq/S06KQghjFMVohhkIhbXHY3z+gLVV9Xo8WfllWKByBz+9XZWQxkM7rx/kLWqx0aUtVVymPoNeF5nAAXgqwrJ1iZRkvbOzHW5sTOHFyK2bHgpTvGgqrliBnlbV48WL8/vd3Y8WqNTBU/KmnnoKPfvQjWgh16O3tw91334OXXn5ZW02yLBqvaj2voj120VvgZbfH0AJhOBxGW1sLZsyYjlmzZmHc+AlaaLbao9qt0lr927bMrVCctI6YjuN7//3344knnlTvtWVJPGbsaFxyyUcxZ85sK2EdLOHVqu+vf30S9/7pAf3eutV7rSuq7MwgKRYL+jNw8Ucv1uNJKGQTfibj8QTmvzUfL7/0Mlav6UAuk9cDpVvCDdTsY6sJliBLr8IU5inWnnDCcTj00EMRDKr3W/eBJVv9EARBEARBEARBEARhz7IrhNXUgr8g+eYDaP307+CbfpwdKwi10brcjmOJcA604KSVYW9/HxLJhBZS9U79sZgWpBKJhBZZaQloer0oen06vckn600DRdWMEtyIhqMI+Pzo7u5BJpNBS0uLCjFdRzwe10JrIZ+DoTK6Vb5sno/lmxjXPgYHTJyIpgjF2pIWTfv7++1HujMYGBjQFp+0WO0fiCNfKCLa1IyWWKt2OZDLZpFNpxH2uhE0ivDkk2jxuzA6GkR7JAhPMY+8uk7xzJLNKLJtK/rVMcosG6nq0KLTq8YlaFvTetyVj+szjQGvh4+iB+HzMfi1xSlFYYrGFKCdYFlZMqhjVS5FStNwI5PNoau7G4sWL8GDDz2K/77tDjzxxFPo7umx+qYrpeVqReXbYIuOdgNXrlyJJYuXwu3yqrYHtWuGvp5eLFZ1ZDM5nWawUDj3+dlH9svqg1cHnrNf7J8TpwKPy4L1uL4VPDqdJbITiqpsM0XV/r4B/OWxx3H33X/CkmXLdX99auz9QT+C6jMUaYpqcZ+WvZFIWIunFPb9voD63AILFy1Wef+I5557Xn++LItchnrjJgiCIAiCIAiCIAjCcCX++p+QXvw3tH/5cRFVhUHj/tpXvnJD38AAJk2aZEcNBkqFFEMNrOhPojOZx1i/F8F8SltEtrZymylbDPR6tVUjhVG3y4VgKAS32wNXiT5MaRVIRc+lSuM/QwtoyVRSi6iOpaljrZrNZLVYSvkqp4774wmEI2E0RSNa3HV7fNqak+lZL0mn09qtQDKZ0gIar0ebouq6R5dDwZfCr9/nhV/F5VR6n9uNSMiHoNcNr8cNs1jQlrEejxd8/JviLR+/t9puYG08g03JHCY3hTCGzlgVhhYn1TjxxfpjpbdxhEm+9vT04r2Fi7RFLWOnTp2CQw6ZuaUPJJ3OYtHCJVi/YT2rRDAYwvjx4zF69Gi0tMb0o+kUovna1taq3wMet6rjpuZmhNT7QjHW8jdqaMGRfmeXLV2GuKp37LhxWkx0cNpXSbmlLa2LX3zxJbzz7ntwqfeU0P0ALU75mP2ECRPQ1s7PAvNUL8/Qbgx4zcCqVau0SFssqfdStS+m+jN6zBjEYs1otgPdDFCwZ6Do7hxbgWI+QxOam5v0uBx88EEYp/pm1UUL2xyeePIJPPXUU+q9N9WYWJ+tUaPaVdqDcdjsQ3WgNe/MQw7GAdOnY9z4cepzG9S+dfn54+eXIv2GDRt0vZMmTdTlC4IgCIIgCIIgCIKwd4g/ehMiR33MPtsx4i/+Bvm+dWj70oPwtO6IPibs7wzNFQCfQYeBguHCE6s3Y2lPGqdOG43JnjxchluLq+VQWKUIxceox4wZrXLSryZlRhfVLhalSqW4ZupH9Sl00hKQ4hrFVQqlFMWo6ZuTmTQAAExdSURBVA3Ek+jq3azOS/AEoohGo4gF/XpTJ4P+UVUi+j+1/LxmtBBIMc2y5LTcAdBHKS0rKQJmVRqXx60tbFPJFLiJUcDerIjiKykVixgY6NfiIa0Y2X9rEymXFiqfX9+Lt7qSOGkyfazy8X3V0DJXAEyn/YXqPm6FbWO/li5dhnv++CesXt2h23/SSSfiwx++QFtKOvT19uPee+/H3197DfT9ecghh+AjF12ICRPGIV+obxlKK8tMOoPevj50qPf6vfcWahGT40O/qKbq31FHHYXzP3QuRo1qs3NVp1xYXbJkGf74x3uxavVqy6LTr96HUkmL2RyXM888E2effQY8arydvm6P9VlieObpZ/HAAw9q6+JAKIhzzz0XJ554nBa1We12uasVZzXPuqYyWe+jV9fPNi1Y8J52qbBx4yb9mfCrdh9++FyceNIJmDRxot4crRpZ9blYsmSpauMzWLJ0qa6H4v7sObPx0Y9+WH2ux9gpBUEQBEEQBEEQBEHY0wzVFUDfUz8DvEG0XaXyVtUtBKE2WvYbCvbD8BoKVxQO6R/US9+YFVDUokDKR6bTqSxKFEANN0wXc7EUyqpFFUrI5ij2lbQlIIXFZDKp/a3SjyXFUm6O1NzUinyoFe/EC5jfn8OmAlAy3SgWijrtwIDlNoCiGi0329raQD+tfMS8paVZC79+fwAJVfZAIq6tVukuIJlOo6DqtjaV2jqZXG7u/u/X9XOTLLoSoNCWzmSRUCGdK9jCX/UJuEumJQuxC+J4Uhz2B/nIu1uPU70QCQfR3t6CA2dMw6kfOAlXXPEJnH76qXqjL/2+qfflzTfn45VX/q6tUBtBgbSgxmDRwsXYsGGjKsPQAvdRRx2JyZMn6/GjZeeSJYuxft36LXkaovtopeMLN4yiJXFEvV/RSFg/lr9NUPHbhfJrkYj+DDiiKlm9eg26u3rgcXthqnbOnXMYLrjgPEyZZImqemMyHdQ4qz/sC/OzHbMPfR8+9KFzMXXKJPWZKWihvWNNBzo61uqyBUEQBEEQBEEQBEEYGZQyCfQ+fBPcLZPQ9rk/btEjBGFHGLKwSjHN+chRfMpkcjDVh5C+LcuhKEXLSAqrseZmLXymUxn7Iv9Q2mMzrJDPWsIeH89mHoqg6XRKW5omEgNg8Ua0CW+k3fjTeuA3S5J4aEUP1vQOYCA+gGzO2qyKYirzsn6Kqpwf2SwFWlWLFtlMLTqOHTcebe1tWiikGEghl4/mJxNJ1c6Utr5MqNciSkhns9i8ebN2a0ArWAqtdG/g83ng0n42dyeWAL01qL/sjMIS/7YNKpYpdDBN1fpS0bqm0kbCIZzxwdNxyiknqbEKaBGZfZ//5ltYsWIli6yJI5BSUFy0aLHKl2UNmDJ1Ck479QOYefBB+r3jGFNwXErrThu212lzNcov8Zj9ss+GFNhfWuQ6babgvmnTJv1es+ympghmzToE0UhEp2VgSloya8mf7h7svE5bpkyZgtmzZ+s+koT6nHRu7tLHgiAIgiAIgiAIgiAMf/K969H78I3wHXgSYpf9wo4VhB1nSGogZStLWqUIZT1m3t+fQCZbQK5YQl4FLUSZlqjK9B6vXz92HQz5kcqmkM6mVf4i5SsUVQLarBZo+Vkq2kKlyqTyBwN8HN5AZ2cXn8BHJBLFplQOL2/KYLkZxSpXDK90ZrG0s1+nD4eC2rqQJoe0PFR/QR01EAyqNvGxeNW+Qh6JgQF43R7dJvr09LgNxKJR7QOWj9on4wls3LgRGzZtQE93NzKpjHZlELA3jNKbczU3waPqGigZSJVcug/EGp/dixYo7YrYP1qdlgfrrWV71PtUfk0lZl6KqUcfdRRmzXqfGhc15qpfGzZuwOIlS/QYEUcI5b9yKFa+t3Ax1q5bp8o00KTG7X0zZ6KtrQUHHDAV48aO0e8FhWlulLVp82Y7J9tpjVEtrNqc+ramrSfI1oJ1lVen3UOoz6olRJv6feR7Sqw6rTpYFfNSLHfay1enDRMnTtA+ibVfV26upq4VCuWWvlvLEgRBEARBEARBEARh+JDftBh9j9yI0LxPoenCm+xYQRgaQxJWy9UqCnSFPB+LdsPj9iART2JgIKktWPlYeTaThtttwOP3omiWtDVp0B9EOplGLldQZXETLMu+ktamLNrv82tdihsgxeMDur72UaNB/60U60pFE0HDg3CpgGAhh9ZACBPGjtUWqAPxuLZspXjqSFv0h6p3x3e5VR059PX1I5vPaZExlU6jp6cP/b2WFSr3UuJj5KNGj8akyZMxfsJEtLa1IeALwqvaUVL54n10NZDR/VmWzOLVzhR68gYMNQa7X1Dj2NuCn974iWx9P6pDgXHbNBQJm5qbcOihs9DU0oyCGsu8Gm/6eaWIbaey69g279qOddoNQDqb0Y/MTz9gGg46aLq+xo2cDjxwhrbk5b9VK1di2fIV+hrZXiB1BGAbXV1ley1x2Eq3Y4HlOGXRZ69laUpXCh69odnmzk5dJT9blvjMtKYWVx0qx27K1Kn46EcvxhVXfgKfuuITeP/7j9AWultx6hcEQRAEQRAEQRAEYbiQXfUaeh74HqLn/x9ETv+KHSsIQ2cIwioFI5d+7J9iKDehoqDq9boRDnjQ3hxBwO9BKptBZ3cnUqkEPEYJrhJ9q5ZQVOkprtIvazyZRiZX0JsrZfMmMgUTpscHw+NFoVBCPJ7QO7FHomE0N0fhcruwYXMnPJl+HD/KjaPCBRzmSePYVgMToh69c3usqRkFVWBfPx/nH0A+V0QuW1R1JZEr5bC5tw8ru1NYXQxiUa6EDarOvNuLSNsoxNpaEY2G0ByLwuPzaBEuoNoTUe1tagoh2hRFU3OrtZlVTze6Eim8uSmBhT15FFz0M8sHyEmleLir2bnyy4XCiRMnYcK48do/Ld/b7u5u9Pb0WhfLKK9x0eLFWLN2jbbo5NjMnDkTo0eP1te4idUhh9B6tY3ypH78fvGiRer96LfrrRQcq/fFshq1T3YSR8yl79X2Ue2WT2BVdiKZwt///jreevsdLbA7LgLYzmp1sxxeD6s+T506GQcffCAOOnCG6nu7/oFBEARBEARBEARBEIThSXrxs+h/6mdoveoPCB97uR0rCDvHkCxWjZIKpvXgOzf44WP2wUgAmQI3dsogHPKjrTkKn5v+Vj3IZotI9A+gyMewtYBnIhiOoOjyoiueRncih854BvECkFXpk/kC+pIJmKqCaDSi8qjz3l79yHosFkXM78JR7V5ceWirCi04ssUDd44WsFn9SLbLo+p1G+jq7MS6teuRUHUUVJv7VHdXGhH83RyF+zt9+N2qHP64JoNXBoCkz6eF34IqgxsT0UkBH3nXfgrUK6U50+VCULUn1h7TIh39yQ7kTKRKPrgMrx6TcrY9G1444iofZ6d1rlv7RTWQVOPe39+vr+k0uhNbe7Ju3Xq899572vcs4ydPsQRG4giYU6dOxfQZ022B0sDy5SuwcuUqfW17wZJ5ykZKXXeETbpkIHyEfyiBm5I5QimhUD5t2lTEWmMo0v2B16fatRq/v/MPuPvuP+Lll/+ud/7fvLkTiURCu7Hg58kpg1apjmUq+2r5cKXvWn6mBUEQBEEQBEEQBEEYjiTffhiJN+5D29WPIPC+M+1YQdh53F/7yldu6BsY0D4jdwRKYQXDwIq+FOL5Eg4aE0MAJSRyJkxvAKlsHm4YaGlugS8Y1o/jU6ji4/bcIijPx/pLLqTyQLZoIgcXCipHrlhUabLwutzwe73IpFNaMKUlZDgcQYg+MU03MokMgvkUWnwuZNMZDPT1wWOa2oqSomgwFEBrSww+T1D7Zs16PZjfX8TjG4p4KxtCR8GLTaqtq5IlrOlPw+MxMSrshq+QV8duuGjVqP65VDBdKlDw4x9VmKlamkpmVR+L2JA3sSyRR3PAg7ltfoz20x2AndZQY8EX9c/Ced0KBbuenh68995CvUEX4QZJtPqkZaUDx27hwkVYt36DFnzHjBm9ZeMli+3LHgwUGzds3Ihly1ZoAZGWq5MmTdCP81tlWuKmI06+/sYbePXVV0E3D3xPTjh+HubOnaOvOWn8fp9+zyimZjJZpDNpRNV7N336dNUna9OnrfCTxHwGVq1ajcWLl+p2uNT7zx39E8mkdk/QsXat3gyrY439Wi2oNHRTwJ3/16tx8vv9aGqK6locotEmLZquVWkp3Hrc6rOkxpabcb3z7rt4990FWLhoIZYsXYrNmzajT32uEomULaRaYavAagnC7LbVd6v/giAIgiAIgiAIgiDsWeKP3oTIUR+zz7YSf/Uu5Na+jfYv/Rnece+zYwVh1zA0YVULhiUUaQHal0J/toRpbREYbi/e6MriVRU25kw0h7xoCdF3pWFtFBT0w6BVZLaAgXQB6ZJbW62WXG5VngrqGkVXa/f6Ik0WEfB5EQoFtS9PinS5TA65nGX1msskUSgWEI40wa/qptAXaYogqOqiP1VaYHJzKopn73X24y9r0liSjyIfCMPwuOBy+1D0BZEwXehPZtHmMzAp7EPA49HXWYdRMlBQ7aH1Y0G1O5NJIZOnC4MiSgV1XaXrzOagWo/D2kPbCKvaMQBFty2C2/bC294WVsm6dRv0Dv+01KUAPmP6dBx88EH6mqWVqn4aLmzcuAnPPPOstlqlsfMB06bhpJNORHNzk06jU7LLKpPfH9DiJkVbWjXzfZo4YTza29u1OMk0VlrmYyWWsMp2WNeBtWs78NZbb2HBggV49933VHh3i/i5fXhXp2OYr/IsXboMk/QmUxN1uwjLpVg7duw4bYna092lLVtZt8tNi12Xdj3R29uHDRs2YPmy5XjnnXe1he6CBQuxbNkydHf3aJcBbo9bvz/022pBK9uhvweCIAiCIAiCIAiCIAydasJq/9/+G8V0P9r/6SG4opYLQ0HYlQzJFQAVMe6BT0mJWhJFs/5cHm/3pvDI2jj+uCaN+1an8FZ/HimKdfks8pksCpk83IYbQX9AP65fNFwosQAGtkSdmy6Piveoc68Wu3KqXG4URb+mFLECoTDaYk0Y296MCWNHIxr0wY88/D4PsoWiFsaowlLiokhL68d8MYd4yY1+dxgljweqBrhVH0xVH8UwlzeAzQUflg8UkTbdemOjUjGPfC6rfW/GEwm9KVYql4HhcSMUjqCttQWeUhFjkMXclgBa3YYWEPnPERlHCvRdS1FRC4Oq6Rwzip4OjmC4ZOkSrFrFR/oNBIIBzJo1S4uXFlYaOyna29vwvvfNRCQSUWW7sXHDJu2blYLmVlHVSluOJUJbF5jPoz4nbjeDRwePx6fjtg++inRe9XFyRM+tUCBvbY3hggs+hI9+7GIcNudQjBrVrjc+c6s8zOv1+uDzBdSrX7chnkhiTUcH3nzzLTz40MO47X/uwG9+cydeevFlvRGabnW1zgiCIAiCIAiCIAiCsOcpFdD311sAbxDtVz8Kw+8YpQnCrmWHhVUtG5ZrSKoEw2UiVyhiXV8GG3IeZEMx9Jb8WN2TRWdfEslkEvF4CgPxNPr7E0imkqBvSlp0sigt0ppaBrXKd3lQKJooqkDLx2AgAJ/Pr60NoeqyNsFSKb0qLtSEdC6PfDGv49OZjCpAlasb6UYynUY+n0PB40XScCPncqNgqPLVMd2nuvjYuao7p5J3pjLoTeaRyeQxMDCg/Y0W1WRkG2LNMbQ2NyHkD6KYLSKTSsHn98Cv2hN1FRDxubUlaTWqxw4fKFprK2GOm2H5Et1WJ3Tp3fMXvPceEomkFkXHjxuHAw+cri2QKZbSipMbjlmvBZ1r2rRp2mKUZRfUe0D/pXwEn7B81rc9fNzecj/Q3NyM8ePHY5yqa9y4sXYYUyfw+jidZ8yYMfpzU85WS1l+rnx6N/9PfeoT+MdPX4kLL7xAW9/OmTMHkydPRmtrqxbQvbbAyuDx+uBxe7Xl9ML3FuHue/6I++//s7bk3Rb2a7i/64IgCIIgCIIgCIKw71FM9qL7oZvgGXsIWj99px0rCLsHY0NHh7myowPz5s2zoxpDq0wDRdCL6l87urGyK41jJrVjY6aAP3dksSlrYLQnh3Mmh/GBiVFEjJJ+pN4wXaAxaipfQFcqi0TRgGn44GI8ZVHDVMEFd7GEEAoIuUvwq0C/psUCNwoytbDKR/v5aDp1OVoUUojLZtLIqnJNjx/haBj+gF/7cy2kMmiO+PFaErh9WQ4r80EYXo+WvVRRKrA3BYQLSRzljeO8cSEc2BqF21eEV2/oRJ+glH6BQjaLZDKlex8KqfIzWQwUing9UcLqRBGnTIxhdsyvR8goUXG2hGJthVnFotER+vjY+j1//JP2JcpOUeD78Icv0FaUDvT1ee+99+Pvr76mxcvZsw/Fxz52McaNHaOuWrUMFkfQdKwsH3/8CTz40COq3LwWVc879xycfbblzJlJmeyll17B/fc9gHg8zkHHzJmH4Kgj3w+/36s3gtqK1Ra+L8VCAa+9/gYWLHhP1+nzeXH+eefgtNNO1ems/jMP/xh4+uln8MCfH9bvGy1iz/jg6Zh37NHIq3btCGyzSxUcDofVGG4rrjYiny8ioTfwGkB3V5cWlHt7etDT06sD+5/NZnTaQrGo+l7CB045Beeffw7CodB2YysIgiAIgiAIgiAIwu5n3dVhtH/sh+j7608QOu5TiJ71TfuKIOw+huRj1ZKMaDnqwvL+NAZyJRw2pgnTYwH4TROjPHkcM96PI0eH0Gr7KqV0SrGL1qOpVAq5Eu1L6ZmU1ylcUmQ1tOWql4/ql/LqmK4DTHi8HgQCIQR18MPr88Ljtnax11avqkFsEzfMWt6XwZt9OSweyKA3nkHE7UbU70JelbkxmUG3CqAwa7rgof9UlTGn6prkzuOEUW7MUW1uivi1eEu/ryXT0P5ds+m0tmDlZk/Rpmakcxlk8nkYoWasThbRl8nhgFgQYwLW5ky0mNVGszxm62oIbRTgdszH6notMFs+Vt+3Uz5WWTfLfXP+W1i5coUWBcOhIA4/fO6WzwObTVH3b88+hxXLV6mx92vXAYyz/Jm+jbfffndLeEuFd95+B2/Nfxvvquv0Scp6nLoo3E6ZPAmRSFTFWZapVtvpY3UVFi1agkK+oEXYI46Yq61iKVjuUAiHEFKv3CjLEm8tK1WOm2OZ6wigzivTELfqGy1dYzFay47DgTOmaxH7sMNmY+bMg9HW1qY/vwPxAZXHpa1zU6k0JkwYj9GjR6k4XcyW8gRBEARBEARBEARB2P3Qx2pm5d8RPftbiJz2ZTtWEHYvO+wKYCuUCw0ticKgeFVELJ/ESTHgEzPbccb4GMZ7DbiLBUtkVCmz+SwSiT54VPr2SBgRj1v7KaX1KzcxcpdK6ryAkMoX9ntUOgO0Vs1m04jH+5FIxvVmQ5SsKHBSAKNVYliVFWtpQdv4cVjrDuCRDQX8cVUWz27OocugR1UTY9wlHNfuwSGRHAKFpKonD6+ZR8DMYIzZj7nNRRzY5IYrn0E+m0MmV0IubyKbKyLeH0c6mdCbaEWbmlR7ssjkC8h7vEiVaO9qjQSHohy2c7jLaxR1N27YqN0umOp9ampq1o/Bl7N06QosW75SC6p8S0pFNTZ0v6DeC4qghZwKfFWhqI6LauwK+aIaRyuOG2IR+shdvXo1li9foc+3//hRgOUwWsInfb1W4oiitYKDc+wInCxr7dp1mD//bR244RX77viWrZa/PI4bo1E8PeWUk3DxxR/B1KnTtKhK8bu/vw+bNm3W6Yb/Oy4IgiAIgiAIgiAI+yaxj9+K8Imftc8EYfczJGGVUpNlgwp4SurIdCNbdCOdKyBomGh2A/5SgU/CqzQe9ceFTDaDRHxAWxFGmyIIqdfWoB/NATd8RgE+M4tAKYeo10BLyIu2aAjRcEhX5lL5g6GQtmLUj9/3x5EYiCOTTqOQz28R4FJFExsKHnR6m9EXGouOQhDdBROBYBgt4QiOGteCc6a24OjmIg7x9OMgbxzHRnL46AQ3jo+VEKAVaq6EZLaIZCKFns2dWL9uPQYScbj9HrCWgb4BHegRtuD2IENRzkW3BBRXa7BVrxsmUMC0WtvRsQ4bN22G22NZd1Kgbo4162tkQI3ze+++h96eXv14P62FW9tatXUmd/hn4OZPo/hqn7e1t22Jb29vRay5Sb/v3Ek/EU9oq1TuvG+JkGWjpg+3tq3auPFaveBQfkz4GXn99ddx++2343/+53/wm9/8r3ZRUE61/NbLtiLrlCmTtbUwxX3GcUOshPqM0EUDqaxbEARBEARBEARBEITdy4Rbkwge/mH7TBD2DEO0WKUFKu1MiQmXYSKVzaLg8sITCmvRsWTyUXqPfhw+m0whnUggEAgiEmkGH6E2TBNBL9DsB6LuIsLIIWimEXYVEeAu9apkbjDEDYy4U3sqmYRZKuld5puiUS1q8RFsPj6fTCT1Dv5eo4R2VWaLmUWkmEC7r4AWPwVgE9lsHvmBJGb4DJwzJYyPHhjBp2c147Pvi+H8qaPQptq9puDGQgSwwetH3udSdRTR3hbB6DGj4PUHkM3lVX39qsuq9y43svT7yk2wthhWbiuoWQK0Yi/rbI4oWC4OEoqbFBf5WL9b9YfC9bSpUzB61Gg7BbB8+XIsXbFMvY90/WBi5iGH4LJLL8UVn7wcn/zEP+CTn7wMnygLn/zU1sDzT33qk7j8sssw65CZVDf1WCxbvgwrVjhWq2Xopm1t49aW7jy0LI2qz42L7h3UBzcRT2Lzpk5teUvKhmULlkDqhK3jSGgt7fV4t7Z1VzZWEARBEARBEARBEARBGPbsuLBqgk/+642f9KkqIV/IoJjLwO8G6FKVYpOhXwtIJQeQTcURCYcQpj9Qww392LzLEquMUhF+PpIP9ari6FPVpPLFy6zL5UYkGkVzc1SLp3zsmpsG0YcmH1lvaorqcvrjcZSSSRwe8+OUVuCEUApnTwpgRpNXi2i0lPSqRuddHqzsziOVKWJiyMBYtwl6Ml2WAe5bV8R/vduP3767GcsSJTS3j0KsOYag14eQL6D9rtKac/TYUdqPp1eVpa1yVVuHu5GiIwBSLGRbc7kCXv3763hvwUItqnLjKu6kf+BBB2o/oySpxpPCa1dXtz6PxZrw/iPm4uCZMzB12hRMn36ACtPqhmkq3cyZB+HwIw633ivTQE9vHxYuWqw3ArM+glsHj83cavFpf8h2EfSbGggGrfJVWL1mNdatW6ev0V+vM0ZWvU7YFual9Wtff/+WTbXoTiASCcPjURNAEARBEARBEARBEARB2C+wFLQdgaqqfije2naKCmo8lUE6m4ZZzCGXzsAsFFHM5zEQjyOTyyHa3IRAKIAShStbqzJt4YrCKnfmd7tc8Pi8KBQLyOWyVhqDtqbqVV33eb1oiTXrXd65idRA3BK2PCq+qakJrS2tiASDmBgATmgBzh7nw/sCBZT6e8BNiygK0tp17UAazy7djHfW9mMgU1ClGxgoFbCwN4MlmTA2esZhadyDTWkTRbeP3l+11S2tY+lTNBC0Nm/iRlw+uOEtAm7dLas/e47yR9+3CpPVYDqKf4SPrdPK929/ew7PPPMsUhQ3VXZadM6dOwfTDzhApyPLl6/EkiXL9PixjAMPmoFpB0y1rzqwz/w8VA/cLIoccMA0HDBdlU1NU/1j2atWr9HXtowbr/E9d0639G+oWG1z2jBu3FiMHTtaf8ZobUp/ry+//Ire7Z9Y9ZW/h1vrLx/DpUuXYuHChdrHKj+bFIzp+oBsFWcFQRAEQRAEQRAEQRCEfZkdFlYpG/GxcAqsOnPJhVC4GbHWNsDjRzJXQG/fANZv2IxEKodAuAkGBUruyG7lZi4Nj7KmgSw8KLo9MPlotYs78VtpuKEVxS3tdoDimAFVVwSRlhbkVO098QRS6bQqm5tfGQj4/Ii6XWguJBHK9MOTy6uSvXCpuvP5AjL5HJo8JRw+JohD28KIebiDvypXtcHrMuB35+D2pOAPFLSbAm7MRWktk8nrEPAH4fP7YRZLMHM5lPJZuFS7nEHc2rNqVLm6RTgsu1a/ENUeCqrWbvSJhOp/KoV4PF430E9qT28v1qzpwN///ir+8Ie78dCDD1suADxu7WJh9uxZOOboo7QvVMId/BctXISuzi4tIjc3N+GQmTMRa6b/VTbSaSj7wBGoFSxL0FgspvIfgqao+jyo8jZv7sTSpcuQ135J7XGwi3SsR+l2obu7R28MtWnTJmzcWB42lx1vrAhW/IYNG7WITGGYjB8/ATNVG7gRFWMY/9JLL+P++/+MZcuWq/FMatcAhUIJRfUeU4QuqvZxrLPZnC7rjTfeUmP3CFavWq034zLNorbKnTRpoq6DaHFVBFZBEARBEARBEARBEIR9GmNDR4e5sqMD8+bNs6MaQ2+b3Om/aBj46+pOrIjnccb0sZgRdCGXz6N/YEBvKkUhEhRUTRNev1dbRXo8BrgZFQXUzkIR73WnkMmbmBIxMDHsA/JFGKUSmiIhbbFK0ZO6GMVcSlX9JWBNMoe+dAF+dT7KXUSrt4SA24NiAXpDK8MoobklBr8/oHenp1iWzmQQ7x+AmxanoQi31EJL0AufakeqCLy0eQAvbUqjp2hiesyDcye2YGrYi0yxiIGBFPxuN6JNIapxyCdVWemkKicEdziC59cOYFlvCqdMbsFhMdUH1VKjpPqohWGKs5bVqz7WBxSL9YuWFGkBec8992LN6rV6rE466UR8+MMXIBgKMJWmt68P9933AP7+99e0oEe3CmNGj1Jj7FPjY70fDpYcrMrmuKny6Bs1XyhoNwq0zqTIzDIYaIU58+ADcf5552DS5Ek6PYXP995biHvuvhfr168H3TYcccRcfOTDF2rLTN32waKHQJWpyqBAyn6+8+4CXQ+tWD/20Y+oV8sK9qmnn8EDf35QvWdsnwdR1cdQOGyVsbV7W1Hx1khWtojCLF1U5HHkUUfgtNNO1T5RyaZNnbj/gQfx1vy3tAsEPT7qPW5uacbUqVMxZcoUtLS06Mf6KShTfGV7urq6sHzFcqxevUZb+brVZ9lU+caOacOHLjwfc+YerltB21gX54ducLVGC4IgCIIgCIIgCIIgCPsCQxBWLYHTElaBx9dsxoqBHM44YCymeYropy9Tn0+LYhToSoUScvkcsrksigU+el9S193ocwfw5Losnl2X09aBx45y4UMzWtFsZrWQFYs2AW5VARUyCmWqrL6Sib9t6Mdf1yTQmTXQHvDgpLEhzPFn4Uv26jr9wbAWvUKhgCVtqewsgpabuWwOfn8QGdWWgWQ/ItEgmkJhZFMFxHMF5ClS+v1wFYsY41Nl+DzoTcS1+NYcDWsRMplIa5E2EvKpOsKqBjee29iLtzcncOKkdsxuoYBnVhFWLZFNR9kHtMylyLxkyVLcc/cftUUp0594MoXVixAKBvU5+09h9U9/uh+vvvoqfD4/6OeTflFL9Eera9C9VcH5a8G2O9CFAcVU57F2bgI2Z+4cnHLySRg1ul1brhoqnuP05wcewlNPPa3riURDuODCC3DiiSeo8igaqjJ1f7bWWR1LVCe0siVPPPEkHnr4YaTSGXg8Xpx37tk4++wzVSkGnnjqKVXvg9pilO2j4FnizmD22PGN3NobhYov05O3gfXms1mc+oGTcPHFH9E+eWldahhuPc4PPfQoFi1aZJWpCi2oz2ZR9ZViKut20V+qyxJWdTvUNdbuiNGUT8e0j8GZZ56OI48+Ah7byrak2sPx4ZZp9cdGEARBEARBEARBEARBGMlYatdOYGmHBjLZHBLJJAJ+P6KRqL5GMcpwG/AH/Fv8oDZFYvB4QljXn8Mb3TmsNaLY4I7gnVQJa9I5ld4S1NLqOJ8vIpPPIKsCha/VfUm8sCGFRbkIugOjsSgfwrPqvN/wYfLkCQgFA+jv7dOWqWZRNUy1i3JYItGPYjGL5tYoos1hjGqJIdYcRU6VuWFzJwYSAwiaRYxCAdNDPrQZQC6bRV8qBRRMNAeCcKuSEgP9up8h1ZcAhTrWodAPvG/R0KqLaVtlNubZ6veTaKGTflvVWHG8eO5g1UBswc9FYc/QFpV8pJ3iLkXDYCioLVytwOOg3qgpFA4h0hRBc3Ozfoyf1piTJ0/GccfNwz9c9g+46MMXaFHVEg4t6Pt0ydKlWnCk0Dpt2jQceOAM+yoj7deGQaHSsuSSGl9y0EEH6sfm3aqPxUIey5Yv1Y/vE4qrVv8oALu0QO9X76lf9ZPWx/pVB3/Fq3PshIAeG/rDpZW0A4VRtmPy5Em45JKP4PTTT0X7qFHaty/ropsHt8er+2wJ1xRUVT9UW91et7rOND71GYpi7uGzcMmlH8KRxxyuPwBF9dkxDO2RV/1znEgIgiAIgiAIgiAIgiAI+yrG+o4Oc9VQLFbVP0plf+3oxKLONE4Y34zDWv3wefmAvqGS0XqPaS2JiekpwlKLLBkG3u1N4q6Vcbyb8MNdMnFgNI+LDohgVqCEZM8AvN4QfEEfcmYKrgItKQN4oz+HP64HVqAZLo8L+byJiUYK/zjdg5PHRrSW193Zpf2Ocvd+iorZbFZbLoYjYXj9Pp2G1pOJVAb9ff3w+1Sbw0HE40k+O46IykODxHhyQFuUtre1w+/1oTcxgGIxj1ikCR51ToFO2yWqvry0sR9vdSZwwsQ2HNbC/m9vsUpxjnVTZuQBLS15xaX+dW7uwltvvat9nnK8Dph2AGYfNgs+n0dn4filVHvfeftdrF27VltN6qIULNt5Ryzs+nik2k/Bln0MBkOIRi2BtbWtBS0tTToNYT+dPGzmkiXL8S4f12dT1flBB8/ArFmHaGtOllku/A4WJx/dELz99jtYsXyljg+psT/88DkYP348lixdgQW2mwBuZrZ1szOnvm37ufW4OvSResD0qTh09vv05mdWeVY7GNi/9es3YfHixVizZg26uruRTCT15lZM6vzqQDcGHMOWlhjGT5iAgw6cgenTp8AXoHVyyRo/9V5bMj4leLfOu+OjJAiCIAiCIAiCIAiCIIwUdl5YXbMZS3uz+OD0cZgZ8ehHt3mNT34znanFJvW3aCKfK2i3APlCFvES8HbSjbd7SvAWi5jb6sX7xzehLehGOp6Cy+VFIOxXufNwqbyFogtv92Vw/8o03k57UfC64M6XcGjEhUsPDGBWs09bGOYyGT7Brd0OJPrj4KPfLW2tCIT8KPJRcNUml4rr74ujv6cXbe0t8DdFEI+n4TMMeD0mksk4NqzfAL8viDETxiOl2m0YHu0OwOdRHStRVDVAxwYUAF/c2I/5nUmcOKkVc2J+3d+awioVO3VI0ZnQn6y+7nLr8y2YLL0C1W6deadRg69VU4Zy7PdW12NtYmXB9JbLAQur7YNri5NWQTWZWbbpB6+rvtIy1MU6HTlzV6HK1W1nPWVtsRqi/rP6WSqqzyQ3Q0ul9MZdFGXZJgqwfr/6LIaC2hKbriasNnJMnPJYljWefLfpHsJxfSAIgiAIgiAIgiAIgiDsmwxdWDUtH6sUVpf35/DBA8bioDB3mGcaWlGWwMfduYlVNqcC/asabni5C30hj6ILKIabkIYbAdOEP5eGV+UJhyOID8RVOSYiTWHkCjlkUxkUVGX5YAjvxgt4fl0cXekCWtwGjp/cjHnjQ4ipvJlsEfFEBqGQX+/y39vVpeUufyQCXzAIn9cNj2Egm8lr8ZbuAYJNQf0YeLIvqTc4CkYC6E8MIJMuIpfMIJlJwB8KojXaAj9dAngM7U+T5remy0RKtf/Jjn4s60ngg1PaMTtGv6gldZmPg1tiniWs6kMrqGM9TOovUxXSCWR7NsLMpqDNZdV4aMmOeTTqjFaR6oj67Jay9CvT28fEOdZCri7Fto7lgfPHCU4m50gnUKgW2xXpWnU5qk0sqBzn1MlWjfI0LEed66K3KYuCJL2S8p+VxsEeiRo4hZHyfHrEVXVsszVu25XijI96I9hXuldwaf+zajy3lEOYk61Qn+hSSYWi/mzqcVcJ9RX1ufbH2lUYpeIo1LItVn8EQRAEQRAEQRAEQRCEfZMhCKvEkqkKMPDEms1Y3JXGB6ePxfua/dpilZsA5fM57ae0VCzA7fHBGwqh6HJhcyKLzqy67nYDPhfafG5M8HtgpBMwKKaGo0gmE8hmMtqvJcvz+vza16bH7UFa1btZ5e/PqNoLOYwJqDLCfmRNA13pPOKZHKIqX7BURFSVHfT7EE8kkc4XEPKH4FFtoFga8ge1xW28WNSP9hfTWRXnB3d0z+Vy8KnrA919gLuAllEtKGYLyKp4w3TD51b9CRooqvJf7crjviX9iKi+XDqzFbOi9OlpamHVGicGQ/1HCY6ndB/AWPVPtZE6as/aFVj14l+Q27RG+/is1C91RgqAPNCPnBMnEV8rM1Sia7OPnbR2u3RgmdZ1CpGMKk9FKDjqVNqK047UOCmqY/VlawYeGVrwZF0sV8Uw0i5mm7LV8dZ216I8QyXMy/aqtuv6nLEj1hH/6hrUH123CnoMVMSW94Gn+iLzO4k4HkxbRN7lw6T3n4QpKsBFlwN0XcFU/AwIgiAIgiAIgiAIgiAI+yJDFFYpSQJ5uPFURzfmb0jhmPExHN7qhVHMaWGSvkP9fp/eQMil0iULRSzrS+K1zgIWJYDufEE/Dj824MGx48OYHSmizWUi6AsiGY8jEeeu/VG0tLSCmwlpUUurYwb0s/6KVDaNXCYNVyiCd/pL+FtHDxL5PA5uCeH4CU2YFlJ1a+tCE6VCAQMDSfSqsoPNITQ3NWMgXUJvDqodblVkEV7Vr4BZQMBtiWdukxalBfgCftWPMErqeiFTRCaTV33PYVMJuL8jj2c3AYeO8uOKGUHMjvp0W3U7bYGOopzlFoCnWnGzj0vqUgmF5ACy3etgZhPq3LlYhl0MRVWNFgjL01nHlgipjvV/Zde3tKNS6FNpdJGW/ayFnVbHq3brLE59tYRCO48tPjp5iW6T05SyQwdaqloHzGRd3ZqcGXRhNuqKTmbH8bJ1VB19UWdQf3li/dVo1VQFZ7x1/UzBV5XO7osVy96rI36W2F59jXnVPKCvXcMLT+t4+FrGqWOPTmsVa5ctCIIgCIIgCIIgCIIg7HMMyRUAxSRKcQXDg6fX9eHR5UlMCntwSruJyTEvosEg/G6fFkQLpRLSqRTWZYp4YnMRf+sy0OcKwXR5Ve2qrEIO44wsTmotYl6rC2P8LnjchraMDKpyuLu7Fr22CGCWrEtdzK3KTqbiWJ0u4c9r83i2B8ireg/yZvGJgyI4dpS1cz+zcmOh/ngSqXQWbq8XedX2lOlF1qAQCrhUn+g11meWEFBpmyM+RAI+JAf6QHebkeYmuG2RkfIcW7OwP4PfLMvg1V5gbruBT80IYXbEr9tOQdJli6t0GUCqWTBqK0qnb/sEVl/Z212FUyIZriNF1xeWOGttaCYIgiAIgiAIgiAIgiDs22yv9DXEsK3yPDpzyWWg0yyhx+2CNxBEyBOCzx1QaVza52lfbwpplXZ1ycD8uIkeVxQFfxAFr4GSx0BRHa9HCK92F7A+byDa0oxYczO8Xi8KBe7Obqoqy4QqderWgYKkC0FvGLk8MFDIweVzw+XxIquuZQt5LcIZLkrAJhLxFMxCCaNGtcEfiSJTcqOoeuAyTB1oBWu63MiqMgteNwyPtZmU10+Zle4BMvqcW0oVqZ+pOsaG/Jja5EXYyMIwWR8vKLY01zrX7dgauQ2OCJdIJjEwEEdJW0Vui9brKuC4cId9btjFcWLedJqbLpV0nG6iHbhrPdOzHB5b+ayx1Qa9LNBOTb+4Oqg+8zWdSWNz52b09/epcy1p69Rb0249d+L4KDxfWQc3gcpmc+jrG0Amk7VSqqRWUOm3lGeVUA7z9vR0Y/26tXozsQ0bNqK3r09vgKZ9nW6p26LynDAunkjoUPX6Ni2wYDIrKf+UBwern7TM7urqVG3qATdn46ZfIqoKgiAIgiAIgiAIgiDsH+ywsEp5qUBhSf3jNj1GyYC3ZGJU0I0xzWGU8kUkk2mkcnn0JxPwBTwIhoPoTBTQk3Xp3f49RVbs1sFtulBy+zHgDiDn9sCtH1c3UCpaguF26MslmAzqn9vrwahIGAeGfZhmpDChNICZTT6MC/pglpjGg1QqjWw+g1DUB7fbhUy+hLzhRsnlVjXR/lSVZKg61XnR40ZG5ctk8yioPnBTIwqraVVGQeWjz9eCWdRWrM0eF44eF8AhbWoMTIqr24pv7IfltqCx2LZxw0asXbsW+XxeC4oU7bg7vTMGFEuz2eyWOKZh+vXr16s4Cnxd2jft5s2bsHr1KmQz9G9rtZeiLjde4nhk0hmsWbMGnZ1dWgSkn1f6s02ns7oOq60qmCqXei82bdyMBe++p8rv1X2mSJpOp7U4y3QsN8fNyVS81VZDddml0pq6no0bNyIeH1DHq9DT06P7R/+7jlBZUOWwPPaN7SuHdaxYsQrvvbdI9XUd1nasxaKFi7BmdYctQBtb2sPxsgRRU7eH48RrjIv3D6C/r0+XyTZSgGZ9bIs1RkV9zDgGiraWPuq8d4bqjxo7PU7qc6DGiWPDMXznnXexYf0mXe9WeFx+LgiCIAiCIAiCIAiCIOxruL/6la/c0DcwgEmTJtlRg8EEn26nMLcplca6eArRoA/jWgLwukro7+9HIZtBS7Mf4ZAfhWIJ724u4L2kF0WPT2uNJW0QasBNLc0w4SlmcEgUODDmh+Wl0rKudLtccHOjKxtLvyqhxDJUOgqfnlIB4WJe5Q3imMnNOGZsEG3FlPa/WiyUkM+mEQwF9a7/qilI5V3IqNeSar8lrCooMqoj/qVbAHc+i3wyiVQqoQW1lOpjMZ8HN7fSwqdqSE+ugM2FAjqTOTSpfLNaw2j1edQRhVpqwLpkdtNmy8F29PX1q7+8bmD9+o1atKOYSCvPcDiMzZs7sXLlKv2qBcGSieXLV+jzUCikxkjL3Fi3bj3WrOlAIBDUAiJFxnA4pMvvV+8zX5cvX66vRaMRLXYuW7YCGzdu0HGsi9bCfG+Tqv/Lli3T8WPHjlVjkVJ5V2oxl8ehUBi9vf1a/Ozs7FTvhUuV2aTb0d3dg4ULF+v6fD6/LiMep/DbqfL06TZRrFy5ciVWrVqjhWHmj0Qium7CfvKzNGHCRMyadYh6Ha/bxvHxeDw6/9Kly/U4JRJJPQ4crxUrVupx6OvrU3Xbrh5cbp2HYu+qVat1uyj4UhBlHevWrVNt26yFYNbBdjhQSOV1jvf69Rv0mHK8Oc4DakzHjh2DWCym278ttd9vQRAEQRAEQRAEQRAEYWQzRGEVWljlhkzBkA8BvxfruuNY3tmPJEraSrPJ70MsGKJMiZzpwqpkCYsTJWRcHphul964iiKllqKKRcSQw6yIC+O8JRiFvBa8KGhSVKXQZVkEbhWqLNHS0AJrIZ9GMZNGS9SPWDRoPe7u8yGZyyPV3Y1IwItIU4tqsFu1B8gWisgUaM1Kq0y7VL6qfIZZQkC1rSUUQEtTRIuHUfpX9XjgUiEYjaDg9mNNysTza3vw+qpNaFXxJ0wZhSlNAXh1eaoPqiL6VLU2f7LrsP9Wo6enV79StKWI19bWipaWGHp7e7WQRwGUwl17e5seFwqGtL6kH9q2tjZ0q35yszAKzRQmx40bp4VFCrCxWLMWOilsRiJhna+1tUXVVdCiZEtLC5qamrFp0yadl+eEYmR//4Cug/XxOts0YcIELULSypVWsr29PRg/fpxuB8VLRxil6BiJRFVdrVp4ZTkTJ07Q7aI1KgVWCpwTJ05S59Zj9RR2mY5Q0GQa9ot9IBQ0E4m4tizlmPGR/IkTJ2phltakFIPj8TjGjBmj+86mWBa2dEfQp9s0ZcpkXQ7b5PX6dDytmqdNm6byp7RIy36yL2Tdug3o6FinBdTRo0frcWDb/D4/XOrzOW7cWC3qOv223ufa77UgCIIgCIIgCIIgCIIw8qk0sRsUfGCcWz3RfLTN5cIpY6K4eNZETIlGsHBtHK91F7EwaWDVQAG9yTzSqSQmN5Uwo6kIbyGjN3ei61ODj3MXs4iU4pgRKWJKzA2/G/qx7kQioa0iKZRRSKQYqP1q8nFxipamAT7Iz0fR4+p1oy+Ip7qK+K93unHrO124fUkCL/X7EA+2IGt4kOSj/EXrsfVSLgs/8vCaBccEVouqtFT1qeBV9aiCtdWp2+OG2+WBLxxGxuPFykwJz3dn8JdlXehOFnHCgZNx8ZzJmN0SgJYDKc5qXc0S1lg6LVgHCwVkCpETJozD6NGjtNDoUW2gRWShkNeWrHzEnZt6UfRtbm7SlqeE6Rjf3NyMpqYmLYw6Wh/HibA85mlujuq4nBoLS7QuIRj06zTWuVVeMMjyoqALhUDAj/Hjx6K9vVULs9lsRr8nFHEpVlIQdfJSaGRdTU0RXS7bOm7cGC0MUyTle8r3mLAdHjW2Pl9AHW87VrzmtJ2wTsePLC1aCV/9fr8W4B1htqurW4u+FJopSFPIpdUsx3bMmNFaCG5vb9fXWO+YMWO1MMxxpihaKGytM6U+v6FQAJMmTcTYsaMRU+NXVO+Fz+fRY8Px3yqqCoIgCIIgCIIgCIIgCPsDQxJWXdrWk2KkCy7ThL9UwvQAcO6B7Tj3sCkIe114Zc1GPLhsA94ayFBlw6HtUXxwXACzfGk0F/oRLGQQUCFWSmB2NI+TR7swNWAiHPBrYXDUqFEIqnwUzWhxSCtEbkCUTGeQz+VRKOZVK0rI5opYEy/i6Y0FPLy2hJf7Q3g7HcYL3Sb+vCaNFxJu9HgjGEglsXHTBgwMJBHx+9AaDsDvtqxmKQbSUpXbWYV8bu3WIJNLoy+ZQMEE6BF0wHDjvXgejyxch/mrNmJyewgXzpmIE8ZGMUrl9aqEHAtVmJZRy2W2baXC2jiipOX6wPLraYmCfPS/pK0waTHJsaAlK0VDPvrubAplYcVZvkJLWywwafFKwZq+QSky0kKTQmQoFNHCIIMlhlqWl05b9GZUqhyKqpZVaZcWRGk1Ggh4bStZ9Ymw028VGHlOq+OsrpfCrDMqFE99Kh9F0FAwhOamGCLhqH78nkJuOSyXAjsf16dVLh+/Z3v4+aCYagnFzbrtfn9Ajxnjpk6drPM6rhPYforOtMDlRl+04KU1LPvndluWy1Z9/Mtzqz+EY8K+08KVFq8J9bnwB1S/1ftEYblc+BUEQRAEQRAEQRAEQRD2D3bYFQD1J0siU0c80c+8qwOKi0YRo3weTB1FkSyEDYkcVvZnkSy5EPJ70O4zMDGqXgNAqyuPaYESjhntxwljwpiEHEKlEuiPEy63LpN72/t8XsSiTTqeG0wVCwVksymkcznk8iV0ZXJ4fmMKL/d40euJweTj2S4DpteNpGppVyKPZlcJE4MmzGIOfr8XTYGALtft52ZaJvyq3JDHQDjgQpNqZ1il8Xq8SGby6CuUsCZTxItrOrF4Qx9GR8P4wIHjcOyYCNrctNvlY/90TGA9gs+hsMRFfaBfiB6vOlD8Y5soAFJYpdBJwZKP64dCQW2xS2Evk0lrIZEWlyyegilfmYaCIoVOCpBsA4XKvr5eLSDy0Xdaa9JalWIrhcq2tnb9KPyGDfRH2qPFSQq3FFwJy6UIyzQUMsnGjZuxadNmbRU6adIU/bi816vGTNVdDuundSnrp+UsXRBY7aNAm0dI5Wd7aJG8YcMG3a+W1hbthmCrOEuR2PLNSjGXfeXYTJkyRbWzXaVzqbhu7UKAedhGtp3jZPlX9erH9NlWCrYUVim00pqV/ae1LttA4dQRadk2j4e+YqO6b4TxdNHAdrJsXps8eYquk4Ixx7ncD7AgCIIgCIIgCIIgCIKw72Os7+gwV3V0YN68eXbUTmDyv6L2n1qEB91F4K31fXhnQ48WRWeOasJhY6NoDvjAh7gpRVGOo6Xn+o2b4XaZGDe6TT/izwf903nu1J5ByOcHfXxysyltUmgW9CZU8YEk3u4ewJ97PFiQi8H0BnTOkotyZ1E/3m9kMjg5lsMnD4piQtCL1EAPipkcAtEYPEFubMTyaINL61Va4jKvC7QBXZ3I4M113Vij6mgOh3HE5FE4pDUIvT1TiRtoqUYYFFVVoIGjowcOAVpXllt9OsdsHwVES6i0rFApAlJ05TGFQF5zxFDmowhLGEf3ATSodDZxYrG00mQaioF83J8WpU65Vho1FrYVqtMM1sHhz+XU2BeLWsClgM2y2UYrWOkcmJ+Wnmy/075ymJb1WhatlkBbjtVWq1ynHbQuJTxnVfl8Ufdxq+Ws5T7AKtOt+u1RcVbb6UZh9eoO3WemoYA6depU7TaB507bWafV363vA485/jznOFltZ7pt0wuCIAiCIAiCIAiCIAj7B7tWWCVmSQXamtK/p4GcCquyBby2thcdmwfQHvLj0IktmBoLotVtwqtSZktubB7IIl0ooLXJi2avoeI9yOYKiKfTCAb82kVAqVTQG2ZRyiwVTWSyebyt8v1uvYEFqRACtjCXM9zaPyof70c2i2ObsvjU9CAOjPq1IpdIJZHK5FSZAYRUoA1uyVBtVnkKpgud+QLe60ni3Y092mJ0zrhROGJMDG2egiqTga1WdVFUs+vRkpoxJM8KW6BAR/GOUAgtF+qcY+txfktIdQS9Wmx/3VRjqHrr2hqnkqg09sl2UFS0LtJylWNBK1BaoFJUzOUyqr1We1gXhU1ah9LSs7Luam1t1H5S2T7mcWDddDXAx/E5HrRItTbPshMoyutgVrpFoLUq20pfr7T0rdZWsjXftueVNLouCIIgCIIgCIIgCIIg7HvssCuARtAylRafNGSkhakBE60eYEZrBK2xCNb1p/HO+l5szhTg9vsQ8HqRyZtI5lwoFF3IF62NjPwuNzwqf7GYBy1PfV66CGBptHA1kEomtauAjDeIBf0FrFf5+fh/idqWysc0tIQ1SnlMDpRwWLMbrW4KgIYWB91uE6lMGqVCER5adqr6aGG7qDeJF1dsxPLOfkwc3YJTD5yIObEgmgzatJagWo2iSktB1apHvahDHttHQ4aWoIsWLcLatWv1I/G0jKRm5/g55WPxvEYrVT6O7sBrFPf4Wihwk69trS9ZLgPho+u8zjIY54isTMe8TjonP32WEj5uv3HjRgQCQd0upl26dClWrlypH9PnI/J8rJ/uBChwMh/LckRHUlm+1TYrHXHawFcGtlPFbmmLdU5h2KX97q5YsUKNRwd6enr1Tv2M52ZZzvvg5OeLVS436Apo61aOJceCfWE6XuerZfW6tX6rbVZ5TtustvOc42pZBzOf005BEARBEATh/2/vzH4bubI7/GNVcWlSOyVKYsvuRUonSJxBMvEYM0AeggxmnvOeP3Ke8pDHIC9OAjiBHRh2DzojtZqSWiu1keJWlfudIt20ppexp+NG2ueTuNStuufeW9LTh1PnOo7jOI7jvP+89YxV9GP+gwQtiEfkYyxUOBpFkdrh25fHHX22faLuYKRHd+f14eKMyuHaeDRSPEy1UI60VC2qGEsXF6FHGml2bkGjgrlVZb0bXV5fKrlT1U25pt88OdU/HQx1XlxQIUoUUfM0DBkNe1rILvXL9Vi/apS0EI7tsfRSpFKxoKGiEKenm0JRx4VEXxycqnVyoeb8jD6+t6rNmZLusJY0De8RNtCEMauhbAAvU8eIttCGVvtj1Br1P588eWLScHNz03baJysTaYm85HF5Nq5C4iHC8535e1ZPNN/Iinqk4b4kie1yTxvHZLkSk3qi7ITP9YhIJCF1SZeW6nYd4/NYPVK30VgJ/QZ2HRmhjI9o3NraskfnGQsJjER9+PChCUjmRwYpgpV5sZs+9WCp20obtVmJubS0YLvvkz16cXEe+hVUq82M+3QsW5c1MB79mc/l5YVdTz+yYvf29vT06VO7D2zq9exZy8ZDOHMNn9RkZZ4hjM2ff8Pl5bqN8fjx17ZG5s5mVmdnpzbm+vp6+EsUbJMqRCrrmZubtYxYNgzLr1mzDF7qzQ4G+XnmMCml4DiO4ziO4ziO4ziO47z/vPWM1fyx+Cx84YH9cdYothEvqZEqStWslvWwMW8Zfl/tn+p3Z9casMHRnZKWKAPQ74bLByqWirYrfzYKbXGiOIkIo+51xzJGKzM13YkiVRLprHOj9s3AJGyUFpSkqeaGV/rZ4ki/vDenD2fuaJgU9TzNdD0cajQY6mqQ6Eglffb8Qp9tH4X5SZ88XNff3l/Rh6VYRcbG0IZ55ht25TI1tpzcsK5winNvQ6ySucmO92yUhExkwyY+9/cPTCIi7xCLCEIkJps9scHVRJJ2wvqRp9VqTcfHpyZTyUolJvITOcq1SEUEKoIQWUi8bvcmjPPcsi+Rg2Sm0sbO+dfXVzYOm0SRoTkRiMwhz1LtmLhst89tHcxxe/t3yuu3JrZxFsKUmMwJKYpgpe3w8LkJTUQtbfRdXFywzbeOj4+/WStCuNXas8xUxOikDAI7/NPGXMmUXVxcsvtFbGQs4pVxWTPzQTQjXxG2CFfkK8KXa9iAiu9IVsZF1LJOxtre3gn9uiZs2fiKNbfbF3Zv2PgKCYsMvr2Bl+M4juM4juM4juM4jvP+8vZLAZhajCyzFLHKj+V4FjgTXmlqj8/PxAXdn6+ouTKn9s1Ijw/aOu8iu0qqlhMNB30lyFQV1e8NxGPbSZG6qz11en2VS5XwKoVYqRYqJdVrJZWzvkqDjuZ0o/XijX6+WtSv789rq0oc6Yujjv55+0S9pKS5uXl9ddbTv24f6LLf11/fW9ffba7q0UxJMynTzdjTKswYQRwIb9RhDQvIRbG95Vmsdjh+fV+QiUhQZCiZktQBJSJSEEm4ufnA5B/ylJ39OUc2K1IRqbdcXzahiHBEECJTySBFZG5tbY775rIUkfno0Z9YJuns7Lxt6nR+3rZ2ZCJxJnVIHzy4Z1mcUZRYtilysVJBrKbWj/GQvsCYkxqw9+/f08pK3eaCgCXDE/nJC6mJMCV7mKzcepg7sZrNZvg/3AjryePPzuabSrHmfN3zlnGLyKWdsgSIZGK3Ws9sfsTnHnI9cyGrl5qwzIM/GWvkXhAfiYsk7Xa6JmWpG0tc1o903dx8aPcaIY3QZU3MnfvOPcizYZlHZXxfqNfrOI7jOI7jOI7jOI7j/Bh4+xmrY8VYYMd8ZVZnFQFpOZ/hO5mmJl+zWEmWWobqg3pN9fkZHbav9d+tY7XTSElcsozRarmoESbNxGqsi86V0ijWbLVm2bEUHYjDdSuVRI/qVf3Z0h19tFTSzzeq+lmjrLViGDZl3Ei/Pe3o0/2OWn1p+7St/bNzba3O6VePmlZHdSHMmQxb5si0EcFUi83zUclZpY5mbGux3FVbW7gu9LFv4+PvCtmkT58+M0nJY+YIRcQjO+oj7hCVCD6yPxGjPL6P2EPEMuTa6qqJSbIt19ebGg2Zcz4Xsn7r9SWThZeX1yYU6YOMRUCSgYmEJLsUgUvWJXKVTMx+v2eZnkjGgwOyYrth7GU7j/zlUXtEJcJxZqZmfRCVnENaAnPON70qmxglFlmgEwHK5lHIX9bFHNhQijkhdrkX4ZZoZ2fHrr97txniFG3+rda+Dg8PtbbWCO3rti7kKfcGobq7+8zWzZj8+zBPxCuSF7ivcVxUlvK3LWi1sRr+54phHbN276jHSn/+DqyTtSFPJ+UFWAeClvns7++bhK7X6xbbcRzHcRzHcRzHcRzHef9562IVn5f7Rd5QVEjW8aEd5YIVNQkIrFL4XK0k2mrMqVYq6cnzth6f3Kg9GCquxOpEiU6HmXppatKwVi6rXIxVyEZikyrbsCrEu5NlWilFWg99lpKCKqENv8uD+1dhuKNw7eEg1fVNV/cXy/r1n67rF415rSBtEWxMKcrnaxsRTb04ttfYuoZ3wz7H13wXkKhATIQoNUQRh83mumWAsskSmZHlclE89r+zs2vlARCdZE8iCqn5iQjd+GBD152Orq4u1ev3TWSm6TAsJ9Pc7JzFGwwHJgTr9QWThTs72zo6OlSSUK/1Q2vjkXjEIWKULFVEJ4/EU2qA+SFBEauIUSDLFaF7cnJmj+AzP9aDtEU6IhuRnYuL82EdpRB/P8SnpMDA5s8tQ9IidMk6ZSzi8UJ8Nptrdg3jLy8vaW1t1e4bYxSLicnX3d2WiU1qxFKmYGOjGdov1G6fWfYrGa2t1q5lnXJPuF8rKw1dhGuurq+0OK5Te3B4ENraqtaqNlfGRfJOMoDpNykdwDnkLeUKkM/cD7J/WbPjOI7jOI7jOI7jOI7z4+Ctb171h4NYzGUm2Z8Yylw1SnujTJ/unenr1r4Ul3QTzavbT1UvDfWLu7P6ZHVW1QLykE6JhjG74qcqjfK6rrhPXmkUqZdl2r8Z6PPDc311eKFqMdHHG8v6qF7TfLgm/CpF1o2FnVmzH4BpsYqkQySS/YiM5BTnyehEbiI6Ly6uTELyuDm1QhGsk8fu6UfGJrVBqYOKAORu8h0ByYv4xCEesRGNfJKFmY+RmpxkzPn5WRuLeSBL2aAJ0ZskRRs3nyPz65v85TqOOYdk5DwxgXiUcaANEcqcySKdZMzymD7zQ5BSS5b19Xo3YjMrSgMcHuY1UtnMCxnLOoB4yGDuC8IYqTk7W7P5PnnyPzYPSiAgSRGn1EEFYrA2sm/zfrM2d0QsWbVz82GMEJcsYmKwNtY5WRtzBsYnJrGJSUat4ziO4ziO4ziO4ziO8+Ph3YnV3CuOP3n8nh33OaCOqdRTolZ3oE932/q35yPtDYsaDHv66Uqsf3y0oEfVWIOUR/PJfu2FnuEzLYVQIU4s9UOMs/5QX51d6b9aJ+r1R/qLjYZ+2lzQ3ShcPaJMAf1HoWcYuUAVVsTsDwMyb4IJ3QBtk3baJu3TcJ7m/DLO/36cvO1F3zwm8SbfX1w7GXN6vLyNa6Zjvug7YfrcNJN4Eyb9ptsQpNPHSE4yU5GvloW7cddqwpJFy+P9PGY/HXfy/fY4ZM4iPJGyZOoyDhJ0mum223ObPp4eDyZznm5zHMdxHMdxHMdxHMdxfpz84GJ17K2Mb/RUaCTDNP+aSz00J4/l9woFfXkx1L+0rvSfx10VleofHs7q7+/OqBQ62ab9Go5lV6JhOD7LUj25uNbnz451fN7VZmNRn9xr6INyrGKaKs2GGkXI1ERJFtlmWhk1A5Bm+cg/CNPibiL03kR+OW/5fbKjb+5d/vmH8G1pSMfp78TMj6dj3haN+fcX8/i+TOJOx77Nq+/PpD2fCxti5TAvzr06JjDmdOzpNU7PZ/qaSfvtOb1u/o7jOI7jOI7jOI7jOM77xbsTqwX7xX/lYEg5yNgqihfwbWiZpSdZrM+Puvps91Sj4UA/aS7oo8aMGsVYxdCXbNdO6PS0M9DnByfaOTxXo1bTJ/fXtDVfVpVYGXVHCxpZhddUkW1WhUiNTawyH6sB+46Ylnq/z/j+vJRJn1f1fRNviv26uK/rC2/q/8fwsrFvj/Xdxn7938BxHMdxHMdxHMdxHMdxct5JKQDk6rfcFW5sIlbzA3uNwvso/NCSKBbqk/qr/7F7rsd7Z5qpxPrJxqI2F6vqptIXR+f6eu9YlSjWxx+s6i8bM5oLoZCuhI+szEBoyNj4iq/U62QUxovzc47jOI7jOI7jOI7jOI7jOG/gHYhVNCnclpg8up1vTDQ5h/xEwlJ/FelJHusofPbCuaedof5950h7Z5eaq1XVH2W66Q/0581F/U1zQasJZQLSsTbNlGTkoo7raka5rOWxf/O5ogwAn7zdnpfjOI7jOI7jOI7jOI7jOM63eQelAHKxyuPW469TiaJZ+EGuRipQdJXz4aIMHxoOLYc1pT3SKC6Ifd5/277WlzsnqpSK+qv7y3pwp6hylpqkJQQ61WKFXxsoxMxswyqCxyEiJxiRIgGQvzuO4ziO4ziO4ziO4ziO47yKd1IK4PtjpvVFFqvYz18a2DfKBUhRlloWaiGaCNLwSbdX+NLXnHIcx3Ecx3Ecx3Ecx3Ecx3kp726npu8FWa55+mr+CD9Zppkq4VvJWnkL72PpOm755uNlvOaU4ziO4ziO4ziO4ziO4zjOS/l/JlbHznSMZaaGTwoI8M7j/FFo8V3dHcdxHMdxHMdxHMdxHMf5v0P6X8UIufTATV4kAAAAAElFTkSuQmCC", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory, TerminationCondition,\n", + " value, Expression, minimize, units)\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (Mixer, MomentumMixingType,\n", + " PressureChanger, Heater,\n", + " Separator, HeatExchanger)\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from SCO2_properties_keras_surrogate import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-19 22:20:40 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 22:20:40 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:43 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 51411\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2674\n", + "\n", + "Total number of variables............................: 5920\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 5669\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 5920\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 9.10e-01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 7.86e-09 7.53e-01 -1.0 9.10e-01 - 9.89e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.119\n", + "Total CPU secs in NLP function evaluations = 0.003\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.3854e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -1.0221e+06 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 719.28\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.5254e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 719.28 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.5254e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 543.23 750.68\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.0875e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.0875e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 396.40 579.39\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -3.1174e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 2.7059e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 396.40\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.0998e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 452.96\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 396.40 396.40 396.40\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 25836. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 396.40 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 579.39 452.96 543.23\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "641.5293430698576 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory('ipopt')\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True)\n", + "\n", + " m.fs.turbine = PressureChanger(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.splitter_1 = Separator(property_package= m.fs.properties,\n", + " outlet_list= [\"bypass\", \"to_cooler\"])\n", + "\n", + " m.fs.co2_cooler = Heater(dynamic= False,\n", + " property_package=m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.main_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.splitter_2 = Separator(property_package= m.fs.properties,\n", + " ideal_separation= False,\n", + " outlet_list= [\"to_FG_cooler\",\n", + " \"to_LTR\"])\n", + "\n", + " m.fs.FG_cooler = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.mixer = Mixer(property_package= m.fs.properties,\n", + " inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"])\n", + "\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet,\n", + " destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet,\n", + " destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet,\n", + " destination=m.fs.LTR_pseudo_shell.inlet)\n", + " m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet,\n", + " destination=m.fs.splitter_1.inlet)\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler,\n", + " destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(source=m.fs.splitter_1.bypass,\n", + " destination=m.fs.bypass_compressor.inlet)\n", + " m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet,\n", + " destination=m.fs.main_compressor.inlet)\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet,\n", + " destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(source=m.fs.main_compressor.outlet,\n", + " destination=m.fs.splitter_2.inlet)\n", + " m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler,\n", + " destination=m.fs.FG_cooler.inlet)\n", + " m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR,\n", + " destination=m.fs.LTR_pseudo_tube.inlet)\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet,\n", + " destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", + " destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline \n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1/3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0) \n", + " m.fs.LTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.LTR_pseudo_tube.heat_duty[0])\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.HTR_pseudo_tube.heat_duty[0])\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW)\n", + " return m\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb new file mode 100644 index 00000000..c4a1dbf3 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb @@ -0,0 +1,665 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - SCO2_flowsheet_optimization_keras_surrogate (Part 3)\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory, TerminationCondition,\n", + " value, Expression, minimize, units)\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (Mixer, MomentumMixingType,\n", + " PressureChanger, Heater,\n", + " Separator, HeatExchanger)\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from SCO2_properties_keras_surrogate import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-19 22:20:40 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 22:20:40 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:41 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 22:20:42 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:43 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:44 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 22:20:45 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 51411\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 2674\n", + "\n", + "Total number of variables............................: 5920\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 5669\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 5920\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 9.10e-01 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 7.86e-09 7.53e-01 -1.0 9.10e-01 - 9.89e-01 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 1\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 1.1641532182693481e-10 7.8580342233181000e-09\n", + "\n", + "\n", + "Number of objective function evaluations = 2\n", + "Number of objective gradient evaluations = 2\n", + "Number of equality constraint evaluations = 2\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 2\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 1\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.119\n", + "Total CPU secs in NLP function evaluations = 0.003\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.3854e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -1.0221e+06 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 719.28\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.5254e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 719.28 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.5254e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 543.23 750.68\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.0875e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.0875e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 396.40 579.39\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -3.1174e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 2.7059e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 396.40\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.0998e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 452.96\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 396.40 396.40 396.40\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 25836. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 396.40 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 579.39 452.96 543.23\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "641.5293430698576 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory('ipopt')\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True)\n", + "\n", + " m.fs.turbine = PressureChanger(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.splitter_1 = Separator(property_package= m.fs.properties,\n", + " outlet_list= [\"bypass\", \"to_cooler\"])\n", + "\n", + " m.fs.co2_cooler = Heater(dynamic= False,\n", + " property_package=m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.main_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.splitter_2 = Separator(property_package= m.fs.properties,\n", + " ideal_separation= False,\n", + " outlet_list= [\"to_FG_cooler\",\n", + " \"to_LTR\"])\n", + "\n", + " m.fs.FG_cooler = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.mixer = Mixer(property_package= m.fs.properties,\n", + " inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"])\n", + "\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet,\n", + " destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet,\n", + " destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet,\n", + " destination=m.fs.LTR_pseudo_shell.inlet)\n", + " m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet,\n", + " destination=m.fs.splitter_1.inlet)\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler,\n", + " destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(source=m.fs.splitter_1.bypass,\n", + " destination=m.fs.bypass_compressor.inlet)\n", + " m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet,\n", + " destination=m.fs.main_compressor.inlet)\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet,\n", + " destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(source=m.fs.main_compressor.outlet,\n", + " destination=m.fs.splitter_2.inlet)\n", + " m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler,\n", + " destination=m.fs.FG_cooler.inlet)\n", + " m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR,\n", + " destination=m.fs.LTR_pseudo_tube.inlet)\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet,\n", + " destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", + " destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline \n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1/3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0) \n", + " m.fs.LTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.LTR_pseudo_tube.heat_duty[0])\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.HTR_pseudo_tube.heat_duty[0])\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW)\n", + " return m\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc.ipynb new file mode 100644 index 00000000..7162da20 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc.ipynb @@ -0,0 +1,1078 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part -1)\n", + "\n", + "## 1. Introduction\n", + "This notebook illustrates the use of KerasSurrogate API leveraging TensorFlow Keras and OMLT package to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "### 1.1 Need for ML Surrogates\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebric, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebric equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train a tanh model from our data and then demonstrate that we can solve an optimization problem with that surrogate model. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random as rn\n", + "import tensorflow as tf\n", + "import tensorflow.keras as keras\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")\n", + "\n", + "# fix environment variables to ensure consist neural network training\n", + "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "np.random.seed(46)\n", + "rn.seed(1342)\n", + "tf.random.set_seed(62)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsquent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "csv_data.columns.values[0:6] =[\"pressure\", \"temperature\",\"enth_mol\",\"entr_mol\",\"CO2_enthalpy\",\"CO2_entropy\"]\n", + "data = csv_data.sample(n=500)\n", + "\n", + "# Creating input_data and output_data from data\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:,2:4]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns \n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogate with TensorFlow Keras\n", + "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", + "\n", + "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", + "\n", + "* Activation function: sigmoid, **tanh**\n", + "* Optimizer: **Adam**\n", + "* Number of hidden layers: 3, **4**, 5, 6\n", + "* Number of neurons per layer: **20**, 40, 60\n", + "\n", + "Important thing to note here is that we do not use ReLU activation function for the training as the flowsheet we intend to solve with this surrogate model is a NLP problem and using ReLU activation function will make it an MINLP. Another thing to note here is the network is smaller (4,20) in order to avoid overfitting. \n", + "\n", + "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", + "\n", + "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/250\n", + "13/13 - 2s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 2s/epoch - 173ms/step\n", + "Epoch 2/250\n", + "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 220ms/epoch - 17ms/step\n", + "Epoch 3/250\n", + "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 228ms/epoch - 18ms/step\n", + "Epoch 4/250\n", + "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 239ms/epoch - 18ms/step\n", + "Epoch 5/250\n", + "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 229ms/epoch - 18ms/step\n", + "Epoch 6/250\n", + "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 202ms/epoch - 16ms/step\n", + "Epoch 7/250\n", + "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 241ms/epoch - 19ms/step\n", + "Epoch 8/250\n", + "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 233ms/epoch - 18ms/step\n", + "Epoch 9/250\n", + "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 227ms/epoch - 17ms/step\n", + "Epoch 10/250\n", + "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 240ms/epoch - 18ms/step\n", + "Epoch 11/250\n", + "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 224ms/epoch - 17ms/step\n", + "Epoch 12/250\n", + "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 227ms/epoch - 17ms/step\n", + "Epoch 13/250\n", + "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 197ms/epoch - 15ms/step\n", + "Epoch 14/250\n", + "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 234ms/epoch - 18ms/step\n", + "Epoch 15/250\n", + "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 207ms/epoch - 16ms/step\n", + "Epoch 16/250\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 215ms/epoch - 17ms/step\n", + "Epoch 17/250\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 227ms/epoch - 17ms/step\n", + "Epoch 18/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 234ms/epoch - 18ms/step\n", + "Epoch 19/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 111ms/epoch - 9ms/step\n", + "Epoch 20/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 246ms/epoch - 19ms/step\n", + "Epoch 21/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 172ms/epoch - 13ms/step\n", + "Epoch 22/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 209ms/epoch - 16ms/step\n", + "Epoch 23/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", + "Epoch 24/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 219ms/epoch - 17ms/step\n", + "Epoch 25/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 212ms/epoch - 16ms/step\n", + "Epoch 26/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 220ms/epoch - 17ms/step\n", + "Epoch 27/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 224ms/epoch - 17ms/step\n", + "Epoch 28/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", + "Epoch 29/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 112ms/epoch - 9ms/step\n", + "Epoch 30/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 223ms/epoch - 17ms/step\n", + "Epoch 31/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 219ms/epoch - 17ms/step\n", + "Epoch 32/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 228ms/epoch - 18ms/step\n", + "Epoch 33/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 112ms/epoch - 9ms/step\n", + "Epoch 34/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 244ms/epoch - 19ms/step\n", + "Epoch 35/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 202ms/epoch - 16ms/step\n", + "Epoch 36/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 224ms/epoch - 17ms/step\n", + "Epoch 37/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 114ms/epoch - 9ms/step\n", + "Epoch 38/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 231ms/epoch - 18ms/step\n", + "Epoch 39/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 107ms/epoch - 8ms/step\n", + "Epoch 40/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 219ms/epoch - 17ms/step\n", + "Epoch 41/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 42/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 100ms/epoch - 8ms/step\n", + "Epoch 43/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0238 - mse: 0.0011 - val_loss: 9.4863e-04 - val_mae: 0.0232 - val_mse: 9.4863e-04 - 225ms/epoch - 17ms/step\n", + "Epoch 44/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0236 - mse: 0.0011 - val_loss: 9.8018e-04 - val_mae: 0.0230 - val_mse: 9.8018e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 45/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0239 - mse: 0.0011 - val_loss: 9.5093e-04 - val_mae: 0.0233 - val_mse: 9.5093e-04 - 121ms/epoch - 9ms/step\n", + "Epoch 46/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.4785e-04 - val_mae: 0.0223 - val_mse: 9.4785e-04 - 234ms/epoch - 18ms/step\n", + "Epoch 47/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 48/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 221ms/epoch - 17ms/step\n", + "Epoch 49/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.2521e-04 - val_mae: 0.0218 - val_mse: 9.2521e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 50/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7818e-04 - val_mae: 0.0231 - val_mse: 9.7818e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 51/250\n", + "13/13 - 0s - loss: 9.9977e-04 - mae: 0.0232 - mse: 9.9977e-04 - val_loss: 9.4350e-04 - val_mae: 0.0221 - val_mse: 9.4350e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 52/250\n", + "13/13 - 0s - loss: 9.8599e-04 - mae: 0.0229 - mse: 9.8599e-04 - val_loss: 9.0638e-04 - val_mae: 0.0230 - val_mse: 9.0638e-04 - 219ms/epoch - 17ms/step\n", + "Epoch 53/250\n", + "13/13 - 0s - loss: 9.8295e-04 - mae: 0.0228 - mse: 9.8295e-04 - val_loss: 9.0667e-04 - val_mae: 0.0215 - val_mse: 9.0667e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 54/250\n", + "13/13 - 0s - loss: 9.7266e-04 - mae: 0.0225 - mse: 9.7266e-04 - val_loss: 9.0391e-04 - val_mae: 0.0224 - val_mse: 9.0391e-04 - 208ms/epoch - 16ms/step\n", + "Epoch 55/250\n", + "13/13 - 0s - loss: 9.5234e-04 - mae: 0.0225 - mse: 9.5234e-04 - val_loss: 8.7426e-04 - val_mae: 0.0219 - val_mse: 8.7426e-04 - 223ms/epoch - 17ms/step\n", + "Epoch 56/250\n", + "13/13 - 0s - loss: 9.4315e-04 - mae: 0.0221 - mse: 9.4315e-04 - val_loss: 8.6742e-04 - val_mae: 0.0224 - val_mse: 8.6742e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 57/250\n", + "13/13 - 0s - loss: 9.9226e-04 - mae: 0.0230 - mse: 9.9226e-04 - val_loss: 8.7793e-04 - val_mae: 0.0225 - val_mse: 8.7793e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 58/250\n", + "13/13 - 0s - loss: 9.4137e-04 - mae: 0.0226 - mse: 9.4137e-04 - val_loss: 8.7477e-04 - val_mae: 0.0225 - val_mse: 8.7477e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 59/250\n", + "13/13 - 0s - loss: 9.2474e-04 - mae: 0.0219 - mse: 9.2474e-04 - val_loss: 8.5320e-04 - val_mae: 0.0212 - val_mse: 8.5320e-04 - 195ms/epoch - 15ms/step\n", + "Epoch 60/250\n", + "13/13 - 0s - loss: 9.1133e-04 - mae: 0.0217 - mse: 9.1133e-04 - val_loss: 8.6082e-04 - val_mae: 0.0217 - val_mse: 8.6082e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 61/250\n", + "13/13 - 0s - loss: 9.1801e-04 - mae: 0.0217 - mse: 9.1801e-04 - val_loss: 8.5403e-04 - val_mae: 0.0223 - val_mse: 8.5403e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 62/250\n", + "13/13 - 0s - loss: 9.1987e-04 - mae: 0.0221 - mse: 9.1987e-04 - val_loss: 8.5714e-04 - val_mae: 0.0219 - val_mse: 8.5714e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 63/250\n", + "13/13 - 0s - loss: 9.0862e-04 - mae: 0.0222 - mse: 9.0862e-04 - val_loss: 8.6160e-04 - val_mae: 0.0225 - val_mse: 8.6160e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 64/250\n", + "13/13 - 0s - loss: 8.9349e-04 - mae: 0.0220 - mse: 8.9349e-04 - val_loss: 8.2851e-04 - val_mae: 0.0214 - val_mse: 8.2851e-04 - 224ms/epoch - 17ms/step\n", + "Epoch 65/250\n", + "13/13 - 0s - loss: 8.7848e-04 - mae: 0.0216 - mse: 8.7848e-04 - val_loss: 8.5189e-04 - val_mae: 0.0218 - val_mse: 8.5189e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 66/250\n", + "13/13 - 0s - loss: 8.9773e-04 - mae: 0.0219 - mse: 8.9773e-04 - val_loss: 8.5650e-04 - val_mae: 0.0211 - val_mse: 8.5650e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 67/250\n", + "13/13 - 0s - loss: 8.7443e-04 - mae: 0.0217 - mse: 8.7443e-04 - val_loss: 8.2545e-04 - val_mae: 0.0214 - val_mse: 8.2545e-04 - 221ms/epoch - 17ms/step\n", + "Epoch 68/250\n", + "13/13 - 0s - loss: 8.9141e-04 - mae: 0.0217 - mse: 8.9141e-04 - val_loss: 8.4471e-04 - val_mae: 0.0219 - val_mse: 8.4471e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 69/250\n", + "13/13 - 0s - loss: 8.9507e-04 - mae: 0.0224 - mse: 8.9507e-04 - val_loss: 8.7916e-04 - val_mae: 0.0217 - val_mse: 8.7916e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 70/250\n", + "13/13 - 0s - loss: 8.5737e-04 - mae: 0.0216 - mse: 8.5737e-04 - val_loss: 8.8807e-04 - val_mae: 0.0215 - val_mse: 8.8807e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 71/250\n", + "13/13 - 0s - loss: 8.5560e-04 - mae: 0.0214 - mse: 8.5560e-04 - val_loss: 8.3750e-04 - val_mae: 0.0213 - val_mse: 8.3750e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 72/250\n", + "13/13 - 0s - loss: 8.5576e-04 - mae: 0.0218 - mse: 8.5576e-04 - val_loss: 8.1156e-04 - val_mae: 0.0210 - val_mse: 8.1156e-04 - 211ms/epoch - 16ms/step\n", + "Epoch 73/250\n", + "13/13 - 0s - loss: 8.4688e-04 - mae: 0.0216 - mse: 8.4688e-04 - val_loss: 8.0221e-04 - val_mae: 0.0210 - val_mse: 8.0221e-04 - 216ms/epoch - 17ms/step\n", + "Epoch 74/250\n", + "13/13 - 0s - loss: 8.3636e-04 - mae: 0.0211 - mse: 8.3636e-04 - val_loss: 7.9384e-04 - val_mae: 0.0208 - val_mse: 7.9384e-04 - 219ms/epoch - 17ms/step\n", + "Epoch 75/250\n", + "13/13 - 0s - loss: 8.4758e-04 - mae: 0.0222 - mse: 8.4758e-04 - val_loss: 8.2932e-04 - val_mae: 0.0212 - val_mse: 8.2932e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 76/250\n", + "13/13 - 0s - loss: 8.4142e-04 - mae: 0.0213 - mse: 8.4142e-04 - val_loss: 8.0552e-04 - val_mae: 0.0209 - val_mse: 8.0552e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 77/250\n", + "13/13 - 0s - loss: 8.5035e-04 - mae: 0.0215 - mse: 8.5035e-04 - val_loss: 8.6014e-04 - val_mae: 0.0215 - val_mse: 8.6014e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 78/250\n", + "13/13 - 0s - loss: 8.9015e-04 - mae: 0.0228 - mse: 8.9015e-04 - val_loss: 9.2548e-04 - val_mae: 0.0225 - val_mse: 9.2548e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 79/250\n", + "13/13 - 0s - loss: 8.1577e-04 - mae: 0.0212 - mse: 8.1577e-04 - val_loss: 8.4703e-04 - val_mae: 0.0211 - val_mse: 8.4703e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 80/250\n", + "13/13 - 0s - loss: 8.0555e-04 - mae: 0.0211 - mse: 8.0555e-04 - val_loss: 8.5652e-04 - val_mae: 0.0214 - val_mse: 8.5652e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 81/250\n", + "13/13 - 0s - loss: 8.3478e-04 - mae: 0.0219 - mse: 8.3478e-04 - val_loss: 9.1057e-04 - val_mae: 0.0222 - val_mse: 9.1057e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 82/250\n", + "13/13 - 0s - loss: 8.2593e-04 - mae: 0.0217 - mse: 8.2593e-04 - val_loss: 8.1172e-04 - val_mae: 0.0209 - val_mse: 8.1172e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 83/250\n", + "13/13 - 0s - loss: 8.2887e-04 - mae: 0.0213 - mse: 8.2887e-04 - val_loss: 8.2033e-04 - val_mae: 0.0211 - val_mse: 8.2033e-04 - 165ms/epoch - 13ms/step\n", + "Epoch 84/250\n", + "13/13 - 0s - loss: 8.1454e-04 - mae: 0.0219 - mse: 8.1454e-04 - val_loss: 8.1589e-04 - val_mae: 0.0211 - val_mse: 8.1589e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 85/250\n", + "13/13 - 0s - loss: 8.0777e-04 - mae: 0.0212 - mse: 8.0777e-04 - val_loss: 7.8637e-04 - val_mae: 0.0208 - val_mse: 7.8637e-04 - 177ms/epoch - 14ms/step\n", + "Epoch 86/250\n", + "13/13 - 0s - loss: 7.8107e-04 - mae: 0.0213 - mse: 7.8107e-04 - val_loss: 7.8138e-04 - val_mae: 0.0212 - val_mse: 7.8138e-04 - 223ms/epoch - 17ms/step\n", + "Epoch 87/250\n", + "13/13 - 0s - loss: 7.9729e-04 - mae: 0.0210 - mse: 7.9729e-04 - val_loss: 7.3667e-04 - val_mae: 0.0204 - val_mse: 7.3667e-04 - 237ms/epoch - 18ms/step\n", + "Epoch 88/250\n", + "13/13 - 0s - loss: 7.5931e-04 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8ms/step\n", + "Epoch 94/250\n", + "13/13 - 0s - loss: 7.4802e-04 - mae: 0.0205 - mse: 7.4802e-04 - val_loss: 7.2386e-04 - val_mae: 0.0201 - val_mse: 7.2386e-04 - 190ms/epoch - 15ms/step\n", + "Epoch 95/250\n", + "13/13 - 0s - loss: 7.2616e-04 - mae: 0.0203 - mse: 7.2616e-04 - val_loss: 7.2728e-04 - val_mae: 0.0204 - val_mse: 7.2728e-04 - 121ms/epoch - 9ms/step\n", + "Epoch 96/250\n", + "13/13 - 0s - loss: 7.2310e-04 - mae: 0.0204 - mse: 7.2310e-04 - val_loss: 7.1349e-04 - val_mae: 0.0206 - val_mse: 7.1349e-04 - 219ms/epoch - 17ms/step\n", + "Epoch 97/250\n", + "13/13 - 0s - loss: 7.0905e-04 - mae: 0.0201 - mse: 7.0905e-04 - val_loss: 7.6242e-04 - val_mae: 0.0205 - val_mse: 7.6242e-04 - 117ms/epoch - 9ms/step\n", + "Epoch 98/250\n", + "13/13 - 0s - loss: 7.1839e-04 - mae: 0.0200 - mse: 7.1839e-04 - val_loss: 7.7098e-04 - val_mae: 0.0202 - val_mse: 7.7098e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 99/250\n", + "13/13 - 0s - loss: 7.3924e-04 - mae: 0.0208 - mse: 7.3924e-04 - val_loss: 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val_mse: 6.8310e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 111/250\n", + "13/13 - 0s - loss: 6.7136e-04 - mae: 0.0197 - mse: 6.7136e-04 - val_loss: 6.5858e-04 - val_mae: 0.0199 - val_mse: 6.5858e-04 - 224ms/epoch - 17ms/step\n", + "Epoch 112/250\n", + "13/13 - 0s - loss: 6.5784e-04 - mae: 0.0195 - mse: 6.5784e-04 - val_loss: 6.5838e-04 - val_mae: 0.0196 - val_mse: 6.5838e-04 - 234ms/epoch - 18ms/step\n", + "Epoch 113/250\n", + "13/13 - 0s - loss: 6.6861e-04 - mae: 0.0198 - mse: 6.6861e-04 - val_loss: 6.9871e-04 - val_mae: 0.0196 - val_mse: 6.9871e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 114/250\n", + "13/13 - 0s - loss: 6.6345e-04 - mae: 0.0196 - mse: 6.6345e-04 - val_loss: 6.8190e-04 - val_mae: 0.0196 - val_mse: 6.8190e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 115/250\n", + "13/13 - 0s - loss: 6.4121e-04 - mae: 0.0193 - mse: 6.4121e-04 - val_loss: 6.6493e-04 - val_mae: 0.0196 - val_mse: 6.6493e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 116/250\n", + "13/13 - 0s - loss: 6.5036e-04 - 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8ms/step\n", + "Epoch 122/250\n", + "13/13 - 0s - loss: 6.4650e-04 - mae: 0.0196 - mse: 6.4650e-04 - val_loss: 8.0733e-04 - val_mae: 0.0210 - val_mse: 8.0733e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 123/250\n", + "13/13 - 0s - loss: 6.5887e-04 - mae: 0.0198 - mse: 6.5887e-04 - val_loss: 6.2666e-04 - val_mae: 0.0191 - val_mse: 6.2666e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 124/250\n", + "13/13 - 0s - loss: 6.1387e-04 - mae: 0.0189 - mse: 6.1387e-04 - val_loss: 6.1020e-04 - val_mae: 0.0188 - val_mse: 6.1020e-04 - 210ms/epoch - 16ms/step\n", + "Epoch 125/250\n", + "13/13 - 0s - loss: 6.1348e-04 - mae: 0.0191 - mse: 6.1348e-04 - val_loss: 6.1093e-04 - val_mae: 0.0193 - val_mse: 6.1093e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 126/250\n", + "13/13 - 0s - loss: 6.1374e-04 - mae: 0.0189 - mse: 6.1374e-04 - val_loss: 6.1062e-04 - val_mae: 0.0188 - val_mse: 6.1062e-04 - 116ms/epoch - 9ms/step\n", + "Epoch 127/250\n", + "13/13 - 0s - loss: 6.1279e-04 - mae: 0.0190 - mse: 6.1279e-04 - val_loss: 6.4391e-04 - val_mae: 0.0190 - val_mse: 6.4391e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 128/250\n", + "13/13 - 0s - loss: 6.0951e-04 - mae: 0.0189 - mse: 6.0951e-04 - val_loss: 5.9592e-04 - val_mae: 0.0188 - val_mse: 5.9592e-04 - 228ms/epoch - 18ms/step\n", + "Epoch 129/250\n", + "13/13 - 0s - loss: 6.2194e-04 - mae: 0.0192 - mse: 6.2194e-04 - val_loss: 5.9344e-04 - val_mae: 0.0188 - val_mse: 5.9344e-04 - 180ms/epoch - 14ms/step\n", + "Epoch 130/250\n", + "13/13 - 0s - loss: 6.1795e-04 - mae: 0.0191 - mse: 6.1795e-04 - val_loss: 5.8880e-04 - val_mae: 0.0188 - val_mse: 5.8880e-04 - 218ms/epoch - 17ms/step\n", + "Epoch 131/250\n", + "13/13 - 0s - loss: 6.6297e-04 - mae: 0.0199 - mse: 6.6297e-04 - val_loss: 7.2306e-04 - val_mae: 0.0197 - val_mse: 7.2306e-04 - 117ms/epoch - 9ms/step\n", + "Epoch 132/250\n", + "13/13 - 0s - loss: 5.8788e-04 - mae: 0.0189 - mse: 5.8788e-04 - val_loss: 6.0686e-04 - val_mae: 0.0189 - val_mse: 6.0686e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 133/250\n", + "13/13 - 0s - loss: 5.7425e-04 - mae: 0.0184 - mse: 5.7425e-04 - val_loss: 5.7895e-04 - val_mae: 0.0183 - val_mse: 5.7895e-04 - 218ms/epoch - 17ms/step\n", + "Epoch 134/250\n", + "13/13 - 0s - loss: 5.8783e-04 - mae: 0.0186 - mse: 5.8783e-04 - val_loss: 5.7846e-04 - val_mae: 0.0188 - val_mse: 5.7846e-04 - 230ms/epoch - 18ms/step\n", + "Epoch 135/250\n", + "13/13 - 0s - loss: 5.8541e-04 - mae: 0.0188 - mse: 5.8541e-04 - val_loss: 6.7887e-04 - val_mae: 0.0191 - val_mse: 6.7887e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 136/250\n", + "13/13 - 0s - loss: 5.9158e-04 - mae: 0.0185 - mse: 5.9158e-04 - val_loss: 5.9231e-04 - val_mae: 0.0188 - val_mse: 5.9231e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 137/250\n", + "13/13 - 0s - loss: 5.9616e-04 - mae: 0.0192 - mse: 5.9616e-04 - val_loss: 7.0218e-04 - val_mae: 0.0212 - val_mse: 7.0218e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 138/250\n", + "13/13 - 0s - loss: 6.2132e-04 - mae: 0.0190 - mse: 6.2132e-04 - val_loss: 6.3436e-04 - val_mae: 0.0186 - val_mse: 6.3436e-04 - 105ms/epoch - 8ms/step\n", + "Epoch 139/250\n", + "13/13 - 0s - loss: 5.8416e-04 - mae: 0.0189 - mse: 5.8416e-04 - val_loss: 5.7793e-04 - val_mae: 0.0184 - val_mse: 5.7793e-04 - 215ms/epoch - 17ms/step\n", + "Epoch 140/250\n", + "13/13 - 0s - loss: 6.5695e-04 - mae: 0.0195 - mse: 6.5695e-04 - val_loss: 5.8062e-04 - val_mae: 0.0189 - val_mse: 5.8062e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 141/250\n", + "13/13 - 0s - loss: 6.4168e-04 - mae: 0.0200 - mse: 6.4168e-04 - val_loss: 6.9879e-04 - val_mae: 0.0196 - val_mse: 6.9879e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 142/250\n", + "13/13 - 0s - loss: 6.5517e-04 - mae: 0.0198 - mse: 6.5517e-04 - val_loss: 6.3928e-04 - val_mae: 0.0193 - val_mse: 6.3928e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 143/250\n", + "13/13 - 0s - loss: 5.8456e-04 - mae: 0.0190 - mse: 5.8456e-04 - val_loss: 5.4596e-04 - val_mae: 0.0181 - val_mse: 5.4596e-04 - 225ms/epoch - 17ms/step\n", + "Epoch 144/250\n", + "13/13 - 0s - loss: 5.9458e-04 - mae: 0.0186 - mse: 5.9458e-04 - val_loss: 5.8598e-04 - val_mae: 0.0181 - val_mse: 5.8598e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 145/250\n", + "13/13 - 0s - loss: 5.6787e-04 - mae: 0.0186 - mse: 5.6787e-04 - val_loss: 5.6263e-04 - val_mae: 0.0186 - val_mse: 5.6263e-04 - 124ms/epoch - 10ms/step\n", + "Epoch 146/250\n", + "13/13 - 0s - loss: 5.3545e-04 - mae: 0.0178 - mse: 5.3545e-04 - val_loss: 5.3802e-04 - val_mae: 0.0179 - val_mse: 5.3802e-04 - 186ms/epoch - 14ms/step\n", + "Epoch 147/250\n", + "13/13 - 0s - loss: 5.2310e-04 - mae: 0.0177 - mse: 5.2310e-04 - val_loss: 5.4103e-04 - val_mae: 0.0179 - val_mse: 5.4103e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 148/250\n", + "13/13 - 0s - loss: 5.2826e-04 - mae: 0.0176 - mse: 5.2826e-04 - val_loss: 5.9310e-04 - val_mae: 0.0181 - val_mse: 5.9310e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 149/250\n", + "13/13 - 0s - loss: 5.3295e-04 - mae: 0.0179 - mse: 5.3295e-04 - val_loss: 5.4002e-04 - val_mae: 0.0176 - val_mse: 5.4002e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 150/250\n", + "13/13 - 0s - loss: 5.1491e-04 - mae: 0.0174 - mse: 5.1491e-04 - val_loss: 5.9602e-04 - val_mae: 0.0179 - val_mse: 5.9602e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 151/250\n", + "13/13 - 0s - loss: 5.2334e-04 - mae: 0.0179 - mse: 5.2334e-04 - val_loss: 5.2811e-04 - val_mae: 0.0178 - val_mse: 5.2811e-04 - 222ms/epoch - 17ms/step\n", + "Epoch 152/250\n", + "13/13 - 0s - loss: 5.2768e-04 - mae: 0.0178 - mse: 5.2768e-04 - val_loss: 5.5139e-04 - val_mae: 0.0184 - val_mse: 5.5139e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 153/250\n", + "13/13 - 0s - loss: 5.2962e-04 - mae: 0.0179 - mse: 5.2962e-04 - val_loss: 5.7462e-04 - val_mae: 0.0178 - val_mse: 5.7462e-04 - 99ms/epoch - 8ms/step\n", + "Epoch 154/250\n", + "13/13 - 0s - loss: 5.0260e-04 - mae: 0.0173 - mse: 5.0260e-04 - val_loss: 5.3387e-04 - val_mae: 0.0181 - val_mse: 5.3387e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 155/250\n", + "13/13 - 0s - loss: 5.0501e-04 - mae: 0.0175 - mse: 5.0501e-04 - val_loss: 5.0751e-04 - val_mae: 0.0172 - val_mse: 5.0751e-04 - 211ms/epoch - 16ms/step\n", + "Epoch 156/250\n", + "13/13 - 0s - loss: 5.0518e-04 - mae: 0.0173 - mse: 5.0518e-04 - val_loss: 5.5553e-04 - val_mae: 0.0174 - val_mse: 5.5553e-04 - 189ms/epoch - 15ms/step\n", + "Epoch 157/250\n", + "13/13 - 0s - loss: 5.0064e-04 - mae: 0.0172 - mse: 5.0064e-04 - val_loss: 5.1205e-04 - val_mae: 0.0172 - val_mse: 5.1205e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 158/250\n", + "13/13 - 0s - loss: 4.9541e-04 - mae: 0.0172 - mse: 4.9541e-04 - val_loss: 5.0799e-04 - val_mae: 0.0172 - val_mse: 5.0799e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 159/250\n", + "13/13 - 0s - loss: 5.4153e-04 - mae: 0.0182 - mse: 5.4153e-04 - val_loss: 5.2077e-04 - val_mae: 0.0171 - val_mse: 5.2077e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 160/250\n", + "13/13 - 0s - loss: 4.8280e-04 - mae: 0.0170 - mse: 4.8280e-04 - val_loss: 5.1410e-04 - val_mae: 0.0168 - val_mse: 5.1410e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 161/250\n", + "13/13 - 0s - loss: 4.8993e-04 - mae: 0.0171 - mse: 4.8993e-04 - val_loss: 5.1744e-04 - val_mae: 0.0171 - val_mse: 5.1744e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 162/250\n", + "13/13 - 0s - loss: 4.8044e-04 - mae: 0.0169 - mse: 4.8044e-04 - val_loss: 5.1099e-04 - val_mae: 0.0168 - val_mse: 5.1099e-04 - 103ms/epoch - 8ms/step\n", + "Epoch 163/250\n", + "13/13 - 0s - loss: 4.9657e-04 - mae: 0.0171 - mse: 4.9657e-04 - val_loss: 4.9877e-04 - val_mae: 0.0171 - val_mse: 4.9877e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 164/250\n", + "13/13 - 0s - loss: 4.8858e-04 - mae: 0.0170 - mse: 4.8858e-04 - val_loss: 5.0099e-04 - val_mae: 0.0169 - val_mse: 5.0099e-04 - 99ms/epoch - 8ms/step\n", + "Epoch 165/250\n", + "13/13 - 0s - loss: 4.7747e-04 - mae: 0.0170 - mse: 4.7747e-04 - val_loss: 5.8449e-04 - val_mae: 0.0174 - val_mse: 5.8449e-04 - 97ms/epoch - 7ms/step\n", + "Epoch 166/250\n", + "13/13 - 0s - loss: 4.9897e-04 - mae: 0.0171 - mse: 4.9897e-04 - val_loss: 4.9512e-04 - val_mae: 0.0173 - val_mse: 4.9512e-04 - 174ms/epoch - 13ms/step\n", + "Epoch 167/250\n", + "13/13 - 0s - loss: 4.8695e-04 - mae: 0.0173 - mse: 4.8695e-04 - val_loss: 5.0306e-04 - val_mae: 0.0165 - val_mse: 5.0306e-04 - 97ms/epoch - 7ms/step\n", + "Epoch 168/250\n", + "13/13 - 0s - loss: 4.7948e-04 - mae: 0.0171 - mse: 4.7948e-04 - val_loss: 6.8895e-04 - val_mae: 0.0193 - val_mse: 6.8895e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 169/250\n", + "13/13 - 0s - loss: 4.8055e-04 - mae: 0.0168 - mse: 4.8055e-04 - val_loss: 4.9053e-04 - val_mae: 0.0171 - val_mse: 4.9053e-04 - 215ms/epoch - 17ms/step\n", + "Epoch 170/250\n", + "13/13 - 0s - loss: 4.5980e-04 - mae: 0.0168 - mse: 4.5980e-04 - val_loss: 5.2267e-04 - val_mae: 0.0170 - val_mse: 5.2267e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 171/250\n", + "13/13 - 0s - loss: 4.6495e-04 - mae: 0.0168 - mse: 4.6495e-04 - val_loss: 4.6718e-04 - val_mae: 0.0165 - val_mse: 4.6718e-04 - 216ms/epoch - 17ms/step\n", + "Epoch 172/250\n", + "13/13 - 0s - loss: 4.6046e-04 - mae: 0.0168 - mse: 4.6046e-04 - val_loss: 4.6731e-04 - val_mae: 0.0166 - val_mse: 4.6731e-04 - 98ms/epoch - 8ms/step\n", + "Epoch 173/250\n", + "13/13 - 0s - loss: 4.6993e-04 - mae: 0.0168 - mse: 4.6993e-04 - val_loss: 4.8190e-04 - val_mae: 0.0167 - val_mse: 4.8190e-04 - 101ms/epoch - 8ms/step\n", + "Epoch 174/250\n", + "13/13 - 0s - loss: 4.8411e-04 - mae: 0.0172 - mse: 4.8411e-04 - val_loss: 5.0800e-04 - val_mae: 0.0164 - val_mse: 5.0800e-04 - 99ms/epoch - 8ms/step\n", + "Epoch 175/250\n", + "13/13 - 0s - loss: 4.5295e-04 - mae: 0.0164 - mse: 4.5295e-04 - val_loss: 6.2583e-04 - val_mae: 0.0182 - val_mse: 6.2583e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 176/250\n", + "13/13 - 0s - loss: 5.3742e-04 - mae: 0.0183 - mse: 5.3742e-04 - val_loss: 5.6727e-04 - val_mae: 0.0187 - val_mse: 5.6727e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 177/250\n", + "13/13 - 0s - loss: 5.3634e-04 - mae: 0.0182 - mse: 5.3634e-04 - val_loss: 4.6197e-04 - val_mae: 0.0157 - val_mse: 4.6197e-04 - 212ms/epoch - 16ms/step\n", + "Epoch 178/250\n", + "13/13 - 0s - loss: 4.8847e-04 - mae: 0.0169 - mse: 4.8847e-04 - val_loss: 4.6646e-04 - val_mae: 0.0160 - val_mse: 4.6646e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 179/250\n", + "13/13 - 0s - loss: 4.3622e-04 - mae: 0.0160 - mse: 4.3622e-04 - val_loss: 5.3203e-04 - val_mae: 0.0164 - val_mse: 5.3203e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 180/250\n", + "13/13 - 0s - loss: 4.7108e-04 - mae: 0.0165 - mse: 4.7108e-04 - val_loss: 4.6548e-04 - val_mae: 0.0161 - val_mse: 4.6548e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 181/250\n", + "13/13 - 0s - loss: 4.3932e-04 - mae: 0.0164 - mse: 4.3932e-04 - val_loss: 4.4195e-04 - val_mae: 0.0157 - val_mse: 4.4195e-04 - 212ms/epoch - 16ms/step\n", + "Epoch 182/250\n", + "13/13 - 0s - loss: 4.3340e-04 - mae: 0.0159 - mse: 4.3340e-04 - val_loss: 4.5463e-04 - val_mae: 0.0158 - val_mse: 4.5463e-04 - 95ms/epoch - 7ms/step\n", + "Epoch 183/250\n", + "13/13 - 0s - loss: 4.2639e-04 - mae: 0.0162 - mse: 4.2639e-04 - val_loss: 4.3874e-04 - val_mae: 0.0156 - val_mse: 4.3874e-04 - 169ms/epoch - 13ms/step\n", + "Epoch 184/250\n", + "13/13 - 0s - loss: 4.4119e-04 - mae: 0.0159 - mse: 4.4119e-04 - val_loss: 4.7791e-04 - val_mae: 0.0169 - val_mse: 4.7791e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 185/250\n", + "13/13 - 0s - loss: 4.4805e-04 - mae: 0.0164 - mse: 4.4805e-04 - val_loss: 4.6275e-04 - val_mae: 0.0163 - val_mse: 4.6275e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 186/250\n", + "13/13 - 0s - loss: 4.4495e-04 - mae: 0.0163 - mse: 4.4495e-04 - val_loss: 4.4746e-04 - val_mae: 0.0155 - val_mse: 4.4746e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 187/250\n", + "13/13 - 0s - loss: 4.7030e-04 - mae: 0.0167 - mse: 4.7030e-04 - val_loss: 5.6234e-04 - val_mae: 0.0169 - val_mse: 5.6234e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 188/250\n", + "13/13 - 0s - loss: 4.4920e-04 - mae: 0.0160 - mse: 4.4920e-04 - val_loss: 4.2347e-04 - val_mae: 0.0154 - val_mse: 4.2347e-04 - 204ms/epoch - 16ms/step\n", + "Epoch 189/250\n", + "13/13 - 0s - loss: 4.1850e-04 - mae: 0.0159 - mse: 4.1850e-04 - val_loss: 4.5828e-04 - val_mae: 0.0156 - val_mse: 4.5828e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 190/250\n", + "13/13 - 0s - loss: 4.2816e-04 - mae: 0.0159 - mse: 4.2816e-04 - val_loss: 4.2983e-04 - val_mae: 0.0155 - val_mse: 4.2983e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 191/250\n", + "13/13 - 0s - loss: 4.1442e-04 - mae: 0.0156 - mse: 4.1442e-04 - val_loss: 4.5135e-04 - val_mae: 0.0154 - val_mse: 4.5135e-04 - 103ms/epoch - 8ms/step\n", + "Epoch 192/250\n", + "13/13 - 0s - loss: 4.1126e-04 - mae: 0.0159 - mse: 4.1126e-04 - val_loss: 4.2590e-04 - val_mae: 0.0151 - val_mse: 4.2590e-04 - 159ms/epoch - 12ms/step\n", + "Epoch 193/250\n", + "13/13 - 0s - loss: 4.1197e-04 - mae: 0.0155 - mse: 4.1197e-04 - val_loss: 4.2111e-04 - val_mae: 0.0151 - val_mse: 4.2111e-04 - 209ms/epoch - 16ms/step\n", + "Epoch 194/250\n", + "13/13 - 0s - loss: 4.0958e-04 - mae: 0.0157 - mse: 4.0958e-04 - val_loss: 4.1117e-04 - val_mae: 0.0149 - val_mse: 4.1117e-04 - 185ms/epoch - 14ms/step\n", + "Epoch 195/250\n", + "13/13 - 0s - loss: 3.9243e-04 - mae: 0.0153 - mse: 3.9243e-04 - val_loss: 4.1405e-04 - val_mae: 0.0150 - val_mse: 4.1405e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 196/250\n", + "13/13 - 0s - loss: 4.0300e-04 - mae: 0.0153 - mse: 4.0300e-04 - val_loss: 4.3989e-04 - val_mae: 0.0150 - val_mse: 4.3989e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 197/250\n", + "13/13 - 0s - loss: 4.0142e-04 - mae: 0.0154 - mse: 4.0142e-04 - val_loss: 4.3665e-04 - val_mae: 0.0151 - val_mse: 4.3665e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 198/250\n", + "13/13 - 0s - loss: 3.9936e-04 - mae: 0.0153 - mse: 3.9936e-04 - val_loss: 4.2897e-04 - val_mae: 0.0149 - val_mse: 4.2897e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 199/250\n", + "13/13 - 0s - loss: 4.0143e-04 - mae: 0.0153 - mse: 4.0143e-04 - val_loss: 4.0877e-04 - val_mae: 0.0148 - val_mse: 4.0877e-04 - 214ms/epoch - 16ms/step\n", + "Epoch 200/250\n", + "13/13 - 0s - loss: 3.9668e-04 - mae: 0.0152 - mse: 3.9668e-04 - val_loss: 4.3571e-04 - val_mae: 0.0150 - val_mse: 4.3571e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 201/250\n", + "13/13 - 0s - loss: 3.9516e-04 - mae: 0.0154 - mse: 3.9516e-04 - val_loss: 5.1984e-04 - val_mae: 0.0161 - val_mse: 5.1984e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 202/250\n", + "13/13 - 0s - loss: 4.5166e-04 - mae: 0.0161 - mse: 4.5166e-04 - val_loss: 5.4696e-04 - val_mae: 0.0182 - val_mse: 5.4696e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 203/250\n", + "13/13 - 0s - loss: 4.5904e-04 - mae: 0.0166 - mse: 4.5904e-04 - val_loss: 4.1240e-04 - val_mae: 0.0150 - val_mse: 4.1240e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 204/250\n", + "13/13 - 0s - loss: 3.9851e-04 - mae: 0.0150 - mse: 3.9851e-04 - val_loss: 4.5210e-04 - val_mae: 0.0154 - val_mse: 4.5210e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 205/250\n", + "13/13 - 0s - loss: 3.8760e-04 - mae: 0.0151 - mse: 3.8760e-04 - val_loss: 4.0982e-04 - val_mae: 0.0149 - val_mse: 4.0982e-04 - 116ms/epoch - 9ms/step\n", + "Epoch 206/250\n", + "13/13 - 0s - loss: 4.1937e-04 - mae: 0.0156 - mse: 4.1937e-04 - val_loss: 3.8857e-04 - val_mae: 0.0145 - val_mse: 3.8857e-04 - 222ms/epoch - 17ms/step\n", + "Epoch 207/250\n", + "13/13 - 0s - loss: 3.7173e-04 - mae: 0.0146 - mse: 3.7173e-04 - val_loss: 3.9353e-04 - val_mae: 0.0147 - val_mse: 3.9353e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 208/250\n", + "13/13 - 0s - loss: 3.9673e-04 - mae: 0.0153 - mse: 3.9673e-04 - val_loss: 3.9003e-04 - val_mae: 0.0145 - val_mse: 3.9003e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 209/250\n", + "13/13 - 0s - loss: 4.2359e-04 - mae: 0.0155 - mse: 4.2359e-04 - val_loss: 3.9027e-04 - val_mae: 0.0146 - val_mse: 3.9027e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 210/250\n", + "13/13 - 0s - loss: 3.9302e-04 - mae: 0.0154 - mse: 3.9302e-04 - val_loss: 4.1320e-04 - val_mae: 0.0152 - val_mse: 4.1320e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 211/250\n", + "13/13 - 0s - loss: 3.6641e-04 - mae: 0.0147 - mse: 3.6641e-04 - val_loss: 3.9564e-04 - val_mae: 0.0141 - val_mse: 3.9564e-04 - 116ms/epoch - 9ms/step\n", + "Epoch 212/250\n", + "13/13 - 0s - loss: 3.6259e-04 - mae: 0.0143 - mse: 3.6259e-04 - val_loss: 3.8787e-04 - val_mae: 0.0146 - val_mse: 3.8787e-04 - 228ms/epoch - 18ms/step\n", + "Epoch 213/250\n", + "13/13 - 0s - loss: 4.0665e-04 - mae: 0.0156 - mse: 4.0665e-04 - val_loss: 5.0910e-04 - val_mae: 0.0160 - val_mse: 5.0910e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 214/250\n", + "13/13 - 0s - loss: 4.5758e-04 - mae: 0.0169 - mse: 4.5758e-04 - val_loss: 4.1241e-04 - val_mae: 0.0141 - val_mse: 4.1241e-04 - 104ms/epoch - 8ms/step\n", + "Epoch 215/250\n", + "13/13 - 0s - loss: 4.0666e-04 - mae: 0.0155 - mse: 4.0666e-04 - val_loss: 4.6639e-04 - val_mae: 0.0151 - val_mse: 4.6639e-04 - 104ms/epoch - 8ms/step\n", + "Epoch 216/250\n", + "13/13 - 0s - loss: 3.6615e-04 - mae: 0.0145 - mse: 3.6615e-04 - val_loss: 3.8294e-04 - val_mae: 0.0138 - val_mse: 3.8294e-04 - 201ms/epoch - 15ms/step\n", + "Epoch 217/250\n", + "13/13 - 0s - loss: 3.8135e-04 - mae: 0.0149 - mse: 3.8135e-04 - val_loss: 5.1259e-04 - val_mae: 0.0162 - val_mse: 5.1259e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 218/250\n", + "13/13 - 0s - loss: 3.5877e-04 - mae: 0.0144 - mse: 3.5877e-04 - val_loss: 3.7918e-04 - val_mae: 0.0142 - val_mse: 3.7918e-04 - 222ms/epoch - 17ms/step\n", + "Epoch 219/250\n", + "13/13 - 0s - loss: 4.1097e-04 - mae: 0.0155 - mse: 4.1097e-04 - val_loss: 3.7973e-04 - val_mae: 0.0144 - val_mse: 3.7973e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 220/250\n", + "13/13 - 0s - loss: 3.7840e-04 - mae: 0.0149 - mse: 3.7840e-04 - val_loss: 4.7988e-04 - val_mae: 0.0153 - val_mse: 4.7988e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 221/250\n", + "13/13 - 0s - loss: 3.5545e-04 - mae: 0.0143 - mse: 3.5545e-04 - val_loss: 3.7230e-04 - val_mae: 0.0136 - val_mse: 3.7230e-04 - 226ms/epoch - 17ms/step\n", + "Epoch 222/250\n", + "13/13 - 0s - loss: 3.4610e-04 - mae: 0.0141 - mse: 3.4610e-04 - val_loss: 4.1371e-04 - val_mae: 0.0142 - val_mse: 4.1371e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 223/250\n", + "13/13 - 0s - loss: 3.7775e-04 - mae: 0.0149 - mse: 3.7775e-04 - val_loss: 3.8045e-04 - val_mae: 0.0142 - val_mse: 3.8045e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 224/250\n", + "13/13 - 0s - loss: 3.5911e-04 - mae: 0.0145 - mse: 3.5911e-04 - val_loss: 3.5609e-04 - val_mae: 0.0134 - val_mse: 3.5609e-04 - 233ms/epoch - 18ms/step\n", + "Epoch 225/250\n", + "13/13 - 0s - loss: 3.5933e-04 - mae: 0.0144 - mse: 3.5933e-04 - val_loss: 3.5900e-04 - val_mae: 0.0134 - val_mse: 3.5900e-04 - 105ms/epoch - 8ms/step\n", + "Epoch 226/250\n", + "13/13 - 0s - loss: 3.6466e-04 - mae: 0.0144 - mse: 3.6466e-04 - val_loss: 3.5378e-04 - val_mae: 0.0135 - val_mse: 3.5378e-04 - 232ms/epoch - 18ms/step\n", + "Epoch 227/250\n", + "13/13 - 0s - loss: 3.5876e-04 - mae: 0.0144 - mse: 3.5876e-04 - val_loss: 3.6523e-04 - val_mae: 0.0133 - val_mse: 3.6523e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 228/250\n", + "13/13 - 0s - loss: 3.4559e-04 - mae: 0.0142 - mse: 3.4559e-04 - val_loss: 3.5907e-04 - val_mae: 0.0139 - val_mse: 3.5907e-04 - 162ms/epoch - 12ms/step\n", + "Epoch 229/250\n", + "13/13 - 0s - loss: 3.4162e-04 - mae: 0.0142 - mse: 3.4162e-04 - val_loss: 4.2194e-04 - val_mae: 0.0141 - val_mse: 4.2194e-04 - 101ms/epoch - 8ms/step\n", + "Epoch 230/250\n", + "13/13 - 0s - loss: 3.6967e-04 - mae: 0.0146 - mse: 3.6967e-04 - val_loss: 3.7720e-04 - val_mae: 0.0138 - val_mse: 3.7720e-04 - 105ms/epoch - 8ms/step\n", + "Epoch 231/250\n", + "13/13 - 0s - loss: 3.3735e-04 - mae: 0.0136 - mse: 3.3735e-04 - val_loss: 3.3976e-04 - val_mae: 0.0129 - val_mse: 3.3976e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 232/250\n", + "13/13 - 0s - loss: 3.3844e-04 - mae: 0.0141 - mse: 3.3844e-04 - val_loss: 3.8716e-04 - val_mae: 0.0135 - val_mse: 3.8716e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 233/250\n", + "13/13 - 0s - loss: 3.6741e-04 - mae: 0.0145 - mse: 3.6741e-04 - val_loss: 3.8668e-04 - val_mae: 0.0136 - val_mse: 3.8668e-04 - 117ms/epoch - 9ms/step\n", + "Epoch 234/250\n", + "13/13 - 0s - loss: 3.4129e-04 - mae: 0.0139 - mse: 3.4129e-04 - val_loss: 3.4933e-04 - val_mae: 0.0133 - val_mse: 3.4933e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 235/250\n", + "13/13 - 0s - loss: 3.2338e-04 - mae: 0.0137 - mse: 3.2338e-04 - val_loss: 3.4566e-04 - val_mae: 0.0133 - val_mse: 3.4566e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 236/250\n", + "13/13 - 0s - loss: 3.1652e-04 - mae: 0.0134 - mse: 3.1652e-04 - val_loss: 3.9728e-04 - val_mae: 0.0136 - val_mse: 3.9728e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 237/250\n", + "13/13 - 0s - loss: 3.2047e-04 - mae: 0.0136 - mse: 3.2047e-04 - val_loss: 3.3756e-04 - val_mae: 0.0130 - val_mse: 3.3756e-04 - 225ms/epoch - 17ms/step\n", + "Epoch 238/250\n", + "13/13 - 0s - loss: 3.3167e-04 - mae: 0.0138 - mse: 3.3167e-04 - val_loss: 3.3191e-04 - val_mae: 0.0126 - val_mse: 3.3191e-04 - 228ms/epoch - 18ms/step\n", + "Epoch 239/250\n", + "13/13 - 0s - loss: 3.2033e-04 - mae: 0.0134 - mse: 3.2033e-04 - val_loss: 3.2969e-04 - val_mae: 0.0128 - val_mse: 3.2969e-04 - 215ms/epoch - 17ms/step\n", + "Epoch 240/250\n", + "13/13 - 0s - loss: 3.5224e-04 - mae: 0.0141 - mse: 3.5224e-04 - val_loss: 3.9061e-04 - val_mae: 0.0148 - val_mse: 3.9061e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 241/250\n", + "13/13 - 0s - loss: 3.9777e-04 - mae: 0.0153 - mse: 3.9777e-04 - val_loss: 3.7065e-04 - val_mae: 0.0137 - val_mse: 3.7065e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 242/250\n", + "13/13 - 0s - loss: 3.2502e-04 - mae: 0.0138 - mse: 3.2502e-04 - val_loss: 3.3236e-04 - val_mae: 0.0124 - val_mse: 3.3236e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 243/250\n", + "13/13 - 0s - loss: 3.0734e-04 - mae: 0.0133 - mse: 3.0734e-04 - val_loss: 3.2635e-04 - val_mae: 0.0126 - val_mse: 3.2635e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 244/250\n", + "13/13 - 0s - loss: 3.2928e-04 - mae: 0.0137 - mse: 3.2928e-04 - val_loss: 3.2871e-04 - val_mae: 0.0125 - val_mse: 3.2871e-04 - 104ms/epoch - 8ms/step\n", + "Epoch 245/250\n", + "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 246/250\n", + "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 247/250\n", + "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 248/250\n", + "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 249/250\n", + "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 250/250\n", + "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 109ms/epoch - 8ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# selected settings for regression (best fit from options above)\n", + "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 4, 20\n", + "loss, metrics = \"mse\", [\"mae\", \"mse\"]\n", + "\n", + "# Create data objects for training using scalar normalization\n", + "n_inputs = len(input_labels)\n", + "n_outputs = len(output_labels)\n", + "x = input_data\n", + "y = output_data\n", + "\n", + "input_scaler = None\n", + "output_scaler = None\n", + "input_scaler = OffsetScaler.create_normalizing_scaler(x)\n", + "output_scaler = OffsetScaler.create_normalizing_scaler(y)\n", + "x = input_scaler.scale(x)\n", + "y = output_scaler.scale(y)\n", + "x = x.to_numpy()\n", + "y = y.to_numpy()\n", + "\n", + "# Create Keras Sequential object and build neural network\n", + "model = tf.keras.Sequential()\n", + "model.add(\n", + " tf.keras.layers.Dense(\n", + " units=n_nodes_per_layer, input_dim=n_inputs, activation=activation\n", + " )\n", + ")\n", + "for i in range(1, n_hidden_layers):\n", + " model.add(tf.keras.layers.Dense(units=n_nodes_per_layer, activation=activation))\n", + "model.add(tf.keras.layers.Dense(units=n_outputs,activation=keras.activations.linear))\n", + "\n", + "# Train surrogate (calls optimizer on neural network and solves for weights)\n", + "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", + "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n", + " \".mdl_co2.h5\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", + ")\n", + "history = model.fit(x=x, y=y, validation_split=0.2, verbose=2, epochs=250, callbacks=[mcp_save])\n", + "\n", + "# Get the training and validation MSE from the history\n", + "train_mse = history.history['mse']\n", + "val_mse = history.history['val_mse']\n", + "\n", + "# Generate a plot of training MSE vs validation MSE\n", + "epochs = range(1, len(train_mse) + 1)\n", + "plt.plot(epochs, train_mse, 'bo-', label='Training MSE')\n", + "plt.plot(epochs, val_mse, 'ro-', label='Validation MSE')\n", + "plt.title('Training MSE vs Validation MSE')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('MSE')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: keras_surrogate\\assets\n" + ] + } + ], + "source": [ + "# Adding input bounds and variables along with scalers and output variable to kerasSurrogate\n", + "xmin, xmax = [7,306], [40,1000]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "\n", + "keras_surrogate = KerasSurrogate(\n", + " model,\n", + " input_labels=list(input_labels),\n", + " output_labels=list(output_labels),\n", + " input_bounds=input_bounds,\n", + " input_scaler=input_scaler,\n", + " output_scaler=output_scaler,\n", + ")\n", + "keras_surrogate.save_to_folder(\"keras_surrogate\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing Surrogates\n", + "\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 [==============================] - 0s 3ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "surrogate_scatter2D(keras_surrogate, data_training)\n", + "surrogate_parity(keras_surrogate, data_training)\n", + "surrogate_residual(keras_surrogate, data_training)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation\n", + "\n", + "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4/4 [==============================] - 0s 5ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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06dIFderUweXLlzFr1iw0a9ZMCksBAQHo2LEjRo4cia+++golJSUYO3YsXnvtNakXbvTo0Vi+fDmmTZuGkSNH4uDBg9iyZQt2794tvdekSZMwfPhw+Pj4oHPnzvjqq6+Qm5uLESNGAADs7e0REhKCSZMmoW7durCzs8NHH30EuVwOPz8/AEDPnj3RunVrDB06FAsXLkR6ejpmzpyJsWPHwtLSUis/o8LCQsyfP18r536a6dOnVxiwq8LT0xNnz54FAEyYMEEqb9KkCT777DOMHj0a33zzDSwsLGBvbw+ZTKbSgzly5Ejp702bNsXSpUvx4osvIicnB7Vq1XruNhIRkWE6ceIE9uzZIz23t7fHxx9/rMMWPRuDmJxgY2ODn376CT169ECrVq0QEhKCF154AUeOHJGCkImJCXbu3AlHR0d069YNgYGB8PLywqZNm6TzeHh4YPfu3Thw4ADat2+PxYsXY/Xq1VAoFFKdgQMHYtGiRZg9ezY6dOiA06dPY9++fUqTDb788kv06dMH/fv3R7du3eDi4iL1FAGAqakpdu3aBVNTU8jlcrz77rsYNmwY5s6dWw0/LcMlhJD+z+eXX35Bjx490LBhQ9SuXRtDhw7Fv//+iwcPHjzxHAkJCXjjjTfg7u6O2rVr4+WXXwbwaLiciIiMS0lJCSIiIpRC2+uvv46mTSfgpZdkiI7WYeOegUwY2uCuEcnOzoa9vT2ysrKU7nfLy8tDSkoKPDw8YGVlBaA09OhqNX1zc/NKdzUHBwcjMzMT27dvVzn2wgsvwN3dHcuXL4enpyfGjBmDgQMHom7duvj9998REhKCe/fuwcHBAVFRUZgwYYLSvYa5ublo3LgxFAoFRo8eDScnJ6SlpUGhUCAxMREdOnTQzAfWIXXXnoiI1EtKSsLmzZuVyj7++GM4ODjA3x+IjwfkciAuTrPvW9H3tyYYxFApPZ1MJtPIcKWuHDx4EH/++ScmTpyIhIQElJSUYPHixTAxKe0U3rJli1J9CwsLFBcXK5UlJSXh33//RWRkpLSUysmTJ6vnAxARkV6JiIhQKYuJmQ1vbxmCgoCwMCAysvRPQ2IQQ6VUs+Tn5yM9PR3//PMPTp06hXnz5qFv377o06cPhg0bhubNm6OwsBDLli3DlStX8P3332PVqlVK52jSpAlycnIQGxuLO3fu4MGDB3B3d4eFhYX0uujoaHz66ac6+pRERKQLhYWFKqHNyckJMTHhiI+XITKytCwoqLSnLShIB418DgxuVO327dsHV1dXNGnSBL169cKhQ4ewdOlS7NixA6ampmjfvj2WLFmCBQsWoG3btli/fr3KxAt/f3+MHj0aAwcOhJOTExYuXAgnJydERUVh69ataN26NSIjI7Fo0SIdfUoiIqpu0dHRmDdvnlLZ2bNvolGjDxEWVjosamg9bI/jPW56rCr3uJFx4LUnIlKvoqHR+HiZVu5jexLe40ZERESkRl5eHhYsWKBSHh4eDm9vw7yP7UkY3IiIiMggbd68WWlrTADo27evtIpAUJDh3cP2NAxuREREZHDUDY3Onj3b4HZCqCoGNyIiIjIY9+/fx5IlS1TKw8PDddCa6sfgRkRERAYhMjJS2qO8zNtvvw0vLy+VutHRj+5vq0nDpVwOhIiIiPReRESESmjz9g5XG9qA0tAWHw9p3baagsGNiIiI9Na9e/fU3s82Z074E0NZTVm37XEcKiUiIiK9pC6w9e/fH1eutH1qKKuJM0oBBjciIiLSQ+pCW9kEhLZta2YoqwwOlVKNFBwcjH79+knPX3nlFUyYMOG5zqmJcxAR0ZNlZGQ8MbQZO/a4UbUKDg7G2rVrAQDm5uZwd3fHsGHDMGPGDJiZae8/x59++gnm5uaVqnv48GF0794d9+7dg4ODwzOdg4iIqk5dYGvWbAjefbe5DlqjnxjcqNr16tULa9asQX5+Pvbs2YOxY8fC3Nwc06dPV6pXUFAACwsLjbxn3bp19eIcRESkXkUTEORy4N13ddAgPcWhUqp2lpaWcHFxQePGjTFmzBgEBAQgOjpaGt78/PPP0aBBA7Rq1QoAcO3aNbz99ttwcHBA3bp10bdvX6SmpkrnKy4uxqRJk+Dg4IB69eph2rRpEEIovefjw5z5+fkIDQ2Fm5sbLC0t0bx5c3z77bdITU1F9+7dAQB16tSBTCZDcHCw2nPcu3cPw4YNQ506dWBjY4PevXvjr7/+ko5HRUXBwcEBMTEx8PLyQq1atdCrVy/cvHlTqnP48GF07twZtra2cHBwwEsvvYSrV69q6CdNRKT/0tLS1IY2b+/wGjkr9Hmxx410ztraGv/++y8AIDY2FnZ2djhw4AAAoLCwEAqFAnK5HL/99hvMzMzw2WefoVevXjh79iwsLCywePFiREVF4bvvvoOXlxcWL16Mn3/+Ga+++mqF7zls2DDEx8dj6dKlaN++PVJSUnDnzh24ublh27Zt6N+/P5KTk2FnZwdra2u15wgODsZff/2F6Oho2NnZITQ0FK+//jouXLggDak+ePAAixYtwvfffw8TExO8++67mDJlCtavX4+ioiL069cPo0aNwsaNG1FQUIDjx4/X+O1aiIjKqAtsI0aMgLu7OwDjnYDwJAxupDNCCMTGxiImJgYfffQRbt++DVtbW6xevVoaIv3hhx9QUlKC1atXS4FmzZo1cHBwwOHDh9GzZ0989dVXmD59Ot566y0AwKpVqxATE1Ph+166dAlbtmzBgQMHEBAQAABo2rSpdLxsSLR+/fpK97iVVxbY/vjjD/j7+wMA1q9fDzc3N2zfvh3/+c9/AJQGz1WrVqFZs2YAgHHjxmHu3LkAgOzsbGRlZaFPnz7S8YoWkiQiqmk4AeHZcKiUEB0N+PuX/lkddu3ahVq1asHKygq9e/fGwIEDMWfOHABAu3btlO5rO3PmDP7++2/Url0btWrVQq1atVC3bl3k5eXh8uXLyMrKws2bN+Hr6yu9xszMDD4+PhW+/+nTp2FqaoqXX375mT/DxYsXYWZmpvS+9erVQ6tWrXDx4kWpzMbGRgplAODq6oqMjAwApQExODgYCoUCb7zxBr7++mulYVQioprozJkzDG3PgT1upLQtSHV0S3fv3h0rV66EhYUFGjRooDSb1NbWVqluTk4OOnXqhPXr16ucx8nJ6Znev6KhT214fBaqTCZTuv9uzZo1GD9+PPbt24fNmzdj5syZOHDgAPz8/KqtjURE1UVdYGvZciSWLXODtzeHRiuDPW5U7duC2Nraonnz5nB3d3/qEiAdO3bEX3/9hfr166N58+ZKD3t7e9jb28PV1RXHjh2TXlNUVISEhIQKz9muXTuUlJTgyJEjao+X9fgVFxdXeA4vLy8UFRUpve+///6L5ORktG7d+omf6XHe3t6YPn064uLi0LZtW2zYsKFKryciMgQV9bItW+ZWI/cU1RYGN0JQEBAXp5//pzNkyBA4Ojqib9+++O2335CSkoLDhw9j/PjxuH79OgDg448/RmRkJLZv346kpCR8+OGHyMzMrPCcTZo0wfDhwzFy5Ehs375dOueWLVsAAI0bN4ZMJsOuXbtw+/Zt5OTkqJyjRYsW6Nu3L0aNGoXff/8dZ86cwbvvvouGDRuib9++lfpsKSkpmD59OuLj43H16lXs378ff/31F+9zI6IaJT4+Xm1oi4kJR3R0zd1TVFsY3Eiv2djY4Ndff4W7uzveeusteHl5ISQkBHl5ebCzswMATJ48GUOHDsXw4cMhl8tRu3ZtvPnmm08878qVKzFgwAB8+OGH8PT0xKhRo5CbmwsAaNiwISIiIhAWFgZnZ2eMGzdO7TnWrFmDTp06oU+fPpDL5RBCYM+ePZVepNfGxgZJSUno378/WrZsiffffx9jx47FBx98UIWfEBGR/oqIiMD+/fuVykaNGoWYmHClW3T0tfNAH8nE4wtekd7Izs6Gvb09srKypJACAHl5eUhJSYGHhwesrKx02EKqbrz2RGQonjQBITq6NLSFhdXMwFbR97cmcHICERERacyWLVuUZteXKT9rNCioZga26sDgRkRERBqhrpftm2/GoFmz+uBqH5rB4EZERETPraJtq5o148QDTWJwIyIiome2evVq/PPPPyrlMTHh8PYunXhAmsPgZsA4r8T48JoTkT5R18v20UcfoU+futW6sLsx4XIgBqj8BuZkXMqueWWXHCEi0gYhRIWzRuvWrcu12bSIPW4GyNTUFA4ODtKelzY2NtIG7FQzCSHw4MEDZGRkwMHBAaamprpuEhEZiceX7li0aJG07mV5nDVaPRjcDJSLiwsASOGNjIODg4N07YmIqkP5/awTE1V72SZNmoTatWvroGXGicHNQMlkMri6uqJ+/fooLCzUdXOoGpibm7OnjYiqXVgYsGBBMXr2/EzlWDjX+Kh2DG4GztTUlF/mRESkFdHRpb1sPXuqHmNo0w0GNyIiIlJL3dBobGwofv2VW+7pCoMbERERKcnPz0dkZKRKeUxMOGeK6hiDGxEREUnULfMBlO6CwNFR3WNwIyIiIgDqQ9uMGTO4dqQeYXAjIiIyctu25eDcucUq5ZyAoH8Y3IiIiIxYRUOjDG36icGNiIjIyJTthqBQqIa2WbNmwcSEO2LqKwY3IiIiI7N06R0oFCtUytnLpv8Y3IiIiIxIREQEunZVLWdoMwwMbkREREZC3f1ss2fPhkwm00Fr6FkwuBEREdVwKSkpWLdunUq5t3c4mNkMC4MbERFRDcZZozULgxsREVENpS60MbAZNgY3IiKiGub06dPYsWOHSjlDm+FjcCMiIqpB1PWymZqaYubMmTpoDWkagxsREVENoS60eXuHIyhIB40hrWBwIyIiMmDR0cCWLUfQosVhlWMcGq15GNyIiIgMWGJiBFq0UC6rU6cOxo8fr5sGkVYxuBERERmQsn1Gw8JKQ9vj2MtWszG4ERERGZDISKBhwx+RmHhe5RhDW83H4EZERKTnyveyKRSqvWzu7u4YMWKEDlpG1c1E1w0gIiIiZdHRgL9/6Z9AaWiLj694aJShzXjIhBBC140g9bKzs2Fvb4+srCzY2dnpujlERFRN/P1Lg5qnJ1CnDtCz51LIZPdU6nFoVD9p8/ubPW5ERER6JiwMkMsBIUqHRh8PbW3btmVoM1IMbkRERDr0+LAoAAQFAXFxwODB6odG+/fvX40tJH3CyQlERETVrPxkg7L71yIjIe1woG4HBIBDo8TgRkREVO3Kh7WwMCA0FLh7tzTQqZuA4O/vj9dee00HLSV9w6FSIiKialI2LNq9e+k9bGFhpb1sdeoAyckVzxplaKMy7HEjIiKqJmU9bUDpPWxlFIoIKBSq9Tk0So9jcCMiIqomZfe0hYU9KlN3P1uvXr3g6+tbjS0jQ8HgRkREVE2Cgh5NQADUhzb2stGTMLgRERFVM84apWdlMJMTgoKC4O7uDisrK7i6umLo0KG4ceOGdHzOnDmQyWQqD1tbW6XzbN26FZ6enrCyskK7du2wZ88epeNCCMyePRuurq6wtrZGQEAA/vrrL6U6d+/exZAhQ2BnZwcHBweEhIQgJydHqc7Zs2fRtWtXWFlZwc3NDQsXLtTwT4SIiAyRutD2n//8h6GNKsVgglv37t2xZcsWJCcnY9u2bbh8+TIGDBggHZ8yZQpu3ryp9GjdujX+85//SHXi4uIwePBghISEIDExEf369UO/fv1w7tw5qc7ChQuxdOlSrFq1CseOHYOtrS0UCgXy8vKkOkOGDMH58+dx4MAB7Nq1C7/++ivef/996Xh2djZ69uyJxo0bIyEhAV988QXmzJmD//73v1r+KRERkb4SQlQ4NNq6dWsdtIgMkcHuVRodHY1+/fohPz8f5ubmKsfPnDmDDh064Ndff0XXrl0BAAMHDkRubi527dol1fPz80OHDh2watUqCCHQoEEDTJ48GVOmTAEAZGVlwdnZGVFRURg0aBAuXryI1q1b48SJE/Dx8QEA7Nu3D6+//jquX7+OBg0aYOXKlfjkk0+Qnp4OCwsLAEBYWBi2b9+OpKSkSn9G7lVKRFQzcGjUuHCv0sfcvXsX69evh7+/v9rQBgCrV69Gy5YtpdAGAPHx8QgICFCqp1AoEP//52anpKQgPT1dqY69vT18fX2lOvHx8XBwcJBCGwAEBATAxMQEx44dk+p069ZNCm1l75OcnIx791Q3CS6Tn5+P7OxspQcRERk2daFtxIgRDG30TAwquIWGhsLW1hb16tVDWloaduzYobZeXl4e1q9fj5CQEKXy9PR0ODs7K5U5OzsjPT1dOl5W9qQ69evXVzpuZmaGunXrKtVRd47y76HO/PnzYW9vLz3c3NwqrEtERPqtsLCwwqFRd3d3HbSIagKdBrewsDC1EwrKP8oPLU6dOhWJiYnYv38/TE1NMWzYMKgb6f35559x//59DB8+vDo/znObPn06srKypMe1a9d03SQiInoGERERmDdvnko5e9noeel0OZDJkycjODj4iXWaNm0q/d3R0RGOjo5o2bIlvLy84ObmhqNHj0Iulyu9ZvXq1ejTp49Kr5eLiwtu3bqlVHbr1i24uLhIx8vKXF1dlep06NBBqpORkaF0jqKiIty9e1fpPOrep/x7qGNpaQlLS8sKjxMRkf4ov1H809ZmGzt2LBwdHauxdVRT6TS4OTk5wcnJ6ZleW1JSAqD0vrDyUlJScOjQIURHR6u8Ri6XIzY2FhMmTJDKDhw4IAU/Dw8PuLi4IDY2Vgpq2dnZOHbsGMaMGSOdIzMzEwkJCejUqRMA4ODBgygpKZFWuZbL5fjkk09QWFgo3YN34MABtGrVCnXq1Hmmz0tERPqhLLDdvVu6v2hkZGlwy8nJweLFi1Xqs5eNNMkgZpUeO3YMJ06cQJcuXVCnTh1cvnwZs2bNwq1bt3D+/HmlXqpZs2bhu+++Q1paGkxNTZXOExcXh5dffhmRkZEIDAzEpk2bMG/ePJw6dQpt27YFACxYsACRkZFYu3YtPDw8MGvWLJw9exYXLlyAlZUVAKB37964desWVq1ahcLCQowYMQI+Pj7YsGEDgNKZqK1atULPnj0RGhqKc+fOYeTIkfjyyy+Vlg15Gs4qJSLSL9HRwJAhQE4O4OlZujl8WJj6zeEBhjZjpc3vb4PYOcHGxgY//fQTwsPDkZubC1dXV/Tq1QszZ85UCm0lJSWIiopCcHCwSmgDAH9/f2zYsAEzZ87EjBkz0KJFC2zfvl0KbQAwbdo05Obm4v3330dmZia6dOmCffv2SaENANavX49x48ahR48eMDExQf/+/bF06VLpuL29Pfbv34+xY8eiU6dOcHR0xOzZs6sU2oiISP9ERpaGtlq1gAULSnva1A2NTpkyRWUBeCJNMIgeN2PFHjciIv1S/r42ufw2vvnmG5U67GUjruNGRERUzaKjAX//0j/LBAUBcXGlQ6MMbaQLBjFUSkREVN0iI4H4+EeTD8qoGxr95JNPYGbGr1TSPva4ERERqVE6HFr6JwBcuXKlwgV1GdqouvC/NCIiIjWCgh71tHGvUdIX7HEjIiKjVv5eNnX3takLbbNnz2ZoI51gjxsRERm18veyAY/+3qjRKezcuVOlPgMb6RKDGxERGbWwsEdLfAClf1coIqAmszG0kc4xuBERkdF5fJ/R8rNG1e2CwMBG+oLBjYiIjI66pT727t2L48ePq9RlaCN9wuBGRERG5/HhUc4aJUPB4EZEREbnaUt9MLCRvmJwIyIio7R27VqkpqaqlDO0kT5jcCMiIqPDoVEyVAxuRERkVDg0SoaMwY2IiIzCokWLkJubq1LO0EaGhFteERGRwVO3VVV5ERERDG1UI7DHjYiIDF7ZumxDhgDr1ysvqMuhUapJGNyIiMjghYWVhracnEeL6nICAtVEHColIiKD88knQO3apX8CpUFt/XpALi8NcepCm62tLUMbGTyZEELouhGkXnZ2Nuzt7ZGVlQU7OztdN4eISG/Url3au2ZiAvz8M4dGSb9o8/ubPW5ERGRwxo8vDW0lJaVDo0BpYGNoo5qOPW56jD1uREQVi45+tN9oYqJqYGvevDmGDBmig5aRsdPm9zcnJxARkUEq22+UvWxkTBjciIjIIHHWKBkjBjciIjI46kJbly5d0KNHDx20hqj6MLgREZFB4dAoGTMGNyIi0jvlJx6ULfXBoVEiBjciItJDZVtYPWkXhL59+6JDhw7V3zgiHWJwIyIivRMWVhraQkMFIiLmqhxnLxsZKy7AS0REOhUdDfj7l/5ZJigIUCgicPo0QxtReexxIyIinXp8WBRQPzQ6bNgweHh4VHPriPQLe9yIiEinwsIebQ5fWFhY4axRhjYibnml17jlFREZE84apZqCm8wTEZFBevz+tehowNMT8PJSvqdNXWgbM2YMQxvRY9jjpsfY40ZEhs7fv/T+tVq1gPXrH93PBpQOjx44kItFixapvI6BjQwZe9yIiMgghYWVhracnEcL6rZqVdrrplBEMLQRVRFnlRIRkdYEBT3qaSvbBaGiBXUnT56MWrVq6aCVRIaDwY2IiLSqLKwBwJ07d7BixQqVOuxlI6ocBjciIqoWnDVK9PwY3IiISOvUhbYZM2bA3NxcB60hMlxVnpxgamqKjIwMlfJ///0XpqamGmkUERHpP3VbVT3uypUrFS6oy9BGVHVV7nGraPWQ/Px8WFhYPHeDiIjIMKjbqqo8Do0SaV6lg9vSpUsBADKZDKtXr1aa+VNcXIxff/0Vnp6emm8hERHppbCwR7NFH6cutM2aNQsmJlyFiuh5VHoB3rI94q5evYpGjRopDYtaWFigSZMmmDt3Lnx9fbXTUiPEBXiJyNCcOnUKO3fuVClnLxsZE21+f1e6xy0lJQUA0L17d/z000+oU6eORhtCRESGjUOjRNpX5XvcDh06pI12EBGRAatoAgIRaVaVg9vIkSOfePy777575sYQEZFh+eWXX/DHH3+olDO0EWlHlYPbvXv3lJ4XFhbi3LlzyMzMxKuvvqqxhhERkX6IjlbesqoMh0aJql+Vg9vPP/+sUlZSUoIxY8agWbNmGmkUERHpD3XLfnBolEg3Kj2r9GmSk5Pxyiuv4ObNm5o4HYGzSolIP5TvccvP34oLFy6o1GFoI3pEm9/fGltQ5/LlyygqKtLU6YiISIfK74oQFATExQGJiREMbUQ6VuWh0kmTJik9F0Lg5s2b2L17N4YPH66xhhERkW5ERwNDhgA5OY+GRzk0SqQfqhzcEhMTlZ6bmJjAyckJixcvfuqMUyIi0n+RkaWhrVYt4I03liIi4p5KHYY2It3gOm5ERKSkbCsrhSICBQWqxxnaiHSnysGtTEZGBpKTkwEArVq1Qv369TXWKCIi0p2goNL72R7HwEake1UObtnZ2Rg7diw2btyIkpISAICpqSkGDhyIFStWwN7eXuONJCKi6sG12Yj0W5VnlY4aNQrHjh3D7t27kZmZiczMTOzatQsnT57EBx98oI02EhGRFpSfOQqoD202NjYMbUR6pMrruNna2iImJgZdunRRKv/tt9/Qq1cv5ObmarSBxozruBGRNvn7ly6sK5eX3s/2OAY2omejze/vKg+V1qtXT+1wqL29PerUqaORRhERkfaFham/lw1gaCPSV1UeKp05cyYmTZqE9PR0qSw9PR1Tp07FrFmzNNo4IiJ6Po8Ph5anLrS1aNGCoY1Ij1V5qNTb2xt///038vPz4e7uDgBIS0uDpaUlWrRooVT31KlTmmupEeJQKRE9r/LDoXFxj8q5oC6R9ujVUGnfvn0hk8k02ggiItKOsjXZwsJKn3PWKJFh09gm86R57HEjIk1SF9p8fX3Rq1cvHbSGqObSq03mmzZtin///VelPDMzE02bNtVIo4iISLMqGhplaCMyLFUeKk1NTUVxcbFKeX5+Pq5fv66RRhER0bOLjgamTQNkMmDQIA6NEtUklQ5u0eWmJMXExCgtCVJcXIzY2Fh4eHhotnVERFRlkZFAcjIwZ45qaAsMDISPj48OWkVEmlDpodJ+/fqhX79+kMlkGD58uPS8X79+GDRoEA4cOIDFixdrraFBQUFwd3eHlZUVXF1dMXToUNy4cUOpTkxMDPz8/FC7dm04OTmhf//+SE1NVapz+PBhdOzYEZaWlmjevDmioqJU3mvFihVo0qQJrKys4Ovri+PHjysdz8vLw9ixY1GvXj3UqlUL/fv3x61bt5TqpKWlITAwEDY2Nqhfvz6mTp2KoqIijfwsiIieJDRUqA1t4eHhDG1EBq7Swa2kpAQlJSVwd3dHRkaG9LykpAT5+flITk5Gnz59tNbQ7t27Y8uWLUhOTsa2bdtw+fJlDBgwQDqekpKCvn374tVXX8Xp06cRExODO3fu4K233lKqExgYiO7du+P06dOYMGEC3nvvPcTExEh1Nm/ejEmTJiE8PBynTp1C+/btoVAokJGRIdWZOHEidu7cia1bt+LIkSO4ceOG0vsUFxcjMDAQBQUFiIuLw9q1axEVFYXZs2dr7edDRASU3st2+vRclXIOjRLVDAY7qzQ6Ohr9+vVDfn4+zM3N8eOPP2Lw4MHIz8+HiUlpHt25cyf69u0r1QkNDcXu3btx7tw56TyDBg1CZmYm9u3bB6B0htWLL76I5cuXAygNrG5ubvjoo48QFhaGrKwsODk5YcOGDVJwTEpKgpeXF+Lj4+Hn54e9e/eiT58+uHHjBpydnQEAq1atQmhoKG7fvg0LC4tKfUbOKiWiqlA3AWHQoEFo1aqVDlpDZLz0ah23uXNV/0+uvOroVbp79y7Wr18Pf39/mJubAwA6deoEExMTrFmzBsHBwcjJycH333+PgIAAqU58fDwCAgKUzqVQKDBhwgQAQEFBARISEjB9+nTpuImJCQICAhAfHw8ASEhIQGFhodJ5PD094e7uLgW3+Ph4tGvXTgptZe8zZswYnD9/Ht7e3lr5uRBRzRYd/WhNtqCgR+VFRUX4/PPPVeqzl42o5qlycPv555+VnhcWFiIlJQVmZmZo1qyZVoNbaGgoli9fjgcPHsDPzw+7du2Sjnl4eGD//v14++238cEHH6C4uBhyuRx79uyR6qSnpyuFKQBwdnZGdnY2Hj58iHv37qG4uFhtnaSkJOkcFhYWcHBwUKlTtg1YRe9Tdqwi+fn5yM/Pl55nZ2c/7UdCREYkMrJ0F4TIyEfBjQvqEhmXKq/jlpiYqPQ4d+4cbt68iR49emDixIlVOldYWBhkMtkTH2WBCQCmTp2KxMRE7N+/H6amphg2bBjKRnrT09MxatQoDB8+HCdOnMCRI0dgYWGBAQMGwFBGg+fPnw97e3vp4ebmpusmEZEe6d4dqFWr9E9AfWgbNWoUQxtRDVblHjd17OzsEBERgTfeeANDhw6t9OsmT56M4ODgJ9Ypv6ivo6MjHB0d0bJlS3h5ecHNzQ1Hjx6FXC7HihUrYG9vj4ULF0r1f/jhB7i5ueHYsWPw8/ODi4uLyuzPW7duwc7ODtbW1jA1NYWpqanaOi4uLgAAFxcXFBQUIDMzU6nX7fE6j89ELTtnWR11pk+fjkmTJknPs7OzGd6ISHLoEJCTA/z++wNERHyhcpyBjajm00hwA4CsrCxkZWVV6TVOTk5wcnJ6pvcrKSkBAGlo8cGDB9KkhDKmpqZKdR8fOgWAAwcOQC6XAwAsLCzQqVMnxMbGol+/ftJrY2NjMW7cOACl99KZm5sjNjYW/fv3BwAkJycjLS1NOo9cLsfnn3+OjIwM1K9fX3ofOzs7tG7dusLPZGlpCUtLy2f6eRBRzRcWBiQmcmiUyJhVObgtXbpU6bkQAjdv3sT333+P3r17a6xh5R07dgwnTpxAly5dUKdOHVy+fBmzZs1Cs2bNpLAUGBiIL7/8EnPnzsXgwYNx//59zJgxA40bN5YmA4wePRrLly/HtGnTMHLkSBw8eBBbtmzB7t27pfeaNGkShg8fDh8fH3Tu3BlfffUVcnNzMWLECACAvb09QkJCMGnSJNStWxd2dnb46KOPIJfL4efnBwDo2bMnWrdujaFDh2LhwoVIT0/HzJkzMXbsWAYzIqqS8hMS1IW2CRMmKC2ITkQ1W5WXA3l8dwQTExM4OTnh1VdfxfTp01G7dm2NNhAA/vzzT3z88cc4c+YMcnNz4erqil69emHmzJlo2LChVG/Tpk1YuHAhLl26BBsbG8jlcixYsACenp5SncOHD2PixIm4cOECGjVqhFmzZqkM1y5fvhxffPEF0tPT0aFDByxduhS+vr7S8by8PEyePBkbN25Efn4+FAoFvvnmG6Vh0KtXr2LMmDE4fPgwbG1tMXz4cERGRsLMrPJZmcuBEJG/P/DXX3cwbtwKlWPsZSPST9r8/jbYddyMAYMbEXHWKJHh0at13AAgMzMTf//9NwCgefPmKktjEBFR1ahbo01daAsLC+MtF0RGrErLgaSmpiIwMBCOjo7w9fWFr68vHB0d0adPH5U9QYmIqPJCQ0vXaAsNLd2eT11oCw8PZ2gjMnKV7nG7du0a/Pz8YG5ujk8//RReXl4AgAsXLmDlypWQy+U4ceIEGjVqpLXGEhHVVGU3rQwaFIF161SPc2iUiIAq3OMWEhKCv//+GzExMbCyslI69vDhQ/Tq1QstWrTA6tWrtdJQY8R73IiMR3S0+lmjs2bNUlnqiIj0m17c47Zv3z5s3rxZJbQBgLW1NT799FMMGjRIo40jIjIGp0+fRmLiDpVy9rIR0eMqHdzu3LmDJk2aVHi8adOmuHv3ribaRERUoz1tbTaAoY2I1Kt0cHN1dZXWPlPn3LlzT9zOiYiISpVtFq8utDGwEdGTVPrGiX79+mHKlCm4ffu2yrGMjAyEhoZK20QREVHFhg37BXPmMLQRUdVVenLCvXv34Ovri/T0dLz77rvw9PSEEAIXL17Ehg0b4OLigqNHj6Ju3brabrPR4OQEopqHC+oS1Xx6MTmhTp06OHbsGGbMmIFNmzYhMzMTAODg4IB33nkH8+bNY2gjIqpARbNGGdiIqCqeacsrIYQ0ZOrk5ASZTKbxhhF73Ihqiu+++w7Xrl1TKWdoI6qZ9KLHrTyZTIb69etrtCFERDURh0aJSJOeKbgREdHTVbRtFRHRs2JwIyLSgPJrs509+xmKi4tV6jC0EdHzYnAjItKAJ63NBjC0EZFmMLgREWlARbsgMLARkSZVKrgtXbq00iccP378MzeGiEjflR8SDQoqLeMEBCKqLpVaDsTDw6NyJ5PJcOXKleduFJXiciBE+sffv3RIVC4H4uIY2ohIlc6XA0lJSdHomxIRGaqwMCA0FLh7l7NGiaj6PfM9bgUFBUhJSUGzZs1gZsZb5YjIOAQFcQICEelOpTeZL/PgwQOEhITAxsYGbdq0QVpaGgDgo48+QmRkpMYbSESkT9T1sllZWTG0EVG1qHJwmz59Os6cOYPDhw/DyspKKg8ICMDmzZs12jgiIn0RHV3x0GhoaKgOWkRExqjKY5zbt2/H5s2b4efnp7RHaZs2bXD58mWNNo6ISB9wAgIR6YsqB7fbt2+r3ac0NzeXm80TUY0SHa3+frY2bdpgwIABOmgRERm7Kg+V+vj4YPfu3dLzsrC2evVqyOVyzbWMiEjHKlpQl6GNiHSlyj1u8+bNQ+/evXHhwgUUFRXh66+/xoULFxAXF4cjR45oo41ERNWKQ6NEpK+q3OPWpUsXnD59GkVFRWjXrh3279+P+vXrIz4+Hp06ddJGG4mIqo260PbKK68wtBGRXqjUzgmkG9w5gaj6CCEwd+5clXIGNiKqKp3vnJCdnV3pEzJgEJGh4dAoERmKSgU3BweHSs8YLS4ufq4GERFVJ3Wh7c0338QLL7ygg9YQET1ZpYLboUOHpL+npqYiLCwMwcHB0izS+Ph4rF27FvPnz9dOK4mINKy4uBifffaZSrm3dziY2YhIX1X5HrcePXrgvffew+DBg5XKN2zYgP/+9784fPiwJttn1HiPG5F2cGiUiLRJm9/fVZ5VGh8fDx8fH5VyHx8fHD9+XCONIiLSFnWhLT7+PXh7M7QRkf6rcnBzc3PD//73P5Xy1atXw83NTSONIiLStIcPH1a41+i+fQ0RFKSDRhERVVGVF+D98ssv0b9/f+zduxe+vr4AgOPHj+Ovv/7Ctm3bNN5AIqLnxaFRIqopnmkdt+vXr+Obb75BUlISAMDLywujR49mj5uG8R43ouenLrR9/PHHcHBwqP7GEJFR0Ob3Nxfg1WMMbkTP7u7du1i2bJlKOXvZiEjbdL4A7+MyMzPx7bff4uLFiwCANm3aYOTIkbC3t9do44iIngWHRomopqpyj9vJkyehUChgbW2Nzp07AwBOnDiBhw8fYv/+/ejYsaNWGmqM2ONGVHXqQltoaCisrKx00BoiMkZ6NVTatWtXNG/eHP/73/9gZlbaYVdUVIT33nsPV65cwa+//qrRBhozBjeiyrt27Rq+++47lXL2shFRddOr4GZtbY3ExER4enoqlV+4cAE+Pj548OCBRhtozBjciCqHQ6NEpE/06h43Ozs7pKWlqQS3a9euoXbt2hprGBFRZagLbTNnzoSpqakOWkNEpF1VDm4DBw5ESEgIFi1aBH9/fwDAH3/8galTp6psg0VEpC0XLlzA1q1bVcrZy0ZENVmVg9uiRYsgk8kwbNgwFBUVAQDMzc0xZswYREZGaryBRESP49AoERmrZ17H7cGDB7h8+TIAoFmzZrCxsdFow4j3uBGpoy60zZ49GzKZTAetISJSpVf3uJWxsbFBu3btNNkWIqIK/f7774iNjVUpZy8bERmTSge3kSNHVqqeuun4RETPo6KhUW9vhjYiMi6VDm5RUVFo3LgxvL29wV2yiKi6qAtt7GUjImNV6eA2ZswYbNy4ESkpKRgxYgTeffdd1K1bV5ttIyIjtmvXLiQkJKiUM7QRkTEzqWzFFStW4ObNm5g2bRp27twJNzc3vP3224iJiWEPHBFpVEREhNrQFhPD0EZExu2ZZ5VevXoVUVFRWLduHYqKinD+/HnUqlVL0+0zapxVSsZI3dDoxo3hkMmABQuAoCAdNIqIqAr0clapiYkJZDIZhBAoLi7WZJuIyAitWbMGaWlpKuXh4eHg6CgRUalKD5UCQH5+PjZu3IjXXnsNLVu2xJ9//only5cjLS2NvW1E9MwiIiIqDG1ERPRIpXvcPvzwQ2zatAlubm4YOXIkNm7cCEdHR222jYiMAGeNEhFVXqXvcTMxMYG7uzu8vb2fuEL5Tz/9pLHGGTve40Y12bx581BYWKhSztBGRIZOL+5xGzZsGLeUISKNUNfLZmFhgenTp+ugNUREhqNKC/ASET0vDo0SET27Z55VSkRUFRVtW8XQRkRUeQxuRKR16kKbu7s7RowYoYPWEBEZLgY3ItIqDo0SEWkOgxsRaQWHRomINI/BjYg0Tl1o8/HxQWBgoA5aQ0RUczC4EZFGcWiUiEh7GNyISCM4NEpEpH0MbkT0XKKjgcRE1dDWs2dPyOVyHbSIiKjmYnAjomcmhEBi4lyVcvayERFpB4MbET0TDo0SEVU/E103oLKCgoLg7u4OKysruLq6YujQobhx44ZSnS1btqBDhw6wsbFB48aN8cUXX6ic5/Dhw+jYsSMsLS3RvHlztVt5rVixAk2aNIGVlRV8fX1x/PhxpeN5eXkYO3Ys6tWrh1q1aqF///64deuWUp20tDQEBgbCxsYG9evXx9SpU1FUVPT8PwgiPaAutDVtOpihjYhIywwmuHXv3h1btmxBcnIytm3bhsuXL2PAgAHS8b1792LIkCEYPXo0zp07h2+++QZffvklli9fLtVJSUlBYGAgunfvjtOnT2PChAl47733EBMTI9XZvHkzJk2ahPDwcJw6dQrt27eHQqFARkaGVGfixInYuXMntm7diiNHjuDGjRt46623pOPFxcUIDAxEQUEB4uLisHbtWkRFRWH27Nla/ikRaVdJSYna0DZnTjhWrmyJ6GjA37/0vjciItI8mRBC6LoRzyI6Ohr9+vVDfn4+zM3N8c4776CwsBBbt26V6ixbtgwLFy5EWloaZDIZQkNDsXv3bpw7d06qM2jQIGRmZmLfvn0AAF9fX7z44otS4CspKYGbmxs++ugjhIWFISsrC05OTtiwYYMUHJOSkuDl5YX4+Hj4+flh79696NOnD27cuAFnZ2cAwKpVqxAaGorbt2/DwsKiUp8xOzsb9vb2yMrKgp2dnUZ+bkTPqqKhUW/vcERGAmFhQGQkEB8PyOVAXFw1N5CISE9o8/vbYHrcyrt79y7Wr18Pf39/mJubAwDy8/NhZWWlVM/a2hrXr1/H1atXAQDx8fEICAhQqqNQKBAfHw8AKCgoQEJCglIdExMTBAQESHUSEhJQWFioVMfT0xPu7u5Snfj4eLRr104KbWXvk52djfPnz1f4ufLz85Gdna30INIH6kLbBx98gPDwcAQFlYa0oKDS8CaXl/5JRESaZ1DBLTQ0FLa2tqhXrx7S0tKwY8cO6ZhCocBPP/2E2NhYlJSU4NKlS1i8eDEA4ObNmwCA9PR0pTAFAM7OzsjOzsbDhw9x584dFBcXq62Tnp4uncPCwgIODg5PrKPuHGXHKjJ//nzY29tLDzc3t8r+aIi0oqCgoMIFdV1cXFTKy4c4IiLSPJ0Gt7CwMMhksic+kpKSpPpTp05FYmIi9u/fD1NTUwwbNgxlI72jRo3CuHHj0KdPH1hYWMDPzw+DBg0CUNprZgimT5+OrKws6XHt2jVdN4mMWEREBObPn69SzgkIRES6o9PlQCZPnozg4OAn1mnatKn0d0dHRzg6OqJly5bw8vKCm5sbjh49CrlcDplMhgULFmDevHlIT0+Hk5MTYmNjlc7h4uKiMvvz1q1bsLOzg7W1NUxNTWFqaqq2TlnvgouLCwoKCpCZmanU6/Z4ncdnopadU10vRRlLS0tYWlo+8edBVB3U9bJNnDhRulcjOhrSfW3sXSMiqj467YpycnKCp6fnEx8V3chfUlICoPS+sPJMTU3RsGFDWFhYYOPGjZDL5XBycgIAyOVyKcyVOXDggLS6u4WFBTp16qRUp6SkBLGxsVKdTp06wdzcXKlOcnIy0tLSpDpyuRx//vmn0kzUAwcOwM7ODq1bt36mnxVRdcjJyalwaLR8aBsypHQSQmRkdbeQiMi4GcQCvMeOHcOJEyfQpUsX1KlTB5cvX8asWbPQrFkzKSzduXMHP/74I1555RXk5eVhzZo10nIdZUaPHo3ly5dj2rRpGDlyJA4ePIgtW7Zg9+7dUp1JkyZh+PDh8PHxQefOnfHVV18hNzcXI0aMAADY29sjJCQEkyZNQt26dWFnZ4ePPvoIcrkcfn5+AEq3+mndujWGDh2KhQsXIj09HTNnzsTYsWPZo0Z6q7IL6kZGAjk5QK1anIRARFTthAE4e/as6N69u6hbt66wtLQUTZo0EaNHjxbXr1+X6ty+fVv4+fkJW1tbYWNjI3r06CGOHj2qcq5Dhw6JDh06CAsLC9G0aVOxZs0alTrLli0T7u7uwsLCQnTu3FnlPA8fPhQffvihqFOnjrCxsRFvvvmmuHnzplKd1NRU0bt3b2FtbS0cHR3F5MmTRWFhYZU+d1ZWlgAgsrKyqvQ6oqqaM2eOyiMvL09t3R07hJDLS/8kIiJV2vz+Nth13IwB13Ejbbt9+za++eYblXJOQCAienba/P42iKFSItI87jVKRGR4GNyIjJC60DZz5kyYmprqoDVERFRZhrHAGRFpxLVr1yqcNVoW2rjfKBGR/mKPG5GRqGhoNCYmHN7ej9ZjK9tvNDKSa7QREekb9rgRGQF1oW327NnYuDEc8fHAtGmPyrnfKBGR/mKPG1ENdunSJWzcuFGlvGwCgkwGpT+B0l429rQREeknBjeiGqoys0YXLHi0dRUREek/BjeiGqiiCQjlcb9RIiLDw3vciGqQhISESoU2QHkSAhERGQb2uBHVEFVdUDcsjMOkRESGhsGNqAaobC/b48OjHCIlIjIsDG5EBuy3337DwYMHVcor6mXjGm1ERIaNwY3IQKnrZXN0dMTYsWMrfA2HR4mIDBuDG5EBquzQ6OM4PEpEZNgY3IgMyM6dO3Hq1CmV8sqENiIiMnxcDoTIQERERKiEtnbt2iE8PJwbwxMRGQn2uBEZgKcNjXLSARGRcWBwI9Jj69atQ0pKikr540OjnHRARGQcGNyI9JS6XrauXbsiJ+dV+Psrb1XFSQdERMaBwY1IDz1paNTfn8OiRETGisGNSI8sWbIE9+/fVykvPzTKYVEiIuPF4EakJ9T1svXp0wedOnVSKuOwKBGR8WJwI9IDz7qgLhERGRcGNyId+vTTT1FSUqJSztBGRETqMLgR6Yi6XrYhQ4agefPmOmgNEREZAgY3omomhMDcuXNVytnLRkRET8PgRlSNPvvsMxQXF6uUl21bVTZblJMPiIhIHQY3omqibmg0Lu4DxMS4AOC2VURE9HTcZJ5Iy4QQakPbpk3huHrVRdoYPiwMkMu5PhsREVVMJoQQum4EqZednQ17e3tkZWXBzs5O182hZ6AusAGlQ6NlOyDI5UBcXDU3jIiItEab398cKiXSEnWhbcKECbC3twfAHRCIiKjqGNyINKyoqAiff/65Svnjs0a5AwIREVUVgxuRBj1paJSIiOh5MbgRaYi60DZt2jRYW1vroDVERFQTMbgRPaf8/HxERkaqlLOXjYiINI3Bjeg5cGiUiIiqE4Mb0TNSF9pmzJgBc3NzHbSGiIiMAYMbURXl5uZi0aJFKuXsZSMiIm1jcCOqAg6NEhGRLnHLK6JKUhfaZs2aBW/v0l0QyrauIiIi0hYGN6KnyMzMVBvawsPDYWJiorQ5PBERkTZxqJToCdQFNkvLeoiOHgdv79KdD7h1FRERVRduMq/HuMm8bqkLbbNnz8ZLL8m4OTwREVVIm9/fHColekxGRkaFQ6MymQxhYaWhjT1sRERU3ThUSlSOusDm4eGBYcOGSc+5OTwREekKgxvR/1dRLxsREZG+YHAjo3fjxg3873//UylnaCMiIn3D4EZGTV0vW/v27dGvX7/qbwwREdFTMLiR0VIX2ry9w3n/GhER6S3OKqUaLzoaSjsbXLlypcL72RjaiIhIn7HHjWq88jsbJCaqBraAgAC89NJLOmgZERFR1TC4UY1XtrOBQsFZo0REZNgY3KjG8/L6CwrFBpVyb2+GNiIiMiwMblQjRUdX3Mt2+vQAbN/eBnI5F9IlIiLDwuBGNdKThkajo4Fbt7hlFRERGR4GN6pxLl++DIXiB5XysvvZuGUVEREZKgY3qlHULfMxYsQIuLu766A1REREmsXgRjUG9xolIqKajsGNDF5SUhI2b96sUs7QRkRENQ2DGxk0db1sY8eOhaOjow5aQ0REpF0MbmSwODRKRETGhsGNDM6ZM2ewfft2lXKGNiIiqukY3MigqOtlmzBhAuzt7XXQGiIiourF4EYGg0OjRERk7BjcSO8dPXoUMTExSmW1atXC5MmTddQiIiIi3WBwI72mrpdtypQpsLW11UFriIiIdIvBjfSSEAJz585VKefQKBERGTMTXTegqvLz89GhQwfIZDKcPn1a6djZs2fRtWtXWFlZwc3NDQsXLlR5/datW+Hp6QkrKyu0a9cOe/bsUTouhMDs2bPh6uoKa2trBAQE4K+//lKqc/fuXQwZMgR2dnZwcHBASEgIcnJyqtwWUu/w4cMqoc3FxYWhjYiIjJ7BBbdp06ahQYMGKuXZ2dno2bMnGjdujISEBHzxxReYM2cO/vvf/0p14uLiMHjwYISEhCAxMRH9+vVDv379cO7cOanOwoULsXTpUqxatQrHjh2Dra0tFAoF8vLypDpDhgzB+fPnceDAAezatQu//vor3n///Sq1hdSLiIjAkSNHlMpCQ0PxwQcf6KhFRERE+kMmhBC6bkRl7d27F5MmTcK2bdvQpk0bJCYmokOHDgCAlStX4pNPPkF6ejosLCwAAGFhYdi+fTuSkpIAAAMHDkRubi527dolndPPzw8dOnTAqlWrIIRAgwYNMHnyZEyZMgUAkJWVBWdnZ0RFRWHQoEG4ePEiWrdujRMnTsDHxwcAsG/fPrz++uu4fv06GjRoUKm2VEZ2djbs7e2RlZUFOzu75/756bOSkhJ8+umnKuXsZSMiIkOjze9vg+lxu3XrFkaNGoXvv/8eNjY2Ksfj4+PRrVs3KSgBgEKhQHJyMu7duyfVCQgIUHqdQqFAfHw8ACAlJQXp6elKdezt7eHr6yvViY+Ph4ODgxTaACAgIAAmJiY4duxYpduiTn5+PrKzs5UexmDv3r0qoa1Zs2YMbURERI8xiMkJQggEBwdj9OjR8PHxQWpqqkqd9PR0eHh4KJU5OztLx+rUqYP09HSprHyd9PR0qV7511VUp379+krHzczMULduXaU6T2uLOvPnz1c7i7ImU/d5Z8yYAXNzcx20hoiISL/ptMctLCwMMpnsiY+kpCQsW7YM9+/fx/Tp03XZXK2bPn06srKypMe1a9d03SStKS4urnBBXYY2IiIi9XTa4zZ58mQEBwc/sU7Tpk1x8OBBxMfHw9LSUumYj48PhgwZgrVr18LFxQW3bt1SOl723MXFRfpTXZ3yx8vKXF1dleqU3Uvn4uKCjIwMpXMUFRXh7t27T32f8u+hjqWlpcpnrIm2bdumNCEEAF544QW8+eabOmoRERGRYdBpcHNycoKTk9NT6y1duhSfffaZ9PzGjRtQKBTYvHkzfH19AQByuRyffPIJCgsLpR6bAwcOoFWrVtLQpFwuR2xsLCZMmCCd68CBA5DL5QAADw8PuLi4IDY2Vgpq2dnZOHbsGMaMGSOdIzMzEwkJCejUqRMA4ODBgygpKalSW4yVul62mTNnwtTUVAetISIiMiwGMTnB3d0dbdu2lR4tW7YEUHoDe6NGjQAA77zzDiwsLBASEoLz589j8+bN+PrrrzFp0iTpPB9//DH27duHxYsXIykpCXPmzMHJkycxbtw4AIBMJsOECRPw2WefITo6Gn/++SeGDRuGBg0aoF+/fgAALy8v9OrVC6NGjcLx48fxxx9/YNy4cRg0aJC0TEll2mJsCgsLKxwaZWgjIiKqHIOYnFAZ9vb22L9/P8aOHYtOnTrB0dERs2fPVlpfzd/fHxs2bMDMmTMxY8YMtGjRAtu3b0fbtm2lOtOmTUNubi7ef/99ZGZmokuXLti3bx+srKykOuvXr8e4cePQo0cPmJiYoH///li6dGmV2lJTREcDkZFAWBgQFKS+zrp165CSkqJU5ufnB4VCUQ0tJCIiqjkMah03Y2MI67j5+wPx8YBcDsTFqR5X18s2e/ZsyGSyamgdERFR9eM6bqRT0dGlAS06WvVYWFhpaAsLUy7Py8urcGiUoY2IiOjZsMdNj+lLj9vTetUet3LlSpWZt6+88gpefvllLbWQiIhIf2jz+7vG3ONG2hMW9ug+tqfh0CgREZH2cKiUKlQ2RAqU9rRVNPkAAHJycjg0SkREpGXscaMKRUaWDpFGRj45tH3xxRd48OCBUlnv3r3RuXNnLbeQiIjIuDC4UYUqM0RaUS8bERERaR6DG1UoKKjinrb79+9jyZIlKuUMbURERNrD4EZVtmrVKpW9WN9880288MILOmoRERGRceDkBHriOm2Pi4iIUAlt4eHhDG1ERETVgMGNlCYhVOTevXu8n42IiEjHOFRKT52EsGTJEty/f1+pLDg4GI0bN66G1hEREVEZ9rgZoceHRoOCKl6nLSIiQiW0hYeHM7QRERHpAIObEarM0Ghubi6HRomIiPQMh0qN0NOGRg8fPowjR44olY0dOxaOjo7V0DoiIiKqCIObEXrS+mzsZSMiItJfDG4EQP2Cuq+88gpefvllHbWIiIiIHsfgRjhw4ADi4uKUyqZMmQJbW1sdtYiIiIjUYXAzclu3bsWFCxeUyjg0SkREpJ8Y3IxUXl4eFixYoFTWs2dPyOVyHbWIiIiInobBzQhdunQJGzduVCqbMWMGzM3NddQiIiIiqgwGNyNUPrT5+flBoVDosDVERERUWQxuRmzUqFFo0KCBrptBRERElSQTQghdN4LUy87Ohr29PbKysmBnZ6fr5hAREVElaPP7m1teERERERkIBjciIiIiA8HgRkRERGQgGNyIiIiIDASDGxEREZGBYHAjIiIiMhAMbkREREQGgsGNiIiIyEAwuBEREREZCAY3IiIiIgPB4EZERERkIBjciIiIiAwEgxsRERGRgWBwIyIiIjIQZrpuAFVMCAEAyM7O1nFLiIiIqLLKvrfLvsc1icFNj92/fx8A4ObmpuOWEBERUVXdv38f9vb2Gj2nTGgjDpJGlJSU4MaNG6hduzZkMpmum6N12dnZcHNzw7Vr12BnZ6fr5tBjeH30G6+PfuP10W+avj5CCNy/fx8NGjSAiYlm70pjj5seMzExQaNGjXTdjGpnZ2fHX2x6jNdHv/H66DdeH/2myeuj6Z62MpycQERERGQgGNyIiIiIDASDG+kNS0tLhIeHw9LSUtdNITV4ffQbr49+4/XRb4Z0fTg5gYiIiMhAsMeNiIiIyEAwuBEREREZCAY3IiIiIgPB4EZERERkIBjcSCPy8/PRoUMHyGQynD59WunY2bNn0bVrV1hZWcHNzQ0LFy5Uef3WrVvh6ekJKysrtGvXDnv27FE6LoTA7Nmz4erqCmtrawQEBOCvv/5SqnP37l0MGTIEdnZ2cHBwQEhICHJycqrclpokKCgI7u7usLKygqurK4YOHYobN24o1dmyZQs6dOgAGxsbNG7cGF988YXKeQ4fPoyOHTvC0tISzZs3R1RUlEqdFStWoEmTJrCysoKvry+OHz+udDwvLw9jx45FvXr1UKtWLfTv3x+3bt1SqpOWlobAwEDY2Nigfv36mDp1KoqKip7/B6GnKnN9YmJi4Ofnh9q1a8PJyQn9+/dHamqqUh1eH+152jWaM2cOZDKZysPW1lbpPPwdpx2V+TckhMCiRYvQsmVLWFpaomHDhvj888+V6hjUvyFBpAHjx48XvXv3FgBEYmKiVJ6VlSWcnZ3FkCFDxLlz58TGjRuFtbW1+L//+z+pzh9//CFMTU3FwoULxYULF8TMmTOFubm5+PPPP6U6kZGRwt7eXmzfvl2cOXNGBAUFCQ8PD/Hw4UOpTq9evUT79u3F0aNHxW+//SaaN28uBg8eXKW21DRLliwR8fHxIjU1Vfzxxx9CLpcLuVwuHd+zZ48wMzMTK1euFJcvXxa7du0Srq6uYtmyZVKdK1euCBsbGzFp0iRx4cIFsWzZMmFqair27dsn1dm0aZOwsLAQ3333nTh//rwYNWqUcHBwELdu3ZLqjB49Wri5uYnY2Fhx8uRJ4efnJ/z9/aXjRUVFom3btiIgIEAkJiaKPXv2CEdHRzF9+nQt/5R052nX58qVK8LS0lJMnz5d/P333yIhIUF069ZNeHt7K9Xh9dGep12j+/fvi5s3byo9WrduLYYPHy7V4e847Xna9RFCiI8++ki0atVK7NixQ1y5ckWcPHlS7N+/XzpuaP+GGNzoue3Zs0d4enqK8+fPqwS3b775RtSpU0fk5+dLZaGhoaJVq1bS87ffflsEBgYqndPX11d88MEHQgghSkpKhIuLi/jiiy+k45mZmcLS0lJs3LhRCCHEhQsXBABx4sQJqc7evXuFTCYT//zzT6XbUtPt2LFDyGQyUVBQIIQQYvDgwWLAgAFKdZYuXSoaNWokSkpKhBBCTJs2TbRp00apzsCBA4VCoZCed+7cWYwdO1Z6XlxcLBo0aCDmz58vhCi9Xubm5mLr1q1SnYsXLwoAIj4+XghR+t+RiYmJSE9Pl+qsXLlS2NnZKV2zmuzx67N161ZhZmYmiouLpTrR0dFKdXh9qtfj1+hxp0+fFgDEr7/+KpXxd1z1efz6XLhwQZiZmYmkpKQKX2No/4Y4VErP5datWxg1ahS+//572NjYqByPj49Ht27dYGFhIZUpFAokJyfj3r17Up2AgACl1ykUCsTHxwMAUlJSkJ6erlTH3t4evr6+Up34+Hg4ODjAx8dHqhMQEAATExMcO3as0m2pye7evYv169fD398f5ubmAEqHuK2srJTqWVtb4/r167h69SqAp1+fgoICJCQkKNUxMTFBQECAVCchIQGFhYVKdTw9PeHu7q50Ddu1awdnZ2el98nOzsb58+c19WPQW+quT6dOnWBiYoI1a9aguLgYWVlZ+P777xEQECDV4fWpPuqu0eNWr16Nli1bomvXrlIZf8dVD3XXZ+fOnWjatCl27doFDw8PNGnSBO+99x7u3r0rvc7Q/g0xuNEzE0IgODgYo0ePVvplUl56errSf6QApOfp6elPrFP+ePnXVVSnfv36SsfNzMxQt27dp75P+feoiUJDQ2Fra4t69eohLS0NO3bskI4pFAr89NNPiI2NRUlJCS5duoTFixcDAG7evAmg4p9bdnY2Hj58iDt37qC4uPip18fCwgIODg5PrMPro3x9PDw8sH//fsyYMQOWlpZwcHDA9evXsWXLFqkOr4/2PekalZeXl4f169cjJCREqZy/47TrSdfnypUruHr1KrZu3Yp169YhKioKCQkJGDBggFTH0P4NMbiRirCwMLU325Z/JCUlYdmyZbh//z6mT5+u6yYblcpenzJTp05FYmIi9u/fD1NTUwwbNgzi/2+YMmrUKIwbNw59+vSBhYUF/Pz8MGjQIACl/0dJVafJ65Oeno5Ro0Zh+PDhOHHiBI4cOQILCwsMGDBAqkNVp8lrVN7PP/+M+/fvY/jw4dX5cWocTV6fkpIS5OfnY926dejatSteeeUVfPvttzh06BCSk5N19RGfi5muG0D6Z/LkyQgODn5inaZNm+LgwYOIj49X2dvNx8cHQ4YMwdq1a+Hi4qIyq6bsuYuLi/Snujrlj5eVubq6KtXp0KGDVCcjI0PpHEVFRbh79+5T36f8exiCyl6fMo6OjnB0dETLli3h5eUFNzc3HD16FHK5HDKZDAsWLMC8efOQnp4OJycnxMbGKp2jop+bnZ0drK2tYWpqClNT06dew4KCAmRmZir9H+njdR6fpWXs12fFihWwt7dXmhn4ww8/wM3NDceOHYOfnx+vzzPQ5DUqb/Xq1ejTp49Krwp/x1WNJq+Pq6srzMzM0LJlS6m+l5cXgNIZnq1atTK8f0OVvhuO6DFXr14Vf/75p/SIiYkRAMSPP/4orl27JoR4dLNs+Rt5p0+frjI5oU+fPkrnlsvlKjfuLlq0SDqelZWl9sbdkydPSnViYmLU3rj7pLbUdFevXhUAxKFDhyqsM3ToUKVZWdOmTRNt27ZVqjN48GCVG3fHjRsnPS8uLhYNGzZUuXH3xx9/lOokJSWpvXG3/Cyt//u//xN2dnYiLy/v2T6wgXn8+kyaNEl07txZqc6NGzcEAPHHH38IIXh9qltF/4auXLkiZDKZ2Llzp8pr+Duu+jx+fcq+l/7++2+pTtkEkuTkZCGE4f0bYnAjjUlJSVGZVZqZmSmcnZ3F0KFDxblz58SmTZuEjY2NynIgZmZmYtGiReLixYsiPDxc7VR5BwcHsWPHDnH27FnRt29ftVPlvb29xbFjx8Tvv/8uWrRooTRVvjJtqUmOHj0qli1bJhITE0VqaqqIjY0V/v7+olmzZtIvidu3b4uVK1eKixcvisTERDF+/HhhZWUljh07Jp2nbKr81KlTxcWLF8WKFSvUTpW3tLQUUVFR4sKFC+L9998XDg4OSrOnRo8eLdzd3cXBgwfFyZMnVabtl02V79mzpzh9+rTYt2+fcHJyqrHLTVTm+sTGxgqZTCYiIiLEpUuXREJCglAoFKJx48biwYMHQgheH22qzDUqM3PmTNGgQQNRVFSkch7+jtOOylyf4uJi0bFjR9GtWzdx6tQpcfLkSeHr6ytee+016TyG9m+IwY00Rl1wE0KIM2fOiC5dughLS0vRsGFDERkZqfLaLVu2iJYtWwoLCwvRpk0bsXv3bqXjJSUlYtasWcLZ2VlYWlqKHj16SP+3VObff/8VgwcPFrVq1RJ2dnZixIgR4v79+1VuS01x9uxZ0b17d1G3bl1haWkpmjRpIkaPHi2uX78u1bl9+7bw8/MTtra2wsbGRvTo0UMcPXpU5VyHDh0SHTp0EBYWFqJp06ZizZo1KnWWLVsm3N3dhYWFhejcubPKeR4+fCg+/PBDUadOHWFjYyPefPNNcfPmTaU6qamponfv3sLa2lo4OjqKyZMni8LCQs38QPRMZa6PEEJs3LhReHt7C1tbW+Hk5CSCgoLExYsXlerw+mhHZa9RcXGxaNSokZgxY0aF5+LvOM2r7PX5559/xFtvvSVq1aolnJ2dRXBwsPj333+V6hjSvyGZELzDlYiIiMgQcNoYERERkYFgcCMiIiIyEAxuRERERAaCwY2IiIjIQDC4ERERERkIBjciIiIiA8HgRkRERGQgGNyIiLRAJpNh+/btum6GksOHD0MmkyEzM1PXTSGiZ8TgRkT0HObMmSNtBE5EpG0MbkREREQGgsGNiIxaSUkJ5s+fDw8PD1hbW6N9+/b48ccfATwaWoyNjYWPjw9sbGzg7++P5ORkAEBUVBQiIiJw5swZyGQyyGQyREVFSee+c+cO3nzzTdjY2KBFixaIjo6uVJvK3jcmJgbe3t6wtrbGq6++ioyMDOzduxdeXl6ws7PDO++8gwcPHkivy8/Px/jx41G/fn1YWVmhS5cuOHHihOZ+WESkcwxuRGTU5s+fj3Xr1mHVqlU4f/48Jk6ciHfffRdHjhyR6nzyySdYvHgxTp48CTMzM4wcORIAMHDgQEyePBlt2rTBzZs3cfPmTQwcOFB6XUREBN5++22cPXsWr7/+OoYMGYK7d+9Wum1z5szB8uXLERcXh2vXruHtt9/GV199hQ0bNmD37t3Yv38/li1bJtWfNm0atm3bhrVr1+LUqVNo3rw5FApFld6TiPRclbakJyKqQfLy8oSNjY2Ii4tTKg8JCRGDBw8Whw4dEgDEL7/8Ih3bvXu3ACAePnwohBAiPDxctG/fXuXcAMTMmTOl5zk5OQKA2Lt371Pbpe5958+fLwCIy5cvS2UffPCBUCgU0vnNzc3F+vXrpeMFBQWiQYMGYuHChUrnvXfv3lPbQET6yUyHmZGISKf+/vtvPHjwAK+99ppSeUFBAby9vaXnL7zwgvR3V1dXAEBGRgbc3d2feP7yr7O1tYWdnR0yMjIq3b7yr3d2doaNjQ2aNm2qVHb8+HEAwOXLl1FYWIiXXnpJOm5ubo7OnTvj4sWLlX5PItJvDG5EZLRycnIAALt370bDhg2VjllaWuLy5csASgNQGZlMBqD03rinKf+6stdW5nXqXi+TyZ77fERk+HiPGxEZrdatW8PS0hJpaWlo3ry50sPNza1S57CwsEBxcbGWW/p0zZo1g4WFBf744w+prLCwECdOnEDr1q112DIi0iT2uBGR0apduzamTJmCiRMnoqSkBF26dEFWVhb++OMP2NnZoXHjxk89R5MmTZCSkoLTp0+jUaNGqF27NiwtLauh9cpsbW0xZswYTJ06FXXr1oW7uzsWLlyIBw8eICQkpNrbQ0TaweBGREbt008/hZOTE+bPn48rV67AwcEBHTt2xIwZMyo1DNm/f3/89NNP6N69OzIzM7FmzRoEBwdrv+FqREZGoqSkBEOHDsX9+/fh4+ODmJgY1KlTRyftISLNkwkhhK4bQURERERPx3vciIiIiAwEgxsRUTUbPXo0atWqpfYxevRoXTePiPQYh0qJiKpZRkYGsrOz1R6zs7ND/fr1q7lFRGQoGNyIiIiIDASHSomIiIgMBIMbERERkYFgcCMiIiIyEAxuRERERAaCwY2IiIjIQDC4ERERERkIBjciIiIiA8HgRkRERGQg/h8Bu7sJt+ciGQAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(keras_surrogate, data_validation)\n", + "surrogate_parity(keras_surrogate, data_validation)\n", + "surrogate_residual(keras_surrogate, data_validation)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_keras_surrogate_embedding_doc.md](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb_doc.md) file." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_test.ipynb new file mode 100644 index 00000000..20ceca57 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_test.ipynb @@ -0,0 +1,1078 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part -1)\n", + "\n", + "## 1. Introduction\n", + "This notebook illustrates the use of KerasSurrogate API leveraging TensorFlow Keras and OMLT package to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "### 1.1 Need for ML Surrogates\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebric, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebric equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train a tanh model from our data and then demonstrate that we can solve an optimization problem with that surrogate model. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random as rn\n", + "import tensorflow as tf\n", + "import tensorflow.keras as keras\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")\n", + "\n", + "# fix environment variables to ensure consist neural network training\n", + "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "np.random.seed(46)\n", + "rn.seed(1342)\n", + "tf.random.set_seed(62)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsquent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "csv_data.columns.values[0:6] =[\"pressure\", \"temperature\",\"enth_mol\",\"entr_mol\",\"CO2_enthalpy\",\"CO2_entropy\"]\n", + "data = csv_data.sample(n=500)\n", + "\n", + "# Creating input_data and output_data from data\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:,2:4]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns \n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogate with TensorFlow Keras\n", + "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", + "\n", + "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", + "\n", + "* Activation function: sigmoid, **tanh**\n", + "* Optimizer: **Adam**\n", + "* Number of hidden layers: 3, **4**, 5, 6\n", + "* Number of neurons per layer: **20**, 40, 60\n", + "\n", + "Important thing to note here is that we do not use ReLU activation function for the training as the flowsheet we intend to solve with this surrogate model is a NLP problem and using ReLU activation function will make it an MINLP. Another thing to note here is the network is smaller (4,20) in order to avoid overfitting. \n", + "\n", + "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", + "\n", + "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/250\n", + "13/13 - 2s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 2s/epoch - 173ms/step\n", + "Epoch 2/250\n", + "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 220ms/epoch - 17ms/step\n", + "Epoch 3/250\n", + "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 228ms/epoch - 18ms/step\n", + "Epoch 4/250\n", + "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 239ms/epoch - 18ms/step\n", + "Epoch 5/250\n", + "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 229ms/epoch - 18ms/step\n", + "Epoch 6/250\n", + "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 202ms/epoch - 16ms/step\n", + "Epoch 7/250\n", + "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 241ms/epoch - 19ms/step\n", + "Epoch 8/250\n", + "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 233ms/epoch - 18ms/step\n", + "Epoch 9/250\n", + "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 227ms/epoch - 17ms/step\n", + "Epoch 10/250\n", + "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 240ms/epoch - 18ms/step\n", + "Epoch 11/250\n", + "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 224ms/epoch - 17ms/step\n", + "Epoch 12/250\n", + "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 227ms/epoch - 17ms/step\n", + "Epoch 13/250\n", + "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 197ms/epoch - 15ms/step\n", + "Epoch 14/250\n", + "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 234ms/epoch - 18ms/step\n", + "Epoch 15/250\n", + "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 207ms/epoch - 16ms/step\n", + "Epoch 16/250\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 215ms/epoch - 17ms/step\n", + "Epoch 17/250\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 227ms/epoch - 17ms/step\n", + "Epoch 18/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 234ms/epoch - 18ms/step\n", + "Epoch 19/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 111ms/epoch - 9ms/step\n", + "Epoch 20/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 246ms/epoch - 19ms/step\n", + "Epoch 21/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 172ms/epoch - 13ms/step\n", + "Epoch 22/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 209ms/epoch - 16ms/step\n", + "Epoch 23/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", + "Epoch 24/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 219ms/epoch - 17ms/step\n", + "Epoch 25/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 212ms/epoch - 16ms/step\n", + "Epoch 26/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 220ms/epoch - 17ms/step\n", + "Epoch 27/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 224ms/epoch - 17ms/step\n", + "Epoch 28/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", + "Epoch 29/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 112ms/epoch - 9ms/step\n", + "Epoch 30/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 223ms/epoch - 17ms/step\n", + "Epoch 31/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 219ms/epoch - 17ms/step\n", + "Epoch 32/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 228ms/epoch - 18ms/step\n", + "Epoch 33/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 112ms/epoch - 9ms/step\n", + "Epoch 34/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 244ms/epoch - 19ms/step\n", + "Epoch 35/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 202ms/epoch - 16ms/step\n", + "Epoch 36/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 224ms/epoch - 17ms/step\n", + "Epoch 37/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 114ms/epoch - 9ms/step\n", + "Epoch 38/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 231ms/epoch - 18ms/step\n", + "Epoch 39/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 107ms/epoch - 8ms/step\n", + "Epoch 40/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 219ms/epoch - 17ms/step\n", + "Epoch 41/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 42/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 100ms/epoch - 8ms/step\n", + "Epoch 43/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0238 - mse: 0.0011 - val_loss: 9.4863e-04 - val_mae: 0.0232 - val_mse: 9.4863e-04 - 225ms/epoch - 17ms/step\n", + "Epoch 44/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0236 - mse: 0.0011 - val_loss: 9.8018e-04 - val_mae: 0.0230 - val_mse: 9.8018e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 45/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0239 - mse: 0.0011 - val_loss: 9.5093e-04 - val_mae: 0.0233 - val_mse: 9.5093e-04 - 121ms/epoch - 9ms/step\n", + "Epoch 46/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.4785e-04 - val_mae: 0.0223 - val_mse: 9.4785e-04 - 234ms/epoch - 18ms/step\n", + "Epoch 47/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 48/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 221ms/epoch - 17ms/step\n", + "Epoch 49/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.2521e-04 - val_mae: 0.0218 - val_mse: 9.2521e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 50/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7818e-04 - val_mae: 0.0231 - val_mse: 9.7818e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 51/250\n", + "13/13 - 0s - loss: 9.9977e-04 - mae: 0.0232 - mse: 9.9977e-04 - val_loss: 9.4350e-04 - val_mae: 0.0221 - val_mse: 9.4350e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 52/250\n", + "13/13 - 0s - loss: 9.8599e-04 - mae: 0.0229 - mse: 9.8599e-04 - val_loss: 9.0638e-04 - val_mae: 0.0230 - val_mse: 9.0638e-04 - 219ms/epoch - 17ms/step\n", + "Epoch 53/250\n", + "13/13 - 0s - loss: 9.8295e-04 - mae: 0.0228 - mse: 9.8295e-04 - val_loss: 9.0667e-04 - val_mae: 0.0215 - val_mse: 9.0667e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 54/250\n", + "13/13 - 0s - loss: 9.7266e-04 - mae: 0.0225 - mse: 9.7266e-04 - val_loss: 9.0391e-04 - val_mae: 0.0224 - val_mse: 9.0391e-04 - 208ms/epoch - 16ms/step\n", + "Epoch 55/250\n", + "13/13 - 0s - loss: 9.5234e-04 - mae: 0.0225 - mse: 9.5234e-04 - val_loss: 8.7426e-04 - val_mae: 0.0219 - val_mse: 8.7426e-04 - 223ms/epoch - 17ms/step\n", + "Epoch 56/250\n", + "13/13 - 0s - loss: 9.4315e-04 - mae: 0.0221 - mse: 9.4315e-04 - val_loss: 8.6742e-04 - val_mae: 0.0224 - val_mse: 8.6742e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 57/250\n", + "13/13 - 0s - loss: 9.9226e-04 - mae: 0.0230 - mse: 9.9226e-04 - val_loss: 8.7793e-04 - val_mae: 0.0225 - val_mse: 8.7793e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 58/250\n", + "13/13 - 0s - loss: 9.4137e-04 - mae: 0.0226 - mse: 9.4137e-04 - val_loss: 8.7477e-04 - val_mae: 0.0225 - val_mse: 8.7477e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 59/250\n", + "13/13 - 0s - loss: 9.2474e-04 - mae: 0.0219 - mse: 9.2474e-04 - val_loss: 8.5320e-04 - val_mae: 0.0212 - val_mse: 8.5320e-04 - 195ms/epoch - 15ms/step\n", + "Epoch 60/250\n", + "13/13 - 0s - loss: 9.1133e-04 - mae: 0.0217 - mse: 9.1133e-04 - val_loss: 8.6082e-04 - val_mae: 0.0217 - val_mse: 8.6082e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 61/250\n", + "13/13 - 0s - loss: 9.1801e-04 - mae: 0.0217 - mse: 9.1801e-04 - val_loss: 8.5403e-04 - val_mae: 0.0223 - val_mse: 8.5403e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 62/250\n", + "13/13 - 0s - loss: 9.1987e-04 - mae: 0.0221 - mse: 9.1987e-04 - val_loss: 8.5714e-04 - val_mae: 0.0219 - val_mse: 8.5714e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 63/250\n", + "13/13 - 0s - loss: 9.0862e-04 - mae: 0.0222 - mse: 9.0862e-04 - val_loss: 8.6160e-04 - val_mae: 0.0225 - val_mse: 8.6160e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 64/250\n", + "13/13 - 0s - loss: 8.9349e-04 - mae: 0.0220 - mse: 8.9349e-04 - val_loss: 8.2851e-04 - val_mae: 0.0214 - val_mse: 8.2851e-04 - 224ms/epoch - 17ms/step\n", + "Epoch 65/250\n", + "13/13 - 0s - loss: 8.7848e-04 - mae: 0.0216 - mse: 8.7848e-04 - val_loss: 8.5189e-04 - val_mae: 0.0218 - val_mse: 8.5189e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 66/250\n", + "13/13 - 0s - loss: 8.9773e-04 - mae: 0.0219 - mse: 8.9773e-04 - val_loss: 8.5650e-04 - val_mae: 0.0211 - val_mse: 8.5650e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 67/250\n", + "13/13 - 0s - loss: 8.7443e-04 - mae: 0.0217 - mse: 8.7443e-04 - val_loss: 8.2545e-04 - val_mae: 0.0214 - val_mse: 8.2545e-04 - 221ms/epoch - 17ms/step\n", + "Epoch 68/250\n", + "13/13 - 0s - loss: 8.9141e-04 - mae: 0.0217 - mse: 8.9141e-04 - val_loss: 8.4471e-04 - val_mae: 0.0219 - val_mse: 8.4471e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 69/250\n", + "13/13 - 0s - loss: 8.9507e-04 - mae: 0.0224 - mse: 8.9507e-04 - val_loss: 8.7916e-04 - val_mae: 0.0217 - val_mse: 8.7916e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 70/250\n", + "13/13 - 0s - loss: 8.5737e-04 - mae: 0.0216 - mse: 8.5737e-04 - val_loss: 8.8807e-04 - val_mae: 0.0215 - val_mse: 8.8807e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 71/250\n", + "13/13 - 0s - loss: 8.5560e-04 - mae: 0.0214 - mse: 8.5560e-04 - val_loss: 8.3750e-04 - val_mae: 0.0213 - val_mse: 8.3750e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 72/250\n", + "13/13 - 0s - loss: 8.5576e-04 - mae: 0.0218 - mse: 8.5576e-04 - val_loss: 8.1156e-04 - val_mae: 0.0210 - val_mse: 8.1156e-04 - 211ms/epoch - 16ms/step\n", + "Epoch 73/250\n", + "13/13 - 0s - loss: 8.4688e-04 - mae: 0.0216 - mse: 8.4688e-04 - val_loss: 8.0221e-04 - val_mae: 0.0210 - val_mse: 8.0221e-04 - 216ms/epoch - 17ms/step\n", + "Epoch 74/250\n", + "13/13 - 0s - loss: 8.3636e-04 - mae: 0.0211 - mse: 8.3636e-04 - val_loss: 7.9384e-04 - val_mae: 0.0208 - val_mse: 7.9384e-04 - 219ms/epoch - 17ms/step\n", + "Epoch 75/250\n", + "13/13 - 0s - loss: 8.4758e-04 - mae: 0.0222 - mse: 8.4758e-04 - val_loss: 8.2932e-04 - val_mae: 0.0212 - val_mse: 8.2932e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 76/250\n", + "13/13 - 0s - loss: 8.4142e-04 - mae: 0.0213 - mse: 8.4142e-04 - val_loss: 8.0552e-04 - val_mae: 0.0209 - val_mse: 8.0552e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 77/250\n", + "13/13 - 0s - loss: 8.5035e-04 - mae: 0.0215 - mse: 8.5035e-04 - val_loss: 8.6014e-04 - val_mae: 0.0215 - val_mse: 8.6014e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 78/250\n", + "13/13 - 0s - loss: 8.9015e-04 - mae: 0.0228 - mse: 8.9015e-04 - val_loss: 9.2548e-04 - val_mae: 0.0225 - val_mse: 9.2548e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 79/250\n", + "13/13 - 0s - loss: 8.1577e-04 - mae: 0.0212 - mse: 8.1577e-04 - val_loss: 8.4703e-04 - val_mae: 0.0211 - val_mse: 8.4703e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 80/250\n", + "13/13 - 0s - loss: 8.0555e-04 - mae: 0.0211 - mse: 8.0555e-04 - val_loss: 8.5652e-04 - val_mae: 0.0214 - val_mse: 8.5652e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 81/250\n", + "13/13 - 0s - loss: 8.3478e-04 - mae: 0.0219 - mse: 8.3478e-04 - val_loss: 9.1057e-04 - val_mae: 0.0222 - val_mse: 9.1057e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 82/250\n", + "13/13 - 0s - loss: 8.2593e-04 - mae: 0.0217 - mse: 8.2593e-04 - val_loss: 8.1172e-04 - val_mae: 0.0209 - val_mse: 8.1172e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 83/250\n", + "13/13 - 0s - loss: 8.2887e-04 - mae: 0.0213 - mse: 8.2887e-04 - val_loss: 8.2033e-04 - val_mae: 0.0211 - val_mse: 8.2033e-04 - 165ms/epoch - 13ms/step\n", + "Epoch 84/250\n", + "13/13 - 0s - loss: 8.1454e-04 - mae: 0.0219 - mse: 8.1454e-04 - val_loss: 8.1589e-04 - val_mae: 0.0211 - val_mse: 8.1589e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 85/250\n", + "13/13 - 0s - loss: 8.0777e-04 - mae: 0.0212 - mse: 8.0777e-04 - val_loss: 7.8637e-04 - val_mae: 0.0208 - val_mse: 7.8637e-04 - 177ms/epoch - 14ms/step\n", + "Epoch 86/250\n", + "13/13 - 0s - loss: 7.8107e-04 - mae: 0.0213 - mse: 7.8107e-04 - val_loss: 7.8138e-04 - val_mae: 0.0212 - val_mse: 7.8138e-04 - 223ms/epoch - 17ms/step\n", + "Epoch 87/250\n", + "13/13 - 0s - loss: 7.9729e-04 - mae: 0.0210 - mse: 7.9729e-04 - val_loss: 7.3667e-04 - val_mae: 0.0204 - val_mse: 7.3667e-04 - 237ms/epoch - 18ms/step\n", + "Epoch 88/250\n", + "13/13 - 0s - loss: 7.5931e-04 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8ms/step\n", + "Epoch 94/250\n", + "13/13 - 0s - loss: 7.4802e-04 - mae: 0.0205 - mse: 7.4802e-04 - val_loss: 7.2386e-04 - val_mae: 0.0201 - val_mse: 7.2386e-04 - 190ms/epoch - 15ms/step\n", + "Epoch 95/250\n", + "13/13 - 0s - loss: 7.2616e-04 - mae: 0.0203 - mse: 7.2616e-04 - val_loss: 7.2728e-04 - val_mae: 0.0204 - val_mse: 7.2728e-04 - 121ms/epoch - 9ms/step\n", + "Epoch 96/250\n", + "13/13 - 0s - loss: 7.2310e-04 - mae: 0.0204 - mse: 7.2310e-04 - val_loss: 7.1349e-04 - val_mae: 0.0206 - val_mse: 7.1349e-04 - 219ms/epoch - 17ms/step\n", + "Epoch 97/250\n", + "13/13 - 0s - loss: 7.0905e-04 - mae: 0.0201 - mse: 7.0905e-04 - val_loss: 7.6242e-04 - val_mae: 0.0205 - val_mse: 7.6242e-04 - 117ms/epoch - 9ms/step\n", + "Epoch 98/250\n", + "13/13 - 0s - loss: 7.1839e-04 - mae: 0.0200 - mse: 7.1839e-04 - val_loss: 7.7098e-04 - val_mae: 0.0202 - val_mse: 7.7098e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 99/250\n", + "13/13 - 0s - loss: 7.3924e-04 - mae: 0.0208 - mse: 7.3924e-04 - val_loss: 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val_mse: 6.8310e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 111/250\n", + "13/13 - 0s - loss: 6.7136e-04 - mae: 0.0197 - mse: 6.7136e-04 - val_loss: 6.5858e-04 - val_mae: 0.0199 - val_mse: 6.5858e-04 - 224ms/epoch - 17ms/step\n", + "Epoch 112/250\n", + "13/13 - 0s - loss: 6.5784e-04 - mae: 0.0195 - mse: 6.5784e-04 - val_loss: 6.5838e-04 - val_mae: 0.0196 - val_mse: 6.5838e-04 - 234ms/epoch - 18ms/step\n", + "Epoch 113/250\n", + "13/13 - 0s - loss: 6.6861e-04 - mae: 0.0198 - mse: 6.6861e-04 - val_loss: 6.9871e-04 - val_mae: 0.0196 - val_mse: 6.9871e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 114/250\n", + "13/13 - 0s - loss: 6.6345e-04 - mae: 0.0196 - mse: 6.6345e-04 - val_loss: 6.8190e-04 - val_mae: 0.0196 - val_mse: 6.8190e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 115/250\n", + "13/13 - 0s - loss: 6.4121e-04 - mae: 0.0193 - mse: 6.4121e-04 - val_loss: 6.6493e-04 - val_mae: 0.0196 - val_mse: 6.6493e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 116/250\n", + "13/13 - 0s - loss: 6.5036e-04 - 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8ms/step\n", + "Epoch 122/250\n", + "13/13 - 0s - loss: 6.4650e-04 - mae: 0.0196 - mse: 6.4650e-04 - val_loss: 8.0733e-04 - val_mae: 0.0210 - val_mse: 8.0733e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 123/250\n", + "13/13 - 0s - loss: 6.5887e-04 - mae: 0.0198 - mse: 6.5887e-04 - val_loss: 6.2666e-04 - val_mae: 0.0191 - val_mse: 6.2666e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 124/250\n", + "13/13 - 0s - loss: 6.1387e-04 - mae: 0.0189 - mse: 6.1387e-04 - val_loss: 6.1020e-04 - val_mae: 0.0188 - val_mse: 6.1020e-04 - 210ms/epoch - 16ms/step\n", + "Epoch 125/250\n", + "13/13 - 0s - loss: 6.1348e-04 - mae: 0.0191 - mse: 6.1348e-04 - val_loss: 6.1093e-04 - val_mae: 0.0193 - val_mse: 6.1093e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 126/250\n", + "13/13 - 0s - loss: 6.1374e-04 - mae: 0.0189 - mse: 6.1374e-04 - val_loss: 6.1062e-04 - val_mae: 0.0188 - val_mse: 6.1062e-04 - 116ms/epoch - 9ms/step\n", + "Epoch 127/250\n", + "13/13 - 0s - loss: 6.1279e-04 - mae: 0.0190 - mse: 6.1279e-04 - val_loss: 6.4391e-04 - val_mae: 0.0190 - val_mse: 6.4391e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 128/250\n", + "13/13 - 0s - loss: 6.0951e-04 - mae: 0.0189 - mse: 6.0951e-04 - val_loss: 5.9592e-04 - val_mae: 0.0188 - val_mse: 5.9592e-04 - 228ms/epoch - 18ms/step\n", + "Epoch 129/250\n", + "13/13 - 0s - loss: 6.2194e-04 - mae: 0.0192 - mse: 6.2194e-04 - val_loss: 5.9344e-04 - val_mae: 0.0188 - val_mse: 5.9344e-04 - 180ms/epoch - 14ms/step\n", + "Epoch 130/250\n", + "13/13 - 0s - loss: 6.1795e-04 - mae: 0.0191 - mse: 6.1795e-04 - val_loss: 5.8880e-04 - val_mae: 0.0188 - val_mse: 5.8880e-04 - 218ms/epoch - 17ms/step\n", + "Epoch 131/250\n", + "13/13 - 0s - loss: 6.6297e-04 - mae: 0.0199 - mse: 6.6297e-04 - val_loss: 7.2306e-04 - val_mae: 0.0197 - val_mse: 7.2306e-04 - 117ms/epoch - 9ms/step\n", + "Epoch 132/250\n", + "13/13 - 0s - loss: 5.8788e-04 - mae: 0.0189 - mse: 5.8788e-04 - val_loss: 6.0686e-04 - val_mae: 0.0189 - val_mse: 6.0686e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 133/250\n", + "13/13 - 0s - loss: 5.7425e-04 - mae: 0.0184 - mse: 5.7425e-04 - val_loss: 5.7895e-04 - val_mae: 0.0183 - val_mse: 5.7895e-04 - 218ms/epoch - 17ms/step\n", + "Epoch 134/250\n", + "13/13 - 0s - loss: 5.8783e-04 - mae: 0.0186 - mse: 5.8783e-04 - val_loss: 5.7846e-04 - val_mae: 0.0188 - val_mse: 5.7846e-04 - 230ms/epoch - 18ms/step\n", + "Epoch 135/250\n", + "13/13 - 0s - loss: 5.8541e-04 - mae: 0.0188 - mse: 5.8541e-04 - val_loss: 6.7887e-04 - val_mae: 0.0191 - val_mse: 6.7887e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 136/250\n", + "13/13 - 0s - loss: 5.9158e-04 - mae: 0.0185 - mse: 5.9158e-04 - val_loss: 5.9231e-04 - val_mae: 0.0188 - val_mse: 5.9231e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 137/250\n", + "13/13 - 0s - loss: 5.9616e-04 - mae: 0.0192 - mse: 5.9616e-04 - val_loss: 7.0218e-04 - val_mae: 0.0212 - val_mse: 7.0218e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 138/250\n", + "13/13 - 0s - loss: 6.2132e-04 - mae: 0.0190 - mse: 6.2132e-04 - val_loss: 6.3436e-04 - val_mae: 0.0186 - val_mse: 6.3436e-04 - 105ms/epoch - 8ms/step\n", + "Epoch 139/250\n", + "13/13 - 0s - loss: 5.8416e-04 - mae: 0.0189 - mse: 5.8416e-04 - val_loss: 5.7793e-04 - val_mae: 0.0184 - val_mse: 5.7793e-04 - 215ms/epoch - 17ms/step\n", + "Epoch 140/250\n", + "13/13 - 0s - loss: 6.5695e-04 - mae: 0.0195 - mse: 6.5695e-04 - val_loss: 5.8062e-04 - val_mae: 0.0189 - val_mse: 5.8062e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 141/250\n", + "13/13 - 0s - loss: 6.4168e-04 - mae: 0.0200 - mse: 6.4168e-04 - val_loss: 6.9879e-04 - val_mae: 0.0196 - val_mse: 6.9879e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 142/250\n", + "13/13 - 0s - loss: 6.5517e-04 - mae: 0.0198 - mse: 6.5517e-04 - val_loss: 6.3928e-04 - val_mae: 0.0193 - val_mse: 6.3928e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 143/250\n", + "13/13 - 0s - loss: 5.8456e-04 - mae: 0.0190 - mse: 5.8456e-04 - val_loss: 5.4596e-04 - val_mae: 0.0181 - val_mse: 5.4596e-04 - 225ms/epoch - 17ms/step\n", + "Epoch 144/250\n", + "13/13 - 0s - loss: 5.9458e-04 - mae: 0.0186 - mse: 5.9458e-04 - val_loss: 5.8598e-04 - val_mae: 0.0181 - val_mse: 5.8598e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 145/250\n", + "13/13 - 0s - loss: 5.6787e-04 - mae: 0.0186 - mse: 5.6787e-04 - val_loss: 5.6263e-04 - val_mae: 0.0186 - val_mse: 5.6263e-04 - 124ms/epoch - 10ms/step\n", + "Epoch 146/250\n", + "13/13 - 0s - loss: 5.3545e-04 - mae: 0.0178 - mse: 5.3545e-04 - val_loss: 5.3802e-04 - val_mae: 0.0179 - val_mse: 5.3802e-04 - 186ms/epoch - 14ms/step\n", + "Epoch 147/250\n", + "13/13 - 0s - loss: 5.2310e-04 - mae: 0.0177 - mse: 5.2310e-04 - val_loss: 5.4103e-04 - val_mae: 0.0179 - val_mse: 5.4103e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 148/250\n", + "13/13 - 0s - loss: 5.2826e-04 - mae: 0.0176 - mse: 5.2826e-04 - val_loss: 5.9310e-04 - val_mae: 0.0181 - val_mse: 5.9310e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 149/250\n", + "13/13 - 0s - loss: 5.3295e-04 - mae: 0.0179 - mse: 5.3295e-04 - val_loss: 5.4002e-04 - val_mae: 0.0176 - val_mse: 5.4002e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 150/250\n", + "13/13 - 0s - loss: 5.1491e-04 - mae: 0.0174 - mse: 5.1491e-04 - val_loss: 5.9602e-04 - val_mae: 0.0179 - val_mse: 5.9602e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 151/250\n", + "13/13 - 0s - loss: 5.2334e-04 - mae: 0.0179 - mse: 5.2334e-04 - val_loss: 5.2811e-04 - val_mae: 0.0178 - val_mse: 5.2811e-04 - 222ms/epoch - 17ms/step\n", + "Epoch 152/250\n", + "13/13 - 0s - loss: 5.2768e-04 - mae: 0.0178 - mse: 5.2768e-04 - val_loss: 5.5139e-04 - val_mae: 0.0184 - val_mse: 5.5139e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 153/250\n", + "13/13 - 0s - loss: 5.2962e-04 - mae: 0.0179 - mse: 5.2962e-04 - val_loss: 5.7462e-04 - val_mae: 0.0178 - val_mse: 5.7462e-04 - 99ms/epoch - 8ms/step\n", + "Epoch 154/250\n", + "13/13 - 0s - loss: 5.0260e-04 - mae: 0.0173 - mse: 5.0260e-04 - val_loss: 5.3387e-04 - val_mae: 0.0181 - val_mse: 5.3387e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 155/250\n", + "13/13 - 0s - loss: 5.0501e-04 - mae: 0.0175 - mse: 5.0501e-04 - val_loss: 5.0751e-04 - val_mae: 0.0172 - val_mse: 5.0751e-04 - 211ms/epoch - 16ms/step\n", + "Epoch 156/250\n", + "13/13 - 0s - loss: 5.0518e-04 - mae: 0.0173 - mse: 5.0518e-04 - val_loss: 5.5553e-04 - val_mae: 0.0174 - val_mse: 5.5553e-04 - 189ms/epoch - 15ms/step\n", + "Epoch 157/250\n", + "13/13 - 0s - loss: 5.0064e-04 - mae: 0.0172 - mse: 5.0064e-04 - val_loss: 5.1205e-04 - val_mae: 0.0172 - val_mse: 5.1205e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 158/250\n", + "13/13 - 0s - loss: 4.9541e-04 - mae: 0.0172 - mse: 4.9541e-04 - val_loss: 5.0799e-04 - val_mae: 0.0172 - val_mse: 5.0799e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 159/250\n", + "13/13 - 0s - loss: 5.4153e-04 - mae: 0.0182 - mse: 5.4153e-04 - val_loss: 5.2077e-04 - val_mae: 0.0171 - val_mse: 5.2077e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 160/250\n", + "13/13 - 0s - loss: 4.8280e-04 - mae: 0.0170 - mse: 4.8280e-04 - val_loss: 5.1410e-04 - val_mae: 0.0168 - val_mse: 5.1410e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 161/250\n", + "13/13 - 0s - loss: 4.8993e-04 - mae: 0.0171 - mse: 4.8993e-04 - val_loss: 5.1744e-04 - val_mae: 0.0171 - val_mse: 5.1744e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 162/250\n", + "13/13 - 0s - loss: 4.8044e-04 - mae: 0.0169 - mse: 4.8044e-04 - val_loss: 5.1099e-04 - val_mae: 0.0168 - val_mse: 5.1099e-04 - 103ms/epoch - 8ms/step\n", + "Epoch 163/250\n", + "13/13 - 0s - loss: 4.9657e-04 - mae: 0.0171 - mse: 4.9657e-04 - val_loss: 4.9877e-04 - val_mae: 0.0171 - val_mse: 4.9877e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 164/250\n", + "13/13 - 0s - loss: 4.8858e-04 - mae: 0.0170 - mse: 4.8858e-04 - val_loss: 5.0099e-04 - val_mae: 0.0169 - val_mse: 5.0099e-04 - 99ms/epoch - 8ms/step\n", + "Epoch 165/250\n", + "13/13 - 0s - loss: 4.7747e-04 - mae: 0.0170 - mse: 4.7747e-04 - val_loss: 5.8449e-04 - val_mae: 0.0174 - val_mse: 5.8449e-04 - 97ms/epoch - 7ms/step\n", + "Epoch 166/250\n", + "13/13 - 0s - loss: 4.9897e-04 - mae: 0.0171 - mse: 4.9897e-04 - val_loss: 4.9512e-04 - val_mae: 0.0173 - val_mse: 4.9512e-04 - 174ms/epoch - 13ms/step\n", + "Epoch 167/250\n", + "13/13 - 0s - loss: 4.8695e-04 - mae: 0.0173 - mse: 4.8695e-04 - val_loss: 5.0306e-04 - val_mae: 0.0165 - val_mse: 5.0306e-04 - 97ms/epoch - 7ms/step\n", + "Epoch 168/250\n", + "13/13 - 0s - loss: 4.7948e-04 - mae: 0.0171 - mse: 4.7948e-04 - val_loss: 6.8895e-04 - val_mae: 0.0193 - val_mse: 6.8895e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 169/250\n", + "13/13 - 0s - loss: 4.8055e-04 - mae: 0.0168 - mse: 4.8055e-04 - val_loss: 4.9053e-04 - val_mae: 0.0171 - val_mse: 4.9053e-04 - 215ms/epoch - 17ms/step\n", + "Epoch 170/250\n", + "13/13 - 0s - loss: 4.5980e-04 - mae: 0.0168 - mse: 4.5980e-04 - val_loss: 5.2267e-04 - val_mae: 0.0170 - val_mse: 5.2267e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 171/250\n", + "13/13 - 0s - loss: 4.6495e-04 - mae: 0.0168 - mse: 4.6495e-04 - val_loss: 4.6718e-04 - val_mae: 0.0165 - val_mse: 4.6718e-04 - 216ms/epoch - 17ms/step\n", + "Epoch 172/250\n", + "13/13 - 0s - loss: 4.6046e-04 - mae: 0.0168 - mse: 4.6046e-04 - val_loss: 4.6731e-04 - val_mae: 0.0166 - val_mse: 4.6731e-04 - 98ms/epoch - 8ms/step\n", + "Epoch 173/250\n", + "13/13 - 0s - loss: 4.6993e-04 - mae: 0.0168 - mse: 4.6993e-04 - val_loss: 4.8190e-04 - val_mae: 0.0167 - val_mse: 4.8190e-04 - 101ms/epoch - 8ms/step\n", + "Epoch 174/250\n", + "13/13 - 0s - loss: 4.8411e-04 - mae: 0.0172 - mse: 4.8411e-04 - val_loss: 5.0800e-04 - val_mae: 0.0164 - val_mse: 5.0800e-04 - 99ms/epoch - 8ms/step\n", + "Epoch 175/250\n", + "13/13 - 0s - loss: 4.5295e-04 - mae: 0.0164 - mse: 4.5295e-04 - val_loss: 6.2583e-04 - val_mae: 0.0182 - val_mse: 6.2583e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 176/250\n", + "13/13 - 0s - loss: 5.3742e-04 - mae: 0.0183 - mse: 5.3742e-04 - val_loss: 5.6727e-04 - val_mae: 0.0187 - val_mse: 5.6727e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 177/250\n", + "13/13 - 0s - loss: 5.3634e-04 - mae: 0.0182 - mse: 5.3634e-04 - val_loss: 4.6197e-04 - val_mae: 0.0157 - val_mse: 4.6197e-04 - 212ms/epoch - 16ms/step\n", + "Epoch 178/250\n", + "13/13 - 0s - loss: 4.8847e-04 - mae: 0.0169 - mse: 4.8847e-04 - val_loss: 4.6646e-04 - val_mae: 0.0160 - val_mse: 4.6646e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 179/250\n", + "13/13 - 0s - loss: 4.3622e-04 - mae: 0.0160 - mse: 4.3622e-04 - val_loss: 5.3203e-04 - val_mae: 0.0164 - val_mse: 5.3203e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 180/250\n", + "13/13 - 0s - loss: 4.7108e-04 - mae: 0.0165 - mse: 4.7108e-04 - val_loss: 4.6548e-04 - val_mae: 0.0161 - val_mse: 4.6548e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 181/250\n", + "13/13 - 0s - loss: 4.3932e-04 - mae: 0.0164 - mse: 4.3932e-04 - val_loss: 4.4195e-04 - val_mae: 0.0157 - val_mse: 4.4195e-04 - 212ms/epoch - 16ms/step\n", + "Epoch 182/250\n", + "13/13 - 0s - loss: 4.3340e-04 - mae: 0.0159 - mse: 4.3340e-04 - val_loss: 4.5463e-04 - val_mae: 0.0158 - val_mse: 4.5463e-04 - 95ms/epoch - 7ms/step\n", + "Epoch 183/250\n", + "13/13 - 0s - loss: 4.2639e-04 - mae: 0.0162 - mse: 4.2639e-04 - val_loss: 4.3874e-04 - val_mae: 0.0156 - val_mse: 4.3874e-04 - 169ms/epoch - 13ms/step\n", + "Epoch 184/250\n", + "13/13 - 0s - loss: 4.4119e-04 - mae: 0.0159 - mse: 4.4119e-04 - val_loss: 4.7791e-04 - val_mae: 0.0169 - val_mse: 4.7791e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 185/250\n", + "13/13 - 0s - loss: 4.4805e-04 - mae: 0.0164 - mse: 4.4805e-04 - val_loss: 4.6275e-04 - val_mae: 0.0163 - val_mse: 4.6275e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 186/250\n", + "13/13 - 0s - loss: 4.4495e-04 - mae: 0.0163 - mse: 4.4495e-04 - val_loss: 4.4746e-04 - val_mae: 0.0155 - val_mse: 4.4746e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 187/250\n", + "13/13 - 0s - loss: 4.7030e-04 - mae: 0.0167 - mse: 4.7030e-04 - val_loss: 5.6234e-04 - val_mae: 0.0169 - val_mse: 5.6234e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 188/250\n", + "13/13 - 0s - loss: 4.4920e-04 - mae: 0.0160 - mse: 4.4920e-04 - val_loss: 4.2347e-04 - val_mae: 0.0154 - val_mse: 4.2347e-04 - 204ms/epoch - 16ms/step\n", + "Epoch 189/250\n", + "13/13 - 0s - loss: 4.1850e-04 - mae: 0.0159 - mse: 4.1850e-04 - val_loss: 4.5828e-04 - val_mae: 0.0156 - val_mse: 4.5828e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 190/250\n", + "13/13 - 0s - loss: 4.2816e-04 - mae: 0.0159 - mse: 4.2816e-04 - val_loss: 4.2983e-04 - val_mae: 0.0155 - val_mse: 4.2983e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 191/250\n", + "13/13 - 0s - loss: 4.1442e-04 - mae: 0.0156 - mse: 4.1442e-04 - val_loss: 4.5135e-04 - val_mae: 0.0154 - val_mse: 4.5135e-04 - 103ms/epoch - 8ms/step\n", + "Epoch 192/250\n", + "13/13 - 0s - loss: 4.1126e-04 - mae: 0.0159 - mse: 4.1126e-04 - val_loss: 4.2590e-04 - val_mae: 0.0151 - val_mse: 4.2590e-04 - 159ms/epoch - 12ms/step\n", + "Epoch 193/250\n", + "13/13 - 0s - loss: 4.1197e-04 - mae: 0.0155 - mse: 4.1197e-04 - val_loss: 4.2111e-04 - val_mae: 0.0151 - val_mse: 4.2111e-04 - 209ms/epoch - 16ms/step\n", + "Epoch 194/250\n", + "13/13 - 0s - loss: 4.0958e-04 - mae: 0.0157 - mse: 4.0958e-04 - val_loss: 4.1117e-04 - val_mae: 0.0149 - val_mse: 4.1117e-04 - 185ms/epoch - 14ms/step\n", + "Epoch 195/250\n", + "13/13 - 0s - loss: 3.9243e-04 - mae: 0.0153 - mse: 3.9243e-04 - val_loss: 4.1405e-04 - val_mae: 0.0150 - val_mse: 4.1405e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 196/250\n", + "13/13 - 0s - loss: 4.0300e-04 - mae: 0.0153 - mse: 4.0300e-04 - val_loss: 4.3989e-04 - val_mae: 0.0150 - val_mse: 4.3989e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 197/250\n", + "13/13 - 0s - loss: 4.0142e-04 - mae: 0.0154 - mse: 4.0142e-04 - val_loss: 4.3665e-04 - val_mae: 0.0151 - val_mse: 4.3665e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 198/250\n", + "13/13 - 0s - loss: 3.9936e-04 - mae: 0.0153 - mse: 3.9936e-04 - val_loss: 4.2897e-04 - val_mae: 0.0149 - val_mse: 4.2897e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 199/250\n", + "13/13 - 0s - loss: 4.0143e-04 - mae: 0.0153 - mse: 4.0143e-04 - val_loss: 4.0877e-04 - val_mae: 0.0148 - val_mse: 4.0877e-04 - 214ms/epoch - 16ms/step\n", + "Epoch 200/250\n", + "13/13 - 0s - loss: 3.9668e-04 - mae: 0.0152 - mse: 3.9668e-04 - val_loss: 4.3571e-04 - val_mae: 0.0150 - val_mse: 4.3571e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 201/250\n", + "13/13 - 0s - loss: 3.9516e-04 - mae: 0.0154 - mse: 3.9516e-04 - val_loss: 5.1984e-04 - val_mae: 0.0161 - val_mse: 5.1984e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 202/250\n", + "13/13 - 0s - loss: 4.5166e-04 - mae: 0.0161 - mse: 4.5166e-04 - val_loss: 5.4696e-04 - val_mae: 0.0182 - val_mse: 5.4696e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 203/250\n", + "13/13 - 0s - loss: 4.5904e-04 - mae: 0.0166 - mse: 4.5904e-04 - val_loss: 4.1240e-04 - val_mae: 0.0150 - val_mse: 4.1240e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 204/250\n", + "13/13 - 0s - loss: 3.9851e-04 - mae: 0.0150 - mse: 3.9851e-04 - val_loss: 4.5210e-04 - val_mae: 0.0154 - val_mse: 4.5210e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 205/250\n", + "13/13 - 0s - loss: 3.8760e-04 - mae: 0.0151 - mse: 3.8760e-04 - val_loss: 4.0982e-04 - val_mae: 0.0149 - val_mse: 4.0982e-04 - 116ms/epoch - 9ms/step\n", + "Epoch 206/250\n", + "13/13 - 0s - loss: 4.1937e-04 - mae: 0.0156 - mse: 4.1937e-04 - val_loss: 3.8857e-04 - val_mae: 0.0145 - val_mse: 3.8857e-04 - 222ms/epoch - 17ms/step\n", + "Epoch 207/250\n", + "13/13 - 0s - loss: 3.7173e-04 - mae: 0.0146 - mse: 3.7173e-04 - val_loss: 3.9353e-04 - val_mae: 0.0147 - val_mse: 3.9353e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 208/250\n", + "13/13 - 0s - loss: 3.9673e-04 - mae: 0.0153 - mse: 3.9673e-04 - val_loss: 3.9003e-04 - val_mae: 0.0145 - val_mse: 3.9003e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 209/250\n", + "13/13 - 0s - loss: 4.2359e-04 - mae: 0.0155 - mse: 4.2359e-04 - val_loss: 3.9027e-04 - val_mae: 0.0146 - val_mse: 3.9027e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 210/250\n", + "13/13 - 0s - loss: 3.9302e-04 - mae: 0.0154 - mse: 3.9302e-04 - val_loss: 4.1320e-04 - val_mae: 0.0152 - val_mse: 4.1320e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 211/250\n", + "13/13 - 0s - loss: 3.6641e-04 - mae: 0.0147 - mse: 3.6641e-04 - val_loss: 3.9564e-04 - val_mae: 0.0141 - val_mse: 3.9564e-04 - 116ms/epoch - 9ms/step\n", + "Epoch 212/250\n", + "13/13 - 0s - loss: 3.6259e-04 - mae: 0.0143 - mse: 3.6259e-04 - val_loss: 3.8787e-04 - val_mae: 0.0146 - val_mse: 3.8787e-04 - 228ms/epoch - 18ms/step\n", + "Epoch 213/250\n", + "13/13 - 0s - loss: 4.0665e-04 - mae: 0.0156 - mse: 4.0665e-04 - val_loss: 5.0910e-04 - val_mae: 0.0160 - val_mse: 5.0910e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 214/250\n", + "13/13 - 0s - loss: 4.5758e-04 - mae: 0.0169 - mse: 4.5758e-04 - val_loss: 4.1241e-04 - val_mae: 0.0141 - val_mse: 4.1241e-04 - 104ms/epoch - 8ms/step\n", + "Epoch 215/250\n", + "13/13 - 0s - loss: 4.0666e-04 - mae: 0.0155 - mse: 4.0666e-04 - val_loss: 4.6639e-04 - val_mae: 0.0151 - val_mse: 4.6639e-04 - 104ms/epoch - 8ms/step\n", + "Epoch 216/250\n", + "13/13 - 0s - loss: 3.6615e-04 - mae: 0.0145 - mse: 3.6615e-04 - val_loss: 3.8294e-04 - val_mae: 0.0138 - val_mse: 3.8294e-04 - 201ms/epoch - 15ms/step\n", + "Epoch 217/250\n", + "13/13 - 0s - loss: 3.8135e-04 - mae: 0.0149 - mse: 3.8135e-04 - val_loss: 5.1259e-04 - val_mae: 0.0162 - val_mse: 5.1259e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 218/250\n", + "13/13 - 0s - loss: 3.5877e-04 - mae: 0.0144 - mse: 3.5877e-04 - val_loss: 3.7918e-04 - val_mae: 0.0142 - val_mse: 3.7918e-04 - 222ms/epoch - 17ms/step\n", + "Epoch 219/250\n", + "13/13 - 0s - loss: 4.1097e-04 - mae: 0.0155 - mse: 4.1097e-04 - val_loss: 3.7973e-04 - val_mae: 0.0144 - val_mse: 3.7973e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 220/250\n", + "13/13 - 0s - loss: 3.7840e-04 - mae: 0.0149 - mse: 3.7840e-04 - val_loss: 4.7988e-04 - val_mae: 0.0153 - val_mse: 4.7988e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 221/250\n", + "13/13 - 0s - loss: 3.5545e-04 - mae: 0.0143 - mse: 3.5545e-04 - val_loss: 3.7230e-04 - val_mae: 0.0136 - val_mse: 3.7230e-04 - 226ms/epoch - 17ms/step\n", + "Epoch 222/250\n", + "13/13 - 0s - loss: 3.4610e-04 - mae: 0.0141 - mse: 3.4610e-04 - val_loss: 4.1371e-04 - val_mae: 0.0142 - val_mse: 4.1371e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 223/250\n", + "13/13 - 0s - loss: 3.7775e-04 - mae: 0.0149 - mse: 3.7775e-04 - val_loss: 3.8045e-04 - val_mae: 0.0142 - val_mse: 3.8045e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 224/250\n", + "13/13 - 0s - loss: 3.5911e-04 - mae: 0.0145 - mse: 3.5911e-04 - val_loss: 3.5609e-04 - val_mae: 0.0134 - val_mse: 3.5609e-04 - 233ms/epoch - 18ms/step\n", + "Epoch 225/250\n", + "13/13 - 0s - loss: 3.5933e-04 - mae: 0.0144 - mse: 3.5933e-04 - val_loss: 3.5900e-04 - val_mae: 0.0134 - val_mse: 3.5900e-04 - 105ms/epoch - 8ms/step\n", + "Epoch 226/250\n", + "13/13 - 0s - loss: 3.6466e-04 - mae: 0.0144 - mse: 3.6466e-04 - val_loss: 3.5378e-04 - val_mae: 0.0135 - val_mse: 3.5378e-04 - 232ms/epoch - 18ms/step\n", + "Epoch 227/250\n", + "13/13 - 0s - loss: 3.5876e-04 - mae: 0.0144 - mse: 3.5876e-04 - val_loss: 3.6523e-04 - val_mae: 0.0133 - val_mse: 3.6523e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 228/250\n", + "13/13 - 0s - loss: 3.4559e-04 - mae: 0.0142 - mse: 3.4559e-04 - val_loss: 3.5907e-04 - val_mae: 0.0139 - val_mse: 3.5907e-04 - 162ms/epoch - 12ms/step\n", + "Epoch 229/250\n", + "13/13 - 0s - loss: 3.4162e-04 - mae: 0.0142 - mse: 3.4162e-04 - val_loss: 4.2194e-04 - val_mae: 0.0141 - val_mse: 4.2194e-04 - 101ms/epoch - 8ms/step\n", + "Epoch 230/250\n", + "13/13 - 0s - loss: 3.6967e-04 - mae: 0.0146 - mse: 3.6967e-04 - val_loss: 3.7720e-04 - val_mae: 0.0138 - val_mse: 3.7720e-04 - 105ms/epoch - 8ms/step\n", + "Epoch 231/250\n", + "13/13 - 0s - loss: 3.3735e-04 - mae: 0.0136 - mse: 3.3735e-04 - val_loss: 3.3976e-04 - val_mae: 0.0129 - val_mse: 3.3976e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 232/250\n", + "13/13 - 0s - loss: 3.3844e-04 - mae: 0.0141 - mse: 3.3844e-04 - val_loss: 3.8716e-04 - val_mae: 0.0135 - val_mse: 3.8716e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 233/250\n", + "13/13 - 0s - loss: 3.6741e-04 - mae: 0.0145 - mse: 3.6741e-04 - val_loss: 3.8668e-04 - val_mae: 0.0136 - val_mse: 3.8668e-04 - 117ms/epoch - 9ms/step\n", + "Epoch 234/250\n", + "13/13 - 0s - loss: 3.4129e-04 - mae: 0.0139 - mse: 3.4129e-04 - val_loss: 3.4933e-04 - val_mae: 0.0133 - val_mse: 3.4933e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 235/250\n", + "13/13 - 0s - loss: 3.2338e-04 - mae: 0.0137 - mse: 3.2338e-04 - val_loss: 3.4566e-04 - val_mae: 0.0133 - val_mse: 3.4566e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 236/250\n", + "13/13 - 0s - loss: 3.1652e-04 - mae: 0.0134 - mse: 3.1652e-04 - val_loss: 3.9728e-04 - val_mae: 0.0136 - val_mse: 3.9728e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 237/250\n", + "13/13 - 0s - loss: 3.2047e-04 - mae: 0.0136 - mse: 3.2047e-04 - val_loss: 3.3756e-04 - val_mae: 0.0130 - val_mse: 3.3756e-04 - 225ms/epoch - 17ms/step\n", + "Epoch 238/250\n", + "13/13 - 0s - loss: 3.3167e-04 - mae: 0.0138 - mse: 3.3167e-04 - val_loss: 3.3191e-04 - val_mae: 0.0126 - val_mse: 3.3191e-04 - 228ms/epoch - 18ms/step\n", + "Epoch 239/250\n", + "13/13 - 0s - loss: 3.2033e-04 - mae: 0.0134 - mse: 3.2033e-04 - val_loss: 3.2969e-04 - val_mae: 0.0128 - val_mse: 3.2969e-04 - 215ms/epoch - 17ms/step\n", + "Epoch 240/250\n", + "13/13 - 0s - loss: 3.5224e-04 - mae: 0.0141 - mse: 3.5224e-04 - val_loss: 3.9061e-04 - val_mae: 0.0148 - val_mse: 3.9061e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 241/250\n", + "13/13 - 0s - loss: 3.9777e-04 - mae: 0.0153 - mse: 3.9777e-04 - val_loss: 3.7065e-04 - val_mae: 0.0137 - val_mse: 3.7065e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 242/250\n", + "13/13 - 0s - loss: 3.2502e-04 - mae: 0.0138 - mse: 3.2502e-04 - val_loss: 3.3236e-04 - val_mae: 0.0124 - val_mse: 3.3236e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 243/250\n", + "13/13 - 0s - loss: 3.0734e-04 - mae: 0.0133 - mse: 3.0734e-04 - val_loss: 3.2635e-04 - val_mae: 0.0126 - val_mse: 3.2635e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 244/250\n", + "13/13 - 0s - loss: 3.2928e-04 - mae: 0.0137 - mse: 3.2928e-04 - val_loss: 3.2871e-04 - val_mae: 0.0125 - val_mse: 3.2871e-04 - 104ms/epoch - 8ms/step\n", + "Epoch 245/250\n", + "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 246/250\n", + "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 247/250\n", + "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 248/250\n", + "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 249/250\n", + "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 250/250\n", + "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 109ms/epoch - 8ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# selected settings for regression (best fit from options above)\n", + "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 4, 20\n", + "loss, metrics = \"mse\", [\"mae\", \"mse\"]\n", + "\n", + "# Create data objects for training using scalar normalization\n", + "n_inputs = len(input_labels)\n", + "n_outputs = len(output_labels)\n", + "x = input_data\n", + "y = output_data\n", + "\n", + "input_scaler = None\n", + "output_scaler = None\n", + "input_scaler = OffsetScaler.create_normalizing_scaler(x)\n", + "output_scaler = OffsetScaler.create_normalizing_scaler(y)\n", + "x = input_scaler.scale(x)\n", + "y = output_scaler.scale(y)\n", + "x = x.to_numpy()\n", + "y = y.to_numpy()\n", + "\n", + "# Create Keras Sequential object and build neural network\n", + "model = tf.keras.Sequential()\n", + "model.add(\n", + " tf.keras.layers.Dense(\n", + " units=n_nodes_per_layer, input_dim=n_inputs, activation=activation\n", + " )\n", + ")\n", + "for i in range(1, n_hidden_layers):\n", + " model.add(tf.keras.layers.Dense(units=n_nodes_per_layer, activation=activation))\n", + "model.add(tf.keras.layers.Dense(units=n_outputs,activation=keras.activations.linear))\n", + "\n", + "# Train surrogate (calls optimizer on neural network and solves for weights)\n", + "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", + "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n", + " \".mdl_co2.h5\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", + ")\n", + "history = model.fit(x=x, y=y, validation_split=0.2, verbose=2, epochs=250, callbacks=[mcp_save])\n", + "\n", + "# Get the training and validation MSE from the history\n", + "train_mse = history.history['mse']\n", + "val_mse = history.history['val_mse']\n", + "\n", + "# Generate a plot of training MSE vs validation MSE\n", + "epochs = range(1, len(train_mse) + 1)\n", + "plt.plot(epochs, train_mse, 'bo-', label='Training MSE')\n", + "plt.plot(epochs, val_mse, 'ro-', label='Validation MSE')\n", + "plt.title('Training MSE vs Validation MSE')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('MSE')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: keras_surrogate\\assets\n" + ] + } + ], + "source": [ + "# Adding input bounds and variables along with scalers and output variable to kerasSurrogate\n", + "xmin, xmax = [7,306], [40,1000]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "\n", + "keras_surrogate = KerasSurrogate(\n", + " model,\n", + " input_labels=list(input_labels),\n", + " output_labels=list(output_labels),\n", + " input_bounds=input_bounds,\n", + " input_scaler=input_scaler,\n", + " output_scaler=output_scaler,\n", + ")\n", + "keras_surrogate.save_to_folder(\"keras_surrogate\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing Surrogates\n", + "\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 [==============================] - 0s 3ms/step\n" + ] + }, + { + "data": { + "image/png": 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CLRKP1Wp1Xmd/TJgwQTPHRASPnu5pgiDYQOj4rcm1I8+ePYuNGzeiW7duiIyMBABs27YNLVu2RF5eHvr06QOO45CZmYlFixahYcOGAID8/HxkZma67SsrKws5OTkAgMrKSuzfvx8zZ850fm40GpGZmYn8/HwAwP79+1FVVeW2n7Zt26J58+ZOEZafn48OHTo4BRj/O+PGjcMvv/yCm266yetxXblyBVeuXHG+Li8vD+EsSY/eRIu/gVfMdoT20Ns9TRCEttCUCJs+fTpWrlyJixcvomvXrsjLy3N+9r///Q/Hjx/H+++/j7fffhs1NTWYNGkS7r//fnzxxRcAgJKSEjdhBABNmjRBeXk5Ll26hHPnzqGmpsbrNr/99ptzH1FRUYiPj6+1TUlJid/f4T/zxYIFCzBv3rwgzoiykGjRBmTZEQ7d0wThgPoNdVBVhM2YMQMLFy70u83hw4fRtm1bAMDUqVMxcuRIHD9+HPPmzcPQoUORl5cHg8EAu92OK1eu4O2338a1114LAFizZg06d+6MI0eO+HRRssTMmTMxefJk5+vy8nIkJyer2CJCa5BlhyCIYKF+Qz1UFWFTpkzB8OHD/W7TsmVL5/8TEhKQkJCAa6+9Fu3atUNycjL27duHjIwMJCYmIiIiwinAAKBdu3YAHJmKbdq0QdOmTWtlMZ46dQpxcXGIiYmByWSCyWTyuk3Tpk0BAE2bNkVlZSXKysrcrGGe23hmVPL75LfxRnR0NKKjo/2eD4LwB1l2CIIIFuo31ENVEdaoUSM0atRI1HftdjsAOGOounfvjurqahw9ehRpaWkAgP/+978AgBYtWgAAMjIy8Mknn7jtZ+fOncjIyADgMLd27twZu3fvxoABA5y/s3v3bkyYMAEA0LlzZ0RGRmL37t0YNGgQAODIkSP4888/nfvJyMjACy+8gNOnT6Nx48bO34mLi0P79u1FHS+hf8gdIA10HrUJXTd2sNlicfasBQ0bWmE2V6jaFr3fF5qICSsoKMB3332HW2+9FQ0aNMDRo0cxe/ZspKWlOYVPZmYmOnXqhBEjRmDp0qWw2+0YP3487rzzTqd1bOzYsVi5ciWmTZuGESNG4IsvvsDmzZuxfft2529NnjwZw4YNQ5cuXXDLLbdg6dKluHDhAh577DEAgNlsxsiRIzF58mQ0bNgQcXFxmDhxIjIyMtC1a1cAwF133YX27dvj0UcfxaJFi1BSUoJZs2Zh/PjxZOkSgd4fQoDcAVJB51Gb0HVjhx9+uAnbtvUDxxlhMNjRv38eOnX6UZW2hMN9oQkRVrduXWzduhXPPvssLly4gMTERPTp0wezZs1yihqj0Yht27Zh4sSJ6NGjB+rVq4e7774br7zyinM/qamp2L59OyZNmoRly5ahWbNmePPNN5GVleXcJjs7G2fOnMGcOXNQUlKCG2+8ETt27HALtF+yZAmMRiMGDRqEK1euICsrC6+++qrzc5PJhLy8PIwbNw4ZGRmoV68ehg0bhvnz5ytwtvSFnh9CV3FZWloq6DvkDvBPqG4VliwA4QS5w9jAZot1CjAA4Dgjtm3rh7S0P1R5Hjyvt6/nU8v3hSZEWIcOHZwZjv5ISkrCli1b/G7Ts2dP/Pijf1U/YcIEp/vRG3Xq1MGqVauwatUqn9u0aNGiluuTCB45O2eh9dqEbhcMQsUloRwsWQAI/cKyZf/sWYtTgPFwnBFnzzZUfVKi1+dTEyKMYAM1RYscWCwWTJgwwdkhFhUZUVgYgdTUaiQlOWIO5eoQ5Zi5Wa1WwRY1wnG+bDYbgMAWAK3c03pBrxZJ1i37DRtaYTDY3YSYwWBHw4ZnFW+LK6xZ6KSERBghGE/R4g2txWfxbV2zBhgzBrDbAaMReOMNYORIlRsXBL46d70OZqFSVlaGzZs3O1/7sgB07DgI/frV19Q9rXX0avEA2He7ms0V6N8/r9b5F9J3yGnhY9lCFyokwoig0ONgdPLkVQEGOP59/HEgKwto1kzdtgnFW+fnbzALF8uOLxFaXV3ttp0vC8D119dR9J5n2VWlBHq2eHiDlUmSa3/QqdOPSEv7A2fPNkTDhmfd2uWr35DbwseqhU4KSIQRojl5Evj9d6B1a+2IFW/8/vtVAcZTUwP88Qd7x1VaWipoIPY1mE2Y0BqdOjXW9UDOE4xFxZcFICnpZsXay7qrSgn0bPHwhCWLX6heDrkC6MvKygCEZqFjHRJhhCi07r5zpXVrxzG4CjGTCWjVSr02+erEtm7dCiDwQOxrMCsvDw8BFsiicu7cuVrf8W4BUE6Ese6qkgvXWEY9WzxcYdHiJ1W/IJW4tFqtbiEDaWl/YNCgLQA4JCef1IUAA0iEESLQg/vOlWbNHCLy8ccdFjCTCXj99avHorSLSEgnFmgg9jWYpaRU+/mWduGvER9oH8iismfPHq/7MZsrdNO5awFP618gi4de3Oh6tfhJKS5d+7hAfaKW7wsSYUTQKOm+83y4fFmIQn0IR450iMg//nBYwFwFmBIuIv64IiOvSNKJseBekwIhAhhArWsUrEWFldgc1pB7AuJt394skgMHDkRSUpJurLh6tfjJIS4DCbvs7GxN3xckwoigUdJ95xqrkJsbg/nzzbDbDTAaOSxaZMNDD12SzBLVrFltESmXi8hVNLrO8gA7gNA7MZstFg0anMPIkW+iqipKFfdaqAgVwIMHD671XjAxJCzF5riitjBUI0bN9ZhTU487309ISND0QOuJXmOc5BCXgYSd2WwWvW8WIBFGBE0g9x2PVLNoi8WCkyeBadNcXaAGTJ8ej+zseGixb+bF5bFj1Zg/vzE4zvD3J0YAHACDc1t/nRh/jl3rg3kTFa4DmlYQKmw9Mx15/GV58bAYmwOwIQyVjlGTMpaI1QzTULMQWUcOcalXqyEPiTBCFL7cdzxSz6K1lMEoFIvFgoMHax8XYHB2Ov46MW/nmIqOuuMZ4+VpXQo0y1bjfLEqDOW0zEl1zKxnmOqx1qInQiY/waBXqyEPiTBCNN7cdzxSz6JZzGCUAm/HZTDYnW7EyMhKVFVFw2aLrdXpeDt3vkRF9+7D0LOnPuu8eeJLLHiztKSl/eF1lj18+K3o0KG/KueLxaBtuS1zUh2zFjJM9fgMek5WfCW4iJ3USC3sWIJEGKEJhLpAtUbt4+LQt28emjUrFjXweTPdm0wc0tMtmnTbuiLEEuPrnPmytOTkLPU6y27T5mbFB0t+gArkflHaOqeEZU4ul5PacXVywZrLVQkLn14zl0mEEZohkAtUCeTo1F2Py2Ipw9atvkWDq1vRW4fnzXS/cGE5mjWLl6StoSJ28BAiSP2dM3+WFrXrg/G4DmTXXFOO6dPNqKkxwGTisHBhOR566EFVXFVKWObkcDnJab1TUwR5ulx99UlKu1yl/i29rVXsCxJhhKbw5wKVGzk79avH1QBJSROwZw+wZIl/t2JxcbHb53xnnJb2B3JyljpFxUMPPQggXpJ2hoLYeB2hlhh/YsGXpSUyshKFhSm1svHUgj/uKVOA7Gx+wmH4W0THq9ImOQOj5QpUl8N6xwsvz3VHlRZBQutnKelydRWlRUVGFBZGIDW1GklJjjgLMaI0HOLnABJhBOEXvvNXMuDdYrGga1fvMXC+3IosZNMFQmy8TiBLTGRkJAD/YsFsrsD8+SV49tlE2O2OxIcbbjiINWtGMXvO1JxwuCJnYLRcA63U1jtf1qeiokTs2pWpighiJYHD9dz464fEiFKtCywhkAgjCD/wg4QQy5SUBBMDx0pnLDVCY6QaNWok2I13992nsGLFp4iMrHQKMEA/50xKlCqnIMdAG+iecS3p4oovwefL+uRaTsbbPSSn25KVBA7++AL1Q3pbbksqSIQRkuDZ2Xh2cloOkBVjmZICoTFwrHTGwRLonhATIxXIjZeUZEdq6nEUFqZo8pwpiZbdQYGsd1u3bhXlRvQUGq71/AD3e+jEiRP49NNP3b4rpduStfpZWu2H1IZEGBEygWJ9/JmotRJUqVZ2pj+XFKvZdEIQ6j4VEyMlxI3H2gDGKiwKLH8Itd6JjaXyJjRccb2HXAXYN99kSO62ZK1+Fj1T4iARRoSMv07Em4l6+/b+mDMnHSkpEZrq5FnIznSF1Wy6QIh1n0oRI8UP0movFM1aiQG94Gm98xZEf+JEMj7+uB/45cGCcUV7Exq8S9KXCPrmmwzs3Hkn/LktxcJS/SyzuQKZmbtqiU2ygvmHRBghK95mjjU1BlRUNNFk3SpWgqV5WMymC0Qgt4WnK1tKQeI5SM+ZcwbHjkUgJaX678XNb5ZdALFe1V3r+Dpn7rFc7vi6/zzvBW/iPTNzF5KSiryKIJstFjt3ZsKf2zJU1K6fVVZWBsBxfnkBBjjOC0uJLqxCIoyQFe/FQ7Vf6Z5FWBKI3iw9/OAWyG2xdevWWvuTUpC47icxEejcWZLdCsbzvPiKFaJAZumoHcvljmu5krVrd9aK2XIlGOvT2bMW8BY3z98T66ZjqX6W1WrF5s2bvZxfI3btysT11x8iS1gASIQRsuI5c9RLpXs1Yd2VFcjSIyaWRa+CRAulRfSAv1iuQOVKfBVFFiIufLkvMzN3iRYnLCVM8G1gJSj/5EnHOsOtW2tnjCERRkiO58yenzl27z4M6ekWzTwcQlFSFGnBlSVEMHXq9CMmTGiNEyeicejQR86OWstZtMGi19IiLOLL+jpo0AeIjy8LWK5EqFUpOzsbZrMZNpsNmzZtqjXhAOy4885d6N49P6TjYc1NLSQoXy7LHN//5ubGYNo0M+x2A4xGDosW2fDQQ5ec/S+rAo1EGCEpvmb2ZnMFunWrRGKiNL/DijVIaVGkhQWKPfElrK6/Ph7XXw+cOOF4L9ysQqxYD8IBX9bX668/LKhcSSjWJ5aC5+UikHU7Oztblv6Y739ttlgsXZoDjnPE3tntBkydGoe//noLZnMF4uOnYPLk+rDbHaWG3njDkWjFAiTCCMlQambPkjVIbVHEuuVIqLAKR6uQ2in9rExklMKXGBJ6HeRafJrFEjJi8Cc2zWazLL8pxB0KAPPn13PWeLTbgccf53DjjaeZyNAnEUaEDN+JBJrZS9XZqC18WIF1y1EwwiocrUJq1nlidRFoufEmhuS4DkL7ukceeYTp8xusC0+tTE1/QvrsWQvsdvfs1JoaA1as+BSpqcdVv8dJhBEhw5vqjx2rxoYNnNsNbzJxmDjxbiZmHEqglGVKC5ajYISVGlYhFixBarmqWFwEWg6EiiGprwNLwfNiWbMGGDMGTLrwPAkkpP31LWrf4yTCCEmwWBzL99SuKm9A585NZP1tVlxySlqmtGA5CkZYKW0VUtOl7SkMfFkPlHBTySHmXcVtUZERhYURSE2tRlKSwx+kpPjwJ4ZKS0vdyqFIbcVhWWD5w2q14tixaowZ09g5oVbShSc2gN6XkGZtZQFPSIQRkqJ0VXlWXHJKW6bUjicSgpDOT6lFoj1Rs1aXnFaSYK17Uot5V3Hr79mUWtyKEX4s1dtiBf76FRamwG4f5vaZNxee1OcwVOubLyHNcnIEiTBCcpQqGsqSS05pyxTLs7tghJVQQQIAxcXFfrcRO6irIeTlzBTj8RfnxSO1mOevY6BnU0pxK1b46cFlKDX8uQh0X/DbSXUOlbC+qb2ygC9IhBGCYSGGxhWWXHJyWaY8zzm/RAigrOUoGILtmIV00HK5DlkS8qEiJs5LLjGv5LMZivALJ4EVDMHcF6Gew2Ctb3qDRBghCJbKQvCw4JKTc0FoIefcbK7AmDH3uKWAszB7l/L35XQdsiTkpSJYYSmHq0aNZ1OP11JNlHLhBWt9c0UPrmISYYQgWCoLIafwCRY5F4QWKj7MZjMSpaqCyzhSuw5ZEPJSI0aMSO2qUcNdrsdrqTZKuPAcMXwpaNjQGvQ9E0ziBauQCCM0h5zCR2x7ePwtCB2KO1cq8cGaSzkY5HAdshxbJxYpl5AJZSKjdDC0Hq+l3nEE4jeG3T7Meb1ycpYGdc/46q+0knhBIowQhdplIYQKH1YIxZ0rlfhg0aUcDHK5m1jOnBKDEDEiRUC1t4xEs7msVluUPJ96u5Z65uRJPhPSEYjP92s5OUuRmno85P1rJfGCRBgRNP6sMqWlpUzc2KwRSlyTVOJDzbIMUuDLwnPhQj3YbLEoLS0FIKxjZalWlxwIESOhPKO+MxIboH//m1RduYHVLDjiKlarFfv2AXa7+z0odQyfFsYhEmFEUASyyvA+eFatKSwQrGtRjlgXVuqrBYM3Cw/HAR988AAMBjuOHs1Dp07C7j+tzJJDQU4xIjQj0RdaFbeBEFtolBWUcOG5LrptMOSEfQwfiTAiKIRaZVi1psiJv3gr3kojZtCSOtZFy2UZeAvPiRPN8MEH9wMQX4dKKoHFSoV4NWJgAvUHAwcOREJCQq3fl/J8qB37w1//3NwYTJtmht1ugNHIYdEiGx566JKmxLwSkxN+3xTD54BEGBEUvqwykZHhJ7pcERpvJda1KGWsi9ZS+b25Ds+evQRegPGocQxqVYj3hhrWvUBW2oSEBNkzd9W0arpadZYuzQHH8YVGDZg6NQ5//fUWzOYKTXkGlGxnMP2aXq2nJMLCDLHZcb7KQgAcOM6INWtGacKlJRdC462CcS3KFbektVR+10GWTztn5RjUqBDvD6UHelasGWoJHP66BprYKHX9tegO9dWvuVpRtWRNDBYSYWFEKNlx/EBYVFQEYCsaNy7Bm2+Ogi93UDjjzyISbCVqOWb4rAycweB5jKwdg9asi1Kip4xEsSKGhUlBqOsusoYSVlQWIBEWRoSaHWexWJyfVVVFgwV3kJJIFfMVzKAl1+xPDwMnS8fAwiCsJnrISAxFxEg5KQhWCCqx7mIoaNE6pyQkwsKUULPjQh10tFY0VOjiyEItImqURNBjWQZWBn/WLHOEcKQSMVJMCoIVgiysu+hPZHk7nnvukaUZmoVEWBgiRXZcoEHHX80mLRYNFbo4ciBx6i1bjEdu4alEADMrmYJqwJJlTm7UzkiUCqlFjNhJgVghGMq6i1LgTzReLcYKl+MBCgqMvncYhpAIC0NCiV9x7VQ9Bx0AzjXAXNfs8uzAtFw0NJCADSRO1Y5zkFMAKZEpyPrgz4plTm70UmdNbREDSCME1bDE+hJZWVlATIz3Yqw1NcAvv1wRtH/WBbxUkAgLQ0JxJXrLVDObK/wOuv46MK0VDRUiYMPJIuKKEpmCehn8tY7DBWXRTZyPmu5kqYSgkv2Or4r3NTXA7t3HcezYOp/FWA8c+ABmMzB48GDEx8d73X84PcMkwsKQUDscqdY2VLpoqBSuMqECVg/xVt7wdw55F7TcmYIsdc6sW+bkQG9ZeDxiRIyU118KIaiEJTZQxfufftoCsznw8cTHx4dF9mMgSISFKSwU/1QyrV8qV1mgjkXNmC+5EXoOwylTkDXLnJyZaKxn4UlBsCJG6uuvBSt6MBXvtXA8akMiLIxgrfinkoO1lK4yfx2L2jFfciL0HIZLpuBVwWNR1SWnxLI5LGThyYWvmFShSH28WoorFCKypD4evZW8IBEWRrBW/FONwVoq65uWOkqpCee4ONbWCVRq2RwWAtjlIDc35u/zpo2YVBZRsi/UoyucRFiYwVrxT6UHa7HWt3CM/fFFqHFxWoXFdQKVXjZHT1bOkyeBadPMzuuotVU/Tp4Evv++Lmy22KCy2rWKv2xMLVvESIQRkiF00FWzaKjYQYS12B810dNAHAysrRPoipKufT1YOX1l9/myigfbF8ntMrtqETLDaJzktMR6UlRkxIkTdXDpkln6RijM779fFWA8NTXAH3+QCCPCFLHWISGC5uLFi6isrERxcbHkRT/FDiLhILCEEspArPVZOYuJB0oLYy1bOQNl9/HXkS+hEGxfE6zLLNh+tLZFyIDp0+ORnR3vJkb05rpr3dpxHK5CzGQCWrVSr01SQCKMEE0o1iF/nZrVasUbb7wBQL6in1oeRPyhZNCqr3Oo5wxRgF1LoB4sVEogNLsv2BIKYrNHg+lH/dXnKiiwIibGsT8tuO6CmYxZrVaYTJVYtCgG06ebUVNjgMnEYeFCG0ymS7BatduvkAgjQkKOG1+Jop96grVgcT1niPKwKnj0OrmQC6muY6jZo0Kez0AWvG++WY9Dhxwxib//bhHkuhMzaRMqnrKzs2E2e3eDBtMneS5z98QTsc7rdf58Bf6er2syMxcgEUaoiK/Cn1FR0hf91GtgvZLB4no9h2IhwaMPpLiOSmSPCrXgnTlzBnFx1TAar1rkAMBk4hAbexpWq8MiJ9ZdqXR8rOfv+LpeWp2UkwgjVMF/4c8CdOokbeyNXgPrlQwW1+s5JAJDAtxBIMuRUq5qfxa8TZs2AQD69XPvV/v2zUNeniOUY+DAJzBmTAPR7kp6xqWDRBihCkJdjlJ2aHruOJQKFtfzOVQTsbF8SokjEuDCA92VclUHsuD5a8eRI3ZdZhpqERJhhKoEsuCwGnvDGqwGiyuFEgkJcgmeULLYlBRHehZYgQg20J0VV7WvdpjNp2E0NvTrriSUgUQYoSpCLDisdGisE66CValUfKkFTyhrMbqLThow5URIRqLW2Lt3s193pVaD3LUIiTBCVcLdgiMUoZaecBKsaiwoLdX+Qsmm01v9J6UJxqIpNCNx8ODBcjVXNvxN2rQa5K5FSIQRquOrMwi0sK5r8UI9LejKI6T0hNaQ6lppfUFpMdl0SolOvT5PPMFYNIuLiwEEnixWV1cr0napCadJG6uQCCOYwLMzcM2YdBUePHwnqVergNDSE8HOwNUcYKW8VnpZUFqoJVgp0anX58kTMefIn+UoMjJS0D5CmThpcdIlBXrPzCURRsiKmEHfM2PSsSyHGT17XnHO9q1WK/bvP6WoK0pJhJaeMBgM3r5ei6ioKNUGWDktOHpwZwuJ5fMnOgEORUVJSE09Llp0quHa1SK+A93NsidICLHglZWVYfPmzaJ/g0X0nplLIoyQDX+Dvr9Zizfh4Trbf+SRR/DOO+9o1hUVDIEsPUI6/4sXL/49wHKKD7BKWHD0kJAg1C1kNlcgM3MXdu68EwAvwA3YtSsT119/SNRva921ywpKnJtAv8GSNUhKq7ue7zsSYYTkCJ1Ve4qH0tJSbN26NaDwuHjxIoDQXVFaiH0RYukRsg6nkAH20iWL5OdDKbdhOMW2JCUV46oAc8BbR8vKyoJeMkovrl0isNWI72PlJlzc2lJgDLwJG9x7771o3rw56tSpg8TERDz66KMoKipyfj537lwYDIZaf/Xq1XPbz/vvv4+2bduiTp066NChAz755BO3zzmOw5w5c5CYmIiYmBhkZmbi999/d9vm7NmzePjhhxEXF4f4+HiMHDkS58+fd9vm4MGDuO2221CnTh0kJydj0aJFEp8RNuFn1StW7HCrQQM4Bv2XXvoKU6bk4eefz8FisaCmJhG//ZaImppE56LPvPAwGBxFeXy5mIRu5401a4AWLYBevRz/rlkjxdHLQ6dOPyInZymGDVuHnJylzgXMheA5wLriOsCuW2eS9XyEcq0Id7xdS94luXnzZlitVlH7pWukDywWCxITE73+8X2snPiqqXbypOw/rUk0Ywm744478PTTTyMxMRF//fUXnnrqKdx///3Yu3cvAOCpp57C2LFj3b7Tu3dv3Hzzzc7Xe/fuxYMPPogFCxagX79+yM3NxYABA/DDDz/g+uuvBwAsWrQIy5cvx/r165GamorZs2cjKysLv/76K+rUqQMAePjhh1FcXIydO3eiqqoKjz32GMaMGYPc3FwAQHl5Oe666y5kZmZi9erV+PnnnzFixAjEx8djzJgxSpwu1Qg0q/7gg/sBGLFhA4dHHwU2bLg6W1q06GrBHaEupmBdUVqNfQnV0uPPomazxWL+fLNHpyn9+RDjNtSCtVIO+MzgoiIjPA1bgVySZ86cCWvXLuEbJYLcf//9qgDjoWr8vtGMCJs0aZLz/y1atMCMGTMwYMAAVFVVITIyEvXr10f9+vWd2/z000/49ddfsXr1aud7y5YtQ58+fTB16lQAwHPPPYedO3di5cqVWL16NTiOw9KlSzFr1izcd999AIC3334bTZo0wUcffYQhQ4bg8OHD2LFjB7777jt06dIFALBixQrcc889ePnll5GUlISNGzeisrISb731FqKionDdddfhwIEDWLx4se5FGI+3QZ/jAN74arcbsH49B34QsduBadPMePLJWGfHH0ycjJDtwj32xdcAe/asxavVUo7zEYyY1LNLw98g55oZvGEDhzfeAO65x30bfy7JTZs2hXTNwsm1Gwi9ZeYpEeTeurXjeXUVYiYT0KqV6F3qGs2IMFfOnj2LjRs3olu3bj5Tg998801ce+21uO2225zv5efnY/LkyW7bZWVl4aOPPgIAFBYWoqSkBJmZmc7PzWYz0tPTkZ+fjyFDhiA/Px/x8fFOAQYAmZmZMBqNKCgowP/93/8hPz8fPXr0cHsws7KysHDhQpw7dw4NGjTw2uYrV67gypUrztfl5eXCTwqDuA76Fy7UwwcfPOCxhfsgYrcbnFl/ckCxL94HWNbOhxBrpdYHR2+DYVlZGU6eBObPb+tWkuTxxzl8+mmZW9081q6ZXtFjZp6cbbVarTCZKrFoUQymTzejpsYAk4nDwoU2mEyXYLVq61wpgaZE2PTp07Fy5UpcvHgRXbt2RV5entftLl++jI0bN2LGjBlu75eUlKBJkyZu7zVp0gQlJSXOz/n3/G3TuHFjt88jIiLQsGFDt21SU1Nr7YP/zJcIW7BgAebNm+f94DUKP+g7Kk7XTq13FWJyLDjtq02slzVQUmSwdD6CsVZqfXB0bZvVasXmzZv/Pu52btvV1Bgwd+5Z5OfnuF0fVq6Z3mH5HmIJ/tnleeKJWKfV/fz5CrzxhuN9tT0NrIU4qCrCZsyYgYULF/rd5vDhw2jbti0AYOrUqRg5ciSOHz+OefPmYejQocjLy6tVK+nDDz9ERUUFhg0b5m2XzDJz5kw3S115eTmSk5NVbJF0eBvob7jhIA4evEGVQYT12BelZ+CsnI9grJXBZgGyjP86YHbk52c43+M4I7Zt64ecnKXIyVmq+jUjCKC29dWXW1tNKy2LIQ6qirApU6Zg+PDhfrdp2bKl8/8JCQlISEjAtddei3bt2iE5ORn79u1DRkaG23fefPNN9OvXr5ZFq2nTpjh16pTbe6dOnULTpk2dn/PvuXbwp06dwo033ujc5vTp0277qK6uxtmzZ9324+13XH/DG9HR0YiOjvb5udJIPWPwNtD36vVF0INI3bp1BW0XyErEeuyL0rNFOc6HWIseS9Y5V+SeRXs77oyMfOzd291tOz7+KzX1eMjnROuuXYIQgq+szawsdS1iqoqwRo0aoVGjRqK+a//7TLrGUAGOuK49e/bg448/rvWdjIwM7N69Gzk5Oc73du7c6RRxqampaNq0KXbv3u0UXeXl5SgoKMC4ceOc+ygrK8P+/fvRuXNnAMAXX3wBu92O9PR05zbPPPOMM2mA/502bdr4dEWyhlwzBs+B3tfAP3DgQK/p1Lz1R+uuKKVQe+AM5VqxYp3jUWoW7XncANwsYYB41703EUnPExEOsJq1qYmYsIKCAnz33Xe49dZb0aBBAxw9ehSzZ89GWlpaLSvYW2+9hcTERNx999219vPkk0/i9ttvxyuvvIK+ffvivffew/fff483/nZWGwwG5OTk4Pnnn0fr1q2dJSqSkpIwYMAAAEC7du3Qp08fjB49GqtXr0ZVVRUmTJiAIUOGICkpCQDw0EMPYd68eRg5ciSmT5+OQ4cOYdmyZViyZIm8J0oCQi3hINWgn5CQ4NfdRAOCMPwNsEoVbgzlWrFgrVSjrInncUthFfQnItV+nliL0yG0g9B7h9WsTU2IsLp162Lr1q149tlnceHCBSQmJqJPnz6YNWuWm/vObrdj3bp1GD58OEwmU639dOvWDbm5uZg1axaefvpptG7dGh999JGzRhgATJs2DRcuXMCYMWNQVlaGW2+9FTt27HDWCAOAjRs3YsKECejduzeMRiMGDRqE5cuXOz83m834/PPPMX78eHTu3BkJCQmYM2cO8+UppCjh4DnoKzXQE75Re4DVMqyUNQnVKsiqKwZgM05HC5BwDe7eadbM8fnjjzssYCYT8Prr6p87TYiwDh064Isvvgi4ndFoxIkTJ/xu88ADD+CBBzxLJVzFYDBg/vz5mD9/vs9tGjZs6CzM6osbbrgB//nPf/w3mDGkKuHA6qBPsS/uaPl8FBUZ8dtv8g9ALJU1CcYq6HrNrFYr9u0D7Hb357KmBigosCImRr1nlmVxyDIkXIO7d6xWKyorK3HPPUBBgRHHjkUgJaUaSUl2FBer627XhAgjlIWFoGg5Bn6KfXFHq+fjhx9uwvz5jRUdgFh4JnzhGT/pes14S56jRExOLRH5zTfrcehQRUiWPLEWGZbFIcvoRbiGaskTGuPlWTqD55DHevdqlc4gEUZ4RaqgaKFiKjs7G2az2fkduR4GMfvVs9mftUEu0P1is8X+LYRcY7OUGYDkTBQIZdLhL36SF9iBRKRYS54/i4y/50YJcahXWA0wD8ayLoUlT2iMl9B7W63SGSTCCJ9IERStVWsLD5n9lcXf/VJWVoa9e6M9amgpazmRK1HA23FLHU8ptYj0Z5H57DP/z43c4lDPsBpgLrSvv3TJErIlT0+V+UmEEbJjsVhqzYpdX7P6rOjF7K81vHWefEV5PVtOPJ+TpCTpY/akEpH+XInbt5/FP/7RwCOT1Pdzw1opEpZhXXwI+e09e0Kz5GmlMr9QSIQRsuNpTXr0UWDDBratSyzFq+jZHSqUcLCc1La6KmtFFnqfBXIl7tq1G3a7e/JToOeGhVIkrKMX8RGqJU8LlfmDgUQYISverEnr11/9nBXrkusAFBPDTrwKuUNrozfLif86ZNVISZHfupGbG4Np04TdZ4EEcXLyCa+ZpK7PDRE8lZWVbou4a1F8sG7JUwMSYYQTT7eG5wPvazt/eAsi9URJ65K32b6n0Fm0yFFjTm2rC7lDfaOU5UTuMh4s1CGz2WIxf75Z1H3mSxDr1VqpJrm5MVi61H0R906dfgxqH2pa1fViyZMaEmGEE9fAytzcmL87ZgOMRg6LFtnw0EOXgnJ/WK1WxMVVw2i8OsN3wAG4+lop65I3q1JWVm2hM326GU88EQuzuUI1qwtL7tBwRu7EEhbqkJ09a/F4PoO7z7wJYr1ZK9Xm5Elg2jSzMyuYX8Q9Le0PwedWbCarVOjNjSgVxsCbEOGExWJBTU0ipk2Ld3GNGDB9ejxqahKDEmArV65EXt5q9Ou3DQaDQ+UYDHZ07PiT2+vMzF2yz5K9W5U4bN9+zkuQqAFnzzZ0vjabKyRZKFko/Lnbu3e98zzx8IJ15cqVsFqtirQn3LFYLEhMTPT5J4UY5q2urs+FFHXIhFjoeAHoihT3mdLPjZ5xeBTchTK/iLsQfFnVT550iLMWLYBevRz/rlkjdevVhfWi1GQJI2ohRR0aVzHlbVbcuPFp7NyZCY4zYteuTMTEXA7atB4M3o/JgF27dsJguF+SxZGlIhyC0InayGE9ErJ2qBr3GesDI2s4gtk5NyEWTD/lq0/Pz9d/yAPrZZJIhBFu+HIhmkwcYmNPw2oVt1ixq+nZZovFrl2Z4A2xrqZ1OfB1TAaDHcnJJ5mthA6QWyfckCPWTcjzqvR9JmRgLC2tg4MHG4R1VjBwNZh9zpwazJuXJKqf8pWRyHFsFn6VGpZDN0iEEU5cAyf79bvJTZj07ZuHvDyHpSrUuK2zZy21Cm7ypvWysjKf1b/F4O+Y+E6MdaFD6ftkORFLMLE+St9n/voQIVnB4VC6xTOYPScnNuh+yl9GYlraFS8TbvULv/pDb30BiTDCSSAXorftxOAvCHnz5s2SBucLPSYSOmzDukuBReQqbyL34CYkK1hvpVt8CUqhweyueC7e7i8jMS/PMTnNy+sPu10b5SL01heQCCN8IpcwUTPWScpj0spMSy9opVNlATnLm/gaBMvKyrB58+aA3/f33AjJCpZi2RuWkEJQ8ou4e4oPISLOc3KqhXIRLLZJLCTCCFVg3QXIw3dunsgx0+Jnw3FxlLQcLsjhWlGivIm37ycmJoZkoRC6qHdKymOw25u7fVerpVukEsv+FnEXQrDlIsS4gvXmRpQKEmGEamjBBRhq5yYU99lwY/Trd5Os2aJEcMgVfyS1a0WokJGran0oAkhoVvBPP32g+koWUiFFJrrSiLXc8ff6sWPVKCyMQGpqNZKS3A9eS25EqSARRshCuM1mQqH2bNggqBAjnWNlkDv+SMpBJ5jyJixbJgLFb3o7NgAoLEzBsWPV0MI4LiQTnTVCtdx99JFFV7F8UsDeVSZ0gbcZPl+XSEsoMQB5mw1znBHduw9Dt27eLSThOGNUA60sHeXNlR3I5c96gLM/S7nnsR092sq5pM+GDRzzg7vQTPTs7Gw1m1mLUCx3WnmWlIZEGCEbWhIJ3mK/lBiA/M2GU1KqSWypiFaWjvLnyg7k8meh/WLhj81mi3WKGMBhSWZ9cBeatV1VVaVG83ziq96YkJIWWnS9KgGJMMIJy+4JsQhta1JSkuIDklJ12YjgCSa2Ss1rI9aVrSe81R3U2uDuSyxHRAgbopXok/3VGxNS0iIUAadnSIQRTuR2T6gh8lh2uShVl40InmCXjnIN3AeUKSLqy1LHFz4OFxHmre5gqCt8SI3n/bFvXxRsttiA1yg+Pj6k/kuqvjRQvbFAJS1CFXB6hkQY4YacD4JagkjM/tSoxq2FbNFwJFBslas70PC3R5nj5A08DmSpU3PtU6XxJpRZsiR7vz8sMBhy0L9/XsAs6FDaLlVNN6FFY7317aEKOL1DIoxQFC08ZHqrxk2Ejq9Bp6jI6OYO5Lirn8kZeCzUUicGtZYDCsVqw6ol2dNd7Hp/uK6ZK+fkS46absEQioALB0iEEYQLlMEjHrkGb5bXCDxw4EKtYGNXlAjiF1P42JfgUXMCEshSHsh6wx/32bMWt9dq4i0Y3RU1XcdamBCHAyTCCMIFyuARh1yDN+tWyV9++aiWO9AVpYL4fVkXgsn6ZWEC4u/8eFpvPEve/PDDTbUsgmoWPPaV+eyKVK5jlicqQrDZYvHNN1Ho2lWb7Q8FWh+FIP7G0WmegtHIub3vCPI9BavVqlLL2MbX4H3yJJv7lRLeHWgwOBrp+Pfq/5VaE9UX/IoPrn++Aqf37bN6nYAUFFiZufctFovzOFzFpWeZCt7VZ7PFqtJOPg4qL281+vXb5rw/ALvbvRLIdSzERbtmDdCiBdCrl+PfNWukOALl+OGHm7B0aQ4eeMCiyfaHClnCCAJULkIsctXS0kqNLqC2OxAA82uiuqKVchz+8Famgnf12Ww2RZYec8Vf5rOjvbXvD0+rpZCYLBasl6FQu8abttovBSTCiIBo3dQtBDXKRWi9Lptcg3eg/e7YsQXbtkVjypR70aFDA0mPiSfYc+7pDtSC+OLxF+SfkZFfazsW8Vamgnf1bdq0SXUBKeT+ELNOrdbDJ/RQ4y1USIQRfmE9JkculCgXwXINMyEEW0tLiv3ecMNBrFkzSvblaQJdGy0uwSUEfgJSUJCOvXszsHdvd+TnZzjXZmQVqe9BLSBk7Ump+g65Jozea7xdLeAaDgYAEmGET7Ru6tYCrAqsYBGToRfsfiMjK50CDJB/eRqWr43cVtT8/AzwIcN8fNWcOWegsFfPL57HFso9qLXBXunwiVAmjP7uQU/xbDIBr7/uuAbhYgAgEUb4ROumbqkJ5wweIchlPeT3W1iY4tV1wVqMmBLIaUX1FV/1008XkJJiZeY8WywWDB482K1shZh7UIuDvRrhE2Kvu7+CsdXV1Rg4EJg06X+wWhsgJaUaSUl27N9vxJgxVy18ejYAkAgjfEJrfV2FT39fssSomY5aDCxbBHzF/WghcNwVqc6xXMfq7TwDHN577w+cOJHP1HmOj48P6ft6sfazvtqG5/1itVq91nw7dMjxb2FhCuz2YW6f6dUAQCUqCK841voqxqJFZTCZHCUbHGt9lcFkKlY9Zf3kSWDPHmXKFfjK4GGpVIIUsJ7q7q0cRDBxP1LeM2LcfFFRUcyfY8BxnjMzdwFwLdViwK5dmbDZYnUVX+XP2q8mSvZvahDoHuInAq7o1QBAljCiFqyv9SW3+8Bmi8XZsxY0bGiF2VwRFhk8WrEIiI37kfqe8XSx8K4VVyIjI2E2mwE4BNilSxZNnGMASEoqBuBeYFTNkg9yIGdgu1iRDmjTPSo1/IQrL68/7HZ9L/ZNIoyoBctrfcklFvgO0FvV7bS0P7xk8EifgaQmWor/C9b1Itc943rdA4mSq8VQ2at75k0wsF7yIVTkDmz3FOk2mw1VVVVu20RERDjdqXz8npB79eRJYN++KNhssUy7IEPFc8LFggFADkiEhSEsx/0EQi6xYLFYMHDgE5g/Px4c55gV81lhOTlLa6W/8x21zRaLHj1GoksXs+bOJY9Yi4BcGXpS1kVjoegr68VQecFQVFTkLLuhlZIPYu9BJQLbgxHpPIH6t6tWMgsMhhzVl2aSG5YMAHJBIizM0LqpW85kgdLSBrU6QN4F462j1kOwfigWAbky9Dz3K7YmFyviR656alJisVhq/b5cZUekRKp7kIXA9kCToZ9/jsKYMVf7KH6SmJb2h+ptJ8RDIiyM0Ercjy8cyQKVWLQoBtOnm1FTI12sgK8O0HWBXdeOWi/LbYRqEZBLvEixXxbFjxaEjSssiJNA6MEtJWQy5C1jkJ8k+rpGrK62QVyFRFgYoaW4H0/kTBbw1wH6WmBXr8H6Whh0xaC2+PFM9tDjOSbEI2Qy1LChFUYj5zZJNBo5PPxwOpKTuzqTQIqKjCgsjECbNkZYLNIu66XlUBZWIREWRgh15Tke4hTngMECciYLCLUGueJ9uQ19BeuzglSxZ2qIn5MngRdeiMXrr+e4CXsW43jIasIO3u5Vs7kC/fptc5sk9uuXh717r4YMfPSRRbZwEylDWeheuwqJsDBBqCvP8aA1ht0+zOuA4Tqj1ytCBmtvLi45lgsJZ67OurW5xuayZecxaVI9cFx953ssx/F4q0BPsIW/SeKxY9WyhZtIHcriK5ZPr2uy+oNEWBgg1JU3cOATfwd+umcH8gOGZ/mGFi0u4skn1TgiNlBquZBwpPas26KppIeffz6HSZPMzkxbVwLF8UhNMC6kUCvQE/Lja5JYWBghW7iJHKEsrE2a1IIq5ocB3lx5qanHaz3IR47YvWYHdu8+DP36jUVeXn+n+43jjJgypb5uKzoLxde59IXeK2FLga9Zt1bOmdVqxbffnqsVM8jjmuwhN8FW6Jd7YXBW0ONxms2nYTRybu85QiROhbTCiSNp6ZQs+/ZEj9clEGQJI5ykplZ7jRlLT7doOqifFbReHkQJWKjrFQr+ymIAtZdackXq+mgO95TnIsgcbrzxNFJSvMctyrkwOEvo8Tj37t0seeFZuYvaeuLruvDJBqmp1T7vXa1CIoxwEhV1GosWRXuNGUtIqAOjsYEs9bm0wsCBAwFAVMwCq+VBWJp5Sl3XS41j81UWA7CjW7d8pKcXuAmwgQMHIiEhQdIBnz+P3hdBNmDFik+Rmnrc53lkZYCTOxOPleOUEqlDJJQoauuJ53XR++SVRBjhhBcX3mLGbLZYDBs2DOvXN9T9Wl6+SEhIEP1dVi2JLFkEpK7rpfaxCcm0TUhIELUOoz+Bwh+vv6WHXLdjEb0PvJ5IORGQMwtY6QxjVievUkIijKiF54PmGpDvmNHvRXp6gaJreclp1Qhm32IGLjkXCpYCFgW0VHW91D42OQYtoQIlkJhlFb0NvEIsev4mDOGYMcjD6uRVSkiEEX7xrAwPGJGfn4H09AK37eSeVctp1Qhm38XFxUHtW+mYCj1BRU3dERPnpXaR2mDRekygJ8FY9Hwdl56C0INFzmXqWIFEGOEXb5XhlU6x57l0ySJbjIjQjj1Yi5waMRVax7O6PBFcnJcnWhGzrKz1KRVSWfS8TRJdA9Wjok7r0lImdpk6rVX1JxEWBoQykwoUV6IUrMSIhGqR08qAqBaetehYrS4vFUKfTT3EeQWCxbU+xSK1Rc91W8++cNGiaMnazQpil6ljZZwIBhJhYUAg4eAv5oCFuBLWYkSknIXzVp+iIiNExGdrBiGz06Iio5vrm+Xq8qEycOBAJCUlBX0vsfA8KoHW3KiuyGnR89YXTp9uxhNPxCpyjpSyUotZpo61cUIoJMLChFCEg9odol6DM12tPhs2cJqYtYlByOzUarXiwIEL4Lgmbu+r5foWi1DLlhgBxqP286gUWrUay2XR821dMwh6RkJNWvJnpWYhbk2r4wSJMEIQanaIYoIzWY8L8Ex4sNsNmpi1BYPQQPJAlgNP1zcLHb4vlCqL4e95LC2tg8LClIDWCpbPo16QSjALfUays7NhNptrfT/UpKVjx6oxf35j5zJcHGfE9u39MWdOOjPFU7UaxE8ijGC6MxYTnKmFuABvCQ9amLUJJZhAcqGWA7EuPKVRs325uTGYNi0edvswGI0cFi2y4aGHLtXaTmvV4LWMFBNYoc+I2WwWVXfOHxaLBQcPerMyGVBR0QQs3EZig/hZgEQY4XX2HmxtGjmEnJjgTKXjAsRa3LwFWLNSN0wKxAaS+7McJCQkaP68yInNFov5880u974B06eb0bPnFWasFUToKO2OZr3OodggflYgEUYAcJ+9nzwJHDkSBZstcLCnnNYJMcGZSsYFeLO4ZWW5izJf4tTbjFaPdcPEBJJrNRZIbc6etbgNkoCwZYrkhvXQAC2i1DOihTqHYsYJlhAswsrLywXvNC4uTlRjCPW5KiwsMBhyApYIYMk6oeSMzZvFbcwYgOMcf1fdoO5WRlcLY7jUDQuXQHI5EWJpZrF8RTChASytY0o4oDqH8iNYhMXHx8NgMPjdhuM4GAwG1NTUhNwwQnk8hYWWSgQoOWPzlaXkaoFzd4P6/r1wsfqEy3HKhZBlbVgqXyGmur/cSQ1kkQsdpZ5juZeSYwnBImzPnj1ytoNgAG+uPK2UCFBqxuYvS8kTPQXaE+ojRHywYHUMJinDmxCTAyWTdciiFzpyLiXHGoJF2O233y5nOwgG8JbiG6g6PqsdiVwztmPHqp3p/55WB44DAH0G2hPaQW2rI0vV/cVY5EJFqTIleidczo/owPyysjKsWbMGhw8fBgBcd911GDFihNcaJQT7eEvxNRo5zJlThIED70RkZGSta8tCR2KzxeKbb6KQmlot+285ZtONYbcPc7p6cnKWOq0OR4+2YjJwlXXIcqBNArn31HaPhmKRCxWp90fPSG34+y8urrY3Qkvrz4oSYd9//z2ysrIQExODW265BQCwePFivPDCC/j888/RqVMnSRtJyIu/FF+gAnylCtaEBF/BeckSI4xGDv363STbOoNX4+WuFivctq0fcnKWIjX1OAAKXHUlmEGDLAfawWq1orKy8u96ZGbY7Qa3emSe111N9yhLFrlQoWfEHXf3cmO3vl9r68+KEmGTJk3Cvffei3/961+IiHDsorq6GqNGjUJOTg7+/e9/S9pIQl5YTfH1N5B7qzgvVxKBr0B8b/FyaruCWCHYQYP1wYOCut3jIZcuzXFWT7fbDZg6NQ5//fUWzOYKZGdnu31P7WdCbYucVATzjOj5fq2dmX617wfgc/1ZVq2Eoi1hrgIMACIiIjBt2jR06dJFssYR4Y2/gfybb6KwZIm7GVqOJIJgl9QJRDi5FVgXVoEQYvVh4RiVuqf459Dbag+uz15VVVVIvyMHLCQsKIWUSQgs9le+Esi6dx8GjoPXceH220fCYmEzVEqUCIuLi8Off/6Jtm3bur1/4sQJxMbGStIwggC8D+RWqxWpqdUwGjm3emBiRFEghC4XIhRyK2gDoVYfFlz0St9Tgdx7rKK2RU5u5EhCYK2/8lcLMiWlGlFRUV7Xj+zcmU0BBogUYdnZ2Rg5ciRefvlldOvWDQDwzTffYOrUqXjwwQclbSBBuOKvHpgQURTKjE2q2bTagzYRGKFWH1biiZS8p/Tg3nMN3NYDciYhsNJfCa0FOW/eaMydm6iZ9SNFibCXX34ZBoMBQ4cORXW1IystMjIS48aNw0svvSRpA3nuvfdeHDhwAKdPn0aDBg2QmZmJhQsXIikpybnNZ599hmeffRa//PIL6tSpgx49euCVV15BSkqKc5svv/wSkydPxi+//ILk5GTMmjULw4cPd/utVatW4Z///CdKSkrQsWNHrFixwpmAAACXL1/GlClT8N577+HKlSvIysrCq6++iiZNmji3+fPPPzFu3Djs2bMH9evXx7Bhw7BgwQI3F64ekTsWQWg9sIEDByIhIcHtu1LM2PQ+m2YJFuJatGr1kRt/z57QPk4tl7tn4LbZXIQxY4qD6h9YuDdd0VMSgi+E9v01Nf/S1PqRohRBVFQUli1bhgULFuDo0aMAgLS0NNStW1fSxrlyxx134Omnn0ZiYiL++usvPPXUU7j//vuxd+9eAEBhYSHuu+8+TJ48GRs3boTNZsOkSZMwcOBA/PDDD85t+vbti7Fjx2Ljxo3YvXs3Ro0ahcTERGRlZQEANm3ahMmTJ2P16tVIT0/H0qVLkZWVhSNHjqBx48YAHIkJ27dvx/vvvw+z2YwJEyZg4MCB+OabbwAANTU16Nu3L5o2bYq9e/eiuLgYQ4cORWRkJF588UXZzpHaKFkQkceXKEpISEBiYqK8P+4DLaVHswZrcVhqWn1YG+g98fXsxcfHM+XCcsUzoYfjjJg3Lwk229KA7mXW7k1vSHG/sn7f8fibELOSXCaEkMwydevWRYcOHaRqi18mTZrk/H+LFi0wY8YMDBgwAFVVVYiMjMT+/ftRU1OD559/Hkaj4wF76qmncN999zm3Wb16NVJTU/HKK68AANq1a4evv/4aS5YscYqwxYsXY/To0XjssccAAKtXr8b27dvx1ltvYcaMGbDZbFizZg1yc3PRq1cvAMDatWvRrl077Nu3D127dsXnn3+OX3/9Fbt27UKTJk1w44034rnnnsP06dMxd+5cXQRde+JtLcWry/ao2zYl8ZUercdrDkjbYbMQh+Wt9pCSQd1aGOiFwEIbvT1zYt3LLNybQgnlflVjIh0KepjwihJhly9fxooVK7Bnzx6cPn0ado9UBd7yJBdnz57Fxo0b0a1bN0RGRgIAOnfuDKPRiLVr12L48OE4f/48NmzYgMzMTOc2+fn5yMzMdNtXVlYWcnJyADhU8v79+zFz5kzn50ajEZmZmcjPzwcA7N+/H1VVVW77adu2LZo3b478/Hx07doV+fn56NChg5t7MisrC+PGjcMvv/yCm266yetxXblyBVeuXHG+DmbR9FCQIgPGW8ZKOC3bM3DgQFRWNsb8+Y2dHTTHGbF9e388//xtsFgaqNxC6ZG6w1YrDsu78HGvPaSEG1pLA70WcA0q59fXFOte1lqMoJj7VWsTaa3VA/OFKBE2cuRIfP7557j//vtxyy23BFzYWyqmT5+OlStX4uLFi+jatSvy8vKcn6WmpuLzzz/H4MGD8fjjj6OmpgYZGRn45JNPnNuUlJS4CSMAaNKkCcrLy3Hp0iWcO3cONTU1Xrf57bffnPuIiopCfHx8rW1KSkr8/g7/mS8WLFiAefPmCTwb0hFqBoy/jJVwWbYnMjISR45EuB0/4AiK/e9/7UhKsurmHMi9FIyScVj+hI/Si9ezNNDzwhQAioqMKCyMQELCBUHfZcnq63kfhuqu02uMoK9aiDU1QEGBFTExbFg3eby5lZV+XqVClAjLy8vDJ598gu7du4f04zNmzMDChQv9bnP48GFnKYypU6di5MiROH78OObNm4ehQ4ciLy8PBoMBJSUlGD16NIYNG4YHH3wQFRUVmDNnDu6//37s3LlTMaEYCjNnzsTkyZOdr8vLy5GcnKzIb4t9wIRmrGh19i50QNm0aZPPWmLffLMehw7pw4KhxFIwSsZhCRU+SqL2QO/6TLtbGxqgf/+r1sHBgwfXmoxqwV0airtOD5mhngSqhchi/8XS8xoqokTYNddcI0k9sClTptTKTPSkZcuWzv8nJCQgISEB1157Ldq1a4fk5GTs27cPGRkZWLVqFcxmMxYtWuTc/p133kFycjIKCgrQtWtXNG3aFKdOnXLb/6lTpxAXF4eYmBiYTCaYTCav2zRt2hQA0LRpU1RWVqKsrMytA/Lc5ttvv621D/4zX0RHRyM6Otrv+WANoRkrrJjpg0WIlbCsrAybN28O2EFr9Ry4olQWllRxWEJj1sQKHzmsPmoP9Py1C2RtiI+PVy35JVRCcS/rrfCr0FqILPVfak9UpESUCHvllVcwffp0rF69Gi1atBD9440aNUKjRo1EfZePQ+NjqC5evOgMyOcxmUxu23q6JwFg586dyMjIAODoUDt37ozdu3djwIABzu/u3r0bEyZMAOCIPYuMjMTu3bsxaNAgAMCRI0fw559/OveTkZGBF154AadPn3ZmVO7cuRNxcXFo3769qOPVCnLHzoQavyYmkDyY2Z/eOmhfKCEUxN5LvoLbx4y5gFGjLnh1lwY6HrlKnviChftIT9YGqfF3b6qRXShVZXsW7jtfuLZd7YmKlIgSYV26dMHly5fRsmVL1K1b1xn4znP2rLRqtKCgAN999x1uvfVWNGjQAEePHsXs2bORlpbmFD59+/bFkiVLMH/+fKc78umnn0aLFi2cgfBjx47FypUrMW3aNIwYMQJffPEFNm/ejO3btzt/a/LkyRg2bBi6dOmCW265BUuXLsWFCxec2ZJmsxkjR47E5MmT0bBhQ8TFxWHixInIyMhA165dAQB33XUX2rdvj0cffRSLFi1CSUkJZs2ahfHjx2vO0sUaocSvKZX5Ey61xFjssP3FeK1eXR+rV9fFvffmYeHCa2tNAP0djxolT9S+j/RkbVCKN96owfz5nOJZrVJWtlf7vvOFxWLB4MGDsXnzZgDi+h+W4hV5RImwBx98EH/99RdefPFFNGnSRPZ4q7p162Lr1q149tlnceHCBSQmJqJPnz6YNWuWU9T06tULubm5WLRoERYtWoS6desiIyMDO3bsQExMDABH8P727dsxadIkLFu2DM2aNcObb77pLE8BOFYDOHPmDObMmYOSkhLceOON2LFjh1ug/ZIlS2A0GjFo0CC3Yq08JpMJeXl5GDduHDIyMlCvXj0MGzYM8+fPl/U8hQvBdmYnTwJ79wKjRwMc53iP9cwfVgg0q2etw/YX4+WAd6k56kINHjzY7VPWjkdN9GRtUGINRIfwT1Itq5WVeC058YxB9PW8Km25DgVRImzv3r3Iz89Hx44dpW6PVzp06IAvvvgi4HZDhgzBkCFD/G7Ts2dP/Pij/zTWCRMmON2P3qhTpw5WrVqFVatW+dymRYsWtVyfhPI4rF9craxFgN3MH7XxV6vqrrtKZftdKQdKb1YcHleXGr/iB+EdFq2dYgjVUiTknmMhq5VwoGax7mARJcLatm2LS5cuSd0WgpAUR90b7wIMYDfzR00C16raCbNMa+FK7VJxteK4wqpLTQlrDY+3EhSpqdVISrKjtNRdaPuyNpSWltb6Lt8+Fp+lUNrk794MtQYZEd6IEmEvvfQSpkyZghdeeAEdOnSoFRMWFxcnSeMIQiy+6t7wsJz5oyZql2yQcvDmrTgFBenYuzcDQG2Xmmff5QslYkmkFKH+8F2CIriCl7NmFWLbtuu8flePk5pAx6Mn9y2hHKJEWJ8+fQAAvXv3dnuf4zgYDAbU1NSE3jJCEyg5exdKoLo3gwZ9gOTkk5J1jiyeg1AJdVbPyrGazRW4665dSE8v8OpS49d+ZWWdQyV+R2gJCn8E+m64Tmq06L7VY/+lJUSJsD179kjdDkKjKDV7D4ZAdW+uv/6wpL/H4jkIFTElG3hYPFZ/AfestVUpAlk7vV1j3vVG5St8o7XkDj32X1pClAi7/fbbBW33j3/8A/Pnz/fZWRP6gOWHU6mZKcvnQCyslWwgpCWQtdPfNab4J32hlf4rGKudGvXaxCBKhAnlnXfewVNPPUUijFAVrc1MWUJr545cJsIJJYaJ4p+8Y7PF4uxZCxo2tDJ3LrQiSvwh1Gr30UcWRWpCSoGsIozjizIRBEEoAN9Jnz592lnU0R/hLtpCsRSzGv/kL/MTkN61dvHiRQCBkxzUuNf8lZtRooisHARqryMr3iHAAPZrQsoqwgiCIJTGYrFQnEsQhGLtZM1SKjTzU6rsTavVinfeeSdgosIjjzyi+L0WuNyMMkVklcRXVjzLNSFJhBEEyCLCo6dMKdY6W0J+hGZ+SpW9KbSkS926dSX5PTnappdM1kBZ8azWhCQRRoQtfPYXWUTcXTg9ejyE//3PhObNr6BpU0dF+cjISJj/rtJK50vbhCK0tSLSlc7eZDlRgeW2eUNs7FqgrHhWa0KSCCN0h9ABICkpicQElHfhEOoSiqtWLTev6yRByG8qLTxYTlRguW2eOJaZCz2gntV4RW/IKsIeeeQRqp5PKA7FAwWH0i4cQn1CXcJHSVwnCYDvDETX9X7VEB4sD/wstw1wXONjx6oxZkxj5zJzdrtj2bnExFKkpxuDvu9Yi1f0hWgRVlZWhm+//RanT5+GnU9D+JuhQ4cCAF577bXQWkcQIiGBFTxUgFMegrXiEO64njt/llrPc6yG8GB54Ge1bbzILixMgd0+zO0zu92Afv0s6N8/D2+91V2Xz4koEbZt2zY8/PDDOH/+POLi4mAwXF0g2WAwOEUYQRDaQWuxI1ogGCuOHgcYKRGzzFKwwkPp8hbEVfHsrf8Brl7nH344gk6d9DfBFiXCpkyZghEjRuDFF19UJeuDIJRCDwUOhaKl2BEe1q1MYq04UsH6+QkGuS21FBupLnz/8/HH/QDUvs4bNxZg797jujv/okTYX3/9hSeeeIIEGKFL9FjgUCi8C+fEiWYADEhOPuH8rKysjKmlilizMnkTPKWlpc62iV0sO5T2uJ4fX2hlUBNiqQ0le1NMbCTL2aIst80XnTr9iMaNS7BmzSif11lvsamiRFhWVha+//57tGzZUur2EISqhGOBQ0+OHm3l1QqwefNmpo5bbSuTK/4FoTrxdp7H7UukamVQE2KplSIpJ5hrxXISEMtt80ezZsWiLPJaFJ1AECLs448/dv6/b9++mDp1Kn799Vd06NABkZGRbtvee++90rWQIBREywUOpXA9aTFDUg0rkyeBBGFa2h+qxtsFWlJHKwgJtg9VVAQbG8maiHGF5bb5Q0xShVZFp2ARNmDAgFrvzZ8/v9Z7BoMBNTU1ITWKINQmUEdss9mc77MQwBuKa851ZqjFDEmW2uxLEObkLFUt3k4pkapU/JncWX5ajI3UI/x1ttliUViYImhRdNYElhAEizDPMhQEoWcCdcSbNm0CwE4AbyiuOYvFguzsbGzatEmTGZIstdmfIFSrVpMSIlXO+DM13Eys19UKF7z1ZaWlpUxatMQiKibs7bffRnZ2NqKjo93er6ysxHvvvUclKoiQYSErMVBHzKLrTqzVg1+SSItWAJbaHEgQqlGrSQmRGkr8mRALmhpuJlbraukNX+LZd1+2VFdxuaJE2GOPPYY+ffqgcePGbu9XVFTgscceIxFGiILFrER/HTFLbjAp26RFKwArbRYrCOUMFlZapAYTfxaMBY2lzFxCOvhYrqKiImzdutX5vhbjcsUgSoRxHOdWoJXn5MmTzhk1QQSDFrMSWXKDSd0mLVoBWGmzP0HILxrvihKTC6VEarCWWJYyOLWaXecNrdWH89YWFvtXOQhKhN10000wGAwwGAzo3bs3IiKufr2mpgaFhYXo06eP5I0k9I+aWYmuHdahQ2WCg0BZcoOx3Ca5YHnQ9CUIExISFLPoeB63rzZJeX5CscSqncGp1ew6T5Ssnydl2IinRSxc+rKgRBifIXngwAFkZWWhfv36zs+ioqKQkpKCQYMGSdpAIrxQevbjvUp2O8GDACtusFDbFIqgUWvWzdKgyaIgVOP8iH1+WSgzAmgzu84TuevnyRk2YrFY3NrFYv8qNUGJsGeffRYAkJKSguzsbNSpU0eWRhHhi9KzH6FVsl3dSGVlZdi8ebNbm1nrHIJtk9gBW+2q7KwMmiwJQlfUiKEU8/yyGF+pdeQQtnKHjZw8CezbFwWbLdbZRhb7VykRFRM2bJhjpfPKykqcPn26VvmK5s2bh94yImxRY/YTzCBQXV0taJ/8kjWuyDUQS2GJEdMulmJ61MabQHU97srKShQXFztfa8G1JQYxz2+4xP8oiRzCVo6wkdqWNQsMhhzNFhQOFlEi7Pfff8eIESOwd+9et/f5gH0q1kqEitDZj+tAF0rR1ECDwNq1O72KC3/4+o4cViEWLDFqx/SwhNoWQqUJNf5MjAVNa8HnSiOnsJVq374sa2q5o9VAlAgbPnw4IiIikJeXh8TERK+ZkgQhN97jucQVTfU3CPjbt6ubsrS01JlircZahmoOOKzE9LCC0GusFwuhFJOAYCxo4SZyxSBnaIdU+xZqWfOGFrJUhSBKhB04cAD79+9H27ZtpW4PQQhGaDyX0IHO2yAQaN/est30tkyMECimxz++3LR6Qsy9JtaCRm5wYcgZ2hHqvl3jvwJZ1jxLu+jJyilKhLVv395rvAtBiCWUmCYpBYDnICBm3ywtE5Odne2zdp+UHRnF9PgmWDctS+JabqSwoOnJDS7HtZczsD3YffuL//JnWQultAsLq6/4Q5QIW7hwIaZNm4YXX3wRHTp0QGRkpNvncXFxkjSOCB9C6YxZi30I9B0p1j4Tagng17j0hVTumnCp6RMswVpFw9HNFspx6MkNLtW1Z7FcChA4/isnZylycpZKYrVjcfUVX4gSYZmZmQCAXr16ucWDUWA+EQpiH4pQBECgjkjMvj2/A9iRkZHv/JyPG5NqIBViCVDCXRMONX2CJViraLjFkoWKntzgUrlY+QntmTNnUFVV5XUb10LrSiEk/is19XjI101rq6+IuhJ79uyRuh0EERJiBYAvC5xrkL2YffPfKShIR35+Bvbu7Y78/AzJA/SFWALkdNeoUZVdS4RqpfU1EEthTdUDenWDS/HMulrBpaqaL4WVTe5rpubqK2IQJcJuv/12/Oc//8Hrr7+Oo0eP4oMPPsA111yDDRs2IDU1Veo2EoQgxMY+COmExO47Pz9DVldJoI5GbncNC6UxWCYUK62/gVhqa6pW0aMbXIpnVq6q+VI870pdM60IdFEibMuWLXj00Ufx8MMP48cff8SVK1cAADabDS+++CI++eQTSRtJEEojZsbn+R0lXCWBOhol2hDOIsAXrveCP0uqr/tM6EDMymxeTfTmBpfymZVjEibF8x7sNRNjSdeKQBclwp5//nmsXr0aQ4cOxXvvved8v3v37nj++eclaxxB+EPOAFQxMz7PBWiVmIkF6mi0MhvUG6FaDHwNxCdONIPZfFjStmoROdzgrGSlSvnMshwzx//+2bMWt9d33nmnm0ctlPMeSOyxkDkpSoQdOXIEPXr0qPW+2WxGWVlZqG0iCEHI4QoLtSN2XYBWqZkY39GcONEMgAHJySecnwVqg5JLK4UboZxDbwMxAGzZcj8qK6+6k/jrF27XTOpnn6WsVCn7DZYnYb7cpDt37pT0PPsS6G+8UYP58znVMydFibCmTZvijz/+QEpKitv7X3/9NVq2bClFuwhCEFI+LFJ1xK4TEblcJZ4z/KNHW/mM+/DXBj62yBM9xhqFKrCVtJTwA/HHH/cDcHUA9XQnuV4/PV4zf0h5rKxlpUrVb4Qq6OS656UqsC0WR+ZkEhOZk6JE2OjRo/Hkk0/irbfegsFgQFFREfLz8/HUU09h9uzZUreRkAm5HjBWzPrBIkVHbLVasXnzZrf35CiWaLFYMHjwYGzevFlQ3EewbdBbrFGoAlspS4lnLFlU1BV88MEDbtv4cifp7ZqpiRorHMiVaSxW0Hne86FmWLq2W203KUuZk6JE2IwZM2C329G7d29cvHgRPXr0QHR0NJ566ilMnDhR6jYSMiDXoMKSWT9UxHTEwTy8oZZtiI+PBxC4Q/G1viUQHsvpAKELbKUsJZ5xhcnJJ5h1J+kVtSrwy5lpLGYiKHWGpcViQXZ2NjZt2iSrm1RIv8qSm1aUCDMYDHjmmWcwdepU/PHHHzh//jzat2+P+vXrS90+QibkWntNL2u6SdUR+zr+7OxsWCwWSayGgToUX0t+6Gm5Fz0RKK4wM3NXrWBmQhrUrsAv1cRUyqQlKc8Jv4SanPGy/sQsPwllKXMypLK5UVFRaN++vVRtIVRCrsFYq4O8VJ2Ov+M3m82SWQ3FdChqDzZqE6oF0Nf3bTab6DXuXPFV4qKoKAm7dmVq7pnSCmq7yaRCSquaXOdEztIiQo6LldImyq9dQDCFXIOxlgd5KTodIccvpYsr2A5FL4ONGEKdHPj7/qZNmyRxtbsOoq4u5LffHirqmdJqnKbSsOSmChWprqec50TOxcW18PsAibCwR67BWMuDvBSdjhLHH0ogr54Gm2AIdXIgpbgOhOcgKvae0lOcptyw5KZiBTFlbnhI3AeGRFiYI9dgrOVBXoqOWMzxB+siC8XlEK6DTajiWM3JhdhnirXyCywS6goHekdMmRseV3EvZ4FtrUIiLMyRazDW4iAvZUcc7PGLdZEFO8sM98Em1MmB9yKqwr8vxi3IX4tA95Rer5kS0BqogQnkuhOShKX2eWZRBJIII2QLUGQl8FEoUnQQwYgc/neUjJ9TuxNUi1CFjOf33YuoGnD0aKuAolmsW9Dzms2ZcwbHjkUgJaUaSUk3A7g5qGvGQlkSFmPU9HbPh0owQiSYSaSa55nF/o9EWJgiV2FAufYrBUp0/EIfcuBqLIXSLq5wHGyECJmLFy+isrISxcXFXvdx991349NPP0Va2h8wGACO4z8xCBLNobgFXa9ZYiLQubOgXdWChYxlilHTBoH6Mj5hRKlJpGv/XVRkRGFhBFJTq5GUZAcgvP9m7Z4iERamyDUjUHum4UtolZWV1apk7w2pMtv84TkIaTl+Tkv4EzJWqxVvvPGGoP1oNemElYxlilHTDmqWsHDFtc/0N5HQonAnERbGyHWzqvUQCJ1h+0OJjt/zNyjWR32Cue5aFc1aFY8E24TyPAj1TggN3dCicCcRRugGLT6APN7ixwYOHIikpCTNzez0jlSiWenYLDnFI4sxXoQyiE3CCmZtSh49TiRIhBGEyrh2Pqmpx53vJyQk0MDFGNnZ2c6lV0IJkFcyNkvu7MpQY7xYSBTQC2qJYTFJWELXpiwqKnJup1UrtD9IhBFhCSsdPwuB0kRtfN0fZrPZuSyR2AB5pWOzPOM0J036H/73PyOSk6+gadNUAKmIiIhwJiUEO1CHEuNF9790KJ3wIFUSlq/noXHjElRVRWPt2p3O/Wqx9FEgSIQRusXXQMpKx89KoDThjtz3hxouFX7QtVqt+OorRwLCiRO+t1ciwJnuf2lROuFBqiQsX8/DmjWjvD6DWit9FAgSYYQu8TWQstTx6zG+QevIeX/wFoFALhU5EzFYykyk+19elLD2SyHUvRdA5vw+gyys+SgVJMII1ZE6jsHfQMpSx6/H+AatI+f94Wo5uOaackyfbkZNjQEmE4eFC8vx0EMP6iKAPdDgH6wYpaD/4GHF2i8ETxcjYMfVQsgO9CzOSYQRqiJHHIO/gVRNKwQPLUOjLEIGcaWsVPw9PGUKkJ0N/PEH0KqVAc2axQOID2nfLCBk8A9GjFJh1+BhydovFFcXY2RkpdMVySN0cqrFPpNEGKEqUrpHhAykZnMF5s8/hblzm6pmhZB6GZpwRKh1JJhBXGkrVbNmjj+9EMzgL1SMsuQ+1QosWfuDwdXF6G9yOnDgQCQkJNT6vlb7TBJhhG4IZoY9fLi6VgiplqEJR4IRVsEM4nzWo16tVN6QMm5I7OCvNzGqNqyFOfiaMNlsNp/f8Rd8n5CQ4HxW9QCJMEJXCJ1hh1vHr6e4GiWsI+Fwf0gVN8RCwgFxFZbKOAidMA0ePBjV1dXYunWr8z09Bd/7g0RYmKOnwdmTcBhIhUBxNYQnUsYNKZlwoHZ9P5b7S1eR68+SpKQY9jxXvq5ffHy84HbpTcyTCAtjWByc1e5k9QjF1RA8/AAWyHUY7ECnRMKB2hl/LPaXrkhVt0suAl0/1tsvFyTCwhjWBme1O1lC/4S7yOcHumPHqrFhAwe73eD8zGTiMHHi3UhJiQhpoJPDAs1Cxh9r/aU3WBUoQq8fq+2XExJhREBKS0t9fialm0HtTpbQJkKFFYl8BxaLBRYL8MYbwOOPAzU1gMkEvP66AZ07N1G7eV7RasYfS6jpSqXr5xvNiLB7770XBw4cwOnTp9GgQQNkZmZi4cKFSEpKcm6zefNmvPjii/jvf/+LRo0aYcKECZg6darbfr788ktMnjwZv/zyC5KTkzFr1iwMHz7cbZtVq1bhn//8J0pKStCxY0esWLECt9xyi/Pzy5cvY8qUKXjvvfdw5coVZGVl4dVXX0WTJlc7sD///BPjxo3Dnj17UL9+fQwbNgwLFixARIRmTrkT12BJb4RifpfLPcIKLMeQ6AGhwopEfm1GjgSysnjXIZvxk2oG/Xs+u56TUS1ZVY8ePYp33nkn4HZyuVJZy9hkCc0ogjvuuANPP/00EhMT8ddff+Gpp57C/fffj7179wIAPv30Uzz88MNYsWIF7rrrLhw+fBijR49GTEwMJkyYAAAoLCxE3759MXbsWGzcuBG7d+/GqFGjkJiYiKysLADApk2bMHnyZKxevRrp6elYunQpsrKycOTIETRu3BgAMGnSJGzfvh3vv/8+zGYzJkyYgIEDB+Kbb74BANTU1KBv375o2rQp9u7di+LiYgwdOhSRkZF48cUXVTh78uJPZAgRIXK7R9SC9RgSrSNEWOld5IcK68kraq0yEOjZ1ZJV1Wq1ChJggHyuVJYyNllDMyJs0qRJzv+3aNECM2bMwIABA1BVVYXIyEhs2LABAwYMwNixYwEALVu2xMyZM7Fw4UKMHz8eBoMBq1evRmpqKl555RUAQLt27fD1119jyZIlThG2ePFijB49Go899hgAYPXq1di+fTveeustzJgxAzabDWvWrEFubi569eoFAFi7di3atWuHffv2oWvXrvj888/x66+/YteuXWjSpAluvPFGPPfcc5g+fTrmzp0bNh1+MCKkc+cmmnKPCIHVGBItzeC9EYywUiIGipAXNVYZ8PdMas2q6r1GV2h9gBgLv94W3pYKzYgwV86ePYuNGzeiW7duiIyMBABcuXIFdevWddsuJiYGJ0+exPHjx5GSkoL8/HxkZma6bZOVlYWcnBwAjpt1//79mDlzpvNzo9GIzMxM5OfnAwD279+Pqqoqt/20bdsWzZs3R35+Prp27Yr8/Hx06NDBzT2ZlZWFcePG4ZdffsFNN90k6fmQCqkH52BFiBbcI1pHSzN4XwQrrFiLgSIXtXhCtdxJce61Ht8Uah8QzOTa0+Dgq/ZXuBgmvKEpETZ9+nSsXLkSFy9eRNeuXZGXl+f8LCsrC5MmTcLw4cNxxx134I8//nBavIqLi5GSkoKSkhI3YQQATZo0QXl5OS5duoRz586hpqbG6za//fYbAKCkpARRUVGIj4+vtU1JSYlzG2/74D/zxZUrV3DlyhXn6/LyciGnRRJYGZxZd49oEb6DCzSD99cRsiYcxAgrFkQ+uajVI5hz7w8txzfZbLH4+ON+4BfIFmPFE1r7i1+FIhzLTgSDqiJsxowZWLhwod9tDh8+jLZt2wIApk6dipEjR+L48eOYN28ehg4diry8PBgMBowePRpHjx5Fv379UFVVhbi4ODz55JOYO3cujEaj399ghQULFmDevHmK/Z7QwTkQpaWlYf8gsQxvOdqzB1iypPYMvnv3YejZ03d6OMvCIVhhpbbIZ9VFHQ6Ecu49hYa/+CabzcZsf1hQkA5egPGEYsUTumA74RtVRdiUKVNqZSZ60rJlS+f/ExISkJCQgGuvvRbt2rVDcnIy9u3bh4yMDBgMBixcuBAvvvgiSkpK0KhRI+zevdttH02bNsWpU6fc9n/q1CnExcUhJiYGJpMJJpPJ6zZNmzZ17qOyshJlZWVu1jDPbb799tta++A/88XMmTMxefJk5+vy8nIkJyf7PT+hEGhwvv76AThxYoPzPV8zHj57kmbv7GKxWNC1K2A0Anb71fdNJiA93WFV8gXrwkFtYaU1WLNqso4voeErvmnTpk0A2OsPbbZY5OdnePlEnBVPa7FxrKKqCGvUqBEaNWok6rv2v0cSV/cdAJhMJlxzzTUAgHfffRcZGRnO38jIyMAnn3zitv3OnTuRkeG4MaOiotC5c2fs3r0bAwYMcP7O7t27nSbqzp07IzIyErt378agQYMAAEeOHMGff/7p3E9GRgZeeOEFnD592plRuXPnTsTFxaF9+/Y+jyk6OhrR0dGizodY/A3OHTvWw4kTjtdCZjw0e2ebZs28ue9IwIQTLFs1WSSQ0PAnNljrD73FsgFAt275okST1mPjWEETMWEFBQX47rvvcOutt6JBgwY4evQoZs+ejbS0NKfwKS0txQcffICePXvi8uXLWLt2Ld5//3189dVXzv2MHTsWK1euxLRp0zBixAh88cUX2Lx5M7Zv3+7cZvLkyRg2bBi6dOmCW265BUuXLsWFCxec2ZJmsxkjR47E5MmT0bBhQ8TFxWHixInIyMhA165dAQB33XUX2rdvj0cffRSLFi1CSUkJZs2ahfHjxysusoQQE2PFokUmj/RvG+rVOweAZjxSwUJWIgtxUYR6sG7VVBIhz6NSQkMJ66SvWLb09AK33wl1f1qIjWMJTYiwunXrYuvWrXj22Wdx4cIFJCYmok+fPpg1a5abqFm/fj2eeuopcByHjIwMfPnll25FVlNTU7F9+3ZMmjQJy5YtQ7NmzfDmm286y1MAQHZ2Ns6cOYM5c+agpKQEN954I3bs2OEWaL9kyRIYjUYMGjTIrVgrj8lkQl5eHsaNG4eMjAzUq1cPw4YNw/z582U+U8HjOjN+4olYp3n9/PkK/G1Vl6wjYkGEKIlrh+bPkqh0ZhC57/QBuRXFE8iyL7RIrBTIbZ3kjyVQLNsjjzwS1P6p9pc0aEKEdejQAV988YXfbRISEpxlJPzRs2dP/Pij/6y/CRMm+M2QqVOnDlatWoVVq1b53KZFixa1XJ8s4tqJ+zKvi+2I1BYhag9SrqUU5s9vDI5zlFLgOCO2b++POXPSqUZVmCN2YkJuRfEIsez7KxI7e3YRgAq3/YUyuZTbOum5MPacOWdw7FgEUlKqkZR0M4CbRfeFVPsrdDQhwgh1ETvjUVOEsDJIWSwWHDzoHm8HADU1BlRUNPEbEC83aovUcCeUsjDkVhSPUMu+ryKxJpMJb7zh2IaV0j6BcH2OExOBzp3F70srtb+00r+RCCMEIXbGo5YIYWmQat3ae+JDq1a+vyN3BxJKzaRwcytLiRQ124jgsVqtsNlsAMStQ+nqwi8udvwrV6ws68+Xp2XNG0oIHH99ZFlZGTZv3ux87eucsmApJhFGCCZQNpAvxIgQPRFsVqISVrxgRKrabmU9EWrNNqlhfcCXAs/nyZdlf/Tou9G4cWPB516OoH0tWtbUQGgfCfg/pyxYikmEhSGuM4jS0tKgv++r4/Y1CFNphOCyElmy4gEU2yY1odRskxKtDPih4u058WbZj4+/2es97GlxKSsrAyDOouYPykIXjtCq/Vo4pyTCwoxgZhCuZGdnw2w2Izc3BvPnm2G3G2A0cli0yIaHHroU0PxMpRG0nZXIcmwbSwh1I6s1MSF36FWEWPb99ZeeFjVHaZ9yPPTQg6LccVR3Sxz+JhNaOKckwsIMsdYTs9mMmppETJt2dSC22w2YPj0e2dnxggZhpUSI1WoVZeEj/BPubuVABOtGVmNiwpo7lHUC9Ze8Ra1792FIT7egWbN4APGifovqbgVPoMmEFs4piTBCEFFRUT4sIY5BJJQBRMogdLGWPiIw5Fb2jxg3slQTk2Biu1hxh+oFs7kC3bpVIjFRXF8mtI5XOFgngyWQpUsLtcxIhBFeGThwIBISEgBc7TjksIRIHYTOQqClniG3MhuEmiwRzoJarmQEsX2ZnHW89I4QSxfrtcxIhBFu8B1UZWVjJCY2cfssUMctZhaoVBB6sMkE4UYwA5OWY9v0ghTJEnoU1N76INfQBDmTEULpy6Ss4xVOCLV0ic3sVwISYYQT1w5qwwYOb7zh6Khd8dVxs1Ic1RveOt7nn09FUlJS2M4u9Vh2QivFGaVCimQJPQnqQH2QFJly4VDSQ2uEYulioX8jEUYAqN1B2e0GPP64Q3B5dtLeOm6hs8CioiK3beUOoPfV8c6Zc4bZAdmzY5DDiqe3shMsTwLkhJIlrhKoDwoUPxToeQrWiqYVwabFyYvQqv18Vr+vfbBwXCTCCADeOygpgu492bp1q3Q7E4CvjvfYsQhmTf6uMSJiS4II/R29lJ1grbaaUoRzbFeweIsfMpk4TJx4t9uEw1sdxWCtaP4Em7+Jp9LCQKuTF1aq9ksBibAww9dsz3sHpf0Ztb/AzeJidh9gi8WCkycRUkkQIZAlRT6UsoToMbZLDvj4oe3b+/+9GDfw+usGdO58NfbVlygRYkXjBUEgwRZoIqqk4NHy5EULAksIJMLCDH8ziGuuKcf06WaXDkr7HbqvwM1///tH/Pvf/r+r9uzv99/lKQniCllSpEPNODs9xXZJhTcR3KnTj5gzJx0VFU28ClZfYkOIFa3470UlQy0Qqqbg0YoLVU+QCAtDfAmLKVOA7Gxtz6i9DXBiAzfVnv0pZaUKZEnRYsyIGugtzk7L+BPBSUl2JCYGtz9v1fE9rWg8WigQ6o1wWcaKNUiEhTknTzosLq1bX51Nsyi+hFoPPC19RUVGFBZGIDW1GklJDjVjs9mwadMm2doqFUpaqXxdd63GjKiFnuLstIpc6wW6TuYmTry7lgATWnTVtZ2sWJ20sMaiXiERFsasWQOMGeMYMIxGeC1JoSSuBWJdCdbSwm/L2vGJQe14Hy3HjKgFxdmpi5TrBXoKJf6Pn9C5EqjoamlpqjMejDWrkxbWWNQrJMLClJMnrwoUwPGvr5IUQpAiziUhIQE1NYluljmx+Ds+kynkpioKq9ZJllHTykBxdt6R263N90GB3IFC+yoxQklI0VUWrU5adaHqARJhYYrUQd/+Av5LS0sFlabIzY1xZgOGarnyd3xt2ojbp5pQXJZ/WCs+q7YFkzWUcGu79kHuSUYcFi4sx0MPPSj4OZFTKLFoddLCGot6hURYmCKHy8RX5yZk4LPZYv+uh+V4HaplTk8uIYrLCgyLQfFkwbyKUm5t/vq6JxkZ0KxZPIB4wfuRUyixZHVy7Zv9JTDx29FkUHpIhIUpcrtM3AP+AxfWKyioiyVLDG7vhWKZ83d8f2eSawaKyxIGBcUTrogRwVK7NL3tO5DVScmldIIpekqTQXkgERbGyOUy8R4Q7/+h7NJFesuVr+MT2smxsK4YERx6soASyiOlS9PfvoHagfvAzapYkoT+nqdQ8xV3Ge6TwWAhERbmSO0yERLw71kWg2+HHJY5b8cnpIwFmdW1CQXFE6EilUvT374B34H7WoC17E4tQyKMEI23+IB9+6Jgt7uLF1e3or+yEUoGM+upjIWcKLGYuNSEa1B8oHidixcvom7duj4/V3LiwVKNLH9QXF9tWMzu1DIkwghR+IoPsNliYTDkeF2DUoiVTMlOT+oyHXpEqcXEpSbcBk+h8TqBUCKeR+tWlHAPTmcxu1PLkAgjROGrE/K1vEezZsCePfKvhRgMSqzNqAeUWkycEI9UcThyx/MItaKwKnQoOJ2t7E49QCKMkBxfy3uwFjTNWntYhgQrEQq8uzqQFSUqKoppoUOZylRTTGpIhBGywC/vERV1GsXFjtHbZAIWL47FlCn1mQia1koQNwtxWSRYiVBwreO2YQMHu/1qORqTicPEiXc767gVC6who2ehwzr+aooRwUEijJAVb5Xyf/zxCVitDZgImtZCEDcLcVlaEawEu1gsFlgs3u4jQ63FsLWCVhIMpMBzksdPtANtR/iHRBihOAkJl9Ghg9qtuIoWgrhZiMvSgmAlHAQSB4E+lzMmK9j7iFWho/UEg2AJprArIRwSYQQRBGoGDEsdlyXmWLQgWMOdQOIg0OdKxGQJvY+UEjrBPgvhWqaBBJb0kAgjCIGoHTAsZVyW2sdCyEMgcSBEPLASfK6U0AnmWeDRQ5kGVjNQww0SYYQowtHvr/bgJGVcltrHQkiL0OxDIdmJrFxzpYSOmGdB62UaaBLGDiTCCFF4iw8oLS31GojvC5qJBQ/FZRHeEJJ9GB9/HmVl9SXLTpSLsrIyAGwKHRYX4RYDrQPJDiTCCNGEIpBoJhY8vGg1mYA2bRzvuY6XJFrDm8DZh44MxGCzE5UMjLdardi8eTOAwELn4sWLfgWj2OfB3/Gyugh3KIRbggFrkAgjVEHP7rDS0lIA0ooiEq1XIQuqfwJZS4Oxpio9QHteV1/1qO655x688847AfcX7PMg5Hj1sgg3EL4JBixBIoyQhJMnge+/rwubLTbgw8tS3IkcuLpkpRJFehatwUBiVBiBsg+FZCeyMED7skrVqVNH0PeDeR5YOF6l0UOCgdYhEUaEzJo1/ELYZhiNk5wFRL3BWynUjjtRCq2IIlZrMXlCYlQ51B6g/VmlKircf1+K+1ft41UDFuPuwg0SYURInDzJCzDHa9cCohQ0rg0oJoRwhQ8qDzRAyxl8HsgqtXPnTue2Ut2/4ShIaB1I9SERRoREOC3sLHQNRy3AH0ugwU6OgVbKmC4tXwNWcQ0+v+aackyfbkZNjQEmE4eFC8vx0EMPyh53J9QqJYULUS8Zj2KhdSDVhUQYERL+CoiePOkQaa1b60OQuQ5Ob7xRg6VLk1SzHoUqPvhj2bMHWLKk9mDXvfsw9OwpfYVsKWO6yIInH/y5nzIFyM7mg/gNaNYsHkC87L8v1CpVUJAesgtRjxmPgRC6DqTNZkNRkRGFhRFITa1GUpLd+X09nQ81IRFGhISvAqKffXbVTWk0OrYZOVLt1oYOv4bj/PkcOM5Ra0npAF6pxIfFYkHXrt5FdHq6RZb1KIXGahUVFTnb6I1wDKJWCzWWqhLiJrPZYrF3b0at74pxIeop41EI/taBLCsrc5YJmT79vz77mnBPfpEKEmFEyHimvANAixaucWIOkZaVdbUzF2raZ9EF4HDBGtze4zgjfv21Pdq3/1VWESC1+JCyCr+U8Bmmvjr6cAyiDgdcn/dAbrKzZy0AjPAkIyOf7gEBBBJQgfoaNZNf9ORlIRFGSILrbHnPnsBxYv5mYjysmrwdLljOQ4hx+OyzPvj887tkcYsJXZZGjGhVswp/ILeqr/sjHIOowwEh/QJvqfF2DwB2pKcXOF+xOInTCqxNdPhY0tzcGEybZobdboDRyDmz8VkdLwJBIoyQHKELTbs+MFqa2TRrBixefAGTJtX9u5PiAHh3TUo1CAhZloZfdkYMaricXN2qgB3duuUjPb3Abwcf7kHU4YDQezjQPZCdna3JQZkVWJro8LGkNlssli7NcYaC2O0GTJ0ah7/+egtmc4UmXaQkwgjJCdbFdbXOmHbix558sj569TqHrVuNmDvX7PaZXIHtgZel0Q6erg7AiL17uyM/P8OvJTEcg6jDHc9sWn5FCsC/y9Jsdn8uieBgqXwFf/0DWee0WB+QRBghC0JdXLXrjNWOH2OVDh0aoEEDYP585QLbAX0s4u2tMwWExbiFWxB1OCMkm9ZXZh9ZQ0OHtfIVLFnnpIJEGCEbQlxcWq8zplZguxruQynxHs/jIJwC7GkdTP94nhtfMYQDBw5EQkKC83W4nzcp8SVy1YAl65xUkAgjVEVo/BjL6MEypRSeMV0ff9wPnhluWp/ZCsXTyuNLYGgxzkUO/JVmSUhIQGJiosotJJSANetcqJAII1SF1RIJwaJ1y5RS8DFdjjpgW5GW9gcKCtKRn5+hm5mtUFytPP4EhhbjXKSG6sIphxbKB7FknQsVEmGE6pAlKbxwteqYzRW4665dSE8v8DqzDYe4HhIYgWGtXIKe0XL5IC1CIoxgArIkhRfU0V+FBEZg9BiQzTLh8NyxAokwgiD8IlcNt3Dv6MvKygCQwBCCHgOyw5lg+xQtuEjFQiKMIHSKFOJJizXctIDVanWuz3f0aCtw3NXPSGB4R28B2eGKmD5Fz5ZzEmEEoUNCFU9WqxXHjlVjzJjGzur8jhpuHG688bTf6vxaWv1ALfjBhI8Hc80Q5TggLe0PlVrGFp6WDaoJpm1CqQupRYElBBJhBKEzQi2Ay5dOKCxMgd0+zO2zmhoDVqz4FKmpx72WTiDLWXB4L1pL8WA8eraAhBtWqxX79gF2u/u1qqkBCgqsiInRr9DyB4kwgvCBFi06UnR0/IAXKFbJc2DU8uoHaiEkHkwvVh6xz1M4Dsx6w3XtR4Mhp9b9/s0363HokDbXfgyV2uWqCYLAmjVAixZAr16Of9esUbtFgeE7ur1718NgcF+GgO/oVq5cCavVKmh/fDA0vy9/sUoO8Wf1uvpBQYFV8G+GG4HOsdYXobZarSguLsYrr5ShRQvu7+eJwyuvlKG4uJjuizCBn7AFut+lrol38iSwZ4/jX1YhSxhBeKBVi45nR+crkyyYjk5IMDTNckNDr4tQu94XS5fmgOP42EIDpk6Nw19/vQWzme6LcEOpBAuthEaQCCMID7S+niUgrKMT6h4KVJ1aDvEXbuipAjgPf70D1UHjt9Oi+58Qh9z3u5Ym0iTCCMIDPaxnCfjv6OSaJVIZAX0jRigJiXtj1WpBwlB7aC0BgGLCCMIDfj1Lk8nxWqvrWfqiqMjodZYoVdyE2VyB1NTjJMD8oMXik2LjJAPFAcl9P4pF7rhQLcQraQ2p42KVgCxhRMjocbYox3qWap0nmy0WZ89a0LChFWZzBQoLI0S5Wz33Q4hHa6UXQnXv+LOQHjhwwWdChxpWi1Bq5AmFVcuf1tFiaASJMCIk9NyZSLmepVrn6YcfbqrVEfXrV+3X3erN+uJtPyxZabRIsAO5WiJeKveOL/f4L798xExCRyg18oTuX26BRzjQSmgEiTCNY7fbVVP1JSXACy8AyclX33vxRaB3b6BpU1WaFDLnzp1DVVWV87XBYIDJZILB4OgwxVgn1AoS5aux84MbxxmxbVs/zJlzBm+84WhDTU1td6unlaaoyIj58xs7s9s4zojt2/vj0iWDfI0n3FBLxCuR+cqS1UJsjTwhyC3wWEYt97sWEl5IhGmYyspKFBYWwu5py1eIy5eBV1+t/f65c8ClS8q3J1RqampQUVH7gb106RK+++47XL58GQCC7iSVyrb07MB8ZaWdPFknoLvV9fh++81b+w2ayhZljWCsWmpmeinl3mHNaiHHguFyCjzW0Zr7XUk0J8KuXLmC9PR0/PTTT/jxxx9x4403Oj87ePAgxo8fj++++w6NGjXCxIkTMW3aNLfvv//++5g9ezaOHTuG1q1bY+HChbjnnnucn3Mch2effRb/+te/UFZWhu7du+O1115D69atnducPXsWEydOxLZt22A0GjFo0CAsW7YM9evXD6otocBxHIqLi2EymZCcnAyjUfkci8pKh5jwJC0N0KKnqrKyEmVlZW7vcRyHc+fOoU2bNvjpp5+c2wWDUtmW3ixYGzZwTrcHABiNHJo1u4zi4oswmYA2bRzvFxc7/vXWEQZqvxaDzNUkWKuWHCJejGszWKEU7PVmzWohlzCUQ+BpgXAUWELQnAibNm0akpKSnAMiT3l5Oe666y5kZmZi9erV+PnnnzFixAjEx8djzJgxAIC9e/fiwQcfxIIFC9CvXz/k5uZiwIAB+OGHH3D99dcDABYtWoTly5dj/fr1SE1NxezZs5GVlYVff/0VderUAQA8/PDDKC4uxs6dO1FVVYXHHnsMY8aMQW5uruC2hEp1dTUuXryIpKQk1K1bV5J9BkudOo6soePHr77XogUQF6dKc0LGaDQiIqL2I2E2m9GoUSNERUWJmqXy2Za+3H9S4trRJSZ6/i6Hvn23IS/vR7/78LT0BWq/2FmuHhM6AiHGqiW1iA/FtRmMUAp0X5SWlmLr1q1Cm60KcglD1ix/hHpoSoR9+umn+Pzzz7FlyxZ8+umnbp9t3LgRlZWVeOuttxAVFYXrrrsOBw4cwOLFi53CZ9myZejTpw+mTp0KAHjuueewc+dOrFy5EqtXrwbHcVi6dClmzZqF++67DwDw9ttvo0mTJvjoo48wZMgQHD58GDt27MB3332HLl26AABWrFiBe+65By+//DKSkpIEtSVUav42QaltXWjUCDCbgStXgOhobVrAAmE0GmE0GhEZGSnaVSBHtmWwvxsbezqgAAO8W/qCcV8KQc8JHf4I1qpltVphMlVi0aIYTJ9uRk2NASYTh4ULbTCZLsFqDc6Fo7Rrk6wfvmHN8keog2ZE2KlTpzB69Gh89NFHXi0/+fn56NGjh5soycrKwsKFC3Hu3Dk0aNAA+fn5mDx5stv3srKy8NFHHwEACgsLUVJSgszMTOfnZrMZ6enpyM/Px5AhQ5Cfn4/4+HinAAOAzMxMGI1GFBQU4P/+7/8EtUUq+IBxNYmK0qf44pHqHEuZbSnmd4uLQ4sdlKr9WqpmLTXBWLX4QG6eJ56IdVpOzp+vwBtvON4PJkZRD6tBEIQvtBgaoQkRxnEchg8fjrFjx6JLly44duxYrW1KSkqQmprq9l6TJk2cnzVo0AAlJSXO91y3KSkpcW7n+j1f2zRu3Njt84iICDRs2NBtm0Bt8caVK1dw5coV5+vy8nKv2xGElglnIRCMa9rTIunLchKMdVYvq0EQhDe0mACgasX8GTNmwGAw+P377bffsGLFClRUVGDmzJlqNld2FixYALPZ7PxLdq39oBOGDx/uvLaRkZFo0qQJ7rzzTrz11ltBZXmuW7cO8fHx8jWUAfRaUZsXAq6EkxAYORI4dsxxbY8dU84N63BtFmPRojKYTBwA/O3aLIPJVKx4FXEWrRYstokIDovFgsTERJ9/LAkwQGVL2JQpUzB8+HC/27Rs2RJffPEF8vPzER0d7fZZly5d8PDDD2P9+vVo2rQpTp065fY5/7rp30WrfG3j+jn/XmJiots2fBZm06ZNcfr0abd9VFdX4+zZswF/x/U3vDFz5kw3d2l5ebkuhVifPn2wdu1a1NTU4NSpU9ixYweefPJJfPDBB/j444+9BseHG6zETFmtVslnlUomKohBroQB13MpNDNVyt8W49qUU5SwaLWQs00k8AhvqDraNWrUCI0aNQq43fLly/H88887XxcVFSErKwubNm1Ceno6ACAjIwPPPPMMqqqqEBkZCQDYuXMn2rRp43T/ZWRkYPfu3cjJyXHua+fOncjIyAAApKamomnTpti9e7dTdJWXl6OgoADjxo1z7qOsrAz79+9H586dAQBffPEF7HZ7UG3xRnR0dC2hKRdyDK5CiY6OdorRa665Bp06dULXrl3Ru3dvrFu3DqNGjcLixYuxdu1a/O9//0PDhg3Rv39/LFq0CPXr18eXX36Jxx57DMDVeK1nn30Wc+fOxYYNG7Bs2TIcOXIE9erVQ69evbB06dJaLmRvCC3zIXcnKUXMlLfrW1paGlQ7PAduX4gpLqlWokIg5BK/cp5LIYh1bcotlFizSgDytYlF0RmusJSZrQmTQ/Pmzd1e8/W40tLS0OzvM/jQQw9h3rx5GDlyJKZPn45Dhw5h2bJlWLJkifN7Tz75JG6//Xa88sor6Nu3L9577z18//33eOPvaaDBYEBOTg6ef/55tG7d2lmiIikpCQMGDAAAtGvXDn369MHo0aOxevVqVFVVYcKECRgyZAiSkpIEt0VN1B4QvNGrVy907NgRW7duxahRo2A0GrF8+XKkpqbif//7H/7xj39g2rRpePXVV9GtWzcsXboUc+bMwZEjRwBcvSeqqqrw3HPPoU2bNjh9+jQmT56M4cOH45NPPgnYhoiICDRu3LiWW/Ty5csoLy/H4MGDUa9ePdnPSagxU0Kur5B1IIXGGonNGFUrUcEXciYMyH0u5YREgXTQuVQfVrwMPJoQYUIwm834/PPPMX78eHTu3BkJCQmYM2eOW0mIbt26ITc3F7NmzcLTTz+N1q1b46OPPnLWCAMcdcguXLiAMWPGoKysDLfeeit27NjhrBEGOMphTJgwAb1793YWa12+fHlQbVETVgeEtm3b4uDBgwDgZq1MSUnB888/j7Fjx+LVV19FVFQUzGYzDAZDLffuiBEjnP9v2bIlli9fjptvvhnnz593K6brC2+uULvdDpPJhMaNG7vdB3IRavB0oOvmbR3ITp2ulq4IV3dIOCcMEPLAksWFYDMzW5MiLCUlBRzH1Xr/hhtuwH/+8x+/333ggQfwwAMP+PzcYDBg/vz5mD9/vs9tGjZs6CzM6gshbSHc4TjO6V7ctWsXFixYgN9++w3l5eWorq7G5cuXcfHiRb/Faffv34+5c+fip59+wrlz55xWrT///BPt27dX5DhCRc6YKW/rSW7f3h9z5qQjKcke1u4QyhwkpIQ1iwvB5kRL1exIgnDl8OHDSE1NxbFjx9CvXz/ccMMN2LJlC/bv349Vq1YB8G/luXDhArKyshAXF4eNGzfiu+++w4cffhjwe3JitVpRXFzs889XRppcGXTe1pOsqTGgoqIJk5lDSsKLX5PJ8VrthAEK5NYuviwuest01hosZmZr0hJG6I8vvvgCP//8MyZNmoT9+/fDbrfjlVdecQbLb9682W37qKgo56oBPL/99husViteeuklZ1bp999/r8wBeCHU2Ds5Yqa8LR6sdifEEq4JA/XqAefPOwbOUK7DyZPAvn1RsNlig6qQToHc2oVFiwvBZmY2iTBCca5cuYKSkhK3EhX8ep5Dhw7FoUOHUFVVhRUrVqB///745ptvsHr1ard9pKSk4Pz589i9ezc6duyIunXronnz5oiKisKKFSswduxYHDp0CM8995xKR8lm7J3n4sFKd0JqZuUKpVkz4LPPpHElXXVJWWAw5NSKvwuE2ueCEAe5ttmD73vuuQcoKDDi2LEIpKRUIynJjuJi9foeEmGE4uzYsQOJiYmIiIhAgwYN0LFjRyxfvhzDhg2D0WhEx44dsXjxYixcuBAzZ85Ejx49sGDBAgwdOtS5j27dumHs2LHIzs6G1Wp1lqhYt24dnn76aSxfvhydOnXCyy+/jHvvvVfFo2UPfvHg7t2HIT3doqgAE5K12aPHSHTpYlZtdipV8K7nfjjOiG3b+iEt7Q/F1wwk16ZySL3eJxE6vvqeQ4fcXytZEYCHRBihKOvWrcO6desCbjdp0iRMmjTJ7b1HH33U7fVrr72G1157ze29Bx98EA8++KDbe96SOMIds7kC3bpVwqUmcS2kHriFZm0uWWJUNZBZKleSt/1wnBFnzzb0KcLkEkFKuTbDPRtQjvU+idBh0SvBQyIsDKFZsb6R6voqGZPkmbWpdOq4q5s0Ls4Io7Ex7ParC7ebTBxatQpuIXfvLikOEyfejaSk2kt0ye0OkXvQp2xAedb7JPQNibAwhAJ+9Y2U11epe8B71qYygczeXBX9+rnXUuvbNw8xMd0BCD8f3oOADejcuYnER6A+LNZfIggtQCIsTCGBpW+0dn3VzNr0Jlb5uDnelWQ2V6Cy8uag983q8kyBCNatSNmABCEOqhNGiKK6uhqVlZU+/6qrq9VuIqEh+KxNg8ExkrOQOm42VyA19XjIQfTNmgE9e2pHjKxZA7RoAfTq5fh3zZrA32Gx/hJBaAGyhBFBU11djdOnTwfcrnHjxl6XAQoXKPYuONTK2iSuItatyGL9JYLQAuE7QhKi8VzgOtTt9ArF3gWPkKxNQj5CcSuy7HoN96xNgl1IhBGEjJDAugpZBtkn1CKjcqzyECqUtUmw3PeQCCMIQhHIMsg+YtyKLK+CoHTWJsuDfTjDct9DIowgCMXQssAqLS0V3FGzLEwCEYxbMdT1UeVG6axNlgf7cIfVc04ijNAVX375Je644w6cO3cO8fHxgr6TkpKCnJwc5OTkyNo2gk2EWiW2bt0KILCgYF2YCEGoW5HlSuSAOms4snpNCTahEhWEogwfPhwGgwFjx46t9dn48eNhMBgwfPhw5RvGCFarFcXFxT7/rFYrk/vWMrz1YuDAgYK2DyQoWBcm4QTvXjWZHK8pa5NgDbKEEYqTnJyM9957D0uWLEFMTAwA4PLly8jNzUXz5s1Vbp16yGlB0YN1Rk4sFkvYiyItu1D9wXLWJkGQCCOCxuhZlTHI7Tp16oSjR49i69atePjhhwE4XD3NmzdHamqqc7srV65g6tSpeO+991BeXo4uXbpgyZIluPnmq5XLP/nkE+Tk5ODEiRPo2rUrhg0bVuv3vv76a8ycORPff/89EhIS8H//939YsGAB6tWrF8xhy46cFhSyzhD+0LtIZzFrkyAAckcSIoiIiEDjxo2RkJDg8y9QodYRI0Zg7dq1ztdvvfUWHnvsMbdtpk2bhi1btmD9+vX44Ycf0KpVK2RlZeHs2bMAgBMnTmDgwIHo378/Dhw4gFGjRmHGjBlu+zh69Cj69OmDQYMG4eDBg9i0aRO+/vprTJgwQcIzQhDaRo8indzvhBYgSxghqpBhqJXwH3nkEcycORPHjx8HAHzzzTd477338OWXXwIALly4gNdeew3r1q3D3XffDQD417/+hZ07d2LNmjWYOnUqXnvtNaSlpeGVV14BALRp0wY///wzFi5c6PydBQsW4OGHH3YG3bdu3RrLly/H7bffjtdeew116tQJ6TiI0NGrG4xQD71b9gj9QCIszFGrkGGjRo3Qt29frFu3DhzHoW/fvkhISHB+fvToUVRVVaF79+7O9yIjI3HLLbfg8OHDAIDDhw8jPT3dbb8ZGRlur3/66SccPHgQGzdudL7HcRzsdjsKCwvRrl07OQ6PEAgNloQc6NGyR+gTEmFhjNKFDD0ZMWKE0y24atUqWX7j/PnzePzxx/HEE0/U+iyckwBYgQZLbUPFSQkiNEiEhTFKFzL0pE+fPqisrITBYEBWVpbbZ2lpaYiKisI333yDFi1aAACqqqrw3XffOV2L7dq1w8cff+z2vX379rm97tSpE3799Ve0krMwEKELpBIU4SRMqDgpQYQGibAwRo1Chq6YTCana9HEF/L5m3r16mHcuHGYOnUqGjZsiObNm2PRokW4ePEiRv7tLx07dixeeeUVTJ06FaNGjcL+/fuxbt06t/1Mnz4dXbt2xYQJEzBq1CjUq1cPv/76K3bu3CnIDUaED1IJinATJno5DoJQAxJhYYyYdeKkJi4uzudnL730Eux2Ox599FFUVFSgS5cu+Oyzz9CgQQMADnfili1bMGnSJKxYsQK33HILXnzxRYwYMcK5jxtuuAFfffUVnnnmGdx2223gOA5paWnIzs6W/diCRU4LSjhZZ0JBKkFBwoQgCCEYOI7j1G4E4Z3y8nKYzWbYbLZaYuXy5csoLCxEampqyBl+J09SIUN/SHmuAyFnpiCLWYjFxcV44403Am43ZswYJCYmKtCi8ERvCRJ0XxFq42/8doUsYQQVMmQIOQc4LQyehDqEmwuVIFiBRBhBEAShK4FF7ndCK5AIIwhCNWiwJOSALHuEViARRhCEatBgScgF3TOEFiARRhCEqtBgSRBEuEILeGscSm6VHzrHBEEQhByQCNMofHFTWs5Ffvhz7FlQliAIgiBCgdyRGiUiIgJ169bFmTNnEBkZCaOR9LQc2O12nDlzBnXr1kVEBD0uBEEQhHTQqKJRDAYDEhMTUVhYiOPHj6vdHF1jNBrRvHlzGAwGtZtCEARB6AgSYRomKioKrVu3JpekzERFRZGlkSAIgpAcEmEax2g0yr6UDkEQBEEQ0kPTe4IgCIIgCBUgEUYQBEEQBKECJMIIgiAIgiBUgGLCGIYvElpeXq5ySwiCIAiCEAo/bgcq9k0ijGEqKioAAMnJySq3hCAIgiCIYKmoqIDZbPb5uYGjNVmYxW63o6ioCLGxsWFZo6q8vBzJyck4ceIE4uLi1G6OZqHzKA10HkOHzqE00HmUBjnPI8dxqKioQFJSkt8SR2QJYxij0YhmzZqp3QzViYuLo45GAug8SgOdx9ChcygNdB6lQa7z6M8CxkOB+QRBEARBECpAIowgCIIgCEIFSIQRzBIdHY1nn30W0dHRajdF09B5lAY6j6FD51Aa6DxKAwvnkQLzCYIgCIIgVIAsYQRBEARBECpAIowgCIIgCEIFSIQRBEEQBEGoAIkwgiAIgiAIFSARRqjOv//9b/Tv3x9JSUkwGAz46KOP3D7nOA5z5sxBYmIiYmJikJmZid9//12dxjJKoHM4fPhwGAwGt78+ffqo01iGWbBgAW6++WbExsaicePGGDBgAI4cOeK2zeXLlzF+/HhYLBbUr18fgwYNwqlTp1RqMZsIOY89e/asdU+OHTtWpRazyWuvvYYbbrjBWUw0IyMDn376qfNzuheFEeg8qnkvkggjVOfChQvo2LEjVq1a5fXzRYsWYfny5Vi9ejUKCgpQr149ZGVl4fLlywq3lF0CnUMA6NOnD4qLi51/7777roIt1AZfffUVxo8fj3379mHnzp2oqqrCXXfdhQsXLji3mTRpErZt24b3338fX331FYqKijBw4EAVW80eQs4jAIwePdrtnly0aJFKLWaTZs2a4aWXXsL+/fvx/fffo1evXrjvvvvwyy+/AKB7USiBziOg4r3IEQRDAOA+/PBD52u73c41bdqU++c//+l8r6ysjIuOjubeffddFVrIPp7nkOM4btiwYdx9992nSnu0zOnTpzkA3FdffcVxnOPei4yM5N5//33nNocPH+YAcPn5+Wo1k3k8zyPHcdztt9/OPfnkk+o1SqM0aNCAe/PNN+leDBH+PHKcuvciWcIIpiksLERJSQkyMzOd75nNZqSnpyM/P1/FlmmPL7/8Eo0bN0abNm0wbtw4WK1WtZvEPDabDQDQsGFDAMD+/ftRVVXldj+2bdsWzZs3p/vRD57nkWfjxo1ISEjA9ddfj5kzZ+LixYtqNE8T1NTU4L333sOFCxeQkZFB96JIPM8jj1r3Ii3gTTBNSUkJAKBJkyZu7zdp0sT5GRGYPn36YODAgUhNTcXRo0fx9NNP4+6770Z+fj5MJpPazWMSu92OnJwcdO/eHddffz0Ax/0YFRWF+Ph4t23pfvSNt/MIAA899BBatGiBpKQkHDx4ENOnT8eRI0ewdetWFVvLHj///DMyMjJw+fJl1K9fHx9++CHat2+PAwcO0L0YBL7OI6DuvUgijCDCgCFDhjj/36FDB9xwww1IS0vDl19+id69e6vYMnYZP348Dh06hK+//lrtpmgaX+dxzJgxzv936NABiYmJ6N27N44ePYq0tDSlm8ksbdq0wYEDB2Cz2fDBBx9g2LBh+Oqrr9RulubwdR7bt2+v6r1I7kiCaZo2bQoAtTJ+Tp065fyMCJ6WLVsiISEBf/zxh9pNYZIJEyYgLy8Pe/bsQbNmzZzvN23aFJWVlSgrK3Pbnu5H7/g6j95IT08HALonPYiKikKrVq3QuXNnLFiwAB07dsSyZcvoXgwSX+fRG0reiyTCCKZJTU1F06ZNsXv3bud75eXlKCgocPPnE8Fx8uRJWK1WJCYmqt0UpuA4DhMmTMCHH36IL774AqmpqW6fd+7cGZGRkW7345EjR/Dnn3/S/ehCoPPojQMHDgAA3ZMBsNvtuHLlCt2LIcKfR28oeS+SO5JQnfPnz7vNOAoLC3HgwAE0bNgQzZs3R05ODp5//nm0bt0aqampmD17NpKSkjBgwAD1Gs0Y/s5hw4YNMW/ePAwaNAhNmzbF0aNHMW3aNLRq1QpZWVkqtpo9xo8fj9zcXPy///f/EBsb64ytMZvNiImJgdlsxsiRIzF58mQ0bNgQcXFxmDhxIjIyMtC1a1eVW88Ogc7j0aNHkZubi3vuuQcWiwUHDx7EpEmT0KNHD9xwww0qt54dZs6cibvvvhvNmzdHRUUFcnNz8eWXX+Kzzz6jezEI/J1H1e9FVXIyCcKFPXv2cABq/Q0bNozjOEeZitmzZ3NNmjThoqOjud69e3NHjhxRt9GM4e8cXrx4kbvrrru4Ro0acZGRkVyLFi240aNHcyUlJWo3mzm8nUMA3Nq1a53bXLp0ifvHP/7BNWjQgKtbty73f//3f1xxcbF6jWaQQOfxzz//5Hr06ME1bNiQi46O5lq1asVNnTqVs9ls6jacMUaMGMG1aNGCi4qK4ho1asT17t2b+/zzz52f070oDH/nUe170cBxHCe/1CMIgiAIgiBcoZgwgiAIgiAIFSARRhAEQRAEoQIkwgiCIAiCIFSARBhBEARBEIQKkAgjCIIgCIJQARJhBEEQBEEQKkAijCAIgiAIQgVIhBEEQRAEQagAiTCCIAiCIAgVIBFGEAQhgsrKSrWbUAsW20QQhG9IhBEEQQDo2bMnJkyYgAkTJsBsNiMhIQGzZ88Gv7JbSkoKnnvuOQwdOhRxcXEYM2YMAODrr7/GbbfdhpiYGCQnJ+OJJ57AhQsXnPt99dVX0bp1a9SpUwdNmjTB/fff7/zsgw8+QIcOHRATEwOLxYLMzEznd3v27ImcnBy3Ng4YMADDhw93vhbbJoIg2IBEGEEQxN+sX78eERER+Pbbb7Fs2TIsXrwYb775pvPzl19+GR07dsSPP/6I2bNn4+jRo+jTpw8GDRqEgwcPYtOmTfj6668xYcIEAMD333+PJ554AvPnz8eRI0ewY8cO9OjRAwBQXFyMBx98ECNGjMDhw4fx5ZdfYuDAgQh2Od9g20QQBDvQAt4EQRBwWJ5Onz6NX375BQaDAQAwY8YMfPzxx/j111+RkpKCm266CR9++KHzO6NGjYLJZMLrr7/ufO/rr7/G7bffjgsXLuCTTz7BY489hpMnTyI2Ntbt93744Qd07twZx44dQ4sWLby258Ybb8TSpUud7w0YMADx8fFYt24dAIhqU506dUI6TwRBSAdZwgiCIP6ma9euTgEGABkZGfj9999RU1MDAOjSpYvb9j/99BPWrVuH+vXrO/+ysrJgt9tRWFiIO++8Ey1atEDLli3x6KOPYuPGjbh48SIAoGPHjujduzc6dOiABx54AP/6179w7ty5oNscbJsIgmAHEmEEQRACqVevntvr8+fP4/HHH8eBAwecfz/99BN+//13pKWlITY2Fj/88APeffddJCYmYs6cOejYsSPKyspgMpmwc+dOfPrpp2jfvj1WrFiBNm3aOIWS0Wis5ZqsqqoKuU0EQbADiTCCIIi/KSgocHu9b98+tG7dGiaTyev2nTp1wq+//opWrVrV+ouKigIAREREIDMzE4sWLcLBgwdx7NgxfPHFFwAAg8GA7t27Y968efjxxx8RFRXldC02atQIxcXFzt+qqanBoUOHAh6DkDYRBMEGJMIIgiD+5s8//8TkyZNx5MgRvPvuu1ixYgWefPJJn9tPnz4de/fuxYQJE3DgwAH8/vvv+H//7/85g+Dz8vKwfPlyHDhwAMePH8fbb78Nu92ONm3aoKCgAC+++CK+//57/Pnnn9i6dSvOnDmDdu3aAQB69eqF7du3Y/v27fjtt98wbtw4lJWVBTyGQG0iCIIdItRuAEEQBCsMHToUly5dwi233AKTyYQnn3zSWfbBGzfccAO++uorPPPMM7jtttvAcRzS0tKQnZ0NAIiPj8fWrVsxd+5cXL58Ga1bt8a7776L6667DocPH8a///1vLF26FOXl5WjRogVeeeUV3H333QCAESNG4KeffsLQoUMRERGBSZMm4Y477gh4DIHaRBAEO1B2JEEQBLxnIxIEQcgJuSMJgiAIgiBUgEQYQRAEQRCECpA7kiAIgiAIQgXIEkYQBEEQBKECJMIIgiAIgiBUgEQYQRAEQRCECpAIIwiCIAiCUAESYQRBEARBECpAIowgCIIgCEIFSIQRBEEQBEGoAIkwgiAIgiAIFSARRhAEQRAEoQL/H6+AZe06G5giAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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", 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", 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2BQDceeed2LBhA6ZPn46LFy9iwYIFWLZsGbKzswEAixcvxtdff4133nkHTz31lGcnxgMsmfFAgkGP3BHpCPutf3yYJOHFEZ2RYNAHOWVEROSJYDQfOHLkCCoqKvC73/0OjRs3Nv+9//77KCwsNK/XpUsX83/Lw/7L0wA40r17d7fTZLmvFi1aIDIy0hzIyMvkfRcWFuLy5cvm4AeonTyyZ8+e+PHHH93etzdYMuOhUT2SkNU+DkdLK5AcG8lAhohIwxw1H/BX/n7hwgUAwJo1a9C6dWurzyIiIswBjWXDXXmQOZPJ5HT7UVFRbqfJdl+2jYYlSXJp34HGYMYLCQY9gxgiojogGM0HOnXqhIiICBw/ftxcjWTJsnRGTXh4OGpqavyRPKdSU1MRHh6O7777Dm3atAFQ22uqoKAAU6dODWhaGMwQEVG9JzcfeHrVftQIEZDmA9HR0XjyySfx+OOPw2QyoW/fvjAajfjuu+8QExNjDhAcSU5ORlFREXbv3o1rrrkG0dHRiIiI8FuaLUVFReHhhx/GU089hWbNmiEpKQkvvfQSKioqMGHChICkQcZghoiICMFpPvD8888jLi4Oubm5+Omnn9CkSRPccMMNePrpp12qzhk5ciRWrVqFAQMGoKysDEuXLsXYsWP9nm7ZvHnzYDKZcP/99+P8+fPo3r07vvzySzRt2jRgaQAASdTxqandmUKciIi059KlSygqKkJKSgoaNWoU7OSQGxz9du48v9mbiYiIiDSNwQwREVEd96c//cmq+7fl35/+9KdgJ89rbDNDRERUxz333HN48sknFT+rC00wGMwQERHVcfHx8YiPjw92MvyG1UxERESkaQxmiIioTgjFkWnJMV/9ZqxmIiIiTQsPD4dOp8PJkycRFxeH8PBw87D/FJqEEKiursaZM2eg0+kQHh7u1fYYzBARkabpdDqkpKSgpKQEJ0+eDHZyyA2RkZFISkqCTuddRRGDGSIi0rzw8HAkJSXhypUrQZuriNwTFhaGBg0a+KQUjcEMERHVCfIsz7YzPVPdxwbAREREpGkMZoiIiEjTGMwQERGRpjGYISIiIk1jMENERESaxmCGiIiINI3BDBEREWkagxkiIiLSNAYzREREpGkMZoiIiEjTGMwQERGRpjGYISIiIk1jMENERESaxmCGiIiINI3BDBEREWkagxkiIiLSNAYzREREpGkMZoiIiEjTGMwQERGRpjGYISIiIk1jMENERESaxmCGiIiINI3BDBEREWlaUIOZzZs347bbbkOrVq0gSRI++eQTq8+FEHj22WeRkJAAvV6PQYMG4fDhw8FJLBEREYWkoAYzFy9eREZGBt58803Fz1966SW8/vrrWLhwIbZu3YqoqCgMHjwYly5dCnBKiYiIKFQ1CObOs7OzkZ2drfiZEAKvvvoq/vKXv+D2228HALz//vto0aIFPvnkE9x9992BTCoRERGFqJBtM1NUVIRTp05h0KBB5mUGgwG9evXCli1bVL9XVVWF8vJyqz8iIiKqu0I2mDl16hQAoEWLFlbLW7RoYf5MSW5uLgwGg/kvMTHRr+kkIiKi4ArZYMZTM2bMgNFoNP8VFxcHO0lERETkRyEbzLRs2RIA8Msvv1gt/+WXX8yfKYmIiEBMTIzVHxEREdVdIRvMpKSkoGXLlvjmm2/My8rLy7F161ZkZmYGMWVEREQUSoLam+nChQs4cuSI+d9FRUXYvXs3mjVrhqSkJEydOhUvvPACrr32WqSkpGDmzJlo1aoVhg8fHrxEExERUUgJajCzfft2DBgwwPzvJ554AgAwZswYLFu2DNOmTcPFixfx4IMPoqysDH379sUXX3yBRo0aBSvJREREFGIkIYQIdiL8qby8HAaDAUajke1niIiINMKd53fItpkhIiIicgWDGSIiItI0BjNERESkaQxmiIiISNMYzBAREZGmMZghIiIiTWMwQ0RERJrGYIaIiIg0jcEMERERaRqDGSIiItI0BjNERESkaQxmiIiISNMYzBAREZGmMZghIiIiTWMwQ0RERJrGYMZLJcZK5BeWosRYGeykEBER1UsNgp0ALVtZcBwzVu2DSQA6CcgdkY5RPZKCnSwiIqJ6hSUzHioxVpoDGQAwCeDpVftZQkNERBRgDGY8VFR60RzIyGqEwNHSiuAkiIiIqJ5iMOOhlNgo6CTrZWGShOTYyOAkiIiIqJ5iMOOhBIMeuSPSESbVRjRhkoQXR3RGgkEf5JQRERHVL2wA7IVRPZKQ1T4OR0srkBwbyUCGiIgoCBjMeCnBoGcQQ0REFESsZiIiIiJNYzBDREREmsZghoiIiDSNwQwRERFpGoMZIiIi0jQGM0RERKRpDGaIiIhI0xjMEBERkaYxmCEiIiJNYzBDREREmsZghoiIiDSNwQwRERFpGoMZIiIi0jQGM0RERKRpDGaIiIhI0xjMEBERkaYxmCEiIiJNYzBDREREmsZghoiIiDSNwQwRERFpGoMZHygxViK/sBQlxspgJ4WIiKjeaRDsBGjdyoLjmLFqH0wC0ElA7oh0jOqRFOxkERER1RssmfFCibHSHMgAgEkAT6/azxIaIiKiAGIw44Wi0ovmQEZWIwSOllYEJ0FERET1UEgHMzU1NZg5cyZSUlKg1+uRmpqK559/HkII518OgJTYKOgk62VhkoTk2MjgJIiIiKgeCuk2M/Pnz8eCBQvw3nvv4brrrsP27dsxbtw4GAwGTJkyJdjJQ4JBj9wR6Xh61X7UCIEwScKLIzojwaAPdtKIiIjqjZAOZvLz83H77bdj6NChAIDk5GQsX74c27ZtC3LKrhrVIwlZ7eNwtLQCybGRDGSIiIgCLKSrmXr37o1vvvkGhw4dAgDs2bMHeXl5yM7OVv1OVVUVysvLrf78LcGgR2ZqcwYyREREQRDSJTM5OTkoLy9HWloawsLCUFNTg7lz5+Lee+9V/U5ubi7mzJkTwFQSERFRMIV0ycy///1v/Otf/8KHH36InTt34r333sPf/vY3vPfee6rfmTFjBoxGo/mvuLg4gCkmIiKiQJNEqHQNUpCYmIicnBxMnjzZvOyFF17ABx98gAMHDri0jfLychgMBhiNRsTExPgrqURERORD7jy/Q7pkpqKiAjqddRLDwsJgMpmClCIiIiIKNSHdZua2227D3LlzkZSUhOuuuw67du3Cyy+/jPHjxwc7aURERBQiQrqa6fz585g5cyZWr16N06dPo1WrVhg9ejSeffZZhIeHu7QNVjMRERFpjzvP75AOZnyBwQwREZH21Jk2M0RERETOMJghIiIiTWMwQ0RERJrGYIaIiIg0jcEMERERaRqDGSIiItI0BjNERESkaQxmiIiISNMYzBAREZGmMZghIiIiTWMwQ0RERJrGYIaIiIg0jcEMERERaRqDGSIiItI0BjNERESkaQxmiIiISNMYzBAREZGmMZghIiIiTWMw44USYyXyC0tRYqwMdlKIiIjqrQbBToBWrSw4jhmr9sEkAJ0E5I5Ix6geScFOFhERUb3DkhkPlBgrzYEMAJgE8PSq/SyhISIiCgIGMx4oKr1oDmRkNULgaGlFcBJERERUjzGY8UBKbBR0kvWyMElCcmyk4vpsW0NEROQ/DGY8kGDQI3dEOsKk2ogmTJLw4ojOSDDo7dZdWXAcfeatxz2Lt6LPvPVYWXA80MklIiKq0yQhhHC+mnaVl5fDYDDAaDQiJibGp9suMVbiaGkFkmMjFQOZEmMl+sxbb1UlFSZJyMsZoLg+ERER1XLn+c3eTF5IMOgdBiWO2tYwmCEiIvINVjP5kbtta4iIiMh9DGb8yJ22NUREROQZVjP52ageSchqH+ewbQ0RERF5jsFMADhrW0NERESeczmYKS8vd3mjvu41RERERKTG5WCmSZMmkCTJ4TpCCEiShJqaGq8TRkREROQKl4OZDRs2+DMdRERERB5xOZjp16+fP9NBRERE5BGPGwCXlZXhnXfewY8//ggAuO666zB+/HgYDAafJY6IiIjIGY/Gmdm+fTtSU1Pxyiuv4OzZszh79ixefvllpKamYufOnb5OIxEREZEqj+Zmuummm9CuXTssXrwYDRrUFu5cuXIFEydOxE8//YTNmzf7PKGe8ufcTEREROQf7jy/PQpm9Ho9du3ahbS0NKvlP/zwA7p3746Kigp3N+k3DGaIiIi0x53nt0fVTDExMTh+/Ljd8uLiYkRHR3uySSIiIiKPeBTMjBo1ChMmTMDKlStRXFyM4uJirFixAhMnTsTo0aN9nUYiIiIiVR71Zvrb3/4GSZLwxz/+EVeuXAEANGzYEA8//DDmzZvn0wQSEREROeJRmxlZRUUFCgsLAQCpqamIjIz0WcJ8hW1miIiItMed57dXE01GRkYiPT3dm00QERERecWjYObSpUt44403sGHDBpw+fRomk8nqc441Q0RERIHiUTAzYcIEfPXVV7jzzjvRs2dPpxNQEhEREfmLR8HM559/jrVr16JPnz6+Tg8RERGRWzzqmt26dWuOJ0NEREQhwaNg5u9//zumT5+OY8eO+To9dk6cOIH77rsPzZs3h16vR3p6OrZv3+73/RIREZE2eFTN1L17d1y6dAlt27ZFZGQkGjZsaPX52bNnfZK4c+fOoU+fPhgwYADWrVuHuLg4HD58GE2bNvXJ9omIiEj7PApmRo8ejRMnTuDFF19EixYt/NYAeP78+UhMTMTSpUvNy1JSUvyyLyIiItImjwbNi4yMxJYtW5CRkeGPNJl16tQJgwcPxs8//4xNmzahdevWmDRpEh544AGXt8FB84iIiLTH7xNNpqWlobKy0qPEueOnn37CggULcO211+LLL7/Eww8/jClTpuC9995T/U5VVRXKy8ut/oiIiKju8qhk5quvvsKcOXMwd+5cpKen27WZ8VUJSHh4OLp37478/HzzsilTpqCgoABbtmxR/M7s2bMxZ84cu+UsmSEiItIOd0pmPApmdLraAh3btjJCCEiShJqaGnc3qahNmzb43e9+hyVLlpiXLViwAC+88AJOnDih+J2qqipUVVWZ/11eXo7ExEQGM0RERBri97mZNmzY4FHC3NWnTx8cPHjQatmhQ4fQpk0b1e9EREQgIiLC30kjIiKiEOFRMNOvXz+X1ps0aRKee+45xMbGerIbPP744+jduzdefPFF3HXXXdi2bRvefvttvP322x5tj4iIiOoej6qZXBUTE4Pdu3ejbdu2Hm/j888/x4wZM3D48GGkpKTgiSeeYG8mIiKiOs7v1Uyu8kWcdOutt+LWW2/1QWqIiIioLvKoazYRERFRqGAwQ0RERJrGYIaIiIg0jcEMERERaZrbwcyVK1fw3HPP4eeff3a67n333cceRERERORXHnXNjo6Oxr59+5CcnOyHJPkWu2YTERFpj98nmrz55puxadMmjxJHRERE5EsejTOTnZ2NnJwc7Nu3D926dUNUVJTV58OGDfNJ4oiIiIic8WqiScUN+nCiSV9gNRMREZH2+H0EYJPJ5FHCiIiIiHzNozYz77//PqqqquyWV1dX4/333/c6UURERESu8qiaKSwsDCUlJYiPj7da/uuvvyI+Pp7VTEREROQVv/dmEkJAkiS75T///DMMBoMnmyQiIiLyiFttZrp27QpJkiBJEgYOHIgGDa5+vaamBkVFRRgyZIjPE0lERESkxq1gZvjw4QCA3bt3Y/DgwWjcuLH5s/DwcCQnJ2PkyJE+TSARERGRI24FM7NmzQIAJCcnY9SoUWjUqJFfEkVERETkKo+6Zo8ZMwZAbe+l06dP23XVTkpK8j5lRERERC7wKJg5fPgwxo8fj/z8fKvlcsPgUOrNFGglxkoUlV5ESmwUEgz6YCeHiIiozvMomBk7diwaNGiAzz//HAkJCYo9m+qjlQXHMWPVPpgEoJOA3BHpGNWDpVRERET+5FEws3v3buzYsQNpaWm+To9mlRgrzYEMAJgE8PSq/chqH2dXQsPSGyIiIt/xKJjp1KkTSktLfZ0WTSsqvWgOZGQ1QuBoaYVVwMLSGyIiIt/yaNC8+fPnY9q0adi4cSN+/fVXlJeXW/3VRymxUdDZ1LaFSRKSYyPN/1YrvSkxVgYwpURERHWLRyUzgwYNAgDcfPPNVu1l6nMD4ASDHrkj0vH0qv2oEQJhkoQXR3S2KpVxtfSGiIiIXOdRMLNhwwZfp6NOGNUjCVnt43C0tALJsZF2AUpKbBQkAJbxjG3pDREREbnHo2qmfv36QafTYfHixcjJyUG7du3Qr18/HD9+HGFhYb5Oo6YkGPTITG2uWNKy+dAZq39LgF3pDREREbnHo2Dm448/xuDBg6HX67Fr1y5UVVUBAIxGI1588UWfJrCukNvLWJbKSBKQ1T4uaGkiIiKqCzwKZl544QUsXLgQixcvRsOGDc3L+/Tpg507d/oscXWJUnsZkwCOllYEJ0FERER1hEfBzMGDB5GVlWW33GAwoKyszNs01Umu9HYiIiIi93kUzLRs2RJHjhyxW56Xl4e2bdt6nai6SO7tFPZb7y+l3k5ERETkPo96Mz3wwAN47LHH8O6770KSJJw8eRJbtmzBk08+iZkzZ/o6jXWGs95ORERE5D6PgpmcnByYTCYMHDgQFRUVyMrKQkREBJ588kk8+uijvk5jnZJg0DOIISIi8iFJCCGcr6asuroaR44cwYULF9CpUyc0btzYl2nzifLychgMBhiNRsTExAQ7OUREROQCd57fHpXMyMLDw9GpUydvNkFERETkFY8aABMRERGFCgYzflBirER+YSknkCQiIgoAr6qZyN7KguPmmbF1EpA7Ih2jeiQFO1lERER1FktmfEieskAe6dckgKdX7VctoWEJDhERkfdYMuNDSlMW1AiBo6UVdt2xPS3BKTFWoqj0IlJio9jFm4iICAxmfEqessAyoFGaskCtBCerfZzDAIVVWERERPZYzeRDrk5Z4KgER427VVhERET1BUtmfMyVKQtcLcGx5E4VFhERUX3Ckhk/SDDokZnaXDXI8GTSSc66TUREpIwlMwFk2XjX3Ukn5QDo6VX7USMEZ90mIiL6DYOZAFFrvOtOMMJZt4mIiOyxmikAfNl411kVFhERUX3DYCYAPOm9FGgcwI+IiLSK1UwB4EnvJUv+HiiP49cQEZGWaapkZt68eZAkCVOnTg12Utxi23tJJwHj+ya79N2VBcfRZ9563LN4K/rMW4+VBcd9mjaOX0NERFqnmWCmoKAAixYtQpcuXYKdFI+M6pGEvJwBeDArBUIAi78tchqcBCLQ0EIVGBERkSOaCGYuXLiAe++9F4sXL0bTpk2DnRyvLPm2CHLs4Cw4CUSgwfFriIhI6zQRzEyePBlDhw7FoEGDgp0Ur7gbnAQi0PBkAD8iIqJQEvINgFesWIGdO3eioKDApfWrqqpQVVVl/nd5ebm/kuY2dxsCB2qgPI5fQ0REWhbSwUxxcTEee+wxfP3112jUqJFL38nNzcWcOXP8nDLPeBKcBCrQSDDoGcQQEZEmSUII4Xy14Pjkk09wxx13ICwszLyspqYGkiRBp9OhqqrK6jNAuWQmMTERRqMRMTExAUu7IyXGSpaCEBEROVBeXg6DweDS8zukS2YGDhyIffv2WS0bN24c0tLSMH36dLtABgAiIiIQERERqCR6hKUgREREvhPSwUx0dDQ6d+5stSwqKgrNmze3Wx4K/D24HREREdkL6WBGS7Q6ii4DMCIi0jrNBTMbN24MdhLsqA1ul9U+LqQDBK0GYERERJY0Mc5MqNPiKLp7is8hh9MYEBFRHcBgxge0NoruyoLjGP5mPmz7sYV6AEZERKSEwYwP+GIU3RJjJfILS/1eMiJXiSn1xw/lAIyIiEiN5trMhCpvBrcLZNsVpSoxoHa/nMaAiIi0iMGMD3kyfkygGw8rTamgA7B6Um9kJGp7Ek8iIqqfWM0UZIFuPKxUJZY7Mp2BDBERaRZLZoLM3cknfYETSxIRUV3Ckpkg80XjYU/3m5nanIEMERFpHktmQoCWSko4YjAREYUaBjMhQguTT3LEYCIiCkWsZiKXqPW64ojBREQUbAxmyI7SAH5anLKBiIjqB1YzkRW1qqRg9LoiIiJyBUtmyMxRVVKwel0RERE5w5IZMnNUlZRg0Guq1xUREdUfDGZCULC6P7tSlaSFXldERFS/sJrJR5QazXoyE/bKguPoM2897lm8FX3mrcfKguP+SK4iViUREZEWSUIIhTmU647y8nIYDAYYjUbExMT4ZR9KjWYBuD0mS4mxEn3mrbcrGcnLGRDQgKLEWMmqJCIiCip3nt+sZvKSUqPZGav2QQhAjklcnQnbWZuVQGFVEhERaQmrmbykFICYLAIZmStjsshtViwFsvuzJ9ViREREwcZgxktKAYhOAmwWuRSUBLPNSjDb6hAREXmDbWZ8YGXBcTy9aj9qhDAHIADslrk6j1Gg26yESlsdIiIiGdvMBJja+CuejskS6DYrodJWh4iIyBMMZnxEKQDRSkNaV8aXCdbYN0RERM6wzQw5bavjansaNiAmIqJgYJsZMlNqq+Nqexq1CSqJiIg84c7zmyUzZJZg0CMztblVkOKoPY3M0QSVRERE/sZghhxyZewbVwIeIiIif2EwQw65MvZNsAf7IyKi+o29mcgpta7nMjngsR1Xh72eiIgoEBjMkEucdTN3FvAQhTIOPUCkbQxmyIo3mbpWxtUhssSeeETax2CGzJipU32j1hPP2Qz3RBRa2ACYALB7NWmXN4M1ujr0gL8Gg+RAk0S+wZIZAuDe/ExqVVFsd0CB5m1porOpPPxZWsmSUCLfYclMCAnmW5qr3avVpjZwdcoDIl9RKk2csWqfW/ePo6EH/Fla6WjbWi6t0XLaSdtYMhMigv2W5kr3arUMOK1ltGq7AwAsrSG/UCpNNAlg6XdFePr3nVzejlpPPH/OJq+27aV5R7Ek7ydNltYEOw+j+o3BTAgIlUaIzrpXq2XABUfPKWfM3xVhybdFzNzIL5SqiABgyeYijOuT4ta9o9QTz5XZ5D2ltG0dYA5kAG01Rg6VPIzqL1YzhYBQmg5AaX4mmVpVVI/kpnbLdRKweHMRGxSTR1yprkgw6DGhb4rdchPgk3vHldGvfbntiTelhEw+4ArL3yiU8jCqn1gyEwLU3jD3nihDZmpzl7YRiMa3alVRGYlN7Zb3adccmw+XWn3fV0X0VLe5U10xvm8KlnxbBMtbx5dTafhzMEjbbQPAkrwij0uCAtkA3/Y3mj4kzW+lWESukIQQwvlq2uXOFOLBtGhTIXLXHbBaFiZJyMsZ4DRjCnRddYmxUjFzl5dHhusw/M182F5YOgn4Ludml3tHUf1TYqxEn3nr7R6Kju6DlQXH7QJsrVZnenosgcwD1H6jadkd8NK6g3Xid6DQ4M7zmyUzISL9GoPdMldKMoJRV6020q+8PL+w1C6QAYCJfdsiwaDHnuJz2Hb0LHomN8OBU+fZaJDMPGl0W5em0vDkWAKdB6j9Rl1aN0FezoA68TuQ9jCYCRGeNjb0Z48LT6k1bhzXNxl//vdufLzzhOL32GiQPL0P6tJUGu4eS6DzAEe/UV36HUhb2AA4RHja2NDV8WECSelYckem43T5JdVARsZGg9rm7Tgj/mx06y/BHlvF33mA7fFp8Teiuo9tZkKMWnsUR0K1zYDtsUz+1w6s2XfK4XdcbSdEoceX7TY8uQ+CIVTGVrHMA3QSMD07DQ9lpfpku2rHp5XfiLTLnec3g5k6ItQzlhJjJXrnrldsS2Npxu99kwlTYHnScNcfaXC3Ibk3jc9D4ZgtLdpUiHnrDkDAN4FVqB0f1T9sAFwPhXpddVHpRaeBDAB0ad3E30khPwh0uw3bIMSTEhJPS1Xkff96oSpk2quVGCsx/4sD5nvMF+3PQrE9HpEaBjPkMXfeatXG0rHkrJ6fXbhDlzej5br7u9qNcZKdhvnrDtjN0ZTWMhoZiU0Vt7Gn+BxyVu2DsPiOKw9/231LgN/GuHGHPwIPf46ATORrId8AODc3Fz169EB0dDTi4+MxfPhwHDx4MNjJqvfcnVhSqdHgyBtau9yIkBNZhjZPG4W6+7sqdUO2DGRkJgEMfzNfcXsrC47XjoPk5oi1SvsGrmaiwWwI649GwGzoS1oS8m1mhgwZgrvvvhs9evTAlStX8PTTT2P//v344YcfEBUV5fT79aXNTCB5U5du27bHUVsf+Y09KjwMd7yVz7p7DXCn7ZYn11F+YSnuWbzVbrltCYna9pT26e2+7+mViNu6tA56ezV3OwK4WiIW6u3xqO6qU21mvvjiC6t/L1u2DPHx8dixYweysrKClKr6zZsibdu2PWptfSyL8yUJqm/RckDE6qfQIVxqHaV+Ha3ZW4KhXRJU5wdTqtqZNqQD5n9hX0Jje10q7ROoDYYclTqUGCtx9mK1YtC0fGsxBqbFo6j0IgAE7Rp0Z8A9d9oLhXp7PCJAA8GMLaPRCABo1qyZ4udVVVWoqqoy/7u8vDwg6dICXz30o8LDFJdHhtvXWnraw8SyOF+p7FAuQl+0+bceHBxBOOjcbVCr1o7qhTU/Yu7aH5Fj0724xFiJd/OKrNaVg5BRPZJwY9tmdtNo2Fa1qO1TAEhrGe30uCSFzwWACe/tMKfngZtSMK6ve7N22/L0XnUl8Aj2DNd8+SB/CPk2M5ZMJhOmTp2KPn36oHPnzorr5ObmwmAwmP8SExMDnEr3BGrALV+2OblYXaO4vKLa5JN9qr0927ZN+Gz3SeSuPWDXiJMzcwee2gPS2azXlm0yLAkB5K49gEWbCgHUXku9c9djsc2kkpIEZLWPAwBkJDbFvJGO23iozbQNAMPfsm9jYxdYOzwLtZ+//W0R+sxbj0WbCl26t23zAH+3D1MrEdtx9JxP96OEbd/IXzRVMjN58mTs378feXl5quvMmDEDTzzxhPnf5eXlIRvQBGrALV+/ibnSy8Gbfaptf9WkTFRUm8z76Z273u677DoaGLZv155WPcpVI2v2luCFNT/afT5v3QFERoRh1qffKwYSJgGrfWS1j8Nro68HBNAtuanivpVm2gZqA6gZH+9DbONw6MMbICU2ymG1lKPAxiRgnjhW7nGV3tpgVxrhSs8sX5eaqJVOTVmxCxerr/h1gspglgiRb4VaCZtmSmYeeeQRfP7559iwYQOuueYa1fUiIiIQExNj9ReKPHmTtf2+qyU6jh40nnCll4M3+1TbfkZiU2SmNjc/PJUeJjoJ7DrqJ/I1t2hTod3btTe9aRIMegztkgCFAhoIADM/+V61S7/lPuS3/kc+3IUpK3Zh86EzqvubNzJdMfMzobbKSD62fSeMise1ZEw3xfQqMf1WymRbGuFqzyxfT/Eh31+2x+/vkk1f50MUPKFYwhbyJTNCCDz66KNYvXo1Nm7ciJQU5SJirXH2Juso6vVF2wRvu206a2zo7T492T5Q+2YbCm8JdY3lNWdJfgDm5QxA7oh0u940rv4WCQY9crLTkLv2gMtp0klXG+26+9Y/qkcS0lpGY/hb9l20LY/tpXUHMX1IGl764qDVcQ3s2BLzLI7XVZbpUsoD5HY5/h67ZlSPJERFNMAjH+6yWh6sCSr9IdRKDuqKUC1hC/lgZvLkyfjwww/x6aefIjo6GqdO1c7tYzAYoNdr9wJ1dGM7mw/F3QtJfhPz9EEjs80cHDU29NU+1XrG2G5fB9/NR0PWSoyVyPl4n2q1ivwAVAtAXX2oPJSVCsilEw7SowMwMSsFQ9MTcLG6xrx9d6u5MhKbYt6IdMz4eJ/q/mqEQJdrmiAvZ4DdcVkeb2S4Dmv2nsKSvJ8cDgxpmS61PGDakA52wZO759IV3do0tZ/d3o8lm77KE1wRKnNm1UWhOjJ0yI8zI6mU5S5duhRjx451+v1QHmdGaVyIrPZxduNgSADyZ9yMBINedayL5Q/ciMzU5g735814Ed4M/e7uPuVeK+/kFTndH8fA8L+5a37A4m+LHK7z6eTeiqPtenLdlBgrsePoOUxZscv6QQvgjXu64oY2TbH50BmHbU0A18ci2lN8TrWExt3xjOTrce+JMry07qBiqY3lNtXGhlG6rj3tueeslNdyJGQJwLyR/n3w+/ue5ZxS/hXI81unxpkJ8VjLK0pvsvmFpYrdRqd/tBfvT+jltKjWUcbl6XgRzkqDfLnPlQXH7UoBHJU+cQwM7zn6/UqMlXgnz3EgA9j3ZJO/60lxdIJBj1sz9LhYfcXuQT+0SyvF7apVB7lybcglNLZVRjoJmJbdwaOu0ZmpzTEso1VtYPNzmWq61EqzbK/rRZsKzQ2K5WN2NGWD/JvuO2HEvLW1czYpBSpZ7eOs6rQEnP9G3pYO+fueDdWSg7oikCVs7gj5YKaus72xlQYFA4DNh0uxp/gcMhKbql5I/ipadZQ52L4he7NP+SGlFL4yM/IPZ9eMWm8eS2rtHrx9qKg96NW2q1Yd5ArLfeUdPoO3NhaaG+Q20Tf06Jq2Cmyub6WaLmcP9xJjJeats29LJE/ZYBugqLVvEgByPt5nFagoNaR39BtpofqGc0r5nzsDNAaKZnoz1RcJBj1+n95S8bPtv40DMapHEvJyBmD5AzciL2eAuVjam95Rjqj1VIkM1/l0n44enMyMfM+Va0bpt5fg2nxEvpgvSA4GbAN+te0qre/OvpJjI7FgU6Hd7NPe9jL0Jl2OZpyXS1Lk/dn+pkrrL9963Ly+O7+RP/MYX3KltyV5z5tr2h8YzISgB7PaKi7vnny1ONn2QvJnt0e1zOFidY1P96mUsQLWvVZkgRpsUCs8OR+uXDNKv/28ken4bsbNVsG0En89VPz5sPLmPvJXd1W1+0Ipfa6UpL2+/og5fe6cSy11rVZ64aO6jdVMISgjsSlG3tAaH+88YV6WdW0s4mMaqX7H30WrSsWKJcZKn+1Troe3bPcg91oZ18d6aHgtFHUHkqfnw9VrxlG7Dmf8VRztr+16eh+52z7InXYntm0UbFmmT23IAlsmUVvllNYy2uVzqbXqG7anq19CvjeTt0K5N5Mze4rPYfHmIqzZVwIB5w8qV2fN9WX3Tndn6lXbhm3PlC6tm6jOpK3Ukn7VpExcrK5xeEyWs3A7WzfUqP1m3vYs8MXvV9d4ck7c6WXobc9ApQbFtm1m5PTLBTpqmbxlo2BX8gVeLxRI7jy/GcyEME8eVM66PfqjVMObrpbuHqPaQ0OeWVvtmJQaRWqlVMfRb+ZNV31ZXe/e7ulkp+6cE1evY2+CcXfSt6f4HAqOnkOP5KaIj2mEncfOYbLNAHky267tzu6Lun69kHv8OThhneqaXZ950hvEsg2N5b8B/43c6E1xrrvHGBUeZg5cLNlONml5TGqNIkNl5EpHnP1mvij6r8vF8Z4G7/I5kdsiOcuoXe2uqna9y7N9W6bR0yEP1I55T3EZ3lYYL0iebsHy347ui1C4Xji6b2iwHEojEGMUOcJgJoR58qBylHl72lXWnxmHO8coH5tlIKMD7EZvtT0mR40iQ73Lt7PfzNlDNJCZfqg9YJQCwRkf70NURAN0a6M8CaUldwMhV9qeqA29YNt7qqzyslslJfJ5B6Aa/I7rm2I367iaUL4v2GYuNNiODK7U9T+QGMyEMHcHJ/LHW7y/Mw5Xj1GpdEUHYPGYbnjg/R0Oj8lRo8hQbsAIuBbsqT1EA5nph+IDRnHuIwCPfLjLpQDBMqN2Z8A/R5/bTn6pFNjUCGEe6M7Zvm3P+8S+KarBb2Zqc8wbme5wagpZqN4X/pwXKNSC8VC3/ehZxaB8x9FzuDUj8OePXbNDnDtdDJ11nXS3S2ugxpVw5RjVHkyR4Q1dOqaJfVPsLnYtjD/h6m9m21U/kGOC+Htf7nY7l9ePCg9T7dLsLI3v5tmXYLjaDVktvUqDQkoSoJREV/atdN6XfFukOiZUfmEpstrHIX/GzZhyczvV9IfyfeGv7uGhOAt0qFObasjV2eR9jSUzGuBqHbU3b/FKAlkt5ewYldrKyMeWmdpc9Zhs31wf7NsWQ7u0REW1STMNGD3phhzIId3d3Zc714e7JT6269/RtTU+2XVSsUuzWhrVpnDQwfkkjO5W85oE8GBWCpZ8W+SwO7VSSYlagP9g37Z4J6/IXNI5vGsr3PFWPkyiNnDKyU7D6F5J+MeGI3YN4l+/uyu6JTuvggsWR3mcpyUre4rPeVQKV5c5O5clxkqI33rL2QbnN7Sxn14jEBjM1CGuVtn4IjhSu9j9Ud2g1lZm2pCr8+YoHZPSm+s7eUUY1zdZc5mUu40uAzkmiCftnuTrY0LfFIzvm6KaYbo7dovt+p/sOolVkzJRfLbSbuJKd6ZhAIC7eyVix7FzEOIsuic3M68r3wPO2ujsO2G026ZOAtJbGzB7WCc8++kP9juF+hxRaud9XN9kjOubbJ7NWw5kgNoHT+66A8Bv96ZtXnFrRivFNIQKtTzO02lVzBNt2iwP5TZD/qaUh2e1jzNf65bnWsLVqlJ53WCdMwYzdYwvBxNLMOhxR1frwfuGd22lmnH4oj7bNkhS7YkEYP4XB9AkUn3eHF+UTmitHt0yvYGaDC7BoLfq2utquyeTABZ/W4Ql3xYp9oJw9/dTW7+i2oRbM1opTlyplMZfL1TZBQkSgA+3FuPDrcVWyywz8cRmkaptdNRK3k0CeHT5bugk5fYz0m/rzFt7AEdLL2LKwGutAnhHv3GCQXniWqB2zqnvcm72eC6rYLLN4wBYdXd3Nd8xV/tpsC2dvyjdozmr9gHi6rUuBKwa/eoA/OO32eyDeQ0xmKmDfNV1ssRYidW7TlgtW73rBFbtPKFYJOtt8KD0RqD0gJA5y7S8LZ0IxUatjizaVIh56w5YPWDlh1VkuA4Xq2tQYqz0eYazsuC4OZCRUFti5mq7J6A2Q1TqZaTWcHvvz2WKY+gora8DEBle21rKWaBv+XtLsB67SOmBZ3sPrJqUqdrQ3FmDW5Oo3Z9O1AZAut++Y/nQWL6tGMu3FWO+ReCX1T4Os2/vhDPlVRjYMd5uBm213lMmAXOjYC32crPM45QCNlfyHbXrUQf76VMc0doLjyNK58Ty2lc6XyYAzaIign7sbABMikqMlfh870nFOn61IllvJhZUK9Vx1IjTct9K5BID+fvulE5oZVI92aLNhchdZ98DBgCOn72IO97K90vjRtvzJAC89MVBxfPkaI4huQTDMn3y72dLbfu2jaXl7d7xVj4WbSpEfmEpACg+wJWOQxLAm/d0xWt3X+80GJFLgGz37w4hgDfu6YrlD9yI1+/pqrrPGR/vQ4mxEisLjqN37nrM/OR7vL7+CIa/ma/4297dM9FumSclD+40xJbzj6dX7fV7w1ql60onXQ1i3f3e6sm9XX5pqWsNh53NA6YkVEqxGMxoRCAnVpRv0LlrDth9JheHW7KcsdjTCQAdVRE4ekA4upEclRg4O59amlSvxFiJeevsf6saIbDz2Dm/BmXunCf5+nCUWdqmL721wW4dy+3b/o6jeiRh1aRMqx4VJlHbTsTRA0etMW2zqAh0T27mNIOXr0O5Z94/RndVnm38t2Vq99ENbZoiM7U5urVpql41hdrur7ZdrAVqx5iRz4V8Hy/fVmzev7wfd4L6/MJSLNpc6PJDWw6yHvlwFz7cVuz3FwLFIFbUBrGO0qmUX+WOSLcr3VLjyxeeUJk4V+lc2pJwNXAIpZ5vrGbSgEBWd6i1UQGuXrgAVOvpPW2z46hKyLK30t4TZXhp3UGn7UDUSgyGXa/e5sfV9ASSK0XYRaUXFatBdABMQvi1V5O750m+PpZ+V4Qlm4vsBjy0TZ+j7avdFxeraxTPB6BeNeloP3IGn2PTvkKuhlJqq3Jrhl6xjY7lvbH50BmH7V1ystNqG+va0AGApFx1JVcfAbC7jyU4b9tgeb1Z3idK5zCtZbTdFAxK3c8t+ath7ageSUhrGW0eSdkynY7aznjTxtBXPQZDpTpb/u3TWkbj1bszcPj0Bbz+zRG79R64qa25gXkotbViMBPivGlU60ldrlo98syhHfH7Lgnm7TjKADxps+NKY0Z5LJVhGa2c3khqGc2Oo8olFbbnc/OhM3YPrmnZHRSnifAXVzM5tbYl07PTzKUK/grK5N/NMp3O3tQSDHo8/ftOGNcnBTuOnnPYy0itETqgPtKts5mjlR44zq4/+aG389g5CAF0S659e3d0HTqbbdzZg/ShfqmABKsB9CQAuSPTzSU3toeok2q7jzsqaVL7bWyvN8uGnkrnUGkKBkejbQPWv22JsRLbj56FJEkujcjszMXqGo96JXnaxtDdQF4pP/bnIIDuUJq7zrKnkkwHmHuDhkoQI2MwE+I8jf49jfbVblDLQAbwz/wsrr4lubJvteOAwkPO9nwqvV0KcfWh4qxLsS+4ksmp9VySJw58KCsVgHIXXF+nWw783Jm2NsGgR7fk2gENl3xbW0qjNB2DbSP0T3adxIC0eNXfMTO1udUx21J74Di7/hIMegztYr/M2TF68yB9KCsVwzJaWQVR8vq2o/lKNl1j3X3Q2l5vziiVgDgKJCUAE/omA7Ce00f+zJV5fRy9oAW6NNVZAGxJLT8O5HhQatRK4wWuVo2aRG0gMz07LeSCGBmDmRDnyQ3qTbTvzg3qD74IkuQMb/qQNLz0hXWVVLc2TZ2eT0e9bgDrLsU52WlIv8bg854MzjI5pcxRrZutK0Gipz0ybAM/AeVrTWn7SgMa2o4BpHYeIBw/rC2P2dWqSSA0JlG0pRREAVePccfRc+bByixLf9y5j52VqDhjG0jaVsllXRuLvCOlePvbIsX5oeT2Po7yKGcvaMHIu1y9t9wpRdRJQOmFSx73PHTnXlbr6CETAEb3SMLyguMwCefDYQQTg5kQ58kN6m2078uxagLNNsObnp2GLq2bWB2Hs/PprJpCJgBzmwZf13U76pacHBupmDnm5QxQ7LIMeDbLsitcudYWbf6ty7jF9rPaxykOaDi0S0urjFgtmO+W3NTp7+hu1aQWye1zlLhzH6t14XaVXL1luV+5NCmxmd5u4D4lcnsfdwMCy/Wz2sfh1bszoPutMXUgfmtnAbCje8S2FFFuhyWPPeRunuLKvSwHO/tOGM0dJNToAKwoOG4OTINVDeYKBjMa4G5w4Yvi1lB8Q3VGKcN7ad1B5OUMcKmkwlG1jaP2A/K+fHmTJxj0mNQ/Ff/YUGi1/KUvDqJ1U71Hwao/6uydXWuLNhVaNWKVt//a6OsVj0GpHYZa0OLOfaHF69kXXD3uBIMeD9yUgre/LXJpu7aBj0kAn+0+WdvOB9alSWoD99lyVCLhStDs64a0vho/xtk9Yhn8PfLhLo+nVXB2L5cYK/FuXhHeyXM8dYZMnrjU9poI1dGRGcxohDuZcbCrioLFnRIp2/PprNpGrWeHK/vyxMqC43jTJpCR9+GsikVte/6os3d0rTnqMq50DIByOwxHQUt9DVL8YVzfFCyxedCFSRKmDelgHowRqG2bk5OdZtUwGbg6TYLcVksWFR7m0v5NDkoknAUEvm5I68vAyJX8OMGgR9Ooi15Nq+DoXv5sz0nkrrW/F9XoAKye1BvxMY0Ur4lQGFfGFoOZOiqrfRxeG309YNNosC7ztERKLSO0rLZxpUuxuz0ZnKVHKW5ytYrFleNTq7N3N7NSCzaKSu0zZ6D24SAfg6MA0TIjZ9Dif2oP3VE9kjDs+lZWbXPUftv56w5gWEYru5IAdygFIs4CAl82pPVHDyNXShG9vRfVvp93+Aze3Gj/YqRGPrfyeDtaeTFmMKNBzh6MoTJuQaB5WiLlakaYYLjapdi2YakOV3tq2HL391AdZt2i27MrVWWuZPS2dfaeZFZq16OjLuPyMUSGh+HR5bsVtxuqb4B1maPu5LZtcxxNk7D50Bm7Qf0s6QDFFwKZ0v3nKCDwVU8mtQaxvih1dRaQe1uirvT9adkdXCqRUWtfCGinDSWDGY1x9mBUe6tQGuCqLvLkxnM3I7RtWLo07yiW5P2Et78twpK8IkwfUtvDKSo8DMfPVrj9e6jNMbR6Um+r0UldqSob1SMJUeFhdg8etZ4/7mZWjq7HBIP1BJRy107LagilcXAA18arIf9wpRQswaA8qF+YJCEyXOdw4LyBafHonxaHWZ9+r1oqp3T/OXqJ80XVutJYK47S4w/eBg6WPdwgAecqqp1+RylvsaWFklFJCHdGhtCe8vJyGAwGGI1GxMTEBDs5XikxVlrNDgv8Voxo0cA1v7AU9yzeavddy0nz6ktJjTtWFhxXLF53Ruk3cYWz38Pd9KhdG9OyO9j1WHDn+Bxxdj1a9mKSUNvOQm4gqnasOgATs1Iwro//xvAh31m0udButvTEZpGKeZAt6bf/sX0CKV0rrpZulhgrrQIBV6t3Hd3HvrpfPOVuQ2TbCVMdZU2uju8TLO48v1kyoyFq1QVr9pZg6G+D2qkV7Wuha10wefpG5On4HJa/x4xV+xAZHobuyc3M+3U3PWrXhhxMyHQSsGpSpkvzzzjLRB02ONx90uqtXeDqdBK229JKMTbZkwf1sw0gXB3aQOlJKwDMW3d1PBNHpc3Hz1ZYjSBsWYLgTvWuqyOfB5q7VdRK07jUFwxmNEQtUHlhzY94ce2Pil1ZleqmQ7VrnRJfdY90hSdFqa6OSSNT+j3UenG4kx61qinbdJkEUFHtqLVCLVcyUbXquchwnWovJrXrTgvF2KTM9rezrfLxhMDVgGXb0bOKQfPtb+ab/21bwuBuI15XRz73FVfyNU8aIrv7ciWf57rwcstZszVEziSUZjSVL/QSY6V55t7lD9yI1ZN7283eKz9wQmGWVkfkWX9dmanXGX/NSuvoN7GkA/DmPV0Vfw+ZSQAzPt6HPcXnnKbX9nPbdIRJEqZnpyn+9p707pqxqjZdjo5dLo5XmiMHsB5Yjeo2yzxoRnaa0/tDiRywzF3jvAGrgPWM4e7Oeq92LfvjAe9qvubKMdjmAymxUVA71bemJyjOxO7ovGgJS2Y0Ri6SX7O3BC+s+dHqM7WurLYN44Z3bWUekTNU29D4snukO6NielICZFlN8t2RM3hrY6FiG5WhXWonSHT01moCcPub+ea6bqX0qh2PUnVNk8iGPundZRLA8Dfz7erXlfapVs0QyvO6kO9ZNZS/vpV9D0AXBqNUolYSKvekUqtudxbIB6K60518zdkxqOUDOdlpdj2YwiQJz9zaEQ9kpWD4W/lWVc91pdcgS2Y0KMGgx9Au9lG2own05LekVZMysXrXCbubybYEwF8lGa5y981KjSulDL4oAUow6HH87EW7QEaebTurfZz5fI7qkYRVkzIxZWA71e3Jm7D9fdQyQ8sSmszU5lZtb+TfPi9ngFuTjSqlSelasd2n7VuuDsAMm15MVL/I18hDWanm6/G7nJsxb6TzUk1bD/dPVSxhsCz5U7oG1YZOUEqnv4Jud/I1R6VFjvKBh7JSMSM7zfxwt/xeRmJTzHOxBCrYzwB3sWRGozYfOmO3zJUJ9JSGFvf3sOCe8NW4Ec5KGZTmCPKkBEh15lkB5K49YH5T0knA4Ota4ov9p8yz0jp7M7X8fTwZHMzd9ihyJqp0PK62t2KjXlJjeT3aXicAFEudLS3c+FPtCMQ2oxJbzhhuuW3boRNcyc8cldR6U4rrbr5mOdWBSQh0T24GwPnYWA/1SzWXhnkybkwoPAPcxWBGg9RGiC2rvKy4risT9/lrWHBP+WLcCEC9ga5cyqA2R5C7DaRdbXhnEsC6/aes0iFZ/LcSy9/HF0GeK5nxqB5JSGsZbZ4ryZN9sVEvucL2OhnaJQEvrv3R4cjQXa5pgvwZNyvOGG5rSd5PbuVnjh7k3j7k3c3XbOdTspyo1Vk+IG+zqPSi1b/l//Zlw+NQwGomDVJ7cM5fd8CqSFCp+sRZQzd3q3c8LYp05XueVJHYko9XqdrEco4gS56UAKlVzbhCAHjgprbm30QCzI34bH8fbxsqulOllpHY1KoaIJSHMqe6w1mjevn+TDDocWtGKwztYt/dX+ZJfqZWfeOsitdVruZrKwuOo3fueiz+tshunwCc5gOeVp+7c85CqSqKJTMalBIb5XAocUd1qs4m7nPnzd/TtxR3vueLt3tHpQyJzfSY0DfF/Obj6QPbm+6oOgDj+iZjXN9kq+J2tWJgtaJnZ1yZVde2xIbVRRQMltfd3p/L8NIXBz0qoXW3JNPRg1xA+KQUF3Cerzman03ep6N705vSFVfPWahVRTGY0aAEg/pQ4vIF56xOVe1mcrUY1NObJVhFmHIpg1qvLgnA0PSWeDCrrUsDyimxzFwiw3UoPluJKSt2Oa1+suzlY1sUrMZyFm9XMxJH14Sj7bG6iIJBqTeUuwG1bX6mk4C7elyDHcfOoVubq/eYHMhHhYc5fJD7oh2fK5bazFRtyXKfavemNxNvuvIMCMWqKAYzGvVQv1RAgt1Q4vKF5E3bClfexj29WXw5u60jzkoZIsN15kAGqC3lWrPvFNbuO+XV8N6WmUtGYlNcrL5izhRsS9MkADm/t+7l46/BtADHg9yFWsZEZMmbgDqrfRxm394JGw+cxjcHzmD5tmIs31ZsHmgPgFUgf0fX1vhk10nFB7m/Z5AuMVZi+9GzWPxtkeLnanOWuds20hlnz4BA5ePuYDCjYUpDicu8bUDrLPPw9GbxVS8lR5xNfqjWqwvw/YiYllVCj3y4y+ozCcCwjFYupduSpxmJ3VsqgGlDOqD4XGXIZUxEvrCy4Ljq7N0CwPSP95nnSQNq86VPdp3EqkmZqKg2BXQGaUcTXQK1g949c2tHu32q5RveBl6OngGByMfdxWBG4xxdcP688TwNlnzVS0mNq6UWjqYh8PWDPMGgR9Ooi/ZtnADsPHYOTaNqi7d9NZiWI6N6JKGs8jLm/VaiZ9m91VKwMyYibzlqd2LJtolbjRCoqDYhM7W54vr+qHYtMVaqBl1Abbs6pUDG07aR3vJ3Pu4JBjN1nD/bO3h6s/jzJnO11EK+GZXehPzxIFcKQCQJeOTDXarjzdQIYQ52UmKjAMBclOxpRrKn+JzV5JNKmadaUTaRlng6Cazt/e9q1a+rs3PLbXMuVteY1383r8jh0Axq96OnbSN9IdQ6BzCYqUMCOSmjzNObxV83mTulFuZBtb4rwpLNRTD9tu607A6KYzMAnp9jpYaIlkO5K2VktsGOvJ5clJyXM8DljKTEWImleUV4W6Uu3tLrd3fFrRbVX0Ra5O4ksIB9IO9K1a+r1cNq1Ui3X98Kn+0+abe+BOAf93RVHEPHUYNlT+ZA8yZfC3YQI5OE8HBaU40oLy+HwWCA0WhETExMsJPjN6HWTS5Y5If2km+vBicvjujs0oifcjfQ+V8csDuPaoNXuXuO5f2UXriER5fvtvtczpiczVsTJknIyxngUkbirC7e0+0ShbqVBceRs2qfuSRSAjD8+lZYrRA8AMA/Rl8N5EuMlegzb73di5Hl/eHKOmrrOfNgVgqe/n0nq20UlV7Evp+NVnnUkOtaYt1vI4rLx+hOJ4ZQfna48/xmyUwdEIrd5ILB9qZ8sG9bjOub7FbR8L1L/s/uPJZVXsa8tdZtS5ydY7U3HflNpsRYad+7SQJWT+qNimqTarAjc7Vdj9o0C+Z9/vb/AhwUj+oeywb4QgDdkmuHXVAKZnTS1c8B16qsXa3WdrfKSycB4/qkmP+t9kJiEsBaixHFAfc6MdSlZweDmTogFLvJBZrSTflOXhHGqUwup/Q2ktgsUvE8qjWStRwV0zJw8fhNRwDxMY3MwY6jInJX2/U4ykR1AFZP7o34mEYhU+9N5GsJBj2Gdrl6XecXliquN7FvW/PnKbFRLk398uuFKtV1LKuClNZTY/tS4eyFRImr+X9denYwmKkD/NlNLhjtcDzhzk2p9jayalKmff0z1DMgnQT8d88JLN9WbG7LMn1ImrkI2HLbtm86RaX2vZsEYNVwz7KNjfRbMY6A/QzAjn4jtXYDOgC5I9PNAwSG8m9L5EtK94QOQPPocHNVkPwSotbQXq2kRF7HchBKmQTHE8vqpNr2at2Sm1oFMp/vPel2Q2ZX8/9Q7GLtKQYzdYC/usmFcl2qLXduSrXAp6LaZHcepw3pYBWcyCTUtmn5cFuxeZlJ/DaIoc3+lIIqR+mVg5Os9nFWjXwB2M0AfEfX1li964Tqb6TU8Hiik+o3orpMKb+cNqSDeQBS4OpLSF7OALuG9molJRKAadkdkNU+TrF9jKN4RJ71+1aVcafcoYPrvRFDsYu1pzQRzLz55pv461//ilOnTiEjIwNvvPEGevbsGexkhRRfd5PTWl2qOzelo0AiM7W53XlsEtnQaqC5u3smmktjbJkAq0G4gNrMJTLcek5XtfQ6mlagxFhpNwPwxztPXN23ym8Ual0oiYLN9p5wVLIrjzdTVHoRp8svYdvRs6oDbs5fdwAXLl12KQDRScCcYdehWVS4XY8lR1VLlqU70m//I37LLzx5UbEdGf1idQ1KjJWayydCPphZuXIlnnjiCSxcuBC9evXCq6++isGDB+PgwYOIj48PdvJCjnA6RJRrtFiX6upD21ngY9ndsMRYicRmkVYjghaVXrQqkbEkd+1+ad1B84STJgB3vJVvV2pim14AVm90tsGJK40I1X6jUOpCSRQKbO8JtRccd0pITAJ4Y32hS/s3CaBdfLTi4Hxq9/rAtHh8c+C0+d8CgE5c7cItf1c+PlclGPQuzfcWys0OQj6Yefnll/HAAw9g3LhxAICFCxdizZo1ePfdd5GTkxPk1IUOX1cJabUu1dWHtiuBj9I5lTMetcZ807I74KGsVNyY0gzD38q3GiZdqdTEMr1KUyxYBieujJuhhd+IKNSoveAA8KiqxxWO7lXFdj0SrAIZmQlAs6gIjyaflblSEh/qzQ50zlcJnurqauzYsQODBg0yL9PpdBg0aBC2bNmi+J2qqiqUl5db/dV1ahdiibHS423KN3eYVNt5V8t1qWoSDLUz8qp1rVY7pwkGPSb0TbH7DgB0ad0EAHCxukZxmHS595MSOQOzZDtDru1vMvKG1nX6NyIKlFE9kpCXMwDLH7gReTkDMKpHksejCMseG9hOcbmzUbaV7nW1PEcnQXWyWFefAY5K4gH/PGN8LaRLZkpLS1FTU4MWLVpYLW/RogUOHDig+J3c3FzMmTMnEMkLGf6qEqrPbS2cndPxfVOw5FvrIcgtAw9PSrZcafej9Js8ObhDvfyNiHzNtmTXk1GEZWGShLt7JqFVE71Vm7uJWSkY1yfF6b2qVA0tD9ppaXp2Gi5W13j1DHCWX2mh2UFIBzOemDFjBp544gnzv8vLy5GYmBjEFPmfP6uE6mtbC2fnNMGgx7yRjtvdeNJLwJUA0vY3qa+/EZG/2d7HSuTxmg6cOq94v3vzUmh7b9vOej89Ow0PZaUqjkvlzjPAWX6lhWYHIT2dQXV1NSIjI/HRRx9h+PDh5uVjxoxBWVkZPv30U6fbqE/TGdheiKFUn6lFrpxTeXoCtUzK2edEFPrk+zgyXIc1+0qs5nKzzBcCcb+r7cMXzwBH6Q/GM8ad53dIBzMA0KtXL/Ts2RNvvPEGAMBkMiEpKQmPPPKISw2A60swA/DB6Q88p0RkK1TzBX+nK9DHXafmZnriiScwZswYdO/eHT179sSrr76Kixcvmns30VWsbvA9nlMishWq+YK/0xWqxw1oIJgZNWoUzpw5g2effRanTp3C9ddfjy+++MKuUTARERHVTyFfzeSt+lTNREREVFe48/wO6XFmiIiIiJxhMENERESaxmCGiIiINI3BDBEREWkagxkiIiLSNAYzREREpGkMZoiIiEjTGMwQERGRpjGYISIiIk0L+ekMvCUPcFxeXh7klBAREZGr5Oe2KxMV1Plg5vz58wCAxMTEIKeEiIiI3HX+/HkYDAaH69T5uZlMJhNOnjyJ6OhoSJLk1bbKy8uRmJiI4uLiejvPE88BzwHAcyDjeeA5AHgOAP+cAyEEzp8/j1atWkGnc9wqps6XzOh0OlxzzTU+3WZMTEy9vWBlPAc8BwDPgYzngecA4DkAfH8OnJXIyNgAmIiIiDSNwQwRERFpGoMZN0RERGDWrFmIiIgIdlKChueA5wDgOZDxPPAcADwHQPDPQZ1vAExERER1G0tmiIiISNMYzBAREZGmMZghIiIiTWMwQ0RERJpWr4OZBQsWoEuXLuZBfjIzM7Fu3Trz55cuXcLkyZPRvHlzNG7cGCNHjsQvv/xitY3jx49j6NChiIyMRHx8PJ566ilcuXIl0IfiM/PmzYMkSZg6dap5WX04D7Nnz4YkSVZ/aWlp5s/rwzkAgBMnTuC+++5D8+bNodfrkZ6eju3bt5s/F0Lg2WefRUJCAvR6PQYNGoTDhw9bbePs2bO49957ERMTgyZNmmDChAm4cOFCoA/FY8nJyXbXgiRJmDx5MoD6cS3U1NRg5syZSElJgV6vR2pqKp5//nmrOXLqw7Vw/vx5TJ06FW3atIFer0fv3r1RUFBg/ryunYPNmzfjtttuQ6tWrSBJEj755BOrz311vHv37sVNN92ERo0aITExES+99JL3iRf12GeffSbWrFkjDh06JA4ePCiefvpp0bBhQ7F//34hhBB/+tOfRGJiovjmm2/E9u3bxY033ih69+5t/v6VK1dE586dxaBBg8SuXbvE2rVrRWxsrJgxY0awDskr27ZtE8nJyaJLly7iscceMy+vD+dh1qxZ4rrrrhMlJSXmvzNnzpg/rw/n4OzZs6JNmzZi7NixYuvWreKnn34SX375pThy5Ih5nXnz5gmDwSA++eQTsWfPHjFs2DCRkpIiKisrzesMGTJEZGRkiP/7v/8T3377rWjXrp0YPXp0MA7JI6dPn7a6Dr7++msBQGzYsEEIUT+uhblz54rmzZuLzz//XBQVFYn//Oc/onHjxuK1114zr1MfroW77rpLdOrUSWzatEkcPnxYzJo1S8TExIiff/5ZCFH3zsHatWvFM888I1atWiUAiNWrV1t97ovjNRqNokWLFuLee+8V+/fvF8uXLxd6vV4sWrTIq7TX62BGSdOmTcWSJUtEWVmZaNiwofjPf/5j/uzHH38UAMSWLVuEELU/vE6nE6dOnTKvs2DBAhETEyOqqqoCnnZvnD9/Xlx77bXi66+/Fv369TMHM/XlPMyaNUtkZGQoflZfzsH06dNF3759VT83mUyiZcuW4q9//at5WVlZmYiIiBDLly8XQgjxww8/CACioKDAvM66deuEJEnixIkT/ku8Hz322GMiNTVVmEymenMtDB06VIwfP95q2YgRI8S9994rhKgf10JFRYUICwsTn3/+udXyG264QTzzzDN1/hzYBjO+Ot633npLNG3a1OpemD59uujQoYNX6a3X1UyWampqsGLFCly8eBGZmZnYsWMHLl++jEGDBpnXSUtLQ1JSErZs2QIA2LJlC9LT09GiRQvzOoMHD0Z5eTm+//77gB+DNyZPnoyhQ4daHS+AenUeDh8+jFatWqFt27a49957cfz4cQD15xx89tln6N69O/7whz8gPj4eXbt2xeLFi82fFxUV4dSpU1bnwWAwoFevXlbnoUmTJujevbt5nUGDBkGn02Hr1q2BOxgfqa6uxgcffIDx48dDkqR6cy307t0b33zzDQ4dOgQA2LNnD/Ly8pCdnQ2gflwLV65cQU1NDRo1amS1XK/XIy8vr16cA0u+Ot4tW7YgKysL4eHh5nUGDx6MgwcP4ty5cx6nr85PNOnMvn37kJmZiUuXLqFx48ZYvXo1OnXqhN27dyM8PBxNmjSxWr9FixY4deoUAODUqVNWGZb8ufyZVqxYsQI7d+60qguWnTp1ql6ch169emHZsmXo0KEDSkpKMGfOHNx0003Yv39/vTkHP/30ExYsWIAnnngCTz/9NAoKCjBlyhSEh4djzJgx5uNQOk7L8xAfH2/1eYMGDdCsWTPNnAdLn3zyCcrKyjB27FgA9ed+yMnJQXl5OdLS0hAWFoaamhrMnTsX9957LwDUi2shOjoamZmZeP7559GxY0e0aNECy5cvx5YtW9CuXbt6cQ4s+ep4T506hZSUFLttyJ81bdrUo/TV+2CmQ4cO2L17N4xGIz766COMGTMGmzZtCnayAqa4uBiPPfYYvv76a7s3kPpEfuMEgC5duqBXr15o06YN/v3vf0Ov1wcxZYFjMpnQvXt3vPjiiwCArl27Yv/+/Vi4cCHGjBkT5NQFxzvvvIPs7Gy0atUq2EkJqH//+9/417/+hQ8//BDXXXcddu/ejalTp6JVq1b16lr45z//ifHjx6N169YICwvDDTfcgNGjR2PHjh3BThrZqPfVTOHh4WjXrh26deuG3NxcZGRk4LXXXkPLli1RXV2NsrIyq/V/+eUXtGzZEgDQsmVLu14M8r/ldULdjh07cPr0adxwww1o0KABGjRogE2bNuH1119HgwYN0KJFi3pxHmw1adIE7du3x5EjR+rNtZCQkIBOnTpZLevYsaO5uk0+DqXjtDwPp0+ftvr8ypUrOHv2rGbOg+zYsWP43//+h4kTJ5qX1Zdr4amnnkJOTg7uvvtupKen4/7778fjjz+O3NxcAPXnWkhNTcWmTZtw4cIFFBcXY9u2bbh8+TLatm1bb86BzFfH66/7o94HM7ZMJhOqqqrQrVs3NGzYEN988435s4MHD+L48ePIzMwEAGRmZmLfvn1WP97XX3+NmJgYu4dCqBo4cCD27duH3bt3m/+6d++Oe++91/zf9eE82Lpw4QIKCwuRkJBQb66FPn364ODBg1bLDh06hDZt2gAAUlJS0LJlS6vzUF5ejq1bt1qdh7KyMqs31/Xr18NkMqFXr14BOArfWbp0KeLj4zF06FDzsvpyLVRUVECns348hIWFwWQyAah/10JUVBQSEhJw7tw5fPnll7j99tvr3Tnw1fFmZmZi8+bNuHz5snmdr7/+Gh06dPC4iglA/e6anZOTIzZt2iSKiorE3r17RU5OjpAkSXz11VdCiNoumElJSWL9+vVi+/btIjMzU2RmZpq/L3fBvOWWW8Tu3bvFF198IeLi4jTVBVOJZW8mIerHefjzn/8sNm7cKIqKisR3330nBg0aJGJjY8Xp06eFEPXjHGzbtk00aNBAzJ07Vxw+fFj861//EpGRkeKDDz4wrzNv3jzRpEkT8emnn4q9e/eK22+/XbFrZteuXcXWrVtFXl6euPbaa0O2K6qampoakZSUJKZPn273WX24FsaMGSNat25t7pq9atUqERsbK6ZNm2Zepz5cC1988YVYt26d+Omnn8RXX30lMjIyRK9evUR1dbUQou6dg/Pnz4tdu3aJXbt2CQDi5ZdfFrt27RLHjh0TQvjmeMvKykSLFi3E/fffL/bv3y9WrFghIiMj2TXbG+PHjxdt2rQR4eHhIi4uTgwcONAcyAghRGVlpZg0aZJo2rSpiIyMFHfccYcoKSmx2sbRo0dFdna20Ov1IjY2Vvz5z38Wly9fDvSh+JRtMFMfzsOoUaNEQkKCCA8PF61btxajRo2yGl+lPpwDIYT473//Kzp37iwiIiJEWlqaePvtt60+N5lMYubMmaJFixYiIiJCDBw4UBw8eNBqnV9//VWMHj1aNG7cWMTExIhx48aJ8+fPB/IwvPbll18KAHbHJkT9uBbKy8vFY489JpKSkkSjRo1E27ZtxTPPPGPVnbY+XAsrV64Ubdu2FeHh4aJly5Zi8uTJoqyszPx5XTsHGzZsEADs/saMGSOE8N3x7tmzR/Tt21dERESI1q1bi3nz5nmddkkIiyEdiYiIiDSGbWaIiIhI0xjMEBERkaYxmCEiIiJNYzBDREREmsZghoiIiDSNwQwRERFpGoMZIiIi0jQGM0RERKRpDGaISFH//v0xderUYCfD72bPno3rr78+2MkgIi8wmCGiOqm6ujqg+xNC4MqVKwHdJxHVYjBDRHbGjh2LTZs24bXXXoMkSZAkCUePHsX+/fuRnZ2Nxo0bo0WLFrj//vtRWlpq/l7//v3x6KOPYurUqWjatClatGiBxYsX4+LFixg3bhyio6PRrl07rFu3zvydjRs3QpIkrFmzBl26dEGjRo1w4403Yv/+/VZpysvLw0033QS9Xo/ExERMmTIFFy9eNH+enJyM559/Hn/84x8RExODBx98EAAwffp0tG/fHpGRkWjbti1mzpxpnrF32bJlmDNnDvbs2WM+zmXLluHo0aOQJAm7d+82b7+srAySJGHjxo1W6V63bh26deuGiIgI5OXlwWQyITc3FykpKdDr9cjIyMBHH33k65+IiCwwmCEiO6+99hoyMzPxwAMPoKSkBCUlJYiOjsbNN9+Mrl27Yvv27fjiiy/wyy+/4K677rL67nvvvYfY2Fhs27YNjz76KB5++GH84Q9/QO/evbFz507ccsstuP/++1FRUWH1vaeeegp///vfUVBQgLi4ONx2223moKOwsBBDhgzByJEjsXfvXqxcuRJ5eXl45JFHrLbxt7/9DRkZGdi1axdmzpwJAIiOjsayZcvwww8/4LXXXsPixYvxyiuvAABGjRqFP//5z7juuuvMxzlq1Ci3zlVOTg7mzZuHH3/8EV26dEFubi7ef/99LFy4EN9//z0ef/xx3Hfffdi0aZNb2yUiN3g9VSUR1Um2s6c///zz4pZbbrFap7i42Gp26X79+om+ffuaP79y5YqIiooS999/v3lZSUmJACC2bNkihLg6U++KFSvM6/z6669Cr9eLlStXCiGEmDBhgnjwwQet9v3tt98KnU4nKisrhRBCtGnTRgwfPtzpcf31r38V3bp1M/971qxZIiMjw2qdoqIiAUDs2rXLvOzcuXMCgNiwYYNVuj/55BPzOpcuXRKRkZEiPz/fansTJkwQo0ePdpo2IvJMg2AGUkSkHXv27MGGDRvQuHFju88KCwvRvn17AECXLl3My8PCwtC8eXOkp6ebl7Vo0QIAcPr0aattZGZmmv+7WbNm6NChA3788Ufzvvfu3Yt//etf5nWEEDCZTCgqKkLHjh0BAN27d7dL28qVK/H666+jsLAQFy5cwJUrVxATE+P28aux3OeRI0dQUVGB3/3ud1brVFdXo2vXrj7bJxFZYzBDRC65cOECbrvtNsyfP9/us4SEBPN/N2zY0OozSZKslkmSBAAwmUxu7fuhhx7ClClT7D5LSkoy/3dUVJTVZ1u2bMG9996LOXPmYPDgwTAYDFixYgX+/ve/O9yfTldbAy+EMC+Tq7xsWe7zwoULAIA1a9agdevWVutFREQ43CcReY7BDBEpCg8PR01NjfnfN9xwAz7++GMkJyejQQPfZx3/93//Zw5Mzp07h0OHDplLXG644Qb88MMPaNeunVvbzM/PR5s2bfDMM8+Ylx07dsxqHdvjBIC4uDgAQElJiblExbIxsJpOnTohIiICx48fR79+/dxKKxF5jg2AiUhRcnIytm7diqNHj6K0tBSTJ0/G2bNnMXr0aBQUFKCwsBBffvklxo0bZxcMeOK5557DN998g/3792Ps2LGIjY3F8OHDAdT2SMrPz8cjjzyC3bt34/Dhw/j000/tGgDbuvbaa3H8+HGsWLEChYWFeP3117F69Wq74ywqKsLu3btRWlqKqqoq6PV63HjjjeaGvZs2bcJf/vIXp8cQHR2NJ598Eo8//jjee+89FBYWYufOnXjjjTfw3nvveXxuiMgxBjNEpOjJJ59EWFgYOnXqhLi4OFRXV+O7775DTU0NbrnlFqSnp2Pq1Klo0qSJuVrGG/PmzcNjjz2Gbt264dSpU/jvf/+L8PBwALXtcDZt2oRDhw7hpptuQteuXfHss8+iVatWDrc5bNgwPP7443jkkUdw/fXXIz8/39zLSTZy5EgMGTIEAwYMQFxcHJYvXw4AePfdd3HlyhV069YNU6dOxQsvvODScTz//POYOXMmcnNz0bFjRwwZMgRr1qxBSkqKB2eFiFwhCctKYSKiANu4cSMGDBiAc+fOoUmTJsFODhFpEEtmiIiISNMYzBAREZGmsZqJiIiINI0lM0RERKRpDGaIiIhI0xjMEBERkaYxmCEiIiJNYzBDREREmsZghoiIiDSNwQwRERFpGoMZIiIi0jQGM0RERKRp/w8ekd4YKrY9/QAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "surrogate_scatter2D(keras_surrogate, data_training)\n", + "surrogate_parity(keras_surrogate, data_training)\n", + "surrogate_residual(keras_surrogate, data_training)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation\n", + "\n", + "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4/4 [==============================] - 0s 5ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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3PmCoT2hoKHJycpCZmYm2bdvCy8sLarXaKvW9XfPmzfHYY4/hmWeeQatWrRASEoLXXnsNZWVlmDFjhk3qUI3BDRERUR1s3bVg5cqV8PX1RUJCAn777Te0aNECffr0wZIlSxqV/nnggQewdetWDBs2DEVFRdi4cSOmTp1q1TrXlJiYCJ1Oh0mTJuHatWuIjIzE7t270bJlS5vVAQBUQgjRcDHlKC4uhkajgVarhbe3t9TVISIiC7t+/TpycnIQFhYGNzc3qatDJqjvvTPl95ujpYiIiEhRGNwQERE5mNmzZxsMN6/5mD17ttTVazL2uSEiInIwK1aswMKFC43uU0KXDQY3REREDsbPzw9+fn5SV8NqmJYiIiIiRWFwQ0REiiTHmXOpfpYawM20FBERKYqrqyucnJxw8eJF+Pr6wtXVVb9MAcmXEAJXrlwxusyDqRjcEBGRojg5OSEsLAz5+fm4ePGi1NUhE6hUKrRt2xbOzs5NOg6DGyIiUhxXV1eEhISgsrJSsrWWyHTNmjVrcmADMLghIiKFqk5vNDXFQfaHHYqJiIhIURjcEBERkaIwuCEiIiJFYXBDREREisLghoiIiBSFwQ0REREpCoMbIiIiUhQGN0RERKQoDG6IiIhIUSQNbtatW4eePXvC29sb3t7eiIqKws6dO+t9zpYtW9C1a1e4ubmhR48e2LFjh41qS0RERPZA0uCmbdu2SExMxNGjR5GRkYG77roL9913H06fPm20fGpqKsaPH48ZM2bg+PHjGDt2LMaOHYusrCwb15yIiIjkSiWEEFJXoqZWrVph1apVmDFjRq19cXFxKC0txbfffqvfNnDgQPTq1Qvr169v1PGLi4uh0Wig1Wrh7e1tsXoTERGR9Zjy+y2bPjdVVVXYvHkzSktLERUVZbRMWloaRowYYbAtNjYWaWlpdR63oqICxcXFBg8iIiJSLsmDm1OnTsHT0xNqtRqzZ8/Gtm3bEB4ebrRsQUEB/P39Dbb5+/ujoKCgzuMnJCRAo9HoH8HBwRatPxEREcmL5MFNly5dkJmZiSNHjuCxxx7DlClT8NNPP1ns+IsXL4ZWq9U/8vLyLHZsIiIikh8XqSvg6uqKjh07AgD69u2L9PR0rF27Fu+8806tsgEBAbh06ZLBtkuXLiEgIKDO46vVaqjVastWmoiIiGRL8pab2+l0OlRUVBjdFxUVhX379hls27t3b519dIiIiMjxSNpys3jxYowePRohISG4du0aPvnkExw8eBC7d+8GAEyePBlBQUFISEgAAMyfPx8xMTFYvXo1xowZg82bNyMjIwPvvvuulKdBREREMiJpcHP58mVMnjwZ+fn50Gg06NmzJ3bv3o27774bAJCbmwsnp78blwYNGoRPPvkEzz//PJYsWYJOnTrhyy+/RPfu3aU6BSIiIpIZ2c1zY22c54aIiMj+2OU8N0RERESWwOCGiIiIFIXBDRERESkKgxsiIiJSFAY3REREpCgMboiIiEhRGNwQERGRojC4ISIiIkVhcENERESKwuDGgvK15UjNLkS+tlzqqhARETksSdeWUpLk9Fws3noKOgE4qYCEcT0Q1y9E6moRERE5HLbcWEC+tlwf2ACATgBLtmaxBYeIiEgCDG4sIKewVB/YVKsSAucLy6SpEBERkQNjcGMBYT7N4aQy3OasUiHUx0OaChERETkwBjcWEKhxR8K4HnBW3YpwnFUqvDKuOwI17hLXjIjkhIMOiGyDHYotJK5fCIZ09sX5wjKE+ngYBDb52nLkFJYizKc5Ax4iB8VBB0S2w+DGggI17rWCF36hEVFdgw6GdPblDQ+RFTAtZUUcRUVEAAcdENkagxsr4hcaEQEcdEBkawxurIhfaEQEcNABka2xz40VVX+hLdmahSoh+IVG5MDqG3RARJbF4MbK+IVGRNWMDTogIstjcGMD/EIjIiKyHfa5ISIiIkVhcENERESKwuCGiIiIFIXBDRERESkKgxsiIiJSFAY3REREpCgMboiIiEhRGNxYSb62HKnZhVwkk4iIyMY4iZ8VJKfn6lcDd1IBCeN6IK5fiNTVIiIicghsubGwfG25PrABAJ0AlmzNYgsOERGRjTC4sbCcwlJ9YFOtSgicLyyTpkJEREQOhsGNhYX5NIeTynCbs0qFUB8PaSpERETkYBjcWFigxh0J43rAWXUrwnFWqfDKuO5cOJOIiMhG2KHYCuL6hWBIZ1+cLyxDqI8HAxsiIiIbYnBjJYEadwY1REREEmBaioiIiBSFwQ0REREpiqTBTUJCAvr16wcvLy/4+flh7NixOHPmTL3PSUpKgkqlMni4ubnZqMZEREQkd5IGN4cOHcKcOXPwww8/YO/evbh58yZGjhyJ0tLSep/n7e2N/Px8/ePChQs2qjERERHJnaQdinft2mXw/6SkJPj5+eHo0aMYMmRInc9TqVQICAiwdvWIiIjIDsmqz41WqwUAtGrVqt5yJSUlaNeuHYKDg3Hffffh9OnTdZatqKhAcXGxwYOIiIiUSzbBjU6nw4IFCxAdHY3u3bvXWa5Lly54//338dVXX2HTpk3Q6XQYNGgQfv/9d6PlExISoNFo9I/g4GBrnQIRERHJgEoIIRouZn2PPfYYdu7ciZSUFLRt27bRz7t58ya6deuG8ePHY+XKlbX2V1RUoKKiQv//4uJiBAcHQ6vVwtvb2yJ1JyIiIusqLi6GRqNp1O+3LCbxmzt3Lr799lscPnzYpMAGAJo1a4bevXvj3LlzRver1Wqo1WpLVJOIiIjsgKRpKSEE5s6di23btmH//v0ICwsz+RhVVVU4deoUAgMDrVBDIiIisjeSttzMmTMHn3zyCb766it4eXmhoKAAAKDRaODufmvpgsmTJyMoKAgJCQkAgBUrVmDgwIHo2LEjioqKsGrVKly4cAEzZ86U7DyIiIhIPiQNbtatWwcAGDp0qMH2jRs3YurUqQCA3NxcODn93cD0119/YdasWSgoKEDLli3Rt29fpKamIjw83FbVJiIiIhmTTYdiWzGlQxIRERHJgym/37IZCk5ERERkCQxuiIiISFEY3BAREZGiMLghIiIiRWFwQ0RERIrC4IaIiIgUhcENERERKQqDGyIiIlIUBjdERESkKAxuyCbyteVIzS5EvrZc6qoQEZHCSbq2FDmG5PRcLN56CjoBOKmAhHE9ENcvROpqERGRQrHlhqwqX1uuD2wAQCeAJVuz2IJDRERWw+CGrCqnsFQf2FSrEgLnC8ukqRARESkegxuyqjCf5nBSGW5zVqkQ6uMhTYWIiEjxGNyQVQVq3JEwrgecVbciHGeVCq+M645AjbvENSMiIqVih2Kyurh+IRjS2RfnC8sQ6uPBwIaIiKyKwQ3ZRKDGnUENERHZBNNSRKRYnF+JyDGx5YaIFInzKxE5LrbcSIh3lUTWwfmViBwbW24kwrtKIuupb34l9v0iUj623EiAd5VE1sX5lYgcG4MbCSh51l5TUm1My5G1cH4lIsfGtJQEqu8qawY4SrirNCXVxrQcWRvnVyJyXGy5kYAS7ypNSbUxLUe2EqhxR1SH1nb9t0VEpmPLjUSUdldpSgdOUzt75mvLkVNYijCf5nZ/nYiIyPoY3EhISbP2mpJqM6Us01dERGQqpqXIIkxJtTW2LNNXRERkDrbckMWYkmprTFnOVUJEROZgcEMWZUqqraGySh1VRkRE1sW0FMmWEkeVERGR9bHlhmRNaaPKiIjI+hjckOwpaVQZERFZH9NSREREpCgMboiIiEhRGNwQUZ24uCkR2SP2uSEiozg7NBHZK7bcEFEtnB2aiOwZgxuSDaZA5KO+2aHJED+3RPLDtBTJgqkpEK4Ubl2cHbpxmLojkidJW24SEhLQr18/eHl5wc/PD2PHjsWZM2cafN6WLVvQtWtXuLm5oUePHtixY4cNakvWYmoKJDk9F9GJ+zFhwxFEJ+5HcnquDWvrGDg7dMOYuiOSL0mDm0OHDmHOnDn44YcfsHfvXty8eRMjR45EaWlpnc9JTU3F+PHjMWPGDBw/fhxjx47F2LFjkZWVZcOakyWZkgLhD4rtxPULQUr8MHw6ayBS4oexReI2TN0RyZekaaldu3YZ/D8pKQl+fn44evQohgwZYvQ5a9euxahRo/DMM88AAFauXIm9e/firbfewvr1661eZ7I8U1IgXCnctjg7dN0cJXXHFDDZI1l1KNZqtQCAVq1a1VkmLS0NI0aMMNgWGxuLtLQ0q9aNrMeUFEj1D0pNSvxBUQKld7R1hNQdU8Bkr2TToVin02HBggWIjo5G9+7d6yxXUFAAf39/g23+/v4oKCgwWr6iogIVFRX6/xcXF1umwmRRjV0gs/oHZcnWLFQJocgfFCVoTEdbJbQIKHlh17pSwEM6+yrqPEmZZBPczJkzB1lZWUhJSbHocRMSErB8+XKLHpOso7EpECX/oChBY34UlTTKSKmpO6aAyZ7JIi01d+5cfPvttzhw4ADatm1bb9mAgABcunTJYNulS5cQEBBgtPzixYuh1Wr1j7y8PIvVm6QTqHFHVIfW/JKVoYY62rJTuH1gCpjsmaTBjRACc+fOxbZt27B//36EhYU1+JyoqCjs27fPYNvevXsRFRVltLxarYa3t7fBg4isp6EfRY4ysg+O0KeIlEvStNScOXPwySef4KuvvoKXl5e+34xGo4G7+60/oMmTJyMoKAgJCQkAgPnz5yMmJgarV6/GmDFjsHnzZmRkZODdd9+V7DyI6G8N9YuS2ygjJfT9AaxzHkwBk71SCSFEw8Ws9OIqldHtGzduxNSpUwEAQ4cORWhoKJKSkvT7t2zZgueffx7nz59Hp06d8Nprr+Ef//hHo16zuLgYGo0GWq2WrThEVpSvLa/zRzE5PbdW8CNFnxul9P1RynkQ1ceU329JgxspMLghkof6gh9bvX504v5aLUgp8cPsqoVCKedB1BBTfr8bnZYyZQg1gwYiaojUo4yUMhpIKedBZEmNDm5atGhRZxqpmhACKpUKVVVVTa4YEZE1ya3vj7mUch5EltTo4ObAgQPWrAcRkU0pZUJIpZwHkSWxzw0ROTSp+/5YilLOg6guVulzc7uioiL897//xc8//wwAuOOOOzB9+nRoNBpzD0lEZHNS9/1pjMYM87aH8yCyFbNabjIyMhAbGwt3d3f0798fAJCeno7y8nLs2bMHffr0sXhFLYUtN0RkTzjMm+gWqw8Fv/POO9GxY0ds2LABLi63Gn8qKysxc+ZM/Pbbbzh8+LB5NbcBBjdEZC84zJvob1ZPS2VkZBgENgDg4uKCZ599FpGRkeYckoiIbsNh3kTmMWttKW9vb+Tm5tbanpeXBy8vryZXioiI6l6ny8PVCanZhVxslKgOZgU3cXFxmDFjBpKTk5GXl4e8vDxs3rwZM2fOxPjx4y1dRyIih2Rs8cqxvdvg/v+kYsKGI4hO3I/k9No3mkSOzqy01L/+9S+oVCpMnjwZlZWVAIBmzZrhscceQ2JiokUrSJallEUCiRxFzcUrPVydcP9/UvWpKp0AlmzNwpDOvvx7JqrBrODG1dUVa9euRUJCArKzswEAHTp0gIcHZ8SUM466ILJP1cO8U7ML2QeHqBHMnucGADw8PNCjRw9L1YWsKF9brg9sAN7xEdkjLrVA1DhmBTfXr1/Hm2++iQMHDuDy5cvQ6XQG+48dO2aRypHlKHnUBVNt5Ci41AJR45gV3MyYMQN79uzBgw8+iP79+ze4oCZJT6l3fDVTbSoAs+4Mw7TBYfyyJ4uRW/Bcsw8Ol1ogMs6sSfw0Gg127NiB6Ohoa9TJqhx5Er/k9Nxad3z23OfG2ARnAPsTkeWwnxqRfFh9Er+goCDOZ2OHlHbHZyzVBrA/EVkG+6kR2S+z5rlZvXo1Fi1ahAsXLli6PmRlgRp3RHVoLasv53xtuVkTkhmb4KxadX8iInPV10+NpGPu9wU5FrNabiIjI3H9+nW0b98eHh4eaNasmcH+q1evWqRypHxNafav7ly5+ItT0N22Twn9iUhaSu2nZs+YJqTGMiu4GT9+PP744w+88sor8Pf3Z4diqqUxnTAt0exfnWrbmHIe76X8Bp0AR5CQRXBkknzka8uRcf4q04TUaGYFN6mpqUhLS0NERISl60MK0Ni7K0sNTw/UuGPJmG6YNjhUMf2JSB6U1k/NHtX8PrmdUqazIMszK7jp2rUrysuZ76TaTGmNsXSzf/UsrkSW5GifKzkNfb/9++R2TBNSXczqUJyYmIinn34aBw8exJ9//oni4mKDBzkuUzphGlsUkM3+RNJJTs9FdOJ+2SzKWdeISIDfF1Q/s1puRo0aBQAYPny4wXYhBFQqFaqqqppeM7JLprbGsNmfSB7kOPTd2PeJE4A3J/RGn3Yt+X1BdTIruDlw4ICl60EKYU4nTEdr9ieSIzku0VLX98mYnm0kqQ/ZD7OCm5iYmEaVe/zxx7FixQr4+PiY8zJkp9gaQ2R/5Dr0nd8nZA6z+tw01qZNm9gHx0HJcbJAIqqbnPvA8fuETGVWy01jmbFsFRERSYStJKQUVg1uiIjIvsipD5ychqWTfWFwQ0REssOlFqgprNrnhoiIyFR1DUvnYpnUWAxuiIisgKtXm48rslNTmRzcVFZWYsWKFfj9998bLPvII4/A29vbrIoREdkruc30a2+qh6XXJIdh6WQ/TA5uXFxcsGrVKlRWVjZYdt26dZzjhogcClMqTSfnYelkH8zqUHzXXXfh0KFDCA0NtXB1yBo44oDIduQ406894rB0agqzgpvRo0cjPj4ep06dQt++fdG8eXOD/ffee69FKkdNxxEHRLYl15l+7ZGchqWTfVEJM2bac3KqO5sl94Uzi4uLodFooNVqFd8fKF9bjujE/bW+ZFPih/ELg8iKktNza62HxJsKoqYx5ffbrJYbnU5nVsXIttg8TiQNplSIpGXWUPAPP/wQFRUVtbbfuHEDH374YZMrRZbBEQdE0uF6SETSMSu4mTZtGrRaba3t165dw7Rp05pcKbIMjjggIiJHZFZaSggBlUpVa/vvv/8OjUbT5EqR5bB5nIiIHI1JwU3v3r2hUqmgUqkwfPhwuLj8/fSqqirk5ORg1KhRjT7e4cOHsWrVKhw9ehT5+fnYtm0bxo4dW2f5gwcPYtiwYbW25+fnIyAgwJRTcSgccUB0C6dFIHIMJgU31YFHZmYmYmNj4enpqd/n6uqK0NBQPPDAA40+XmlpKSIiIjB9+nSMGzeu0c87c+aMQU9pPz+/Rj+XiBwTp0UgchwmBTdLly4FAISGhiIuLg5ubm5NevHRo0dj9OjRJj/Pz88PLVq0aNJrE5HjqGvW4CGdfdmCQ6RAZvW5mTJlCoBbo6MuX75ca2h4SIh174Z69eqFiooKdO/eHcuWLUN0dHSdZSsqKgxGdhUXF1u1bkQkP3KbFoHpMSLrMiu4OXv2LKZPn47U1FSD7dUdja01iV9gYCDWr1+PyMhIVFRU4L333sPQoUNx5MgR9OnTx+hzEhISsHz5cqvUh4isy1JBgJxmDWZ6jMj6zJqhODo6Gi4uLoiPj0dgYGCtkVMRERGmV0SlarBDsTExMTEICQnBRx99ZHS/sZab4OBgh5ihmEhuTAlWLB0EyGHWYM4aTmQ+q89QnJmZiaNHj6Jr165mVdCS+vfvj5SUlDr3q9VqqNVqG9aI5ITN/01nqWtoSrBijT4yNadF8HB1QumNKuRry236uZBbeoxIqcwKbsLDw1FYWGjpupglMzMTgYGBUleDZIjN/01nqWtoarBirSAgUOOOw79ekexzIaf0GJGSmTVD8auvvopnn30WBw8exJ9//oni4mKDR2OVlJQgMzMTmZmZAICcnBxkZmYiNzcXALB48WJMnjxZX37NmjX46quvcO7cOWRlZWHBggXYv38/5syZY85pkILV9WOary2XtmJ2xJLXsL5gxRhrLR0i9eeCs4YT2YZZLTcjRowAANx1110G/W1M7VCckZFhMCnfU089BeDWaKykpCTk5+frAx3g1uisp59+Gn/88Qc8PDzQs2dPfPfdd0Yn9iPHxub/pmvMNWxsysrUFovqIOD2PjJNfe/k8LngrOFE1mdWcHPgwAGLvPjQoUNRX3/mpKQkg/8/++yzePbZZy3y2qRsbP5vuoauoSkpK3OCFWsEAXL5XHDWcCLrMistFRMTAycnJ2zYsAHx8fHo2LEjYmJikJubC2dnZ0vXkchkbP5vuvquoTnpnbh+IUiJH4ZPZw1ESvywRvVzsfTK2vxcEDkGs1puvvjiC0yaNAkTJ07E8ePH9UOttVotXnnlFezYscOilSQyB5v/m66ua2huekcOLRb8XBApn1ktNy+99BLWr1+PDRs2oFmzZvrt0dHROHbsmMUqR9RUlr7zd0TGrqG1OvzaCj8XRMpmVnBz5swZDBkypNZ2jUaDoqKiptaJiGSO6R3K15YjNbuQIxBJlsxKSwUEBODcuXMIDQ012J6SkoL27dtbol5EJHNM7zguziFFcmdWy82sWbMwf/58HDlyBCqVChcvXsTHH3+MhQsX4rHHHrN0HYlIppjecTxSzxVEtmePrXRmtdzEx8dDp9Nh+PDhKCsrw5AhQ6BWq7Fw4UI88cQTlq4jEckEl7OwH9Z6r+QwVxDZjr220pm1cGa1Gzdu4Ny5cygpKUF4eDg8PT0tWTerMGXhLSL6m71+ycmBrYNCa75XXPzTccjtvTbl99ustFQ1V1dXhIeHo3///nYR2BCReZiKMF9yei6iE/djwoYjiE7cj+T03Iaf1ATWfq/YmdxxmLpsipyYlZYiIsfCVIR5TF0w1BItPLZ4r9iZ3DHIZUZvczC4IaIG2fOXnJRMCTQslUqy1XslhwkZybqstcabLTQpLUVEjoGpCPM0drJDS6aS+F6RJZmzbIocsOWGiBqFqQjTNfbO19KpJL5XZEn22ErH4IaIGs0ev+Sk1phAwxqpJL5X5MiYliIisrKGJjtkKonIsthyQ0QkA0pJJXGiR5IDBjdERDJh76kkTvRIcsG0FBHVYo9ryTgiOb1PnOiR5IQtN0RkgHff9kFu7xMneiQ5YcsNEenx7ts+yPF9auycPkS2wOCGiPTseS0ZRyLH94kjvkhOmJYiIj0us2Af5Po+KWXEF9k/ttwQkR7vvu2DnN+nhub0IbIFlRBCNFxMOYqLi6HRaKDVauHt7S11dYhkKV9bzrtvO8D3iRyJKb/fTEsRUS32Pt/K7ZQ6sZzS3iciS2FwQ0SKJrch00RkfexzQ0SKJcch00RkfQxuiEix5Dhkmkjp5DBzNtNSRKRYch0yTaRUckkDs+WGiBRLzkOmiZRGTmlgttwQkaJxYjki25DT+mIMbohI8Thkmsj65JQGZlqKiIiIGq2uDsNySgOz5YaIiIyyp8kP7amu9qyhDsNySQMzuCEiu8MfMuuTy6iXxrCnutqzujoMD+nsa/B3KIc0MNNSDk4O8xEQmSI5PRfRifsxYcMRRCfuR3J6rtRVUhw5jXppiD3V1d7Z07xRbLlxYLzbIXvT2DtHaho5jXppiD3V1d7JqcNwQ9hy46B4t0P2yJ7uHO1Z9Y9YTQ39iEnVCmxOXck8cuow3BC23Dgo3u2QPbKnO0d7Vv0jtmRrFqqEaPBHTMpWYFPrSk0jlw7DDWFw46D4I0H2iD9kttPYHzE5pArt5QdXKeTQYbghkqalDh8+jHvuuQdt2rSBSqXCl19+2eBzDh48iD59+kCtVqNjx45ISkqyej2VyJ6aF4lqiusXgpT4Yfh01kCkxA9jPzErCtS4I6pD63q/F+SSKmxMXclxSNpyU1paioiICEyfPh3jxo1rsHxOTg7GjBmD2bNn4+OPP8a+ffswc+ZMBAYGIjY21gY1Vhbe7ZC9soc7R0fBVmCSI5UQQjRczPpUKhW2bduGsWPH1llm0aJF2L59O7KysvTbHn74YRQVFWHXrl2Nep3i4mJoNBpotVp4e3s3tdpERA4vOT23VqpwSGdfzkVEFmXK77dd9blJS0vDiBEjDLbFxsZiwYIFdT6noqICFRUV+v8XFxdbq3pERA7p9lbgw79eQXTifk4zQZKxq6HgBQUF8Pf3N9jm7++P4uJilJcbH36YkJAAjUajfwQHB9uiqkREDqW6zwsAi0wzwQlGqSnsKrgxx+LFi6HVavWPvLw8qatERKRYluhgzFmoqansKrgJCAjApUuXDLZdunQJ3t7ecHc3ntNVq9Xw9vY2eBCRdHfGvCNXtqZOqscJRskS7KrPTVRUFHbs2GGwbe/evYiKipKoRiRHXFSxYVJNusYlP5SvqXMRcYJRsgRJg5uSkhKcO3dO//+cnBxkZmaiVatWCAkJweLFi/HHH3/gww8/BADMnj0bb731Fp599llMnz4d+/fvx2effYbt27dLdQokM/zxbJhUk67JYbI3so2mTDPBoeVkCZKmpTIyMtC7d2/07t0bAPDUU0+hd+/eePHFFwEA+fn5yM39O9caFhaG7du3Y+/evYiIiMDq1avx3nvvcY4bAsDm7MaSatI1uUz2RrZh7qR6nGCULEHSlpuhQ4eivml2jM0+PHToUBw/ftyKtSJ7xebsxpHqzph35NRYnGCUmsquOhQT1YerAzeOVHfGvCMnU3A5BWoK2cxQbCucoVjZjM2Uyj43xuVryyW5M5bqdYnIOHsZhGHK7zeDG1Ic/ngSETWOPQ3CMOX3m2kpB6fEOUfYnE1E1DAlD8Kwq3luyLLsKWInIiLLUvIgDLbcOCglR+xERNQwJQ/CYHDjoDjnCBGRbcg1/a/kEYxMSzkozjlCRGR9ck//K3VOIbbcOCglR+xERHJgL+l/JQ7CYMuNA1NqxE5EJAdK7rArdwxuHFygxp1/ZEREVsD0v3SYliIiIrICpv+lw5YbIiIiK2H6XxoMboiIyC7Zy5pITP/bHoMbIiKyO3IfYk3SYp8bIiKyK/YyxNoRyHWCQrbcEBGRXeEQa3mQc+sZW26IiMiuKHlNJHsh99YzBjdERGRXOMTaNNZIHcl9fUKmpYiIyO5wiHXjWCt1JPcJCtlyQ0REdkmJayJZkjVTR3JvPWPLDRERkQJZu+O1nFvPGNwQEZFi2MvEfrZgi9SRXCcoZFrKzsh1TgEiIqklp+ciOnE/Jmw4gujE/UhOz5W6SpKSe+rImlRCCNFwMeUoLi6GRqOBVquFt7e31NUxiZznFCAiklK+thzRiftrtVKkxA9ziB/z+uRry2WZOjKVKb/fbLmxE3KfU4CISEpyH5osJUfseM3gxk7wD5eIqG6c2I9qYnBjJ+z9D5d9hYjImhy5fwnVxtFSdqL6D3fJ1ixUCWFXf7jsK6QsHI1CciXnoclkW+xQbGfsrWMYO/kpCwNVsjcMxpXDlN9vttzYGbnOKVAXrt6rHHV1ah/S2ZfvJckSg3HHxT43ZFX23leI/sZO7WRPHGmEKfs01sbghqyKnfyUg4Eq2RNHCcY5caFxTEuR1Smhkx/z9vbdqZ0cj9xXrbYEporrxuCGbMLe+grVDGYO/3pF0Xl7UwI3JQSq5BgcIRhnn8a6Mbghus3tnRCFAKq/P5R2Z2ROh0t7C1TJcSk9GHeE1ilzsc8NUQ3GmnlvnytBKXl7R+pwSY5LyUsPsE9j3dhyQ1SDsWbe2ynlzohN2kT2T+mtU+ZicEMOy1hfE2PNvCoVoBKADsq6M2KTNimJI3f6Z6q4NgY35JDq6mtSVydEe7wzaujL3hE6XJJj4GR9dDtZLL/w9ttvY9WqVSgoKEBERATefPNN9O/f32jZpKQkTJs2zWCbWq3G9evXG/Va9r78AjVdY5aEsLdlLm5nype9vZ8rOTYu8eI4TPn9lrxDcXJyMp566iksXboUx44dQ0REBGJjY3H58uU6n+Pt7Y38/Hz948KFCzasMdm7xkzuZc+dEE3tKGzsXDnjKdkLR5msj0wjeXDz73//G7NmzcK0adMQHh6O9evXw8PDA++//36dz1GpVAgICNA//P39bVhjsndKn2m3qV/2nPGU7InS/56N4c1HwyQNbm7cuIGjR49ixIgR+m1OTk4YMWIE0tLS6nxeSUkJ2rVrh+DgYNx33304ffq0LapLCqH04ZNN+bLn8HCyN0r/e74dbz4aR9IOxYWFhaiqqqrV8uLv749ffvnF6HO6dOmC999/Hz179oRWq8W//vUvDBo0CKdPn0bbtm1rla+oqEBFRYX+/8XFxZY9CbJLSh4+2ZSOwhweTvZIyX/PNXG5hcazu9FSUVFRiIqK0v9/0KBB6NatG9555x2sXLmyVvmEhAQsX77cllUkO6Hk4ZPVX/bHLvwFnRCIDG3VqOdxeDjZKyX/PVfjzUfjSZqW8vHxgbOzMy5dumSw/dKlSwgICGjUMZo1a4bevXvj3LlzRvcvXrwYWq1W/8jLy2tyvYnsweFfr+CJT4/jiU8zG9187WhN/ET2xBH7F5lL0uDG1dUVffv2xb59+/TbdDod9u3bZ9A6U5+qqiqcOnUKgYGBRver1Wp4e3sbPIiUril9Z+L6hSAlfhg+nTUQKfHDOF8IkUzw5qPxJE9LPfXUU5gyZQoiIyPRv39/rFmzBqWlpfq5bCZPnoygoCAkJCQAAFasWIGBAweiY8eOKCoqwqpVq3DhwgXMnDlTytMgkpWmNl87QhM/kdwZm4jTUfoXNZXkwU1cXByuXLmCF198EQUFBejVqxd27dql72Scm5sLJ6e/G5j++usvzJo1CwUFBWjZsiX69u2L1NRUhIeHS3UKRLLDvjNE9q2+iTh589EwWcxQbEucoZgcRXJ6bq0RU0wxEckfZ102zpTfb8lbbojIOth8TWQ/aqagOCqq6RjcECkYm6+J5O/2FNSi0V2ZVm4iyZdfICIiclTGRja+tvMMFo3qylFRTcCWGyIiIonUlYLq2bYFUuKHMa1sJgY3REREEqlvZCPTyuZjWoqIiEginJjPOthyQ0REJCGObLQ8BjdEREQSYwrKspiWIiIiMiJfW47U7MJGrclG8sKWGyIiotvUt/wByR9bbmSAdwdERPJhbO6ZJVuz+B1tR9hyIzHeHRARyQuXP7B/bLmREO8OiIikUV+LefXcMzVx+QP7wuBGQvXdHRARkXUkp+ciOnE/Jmw4gujE/UhOzzXYz7ln7B/TUhKqb2ZKIiKyvLpazId09jUIXjj3jH1jy42EeHdARGRbprSYB2rcEdWhNb+T7RBbbiTGuwMiItthi7ljYMuNDPDuwP5w+D6RfWKLuWNgyw2RiTh8n8i+scVc+dhyQ2QCDt8nUga2mCsbgxsiE3D4PhGR/DG4ITIBJ/ciIpI/BjdEJmBnRCIi+WOHYiITsTMiEZG8MbghMkOgxp1BDRGRTDEtRURERIrC4IaIiIgUhcENERERKQqDGyIiIlIUBjdEREQWxvXnpMXRUkRERBbE9eekx5YbIiOkuOuy5GvyrpFIGlx/Th7YckN0Gynuuiz5mrxrJJJOfevPcW4s22HLDVENUtx1WfI1eddIZD2NaRE1tv4cAJz8o8h6FaNaGNwQ1SDFqt+WfE2uWk5kHcnpuYhO3I8JG44gOnE/3jmcbTTQCdS4Y9GorrWe/9rOM7zJsCGmpYhqqL7rqhkgWHvVb1NfM19bjpzCUoT5NK/VzC1F/YmUzliLaMKOXwAYT/32aKupdQympmyLLTdENUix6rcpr3n73WNyeq7k9SdSOmMtotWMpX6NpaZ4k2FbKiFEHW+ZMhUXF0Oj0UCr1cLb21vq6pBM5WvLbb7qd0Ovma8tR3Ti/lqtMinxw2qVl6L+REpl7G/vdp/OGoioDq31/09Oz8WSrVmoEkJ/k8GO/U1jyu8301JERkix6ndDr2nKKAyuWk5kOdUtotXByu2MtcrE9QvBkM6+vMmQCIMbIjvB/jRE0qkZrJz8vQiv7Tpj0CpTM3ip2S+uZmsO2Q6DGyI7cfvdI/vTENlWdYtoVIfWuLdXG6OtMpxnSh5k0aH47bffRmhoKNzc3DBgwAD8+OOP9ZbfsmULunbtCjc3N/To0QM7duywUU2JpBXXLwQp8cPw6ayBSIkfxi9NIolUBzm3t9hwnil5kDy4SU5OxlNPPYWlS5fi2LFjiIiIQGxsLC5fvmy0fGpqKsaPH48ZM2bg+PHjGDt2LMaOHYusrCwb15xIGsa+VIlIepxnSj4kHy01YMAA9OvXD2+99RYAQKfTITg4GE888QTi4+NrlY+Li0NpaSm+/fZb/baBAweiV69eWL9+fYOvx9FSRERkDaaMaCTTmfL7LWnLzY0bN3D06FGMGDFCv83JyQkjRoxAWlqa0eekpaUZlAeA2NjYOstXVFSguLjY4EFERGRpnGdKPiTtUFxYWIiqqir4+/sbbPf398cvv/xi9DkFBQVGyxcUFBgtn5CQgOXLl1umwkRERPXgEHB5kLzPjbUtXrwYWq1W/8jLy5O6SkREpGDsFyc9SVtufHx84OzsjEuXLhlsv3TpEgICAow+JyAgwKTyarUaarXaMhUmIiIi2ZO05cbV1RV9+/bFvn379Nt0Oh327duHqKgoo8+JiooyKA8Ae/furbM8ERERORbJJ/F76qmnMGXKFERGRqJ///5Ys2YNSktLMW3aNADA5MmTERQUhISEBADA/PnzERMTg9WrV2PMmDHYvHkzMjIy8O6770p5GkRERCQTkgc3cXFxuHLlCl588UUUFBSgV69e2LVrl77TcG5uLpyc/m5gGjRoED755BM8//zzWLJkCTp16oQvv/wS3bt3l+oUiIiISEYkn+fG1jjPDRERkf2xm3luiIiIiCyNwQ0REREpCoMbIiIiUhQGN0RERKQoDG6IiIhIURjcEBERkaJIPs+NrVWPfOfq4ERERPaj+ne7MTPYOFxwc+3aNQBAcHCwxDUhIiIiU127dg0ajabeMg43iZ9Op8PFixfh5eUFlUrV6OcVFxcjODgYeXl5Dj35H68Dr0E1XodbeB14DarxOtxiresghMC1a9fQpk0bg5ULjHG4lhsnJye0bdvW7Od7e3s79Ie2Gq8Dr0E1XodbeB14DarxOtxijevQUItNNXYoJiIiIkVhcENERESKwuCmkdRqNZYuXQq1Wi11VSTF68BrUI3X4RZeB16DarwOt8jhOjhch2IiIiJSNrbcEBERkaIwuCEiIiJFYXBDREREisLghoiIiBTFoYObdevWoWfPnvqJhqKiorBz5079/uvXr2POnDlo3bo1PD098cADD+DSpUsGx8jNzcWYMWPg4eEBPz8/PPPMM6isrLT1qVhMYmIiVCoVFixYoN/mCNdh2bJlUKlUBo+uXbvq9zvCNaj2xx9/4JFHHkHr1q3h7u6OHj16ICMjQ79fCIEXX3wRgYGBcHd3x4gRI3D27FmDY1y9ehUTJ06Et7c3WrRogRkzZqCkpMTWp2K20NDQWp8HlUqFOXPmAHCMz0NVVRVeeOEFhIWFwd3dHR06dMDKlSsN1vVxhM8CcGu6/wULFqBdu3Zwd3fHoEGDkJ6ert+vxOtw+PBh3HPPPWjTpg1UKhW+/PJLg/2WOueTJ0/izjvvhJubG4KDg/Haa69Z5gSEA/v666/F9u3bxa+//irOnDkjlixZIpo1ayaysrKEEELMnj1bBAcHi3379omMjAwxcOBAMWjQIP3zKysrRffu3cWIESPE8ePHxY4dO4SPj49YvHixVKfUJD/++KMIDQ0VPXv2FPPnz9dvd4TrsHTpUnHHHXeI/Px8/ePKlSv6/Y5wDYQQ4urVq6Jdu3Zi6tSp4siRI+K3334Tu3fvFufOndOXSUxMFBqNRnz55ZfixIkT4t577xVhYWGivLxcX2bUqFEiIiJC/PDDD+J///uf6Nixoxg/frwUp2SWy5cvG3wW9u7dKwCIAwcOCCEc4/Pw8ssvi9atW4tvv/1W5OTkiC1btghPT0+xdu1afRlH+CwIIcRDDz0kwsPDxaFDh8TZs2fF0qVLhbe3t/j999+FEMq8Djt27BDPPfec2Lp1qwAgtm3bZrDfEues1WqFv7+/mDhxosjKyhKffvqpcHd3F++8806T6+/QwY0xLVu2FO+9954oKioSzZo1E1u2bNHv+/nnnwUAkZaWJoS49eY7OTmJgoICfZl169YJb29vUVFRYfO6N8W1a9dEp06dxN69e0VMTIw+uHGU67B06VIRERFhdJ+jXAMhhFi0aJEYPHhwnft1Op0ICAgQq1at0m8rKioSarVafPrpp0IIIX766ScBQKSnp+vL7Ny5U6hUKvHHH39Yr/JWNH/+fNGhQweh0+kc5vMwZswYMX36dINt48aNExMnThRCOM5noaysTDg7O4tvv/3WYHufPn3Ec8895xDX4fbgxlLn/J///Ee0bNnS4G9i0aJFokuXLk2us0OnpWqqqqrC5s2bUVpaiqioKBw9ehQ3b97EiBEj9GW6du2KkJAQpKWlAQDS0tLQo0cP+Pv768vExsaiuLgYp0+ftvk5NMWcOXMwZswYg/MF4FDX4ezZs2jTpg3at2+PiRMnIjc3F4BjXYOvv/4akZGR+Oc//wk/Pz/07t0bGzZs0O/PyclBQUGBwbXQaDQYMGCAwbVo0aIFIiMj9WVGjBgBJycnHDlyxHYnYyE3btzApk2bMH36dKhUKof5PAwaNAj79u3Dr7/+CgA4ceIEUlJSMHr0aACO81morKxEVVUV3NzcDLa7u7sjJSXFYa5DTZY657S0NAwZMgSurq76MrGxsThz5gz++uuvJtXR4RbOvN2pU6cQFRWF69evw9PTE9u2bUN4eDgyMzPh6uqKFi1aGJT39/dHQUEBAKCgoMDgy6t6f/U+e7F582YcO3bMIIdcraCgwCGuw4ABA5CUlIQuXbogPz8fy5cvx5133omsrCyHuQYA8Ntvv2HdunV46qmnsGTJEqSnp2PevHlwdXXFlClT9Odi7FxrXgs/Pz+D/S4uLmjVqpVdXYtqX375JYqKijB16lQAjvM3ER8fj+LiYnTt2hXOzs6oqqrCyy+/jIkTJwKAw3wWvLy8EBUVhZUrV6Jbt27w9/fHp59+irS0NHTs2NFhrkNNljrngoIChIWF1TpG9b6WLVuaXUeHD266dOmCzMxMaLVafP7555gyZQoOHTokdbVsJi8vD/Pnz8fevXtr3Zk4kuq7UQDo2bMnBgwYgHbt2uGzzz6Du7u7hDWzLZ1Oh8jISLzyyisAgN69eyMrKwvr16/HlClTJK6dNP773/9i9OjRaNOmjdRVsanPPvsMH3/8MT755BPccccdyMzMxIIFC9CmTRuH+yx89NFHmD59OoKCguDs7Iw+ffpg/PjxOHr0qNRVozo4fFrK1dUVHTt2RN++fZGQkICIiAisXbsWAQEBuHHjBoqKigzKX7p0CQEBAQCAgICAWiMkqv9fXUbujh49isuXL6NPnz5wcXGBi4sLDh06hDfeeAMuLi7w9/d3iOtwuxYtWqBz5844d+6cw3wWACAwMBDh4eEG27p166ZP0VWfi7FzrXktLl++bLC/srISV69etatrAQAXLlzAd999h5kzZ+q3Ocrn4ZlnnkF8fDwefvhh9OjRA5MmTcKTTz6JhIQEAI71WejQoQMOHTqEkpIS5OXl4ccff8TNmzfRvn17h7oO1Sx1ztb8O3H44OZ2Op0OFRUV6Nu3L5o1a4Z9+/bp9505cwa5ubmIiooCAERFReHUqVMGb+DevXvh7e1d6wdCroYPH45Tp04hMzNT/4iMjMTEiRP1/3aE63C7kpISZGdnIzAw0GE+CwAQHR2NM2fOGGz79ddf0a5dOwBAWFgYAgICDK5FcXExjhw5YnAtioqKDO5q9+/fD51OhwEDBtjgLCxn48aN8PPzw5gxY/TbHOXzUFZWBicnw58IZ2dn6HQ6AI73WQCA5s2bIzAwEH/99Rd2796N++67zyGvg6XOOSoqCocPH8bNmzf1Zfbu3YsuXbo0KSUFwLGHgsfHx4tDhw6JnJwccfLkSREfHy9UKpXYs2ePEOLWcM+QkBCxf/9+kZGRIaKiokRUVJT++dXDPUeOHCkyMzPFrl27hK+vr10N9zSm5mgpIRzjOjz99NPi4MGDIicnR3z//fdixIgRwsfHR1y+fFkI4RjXQIhb0wG4uLiIl19+WZw9e1Z8/PHHwsPDQ2zatElfJjExUbRo0UJ89dVX4uTJk+K+++4zOgS0d+/e4siRIyIlJUV06tRJ1sNejamqqhIhISFi0aJFtfY5wudhypQpIigoSD8UfOvWrcLHx0c8++yz+jKO8lnYtWuX2Llzp/jtt9/Enj17REREhBgwYIC4ceOGEEKZ1+HatWvi+PHj4vjx4wKA+Pe//y2OHz8uLly4IISwzDkXFRUJf39/MWnSJJGVlSU2b94sPDw8OBS8qaZPny7atWsnXF1dha+vrxg+fLg+sBFCiPLycvH444+Lli1bCg8PD3H//feL/Px8g2OcP39ejB49Wri7uwsfHx/x9NNPi5s3b9r6VCzq9uDGEa5DXFycCAwMFK6uriIoKEjExcUZzO3iCNeg2jfffCO6d+8u1Gq16Nq1q3j33XcN9ut0OvHCCy8If39/oVarxfDhw8WZM2cMyvz5559i/PjxwtPTU3h7e4tp06aJa9eu2fI0mmz37t0CQK1zE8IxPg/FxcVi/vz5IiQkRLi5uYn27duL5557zmDYrqN8FpKTk0X79u2Fq6urCAgIEHPmzBFFRUX6/Uq8DgcOHBAAaj2mTJkihLDcOZ84cUIMHjxYqNVqERQUJBITEy1Sf5UQNaabJCIiIrJz7HNDREREisLghoiIiBSFwQ0REREpCoMbIiIiUhQGN0RERKQoDG6IiIhIURjcEBERkaIwuCEiIiJFYXBDRI0ydOhQLFiwQOpqWN2yZcvQq1cvqatBRE3A4IaIHMKNGzds+npCCFRWVtr0NYnoFgY3RNSgqVOn4tChQ1i7di1UKhVUKhXOnz+PrKwsjB49Gp6envD398ekSZNQWFiof97QoUPxxBNPYMGCBWjZsiX8/f2xYcMGlJaWYtq0afDy8kLHjh2xc+dO/XMOHjwIlUqF7du3o2fPnnBzc8PAgQORlZVlUKeUlBTceeedcHd3R3BwMObNm4fS0lL9/tDQUKxcuRKTJ0+Gt7c3Hn30UQDAokWL0LlzZ3h4eKB9+/Z44YUX9KsSJyUlYfny5Thx4oT+PJOSknD+/HmoVCpkZmbqj19UVASVSoWDBw8a1Hvnzp3o27cv1Go1UlJSoNPpkJCQgLCwMLi7uyMiIgKff/65pd8iIqqBwQ0RNWjt2rWIiorCrFmzkJ+fj/z8fHh5eeGuu+5C7969kZGRgV27duHSpUt46KGHDJ77wQcfwMfHBz/++COeeOIJPPbYY/jnP/+JQYMG4dixYxg5ciQmTZqEsrIyg+c988wzWL16NdLT0+Hr64t77rlHH4RkZ2dj1KhReOCBB3Dy5EkkJycjJSUFc+fONTjGv/71L0REROD48eN44YUXAABeXl5ISkrCTz/9hLVr12LDhg14/fXXAQBxcXF4+umncccdd+jPMy4uzqRrFR8fj8TERPz888/o2bMnEhIS8OGHH2L9+vU4ffo0nnzySTzyyCM4dOiQScclIhNYZPlNIlK821eLX7lypRg5cqRBmby8PIOVtGNiYsTgwYP1+ysrK0Xz5s3FpEmT9Nvy8/MFAJGWliaE+Hs14s2bN+vL/Pnnn8Ld3V0kJycLIYSYMWOGePTRRw1e+3//+59wcnIS5eXlQggh2rVrJ8aOHdvgea1atUr07dtX//+lS5eKiIgIgzI5OTkCgDh+/Lh+219//SUAiAMHDhjU+8svv9SXuX79uvDw8BCpqakGx5sxY4YYP358g3UjIvO4SBlYEZH9OnHiBA4cOABPT89a+7Kzs9G5c2cAQM+ePfXbnZ2d0bp1a/To0UO/zd/fHwBw+fJlg2NERUXp/92qVSt06dIFP//8s/61T548iY8//lhfRggBnU6HnJwcdOvWDQAQGRlZq27Jycl44403kJ2djZKSElRWVsLb29vk869Lzdc8d+4cysrKcPfddxuUuXHjBnr37m2x1yQiQwxuiMgsJSUluOeee/Dqq6/W2hcYGKj/d7NmzQz2qVQqg20qlQoAoNPpTHrt//u//8O8efNq7QsJCdH/u3nz5gb70tLSMHHiRCxfvhyxsbHQaDTYvHkzVq9eXe/rOTndyuALIfTbqlNkt6v5miUlJQCA7du3IygoyKCcWq2u9zWJyHwMboioUVxdXVFVVaX/f58+ffDFF18gNDQULi6W/yr54Ycf9IHKX3/9hV9//VXfItOnTx/89NNP6Nixo0nHTE1NRbt27fDcc8/pt124cMGgzO3nCQC+vr4AgPz8fH2LS83OxXUJDw+HWq1Gbm4uYmJiTKorEZmPHYqJqFFCQ0Nx5MgRnD9/HoWFhZgzZw6uXr2K8ePHIz09HdnZ2di9ezemTZtWKzgwx4oVK7Bv3z5kZWVh6tSp8PHxwdixYwHcGvGUmpqKuXPnIjMzE2fPnsVXX31Vq0Px7Tp16oTc3Fxs3rwZ2dnZeOONN7Bt27Za55mTk4PMzEwUFhaioqIC7u7uGDhwoL6j8KFDh/D88883eA5eXl5YuHAhnnzySXzwwQfIzs7GsWPH8Oabb+KDDz4w+9oQUf0Y3BBRoyxcuBDOzs4IDw+Hr68vbty4ge+//x5VVVUYOXIkevTogQULFqBFixb6NE5TJCYmYv78+ejbty8KCgrwzTffwNXVFcCtfjyHDh3Cr7/+ijvvvBO9e/fGiy++iDZt2tR7zHvvvRdPPvkk5s6di169eiE1NVU/iqraAw88gFGjRmHYsGHw9fXFp59+CgB4//33UVlZib59+2LBggV46aWXGnUeK1euxAsvvICEhAR069YNo0aNwvbt2xEWFmbGVSGixlCJmklkIiKJHTx4EMOGDcNff/2FFi1aSF0dIrJDbLkhIiIiRWFwQ0RERIrCtBQREREpCltuiIiISFEY3BAREZGiMLghIiIiRWFwQ0RERIrC4IaIiIgUhcENERERKQqDGyIiIlIUBjdERESkKAxuiIiISFH+P6mFqBfr0aimAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(keras_surrogate, data_validation)\n", + "surrogate_parity(keras_surrogate, data_validation)\n", + "surrogate_residual(keras_surrogate, data_validation)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_keras_surrogate_embedding_test.ipynb](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb_test.ipynb) file." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_usr.ipynb new file mode 100644 index 00000000..57cff087 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_usr.ipynb @@ -0,0 +1,1078 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part -1)\n", + "\n", + "## 1. Introduction\n", + "This notebook illustrates the use of KerasSurrogate API leveraging TensorFlow Keras and OMLT package to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "### 1.1 Need for ML Surrogates\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebric, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebric equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train a tanh model from our data and then demonstrate that we can solve an optimization problem with that surrogate model. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random as rn\n", + "import tensorflow as tf\n", + "import tensorflow.keras as keras\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.sampling.scaling import OffsetScaler\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")\n", + "\n", + "# fix environment variables to ensure consist neural network training\n", + "os.environ[\"PYTHONHASHSEED\"] = \"0\"\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "np.random.seed(46)\n", + "rn.seed(1342)\n", + "tf.random.set_seed(62)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsquent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "csv_data.columns.values[0:6] =[\"pressure\", \"temperature\",\"enth_mol\",\"entr_mol\",\"CO2_enthalpy\",\"CO2_entropy\"]\n", + "data = csv_data.sample(n=500)\n", + "\n", + "# Creating input_data and output_data from data\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:,2:4]\n", + "\n", + "# Define labels, and split training and validation data\n", + "input_labels = input_data.columns\n", + "output_labels = output_data.columns \n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogate with TensorFlow Keras\n", + "TensorFlow Keras provides an interface to pass regression settings, build neural networks and train surrogate models. Keras enables the usage of two API formats: Sequential and Functional. While the Functional API offers more versatility, including multiple input and output layers in a single neural network, the Sequential API is more stable and user-friendly. Further, the Sequential API integrates cleanly with existing IDAES surrogate tools and will be utilized in this example.\n", + "\n", + "In the code below, we build the neural network structure based on our training data structure and desired regression settings. Offline, neural network models were trained for the list of settings below, and the options bolded and italicized were determined to have the minimum mean squared error for the dataset:\n", + "\n", + "* Activation function: sigmoid, **tanh**\n", + "* Optimizer: **Adam**\n", + "* Number of hidden layers: 3, **4**, 5, 6\n", + "* Number of neurons per layer: **20**, 40, 60\n", + "\n", + "Important thing to note here is that we do not use ReLU activation function for the training as the flowsheet we intend to solve with this surrogate model is a NLP problem and using ReLU activation function will make it an MINLP. Another thing to note here is the network is smaller (4,20) in order to avoid overfitting. \n", + "\n", + "Typically, Sequential Keras models are built vertically; the dataset is scaled and normalized. The network is defined for the input layer, hidden layers, and output layer for the passed activation functions and network/layer sizes. Then, the model is compiled using the passed optimizer and trained using a desired number of epochs. Keras internally validates while training and updates each epoch's model weight (coefficient) values.\n", + "\n", + "Finally, after training the model, we save the results and model expressions to a folder that contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/250\n", + "13/13 - 2s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 2s/epoch - 173ms/step\n", + "Epoch 2/250\n", + "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 220ms/epoch - 17ms/step\n", + "Epoch 3/250\n", + "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 228ms/epoch - 18ms/step\n", + "Epoch 4/250\n", + "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 239ms/epoch - 18ms/step\n", + "Epoch 5/250\n", + "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 229ms/epoch - 18ms/step\n", + "Epoch 6/250\n", + "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 202ms/epoch - 16ms/step\n", + "Epoch 7/250\n", + "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 241ms/epoch - 19ms/step\n", + "Epoch 8/250\n", + "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 233ms/epoch - 18ms/step\n", + "Epoch 9/250\n", + "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 227ms/epoch - 17ms/step\n", + "Epoch 10/250\n", + "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 240ms/epoch - 18ms/step\n", + "Epoch 11/250\n", + "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 224ms/epoch - 17ms/step\n", + "Epoch 12/250\n", + "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 227ms/epoch - 17ms/step\n", + "Epoch 13/250\n", + "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 197ms/epoch - 15ms/step\n", + "Epoch 14/250\n", + "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 234ms/epoch - 18ms/step\n", + "Epoch 15/250\n", + "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 207ms/epoch - 16ms/step\n", + "Epoch 16/250\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 215ms/epoch - 17ms/step\n", + "Epoch 17/250\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 227ms/epoch - 17ms/step\n", + "Epoch 18/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 234ms/epoch - 18ms/step\n", + "Epoch 19/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 111ms/epoch - 9ms/step\n", + "Epoch 20/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 246ms/epoch - 19ms/step\n", + "Epoch 21/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 172ms/epoch - 13ms/step\n", + "Epoch 22/250\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 209ms/epoch - 16ms/step\n", + "Epoch 23/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", + "Epoch 24/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 219ms/epoch - 17ms/step\n", + "Epoch 25/250\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 212ms/epoch - 16ms/step\n", + "Epoch 26/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 220ms/epoch - 17ms/step\n", + "Epoch 27/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 224ms/epoch - 17ms/step\n", + "Epoch 28/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", + "Epoch 29/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 112ms/epoch - 9ms/step\n", + "Epoch 30/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 223ms/epoch - 17ms/step\n", + "Epoch 31/250\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 219ms/epoch - 17ms/step\n", + "Epoch 32/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 228ms/epoch - 18ms/step\n", + "Epoch 33/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 112ms/epoch - 9ms/step\n", + "Epoch 34/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 244ms/epoch - 19ms/step\n", + "Epoch 35/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 202ms/epoch - 16ms/step\n", + "Epoch 36/250\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 224ms/epoch - 17ms/step\n", + "Epoch 37/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 114ms/epoch - 9ms/step\n", + "Epoch 38/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 231ms/epoch - 18ms/step\n", + "Epoch 39/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 107ms/epoch - 8ms/step\n", + "Epoch 40/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 219ms/epoch - 17ms/step\n", + "Epoch 41/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 167ms/epoch - 13ms/step\n", + "Epoch 42/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 100ms/epoch - 8ms/step\n", + "Epoch 43/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0238 - mse: 0.0011 - val_loss: 9.4863e-04 - val_mae: 0.0232 - val_mse: 9.4863e-04 - 225ms/epoch - 17ms/step\n", + "Epoch 44/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0236 - mse: 0.0011 - val_loss: 9.8018e-04 - val_mae: 0.0230 - val_mse: 9.8018e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 45/250\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0239 - mse: 0.0011 - val_loss: 9.5093e-04 - val_mae: 0.0233 - val_mse: 9.5093e-04 - 121ms/epoch - 9ms/step\n", + "Epoch 46/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.4785e-04 - val_mae: 0.0223 - val_mse: 9.4785e-04 - 234ms/epoch - 18ms/step\n", + "Epoch 47/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 48/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 221ms/epoch - 17ms/step\n", + "Epoch 49/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.2521e-04 - val_mae: 0.0218 - val_mse: 9.2521e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 50/250\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7818e-04 - val_mae: 0.0231 - val_mse: 9.7818e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 51/250\n", + "13/13 - 0s - loss: 9.9977e-04 - mae: 0.0232 - mse: 9.9977e-04 - val_loss: 9.4350e-04 - val_mae: 0.0221 - val_mse: 9.4350e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 52/250\n", + "13/13 - 0s - loss: 9.8599e-04 - mae: 0.0229 - mse: 9.8599e-04 - val_loss: 9.0638e-04 - val_mae: 0.0230 - val_mse: 9.0638e-04 - 219ms/epoch - 17ms/step\n", + "Epoch 53/250\n", + "13/13 - 0s - loss: 9.8295e-04 - mae: 0.0228 - mse: 9.8295e-04 - val_loss: 9.0667e-04 - val_mae: 0.0215 - val_mse: 9.0667e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 54/250\n", + "13/13 - 0s - loss: 9.7266e-04 - mae: 0.0225 - mse: 9.7266e-04 - val_loss: 9.0391e-04 - val_mae: 0.0224 - val_mse: 9.0391e-04 - 208ms/epoch - 16ms/step\n", + "Epoch 55/250\n", + "13/13 - 0s - loss: 9.5234e-04 - mae: 0.0225 - mse: 9.5234e-04 - val_loss: 8.7426e-04 - val_mae: 0.0219 - val_mse: 8.7426e-04 - 223ms/epoch - 17ms/step\n", + "Epoch 56/250\n", + "13/13 - 0s - loss: 9.4315e-04 - mae: 0.0221 - mse: 9.4315e-04 - val_loss: 8.6742e-04 - val_mae: 0.0224 - val_mse: 8.6742e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 57/250\n", + "13/13 - 0s - loss: 9.9226e-04 - mae: 0.0230 - mse: 9.9226e-04 - val_loss: 8.7793e-04 - val_mae: 0.0225 - val_mse: 8.7793e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 58/250\n", + "13/13 - 0s - loss: 9.4137e-04 - mae: 0.0226 - mse: 9.4137e-04 - val_loss: 8.7477e-04 - val_mae: 0.0225 - val_mse: 8.7477e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 59/250\n", + "13/13 - 0s - loss: 9.2474e-04 - mae: 0.0219 - mse: 9.2474e-04 - val_loss: 8.5320e-04 - val_mae: 0.0212 - val_mse: 8.5320e-04 - 195ms/epoch - 15ms/step\n", + "Epoch 60/250\n", + "13/13 - 0s - loss: 9.1133e-04 - mae: 0.0217 - mse: 9.1133e-04 - val_loss: 8.6082e-04 - val_mae: 0.0217 - val_mse: 8.6082e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 61/250\n", + "13/13 - 0s - loss: 9.1801e-04 - mae: 0.0217 - mse: 9.1801e-04 - val_loss: 8.5403e-04 - val_mae: 0.0223 - val_mse: 8.5403e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 62/250\n", + "13/13 - 0s - loss: 9.1987e-04 - mae: 0.0221 - mse: 9.1987e-04 - val_loss: 8.5714e-04 - val_mae: 0.0219 - val_mse: 8.5714e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 63/250\n", + "13/13 - 0s - loss: 9.0862e-04 - mae: 0.0222 - mse: 9.0862e-04 - val_loss: 8.6160e-04 - val_mae: 0.0225 - val_mse: 8.6160e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 64/250\n", + "13/13 - 0s - loss: 8.9349e-04 - mae: 0.0220 - mse: 8.9349e-04 - val_loss: 8.2851e-04 - val_mae: 0.0214 - val_mse: 8.2851e-04 - 224ms/epoch - 17ms/step\n", + "Epoch 65/250\n", + "13/13 - 0s - loss: 8.7848e-04 - mae: 0.0216 - mse: 8.7848e-04 - val_loss: 8.5189e-04 - val_mae: 0.0218 - val_mse: 8.5189e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 66/250\n", + "13/13 - 0s - loss: 8.9773e-04 - mae: 0.0219 - mse: 8.9773e-04 - val_loss: 8.5650e-04 - val_mae: 0.0211 - val_mse: 8.5650e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 67/250\n", + "13/13 - 0s - loss: 8.7443e-04 - mae: 0.0217 - mse: 8.7443e-04 - val_loss: 8.2545e-04 - val_mae: 0.0214 - val_mse: 8.2545e-04 - 221ms/epoch - 17ms/step\n", + "Epoch 68/250\n", + "13/13 - 0s - loss: 8.9141e-04 - mae: 0.0217 - mse: 8.9141e-04 - val_loss: 8.4471e-04 - val_mae: 0.0219 - val_mse: 8.4471e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 69/250\n", + "13/13 - 0s - loss: 8.9507e-04 - mae: 0.0224 - mse: 8.9507e-04 - val_loss: 8.7916e-04 - val_mae: 0.0217 - val_mse: 8.7916e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 70/250\n", + "13/13 - 0s - loss: 8.5737e-04 - mae: 0.0216 - mse: 8.5737e-04 - val_loss: 8.8807e-04 - val_mae: 0.0215 - val_mse: 8.8807e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 71/250\n", + "13/13 - 0s - loss: 8.5560e-04 - mae: 0.0214 - mse: 8.5560e-04 - val_loss: 8.3750e-04 - val_mae: 0.0213 - val_mse: 8.3750e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 72/250\n", + "13/13 - 0s - loss: 8.5576e-04 - mae: 0.0218 - mse: 8.5576e-04 - val_loss: 8.1156e-04 - val_mae: 0.0210 - val_mse: 8.1156e-04 - 211ms/epoch - 16ms/step\n", + "Epoch 73/250\n", + "13/13 - 0s - loss: 8.4688e-04 - mae: 0.0216 - mse: 8.4688e-04 - val_loss: 8.0221e-04 - val_mae: 0.0210 - val_mse: 8.0221e-04 - 216ms/epoch - 17ms/step\n", + "Epoch 74/250\n", + "13/13 - 0s - loss: 8.3636e-04 - mae: 0.0211 - mse: 8.3636e-04 - val_loss: 7.9384e-04 - val_mae: 0.0208 - val_mse: 7.9384e-04 - 219ms/epoch - 17ms/step\n", + "Epoch 75/250\n", + "13/13 - 0s - loss: 8.4758e-04 - mae: 0.0222 - mse: 8.4758e-04 - val_loss: 8.2932e-04 - val_mae: 0.0212 - val_mse: 8.2932e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 76/250\n", + "13/13 - 0s - loss: 8.4142e-04 - mae: 0.0213 - mse: 8.4142e-04 - val_loss: 8.0552e-04 - val_mae: 0.0209 - val_mse: 8.0552e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 77/250\n", + "13/13 - 0s - loss: 8.5035e-04 - mae: 0.0215 - mse: 8.5035e-04 - val_loss: 8.6014e-04 - val_mae: 0.0215 - val_mse: 8.6014e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 78/250\n", + "13/13 - 0s - loss: 8.9015e-04 - mae: 0.0228 - mse: 8.9015e-04 - val_loss: 9.2548e-04 - val_mae: 0.0225 - val_mse: 9.2548e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 79/250\n", + "13/13 - 0s - loss: 8.1577e-04 - mae: 0.0212 - mse: 8.1577e-04 - val_loss: 8.4703e-04 - val_mae: 0.0211 - val_mse: 8.4703e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 80/250\n", + "13/13 - 0s - loss: 8.0555e-04 - mae: 0.0211 - mse: 8.0555e-04 - val_loss: 8.5652e-04 - val_mae: 0.0214 - val_mse: 8.5652e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 81/250\n", + "13/13 - 0s - loss: 8.3478e-04 - mae: 0.0219 - mse: 8.3478e-04 - val_loss: 9.1057e-04 - val_mae: 0.0222 - val_mse: 9.1057e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 82/250\n", + "13/13 - 0s - loss: 8.2593e-04 - mae: 0.0217 - mse: 8.2593e-04 - val_loss: 8.1172e-04 - val_mae: 0.0209 - val_mse: 8.1172e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 83/250\n", + "13/13 - 0s - loss: 8.2887e-04 - mae: 0.0213 - mse: 8.2887e-04 - val_loss: 8.2033e-04 - val_mae: 0.0211 - val_mse: 8.2033e-04 - 165ms/epoch - 13ms/step\n", + "Epoch 84/250\n", + "13/13 - 0s - loss: 8.1454e-04 - mae: 0.0219 - mse: 8.1454e-04 - val_loss: 8.1589e-04 - val_mae: 0.0211 - val_mse: 8.1589e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 85/250\n", + "13/13 - 0s - loss: 8.0777e-04 - mae: 0.0212 - mse: 8.0777e-04 - val_loss: 7.8637e-04 - val_mae: 0.0208 - val_mse: 7.8637e-04 - 177ms/epoch - 14ms/step\n", + "Epoch 86/250\n", + "13/13 - 0s - loss: 7.8107e-04 - mae: 0.0213 - mse: 7.8107e-04 - val_loss: 7.8138e-04 - val_mae: 0.0212 - val_mse: 7.8138e-04 - 223ms/epoch - 17ms/step\n", + "Epoch 87/250\n", + "13/13 - 0s - loss: 7.9729e-04 - mae: 0.0210 - mse: 7.9729e-04 - val_loss: 7.3667e-04 - val_mae: 0.0204 - val_mse: 7.3667e-04 - 237ms/epoch - 18ms/step\n", + "Epoch 88/250\n", + "13/13 - 0s - loss: 7.5931e-04 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8ms/step\n", + "Epoch 94/250\n", + "13/13 - 0s - loss: 7.4802e-04 - mae: 0.0205 - mse: 7.4802e-04 - val_loss: 7.2386e-04 - val_mae: 0.0201 - val_mse: 7.2386e-04 - 190ms/epoch - 15ms/step\n", + "Epoch 95/250\n", + "13/13 - 0s - loss: 7.2616e-04 - mae: 0.0203 - mse: 7.2616e-04 - val_loss: 7.2728e-04 - val_mae: 0.0204 - val_mse: 7.2728e-04 - 121ms/epoch - 9ms/step\n", + "Epoch 96/250\n", + "13/13 - 0s - loss: 7.2310e-04 - mae: 0.0204 - mse: 7.2310e-04 - val_loss: 7.1349e-04 - val_mae: 0.0206 - val_mse: 7.1349e-04 - 219ms/epoch - 17ms/step\n", + "Epoch 97/250\n", + "13/13 - 0s - loss: 7.0905e-04 - mae: 0.0201 - mse: 7.0905e-04 - val_loss: 7.6242e-04 - val_mae: 0.0205 - val_mse: 7.6242e-04 - 117ms/epoch - 9ms/step\n", + "Epoch 98/250\n", + "13/13 - 0s - loss: 7.1839e-04 - mae: 0.0200 - mse: 7.1839e-04 - val_loss: 7.7098e-04 - val_mae: 0.0202 - val_mse: 7.7098e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 99/250\n", + "13/13 - 0s - loss: 7.3924e-04 - mae: 0.0208 - mse: 7.3924e-04 - val_loss: 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val_mse: 6.8310e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 111/250\n", + "13/13 - 0s - loss: 6.7136e-04 - mae: 0.0197 - mse: 6.7136e-04 - val_loss: 6.5858e-04 - val_mae: 0.0199 - val_mse: 6.5858e-04 - 224ms/epoch - 17ms/step\n", + "Epoch 112/250\n", + "13/13 - 0s - loss: 6.5784e-04 - mae: 0.0195 - mse: 6.5784e-04 - val_loss: 6.5838e-04 - val_mae: 0.0196 - val_mse: 6.5838e-04 - 234ms/epoch - 18ms/step\n", + "Epoch 113/250\n", + "13/13 - 0s - loss: 6.6861e-04 - mae: 0.0198 - mse: 6.6861e-04 - val_loss: 6.9871e-04 - val_mae: 0.0196 - val_mse: 6.9871e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 114/250\n", + "13/13 - 0s - loss: 6.6345e-04 - mae: 0.0196 - mse: 6.6345e-04 - val_loss: 6.8190e-04 - val_mae: 0.0196 - val_mse: 6.8190e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 115/250\n", + "13/13 - 0s - loss: 6.4121e-04 - mae: 0.0193 - mse: 6.4121e-04 - val_loss: 6.6493e-04 - val_mae: 0.0196 - val_mse: 6.6493e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 116/250\n", + "13/13 - 0s - loss: 6.5036e-04 - 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8ms/step\n", + "Epoch 122/250\n", + "13/13 - 0s - loss: 6.4650e-04 - mae: 0.0196 - mse: 6.4650e-04 - val_loss: 8.0733e-04 - val_mae: 0.0210 - val_mse: 8.0733e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 123/250\n", + "13/13 - 0s - loss: 6.5887e-04 - mae: 0.0198 - mse: 6.5887e-04 - val_loss: 6.2666e-04 - val_mae: 0.0191 - val_mse: 6.2666e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 124/250\n", + "13/13 - 0s - loss: 6.1387e-04 - mae: 0.0189 - mse: 6.1387e-04 - val_loss: 6.1020e-04 - val_mae: 0.0188 - val_mse: 6.1020e-04 - 210ms/epoch - 16ms/step\n", + "Epoch 125/250\n", + "13/13 - 0s - loss: 6.1348e-04 - mae: 0.0191 - mse: 6.1348e-04 - val_loss: 6.1093e-04 - val_mae: 0.0193 - val_mse: 6.1093e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 126/250\n", + "13/13 - 0s - loss: 6.1374e-04 - mae: 0.0189 - mse: 6.1374e-04 - val_loss: 6.1062e-04 - val_mae: 0.0188 - val_mse: 6.1062e-04 - 116ms/epoch - 9ms/step\n", + "Epoch 127/250\n", + "13/13 - 0s - loss: 6.1279e-04 - mae: 0.0190 - mse: 6.1279e-04 - val_loss: 6.4391e-04 - val_mae: 0.0190 - val_mse: 6.4391e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 128/250\n", + "13/13 - 0s - loss: 6.0951e-04 - mae: 0.0189 - mse: 6.0951e-04 - val_loss: 5.9592e-04 - val_mae: 0.0188 - val_mse: 5.9592e-04 - 228ms/epoch - 18ms/step\n", + "Epoch 129/250\n", + "13/13 - 0s - loss: 6.2194e-04 - mae: 0.0192 - mse: 6.2194e-04 - val_loss: 5.9344e-04 - val_mae: 0.0188 - val_mse: 5.9344e-04 - 180ms/epoch - 14ms/step\n", + "Epoch 130/250\n", + "13/13 - 0s - loss: 6.1795e-04 - mae: 0.0191 - mse: 6.1795e-04 - val_loss: 5.8880e-04 - val_mae: 0.0188 - val_mse: 5.8880e-04 - 218ms/epoch - 17ms/step\n", + "Epoch 131/250\n", + "13/13 - 0s - loss: 6.6297e-04 - mae: 0.0199 - mse: 6.6297e-04 - val_loss: 7.2306e-04 - val_mae: 0.0197 - val_mse: 7.2306e-04 - 117ms/epoch - 9ms/step\n", + "Epoch 132/250\n", + "13/13 - 0s - loss: 5.8788e-04 - mae: 0.0189 - mse: 5.8788e-04 - val_loss: 6.0686e-04 - val_mae: 0.0189 - val_mse: 6.0686e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 133/250\n", + "13/13 - 0s - loss: 5.7425e-04 - mae: 0.0184 - mse: 5.7425e-04 - val_loss: 5.7895e-04 - val_mae: 0.0183 - val_mse: 5.7895e-04 - 218ms/epoch - 17ms/step\n", + "Epoch 134/250\n", + "13/13 - 0s - loss: 5.8783e-04 - mae: 0.0186 - mse: 5.8783e-04 - val_loss: 5.7846e-04 - val_mae: 0.0188 - val_mse: 5.7846e-04 - 230ms/epoch - 18ms/step\n", + "Epoch 135/250\n", + "13/13 - 0s - loss: 5.8541e-04 - mae: 0.0188 - mse: 5.8541e-04 - val_loss: 6.7887e-04 - val_mae: 0.0191 - val_mse: 6.7887e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 136/250\n", + "13/13 - 0s - loss: 5.9158e-04 - mae: 0.0185 - mse: 5.9158e-04 - val_loss: 5.9231e-04 - val_mae: 0.0188 - val_mse: 5.9231e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 137/250\n", + "13/13 - 0s - loss: 5.9616e-04 - mae: 0.0192 - mse: 5.9616e-04 - val_loss: 7.0218e-04 - val_mae: 0.0212 - val_mse: 7.0218e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 138/250\n", + "13/13 - 0s - loss: 6.2132e-04 - mae: 0.0190 - mse: 6.2132e-04 - val_loss: 6.3436e-04 - val_mae: 0.0186 - val_mse: 6.3436e-04 - 105ms/epoch - 8ms/step\n", + "Epoch 139/250\n", + "13/13 - 0s - loss: 5.8416e-04 - mae: 0.0189 - mse: 5.8416e-04 - val_loss: 5.7793e-04 - val_mae: 0.0184 - val_mse: 5.7793e-04 - 215ms/epoch - 17ms/step\n", + "Epoch 140/250\n", + "13/13 - 0s - loss: 6.5695e-04 - mae: 0.0195 - mse: 6.5695e-04 - val_loss: 5.8062e-04 - val_mae: 0.0189 - val_mse: 5.8062e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 141/250\n", + "13/13 - 0s - loss: 6.4168e-04 - mae: 0.0200 - mse: 6.4168e-04 - val_loss: 6.9879e-04 - val_mae: 0.0196 - val_mse: 6.9879e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 142/250\n", + "13/13 - 0s - loss: 6.5517e-04 - mae: 0.0198 - mse: 6.5517e-04 - val_loss: 6.3928e-04 - val_mae: 0.0193 - val_mse: 6.3928e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 143/250\n", + "13/13 - 0s - loss: 5.8456e-04 - mae: 0.0190 - mse: 5.8456e-04 - val_loss: 5.4596e-04 - val_mae: 0.0181 - val_mse: 5.4596e-04 - 225ms/epoch - 17ms/step\n", + "Epoch 144/250\n", + "13/13 - 0s - loss: 5.9458e-04 - mae: 0.0186 - mse: 5.9458e-04 - val_loss: 5.8598e-04 - val_mae: 0.0181 - val_mse: 5.8598e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 145/250\n", + "13/13 - 0s - loss: 5.6787e-04 - mae: 0.0186 - mse: 5.6787e-04 - val_loss: 5.6263e-04 - val_mae: 0.0186 - val_mse: 5.6263e-04 - 124ms/epoch - 10ms/step\n", + "Epoch 146/250\n", + "13/13 - 0s - loss: 5.3545e-04 - mae: 0.0178 - mse: 5.3545e-04 - val_loss: 5.3802e-04 - val_mae: 0.0179 - val_mse: 5.3802e-04 - 186ms/epoch - 14ms/step\n", + "Epoch 147/250\n", + "13/13 - 0s - loss: 5.2310e-04 - mae: 0.0177 - mse: 5.2310e-04 - val_loss: 5.4103e-04 - val_mae: 0.0179 - val_mse: 5.4103e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 148/250\n", + "13/13 - 0s - loss: 5.2826e-04 - mae: 0.0176 - mse: 5.2826e-04 - val_loss: 5.9310e-04 - val_mae: 0.0181 - val_mse: 5.9310e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 149/250\n", + "13/13 - 0s - loss: 5.3295e-04 - mae: 0.0179 - mse: 5.3295e-04 - val_loss: 5.4002e-04 - val_mae: 0.0176 - val_mse: 5.4002e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 150/250\n", + "13/13 - 0s - loss: 5.1491e-04 - mae: 0.0174 - mse: 5.1491e-04 - val_loss: 5.9602e-04 - val_mae: 0.0179 - val_mse: 5.9602e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 151/250\n", + "13/13 - 0s - loss: 5.2334e-04 - mae: 0.0179 - mse: 5.2334e-04 - val_loss: 5.2811e-04 - val_mae: 0.0178 - val_mse: 5.2811e-04 - 222ms/epoch - 17ms/step\n", + "Epoch 152/250\n", + "13/13 - 0s - loss: 5.2768e-04 - mae: 0.0178 - mse: 5.2768e-04 - val_loss: 5.5139e-04 - val_mae: 0.0184 - val_mse: 5.5139e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 153/250\n", + "13/13 - 0s - loss: 5.2962e-04 - mae: 0.0179 - mse: 5.2962e-04 - val_loss: 5.7462e-04 - val_mae: 0.0178 - val_mse: 5.7462e-04 - 99ms/epoch - 8ms/step\n", + "Epoch 154/250\n", + "13/13 - 0s - loss: 5.0260e-04 - mae: 0.0173 - mse: 5.0260e-04 - val_loss: 5.3387e-04 - val_mae: 0.0181 - val_mse: 5.3387e-04 - 102ms/epoch - 8ms/step\n", + "Epoch 155/250\n", + "13/13 - 0s - loss: 5.0501e-04 - mae: 0.0175 - mse: 5.0501e-04 - val_loss: 5.0751e-04 - val_mae: 0.0172 - val_mse: 5.0751e-04 - 211ms/epoch - 16ms/step\n", + "Epoch 156/250\n", + "13/13 - 0s - loss: 5.0518e-04 - mae: 0.0173 - mse: 5.0518e-04 - val_loss: 5.5553e-04 - val_mae: 0.0174 - val_mse: 5.5553e-04 - 189ms/epoch - 15ms/step\n", + "Epoch 157/250\n", + "13/13 - 0s - loss: 5.0064e-04 - mae: 0.0172 - mse: 5.0064e-04 - val_loss: 5.1205e-04 - val_mae: 0.0172 - val_mse: 5.1205e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 158/250\n", + "13/13 - 0s - loss: 4.9541e-04 - mae: 0.0172 - mse: 4.9541e-04 - val_loss: 5.0799e-04 - val_mae: 0.0172 - val_mse: 5.0799e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 159/250\n", + "13/13 - 0s - loss: 5.4153e-04 - mae: 0.0182 - mse: 5.4153e-04 - val_loss: 5.2077e-04 - val_mae: 0.0171 - val_mse: 5.2077e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 160/250\n", + "13/13 - 0s - loss: 4.8280e-04 - mae: 0.0170 - mse: 4.8280e-04 - val_loss: 5.1410e-04 - val_mae: 0.0168 - val_mse: 5.1410e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 161/250\n", + "13/13 - 0s - loss: 4.8993e-04 - mae: 0.0171 - mse: 4.8993e-04 - val_loss: 5.1744e-04 - val_mae: 0.0171 - val_mse: 5.1744e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 162/250\n", + "13/13 - 0s - loss: 4.8044e-04 - mae: 0.0169 - mse: 4.8044e-04 - val_loss: 5.1099e-04 - val_mae: 0.0168 - val_mse: 5.1099e-04 - 103ms/epoch - 8ms/step\n", + "Epoch 163/250\n", + "13/13 - 0s - loss: 4.9657e-04 - mae: 0.0171 - mse: 4.9657e-04 - val_loss: 4.9877e-04 - val_mae: 0.0171 - val_mse: 4.9877e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 164/250\n", + "13/13 - 0s - loss: 4.8858e-04 - mae: 0.0170 - mse: 4.8858e-04 - val_loss: 5.0099e-04 - val_mae: 0.0169 - val_mse: 5.0099e-04 - 99ms/epoch - 8ms/step\n", + "Epoch 165/250\n", + "13/13 - 0s - loss: 4.7747e-04 - mae: 0.0170 - mse: 4.7747e-04 - val_loss: 5.8449e-04 - val_mae: 0.0174 - val_mse: 5.8449e-04 - 97ms/epoch - 7ms/step\n", + "Epoch 166/250\n", + "13/13 - 0s - loss: 4.9897e-04 - mae: 0.0171 - mse: 4.9897e-04 - val_loss: 4.9512e-04 - val_mae: 0.0173 - val_mse: 4.9512e-04 - 174ms/epoch - 13ms/step\n", + "Epoch 167/250\n", + "13/13 - 0s - loss: 4.8695e-04 - mae: 0.0173 - mse: 4.8695e-04 - val_loss: 5.0306e-04 - val_mae: 0.0165 - val_mse: 5.0306e-04 - 97ms/epoch - 7ms/step\n", + "Epoch 168/250\n", + "13/13 - 0s - loss: 4.7948e-04 - mae: 0.0171 - mse: 4.7948e-04 - val_loss: 6.8895e-04 - val_mae: 0.0193 - val_mse: 6.8895e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 169/250\n", + "13/13 - 0s - loss: 4.8055e-04 - mae: 0.0168 - mse: 4.8055e-04 - val_loss: 4.9053e-04 - val_mae: 0.0171 - val_mse: 4.9053e-04 - 215ms/epoch - 17ms/step\n", + "Epoch 170/250\n", + "13/13 - 0s - loss: 4.5980e-04 - mae: 0.0168 - mse: 4.5980e-04 - val_loss: 5.2267e-04 - val_mae: 0.0170 - val_mse: 5.2267e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 171/250\n", + "13/13 - 0s - loss: 4.6495e-04 - mae: 0.0168 - mse: 4.6495e-04 - val_loss: 4.6718e-04 - val_mae: 0.0165 - val_mse: 4.6718e-04 - 216ms/epoch - 17ms/step\n", + "Epoch 172/250\n", + "13/13 - 0s - loss: 4.6046e-04 - mae: 0.0168 - mse: 4.6046e-04 - val_loss: 4.6731e-04 - val_mae: 0.0166 - val_mse: 4.6731e-04 - 98ms/epoch - 8ms/step\n", + "Epoch 173/250\n", + "13/13 - 0s - loss: 4.6993e-04 - mae: 0.0168 - mse: 4.6993e-04 - val_loss: 4.8190e-04 - val_mae: 0.0167 - val_mse: 4.8190e-04 - 101ms/epoch - 8ms/step\n", + "Epoch 174/250\n", + "13/13 - 0s - loss: 4.8411e-04 - mae: 0.0172 - mse: 4.8411e-04 - val_loss: 5.0800e-04 - val_mae: 0.0164 - val_mse: 5.0800e-04 - 99ms/epoch - 8ms/step\n", + "Epoch 175/250\n", + "13/13 - 0s - loss: 4.5295e-04 - mae: 0.0164 - mse: 4.5295e-04 - val_loss: 6.2583e-04 - val_mae: 0.0182 - val_mse: 6.2583e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 176/250\n", + "13/13 - 0s - loss: 5.3742e-04 - mae: 0.0183 - mse: 5.3742e-04 - val_loss: 5.6727e-04 - val_mae: 0.0187 - val_mse: 5.6727e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 177/250\n", + "13/13 - 0s - loss: 5.3634e-04 - mae: 0.0182 - mse: 5.3634e-04 - val_loss: 4.6197e-04 - val_mae: 0.0157 - val_mse: 4.6197e-04 - 212ms/epoch - 16ms/step\n", + "Epoch 178/250\n", + "13/13 - 0s - loss: 4.8847e-04 - mae: 0.0169 - mse: 4.8847e-04 - val_loss: 4.6646e-04 - val_mae: 0.0160 - val_mse: 4.6646e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 179/250\n", + "13/13 - 0s - loss: 4.3622e-04 - mae: 0.0160 - mse: 4.3622e-04 - val_loss: 5.3203e-04 - val_mae: 0.0164 - val_mse: 5.3203e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 180/250\n", + "13/13 - 0s - loss: 4.7108e-04 - mae: 0.0165 - mse: 4.7108e-04 - val_loss: 4.6548e-04 - val_mae: 0.0161 - val_mse: 4.6548e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 181/250\n", + "13/13 - 0s - loss: 4.3932e-04 - mae: 0.0164 - mse: 4.3932e-04 - val_loss: 4.4195e-04 - val_mae: 0.0157 - val_mse: 4.4195e-04 - 212ms/epoch - 16ms/step\n", + "Epoch 182/250\n", + "13/13 - 0s - loss: 4.3340e-04 - mae: 0.0159 - mse: 4.3340e-04 - val_loss: 4.5463e-04 - val_mae: 0.0158 - val_mse: 4.5463e-04 - 95ms/epoch - 7ms/step\n", + "Epoch 183/250\n", + "13/13 - 0s - loss: 4.2639e-04 - mae: 0.0162 - mse: 4.2639e-04 - val_loss: 4.3874e-04 - val_mae: 0.0156 - val_mse: 4.3874e-04 - 169ms/epoch - 13ms/step\n", + "Epoch 184/250\n", + "13/13 - 0s - loss: 4.4119e-04 - mae: 0.0159 - mse: 4.4119e-04 - val_loss: 4.7791e-04 - val_mae: 0.0169 - val_mse: 4.7791e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 185/250\n", + "13/13 - 0s - loss: 4.4805e-04 - mae: 0.0164 - mse: 4.4805e-04 - val_loss: 4.6275e-04 - val_mae: 0.0163 - val_mse: 4.6275e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 186/250\n", + "13/13 - 0s - loss: 4.4495e-04 - mae: 0.0163 - mse: 4.4495e-04 - val_loss: 4.4746e-04 - val_mae: 0.0155 - val_mse: 4.4746e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 187/250\n", + "13/13 - 0s - loss: 4.7030e-04 - mae: 0.0167 - mse: 4.7030e-04 - val_loss: 5.6234e-04 - val_mae: 0.0169 - val_mse: 5.6234e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 188/250\n", + "13/13 - 0s - loss: 4.4920e-04 - mae: 0.0160 - mse: 4.4920e-04 - val_loss: 4.2347e-04 - val_mae: 0.0154 - val_mse: 4.2347e-04 - 204ms/epoch - 16ms/step\n", + "Epoch 189/250\n", + "13/13 - 0s - loss: 4.1850e-04 - mae: 0.0159 - mse: 4.1850e-04 - val_loss: 4.5828e-04 - val_mae: 0.0156 - val_mse: 4.5828e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 190/250\n", + "13/13 - 0s - loss: 4.2816e-04 - mae: 0.0159 - mse: 4.2816e-04 - val_loss: 4.2983e-04 - val_mae: 0.0155 - val_mse: 4.2983e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 191/250\n", + "13/13 - 0s - loss: 4.1442e-04 - mae: 0.0156 - mse: 4.1442e-04 - val_loss: 4.5135e-04 - val_mae: 0.0154 - val_mse: 4.5135e-04 - 103ms/epoch - 8ms/step\n", + "Epoch 192/250\n", + "13/13 - 0s - loss: 4.1126e-04 - mae: 0.0159 - mse: 4.1126e-04 - val_loss: 4.2590e-04 - val_mae: 0.0151 - val_mse: 4.2590e-04 - 159ms/epoch - 12ms/step\n", + "Epoch 193/250\n", + "13/13 - 0s - loss: 4.1197e-04 - mae: 0.0155 - mse: 4.1197e-04 - val_loss: 4.2111e-04 - val_mae: 0.0151 - val_mse: 4.2111e-04 - 209ms/epoch - 16ms/step\n", + "Epoch 194/250\n", + "13/13 - 0s - loss: 4.0958e-04 - mae: 0.0157 - mse: 4.0958e-04 - val_loss: 4.1117e-04 - val_mae: 0.0149 - val_mse: 4.1117e-04 - 185ms/epoch - 14ms/step\n", + "Epoch 195/250\n", + "13/13 - 0s - loss: 3.9243e-04 - mae: 0.0153 - mse: 3.9243e-04 - val_loss: 4.1405e-04 - val_mae: 0.0150 - val_mse: 4.1405e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 196/250\n", + "13/13 - 0s - loss: 4.0300e-04 - mae: 0.0153 - mse: 4.0300e-04 - val_loss: 4.3989e-04 - val_mae: 0.0150 - val_mse: 4.3989e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 197/250\n", + "13/13 - 0s - loss: 4.0142e-04 - mae: 0.0154 - mse: 4.0142e-04 - val_loss: 4.3665e-04 - val_mae: 0.0151 - val_mse: 4.3665e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 198/250\n", + "13/13 - 0s - loss: 3.9936e-04 - mae: 0.0153 - mse: 3.9936e-04 - val_loss: 4.2897e-04 - val_mae: 0.0149 - val_mse: 4.2897e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 199/250\n", + "13/13 - 0s - loss: 4.0143e-04 - mae: 0.0153 - mse: 4.0143e-04 - val_loss: 4.0877e-04 - val_mae: 0.0148 - val_mse: 4.0877e-04 - 214ms/epoch - 16ms/step\n", + "Epoch 200/250\n", + "13/13 - 0s - loss: 3.9668e-04 - mae: 0.0152 - mse: 3.9668e-04 - val_loss: 4.3571e-04 - val_mae: 0.0150 - val_mse: 4.3571e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 201/250\n", + "13/13 - 0s - loss: 3.9516e-04 - mae: 0.0154 - mse: 3.9516e-04 - val_loss: 5.1984e-04 - val_mae: 0.0161 - val_mse: 5.1984e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 202/250\n", + "13/13 - 0s - loss: 4.5166e-04 - mae: 0.0161 - mse: 4.5166e-04 - val_loss: 5.4696e-04 - val_mae: 0.0182 - val_mse: 5.4696e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 203/250\n", + "13/13 - 0s - loss: 4.5904e-04 - mae: 0.0166 - mse: 4.5904e-04 - val_loss: 4.1240e-04 - val_mae: 0.0150 - val_mse: 4.1240e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 204/250\n", + "13/13 - 0s - loss: 3.9851e-04 - mae: 0.0150 - mse: 3.9851e-04 - val_loss: 4.5210e-04 - val_mae: 0.0154 - val_mse: 4.5210e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 205/250\n", + "13/13 - 0s - loss: 3.8760e-04 - mae: 0.0151 - mse: 3.8760e-04 - val_loss: 4.0982e-04 - val_mae: 0.0149 - val_mse: 4.0982e-04 - 116ms/epoch - 9ms/step\n", + "Epoch 206/250\n", + "13/13 - 0s - loss: 4.1937e-04 - mae: 0.0156 - mse: 4.1937e-04 - val_loss: 3.8857e-04 - val_mae: 0.0145 - val_mse: 3.8857e-04 - 222ms/epoch - 17ms/step\n", + "Epoch 207/250\n", + "13/13 - 0s - loss: 3.7173e-04 - mae: 0.0146 - mse: 3.7173e-04 - val_loss: 3.9353e-04 - val_mae: 0.0147 - val_mse: 3.9353e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 208/250\n", + "13/13 - 0s - loss: 3.9673e-04 - mae: 0.0153 - mse: 3.9673e-04 - val_loss: 3.9003e-04 - val_mae: 0.0145 - val_mse: 3.9003e-04 - 114ms/epoch - 9ms/step\n", + "Epoch 209/250\n", + "13/13 - 0s - loss: 4.2359e-04 - mae: 0.0155 - mse: 4.2359e-04 - val_loss: 3.9027e-04 - val_mae: 0.0146 - val_mse: 3.9027e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 210/250\n", + "13/13 - 0s - loss: 3.9302e-04 - mae: 0.0154 - mse: 3.9302e-04 - val_loss: 4.1320e-04 - val_mae: 0.0152 - val_mse: 4.1320e-04 - 113ms/epoch - 9ms/step\n", + "Epoch 211/250\n", + "13/13 - 0s - loss: 3.6641e-04 - mae: 0.0147 - mse: 3.6641e-04 - val_loss: 3.9564e-04 - val_mae: 0.0141 - val_mse: 3.9564e-04 - 116ms/epoch - 9ms/step\n", + "Epoch 212/250\n", + "13/13 - 0s - loss: 3.6259e-04 - mae: 0.0143 - mse: 3.6259e-04 - val_loss: 3.8787e-04 - val_mae: 0.0146 - val_mse: 3.8787e-04 - 228ms/epoch - 18ms/step\n", + "Epoch 213/250\n", + "13/13 - 0s - loss: 4.0665e-04 - mae: 0.0156 - mse: 4.0665e-04 - val_loss: 5.0910e-04 - val_mae: 0.0160 - val_mse: 5.0910e-04 - 120ms/epoch - 9ms/step\n", + "Epoch 214/250\n", + "13/13 - 0s - loss: 4.5758e-04 - mae: 0.0169 - mse: 4.5758e-04 - val_loss: 4.1241e-04 - val_mae: 0.0141 - val_mse: 4.1241e-04 - 104ms/epoch - 8ms/step\n", + "Epoch 215/250\n", + "13/13 - 0s - loss: 4.0666e-04 - mae: 0.0155 - mse: 4.0666e-04 - val_loss: 4.6639e-04 - val_mae: 0.0151 - val_mse: 4.6639e-04 - 104ms/epoch - 8ms/step\n", + "Epoch 216/250\n", + "13/13 - 0s - loss: 3.6615e-04 - mae: 0.0145 - mse: 3.6615e-04 - val_loss: 3.8294e-04 - val_mae: 0.0138 - val_mse: 3.8294e-04 - 201ms/epoch - 15ms/step\n", + "Epoch 217/250\n", + "13/13 - 0s - loss: 3.8135e-04 - mae: 0.0149 - mse: 3.8135e-04 - val_loss: 5.1259e-04 - val_mae: 0.0162 - val_mse: 5.1259e-04 - 119ms/epoch - 9ms/step\n", + "Epoch 218/250\n", + "13/13 - 0s - loss: 3.5877e-04 - mae: 0.0144 - mse: 3.5877e-04 - val_loss: 3.7918e-04 - val_mae: 0.0142 - val_mse: 3.7918e-04 - 222ms/epoch - 17ms/step\n", + "Epoch 219/250\n", + "13/13 - 0s - loss: 4.1097e-04 - mae: 0.0155 - mse: 4.1097e-04 - val_loss: 3.7973e-04 - val_mae: 0.0144 - val_mse: 3.7973e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 220/250\n", + "13/13 - 0s - loss: 3.7840e-04 - mae: 0.0149 - mse: 3.7840e-04 - val_loss: 4.7988e-04 - val_mae: 0.0153 - val_mse: 4.7988e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 221/250\n", + "13/13 - 0s - loss: 3.5545e-04 - mae: 0.0143 - mse: 3.5545e-04 - val_loss: 3.7230e-04 - val_mae: 0.0136 - val_mse: 3.7230e-04 - 226ms/epoch - 17ms/step\n", + "Epoch 222/250\n", + "13/13 - 0s - loss: 3.4610e-04 - mae: 0.0141 - mse: 3.4610e-04 - val_loss: 4.1371e-04 - val_mae: 0.0142 - val_mse: 4.1371e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 223/250\n", + "13/13 - 0s - loss: 3.7775e-04 - mae: 0.0149 - mse: 3.7775e-04 - val_loss: 3.8045e-04 - val_mae: 0.0142 - val_mse: 3.8045e-04 - 115ms/epoch - 9ms/step\n", + "Epoch 224/250\n", + "13/13 - 0s - loss: 3.5911e-04 - mae: 0.0145 - mse: 3.5911e-04 - val_loss: 3.5609e-04 - val_mae: 0.0134 - val_mse: 3.5609e-04 - 233ms/epoch - 18ms/step\n", + "Epoch 225/250\n", + "13/13 - 0s - loss: 3.5933e-04 - mae: 0.0144 - mse: 3.5933e-04 - val_loss: 3.5900e-04 - val_mae: 0.0134 - val_mse: 3.5900e-04 - 105ms/epoch - 8ms/step\n", + "Epoch 226/250\n", + "13/13 - 0s - loss: 3.6466e-04 - mae: 0.0144 - mse: 3.6466e-04 - val_loss: 3.5378e-04 - val_mae: 0.0135 - val_mse: 3.5378e-04 - 232ms/epoch - 18ms/step\n", + "Epoch 227/250\n", + "13/13 - 0s - loss: 3.5876e-04 - mae: 0.0144 - mse: 3.5876e-04 - val_loss: 3.6523e-04 - val_mae: 0.0133 - val_mse: 3.6523e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 228/250\n", + "13/13 - 0s - loss: 3.4559e-04 - mae: 0.0142 - mse: 3.4559e-04 - val_loss: 3.5907e-04 - val_mae: 0.0139 - val_mse: 3.5907e-04 - 162ms/epoch - 12ms/step\n", + "Epoch 229/250\n", + "13/13 - 0s - loss: 3.4162e-04 - mae: 0.0142 - mse: 3.4162e-04 - val_loss: 4.2194e-04 - val_mae: 0.0141 - val_mse: 4.2194e-04 - 101ms/epoch - 8ms/step\n", + "Epoch 230/250\n", + "13/13 - 0s - loss: 3.6967e-04 - mae: 0.0146 - mse: 3.6967e-04 - val_loss: 3.7720e-04 - val_mae: 0.0138 - val_mse: 3.7720e-04 - 105ms/epoch - 8ms/step\n", + "Epoch 231/250\n", + "13/13 - 0s - loss: 3.3735e-04 - mae: 0.0136 - mse: 3.3735e-04 - val_loss: 3.3976e-04 - val_mae: 0.0129 - val_mse: 3.3976e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 232/250\n", + "13/13 - 0s - loss: 3.3844e-04 - mae: 0.0141 - mse: 3.3844e-04 - val_loss: 3.8716e-04 - val_mae: 0.0135 - val_mse: 3.8716e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 233/250\n", + "13/13 - 0s - loss: 3.6741e-04 - mae: 0.0145 - mse: 3.6741e-04 - val_loss: 3.8668e-04 - val_mae: 0.0136 - val_mse: 3.8668e-04 - 117ms/epoch - 9ms/step\n", + "Epoch 234/250\n", + "13/13 - 0s - loss: 3.4129e-04 - mae: 0.0139 - mse: 3.4129e-04 - val_loss: 3.4933e-04 - val_mae: 0.0133 - val_mse: 3.4933e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 235/250\n", + "13/13 - 0s - loss: 3.2338e-04 - mae: 0.0137 - mse: 3.2338e-04 - val_loss: 3.4566e-04 - val_mae: 0.0133 - val_mse: 3.4566e-04 - 108ms/epoch - 8ms/step\n", + "Epoch 236/250\n", + "13/13 - 0s - loss: 3.1652e-04 - mae: 0.0134 - mse: 3.1652e-04 - val_loss: 3.9728e-04 - val_mae: 0.0136 - val_mse: 3.9728e-04 - 111ms/epoch - 9ms/step\n", + "Epoch 237/250\n", + "13/13 - 0s - loss: 3.2047e-04 - mae: 0.0136 - mse: 3.2047e-04 - val_loss: 3.3756e-04 - val_mae: 0.0130 - val_mse: 3.3756e-04 - 225ms/epoch - 17ms/step\n", + "Epoch 238/250\n", + "13/13 - 0s - loss: 3.3167e-04 - mae: 0.0138 - mse: 3.3167e-04 - val_loss: 3.3191e-04 - val_mae: 0.0126 - val_mse: 3.3191e-04 - 228ms/epoch - 18ms/step\n", + "Epoch 239/250\n", + "13/13 - 0s - loss: 3.2033e-04 - mae: 0.0134 - mse: 3.2033e-04 - val_loss: 3.2969e-04 - val_mae: 0.0128 - val_mse: 3.2969e-04 - 215ms/epoch - 17ms/step\n", + "Epoch 240/250\n", + "13/13 - 0s - loss: 3.5224e-04 - mae: 0.0141 - mse: 3.5224e-04 - val_loss: 3.9061e-04 - val_mae: 0.0148 - val_mse: 3.9061e-04 - 110ms/epoch - 8ms/step\n", + "Epoch 241/250\n", + "13/13 - 0s - loss: 3.9777e-04 - mae: 0.0153 - mse: 3.9777e-04 - val_loss: 3.7065e-04 - val_mae: 0.0137 - val_mse: 3.7065e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 242/250\n", + "13/13 - 0s - loss: 3.2502e-04 - mae: 0.0138 - mse: 3.2502e-04 - val_loss: 3.3236e-04 - val_mae: 0.0124 - val_mse: 3.3236e-04 - 109ms/epoch - 8ms/step\n", + "Epoch 243/250\n", + "13/13 - 0s - loss: 3.0734e-04 - mae: 0.0133 - mse: 3.0734e-04 - val_loss: 3.2635e-04 - val_mae: 0.0126 - val_mse: 3.2635e-04 - 227ms/epoch - 17ms/step\n", + "Epoch 244/250\n", + "13/13 - 0s - loss: 3.2928e-04 - mae: 0.0137 - mse: 3.2928e-04 - val_loss: 3.2871e-04 - val_mae: 0.0125 - val_mse: 3.2871e-04 - 104ms/epoch - 8ms/step\n", + "Epoch 245/250\n", + "13/13 - 0s - loss: 2.9711e-04 - mae: 0.0131 - mse: 2.9711e-04 - val_loss: 3.2920e-04 - val_mae: 0.0121 - val_mse: 3.2920e-04 - 112ms/epoch - 9ms/step\n", + "Epoch 246/250\n", + "13/13 - 0s - loss: 3.2661e-04 - mae: 0.0134 - mse: 3.2661e-04 - val_loss: 3.6936e-04 - val_mae: 0.0134 - val_mse: 3.6936e-04 - 107ms/epoch - 8ms/step\n", + "Epoch 247/250\n", + "13/13 - 0s - loss: 2.9618e-04 - mae: 0.0128 - mse: 2.9618e-04 - val_loss: 3.3549e-04 - val_mae: 0.0123 - val_mse: 3.3549e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 248/250\n", + "13/13 - 0s - loss: 2.9979e-04 - mae: 0.0130 - mse: 2.9979e-04 - val_loss: 3.8099e-04 - val_mae: 0.0135 - val_mse: 3.8099e-04 - 118ms/epoch - 9ms/step\n", + "Epoch 249/250\n", + "13/13 - 0s - loss: 3.0599e-04 - mae: 0.0131 - mse: 3.0599e-04 - val_loss: 3.2729e-04 - val_mae: 0.0122 - val_mse: 3.2729e-04 - 106ms/epoch - 8ms/step\n", + "Epoch 250/250\n", + "13/13 - 0s - loss: 3.1256e-04 - mae: 0.0134 - mse: 3.1256e-04 - val_loss: 3.3855e-04 - val_mae: 0.0134 - val_mse: 3.3855e-04 - 109ms/epoch - 8ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# selected settings for regression (best fit from options above)\n", + "activation, optimizer, n_hidden_layers, n_nodes_per_layer = \"tanh\", \"Adam\", 4, 20\n", + "loss, metrics = \"mse\", [\"mae\", \"mse\"]\n", + "\n", + "# Create data objects for training using scalar normalization\n", + "n_inputs = len(input_labels)\n", + "n_outputs = len(output_labels)\n", + "x = input_data\n", + "y = output_data\n", + "\n", + "input_scaler = None\n", + "output_scaler = None\n", + "input_scaler = OffsetScaler.create_normalizing_scaler(x)\n", + "output_scaler = OffsetScaler.create_normalizing_scaler(y)\n", + "x = input_scaler.scale(x)\n", + "y = output_scaler.scale(y)\n", + "x = x.to_numpy()\n", + "y = y.to_numpy()\n", + "\n", + "# Create Keras Sequential object and build neural network\n", + "model = tf.keras.Sequential()\n", + "model.add(\n", + " tf.keras.layers.Dense(\n", + " units=n_nodes_per_layer, input_dim=n_inputs, activation=activation\n", + " )\n", + ")\n", + "for i in range(1, n_hidden_layers):\n", + " model.add(tf.keras.layers.Dense(units=n_nodes_per_layer, activation=activation))\n", + "model.add(tf.keras.layers.Dense(units=n_outputs,activation=keras.activations.linear))\n", + "\n", + "# Train surrogate (calls optimizer on neural network and solves for weights)\n", + "model.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", + "mcp_save = tf.keras.callbacks.ModelCheckpoint(\n", + " \".mdl_co2.h5\", save_best_only=True, monitor=\"val_loss\", mode=\"min\"\n", + ")\n", + "history = model.fit(x=x, y=y, validation_split=0.2, verbose=2, epochs=250, callbacks=[mcp_save])\n", + "\n", + "# Get the training and validation MSE from the history\n", + "train_mse = history.history['mse']\n", + "val_mse = history.history['val_mse']\n", + "\n", + "# Generate a plot of training MSE vs validation MSE\n", + "epochs = range(1, len(train_mse) + 1)\n", + "plt.plot(epochs, train_mse, 'bo-', label='Training MSE')\n", + "plt.plot(epochs, val_mse, 'ro-', label='Validation MSE')\n", + "plt.title('Training MSE vs Validation MSE')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('MSE')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: keras_surrogate\\assets\n" + ] + } + ], + "source": [ + "# Adding input bounds and variables along with scalers and output variable to kerasSurrogate\n", + "xmin, xmax = [7,306], [40,1000]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "\n", + "keras_surrogate = KerasSurrogate(\n", + " model,\n", + " input_labels=list(input_labels),\n", + " output_labels=list(output_labels),\n", + " input_bounds=input_bounds,\n", + " input_scaler=input_scaler,\n", + " output_scaler=output_scaler,\n", + ")\n", + "keras_surrogate.save_to_folder(\"keras_surrogate\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing Surrogates\n", + "\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13/13 [==============================] - 0s 3ms/step\n" + ] + }, + { + "data": { + "image/png": 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CLRKP1Wp1Xmd/TJgwQTPHRASPnu5pgiDYQOj4rcm1I8+ePYuNGzeiW7duiIyMBABs27YNLVu2RF5eHvr06QOO45CZmYlFixahYcOGAID8/HxkZma67SsrKws5OTkAgMrKSuzfvx8zZ850fm40GpGZmYn8/HwAwP79+1FVVeW2n7Zt26J58+ZOEZafn48OHTo4BRj/O+PGjcMvv/yCm266yetxXblyBVeuXHG+Li8vD+EsSY/eRIu/gVfMdoT20Ns9TRCEttCUCJs+fTpWrlyJixcvomvXrsjLy3N+9r///Q/Hjx/H+++/j7fffhs1NTWYNGkS7r//fnzxxRcAgJKSEjdhBABNmjRBeXk5Ll26hHPnzqGmpsbrNr/99ptzH1FRUYiPj6+1TUlJid/f4T/zxYIFCzBv3rwgzoiykGjRBmTZEQ7d0wThgPoNdVBVhM2YMQMLFy70u83hw4fRtm1bAMDUqVMxcuRIHD9+HPPmzcPQoUORl5cHg8EAu92OK1eu4O2338a1114LAFizZg06d+6MI0eO+HRRssTMmTMxefJk5+vy8nIkJyer2CJCa5BlhyCIYKF+Qz1UFWFTpkzB8OHD/W7TsmVL5/8TEhKQkJCAa6+9Fu3atUNycjL27duHjIwMJCYmIiIiwinAAKBdu3YAHJmKbdq0QdOmTWtlMZ46dQpxcXGIiYmByWSCyWTyuk3Tpk0BAE2bNkVlZSXKysrcrGGe23hmVPL75LfxRnR0NKKjo/2eD4LwB1l2CIIIFuo31ENVEdaoUSM0atRI1HftdjsAOGOounfvjurqahw9ehRpaWkAgP/+978AgBYtWgAAMjIy8Mknn7jtZ+fOncjIyADgMLd27twZu3fvxoABA5y/s3v3bkyYMAEA0LlzZ0RGRmL37t0YNGgQAODIkSP4888/nfvJyMjACy+8gNOnT6Nx48bO34mLi0P79u1FHS+hf8gdIA10HrUJXTd2sNlicfasBQ0bWmE2V6jaFr3fF5qICSsoKMB3332HW2+9FQ0aNMDRo0cxe/ZspKWlOYVPZmYmOnXqhBEjRmDp0qWw2+0YP3487rzzTqd1bOzYsVi5ciWmTZuGESNG4IsvvsDmzZuxfft2529NnjwZw4YNQ5cuXXDLLbdg6dKluHDhAh577DEAgNlsxsiRIzF58mQ0bNgQcXFxmDhxIjIyMtC1a1cAwF133YX27dvj0UcfxaJFi1BSUoJZs2Zh/PjxZOkSgd4fQoDcAVJB51Gb0HVjhx9+uAnbtvUDxxlhMNjRv38eOnX6UZW2hMN9oQkRVrduXWzduhXPPvssLly4gMTERPTp0wezZs1yihqj0Yht27Zh4sSJ6NGjB+rVq4e7774br7zyinM/qamp2L59OyZNmoRly5ahWbNmePPNN5GVleXcJjs7G2fOnMGcOXNQUlKCG2+8ETt27HALtF+yZAmMRiMGDRqEK1euICsrC6+++qrzc5PJhLy8PIwbNw4ZGRmoV68ehg0bhvnz5ytwtvSFnh9CV3FZWloq6DvkDvBPqG4VliwA4QS5w9jAZot1CjAA4Dgjtm3rh7S0P1R5Hjyvt6/nU8v3hSZEWIcOHZwZjv5ISkrCli1b/G7Ts2dP/Pijf1U/YcIEp/vRG3Xq1MGqVauwatUqn9u0aNGiluuTCB45O2eh9dqEbhcMQsUloRwsWQAI/cKyZf/sWYtTgPFwnBFnzzZUfVKi1+dTEyKMYAM1RYscWCwWTJgwwdkhFhUZUVgYgdTUaiQlOWIO5eoQ5Zi5Wa1WwRY1wnG+bDYbgMAWAK3c03pBrxZJ1i37DRtaYTDY3YSYwWBHw4ZnFW+LK6xZ6KSERBghGE/R4g2txWfxbV2zBhgzBrDbAaMReOMNYORIlRsXBL46d70OZqFSVlaGzZs3O1/7sgB07DgI/frV19Q9rXX0avEA2He7ms0V6N8/r9b5F9J3yGnhY9lCFyokwoig0ONgdPLkVQEGOP59/HEgKwto1kzdtgnFW+fnbzALF8uOLxFaXV3ttp0vC8D119dR9J5n2VWlBHq2eHiDlUmSa3/QqdOPSEv7A2fPNkTDhmfd2uWr35DbwseqhU4KSIQRojl5Evj9d6B1a+2IFW/8/vtVAcZTUwP88Qd7x1VaWipoIPY1mE2Y0BqdOjXW9UDOE4xFxZcFICnpZsXay7qrSgn0bPHwhCWLX6heDrkC6MvKygCEZqFjHRJhhCi07r5zpXVrxzG4CjGTCWjVSr02+erEtm7dCiDwQOxrMCsvDw8BFsiicu7cuVrf8W4BUE6Ese6qkgvXWEY9WzxcYdHiJ1W/IJW4tFqtbiEDaWl/YNCgLQA4JCef1IUAA0iEESLQg/vOlWbNHCLy8ccdFjCTCXj99avHorSLSEgnFmgg9jWYpaRU+/mWduGvER9oH8iismfPHq/7MZsrdNO5awFP618gi4de3Oh6tfhJKS5d+7hAfaKW7wsSYUTQKOm+83y4fFmIQn0IR450iMg//nBYwFwFmBIuIv64IiOvSNKJseBekwIhAhhArWsUrEWFldgc1pB7AuJt394skgMHDkRSUpJurLh6tfjJIS4DCbvs7GxN3xckwoigUdJ95xqrkJsbg/nzzbDbDTAaOSxaZMNDD12SzBLVrFltESmXi8hVNLrO8gA7gNA7MZstFg0anMPIkW+iqipKFfdaqAgVwIMHD671XjAxJCzF5riitjBUI0bN9ZhTU487309ISND0QOuJXmOc5BCXgYSd2WwWvW8WIBFGBE0g9x2PVLNoi8WCkyeBadNcXaAGTJ8ej+zseGixb+bF5bFj1Zg/vzE4zvD3J0YAHACDc1t/nRh/jl3rg3kTFa4DmlYQKmw9Mx15/GV58bAYmwOwIQyVjlGTMpaI1QzTULMQWUcOcalXqyEPiTBCFL7cdzxSz6K1lMEoFIvFgoMHax8XYHB2Ov46MW/nmIqOuuMZ4+VpXQo0y1bjfLEqDOW0zEl1zKxnmOqx1qInQiY/waBXqyEPiTBCNN7cdzxSz6JZzGCUAm/HZTDYnW7EyMhKVFVFw2aLrdXpeDt3vkRF9+7D0LOnPuu8eeJLLHiztKSl/eF1lj18+K3o0KG/KueLxaBtuS1zUh2zFjJM9fgMek5WfCW4iJ3USC3sWIJEGKEJhLpAtUbt4+LQt28emjUrFjXweTPdm0wc0tMtmnTbuiLEEuPrnPmytOTkLPU6y27T5mbFB0t+gArkflHaOqeEZU4ul5PacXVywZrLVQkLn14zl0mEEZohkAtUCeTo1F2Py2Ipw9atvkWDq1vRW4fnzXS/cGE5mjWLl6StoSJ28BAiSP2dM3+WFrXrg/G4DmTXXFOO6dPNqKkxwGTisHBhOR566EFVXFVKWObkcDnJab1TUwR5ulx99UlKu1yl/i29rVXsCxJhhKbw5wKVGzk79avH1QBJSROwZw+wZIl/t2JxcbHb53xnnJb2B3JyljpFxUMPPQggXpJ2hoLYeB2hlhh/YsGXpSUyshKFhSm1svHUgj/uKVOA7Gx+wmH4W0THq9ImOQOj5QpUl8N6xwsvz3VHlRZBQutnKelydRWlRUVGFBZGIDW1GklJjjgLMaI0HOLnABJhBOEXvvNXMuDdYrGga1fvMXC+3IosZNMFQmy8TiBLTGRkJAD/YsFsrsD8+SV49tlE2O2OxIcbbjiINWtGMXvO1JxwuCJnYLRcA63U1jtf1qeiokTs2pWpighiJYHD9dz464fEiFKtCywhkAgjCD/wg4QQy5SUBBMDx0pnLDVCY6QaNWok2I13992nsGLFp4iMrHQKMEA/50xKlCqnIMdAG+iecS3p4oovwefL+uRaTsbbPSSn25KVBA7++AL1Q3pbbksqSIQRkuDZ2Xh2cloOkBVjmZICoTFwrHTGwRLonhATIxXIjZeUZEdq6nEUFqZo8pwpiZbdQYGsd1u3bhXlRvQUGq71/AD3e+jEiRP49NNP3b4rpduStfpZWu2H1IZEGBEygWJ9/JmotRJUqVZ2pj+XFKvZdEIQ6j4VEyMlxI3H2gDGKiwKLH8Itd6JjaXyJjRccb2HXAXYN99kSO62ZK1+Fj1T4iARRoSMv07Em4l6+/b+mDMnHSkpEZrq5FnIznSF1Wy6QIh1n0oRI8UP0movFM1aiQG94Gm98xZEf+JEMj7+uB/45cGCcUV7Exq8S9KXCPrmmwzs3Hkn/LktxcJS/SyzuQKZmbtqiU2ygvmHRBghK95mjjU1BlRUNNFk3SpWgqV5WMymC0Qgt4WnK1tKQeI5SM+ZcwbHjkUgJaX678XNb5ZdALFe1V3r+Dpn7rFc7vi6/zzvBW/iPTNzF5KSiryKIJstFjt3ZsKf2zJU1K6fVVZWBsBxfnkBBjjOC0uJLqxCIoyQFe/FQ7Vf6Z5FWBKI3iw9/OAWyG2xdevWWvuTUpC47icxEejcWZLdCsbzvPiKFaJAZumoHcvljmu5krVrd9aK2XIlGOvT2bMW8BY3z98T66ZjqX6W1WrF5s2bvZxfI3btysT11x8iS1gASIQRsuI5c9RLpXs1Yd2VFcjSIyaWRa+CRAulRfSAv1iuQOVKfBVFFiIufLkvMzN3iRYnLCVM8G1gJSj/5EnHOsOtW2tnjCERRkiO58yenzl27z4M6ekWzTwcQlFSFGnBlSVEMHXq9CMmTGiNEyeicejQR86OWstZtMGi19IiLOLL+jpo0AeIjy8LWK5EqFUpOzsbZrMZNpsNmzZtqjXhAOy4885d6N49P6TjYc1NLSQoXy7LHN//5ubGYNo0M+x2A4xGDosW2fDQQ5ec/S+rAo1EGCEpvmb2ZnMFunWrRGKiNL/DijVIaVGkhQWKPfElrK6/Ph7XXw+cOOF4L9ysQqxYD8IBX9bX668/LKhcSSjWJ5aC5+UikHU7Oztblv6Y739ttlgsXZoDjnPE3tntBkydGoe//noLZnMF4uOnYPLk+rDbHaWG3njDkWjFAiTCCMlQambPkjVIbVHEuuVIqLAKR6uQ2in9rExklMKXGBJ6HeRafJrFEjJi8Cc2zWazLL8pxB0KAPPn13PWeLTbgccf53DjjaeZyNAnEUaEDN+JBJrZS9XZqC18WIF1y1EwwiocrUJq1nlidRFoufEmhuS4DkL7ukceeYTp8xusC0+tTE1/QvrsWQvsdvfs1JoaA1as+BSpqcdVv8dJhBEhw5vqjx2rxoYNnNsNbzJxmDjxbiZmHEqglGVKC5ajYISVGlYhFixBarmqWFwEWg6EiiGprwNLwfNiWbMGGDMGTLrwPAkkpP31LWrf4yTCCEmwWBzL99SuKm9A585NZP1tVlxySlqmtGA5CkZYKW0VUtOl7SkMfFkPlHBTySHmXcVtUZERhYURSE2tRlKSwx+kpPjwJ4ZKS0vdyqFIbcVhWWD5w2q14tixaowZ09g5oVbShSc2gN6XkGZtZQFPSIQRkqJ0VXlWXHJKW6bUjicSgpDOT6lFoj1Rs1aXnFaSYK17Uot5V3Hr79mUWtyKEX4s1dtiBf76FRamwG4f5vaZNxee1OcwVOubLyHNcnIEiTBCcpQqGsqSS05pyxTLs7tghJVQQQIAxcXFfrcRO6irIeTlzBTj8RfnxSO1mOevY6BnU0pxK1b46cFlKDX8uQh0X/DbSXUOlbC+qb2ygC9IhBGCYSGGxhWWXHJyWaY8zzm/RAigrOUoGILtmIV00HK5DlkS8qEiJs5LLjGv5LMZivALJ4EVDMHcF6Gew2Ctb3qDRBghCJbKQvCw4JKTc0FoIefcbK7AmDH3uKWAszB7l/L35XQdsiTkpSJYYSmHq0aNZ1OP11JNlHLhBWt9c0UPrmISYYQgWCoLIafwCRY5F4QWKj7MZjMSpaqCyzhSuw5ZEPJSI0aMSO2qUcNdrsdrqTZKuPAcMXwpaNjQGvQ9E0ziBauQCCM0h5zCR2x7ePwtCB2KO1cq8cGaSzkY5HAdshxbJxYpl5AJZSKjdDC0Hq+l3nEE4jeG3T7Meb1ycpYGdc/46q+0knhBIowQhdplIYQKH1YIxZ0rlfhg0aUcDHK5m1jOnBKDEDEiRUC1t4xEs7msVluUPJ96u5Z65uRJPhPSEYjP92s5OUuRmno85P1rJfGCRBgRNP6sMqWlpUzc2KwRSlyTVOJDzbIMUuDLwnPhQj3YbLEoLS0FIKxjZalWlxwIESOhPKO+MxIboH//m1RduYHVLDjiKlarFfv2AXa7+z0odQyfFsYhEmFEUASyyvA+eFatKSwQrGtRjlgXVuqrBYM3Cw/HAR988AAMBjuOHs1Dp07C7j+tzJJDQU4xIjQj0RdaFbeBEFtolBWUcOG5LrptMOSEfQwfiTAiKIRaZVi1psiJv3gr3kojZtCSOtZFy2UZeAvPiRPN8MEH9wMQX4dKKoHFSoV4NWJgAvUHAwcOREJCQq3fl/J8qB37w1//3NwYTJtmht1ugNHIYdEiGx566JKmxLwSkxN+3xTD54BEGBEUvqwykZHhJ7pcERpvJda1KGWsi9ZS+b25Ds+evQRegPGocQxqVYj3hhrWvUBW2oSEBNkzd9W0arpadZYuzQHH8YVGDZg6NQ5//fUWzOYKTXkGlGxnMP2aXq2nJMLCDLHZcb7KQgAcOM6INWtGacKlJRdC462CcS3KFbektVR+10GWTztn5RjUqBDvD6UHelasGWoJHP66BprYKHX9tegO9dWvuVpRtWRNDBYSYWFEKNlx/EBYVFQEYCsaNy7Bm2+Ogi93UDjjzyISbCVqOWb4rAycweB5jKwdg9asi1Kip4xEsSKGhUlBqOsusoYSVlQWIBEWRoSaHWexWJyfVVVFgwV3kJJIFfMVzKAl1+xPDwMnS8fAwiCsJnrISAxFxEg5KQhWCCqx7mIoaNE6pyQkwsKUULPjQh10tFY0VOjiyEItImqURNBjWQZWBn/WLHOEcKQSMVJMCoIVgiysu+hPZHk7nnvukaUZmoVEWBgiRXZcoEHHX80mLRYNFbo4ciBx6i1bjEdu4alEADMrmYJqwJJlTm7UzkiUCqlFjNhJgVghGMq6i1LgTzReLcYKl+MBCgqMvncYhpAIC0NCiV9x7VQ9Bx0AzjXAXNfs8uzAtFw0NJCADSRO1Y5zkFMAKZEpyPrgz4plTm70UmdNbREDSCME1bDE+hJZWVlATIz3Yqw1NcAvv1wRtH/WBbxUkAgLQ0JxJXrLVDObK/wOuv46MK0VDRUiYMPJIuKKEpmCehn8tY7DBWXRTZyPmu5kqYSgkv2Or4r3NTXA7t3HcezYOp/FWA8c+ABmMzB48GDEx8d73X84PcMkwsKQUDscqdY2VLpoqBSuMqECVg/xVt7wdw55F7TcmYIsdc6sW+bkQG9ZeDxiRIyU118KIaiEJTZQxfufftoCsznw8cTHx4dF9mMgSISFKSwU/1QyrV8qV1mgjkXNmC+5EXoOwylTkDXLnJyZaKxn4UlBsCJG6uuvBSt6MBXvtXA8akMiLIxgrfinkoO1lK4yfx2L2jFfciL0HIZLpuBVwWNR1SWnxLI5LGThyYWvmFShSH28WoorFCKypD4evZW8IBEWRrBW/FONwVoq65uWOkqpCee4ONbWCVRq2RwWAtjlIDc35u/zpo2YVBZRsi/UoyucRFiYwVrxT6UHa7HWt3CM/fFFqHFxWoXFdQKVXjZHT1bOkyeBadPMzuuotVU/Tp4Evv++Lmy22KCy2rWKv2xMLVvESIQRkiF00FWzaKjYQYS12B810dNAHAysrRPoipKufT1YOX1l9/myigfbF8ntMrtqETLDaJzktMR6UlRkxIkTdXDpkln6RijM779fFWA8NTXAH3+QCCPCFLHWISGC5uLFi6isrERxcbHkRT/FDiLhILCEEspArPVZOYuJB0oLYy1bOQNl9/HXkS+hEGxfE6zLLNh+tLZFyIDp0+ORnR3vJkb05rpr3dpxHK5CzGQCWrVSr01SQCKMEE0o1iF/nZrVasUbb7wBQL6in1oeRPyhZNCqr3Oo5wxRgF1LoB4sVEogNLsv2BIKYrNHg+lH/dXnKiiwIibGsT8tuO6CmYxZrVaYTJVYtCgG06ebUVNjgMnEYeFCG0ymS7BatduvkAgjQkKOG1+Jop96grVgcT1niPKwKnj0OrmQC6muY6jZo0Kez0AWvG++WY9Dhxwxib//bhHkuhMzaRMqnrKzs2E2e3eDBtMneS5z98QTsc7rdf58Bf6er2syMxcgEUaoiK/Cn1FR0hf91GtgvZLB4no9h2IhwaMPpLiOSmSPCrXgnTlzBnFx1TAar1rkAMBk4hAbexpWq8MiJ9ZdqXR8rOfv+LpeWp2UkwgjVMF/4c8CdOokbeyNXgPrlQwW1+s5JAJDAtxBIMuRUq5qfxa8TZs2AQD69XPvV/v2zUNeniOUY+DAJzBmTAPR7kp6xqWDRBihCkJdjlJ2aHruOJQKFtfzOVQTsbF8SokjEuDCA92VclUHsuD5a8eRI3ZdZhpqERJhhKoEsuCwGnvDGqwGiyuFEgkJcgmeULLYlBRHehZYgQg20J0VV7WvdpjNp2E0NvTrriSUgUQYoSpCLDisdGisE66CValUfKkFTyhrMbqLThow5URIRqLW2Lt3s193pVaD3LUIiTBCVcLdgiMUoZaecBKsaiwoLdX+Qsmm01v9J6UJxqIpNCNx8ODBcjVXNvxN2rQa5K5FSIQRquOrMwi0sK5r8UI9LejKI6T0hNaQ6lppfUFpMdl0SolOvT5PPMFYNIuLiwEEnixWV1cr0napCadJG6uQCCOYwLMzcM2YdBUePHwnqVergNDSE8HOwNUcYKW8VnpZUFqoJVgp0anX58kTMefIn+UoMjJS0D5CmThpcdIlBXrPzCURRsiKmEHfM2PSsSyHGT17XnHO9q1WK/bvP6WoK0pJhJaeMBgM3r5ei6ioKNUGWDktOHpwZwuJ5fMnOgEORUVJSE09Llp0quHa1SK+A93NsidICLHglZWVYfPmzaJ/g0X0nplLIoyQDX+Dvr9Zizfh4Trbf+SRR/DOO+9o1hUVDIEsPUI6/4sXL/49wHKKD7BKWHD0kJAg1C1kNlcgM3MXdu68EwAvwA3YtSsT119/SNRva921ywpKnJtAv8GSNUhKq7ue7zsSYYTkCJ1Ve4qH0tJSbN26NaDwuHjxIoDQXVFaiH0RYukRsg6nkAH20iWL5OdDKbdhOMW2JCUV46oAc8BbR8vKyoJeMkovrl0isNWI72PlJlzc2lJgDLwJG9x7771o3rw56tSpg8TERDz66KMoKipyfj537lwYDIZaf/Xq1XPbz/vvv4+2bduiTp066NChAz755BO3zzmOw5w5c5CYmIiYmBhkZmbi999/d9vm7NmzePjhhxEXF4f4+HiMHDkS58+fd9vm4MGDuO2221CnTh0kJydj0aJFEp8RNuFn1StW7HCrQQM4Bv2XXvoKU6bk4eefz8FisaCmJhG//ZaImppE56LPvPAwGBxFeXy5mIRu5401a4AWLYBevRz/rlkjxdHLQ6dOPyInZymGDVuHnJylzgXMheA5wLriOsCuW2eS9XyEcq0Id7xdS94luXnzZlitVlH7pWukDywWCxITE73+8X2snPiqqXbypOw/rUk0Ywm744478PTTTyMxMRF//fUXnnrqKdx///3Yu3cvAOCpp57C2LFj3b7Tu3dv3Hzzzc7Xe/fuxYMPPogFCxagX79+yM3NxYABA/DDDz/g+uuvBwAsWrQIy5cvx/r165GamorZs2cjKysLv/76K+rUqQMAePjhh1FcXIydO3eiqqoKjz32GMaMGYPc3FwAQHl5Oe666y5kZmZi9erV+PnnnzFixAjEx8djzJgxSpwu1Qg0q/7gg/sBGLFhA4dHHwU2bLg6W1q06GrBHaEupmBdUVqNfQnV0uPPomazxWL+fLNHpyn9+RDjNtSCtVIO+MzgoiIjPA1bgVySZ86cCWvXLuEbJYLcf//9qgDjoWr8vtGMCJs0aZLz/y1atMCMGTMwYMAAVFVVITIyEvXr10f9+vWd2/z000/49ddfsXr1aud7y5YtQ58+fTB16lQAwHPPPYedO3di5cqVWL16NTiOw9KlSzFr1izcd999AIC3334bTZo0wUcffYQhQ4bg8OHD2LFjB7777jt06dIFALBixQrcc889ePnll5GUlISNGzeisrISb731FqKionDdddfhwIEDWLx4se5FGI+3QZ/jAN74arcbsH49B34QsduBadPMePLJWGfHH0ycjJDtwj32xdcAe/asxavVUo7zEYyY1LNLw98g55oZvGEDhzfeAO65x30bfy7JTZs2hXTNwsm1Gwi9ZeYpEeTeurXjeXUVYiYT0KqV6F3qGs2IMFfOnj2LjRs3olu3bj5Tg998801ce+21uO2225zv5efnY/LkyW7bZWVl4aOPPgIAFBYWoqSkBJmZmc7PzWYz0tPTkZ+fjyFDhiA/Px/x8fFOAQYAmZmZMBqNKCgowP/93/8hPz8fPXr0cHsws7KysHDhQpw7dw4NGjTw2uYrV67gypUrztfl5eXCTwqDuA76Fy7UwwcfPOCxhfsgYrcbnFl/ckCxL94HWNbOhxBrpdYHR2+DYVlZGU6eBObPb+tWkuTxxzl8+mmZW9081q6ZXtFjZp6cbbVarTCZKrFoUQymTzejpsYAk4nDwoU2mEyXYLVq61wpgaZE2PTp07Fy5UpcvHgRXbt2RV5entftLl++jI0bN2LGjBlu75eUlKBJkyZu7zVp0gQlJSXOz/n3/G3TuHFjt88jIiLQsGFDt21SU1Nr7YP/zJcIW7BgAebNm+f94DUKP+g7Kk7XTq13FWJyLDjtq02slzVQUmSwdD6CsVZqfXB0bZvVasXmzZv/Pu52btvV1Bgwd+5Z5OfnuF0fVq6Z3mH5HmIJ/tnleeKJWKfV/fz5CrzxhuN9tT0NrIU4qCrCZsyYgYULF/rd5vDhw2jbti0AYOrUqRg5ciSOHz+OefPmYejQocjLy6tVK+nDDz9ERUUFhg0b5m2XzDJz5kw3S115eTmSk5NVbJF0eBvob7jhIA4evEGVQYT12BelZ+CsnI9grJXBZgGyjP86YHbk52c43+M4I7Zt64ecnKXIyVmq+jUjCKC29dWXW1tNKy2LIQ6qirApU6Zg+PDhfrdp2bKl8/8JCQlISEjAtddei3bt2iE5ORn79u1DRkaG23fefPNN9OvXr5ZFq2nTpjh16pTbe6dOnULTpk2dn/PvuXbwp06dwo033ujc5vTp0277qK6uxtmzZ9324+13XH/DG9HR0YiOjvb5udJIPWPwNtD36vVF0INI3bp1BW0XyErEeuyL0rNFOc6HWIseS9Y5V+SeRXs77oyMfOzd291tOz7+KzX1eMjnROuuXYIQgq+szawsdS1iqoqwRo0aoVGjRqK+a//7TLrGUAGOuK49e/bg448/rvWdjIwM7N69Gzk5Oc73du7c6RRxqampaNq0KXbv3u0UXeXl5SgoKMC4ceOc+ygrK8P+/fvRuXNnAMAXX3wBu92O9PR05zbPPPOMM2mA/502bdr4dEWyhlwzBs+B3tfAP3DgQK/p1Lz1R+uuKKVQe+AM5VqxYp3jUWoW7XncANwsYYB41703EUnPExEOsJq1qYmYsIKCAnz33Xe49dZb0aBBAxw9ehSzZ89GWlpaLSvYW2+9hcTERNx999219vPkk0/i9ttvxyuvvIK+ffvivffew/fff483/nZWGwwG5OTk4Pnnn0fr1q2dJSqSkpIwYMAAAEC7du3Qp08fjB49GqtXr0ZVVRUmTJiAIUOGICkpCQDw0EMPYd68eRg5ciSmT5+OQ4cOYdmyZViyZIm8J0oCQi3hINWgn5CQ4NfdRAOCMPwNsEoVbgzlWrFgrVSjrInncUthFfQnItV+nliL0yG0g9B7h9WsTU2IsLp162Lr1q149tlnceHCBSQmJqJPnz6YNWuWm/vObrdj3bp1GD58OEwmU639dOvWDbm5uZg1axaefvpptG7dGh999JGzRhgATJs2DRcuXMCYMWNQVlaGW2+9FTt27HDWCAOAjRs3YsKECejduzeMRiMGDRqE5cuXOz83m834/PPPMX78eHTu3BkJCQmYM2cO8+UppCjh4DnoKzXQE75Re4DVMqyUNQnVKsiqKwZgM05HC5BwDe7eadbM8fnjjzssYCYT8Prr6p87TYiwDh064Isvvgi4ndFoxIkTJ/xu88ADD+CBBzxLJVzFYDBg/vz5mD9/vs9tGjZs6CzM6osbbrgB//nPf/w3mDGkKuHA6qBPsS/uaPl8FBUZ8dtv8g9ALJU1CcYq6HrNrFYr9u0D7Hb357KmBigosCImRr1nlmVxyDIkXIO7d6xWKyorK3HPPUBBgRHHjkUgJaUaSUl2FBer627XhAgjlIWFoGg5Bn6KfXFHq+fjhx9uwvz5jRUdgFh4JnzhGT/pes14S56jRExOLRH5zTfrcehQRUiWPLEWGZbFIcvoRbiGaskTGuPlWTqD55DHevdqlc4gEUZ4RaqgaKFiKjs7G2az2fkduR4GMfvVs9mftUEu0P1is8X+LYRcY7OUGYDkTBQIZdLhL36SF9iBRKRYS54/i4y/50YJcahXWA0wD8ayLoUlT2iMl9B7W63SGSTCCJ9IERStVWsLD5n9lcXf/VJWVoa9e6M9amgpazmRK1HA23FLHU8ptYj0Z5H57DP/z43c4lDPsBpgLrSvv3TJErIlT0+V+UmEEbJjsVhqzYpdX7P6rOjF7K81vHWefEV5PVtOPJ+TpCTpY/akEpH+XInbt5/FP/7RwCOT1Pdzw1opEpZhXXwI+e09e0Kz5GmlMr9QSIQRsuNpTXr0UWDDBratSyzFq+jZHSqUcLCc1La6KmtFFnqfBXIl7tq1G3a7e/JToOeGhVIkrKMX8RGqJU8LlfmDgUQYISverEnr11/9nBXrkusAFBPDTrwKuUNrozfLif86ZNVISZHfupGbG4Np04TdZ4EEcXLyCa+ZpK7PDRE8lZWVbou4a1F8sG7JUwMSYYQTT7eG5wPvazt/eAsi9URJ65K32b6n0Fm0yFFjTm2rC7lDfaOU5UTuMh4s1CGz2WIxf75Z1H3mSxDr1VqpJrm5MVi61H0R906dfgxqH2pa1fViyZMaEmGEE9fAytzcmL87ZgOMRg6LFtnw0EOXgnJ/WK1WxMVVw2i8OsN3wAG4+lop65I3q1JWVm2hM326GU88EQuzuUI1qwtL7tBwRu7EEhbqkJ09a/F4PoO7z7wJYr1ZK9Xm5Elg2jSzMyuYX8Q9Le0PwedWbCarVOjNjSgVxsCbEOGExWJBTU0ipk2Ld3GNGDB9ejxqahKDEmArV65EXt5q9Ou3DQaDQ+UYDHZ07PiT2+vMzF2yz5K9W5U4bN9+zkuQqAFnzzZ0vjabKyRZKFko/Lnbu3e98zzx8IJ15cqVsFqtirQn3LFYLEhMTPT5J4UY5q2urs+FFHXIhFjoeAHoihT3mdLPjZ5xeBTchTK/iLsQfFnVT550iLMWLYBevRz/rlkjdevVhfWi1GQJI2ohRR0aVzHlbVbcuPFp7NyZCY4zYteuTMTEXA7atB4M3o/JgF27dsJguF+SxZGlIhyC0InayGE9ErJ2qBr3GesDI2s4gtk5NyEWTD/lq0/Pz9d/yAPrZZJIhBFu+HIhmkwcYmNPw2oVt1ixq+nZZovFrl2Z4A2xrqZ1OfB1TAaDHcnJJ5mthA6QWyfckCPWTcjzqvR9JmRgLC2tg4MHG4R1VjBwNZh9zpwazJuXJKqf8pWRyHFsFn6VGpZDN0iEEU5cAyf79bvJTZj07ZuHvDyHpSrUuK2zZy21Cm7ypvWysjKf1b/F4O+Y+E6MdaFD6ftkORFLMLE+St9n/voQIVnB4VC6xTOYPScnNuh+yl9GYlraFS8TbvULv/pDb30BiTDCSSAXorftxOAvCHnz5s2SBucLPSYSOmzDukuBReQqbyL34CYkK1hvpVt8CUqhweyueC7e7i8jMS/PMTnNy+sPu10b5SL01heQCCN8IpcwUTPWScpj0spMSy9opVNlATnLm/gaBMvKyrB58+aA3/f33AjJCpZi2RuWkEJQ8ou4e4oPISLOc3KqhXIRLLZJLCTCCFVg3QXIw3dunsgx0+Jnw3FxlLQcLsjhWlGivIm37ycmJoZkoRC6qHdKymOw25u7fVerpVukEsv+FnEXQrDlIsS4gvXmRpQKEmGEamjBBRhq5yYU99lwY/Trd5Os2aJEcMgVfyS1a0WokJGran0oAkhoVvBPP32g+koWUiFFJrrSiLXc8ff6sWPVKCyMQGpqNZKS3A9eS25EqSARRshCuM1mQqH2bNggqBAjnWNlkDv+SMpBJ5jyJixbJgLFb3o7NgAoLEzBsWPV0MI4LiQTnTVCtdx99JFFV7F8UsDeVSZ0gbcZPl+XSEsoMQB5mw1znBHduw9Dt27eLSThOGNUA60sHeXNlR3I5c96gLM/S7nnsR092sq5pM+GDRzzg7vQTPTs7Gw1m1mLUCx3WnmWlIZEGCEbWhIJ3mK/lBiA/M2GU1KqSWypiFaWjvLnyg7k8meh/WLhj81mi3WKGMBhSWZ9cBeatV1VVaVG83ziq96YkJIWWnS9KgGJMMIJy+4JsQhta1JSkuIDklJ12YjgCSa2Ss1rI9aVrSe81R3U2uDuSyxHRAgbopXok/3VGxNS0iIUAadnSIQRTuR2T6gh8lh2uShVl40InmCXjnIN3AeUKSLqy1LHFz4OFxHmre5gqCt8SI3n/bFvXxRsttiA1yg+Pj6k/kuqvjRQvbFAJS1CFXB6hkQY4YacD4JagkjM/tSoxq2FbNFwJFBslas70PC3R5nj5A08DmSpU3PtU6XxJpRZsiR7vz8sMBhy0L9/XsAs6FDaLlVNN6FFY7317aEKOL1DIoxQFC08ZHqrxk2Ejq9Bp6jI6OYO5Lirn8kZeCzUUicGtZYDCsVqw6ol2dNd7Hp/uK6ZK+fkS46absEQioALB0iEEYQLlMEjHrkGb5bXCDxw4EKtYGNXlAjiF1P42JfgUXMCEshSHsh6wx/32bMWt9dq4i0Y3RU1XcdamBCHAyTCCMIFyuARh1yDN+tWyV9++aiWO9AVpYL4fVkXgsn6ZWEC4u/8eFpvPEve/PDDTbUsgmoWPPaV+eyKVK5jlicqQrDZYvHNN1Ho2lWb7Q8FWh+FIP7G0WmegtHIub3vCPI9BavVqlLL2MbX4H3yJJv7lRLeHWgwOBrp+Pfq/5VaE9UX/IoPrn++Aqf37bN6nYAUFFiZufctFovzOFzFpWeZCt7VZ7PFqtJOPg4qL281+vXb5rw/ALvbvRLIdSzERbtmDdCiBdCrl+PfNWukOALl+OGHm7B0aQ4eeMCiyfaHClnCCAJULkIsctXS0kqNLqC2OxAA82uiuqKVchz+8Famgnf12Ww2RZYec8Vf5rOjvbXvD0+rpZCYLBasl6FQu8abttovBSTCiIBo3dQtBDXKRWi9Lptcg3eg/e7YsQXbtkVjypR70aFDA0mPiSfYc+7pDtSC+OLxF+SfkZFfazsW8Vamgnf1bdq0SXUBKeT+ELNOrdbDJ/RQ4y1USIQRfmE9JkculCgXwXINMyEEW0tLiv3ecMNBrFkzSvblaQJdGy0uwSUEfgJSUJCOvXszsHdvd+TnZzjXZmQVqe9BLSBk7Ump+g65Jozea7xdLeAaDgYAEmGET7Ru6tYCrAqsYBGToRfsfiMjK50CDJB/eRqWr43cVtT8/AzwIcN8fNWcOWegsFfPL57HFso9qLXBXunwiVAmjP7uQU/xbDIBr7/uuAbhYgAgEUb4ROumbqkJ5wweIchlPeT3W1iY4tV1wVqMmBLIaUX1FV/1008XkJJiZeY8WywWDB482K1shZh7UIuDvRrhE2Kvu7+CsdXV1Rg4EJg06X+wWhsgJaUaSUl27N9vxJgxVy18ejYAkAgjfEJrfV2FT39fssSomY5aDCxbBHzF/WghcNwVqc6xXMfq7TwDHN577w+cOJHP1HmOj48P6ft6sfazvtqG5/1itVq91nw7dMjxb2FhCuz2YW6f6dUAQCUqCK841voqxqJFZTCZHCUbHGt9lcFkKlY9Zf3kSWDPHmXKFfjK4GGpVIIUsJ7q7q0cRDBxP1LeM2LcfFFRUcyfY8BxnjMzdwFwLdViwK5dmbDZYnUVX+XP2q8mSvZvahDoHuInAq7o1QBAljCiFqyv9SW3+8Bmi8XZsxY0bGiF2VwRFhk8WrEIiI37kfqe8XSx8K4VVyIjI2E2mwE4BNilSxZNnGMASEoqBuBeYFTNkg9yIGdgu1iRDmjTPSo1/IQrL68/7HZ9L/ZNIoyoBctrfcklFvgO0FvV7bS0P7xk8EifgaQmWor/C9b1Itc943rdA4mSq8VQ2at75k0wsF7yIVTkDmz3FOk2mw1VVVVu20RERDjdqXz8npB79eRJYN++KNhssUy7IEPFc8LFggFADkiEhSEsx/0EQi6xYLFYMHDgE5g/Px4c55gV81lhOTlLa6W/8x21zRaLHj1GoksXs+bOJY9Yi4BcGXpS1kVjoegr68VQecFQVFTkLLuhlZIPYu9BJQLbgxHpPIH6t6tWMgsMhhzVl2aSG5YMAHJBIizM0LqpW85kgdLSBrU6QN4F462j1kOwfigWAbky9Dz3K7YmFyviR656alJisVhq/b5cZUekRKp7kIXA9kCToZ9/jsKYMVf7KH6SmJb2h+ptJ8RDIiyM0Ercjy8cyQKVWLQoBtOnm1FTI12sgK8O0HWBXdeOWi/LbYRqEZBLvEixXxbFjxaEjSssiJNA6MEtJWQy5C1jkJ8k+rpGrK62QVyFRFgYoaW4H0/kTBbw1wH6WmBXr8H6Whh0xaC2+PFM9tDjOSbEI2Qy1LChFUYj5zZJNBo5PPxwOpKTuzqTQIqKjCgsjECbNkZYLNIu66XlUBZWIREWRgh15Tke4hTngMECciYLCLUGueJ9uQ19BeuzglSxZ2qIn5MngRdeiMXrr+e4CXsW43jIasIO3u5Vs7kC/fptc5sk9uuXh717r4YMfPSRRbZwEylDWeheuwqJsDBBqCvP8aA1ht0+zOuA4Tqj1ytCBmtvLi45lgsJZ67OurW5xuayZecxaVI9cFx953ssx/F4q0BPsIW/SeKxY9WyhZtIHcriK5ZPr2uy+oNEWBgg1JU3cOATfwd+umcH8gOGZ/mGFi0u4skn1TgiNlBquZBwpPas26KppIeffz6HSZPMzkxbVwLF8UhNMC6kUCvQE/Lja5JYWBghW7iJHKEsrE2a1IIq5ocB3lx5qanHaz3IR47YvWYHdu8+DP36jUVeXn+n+43jjJgypb5uKzoLxde59IXeK2FLga9Zt1bOmdVqxbffnqsVM8jjmuwhN8FW6Jd7YXBW0ONxms2nYTRybu85QiROhbTCiSNp6ZQs+/ZEj9clEGQJI5ykplZ7jRlLT7doOqifFbReHkQJWKjrFQr+ymIAtZdackXq+mgO95TnIsgcbrzxNFJSvMctyrkwOEvo8Tj37t0seeFZuYvaeuLruvDJBqmp1T7vXa1CIoxwEhV1GosWRXuNGUtIqAOjsYEs9bm0wsCBAwFAVMwCq+VBWJp5Sl3XS41j81UWA7CjW7d8pKcXuAmwgQMHIiEhQdIBnz+P3hdBNmDFik+Rmnrc53lkZYCTOxOPleOUEqlDJJQoauuJ53XR++SVRBjhhBcX3mLGbLZYDBs2DOvXN9T9Wl6+SEhIEP1dVi2JLFkEpK7rpfaxCcm0TUhIELUOoz+Bwh+vv6WHXLdjEb0PvJ5IORGQMwtY6QxjVievUkIijKiF54PmGpDvmNHvRXp6gaJreclp1Qhm32IGLjkXCpYCFgW0VHW91D42OQYtoQIlkJhlFb0NvEIsev4mDOGYMcjD6uRVSkiEEX7xrAwPGJGfn4H09AK37eSeVctp1Qhm38XFxUHtW+mYCj1BRU3dERPnpXaR2mDRekygJ8FY9Hwdl56C0INFzmXqWIFEGOEXb5XhlU6x57l0ySJbjIjQjj1Yi5waMRVax7O6PBFcnJcnWhGzrKz1KRVSWfS8TRJdA9Wjok7r0lImdpk6rVX1JxEWBoQykwoUV6IUrMSIhGqR08qAqBaetehYrS4vFUKfTT3EeQWCxbU+xSK1Rc91W8++cNGiaMnazQpil6ljZZwIBhJhYUAg4eAv5oCFuBLWYkSknIXzVp+iIiNExGdrBiGz06Iio5vrm+Xq8qEycOBAJCUlBX0vsfA8KoHW3KiuyGnR89YXTp9uxhNPxCpyjpSyUotZpo61cUIoJMLChFCEg9odol6DM12tPhs2cJqYtYlByOzUarXiwIEL4Lgmbu+r5foWi1DLlhgBxqP286gUWrUay2XR821dMwh6RkJNWvJnpWYhbk2r4wSJMEIQanaIYoIzWY8L8Ex4sNsNmpi1BYPQQPJAlgNP1zcLHb4vlCqL4e95LC2tg8LClIDWCpbPo16QSjALfUays7NhNptrfT/UpKVjx6oxf35j5zJcHGfE9u39MWdOOjPFU7UaxE8ijGC6MxYTnKmFuABvCQ9amLUJJZhAcqGWA7EuPKVRs325uTGYNi0edvswGI0cFi2y4aGHLtXaTmvV4LWMFBNYoc+I2WwWVXfOHxaLBQcPerMyGVBR0QQs3EZig/hZgEQY4XX2HmxtGjmEnJjgTKXjAsRa3LwFWLNSN0wKxAaS+7McJCQkaP68yInNFov5880u974B06eb0bPnFWasFUToKO2OZr3OodggflYgEUYAcJ+9nzwJHDkSBZstcLCnnNYJMcGZSsYFeLO4ZWW5izJf4tTbjFaPdcPEBJJrNRZIbc6etbgNkoCwZYrkhvXQAC2i1DOihTqHYsYJlhAswsrLywXvNC4uTlRjCPW5KiwsMBhyApYIYMk6oeSMzZvFbcwYgOMcf1fdoO5WRlcLY7jUDQuXQHI5EWJpZrF8RTChASytY0o4oDqH8iNYhMXHx8NgMPjdhuM4GAwG1NTUhNwwQnk8hYWWSgQoOWPzlaXkaoFzd4P6/r1wsfqEy3HKhZBlbVgqXyGmur/cSQ1kkQsdpZ5juZeSYwnBImzPnj1ytoNgAG+uPK2UCFBqxuYvS8kTPQXaE+ojRHywYHUMJinDmxCTAyWTdciiFzpyLiXHGoJF2O233y5nOwgG8JbiG6g6PqsdiVwztmPHqp3p/55WB44DAH0G2hPaQW2rI0vV/cVY5EJFqTIleidczo/owPyysjKsWbMGhw8fBgBcd911GDFihNcaJQT7eEvxNRo5zJlThIED70RkZGSta8tCR2KzxeKbb6KQmlot+285ZtONYbcPc7p6cnKWOq0OR4+2YjJwlXXIcqBNArn31HaPhmKRCxWp90fPSG34+y8urrY3Qkvrz4oSYd9//z2ysrIQExODW265BQCwePFivPDCC/j888/RqVMnSRtJyIu/FF+gAnylCtaEBF/BeckSI4xGDv363STbOoNX4+WuFivctq0fcnKWIjX1OAAKXHUlmEGDLAfawWq1orKy8u96ZGbY7Qa3emSe111N9yhLFrlQoWfEHXf3cmO3vl9r68+KEmGTJk3Cvffei3/961+IiHDsorq6GqNGjUJOTg7+/e9/S9pIQl5YTfH1N5B7qzgvVxKBr0B8b/FyaruCWCHYQYP1wYOCut3jIZcuzXFWT7fbDZg6NQ5//fUWzOYKZGdnu31P7WdCbYucVATzjOj5fq2dmX617wfgc/1ZVq2Eoi1hrgIMACIiIjBt2jR06dJFssYR4Y2/gfybb6KwZIm7GVqOJIJgl9QJRDi5FVgXVoEQYvVh4RiVuqf459Dbag+uz15VVVVIvyMHLCQsKIWUSQgs9le+Esi6dx8GjoPXceH220fCYmEzVEqUCIuLi8Off/6Jtm3bur1/4sQJxMbGStIwggC8D+RWqxWpqdUwGjm3emBiRFEghC4XIhRyK2gDoVYfFlz0St9Tgdx7rKK2RU5u5EhCYK2/8lcLMiWlGlFRUV7Xj+zcmU0BBogUYdnZ2Rg5ciRefvlldOvWDQDwzTffYOrUqXjwwQclbSBBuOKvHpgQURTKjE2q2bTagzYRGKFWH1biiZS8p/Tg3nMN3NYDciYhsNJfCa0FOW/eaMydm6iZ9SNFibCXX34ZBoMBQ4cORXW1IystMjIS48aNw0svvSRpA3nuvfdeHDhwAKdPn0aDBg2QmZmJhQsXIikpybnNZ599hmeffRa//PIL6tSpgx49euCVV15BSkqKc5svv/wSkydPxi+//ILk5GTMmjULw4cPd/utVatW4Z///CdKSkrQsWNHrFixwpmAAACXL1/GlClT8N577+HKlSvIysrCq6++iiZNmji3+fPPPzFu3Djs2bMH9evXx7Bhw7BgwQI3F64ekTsWQWg9sIEDByIhIcHtu1LM2PQ+m2YJFuJatGr1kRt/z57QPk4tl7tn4LbZXIQxY4qD6h9YuDdd0VMSgi+E9v01Nf/S1PqRohRBVFQUli1bhgULFuDo0aMAgLS0NNStW1fSxrlyxx134Omnn0ZiYiL++usvPPXUU7j//vuxd+9eAEBhYSHuu+8+TJ48GRs3boTNZsOkSZMwcOBA/PDDD85t+vbti7Fjx2Ljxo3YvXs3Ro0ahcTERGRlZQEANm3ahMmTJ2P16tVIT0/H0qVLkZWVhSNHjqBx48YAHIkJ27dvx/vvvw+z2YwJEyZg4MCB+OabbwAANTU16Nu3L5o2bYq9e/eiuLgYQ4cORWRkJF588UXZzpHaKFkQkceXKEpISEBiYqK8P+4DLaVHswZrcVhqWn1YG+g98fXsxcfHM+XCcsUzoYfjjJg3Lwk229KA7mXW7k1vSHG/sn7f8fibELOSXCaEkMwydevWRYcOHaRqi18mTZrk/H+LFi0wY8YMDBgwAFVVVYiMjMT+/ftRU1OD559/Hkaj4wF76qmncN999zm3Wb16NVJTU/HKK68AANq1a4evv/4aS5YscYqwxYsXY/To0XjssccAAKtXr8b27dvx1ltvYcaMGbDZbFizZg1yc3PRq1cvAMDatWvRrl077Nu3D127dsXnn3+OX3/9Fbt27UKTJk1w44034rnnnsP06dMxd+5cXQRde+JtLcWry/ao2zYl8ZUercdrDkjbYbMQh+Wt9pCSQd1aGOiFwEIbvT1zYt3LLNybQgnlflVjIh0KepjwihJhly9fxooVK7Bnzx6cPn0ado9UBd7yJBdnz57Fxo0b0a1bN0RGRgIAOnfuDKPRiLVr12L48OE4f/48NmzYgMzMTOc2+fn5yMzMdNtXVlYWcnJyADhU8v79+zFz5kzn50ajEZmZmcjPzwcA7N+/H1VVVW77adu2LZo3b478/Hx07doV+fn56NChg5t7MisrC+PGjcMvv/yCm266yetxXblyBVeuXHG+DmbR9FCQIgPGW8ZKOC3bM3DgQFRWNsb8+Y2dHTTHGbF9e388//xtsFgaqNxC6ZG6w1YrDsu78HGvPaSEG1pLA70WcA0q59fXFOte1lqMoJj7VWsTaa3VA/OFKBE2cuRIfP7557j//vtxyy23BFzYWyqmT5+OlStX4uLFi+jatSvy8vKcn6WmpuLzzz/H4MGD8fjjj6OmpgYZGRn45JNPnNuUlJS4CSMAaNKkCcrLy3Hp0iWcO3cONTU1Xrf57bffnPuIiopCfHx8rW1KSkr8/g7/mS8WLFiAefPmCTwb0hFqBoy/jJVwWbYnMjISR45EuB0/4AiK/e9/7UhKsurmHMi9FIyScVj+hI/Si9ezNNDzwhQAioqMKCyMQELCBUHfZcnq63kfhuqu02uMoK9aiDU1QEGBFTExbFg3eby5lZV+XqVClAjLy8vDJ598gu7du4f04zNmzMDChQv9bnP48GFnKYypU6di5MiROH78OObNm4ehQ4ciLy8PBoMBJSUlGD16NIYNG4YHH3wQFRUVmDNnDu6//37s3LlTMaEYCjNnzsTkyZOdr8vLy5GcnKzIb4t9wIRmrGh19i50QNm0aZPPWmLffLMehw7pw4KhxFIwSsZhCRU+SqL2QO/6TLtbGxqgf/+r1sHBgwfXmoxqwV0airtOD5mhngSqhchi/8XS8xoqokTYNddcI0k9sClTptTKTPSkZcuWzv8nJCQgISEB1157Ldq1a4fk5GTs27cPGRkZWLVqFcxmMxYtWuTc/p133kFycjIKCgrQtWtXNG3aFKdOnXLb/6lTpxAXF4eYmBiYTCaYTCav2zRt2hQA0LRpU1RWVqKsrMytA/Lc5ttvv621D/4zX0RHRyM6Otrv+WANoRkrrJjpg0WIlbCsrAybN28O2EFr9Ry4olQWllRxWEJj1sQKHzmsPmoP9Py1C2RtiI+PVy35JVRCcS/rrfCr0FqILPVfak9UpESUCHvllVcwffp0rF69Gi1atBD9440aNUKjRo1EfZePQ+NjqC5evOgMyOcxmUxu23q6JwFg586dyMjIAODoUDt37ozdu3djwIABzu/u3r0bEyZMAOCIPYuMjMTu3bsxaNAgAMCRI0fw559/OveTkZGBF154AadPn3ZmVO7cuRNxcXFo3769qOPVCnLHzoQavyYmkDyY2Z/eOmhfKCEUxN5LvoLbx4y5gFGjLnh1lwY6HrlKnviChftIT9YGqfF3b6qRXShVZXsW7jtfuLZd7YmKlIgSYV26dMHly5fRsmVL1K1b1xn4znP2rLRqtKCgAN999x1uvfVWNGjQAEePHsXs2bORlpbmFD59+/bFkiVLMH/+fKc78umnn0aLFi2cgfBjx47FypUrMW3aNIwYMQJffPEFNm/ejO3btzt/a/LkyRg2bBi6dOmCW265BUuXLsWFCxec2ZJmsxkjR47E5MmT0bBhQ8TFxWHixInIyMhA165dAQB33XUX2rdvj0cffRSLFi1CSUkJZs2ahfHjx2vO0sUaocSvKZX5Ey61xFjssP3FeK1eXR+rV9fFvffmYeHCa2tNAP0djxolT9S+j/RkbVCKN96owfz5nOJZrVJWtlf7vvOFxWLB4MGDsXnzZgDi+h+W4hV5RImwBx98EH/99RdefPFFNGnSRPZ4q7p162Lr1q149tlnceHCBSQmJqJPnz6YNWuWU9T06tULubm5WLRoERYtWoS6desiIyMDO3bsQExMDABH8P727dsxadIkLFu2DM2aNcObb77pLE8BOFYDOHPmDObMmYOSkhLceOON2LFjh1ug/ZIlS2A0GjFo0CC3Yq08JpMJeXl5GDduHDIyMlCvXj0MGzYM8+fPl/U8hQvBdmYnTwJ79wKjRwMc53iP9cwfVgg0q2etw/YX4+WAd6k56kINHjzY7VPWjkdN9GRtUGINRIfwT1Itq5WVeC058YxB9PW8Km25DgVRImzv3r3Iz89Hx44dpW6PVzp06IAvvvgi4HZDhgzBkCFD/G7Ts2dP/Pij/zTWCRMmON2P3qhTpw5WrVqFVatW+dymRYsWtVyfhPI4rF9craxFgN3MH7XxV6vqrrtKZftdKQdKb1YcHleXGr/iB+EdFq2dYgjVUiTknmMhq5VwoGax7mARJcLatm2LS5cuSd0WgpAUR90b7wIMYDfzR00C16raCbNMa+FK7VJxteK4wqpLTQlrDY+3EhSpqdVISrKjtNRdaPuyNpSWltb6Lt8+Fp+lUNrk794MtQYZEd6IEmEvvfQSpkyZghdeeAEdOnSoFRMWFxcnSeMIQiy+6t7wsJz5oyZql2yQcvDmrTgFBenYuzcDQG2Xmmff5QslYkmkFKH+8F2CIriCl7NmFWLbtuu8flePk5pAx6Mn9y2hHKJEWJ8+fQAAvXv3dnuf4zgYDAbU1NSE3jJCEyg5exdKoLo3gwZ9gOTkk5J1jiyeg1AJdVbPyrGazRW4665dSE8v8OpS49d+ZWWdQyV+R2gJCn8E+m64Tmq06L7VY/+lJUSJsD179kjdDkKjKDV7D4ZAdW+uv/6wpL/H4jkIFTElG3hYPFZ/AfestVUpAlk7vV1j3vVG5St8o7XkDj32X1pClAi7/fbbBW33j3/8A/Pnz/fZWRP6gOWHU6mZKcvnQCyslWwgpCWQtdPfNab4J32hlf4rGKudGvXaxCBKhAnlnXfewVNPPUUijFAVrc1MWUJr545cJsIJJYaJ4p+8Y7PF4uxZCxo2tDJ3LrQiSvwh1Gr30UcWRWpCSoGsIozjizIRBEEoAN9Jnz592lnU0R/hLtpCsRSzGv/kL/MTkN61dvHiRQCBkxzUuNf8lZtRooisHARqryMr3iHAAPZrQsoqwgiCIJTGYrFQnEsQhGLtZM1SKjTzU6rsTavVinfeeSdgosIjjzyi+L0WuNyMMkVklcRXVjzLNSFJhBEEyCLCo6dMKdY6W0J+hGZ+SpW9KbSkS926dSX5PTnappdM1kBZ8azWhCQRRoQtfPYXWUTcXTg9ejyE//3PhObNr6BpU0dF+cjISJj/rtJK50vbhCK0tSLSlc7eZDlRgeW2eUNs7FqgrHhWa0KSCCN0h9ABICkpicQElHfhEOoSiqtWLTev6yRByG8qLTxYTlRguW2eOJaZCz2gntV4RW/IKsIeeeQRqp5PKA7FAwWH0i4cQn1CXcJHSVwnCYDvDETX9X7VEB4sD/wstw1wXONjx6oxZkxj5zJzdrtj2bnExFKkpxuDvu9Yi1f0hWgRVlZWhm+//RanT5+GnU9D+JuhQ4cCAF577bXQWkcQIiGBFTxUgFMegrXiEO64njt/llrPc6yG8GB54Ge1bbzILixMgd0+zO0zu92Afv0s6N8/D2+91V2Xz4koEbZt2zY8/PDDOH/+POLi4mAwXF0g2WAwOEUYQRDaQWuxI1ogGCuOHgcYKRGzzFKwwkPp8hbEVfHsrf8Brl7nH344gk6d9DfBFiXCpkyZghEjRuDFF19UJeuDIJRCDwUOhaKl2BEe1q1MYq04UsH6+QkGuS21FBupLnz/8/HH/QDUvs4bNxZg797jujv/okTYX3/9hSeeeIIEGKFL9FjgUCi8C+fEiWYADEhOPuH8rKysjKmlilizMnkTPKWlpc62iV0sO5T2uJ4fX2hlUBNiqQ0le1NMbCTL2aIst80XnTr9iMaNS7BmzSif11lvsamiRFhWVha+//57tGzZUur2EISqhGOBQ0+OHm3l1QqwefNmpo5bbSuTK/4FoTrxdp7H7UukamVQE2KplSIpJ5hrxXISEMtt80ezZsWiLPJaFJ1AECLs448/dv6/b9++mDp1Kn799Vd06NABkZGRbtvee++90rWQIBREywUOpXA9aTFDUg0rkyeBBGFa2h+qxtsFWlJHKwgJtg9VVAQbG8maiHGF5bb5Q0xShVZFp2ARNmDAgFrvzZ8/v9Z7BoMBNTU1ITWKINQmUEdss9mc77MQwBuKa851ZqjFDEmW2uxLEObkLFUt3k4pkapU/JncWX5ajI3UI/x1ttliUViYImhRdNYElhAEizDPMhQEoWcCdcSbNm0CwE4AbyiuOYvFguzsbGzatEmTGZIstdmfIFSrVpMSIlXO+DM13Eys19UKF7z1ZaWlpUxatMQiKibs7bffRnZ2NqKjo93er6ysxHvvvUclKoiQYSErMVBHzKLrTqzVg1+SSItWAJbaHEgQqlGrSQmRGkr8mRALmhpuJlbraukNX+LZd1+2VFdxuaJE2GOPPYY+ffqgcePGbu9XVFTgscceIxFGiILFrER/HTFLbjAp26RFKwArbRYrCOUMFlZapAYTfxaMBY2lzFxCOvhYrqKiImzdutX5vhbjcsUgSoRxHOdWoJXn5MmTzhk1QQSDFrMSWXKDSd0mLVoBWGmzP0HILxrvihKTC6VEarCWWJYyOLWaXecNrdWH89YWFvtXOQhKhN10000wGAwwGAzo3bs3IiKufr2mpgaFhYXo06eP5I0k9I+aWYmuHdahQ2WCg0BZcoOx3Ca5YHnQ9CUIExISFLPoeB63rzZJeX5CscSqncGp1ew6T5Ssnydl2IinRSxc+rKgRBifIXngwAFkZWWhfv36zs+ioqKQkpKCQYMGSdpAIrxQevbjvUp2O8GDACtusFDbFIqgUWvWzdKgyaIgVOP8iH1+WSgzAmgzu84TuevnyRk2YrFY3NrFYv8qNUGJsGeffRYAkJKSguzsbNSpU0eWRhHhi9KzH6FVsl3dSGVlZdi8ebNbm1nrHIJtk9gBW+2q7KwMmiwJQlfUiKEU8/yyGF+pdeQQtnKHjZw8CezbFwWbLdbZRhb7VykRFRM2bJhjpfPKykqcPn26VvmK5s2bh94yImxRY/YTzCBQXV0taJ/8kjWuyDUQS2GJEdMulmJ61MabQHU97srKShQXFztfa8G1JQYxz2+4xP8oiRzCVo6wkdqWNQsMhhzNFhQOFlEi7Pfff8eIESOwd+9et/f5gH0q1kqEitDZj+tAF0rR1ECDwNq1O72KC3/4+o4cViEWLDFqx/SwhNoWQqUJNf5MjAVNa8HnSiOnsJVq374sa2q5o9VAlAgbPnw4IiIikJeXh8TERK+ZkgQhN97jucQVTfU3CPjbt6ubsrS01JlircZahmoOOKzE9LCC0GusFwuhFJOAYCxo4SZyxSBnaIdU+xZqWfOGFrJUhSBKhB04cAD79+9H27ZtpW4PQQhGaDyX0IHO2yAQaN/est30tkyMECimxz++3LR6Qsy9JtaCRm5wYcgZ2hHqvl3jvwJZ1jxLu+jJyilKhLVv395rvAtBiCWUmCYpBYDnICBm3ywtE5Odne2zdp+UHRnF9PgmWDctS+JabqSwoOnJDS7HtZczsD3YffuL//JnWQultAsLq6/4Q5QIW7hwIaZNm4YXX3wRHTp0QGRkpNvncXFxkjSOCB9C6YxZi30I9B0p1j4Tagng17j0hVTumnCp6RMswVpFw9HNFspx6MkNLtW1Z7FcChA4/isnZylycpZKYrVjcfUVX4gSYZmZmQCAXr16ucWDUWA+EQpiH4pQBECgjkjMvj2/A9iRkZHv/JyPG5NqIBViCVDCXRMONX2CJViraLjFkoWKntzgUrlY+QntmTNnUFVV5XUb10LrSiEk/is19XjI101rq6+IuhJ79uyRuh0EERJiBYAvC5xrkL2YffPfKShIR35+Bvbu7Y78/AzJA/SFWALkdNeoUZVdS4RqpfU1EEthTdUDenWDS/HMulrBpaqaL4WVTe5rpubqK2IQJcJuv/12/Oc//8Hrr7+Oo0eP4oMPPsA111yDDRs2IDU1Veo2EoQgxMY+COmExO47Pz9DVldJoI5GbncNC6UxWCYUK62/gVhqa6pW0aMbXIpnVq6q+VI870pdM60IdFEibMuWLXj00Ufx8MMP48cff8SVK1cAADabDS+++CI++eQTSRtJEEojZsbn+R0lXCWBOhol2hDOIsAXrveCP0uqr/tM6EDMymxeTfTmBpfymZVjEibF8x7sNRNjSdeKQBclwp5//nmsXr0aQ4cOxXvvved8v3v37nj++eclaxxB+EPOAFQxMz7PBWiVmIkF6mi0MhvUG6FaDHwNxCdONIPZfFjStmoROdzgrGSlSvnMshwzx//+2bMWt9d33nmnm0ctlPMeSOyxkDkpSoQdOXIEPXr0qPW+2WxGWVlZqG0iCEHI4QoLtSN2XYBWqZkY39GcONEMgAHJySecnwVqg5JLK4UboZxDbwMxAGzZcj8qK6+6k/jrF27XTOpnn6WsVCn7DZYnYb7cpDt37pT0PPsS6G+8UYP58znVMydFibCmTZvijz/+QEpKitv7X3/9NVq2bClFuwhCEFI+LFJ1xK4TEblcJZ4z/KNHW/mM+/DXBj62yBM9xhqFKrCVtJTwA/HHH/cDcHUA9XQnuV4/PV4zf0h5rKxlpUrVb4Qq6OS656UqsC0WR+ZkEhOZk6JE2OjRo/Hkk0/irbfegsFgQFFREfLz8/HUU09h9uzZUreRkAm5HjBWzPrBIkVHbLVasXnzZrf35CiWaLFYMHjwYGzevFlQ3EewbdBbrFGoAlspS4lnLFlU1BV88MEDbtv4cifp7ZqpiRorHMiVaSxW0Hne86FmWLq2W203KUuZk6JE2IwZM2C329G7d29cvHgRPXr0QHR0NJ566ilMnDhR6jYSMiDXoMKSWT9UxHTEwTy8oZZtiI+PBxC4Q/G1viUQHsvpAKELbKUsJZ5xhcnJJ5h1J+kVtSrwy5lpLGYiKHWGpcViQXZ2NjZt2iSrm1RIv8qSm1aUCDMYDHjmmWcwdepU/PHHHzh//jzat2+P+vXrS90+QibkWntNL2u6SdUR+zr+7OxsWCwWSayGgToUX0t+6Gm5Fz0RKK4wM3NXrWBmQhrUrsAv1cRUyqQlKc8Jv4SanPGy/sQsPwllKXMypLK5UVFRaN++vVRtIVRCrsFYq4O8VJ2Ov+M3m82SWQ3FdChqDzZqE6oF0Nf3bTab6DXuXPFV4qKoKAm7dmVq7pnSCmq7yaRCSquaXOdEztIiQo6LldImyq9dQDCFXIOxlgd5KTodIccvpYsr2A5FL4ONGEKdHPj7/qZNmyRxtbsOoq4u5LffHirqmdJqnKbSsOSmChWprqec50TOxcW18PsAibCwR67BWMuDvBSdjhLHH0ogr54Gm2AIdXIgpbgOhOcgKvae0lOcptyw5KZiBTFlbnhI3AeGRFiYI9dgrOVBXoqOWMzxB+siC8XlEK6DTajiWM3JhdhnirXyCywS6goHekdMmRseV3EvZ4FtrUIiLMyRazDW4iAvZUcc7PGLdZEFO8sM98Em1MmB9yKqwr8vxi3IX4tA95Rer5kS0BqogQnkuhOShKX2eWZRBJIII2QLUGQl8FEoUnQQwYgc/neUjJ9TuxNUi1CFjOf33YuoGnD0aKuAolmsW9Dzms2ZcwbHjkUgJaUaSUk3A7g5qGvGQlkSFmPU9HbPh0owQiSYSaSa55nF/o9EWJgiV2FAufYrBUp0/EIfcuBqLIXSLq5wHGyECJmLFy+isrISxcXFXvdx991349NPP0Va2h8wGACO4z8xCBLNobgFXa9ZYiLQubOgXdWChYxlilHTBoH6Mj5hRKlJpGv/XVRkRGFhBFJTq5GUZAcgvP9m7Z4iERamyDUjUHum4UtolZWV1apk7w2pMtv84TkIaTl+Tkv4EzJWqxVvvPGGoP1oNemElYxlilHTDmqWsHDFtc/0N5HQonAnERbGyHWzqvUQCJ1h+0OJjt/zNyjWR32Cue5aFc1aFY8E24TyPAj1TggN3dCicCcRRugGLT6APN7ixwYOHIikpCTNzez0jlSiWenYLDnFI4sxXoQyiE3CCmZtSh49TiRIhBGEyrh2Pqmpx53vJyQk0MDFGNnZ2c6lV0IJkFcyNkvu7MpQY7xYSBTQC2qJYTFJWELXpiwqKnJup1UrtD9IhBFhCSsdPwuB0kRtfN0fZrPZuSyR2AB5pWOzPOM0J036H/73PyOSk6+gadNUAKmIiIhwJiUEO1CHEuNF9790KJ3wIFUSlq/noXHjElRVRWPt2p3O/Wqx9FEgSIQRusXXQMpKx89KoDThjtz3hxouFX7QtVqt+OorRwLCiRO+t1ciwJnuf2lROuFBqiQsX8/DmjWjvD6DWit9FAgSYYQu8TWQstTx6zG+QevIeX/wFoFALhU5EzFYykyk+19elLD2SyHUvRdA5vw+gyys+SgVJMII1ZE6jsHfQMpSx6/H+AatI+f94Wo5uOaackyfbkZNjQEmE4eFC8vx0EMP6iKAPdDgH6wYpaD/4GHF2i8ETxcjYMfVQsgO9CzOSYQRqiJHHIO/gVRNKwQPLUOjLEIGcaWsVPw9PGUKkJ0N/PEH0KqVAc2axQOID2nfLCBk8A9GjFJh1+BhydovFFcXY2RkpdMVySN0cqrFPpNEGKEqUrpHhAykZnMF5s8/hblzm6pmhZB6GZpwRKh1JJhBXGkrVbNmjj+9EMzgL1SMsuQ+1QosWfuDwdXF6G9yOnDgQCQkJNT6vlb7TBJhhG4IZoY9fLi6VgiplqEJR4IRVsEM4nzWo16tVN6QMm5I7OCvNzGqNqyFOfiaMNlsNp/f8Rd8n5CQ4HxW9QCJMEJXCJ1hh1vHr6e4GiWsI+Fwf0gVN8RCwgFxFZbKOAidMA0ePBjV1dXYunWr8z09Bd/7g0RYmKOnwdmTcBhIhUBxNYQnUsYNKZlwoHZ9P5b7S1eR68+SpKQY9jxXvq5ffHy84HbpTcyTCAtjWByc1e5k9QjF1RA8/AAWyHUY7ECnRMKB2hl/LPaXrkhVt0suAl0/1tsvFyTCwhjWBme1O1lC/4S7yOcHumPHqrFhAwe73eD8zGTiMHHi3UhJiQhpoJPDAs1Cxh9r/aU3WBUoQq8fq+2XExJhREBKS0t9fialm0HtTpbQJkKFFYl8BxaLBRYL8MYbwOOPAzU1gMkEvP66AZ07N1G7eV7RasYfS6jpSqXr5xvNiLB7770XBw4cwOnTp9GgQQNkZmZi4cKFSEpKcm6zefNmvPjii/jvf/+LRo0aYcKECZg6darbfr788ktMnjwZv/zyC5KTkzFr1iwMHz7cbZtVq1bhn//8J0pKStCxY0esWLECt9xyi/Pzy5cvY8qUKXjvvfdw5coVZGVl4dVXX0WTJlc7sD///BPjxo3Dnj17UL9+fQwbNgwLFixARIRmTrkT12BJb4RifpfLPcIKLMeQ6AGhwopEfm1GjgSysnjXIZvxk2oG/Xs+u56TUS1ZVY8ePYp33nkn4HZyuVJZy9hkCc0ogjvuuANPP/00EhMT8ddff+Gpp57C/fffj7179wIAPv30Uzz88MNYsWIF7rrrLhw+fBijR49GTEwMJkyYAAAoLCxE3759MXbsWGzcuBG7d+/GqFGjkJiYiKysLADApk2bMHnyZKxevRrp6elYunQpsrKycOTIETRu3BgAMGnSJGzfvh3vv/8+zGYzJkyYgIEDB+Kbb74BANTU1KBv375o2rQp9u7di+LiYgwdOhSRkZF48cUXVTh78uJPZAgRIXK7R9SC9RgSrSNEWOld5IcK68kraq0yEOjZ1ZJV1Wq1ChJggHyuVJYyNllDMyJs0qRJzv+3aNECM2bMwIABA1BVVYXIyEhs2LABAwYMwNixYwEALVu2xMyZM7Fw4UKMHz8eBoMBq1evRmpqKl555RUAQLt27fD1119jyZIlThG2ePFijB49Go899hgAYPXq1di+fTveeustzJgxAzabDWvWrEFubi569eoFAFi7di3atWuHffv2oWvXrvj888/x66+/YteuXWjSpAluvPFGPPfcc5g+fTrmzp0bNh1+MCKkc+cmmnKPCIHVGBItzeC9EYywUiIGipAXNVYZ8PdMas2q6r1GV2h9gBgLv94W3pYKzYgwV86ePYuNGzeiW7duiIyMBABcuXIFdevWddsuJiYGJ0+exPHjx5GSkoL8/HxkZma6bZOVlYWcnBwAjpt1//79mDlzpvNzo9GIzMxM5OfnAwD279+Pqqoqt/20bdsWzZs3R35+Prp27Yr8/Hx06NDBzT2ZlZWFcePG4ZdffsFNN90k6fmQCqkH52BFiBbcI1pHSzN4XwQrrFiLgSIXtXhCtdxJce61Ht8Uah8QzOTa0+Dgq/ZXuBgmvKEpETZ9+nSsXLkSFy9eRNeuXZGXl+f8LCsrC5MmTcLw4cNxxx134I8//nBavIqLi5GSkoKSkhI3YQQATZo0QXl5OS5duoRz586hpqbG6za//fYbAKCkpARRUVGIj4+vtU1JSYlzG2/74D/zxZUrV3DlyhXn6/LyciGnRRJYGZxZd49oEb6DCzSD99cRsiYcxAgrFkQ+uajVI5hz7w8txzfZbLH4+ON+4BfIFmPFE1r7i1+FIhzLTgSDqiJsxowZWLhwod9tDh8+jLZt2wIApk6dipEjR+L48eOYN28ehg4diry8PBgMBowePRpHjx5Fv379UFVVhbi4ODz55JOYO3cujEaj399ghQULFmDevHmK/Z7QwTkQpaWlYf8gsQxvOdqzB1iypPYMvnv3YejZ03d6OMvCIVhhpbbIZ9VFHQ6Ecu49hYa/+CabzcZsf1hQkA5egPGEYsUTumA74RtVRdiUKVNqZSZ60rJlS+f/ExISkJCQgGuvvRbt2rVDcnIy9u3bh4yMDBgMBixcuBAvvvgiSkpK0KhRI+zevdttH02bNsWpU6fc9n/q1CnExcUhJiYGJpMJJpPJ6zZNmzZ17qOyshJlZWVu1jDPbb799tta++A/88XMmTMxefJk5+vy8nIkJyf7PT+hEGhwvv76AThxYoPzPV8zHj57kmbv7GKxWNC1K2A0Anb71fdNJiA93WFV8gXrwkFtYaU1WLNqso4voeErvmnTpk0A2OsPbbZY5OdnePlEnBVPa7FxrKKqCGvUqBEaNWok6rv2v0cSV/cdAJhMJlxzzTUAgHfffRcZGRnO38jIyMAnn3zitv3OnTuRkeG4MaOiotC5c2fs3r0bAwYMcP7O7t27nSbqzp07IzIyErt378agQYMAAEeOHMGff/7p3E9GRgZeeOEFnD592plRuXPnTsTFxaF9+/Y+jyk6OhrR0dGizodY/A3OHTvWw4kTjtdCZjw0e2ebZs28ue9IwIQTLFs1WSSQ0PAnNljrD73FsgFAt275okST1mPjWEETMWEFBQX47rvvcOutt6JBgwY4evQoZs+ejbS0NKfwKS0txQcffICePXvi8uXLWLt2Ld5//3189dVXzv2MHTsWK1euxLRp0zBixAh88cUX2Lx5M7Zv3+7cZvLkyRg2bBi6dOmCW265BUuXLsWFCxec2ZJmsxkjR47E5MmT0bBhQ8TFxWHixInIyMhA165dAQB33XUX2rdvj0cffRSLFi1CSUkJZs2ahfHjxysusoQQE2PFokUmj/RvG+rVOweAZjxSwUJWIgtxUYR6sG7VVBIhz6NSQkMJ66SvWLb09AK33wl1f1qIjWMJTYiwunXrYuvWrXj22Wdx4cIFJCYmok+fPpg1a5abqFm/fj2eeuopcByHjIwMfPnll25FVlNTU7F9+3ZMmjQJy5YtQ7NmzfDmm286y1MAQHZ2Ns6cOYM5c+agpKQEN954I3bs2OEWaL9kyRIYjUYMGjTIrVgrj8lkQl5eHsaNG4eMjAzUq1cPw4YNw/z582U+U8HjOjN+4olYp3n9/PkK/G1Vl6wjYkGEKIlrh+bPkqh0ZhC57/QBuRXFE8iyL7RIrBTIbZ3kjyVQLNsjjzwS1P6p9pc0aEKEdejQAV988YXfbRISEpxlJPzRs2dP/Pij/6y/CRMm+M2QqVOnDlatWoVVq1b53KZFixa1XJ8s4tqJ+zKvi+2I1BYhag9SrqUU5s9vDI5zlFLgOCO2b++POXPSqUZVmCN2YkJuRfEIsez7KxI7e3YRgAq3/YUyuZTbOum5MPacOWdw7FgEUlKqkZR0M4CbRfeFVPsrdDQhwgh1ETvjUVOEsDJIWSwWHDzoHm8HADU1BlRUNPEbEC83aovUcCeUsjDkVhSPUMu+ryKxJpMJb7zh2IaV0j6BcH2OExOBzp3F70srtb+00r+RCCMEIXbGo5YIYWmQat3ae+JDq1a+vyN3BxJKzaRwcytLiRQ124jgsVqtsNlsAMStQ+nqwi8udvwrV6ws68+Xp2XNG0oIHH99ZFlZGTZv3ux87eucsmApJhFGCCZQNpAvxIgQPRFsVqISVrxgRKrabmU9EWrNNqlhfcCXAs/nyZdlf/Tou9G4cWPB516OoH0tWtbUQGgfCfg/pyxYikmEhSGuM4jS0tKgv++r4/Y1CFNphOCyElmy4gEU2yY1odRskxKtDPih4u058WbZj4+/2es97GlxKSsrAyDOouYPykIXjtCq/Vo4pyTCwoxgZhCuZGdnw2w2Izc3BvPnm2G3G2A0cli0yIaHHroU0PxMpRG0nZXIcmwbSwh1I6s1MSF36FWEWPb99ZeeFjVHaZ9yPPTQg6LccVR3Sxz+JhNaOKckwsIMsdYTs9mMmppETJt2dSC22w2YPj0e2dnxggZhpUSI1WoVZeEj/BPubuVABOtGVmNiwpo7lHUC9Ze8Ra1792FIT7egWbN4APGifovqbgVPoMmEFs4piTBCEFFRUT4sIY5BJJQBRMogdLGWPiIw5Fb2jxg3slQTk2Biu1hxh+oFs7kC3bpVIjFRXF8mtI5XOFgngyWQpUsLtcxIhBFeGThwIBISEgBc7TjksIRIHYTOQqClniG3MhuEmiwRzoJarmQEsX2ZnHW89I4QSxfrtcxIhBFu8B1UZWVjJCY2cfssUMctZhaoVBB6sMkE4UYwA5OWY9v0ghTJEnoU1N76INfQBDmTEULpy6Ss4xVOCLV0ic3sVwISYYQT1w5qwwYOb7zh6Khd8dVxs1Ic1RveOt7nn09FUlJS2M4u9Vh2QivFGaVCimQJPQnqQH2QFJly4VDSQ2uEYulioX8jEUYAqN1B2e0GPP64Q3B5dtLeOm6hs8CioiK3beUOoPfV8c6Zc4bZAdmzY5DDiqe3shMsTwLkhJIlrhKoDwoUPxToeQrWiqYVwabFyYvQqv18Vr+vfbBwXCTCCADeOygpgu492bp1q3Q7E4CvjvfYsQhmTf6uMSJiS4II/R29lJ1grbaaUoRzbFeweIsfMpk4TJx4t9uEw1sdxWCtaP4Em7+Jp9LCQKuTF1aq9ksBibAww9dsz3sHpf0Ztb/AzeJidh9gi8WCkycRUkkQIZAlRT6UsoToMbZLDvj4oe3b+/+9GDfw+usGdO58NfbVlygRYkXjBUEgwRZoIqqk4NHy5EULAksIJMLCDH8ziGuuKcf06WaXDkr7HbqvwM1///tH/Pvf/r+r9uzv99/lKQniCllSpEPNODs9xXZJhTcR3KnTj5gzJx0VFU28ClZfYkOIFa3470UlQy0Qqqbg0YoLVU+QCAtDfAmLKVOA7Gxtz6i9DXBiAzfVnv0pZaUKZEnRYsyIGugtzk7L+BPBSUl2JCYGtz9v1fE9rWg8WigQ6o1wWcaKNUiEhTknTzosLq1bX51Nsyi+hFoPPC19RUVGFBZGIDW1GklJDjVjs9mwadMm2doqFUpaqXxdd63GjKiFnuLstIpc6wW6TuYmTry7lgATWnTVtZ2sWJ20sMaiXiERFsasWQOMGeMYMIxGeC1JoSSuBWJdCdbSwm/L2vGJQe14Hy3HjKgFxdmpi5TrBXoKJf6Pn9C5EqjoamlpqjMejDWrkxbWWNQrJMLClJMnrwoUwPGvr5IUQpAiziUhIQE1NYluljmx+Ds+kynkpioKq9ZJllHTykBxdt6R263N90GB3IFC+yoxQklI0VUWrU5adaHqARJhYYrUQd/+Av5LS0sFlabIzY1xZgOGarnyd3xt2ojbp5pQXJZ/WCs+q7YFkzWUcGu79kHuSUYcFi4sx0MPPSj4OZFTKLFoddLCGot6hURYmCKHy8RX5yZk4LPZYv+uh+V4HaplTk8uIYrLCgyLQfFkwbyKUm5t/vq6JxkZ0KxZPIB4wfuRUyixZHVy7Zv9JTDx29FkUHpIhIUpcrtM3AP+AxfWKyioiyVLDG7vhWKZ83d8f2eSawaKyxIGBcUTrogRwVK7NL3tO5DVScmldIIpekqTQXkgERbGyOUy8R4Q7/+h7NJFesuVr+MT2smxsK4YERx6soASyiOlS9PfvoHagfvAzapYkoT+nqdQ8xV3Ge6TwWAhERbmSO0yERLw71kWg2+HHJY5b8cnpIwFmdW1CQXFE6EilUvT374B34H7WoC17E4tQyKMEI23+IB9+6Jgt7uLF1e3or+yEUoGM+upjIWcKLGYuNSEa1B8oHidixcvom7duj4/V3LiwVKNLH9QXF9tWMzu1DIkwghR+IoPsNliYTDkeF2DUoiVTMlOT+oyHXpEqcXEpSbcBk+h8TqBUCKeR+tWlHAPTmcxu1PLkAgjROGrE/K1vEezZsCePfKvhRgMSqzNqAeUWkycEI9UcThyx/MItaKwKnQoOJ2t7E49QCKMkBxfy3uwFjTNWntYhgQrEQq8uzqQFSUqKoppoUOZylRTTGpIhBGywC/vERV1GsXFjtHbZAIWL47FlCn1mQia1koQNwtxWSRYiVBwreO2YQMHu/1qORqTicPEiXc767gVC6who2ehwzr+aooRwUEijJAVb5Xyf/zxCVitDZgImtZCEDcLcVlaEawEu1gsFlgs3u4jQ63FsLWCVhIMpMBzksdPtANtR/iHRBihOAkJl9Ghg9qtuIoWgrhZiMvSgmAlHAQSB4E+lzMmK9j7iFWho/UEg2AJprArIRwSYQQRBGoGDEsdlyXmWLQgWMOdQOIg0OdKxGQJvY+UEjrBPgvhWqaBBJb0kAgjCIGoHTAsZVyW2sdCyEMgcSBEPLASfK6U0AnmWeDRQ5kGVjNQww0SYYQowtHvr/bgJGVcltrHQkiL0OxDIdmJrFxzpYSOmGdB62UaaBLGDiTCCFF4iw8oLS31GojvC5qJBQ/FZRHeEJJ9GB9/HmVl9SXLTpSLsrIyAGwKHRYX4RYDrQPJDiTCCNGEIpBoJhY8vGg1mYA2bRzvuY6XJFrDm8DZh44MxGCzE5UMjLdardi8eTOAwELn4sWLfgWj2OfB3/Gyugh3KIRbggFrkAgjVEHP7rDS0lIA0ooiEq1XIQuqfwJZS4Oxpio9QHteV1/1qO655x688847AfcX7PMg5Hj1sgg3EL4JBixBIoyQhJMnge+/rwubLTbgw8tS3IkcuLpkpRJFehatwUBiVBiBsg+FZCeyMED7skrVqVNH0PeDeR5YOF6l0UOCgdYhEUaEzJo1/ELYZhiNk5wFRL3BWynUjjtRCq2IIlZrMXlCYlQ51B6g/VmlKircf1+K+1ft41UDFuPuwg0SYURInDzJCzDHa9cCohQ0rg0oJoRwhQ8qDzRAyxl8HsgqtXPnTue2Ut2/4ShIaB1I9SERRoREOC3sLHQNRy3AH0ugwU6OgVbKmC4tXwNWcQ0+v+aackyfbkZNjQEmE4eFC8vx0EMPyh53J9QqJYULUS8Zj2KhdSDVhUQYERL+CoiePOkQaa1b60OQuQ5Ob7xRg6VLk1SzHoUqPvhj2bMHWLKk9mDXvfsw9OwpfYVsKWO6yIInH/y5nzIFyM7mg/gNaNYsHkC87L8v1CpVUJAesgtRjxmPgRC6DqTNZkNRkRGFhRFITa1GUpLd+X09nQ81IRFGhISvAqKffXbVTWk0OrYZOVLt1oYOv4bj/PkcOM5Ra0npAF6pxIfFYkHXrt5FdHq6RZb1KIXGahUVFTnb6I1wDKJWCzWWqhLiJrPZYrF3b0at74pxIeop41EI/taBLCsrc5YJmT79vz77mnBPfpEKEmFEyHimvANAixaucWIOkZaVdbUzF2raZ9EF4HDBGtze4zgjfv21Pdq3/1VWESC1+JCyCr+U8Bmmvjr6cAyiDgdcn/dAbrKzZy0AjPAkIyOf7gEBBBJQgfoaNZNf9ORlIRFGSILrbHnPnsBxYv5mYjysmrwdLljOQ4hx+OyzPvj887tkcYsJXZZGjGhVswp/ILeqr/sjHIOowwEh/QJvqfF2DwB2pKcXOF+xOInTCqxNdPhY0tzcGEybZobdboDRyDmz8VkdLwJBIoyQHKELTbs+MFqa2TRrBixefAGTJtX9u5PiAHh3TUo1CAhZloZfdkYMaricXN2qgB3duuUjPb3Abwcf7kHU4YDQezjQPZCdna3JQZkVWJro8LGkNlssli7NcYaC2O0GTJ0ah7/+egtmc4UmXaQkwgjJCdbFdbXOmHbix558sj569TqHrVuNmDvX7PaZXIHtgZel0Q6erg7AiL17uyM/P8OvJTEcg6jDHc9sWn5FCsC/y9Jsdn8uieBgqXwFf/0DWee0WB+QRBghC0JdXLXrjNWOH2OVDh0aoEEDYP585QLbAX0s4u2tMwWExbiFWxB1OCMkm9ZXZh9ZQ0OHtfIVLFnnpIJEGCEbQlxcWq8zplZguxruQynxHs/jIJwC7GkdTP94nhtfMYQDBw5EQkKC83W4nzcp8SVy1YAl65xUkAgjVEVo/BjL6MEypRSeMV0ff9wPnhluWp/ZCsXTyuNLYGgxzkUO/JVmSUhIQGJiosotJJSANetcqJAII1SF1RIJwaJ1y5RS8DFdjjpgW5GW9gcKCtKRn5+hm5mtUFytPP4EhhbjXKSG6sIphxbKB7FknQsVEmGE6pAlKbxwteqYzRW4665dSE8v8DqzDYe4HhIYgWGtXIKe0XL5IC1CIoxgArIkhRfU0V+FBEZg9BiQzTLh8NyxAokwgiD8IlcNt3Dv6MvKygCQwBCCHgOyw5lg+xQtuEjFQiKMIHSKFOJJizXctIDVanWuz3f0aCtw3NXPSGB4R28B2eGKmD5Fz5ZzEmEEoUNCFU9WqxXHjlVjzJjGzur8jhpuHG688bTf6vxaWv1ALfjBhI8Hc80Q5TggLe0PlVrGFp6WDaoJpm1CqQupRYElBBJhBKEzQi2Ay5dOKCxMgd0+zO2zmhoDVqz4FKmpx72WTiDLWXB4L1pL8WA8eraAhBtWqxX79gF2u/u1qqkBCgqsiInRr9DyB4kwgvCBFi06UnR0/IAXKFbJc2DU8uoHaiEkHkwvVh6xz1M4Dsx6w3XtR4Mhp9b9/s0363HokDbXfgyV2uWqCYLAmjVAixZAr16Of9esUbtFgeE7ur1718NgcF+GgO/oVq5cCavVKmh/fDA0vy9/sUoO8Wf1uvpBQYFV8G+GG4HOsdYXobZarSguLsYrr5ShRQvu7+eJwyuvlKG4uJjuizCBn7AFut+lrol38iSwZ4/jX1YhSxhBeKBVi45nR+crkyyYjk5IMDTNckNDr4tQu94XS5fmgOP42EIDpk6Nw19/vQWzme6LcEOpBAuthEaQCCMID7S+niUgrKMT6h4KVJ1aDvEXbuipAjgPf70D1UHjt9Oi+58Qh9z3u5Ym0iTCCMIDPaxnCfjv6OSaJVIZAX0jRigJiXtj1WpBwlB7aC0BgGLCCMIDfj1Lk8nxWqvrWfqiqMjodZYoVdyE2VyB1NTjJMD8oMXik2LjJAPFAcl9P4pF7rhQLcQraQ2p42KVgCxhRMjocbYox3qWap0nmy0WZ89a0LChFWZzBQoLI0S5Wz33Q4hHa6UXQnXv+LOQHjhwwWdChxpWi1Bq5AmFVcuf1tFiaASJMCIk9NyZSLmepVrn6YcfbqrVEfXrV+3X3erN+uJtPyxZabRIsAO5WiJeKveOL/f4L798xExCRyg18oTuX26BRzjQSmgEiTCNY7fbVVP1JSXACy8AyclX33vxRaB3b6BpU1WaFDLnzp1DVVWV87XBYIDJZILB4OgwxVgn1AoS5aux84MbxxmxbVs/zJlzBm+84WhDTU1td6unlaaoyIj58xs7s9s4zojt2/vj0iWDfI0n3FBLxCuR+cqS1UJsjTwhyC3wWEYt97sWEl5IhGmYyspKFBYWwu5py1eIy5eBV1+t/f65c8ClS8q3J1RqampQUVH7gb106RK+++47XL58GQCC7iSVyrb07MB8ZaWdPFknoLvV9fh++81b+w2ayhZljWCsWmpmeinl3mHNaiHHguFyCjzW0Zr7XUk0J8KuXLmC9PR0/PTTT/jxxx9x4403Oj87ePAgxo8fj++++w6NGjXCxIkTMW3aNLfvv//++5g9ezaOHTuG1q1bY+HChbjnnnucn3Mch2effRb/+te/UFZWhu7du+O1115D69atnducPXsWEydOxLZt22A0GjFo0CAsW7YM9evXD6otocBxHIqLi2EymZCcnAyjUfkci8pKh5jwJC0N0KKnqrKyEmVlZW7vcRyHc+fOoU2bNvjpp5+c2wWDUtmW3ixYGzZwTrcHABiNHJo1u4zi4oswmYA2bRzvFxc7/vXWEQZqvxaDzNUkWKuWHCJejGszWKEU7PVmzWohlzCUQ+BpgXAUWELQnAibNm0akpKSnAMiT3l5Oe666y5kZmZi9erV+PnnnzFixAjEx8djzJgxAIC9e/fiwQcfxIIFC9CvXz/k5uZiwIAB+OGHH3D99dcDABYtWoTly5dj/fr1SE1NxezZs5GVlYVff/0VderUAQA8/PDDKC4uxs6dO1FVVYXHHnsMY8aMQW5uruC2hEp1dTUuXryIpKQk1K1bV5J9BkudOo6soePHr77XogUQF6dKc0LGaDQiIqL2I2E2m9GoUSNERUWJmqXy2Za+3H9S4trRJSZ6/i6Hvn23IS/vR7/78LT0BWq/2FmuHhM6AiHGqiW1iA/FtRmMUAp0X5SWlmLr1q1Cm60KcglD1ix/hHpoSoR9+umn+Pzzz7FlyxZ8+umnbp9t3LgRlZWVeOuttxAVFYXrrrsOBw4cwOLFi53CZ9myZejTpw+mTp0KAHjuueewc+dOrFy5EqtXrwbHcVi6dClmzZqF++67DwDw9ttvo0mTJvjoo48wZMgQHD58GDt27MB3332HLl26AABWrFiBe+65By+//DKSkpIEtSVUav42QaltXWjUCDCbgStXgOhobVrAAmE0GmE0GhEZGSnaVSBHtmWwvxsbezqgAAO8W/qCcV8KQc8JHf4I1qpltVphMlVi0aIYTJ9uRk2NASYTh4ULbTCZLsFqDc6Fo7Rrk6wfvmHN8keog2ZE2KlTpzB69Gh89NFHXi0/+fn56NGjh5soycrKwsKFC3Hu3Dk0aNAA+fn5mDx5stv3srKy8NFHHwEACgsLUVJSgszMTOfnZrMZ6enpyM/Px5AhQ5Cfn4/4+HinAAOAzMxMGI1GFBQU4P/+7/8EtUUq+IBxNYmK0qf44pHqHEuZbSnmd4uLQ4sdlKr9WqpmLTXBWLX4QG6eJ56IdVpOzp+vwBtvON4PJkZRD6tBEIQvtBgaoQkRxnEchg8fjrFjx6JLly44duxYrW1KSkqQmprq9l6TJk2cnzVo0AAlJSXO91y3KSkpcW7n+j1f2zRu3Njt84iICDRs2NBtm0Bt8caVK1dw5coV5+vy8nKv2xGElglnIRCMa9rTIunLchKMdVYvq0EQhDe0mACgasX8GTNmwGAw+P377bffsGLFClRUVGDmzJlqNld2FixYALPZ7PxLdq39oBOGDx/uvLaRkZFo0qQJ7rzzTrz11ltBZXmuW7cO8fHx8jWUAfRaUZsXAq6EkxAYORI4dsxxbY8dU84N63BtFmPRojKYTBwA/O3aLIPJVKx4FXEWrRYstokIDovFgsTERJ9/LAkwQGVL2JQpUzB8+HC/27Rs2RJffPEF8vPzER0d7fZZly5d8PDDD2P9+vVo2rQpTp065fY5/7rp30WrfG3j+jn/XmJiots2fBZm06ZNcfr0abd9VFdX4+zZswF/x/U3vDFz5kw3d2l5ebkuhVifPn2wdu1a1NTU4NSpU9ixYweefPJJfPDBB/j444+9BseHG6zETFmtVslnlUomKohBroQB13MpNDNVyt8W49qUU5SwaLWQs00k8AhvqDraNWrUCI0aNQq43fLly/H88887XxcVFSErKwubNm1Ceno6ACAjIwPPPPMMqqqqEBkZCQDYuXMn2rRp43T/ZWRkYPfu3cjJyXHua+fOncjIyAAApKamomnTpti9e7dTdJWXl6OgoADjxo1z7qOsrAz79+9H586dAQBffPEF7HZ7UG3xRnR0dC2hKRdyDK5CiY6OdorRa665Bp06dULXrl3Ru3dvrFu3DqNGjcLixYuxdu1a/O9//0PDhg3Rv39/LFq0CPXr18eXX36Jxx57DMDVeK1nn30Wc+fOxYYNG7Bs2TIcOXIE9erVQ69evbB06dJaLmRvCC3zIXcnKUXMlLfrW1paGlQ7PAduX4gpLqlWokIg5BK/cp5LIYh1bcotlFizSgDytYlF0RmusJSZrQmTQ/Pmzd1e8/W40tLS0OzvM/jQQw9h3rx5GDlyJKZPn45Dhw5h2bJlWLJkifN7Tz75JG6//Xa88sor6Nu3L9577z18//33eOPvaaDBYEBOTg6ef/55tG7d2lmiIikpCQMGDAAAtGvXDn369MHo0aOxevVqVFVVYcKECRgyZAiSkpIEt0VN1B4QvNGrVy907NgRW7duxahRo2A0GrF8+XKkpqbif//7H/7xj39g2rRpePXVV9GtWzcsXboUc+bMwZEjRwBcvSeqqqrw3HPPoU2bNjh9+jQmT56M4cOH45NPPgnYhoiICDRu3LiWW/Ty5csoLy/H4MGDUa9ePdnPSagxU0Kur5B1IIXGGonNGFUrUcEXciYMyH0u5YREgXTQuVQfVrwMPJoQYUIwm834/PPPMX78eHTu3BkJCQmYM2eOW0mIbt26ITc3F7NmzcLTTz+N1q1b46OPPnLWCAMcdcguXLiAMWPGoKysDLfeeit27NjhrBEGOMphTJgwAb1793YWa12+fHlQbVETVgeEtm3b4uDBgwDgZq1MSUnB888/j7Fjx+LVV19FVFQUzGYzDAZDLffuiBEjnP9v2bIlli9fjptvvhnnz593K6brC2+uULvdDpPJhMaNG7vdB3IRavB0oOvmbR3ITp2ulq4IV3dIOCcMEPLAksWFYDMzW5MiLCUlBRzH1Xr/hhtuwH/+8x+/333ggQfwwAMP+PzcYDBg/vz5mD9/vs9tGjZs6CzM6gshbSHc4TjO6V7ctWsXFixYgN9++w3l5eWorq7G5cuXcfHiRb/Faffv34+5c+fip59+wrlz55xWrT///BPt27dX5DhCRc6YKW/rSW7f3h9z5qQjKcke1u4QyhwkpIQ1iwvB5kRL1exIgnDl8OHDSE1NxbFjx9CvXz/ccMMN2LJlC/bv349Vq1YB8G/luXDhArKyshAXF4eNGzfiu+++w4cffhjwe3JitVpRXFzs889XRppcGXTe1pOsqTGgoqIJk5lDSsKLX5PJ8VrthAEK5NYuviwuest01hosZmZr0hJG6I8vvvgCP//8MyZNmoT9+/fDbrfjlVdecQbLb9682W37qKgo56oBPL/99husViteeuklZ1bp999/r8wBeCHU2Ds5Yqa8LR6sdifEEq4JA/XqAefPOwbOUK7DyZPAvn1RsNlig6qQToHc2oVFiwvBZmY2iTBCca5cuYKSkhK3EhX8ep5Dhw7FoUOHUFVVhRUrVqB///745ptvsHr1ard9pKSk4Pz589i9ezc6duyIunXronnz5oiKisKKFSswduxYHDp0CM8995xKR8lm7J3n4sFKd0JqZuUKpVkz4LPPpHElXXVJWWAw5NSKvwuE2ueCEAe5ttmD73vuuQcoKDDi2LEIpKRUIynJjuJi9foeEmGE4uzYsQOJiYmIiIhAgwYN0LFjRyxfvhzDhg2D0WhEx44dsXjxYixcuBAzZ85Ejx49sGDBAgwdOtS5j27dumHs2LHIzs6G1Wp1lqhYt24dnn76aSxfvhydOnXCyy+/jHvvvVfFo2UPfvHg7t2HIT3doqgAE5K12aPHSHTpYlZtdipV8K7nfjjOiG3b+iEt7Q/F1wwk16ZySL3eJxE6vvqeQ4fcXytZEYCHRBihKOvWrcO6desCbjdp0iRMmjTJ7b1HH33U7fVrr72G1157ze29Bx98EA8++KDbe96SOMIds7kC3bpVwqUmcS2kHriFZm0uWWJUNZBZKleSt/1wnBFnzzb0KcLkEkFKuTbDPRtQjvU+idBh0SvBQyIsDKFZsb6R6voqGZPkmbWpdOq4q5s0Ls4Io7Ex7ParC7ebTBxatQpuIXfvLikOEyfejaSk2kt0ye0OkXvQp2xAedb7JPQNibAwhAJ+9Y2U11epe8B71qYygczeXBX9+rnXUuvbNw8xMd0BCD8f3oOADejcuYnER6A+LNZfIggtQCIsTCGBpW+0dn3VzNr0Jlb5uDnelWQ2V6Cy8uag983q8kyBCNatSNmABCEOqhNGiKK6uhqVlZU+/6qrq9VuIqEh+KxNg8ExkrOQOm42VyA19XjIQfTNmgE9e2pHjKxZA7RoAfTq5fh3zZrA32Gx/hJBaAGyhBFBU11djdOnTwfcrnHjxl6XAQoXKPYuONTK2iSuItatyGL9JYLQAuE7QhKi8VzgOtTt9ArF3gWPkKxNQj5CcSuy7HoN96xNgl1IhBGEjJDAugpZBtkn1CKjcqzyECqUtUmw3PeQCCMIQhHIMsg+YtyKLK+CoHTWJsuDfTjDct9DIowgCMXQssAqLS0V3FGzLEwCEYxbMdT1UeVG6axNlgf7cIfVc04ijNAVX375Je644w6cO3cO8fHxgr6TkpKCnJwc5OTkyNo2gk2EWiW2bt0KILCgYF2YCEGoW5HlSuSAOms4snpNCTahEhWEogwfPhwGgwFjx46t9dn48eNhMBgwfPhw5RvGCFarFcXFxT7/rFYrk/vWMrz1YuDAgYK2DyQoWBcm4QTvXjWZHK8pa5NgDbKEEYqTnJyM9957D0uWLEFMTAwA4PLly8jNzUXz5s1Vbp16yGlB0YN1Rk4sFkvYiyItu1D9wXLWJkGQCCOCxuhZlTHI7Tp16oSjR49i69atePjhhwE4XD3NmzdHamqqc7srV65g6tSpeO+991BeXo4uXbpgyZIluPnmq5XLP/nkE+Tk5ODEiRPo2rUrhg0bVuv3vv76a8ycORPff/89EhIS8H//939YsGAB6tWrF8xhy46cFhSyzhD+0LtIZzFrkyAAckcSIoiIiEDjxo2RkJDg8y9QodYRI0Zg7dq1ztdvvfUWHnvsMbdtpk2bhi1btmD9+vX44Ycf0KpVK2RlZeHs2bMAgBMnTmDgwIHo378/Dhw4gFGjRmHGjBlu+zh69Cj69OmDQYMG4eDBg9i0aRO+/vprTJgwQcIzQhDaRo8indzvhBYgSxghqpBhqJXwH3nkEcycORPHjx8HAHzzzTd477338OWXXwIALly4gNdeew3r1q3D3XffDQD417/+hZ07d2LNmjWYOnUqXnvtNaSlpeGVV14BALRp0wY///wzFi5c6PydBQsW4OGHH3YG3bdu3RrLly/H7bffjtdeew116tQJ6TiI0NGrG4xQD71b9gj9QCIszFGrkGGjRo3Qt29frFu3DhzHoW/fvkhISHB+fvToUVRVVaF79+7O9yIjI3HLLbfg8OHDAIDDhw8jPT3dbb8ZGRlur3/66SccPHgQGzdudL7HcRzsdjsKCwvRrl07OQ6PEAgNloQc6NGyR+gTEmFhjNKFDD0ZMWKE0y24atUqWX7j/PnzePzxx/HEE0/U+iyckwBYgQZLbUPFSQkiNEiEhTFKFzL0pE+fPqisrITBYEBWVpbbZ2lpaYiKisI333yDFi1aAACqqqrw3XffOV2L7dq1w8cff+z2vX379rm97tSpE3799Ve0krMwEKELpBIU4SRMqDgpQYQGibAwRo1Chq6YTCana9HEF/L5m3r16mHcuHGYOnUqGjZsiObNm2PRokW4ePEiRv7tLx07dixeeeUVTJ06FaNGjcL+/fuxbt06t/1Mnz4dXbt2xYQJEzBq1CjUq1cPv/76K3bu3CnIDUaED1IJinATJno5DoJQAxJhYYyYdeKkJi4uzudnL730Eux2Ox599FFUVFSgS5cu+Oyzz9CgQQMADnfili1bMGnSJKxYsQK33HILXnzxRYwYMcK5jxtuuAFfffUVnnnmGdx2223gOA5paWnIzs6W/diCRU4LSjhZZ0JBKkFBwoQgCCEYOI7j1G4E4Z3y8nKYzWbYbLZaYuXy5csoLCxEampqyBl+J09SIUN/SHmuAyFnpiCLWYjFxcV44403Am43ZswYJCYmKtCi8ERvCRJ0XxFq42/8doUsYQQVMmQIOQc4LQyehDqEmwuVIFiBRBhBEAShK4FF7ndCK5AIIwhCNWiwJOSALHuEViARRhCEatBgScgF3TOEFiARRhCEqtBgSRBEuEILeGscSm6VHzrHBEEQhByQCNMofHFTWs5Ffvhz7FlQliAIgiBCgdyRGiUiIgJ169bFmTNnEBkZCaOR9LQc2O12nDlzBnXr1kVEBD0uBEEQhHTQqKJRDAYDEhMTUVhYiOPHj6vdHF1jNBrRvHlzGAwGtZtCEARB6AgSYRomKioKrVu3JpekzERFRZGlkSAIgpAcEmEax2g0yr6UDkEQBEEQ0kPTe4IgCIIgCBUgEUYQBEEQBKECJMIIgiAIgiBUgGLCGIYvElpeXq5ySwiCIAiCEAo/bgcq9k0ijGEqKioAAMnJySq3hCAIgiCIYKmoqIDZbPb5uYGjNVmYxW63o6ioCLGxsWFZo6q8vBzJyck4ceIE4uLi1G6OZqHzKA10HkOHzqE00HmUBjnPI8dxqKioQFJSkt8SR2QJYxij0YhmzZqp3QzViYuLo45GAug8SgOdx9ChcygNdB6lQa7z6M8CxkOB+QRBEARBECpAIowgCIIgCEIFSIQRzBIdHY1nn30W0dHRajdF09B5lAY6j6FD51Aa6DxKAwvnkQLzCYIgCIIgVIAsYQRBEARBECpAIowgCIIgCEIFSIQRBEEQBEGoAIkwgiAIgiAIFSARRqjOv//9b/Tv3x9JSUkwGAz46KOP3D7nOA5z5sxBYmIiYmJikJmZid9//12dxjJKoHM4fPhwGAwGt78+ffqo01iGWbBgAW6++WbExsaicePGGDBgAI4cOeK2zeXLlzF+/HhYLBbUr18fgwYNwqlTp1RqMZsIOY89e/asdU+OHTtWpRazyWuvvYYbbrjBWUw0IyMDn376qfNzuheFEeg8qnkvkggjVOfChQvo2LEjVq1a5fXzRYsWYfny5Vi9ejUKCgpQr149ZGVl4fLlywq3lF0CnUMA6NOnD4qLi51/7777roIt1AZfffUVxo8fj3379mHnzp2oqqrCXXfdhQsXLji3mTRpErZt24b3338fX331FYqKijBw4EAVW80eQs4jAIwePdrtnly0aJFKLWaTZs2a4aWXXsL+/fvx/fffo1evXrjvvvvwyy+/AKB7USiBziOg4r3IEQRDAOA+/PBD52u73c41bdqU++c//+l8r6ysjIuOjubeffddFVrIPp7nkOM4btiwYdx9992nSnu0zOnTpzkA3FdffcVxnOPei4yM5N5//33nNocPH+YAcPn5+Wo1k3k8zyPHcdztt9/OPfnkk+o1SqM0aNCAe/PNN+leDBH+PHKcuvciWcIIpiksLERJSQkyMzOd75nNZqSnpyM/P1/FlmmPL7/8Eo0bN0abNm0wbtw4WK1WtZvEPDabDQDQsGFDAMD+/ftRVVXldj+2bdsWzZs3p/vRD57nkWfjxo1ISEjA9ddfj5kzZ+LixYtqNE8T1NTU4L333sOFCxeQkZFB96JIPM8jj1r3Ii3gTTBNSUkJAKBJkyZu7zdp0sT5GRGYPn36YODAgUhNTcXRo0fx9NNP4+6770Z+fj5MJpPazWMSu92OnJwcdO/eHddffz0Ax/0YFRWF+Ph4t23pfvSNt/MIAA899BBatGiBpKQkHDx4ENOnT8eRI0ewdetWFVvLHj///DMyMjJw+fJl1K9fHx9++CHat2+PAwcO0L0YBL7OI6DuvUgijCDCgCFDhjj/36FDB9xwww1IS0vDl19+id69e6vYMnYZP348Dh06hK+//lrtpmgaX+dxzJgxzv936NABiYmJ6N27N44ePYq0tDSlm8ksbdq0wYEDB2Cz2fDBBx9g2LBh+Oqrr9RulubwdR7bt2+v6r1I7kiCaZo2bQoAtTJ+Tp065fyMCJ6WLVsiISEBf/zxh9pNYZIJEyYgLy8Pe/bsQbNmzZzvN23aFJWVlSgrK3Pbnu5H7/g6j95IT08HALonPYiKikKrVq3QuXNnLFiwAB07dsSyZcvoXgwSX+fRG0reiyTCCKZJTU1F06ZNsXv3bud75eXlKCgocPPnE8Fx8uRJWK1WJCYmqt0UpuA4DhMmTMCHH36IL774AqmpqW6fd+7cGZGRkW7345EjR/Dnn3/S/ehCoPPojQMHDgAA3ZMBsNvtuHLlCt2LIcKfR28oeS+SO5JQnfPnz7vNOAoLC3HgwAE0bNgQzZs3R05ODp5//nm0bt0aqampmD17NpKSkjBgwAD1Gs0Y/s5hw4YNMW/ePAwaNAhNmzbF0aNHMW3aNLRq1QpZWVkqtpo9xo8fj9zcXPy///f/EBsb64ytMZvNiImJgdlsxsiRIzF58mQ0bNgQcXFxmDhxIjIyMtC1a1eVW88Ogc7j0aNHkZubi3vuuQcWiwUHDx7EpEmT0KNHD9xwww0qt54dZs6cibvvvhvNmzdHRUUFcnNz8eWXX+Kzzz6jezEI/J1H1e9FVXIyCcKFPXv2cABq/Q0bNozjOEeZitmzZ3NNmjThoqOjud69e3NHjhxRt9GM4e8cXrx4kbvrrru4Ro0acZGRkVyLFi240aNHcyUlJWo3mzm8nUMA3Nq1a53bXLp0ifvHP/7BNWjQgKtbty73f//3f1xxcbF6jWaQQOfxzz//5Hr06ME1bNiQi46O5lq1asVNnTqVs9ls6jacMUaMGMG1aNGCi4qK4ho1asT17t2b+/zzz52f070oDH/nUe170cBxHCe/1CMIgiAIgiBcoZgwgiAIgiAIFSARRhAEQRAEoQIkwgiCIAiCIFSARBhBEARBEIQKkAgjCIIgCIJQARJhBEEQBEEQKkAijCAIgiAIQgVIhBEEQRAEQagAiTCCIAiCIAgVIBFGEAQhgsrKSrWbUAsW20QQhG9IhBEEQQDo2bMnJkyYgAkTJsBsNiMhIQGzZ88Gv7JbSkoKnnvuOQwdOhRxcXEYM2YMAODrr7/GbbfdhpiYGCQnJ+OJJ57AhQsXnPt99dVX0bp1a9SpUwdNmjTB/fff7/zsgw8+QIcOHRATEwOLxYLMzEznd3v27ImcnBy3Ng4YMADDhw93vhbbJoIg2IBEGEEQxN+sX78eERER+Pbbb7Fs2TIsXrwYb775pvPzl19+GR07dsSPP/6I2bNn4+jRo+jTpw8GDRqEgwcPYtOmTfj6668xYcIEAMD333+PJ554AvPnz8eRI0ewY8cO9OjRAwBQXFyMBx98ECNGjMDhw4fx5ZdfYuDAgQh2Od9g20QQBDvQAt4EQRBwWJ5Onz6NX375BQaDAQAwY8YMfPzxx/j111+RkpKCm266CR9++KHzO6NGjYLJZMLrr7/ufO/rr7/G7bffjgsXLuCTTz7BY489hpMnTyI2Ntbt93744Qd07twZx44dQ4sWLby258Ybb8TSpUud7w0YMADx8fFYt24dAIhqU506dUI6TwRBSAdZwgiCIP6ma9euTgEGABkZGfj9999RU1MDAOjSpYvb9j/99BPWrVuH+vXrO/+ysrJgt9tRWFiIO++8Ey1atEDLli3x6KOPYuPGjbh48SIAoGPHjujduzc6dOiABx54AP/6179w7ty5oNscbJsIgmAHEmEEQRACqVevntvr8+fP4/HHH8eBAwecfz/99BN+//13pKWlITY2Fj/88APeffddJCYmYs6cOejYsSPKyspgMpmwc+dOfPrpp2jfvj1WrFiBNm3aOIWS0Wis5ZqsqqoKuU0EQbADiTCCIIi/KSgocHu9b98+tG7dGiaTyev2nTp1wq+//opWrVrV+ouKigIAREREIDMzE4sWLcLBgwdx7NgxfPHFFwAAg8GA7t27Y968efjxxx8RFRXldC02atQIxcXFzt+qqanBoUOHAh6DkDYRBMEGJMIIgiD+5s8//8TkyZNx5MgRvPvuu1ixYgWefPJJn9tPnz4de/fuxYQJE3DgwAH8/vvv+H//7/85g+Dz8vKwfPlyHDhwAMePH8fbb78Nu92ONm3aoKCgAC+++CK+//57/Pnnn9i6dSvOnDmDdu3aAQB69eqF7du3Y/v27fjtt98wbtw4lJWVBTyGQG0iCIIdItRuAEEQBCsMHToUly5dwi233AKTyYQnn3zSWfbBGzfccAO++uorPPPMM7jtttvAcRzS0tKQnZ0NAIiPj8fWrVsxd+5cXL58Ga1bt8a7776L6667DocPH8a///1vLF26FOXl5WjRogVeeeUV3H333QCAESNG4KeffsLQoUMRERGBSZMm4Y477gh4DIHaRBAEO1B2JEEQBLxnIxIEQcgJuSMJgiAIgiBUgEQYQRAEQRCECpA7kiAIgiAIQgXIEkYQBEEQBKECJMIIgiAIgiBUgEQYQRAEQRCECpAIIwiCIAiCUAESYQRBEARBECpAIowgCIIgCEIFSIQRBEEQBEGoAIkwgiAIgiAIFSARRhAEQRAEoQL/H6+AZe06G5giAAAAAElFTkSuQmCC", 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", 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", 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REWbMmNFkn4OIiMioVpPx+fjjj1U/r1y5EjExMdixYwdGjBgBq9WKFStWYPXq1bjiiisAAG+//TYuvPBCfPPNN7j00kub47JbvL59+2LPnj0AgOzsbPnxxMRE/P3vf8ddd92FV155BcHBwTCbzRAEQc4cSW677Tb59z179sSLL76Iiy++GKdOnUKHDh2a5HMQEREZ0WoyPo6sVisAoFOnTgCAHTt2oKamBhkZGfIxffv2Rffu3ZGXl6d7nnPnzqGyslL1y5+IoigvXW3duhVXXnklunXrhvDwcNx8882wWCw4c+aMy3Ps2LEDkyZNQvfu3REeHo6RI0cCAI4cOdLo109EROSJVhn42Gw2ZGdnY/jw4RgwYAAA4NixYwgODnbaddSlSxccO3ZM91yLFy+G2WyWfyUkJDTmpbc4+/btQ1JSEoqKijBx4kQMGjQI69evx44dO/CPf/wDgOvlt9OnT2PcuHGIiIjAe++9h++++w7/+c9/3L6OiIioObSapS6lWbNmoaCgAF9//XWDz/XQQw/h/vvvl3+urKz0m+Dns88+w48//oj77rsPO3bsgM1mw/PPPy8PX127dq3q+ODgYKcREj///DMsFgueeuop+b59//33TfMBiIioWbjapSytyNT/HI7y8ih06mSB2VwlP95c459aXeAze/ZsbN68GV9++SXi4+Plx7t27Yrq6mpUVFSosj6///67U02KUkhICEJCQhrzkluEc+fO4dixY6rt7IsXL8bEiRNxyy23oKCgADU1NXjppZcwadIkbN++Ha+99prqHImJiTh16hQ+/fRTDB48GGFhYejevTuCg4Px0ksv4a677kJBQQEef/zxZvqURETU2IzuUp42bRr+979YPPaYGTabAJNJxMKFv2HKlJMICgpCdXU1SktLm7yLc6sJfERRxN13343//Oc/+Pzzz5GUlKR6PjU1FUFBQfj0008xdepUAMD+/ftx5MgRpKenN8cltygff/wxYmNjERgYiI4dO2Lw4MF48cUXMX36dJhMJgwePBhLlizB008/jYceeggjRozA4sWLccstt8jnGDZsGO666y5kZmbCYrHI29lXrlyJv/3tb3jxxReRkpKC5557DpMnT27GT0tERHoa2lPOaBnDmTOdMG+ePegBAJtNwKJFcbBa16oyP0DTdnFuNYHPrFmzsHr1avz3v/9FeHi4XLdjNpsRGhoKs9mMrKws3H///ejUqRMiIiJw9913Iz09vUXs6DKa0muM1N/KlSuxcuVKt8fdd999uO+++1SP3XzzzaqfX331Vbz66quqx66//npcf/31qsdEUfTuYomIqNE0ZU+5wsJAOeiRiKIJ5eWdnAKfpqwJbTWBj/RlO2rUKNXjb7/9ttwz5oUXXoDJZMLUqVNVDQxbgqioKMyePbvFDm0jIqK2ryl7yiUl1cJkElXBjyDY0KlTeYPP3RCtJvAxkkFo164d/vGPf8i7kVoaBjVERORLLXkKelycDc88Y8XcuREQRRMEwYZJkzY7ZXuaWqsJfIiIiKhec49CMuKGG87it9/eQnl5J3TqVN7sQQ/AwIeIiKhVaimjkPS2q0vM5qoWEfBIGPgQERH5KXdBizs7dw7Bpk0TVUtZKSm7Gu39fIGBDxERkR/yNGiRSLuPrdZw+fWAfcfWpk0TkZx8EGZzFaxWK2pqahr8fr7GwIeIiKgN8CSb4i5oKSsrUx3vWCSdmZmJ7duD5ddLlNvV16xZY/j9mrKLMwMfIiKiRtbYu6+MZlOkAKO8PMpl0LJhwwan186ePRsA5IJqqzUcgpCtOo/ednW99xs+fDpGjWraXc8MfIiIiAzwNnhp7N1X7rIpSlJPuaKiWrzzjmc9dhw/u9lchUmTNjsFXFrZpk6dLBAEmyr4CQgA0tKi0NQbzhj4UIN9/vnnGD16NE6ePKmak+ZKYmIisrOzkZ2d3ajXRkTkCw0JXhp795W77I2jqCh7sLF8OXDnnUBdHQz12Dlw4IDTYykpu5CcfNDtdnXHICkgQMTrrwtQjNxsMgx8/MCMGTOwatUq3HnnnU6DR2fNmoVXXnkF06dPNzTWgojIHxkNSkpKSpyOdayXMVKLYyS7JC1baWVTlNkbvfqZrCxg3DggP9+C7dtXua0L2rZtm+bjRrerK4Oku+++CqmpXdy+pjEw8PETCQkJeP/99/HCCy8gNDQUAPDHH39g9erV6N69ezNfHRFR66IXvGjVxigZqcXxJLskjULq1q0S8+ebUVcnICBAxNNPV+KGG653WzsUHw8EBFSjoKD+MzRky7nRnj5xcTaPzutLJveHUFuQkpKChIQE1X+UGzZsQPfu3TFkyBD5sXPnzuGee+5BTEwM2rVrh8suuwzfffed6lz/+9//cMEFFyA0NBSjR49GUVGR0/t9/fXXuPzyyxEaGoqEhATcc889OH36dKN9PiKiprJz5xAsXZqNVaumY+nSbOzcOcT9i6Bfi2O1hquO82RpLCoqCrGxsZgzJxJFRQK2bQOKigTMmROJ2NhYj2uGPP1sVms4CgsTYbWGe31fmhoDn2ZSXAxs22b/Z1O57bbb8Pbbb8s/v/XWW7j11ltVx8ybNw/r16/HqlWrsHPnTvTq1Qvjxo1Debk9ZXr06FFMmTIFkyZNwu7du3H77bfjwQcfVJ3j0KFDGD9+PKZOnYo9e/ZgzZo1+Prrr+UdAURErZWR4EUZDCi5qsVx955a53MUHw+MGgXDdTMWiwWlpaXyUpzRwEyiDHReeCEbGzdOcnptcXGsoWtvSlzqagYrVgAzZwI2G2Ay2QvMsrIa/31vuukmPPTQQzh8+DAAYPv27Xj//ffx+eefAwBOnz6NV199FStXrsRVV10FAHjjjTeQk5ODFStWYO7cuXj11VeRnJyM559/HgDQp08f/Pjjj3j66afl91m8eDFuvPFGuXC5d+/eePHFFzFy5Ei8+uqraNeuXeN/WCKiRuCukNjVUpa7WhwtjdX0T2s5Te+z7d3bD/367VUtXTkGSVp5FFE0YcWK2zWvXVn31NSDVBn4NLHi4vqgB7D/88477QVmjV3d3rlzZ0yYMAErV66EKIqYMGECoqOj5ecPHTqEmpoaDB8+XH4sKCgIl1xyCfbt2wcA2LdvH9LS0lTnTU9PV/38ww8/YM+ePXjvvffkx0RRhM1mQ2FhIS688MLG+HhERD7jWFwsfVG7Cl7cbSv3ZPs34Nk2dU9pLadpfTZAxCefjMeWLWPlwMVqDcdPP/V3CpKcibrX7lgL1ZSDVBn4NLEDB+qDHkldHXDwYOMHPoB9uUtacvrHP/7RKO9x6tQp3HnnnbjnnnucnmMhNRG1dI7ZkPqC3XCXwUthYaLbbeWutn877r5yl10qKyvzabbE8bMBIgBBft9Nmybi7Nl22Lo1w+l5OxsEAeefs8ExC+Rqi31jD1JVYuDTxHr3ti9vKYOfgACgV6+mef/x48ejuroagiBg3LhxqueSk5MRHByM7du3o0ePHgCAmpoafPfdd/Ky1YUXXoiNGzeqXvfNN9+ofk5JScHevXvRq6k+FBGRDym/hPWWmrSCF3dLWVOmTFFl2ZW0Ahh355OyJr7Mlkifbe/efvjkk/Gq50TRpAh6AHvQYw9+pHsj3ZegoGp5mUvr2psTi5ubWHy8vaYnIMD+c0AA8PrrTZPtsb9fAPbt24e9e/ciQLqI89q3b4+//OUvmDt3Lj7++GPs3bsXd9xxB86cOYOs80VId911Fw4cOIC5c+di//79WL16tVP/n/nz5yM3NxezZ8/G7t27ceDAAfz3v/9lcTMRtSquin3N5irMnHkB5s69HlOmTAFQnzERBPvfbB2XsqKjoxEbG6v5SytwcXc+iS+yJcoCarO5Cv367ZXft57jMhgACBg37mNkZy9FSsoumM1VSEo6jPj4UkPX3hyY8WkGUtOogwftmZ6m7lwZERGh+9xTTz0Fm82Gm2++GVVVVRg6dCg++eQTdOzYEYB9qWr9+vW477778NJLL+GSSy7Bk08+idtuu00+x6BBg/DFF19gwYIFuPzyyyGKIpKTk5GZmdnon42IyFfcLTXl5ORg9uzZiIuLk5/3ZClLj/I45fmCgqpRUxMiBycSqf7I22UvvazWpEmbsXnzJNhsAkwmEVdeudUh42MPaBwLn43ci+YkiKIoNvdFtCSVlZUwm82wWq1OAcIff/yBwsJCJCUlcWdSI+O9JqLmUlpaiuXLl8NqDcfSpdlOxb5jxuRg+PA8AMDMmTMRGxvr8yGkFosFJSUl8nKW0d1dRpe9XH1GQbAhO3spzOaq8/VNnXDllT3w669fNtouM+k+NoSr728lZnyIiIg0mM1VyMjYipycMagv4hWwdWsGBgwoUGUwfL0jKSoqSg6kPNnd5Rh86QVkUpbIXVZL+vXrr/Y2KA3N4jSkK7SvMPAhIiLSERdXCvXOJde7kxqDJ/11lIyMvvCmt5Cr2VyuApvGyhZ5ioEPERGRDm8Cg6a4Bq3+Oo6MFD172lvIFVeBjbusldH6J19g4ENERKSg/BL2NjDQWmKyWq2oqakBAAQGBiIyMtLpfV3t7nLVX0cKICoqKjyulTFSQO2Ou8Bm0KCpmlmr4cOnY9Qo3y8VusLAxwusB298vMdE5K3iYnuz2N69gdDQ+gCkpMSEwsJAJCXVytPBtYKNs2ejkJh4K374YR3M5iqXdS1Wq9Xp/SsqKrB27VrFMcbrWpTFyVq7u/T660hLb2vXrsW0adMQExPjdG5X12E2V+HQoV6K4MWGYcPykJaWr3nNjufSW44bMOAaDBtWjZqaCM0edmlpUWjCmAcAAx+PSH1vqqurERoa2sxX07ZJ/6Ny7DVERKRFyrCsXh2KefPM8hbsiRO3IyVll8tlGKnHmPr13SEI2fJxyroW5Zf+mjVr5GtQBwP2x/TeVy8IUWaJoqKiMHv2bFRXV6OsrAwbNmxAv357sWXLWJdLb1LQNW3aNPkxd/U1WrO3cnOHIzc33SkA0jpXcvJBzSXBgoIPcPSo/XVLlszBnDkdUFfX9D3slBj4eCAwMBBhYWE4ceIEgoKCYDKx/2NjsNlsOHHiBMLCwhAYyD+iROSaVMRbvzXbvgxkswnYtGkiYmKOuVyGOX78ONauXev0eq3dU3oBhF4woPW+yrEP7op8HbNRniy91dbWAjC2K0wrY2NnD4Dy8tJdfqbs7KVur2vatCpMndqh2XrYSfit4gFBEBAbG4vCwkJ5wjk1DpPJhO7du0MQBPcHE1Gbo1yucvcFKWVJ9JZbjh7t7nLLthQguNvarRdA6AVWU6eu1zyfsgmg0cGjWsteR4/GAxCQkHDU5f1x9bmk54OCzmkUUKuPd/WZyss7GdrqHh/ffAGPhIGPh4KDg9G7d+8mHajmj4KDg5lRI2rFPAlcHK1YAcycaa8HMZnsY37GjXN/Pr0dWAkJRwztzHK3g0svgDhyRDuwAkSNYMI5uDCyPT4qKgqZmZny0pon9Th6n6ukJA7//OctcoZm0KA9+OGHQdCbZiWKJhw/Hg3HAaQmkyjfI1db3VsKBj5eMJlM7CZMRKRDK3A5P+7PiRQgdegA/PabFcHB1Zg5Mxo2m7RcBdxxh32zgygKLs+ntwwkzY1ytzzkbhlJL4Do3l07sEpIKHY632WXfYWvv77cq+3x5vOFQ3r1ONJylLRsFhQUpPu5MjK2OmWe9uwZhNtvfxN79/ZHbm46nAMgEV9+ORr2XWX24EcQbFi4sARAyw52lBj4EBGRzxQX1wc9gP2fd95pz9g4ZmqUAZL9y9QM+xeqY8PA+p/t5xNx0UXHkZgY6FQDo7fcYrTjsKvjvAmsUlJ24ezZdsjJsQcZX399OQYN2oM9ewZpBldG5m7p1eM4LpvV1NTgpptuQlhYGABg4cIT+OGH0ygo+EA3e1VTE4yxY7ciLS0f+flpyMtLd9pCb5/GLmLq1LVISCjG6NFDsW1b/Xn0CrelzybxdrZYQzHwISIin7BYLPjmG8BmU3+Z1dUB+fkWhIbWF+s6Bkj1X6qOX7LO6uoEvPTSR0hKOizvyFLSW24xugzj6jhPAyurNRxbt2ZAyp5ImZWsrDdRUxPsFFxJs7kA+64sZa8faeu8dkNDyOeXls2kc0lb5GNjgcREi7zLytWyntlcJQdAP/3UD1u2OG+hb9/+DMzmKmxTRD2udo8pP5vE6GwxX2LgQ0REDabcWSUIzkMvt29fhYKCKjlQ0QqQ6gmKL2UpA6QMhEQEBdnrLJuj3tKTwMpVZiUpyfUmGWUvIMf3mTRpMzZunAjH5SitZTO9LfLdulWqtv4/8MBBhIUpexTZMzd6S3mO7+PJTDGta2sqDHyIiKjBpC8wd3Uyyq3jjgGSRBBsckbk9On2WLfuOscjUFNj3+VktVrl2hdvSbUwjcHIyAtPGhxOmzbt/G7XNUhOPqhajjLaVVrKsMyYYcFvv70gZ6mUQY8ycwPYkJx8CL/+muzyfdztimspGPgQEZFPuaqTkbaO641hUNbNADgfIOkHDjU1NYbnPGVmZjoFSb6cEaVcmiosLEROTo7bQNDTBoeRkZGIjY3F7NmzUVJSArN5A9LS8r0aN1FdXa2ZpdIqnj50qDfsu8e2O+0ek65Va0u89O+qJUxllzDwISLycw3Zeq7HSD2N84yoYKcvbyMN+5TLN3rcFdK6e/2ZM2fkImF357dYLMjJydH8nI71P942OIyKilJl2ZTb2xs6+dxVM8O8vHSkpeXLjzgGblqF2768Nl9g4ENE5Mc82XreGJQBkl72w8iOrIYWyCqDFmUApBwseubMGafholoBlfNwUu1sh97SkKsGh8qdUVKxs7vamqKiIhQWFsrZNsA+iUD5s/I6XTUzdNfQUVm4HRRUjYqKjli3biqUxd1GGjY2JgY+RER+ysjW8+JiIDfX/vthwxqv6667L29vGuMpgxgjA0qlAm3lNSkDFr0AxtXOJFe7nLR3Z7lucKi1M8pdbc2WLVvc3iutzI1WM0PlMuPRowm6hdsnT3ZyWC7TvrbmwMCHiMhPHTignpYN2LeeHzxoD3BWrADuuAMQ7f0DIQjAG280TkbI14WxyiDG3YBSKWhRZmqMLOFI59BbIjMSzLlrLAi4b3BopIDaFb3MjdTMUFk8nZGxFeXlUSgoGHB+m76aINgQFFStG/R4em2NgYEPEZEfslgsiIiohckUI3dJBoCAABHh4cfx44/BuOOOjnLQA9gDIL1mhA3l6Ze3lH0pK9Puoi8FI+6CD62gRes1P/wwGNKWer3lGinDJC1HGQnmtJbxQkP/cArUAKCwMFGzONhdHZQyUwXYMzUAkJBwFGZzlaFmhuXlnVBSEqcIyrR6Ldnft6YmxGXQo7w2XxaXG8XAh4jIzyizIVdema4qpJ0wYTM2b96FwsJEiOJ0p9cqM0JKRr/A9LaOezJ1XJmNeecdEc88U4EbbjirWW/jTSZJu7jXsZu0+hyOy2SA+2BuypQpAOyN/ZRBSseOJ1UNDg8d6nV+arx+cbBzobi9QPzQoV6Kfj+OPZHsM7769fvJbTNDAPJcL637AQDXXrsOAwbs092JN3XqOiQkFOPWW8cgOvpidm4mIqKmIWU5du4covgbvH0ZQ11/IqrGRQD2gZSieBAWSyfVl5bRnVWuGClidszG2GwC5s6NwG+/vQWzucqp3sabZSDt2ht1hsPxHHoFzcqlK8dgLjo6WvUarSW5Tp3KDTUFlN6vpCRW9X72jF39tnS1+hlfrsZoAK52etXfj4SEYgD6QeyAAfvkzx0bG6t7rsbGwIeIyA9p9WrZujUDAwYUKOpPNqm6AwuCDRMnbsaXX+7Cl186F/Ua/du7FCBVVFTgxIkTqpEHekXMo0ePxrZt29xmcBwDEE8ySa5e4y4wUHIMYDIytiIursTljDCt5bWNGydixIgv3Was1M0G6wM0V4GK4/mUu7FuvDENAwZcDOBiWK1WrFmzxmUwqHU/jM5Gaw4MfIiI/JAn9SdHj9rXtRISilVfYN6OG5ACpNjYWERGRqoCHz0dO3aE1RqO06fDIE0Gl7jL4HjzJaz1miuu+MztObQCmK1bM5CdvdTpNRUVFee7MOtlVEz48stRcJVtcg5g9WecuaIcozFgwDinjIxeIbargE4viG2Ouh4lBj5ERH7I6BKQ/ctrn+qx+mUVE5pqxeLdd0PkOhd7IGAPfoyOafBmO7zja4ycw1VAKT0vFSgrZ3G5GjxqD2a0syvulqCMMrLTymgAqdUhW9JcdT1KDHyIiPxMRUWF7hIQUL97CFB/UQPOhcWN1fDQcSfS0qW9VFkNQRAxdepapyyUN6TdWBUVFV6NVggODpazX3oBZUlJnFwcrFWg7DzCw5GAceM+Rr9+e1XX5Tpgcj3lXnl9RoJH6Tq1jpsyZQqio6NbRGDjDgMfIiI/YrFY5EyD49/glbuH7F+aIpRZleTkg06FxQ3d3q617OFYI5OenqeZRWnf/ozul7XR5ZQzZ85g+fLlivfV3j01ZswYhIeH63ZuLi21zxYz0ptHr0A5Ofmgqn2AkiDY5KBH2g329ts5TgXUzqRlQa0p9zZce+06l8Gj0fsYFxfX4gMeCQMfIiI/olX8qzV+QPklKX1RT5263unLVW97u1HK3WAVFRUoLgYee6yvvJtMFE3IzU3XzKIEBenXGBndZWa0309SUpLhnUiOAaW7eqoxY8YgJ8cexDjvvAKkHXf1W+dDsWZNV7z2WjakwDQlZQd27LjY4XUCrr3232jf/owc2OrttHK8LxJfzEFraRj4EBE1g8YYDNoQ7mpFpCyQYwAiNTy0WAK9/vKLioqSM1GFhYmw2S50OMKE9PTtcgdhQIQomrBixe1O08wLCirkVwUHB7sNVqRMjbvgpKKiwuW5HDMjjktCruqpwsPDAbjaRm8vkA4N/QMAsGhRT9UxomjCzp2p0Cr6VmZzXNXouFqqak1BjREMfIiImlhzDwbV4rpWpP5L1HEZR2p4CLieWeWOuxqZtLR89Ov3E95883Y4DrxUTjP/5z/1R1J4+vmVwcnatWtdnksrM1JWVqa5HKVXU2M2V52fkSV1iVZvTbffd0ArKySKJgwbtl01XkLvPbSWtZq7t05TYuBDRNSEjAwGbQ5atSnSl6zyS9RV1sDb7e3urkN6b62lIFE0IScnA3rTv41ek1ZxsSgCBQUDEBdXik6dLCgpKUF1dbXu0o7jhPcNGzqqOi672/5ttYZjz55BqK/Dce4WrUcKDqXxEp72zmnuLeZNiYEPEVETcjcYtDk5BjUAVF+iyh1PSUmHff7+0vmTkw8iO3up0xe4XlbG05EUepyLi03IyRkDaRv5oUObkZJin44ubdl2nPouNfyzWsMVheL1/Xyyst48H8DVj4I4e/YsAGPdkbUzPursjqtdV1paW41OQzHwISLykjd1Or1725e3lMFPQADQq1fjXKOntHrXAK4nnPvC6tWhbudRGZ1mDrgufNajXVysPZh0zZo1Lu+JXs2QtFSnPD4iIgKA61EZynYDjjve0tLy3QZ5/rSU5Q4DHyIiL3hbpxMfbz/2zjvtmZ6AAOD115s/2+OKux1PDWGxWFBUVIt582JUO7n0zu9umrlUDCwVPpeVlRnOaLirc1JmkqzWcNU4D+maY2KOIT6+1GWhsnT85s2TcN99/QDUAjDeHdmbURD+tJTlDgMfIiIPeVunI9V+XH01kJ9vQlFRIBIT7UskpaVNs+TgzRegNxPOjZAmmtt3cqknwbs6v2NWKiVlF2JijmHFits1grOlmsNLJcr74Vznoz8qIj8/DVr1Rm++eTsmT7ZncpxrptTH22wCVq78WrVsaKQ7srsO0o7LWv62lOUOAx8iIg95U6cjfck7KihQ/9yQnVFGOO4+Kisrw4YNG1y+xpsJ50a428ll9PxWaziOHOnh0fBSSVRUFDIzM+W6nI4dT8rDOktK4px2YgFAQUE/5OYO07ma+myVMogJCqpWBWauPqM34zWUuKzlGgMfIiI3HGt5vKnTMbq7yNVx3tQUab3G08DKmwnnjXV+x5ESepPJAWPBk8ViQU1NjWa9zvDheRgwoECns7U+ZcClDGIa8x6ScQx8iIhccKzlWbLkFKZNq8Izz4Ri/nwz6uoEBASIePppKwICzsJiaZxlBW9qinzZL8jIEkxwcLDXjRldnX/YsGHIzc11CHJsSE3dgZ07Ux26TWsP89Ty448n8fzzmxEUdA6bNmktkx100dlan17A5c2UeG+wnsc1Bj5ERDqKi4E77oC8xdlmA+67LwyHDy+H2VyFe+4Jl7/ETp2qwvmRTz5frvKmpsjoazz5knQ1oDIuLg4ffBDlNtBSBkYBAdrnt1rDVYNS33uvBEFBsQ6Bh0ljRAOgN8zTkT0ojDxfW6TueAw41xi5n4JubFp8Q5exXMnMzETnzp1Zz+MGAx8iIh3LltUHPRK9ZQwl5XKV9EUfEeE+U6DHm5oio69xrPmpqKhAba19l9HJkyexbds2t9cXHR2Ns2ej3AZajhmoZ54JdTqXY1ZHmhnmareVknKYp+OEd8C+tJWfb8Mdd0TLu8jsQY/rZTLXO75suP12e11QY2Zypk2bphqQqsQCZuMY+BARaSguBpYs0XrGeNGt+os+BhMnDvGq9403NUWevEb5haksii0tLTUU+FRUVOCnn4Jhs6m/eOvqgPx8C86dq0BFRQfMnBkDm80eXNhswLx5Ztx7b7gcpBw9mqDaIq7MwmjV8DhSZlsca3a6davEjBkW3Hbb9vOPO57H9TKZ3o4v6dj4+FLNaxo9erShe+gqqAEY2PgSAx8iIg1aGRMAGDYsz9Df6J2XmgSvet9YLBYEBFR7VFPkzWsaYu3atbBawyEI2U67lrZvX4WCgirNLes2m4Dy8k6qqeGuCdBalgJsuPbadfJATq2+Q3PnmhEbe9DN+9jPn5X1pmYg47hLy0iGp2PHjm4+k11kZCR3YjURBj5E5Nf0inGjo0/CZIqUMxQAYDKJSEvLN3RercDJ0943jlvgjdQUefMaX3C3M0tvy3pQULVHRcNZWW9i797+TsM4BwzYJx+n3XdIwBNP/GHgfUyoqdGve/K0RicoKMjQcSxIbjoMfIjIb+nterJYLNiw4WVMnKheLpk40fj2Y62lJqluxHFLtpLyC9Bxa7uRmiJvXqPH0y9jV7uWzOYqzQnlNTUhLoIRGwQBquPj40sRH1/qchinvabHOTO0b19/jcc93wLvic6dOztNbXfEZaymxcCHiPySq11Pp06dANCw7cfx8cDNNwOrVtU/lplZjW7dbsO8eWbYbAJMJhHPPGPFDTfYh1S2tC9Ax8JnLdJQToleoLVz5xDFTC37KIaUlF3nl8icM0FTp9qXrgDoBlKudk4NG5aH3NzhqsdF0YRhw7arskUXXrgPe/deCCM7sowYO3YsEhMTAbS8f59kx8CHiPyS3q6nHTus2L3b/Re5KxUVFSgpMeGdd2KgzCasXRuCtWtDFAW+AubPj0RmZmSLndXl7ovbPp08UTN7JXHugWOfVD5gQIHuEply6cqbQCQtLV8OcCSCYENaWr5TtsiegfNNb53ExETW6rRwbTLw+cc//oFnn30Wx44dw+DBg/HSSy/hkksuae7LIqIWRG/XU3z8H9i9u2HnXrt2rW4xryN329JbMvtSYQxstukuJ7a7m/XVGI393NUcGckeTZkyBYGBgVi7dm2Dr4dajjYX+KxZswb3338/XnvtNaSlpWHp0qUYN24c9u/fj5iYmOa+PCJqAVztegoOPm74PI7DIAH70s/y5f/D6dNhcK4nqa9Zkbjblt4SSRPVldvTld2OAahqmLRqbhxrafSCD1f1UO40NKCKjo72uM6JRcotX5sLfJYsWYI77rgDt956KwDgtddew4cffoi33noLDz74YDNfHRH5irejEdztenIzr1NFaxjk6tWhinlOIhw7+gKQsxBNMerC19xNVM/PT3PacWWnzHYZq6XRmp8lZZS0+t441hsBDe+UrKxzUjZ3VAoKCoLZbGZNTyvRpgKf6upq7NixAw899JD8mMlkQkZGBvLy8jRfc+7cOZw7d07+ubKystGvk4gapiEzqIzuevJGcbG9KV99czwBgiBi6tS1co8ZAKoshDdbzBuSBWko6f6VlMRCa0dUbm46pMyOlAWyd79WHgc5M6RHqxfPhx9OwsKFaUhMDGzSAEN6L9butA1tKvApKytDXV0dunTponq8S5cu+PnnnzVfs3jxYixatKgpLo+IfMCbuVVaGiN4sBdMq+t4RNGE9u3PGKopcQzKtJZNtLIgyuNcLbUoP3NDlmSs1nBs3ZoBdRZHPB/gONfyODLSz0irLqiuTkBVVRfoxTxcZiIj2lTg442HHnoI999/v/xzZWUlEhISmvGKiMgVb+ZWOXK1hOIJxy9ae8G0qAp+GtIXxnE7eUmJCY89FiNnlETRhM2bJ+HsWUH3NZLVq0Px2GP12+j79xe8mtReUVGhM7BTgNY4CftWdcBVfY9WrZR9V5z6Xrqrh4qKisK0adNYjEwueT81rwWKjo5GQEAAfv/9d9Xjv//+O7p27ar5mpCQEERERKh+EVHLJe3GUvKkQLikxOS0hLJp00RYreEeXce0adOcllvi44GFC3+DINgjM3d9YaQp5K7eOyoqCrGxsQgODkZhYaBTRslmE/DEE6dgsVicXiP9qquLxbx5kapt9Hfeac+eKRUXA9u2OT8usVgsWLt2rdyF2R1BsCE9PQ9jxmx1uicA5M8u1Uopf6WmdsHy5YI8wT0gAHj9dffBrat5V0RAG8v4BAcHIzU1FZ9++imuueYaAIDNZsOnn36K2bNnN+/FEZFPxMfba3ruvNOe6TH6hSgpLAx0ubXaKMddohaLBdXV1Rg9+ldYrWvd7iTSyjrpkQqKteZhAcBrr4UhNHQ5FiyYrjm365tvoDtANDTUHigZqZuSskhaW8Wdl7nsj9mbCNqQmroDSUmFSEgoxqFDveQCcGmA6Jw5zvfy6quB/HwTiooCkZhYi7g4G0pLm64xIJfO2qY2FfgAwP3334/p06dj6NChuOSSS7B06VKcPn1a3uVFRK2XL74Qk5JqNTsFOy5H6S2/FBYGok8fE6Ki6odPOu4UM5tdN93TKtzdtGkiFi48Aa36WWXAkZ7u3JEYsAduRUW12LMH6NABOHXKPm9swwbtgEk5QHTKlHswc2ZHj+qmHLeKKweNOgdCJuzYcTF27kxVja2QPvv8+WZkZtrfy/FeSgoK1D/rFYIbDVZuuukmhIWF6T7PHVptV5sLfDIzM3HixAksXLgQx44dw0UXXYSPP/7YqeCZiFoXb78QHbe9x8XZXDa2kzhuVXeVETEy90pJr6FfUVEgUlPVxxYXA998EwyrNRxmcxXS0vJVO6cAexBTUhKHSy6JUdU/mUyRmDhxCFJSdmnOyZI+87ffnoTNpp4iXlcHrFhhxZQpNgwcqD1hXFmkLQVCR4/G4/jxGHz55Sin40XRhJycDDhWWdTVCXKNltF7qXeckTEbDGr8W5sLfAD7//i4tEXUtnjzhegYrCxZcgpjx1YYamynzBz4aieZRG9SeWKiukdM/fVHQRCy5SLsyZPVgVt9UONc/7Np00ScPdtOc06W5KefPtBYQhPx6KNmLFpkwwsvnMK993aQn9HbEafM+jhuda9ncvrsvm7iyKCGXGmTgQ8RkVawct99YcjO/kheitIKeDIzM9G5c2fVl6cvdpIp6Y1TCAvrDYvF3qPG8fqVnZEdAzftXVb1r1MuLTnOydK6HmXQIoomzJnTHlOnAr//bsKWLRlyxkm5I855HpcAreBHHajZmzi+/rrQKkd2UOvEwIeI2iStYMVIEbPZbHbKGOjN9WpIlkIr67R2rT0Lc9NNN2HXrkingmTl9TsGbo5ZlHrOj2vdh5SUXYiJOYY337wdWktRs2adwaZNMRDFLqrzSMGY3hb3/v1/xN69/Z1aBwwYUIDy8k6YM+dPuktpRI2BgQ8RtUlawYo3PXVczfWSRk0YNXr0aGzbtk3+WS/r9O677+oWJGtdv17GxjG74u48FRUdod3lRMTGjdqFwFIQpbd8N3ZsDsaOzVEFeKNHj0bHjh0RGBiIyMg/UFpaCoC7qKhpMPAhombjzbwtox2XpW3v9TU+IiZOVBcxuzuXu7le0qiJzMxMQ9fesaNzZkPvGtxNF3eUnHwQU6euByAiMrICNTXBcqARGvqH6jzp6c4jfHbuHIKNGydqnFmvVsdOCqI8mYauDP4cGb2XRN5i4ENEzcKbeVtGOy5L294rKkIBmKH1xW3kXEbnetXU1Lj9vN58HqPTxY2eJz8/Dbm56cjNHY68vHSn+hznbI/jdHk1QRAxf/6vGDmyHwBg6NA6JCcv9XoaOuD9vSQyioEPETU5b3ZJ6XVcTk4+qPqCraiowNq1a1FcHIsVK26XdzpJO5yk4Zh65/JGYKCx/5UGBQXJv9fr5eP4edwNUTV6HgDIy3MeIKpfnwNcffWH+OijCU7PSVmjtLR8tGtXhfz8+ufc9TByx+i95LIYeYuBDxE1OW92SR092s5Qka4gCIplG+3jAUH3XN6IjIzE7NmzUVRUi8LCQCQl2ZsqKgUHB6sySHq9fH76qR/6999rOHjQO4/jfXH1ft27H9Gsz+nT5wACA5W1QzYMG2YPeBprKrx0L9mHhxoLAx8ianLe7JIaOtTsNADUZBKRnv4nDB1ajbg4G4KDg1FUVKuzbKMu6jXSvdkTH3wQ5XbpTiriBbR7+QAitmwZj5ycsZpLb1r1QJ06WeC4JCUINlRXByE391J0734Y8fGlLt9PEGwYNGgP9uwZ5FSfY3S5zZcY1FBjYuBDRE3KyC4prXlTjq8RBBGiCNx1V0eYTCKWL7dPG9+2zaK5bONYbKtXiHvmzBmPP1NJicnjpTt3vXM2bZqImJhjqKkJQadOFqeREHPnHkRY2L9w6FAvqGuYbOjWrRj/+tcNkHrpDB78A/7f//uvy/fbs2cQsrLeVBVFK6+1KQIeoqbAwIeImozRXVLKsRNarzl6NB7r1l0LKcthnzYuYtw4QXMWF2BDVtabiI+vz7joZTLeffdd3TlQeuxT09WPaS3dOdalSNfw00/9sGXLeNVzomg6X6PkPPtKFE147rneuO222PPZrfrARxCA4uIExWMCfvhhMC6++FtFr5474FjwLYom1NQEIynpsOHPTdQa6ZfrExH5mNYuqaSkw07ZBOVxWq9p3/4stJrs7dhhRXX1r5g0aTMEwR6JCIINkydvVgU9nry/EWbzcZhMouqxgAAR4eG/w2KxALAXdO/ZE4WRI290uob+/ffK11tPVBUsO35em03AkSPdNet2nHexCTh6tDsAoKYmRON5AGjYUh9Ra8GMDxG1Olr1KiaTiC++WHG+LgVe16VYreHYvj0Yl14KnDzZDoWFiW57BuXmrsXEieot5RMmbMZ779l3TCUlXYvHHmt/vv4nWR4cKnFe9nK9jRywB3R6Rcn2nWzK4EZEQsIR3XsHiBgzZmuLWM7ibi1qbAx8iKjV0WqW98ADBxEW1rC6FKkfzgsvmCAIANARojgdJpOIZ56x4oYbzqqOLysrw4YNGwA4L50dOtQLS5dmK+pp7JTb6h1HRkivDwqqlpe56onnf9XXJMXHl2LQoD344YfBkOp5Bg3aAwCqxwYP/kHOeGndu4yMrRg+3LmpYWObNm0aIiMj5Z+5W4uaAgMfImqVHAMNZdDjDed+OPXP2WwC5s+PRGZmpMsO01KwpT2ws57ezDBlsKY9gkJEevp2eTu51RqOH34YBGU9z549g5CdvRQXX/wtjh7tjoSEI07LfM2xUyszMxNms1n+mUEONRcGPkTUavlyt5GrCeeAZ9PY3Z3LyNZ5raGhomhCXl460tLsHQPz89Og16soKemwZl2TxJf37uqrr0aHDh1U2RslBjnUkjDwIaJGJ83kiohwv59CWWNjdH6XL2jXvtSzFysfh8US6PZLXKu3jnJwqKuZW0r2QmT9Rov2TsyOmq5IecqUKYiLi2NQQ60KAx8ialTqmVwxToW9SsoaG6kJ4NVXN811ahUYCwJUxcqbN9uv2912d63eOmPGbEVcXImhpSWpUWFQ0DnNQuSSkjhodZ8GgGHD8pqsSJlBD7VGDHyIyKeUE9cBx5lc2oW9gHONjdQEcNeudk127Y61LwA062Ck7e4lJSanXV/1Az/VvXUGDChwO3OrvDwKJSWx2Lo1Qw64+vbdh337+kFZx7N1awayst7U3NElLYM1xJQpUxAUFISamhoEBgZqLmFx+YpaKwY+ROQzjhPX77/feSaXKJowfPh0DBum7pWzfXswXnjBsTcPYLF0lGc3KXdRNZRUbOt4TsfaF71gxf5ZY2CzTVdNRDc6O0tJOV3dsaPyvn0XQq/ZoF736YaKjo5GbGxsg89D1BIx8CEin9CauP7CCzi/XFR/XEAAkJYWBeX3qsViQVJSrdMsLqmuBgj02RfxtGnTEBMT06BsRf2ICvWIieTkg5q1Qq6KmUeMuAGPPdZLniLv3FzQpHu+pKTDTb47i6i1Y+BDRD6hN3H9rrtO4Y032uvO5FKOpNBqAqisqzFqypQpiI6OdnrcV8szu3ef1sxkSbupPMnEWCydVMGeI6nPjnL5S3k+ztEi8gwDHyLyCa2J64JgQ2joctxzD3RncinHQ7jqL1NdXW24q29jF93+9NMHEIRshyUtG06fbg+rNVz3c1it4Th6NAEAkJBw9PzIjFqn++a4AywlZRcGDChosswOuydTW8bAh4gaTHt6unNmwpHWTCxXGYyoqCinYMlRUxTdao+YELBu3XWqYEX5OXbuHIKNGyehfinLPkMsM/MkFi2qw8KFXVWdlB13gHmT2Rk9ejQ6duwIADh58iS2bdvm9jXTpk1j0TK1aQx8iKhBXE1cb4zMRFN8KUs7rFzN6JKyOkePxmP9+mvlGh1lvY+rnV6ACRs3TkRy8lKYzVXIzvb9fevdu7dcG2WxWAwFPjExMT55b6KWioEPETWI1vR0XwY89du8TWiMjUaOyzrKHVbK7I0Ws7kK5eVn3e7i0u/kXH9cY9fqtJRsGVFzY+BDRC2WMgh55x0Ry5cDWVnqXkEN7e4sBQQnTpzA0aMiHnusryp7s3nzJNx6axz69u2Ampoap+30Wk0GHXdx6XeFbtwuy45BHYMaIsde6ERELYRzQ0MBd94JPPss0KMHcMUV9n+uWKF+XXExsG2b/Z+eWLNmDd5771unHVY2m4APPijAmjVrEBQUpHpu584hiinq9j37gmBDerp60rlUEyQI9RXMgiBi8mTf9N3RwlodIm3M+BBRi6S1PFRXB8yfL8oZGXt3ZxEXXXQciYmB+OCDKFUDRSlD5E51dTWs1nCcPh3mMntTU1MDoH531saNE1H/90cBgA2iCOTmDkdeXrpqmUxZEwQACQnFjbq0pTcwlMjfMfAhomalt3VarxGgczAk4KWXPkKnTuVYtuw+OWPjGBS5yn6sXh2KpUuz5R1a0vto9eBRd1l2VP+YVpGzvY5nn7tbQkSNiIEPETUp5Y4pqZhWWXQrjZBw3DLu2MhPImVkysujnJappKAoKemw7mDR4mJg3jyzonOyCaJow7XXrnXKypSVtXMR9DhzN6qiMbEXD5E2Bj5E1CSmTJmCLVsS8NhjZthsAkwmEf37C8jK0i+61Roaevp0e+TmpkMa5aDMyLhapqqurnYqirZYLPjmG8Bmc3x/E9q3P+MUsPzyi97uLOm97a/Vev/GotWlmruziPQx8CGiBjGaWSgra4d588yKpSjB0FKUtM3bcZt5evp2pKXlq5aRXI2KWL06FPPm1df/LFlyChUVL6O4OBaCcLvLXVmAtMTVUeMKRVx77b+RkFCMQ4d6NcrQUFc4UJTIMwx8iKhB3PWHsVqtWLNmDVatyoXN1lv1nN5SlGMDQccdXqJoQl5eOtLS8lXnczUqwp5psh9nswFz5rTHFVekK5bO1GMilAFL/ftrzdQS5SUxVyM33HHM3Ej3zR0uaRF5hoEPURvhy942njKyrOJuarm0FPXEE+F4/fVsVdakY8eTmk0Cjx6NR3n5WVWHZa1GgHr1Pzk5GXDclZWV9Sbi40udXq9f16Ou4/G2EaFj5iY2NpYNB4kaAQMfojZgxQp4vI27qQMld0tRK1aIWLhQhCh2kF8j7YzKynpTM2hat+5aKGt99DosG90hBphQU+OcQdFvQKhfxzN69GgEBQVhy5Ytrm6LSwxqiHyPgQ9RK2axWFBUVIuZM2M82sbtTaDkC66WopYu7aq5lCSK9mDEMWhSFhJrbR1X8nSHmNKUKVMAAIcOKYeS6i+LSYzMxSKipsfAh6iVkoaDFhYmwmabrnpOr3bG20DJl/SWolztlurUqRxJSYfloOn06fZYt+461XHuto5rBV2hoX+4LUYOCgpCTU2N6vVBQdWoqQlutEGsRNR4GPgQtVJS7YeR2hnAu0BJj6+XyfSWkhyDESloslrDXX5mvenqjkGXkWJkZYFxYw4SZZEyUdNg4EPUyrmrnZEUFdWisDDR7VBNV8W0QOMskzl+BsCGYcPyVNvVJVJQo1yqUn7mnTuHYPPmSXKvoIkTNyElZZf8uqCgc6ipCZGDosaeii7R6rcjYZEyUdNh4EPUBrjLXNiDlRjYbNMhCDYMGrQHe/YM8qjfTGMvkxnJvjj28snI2Iq4uBL5eKs1/Pz8rPpeQZs2TcTZs+10t63rFUT7GvvtELUMDHyI2gi9zEVBQQVmzuwqByuiaMKePYOQlfWm4TqVhiyTebKE4yr7otXLZ+vWDGRnL5Vfk5+fBmXnZOVx9Rmu+vvgqiB6ypQpCAwMxNq1aw1fPxG1fAx8iNoAvZoWAHjvvW9hs12oekzaKZWUdNjQ+T2tJ1Jy1eBQmstlhFYBtLKg2WoNR15eusYrtbehO77ekd6yFBG1bgx8iFo5x+Ufx+Ubd8GKJ4zWEznyRf2Ku89x9GiCZoCTmroDO3em6gQ/jT9Li4haFgY+RK2A1i6qiooKzeUfx+UbV8GKVqaopMSEn3/W37HVkLEMelxlrCSuPsfOnUPO1/aoCYINI0Z8hU6dTiInZwykZS47EWPGbNV9v+DgYLeF3p7gri2iloGBD1ELZbFYUF1dfX64Zv1E82eeseLqq0uxdu1alJcnulz+kWgFK1qZIgB47LGY8zu27O91ww1nUVFRoXoPX+yEslgssFqtqusAbBgzZiuGD8/TfI3W55CCP8faHmVgJJ1PGlEhFUYPH56HzMxMmM1m1WulXValperRFe7o7dziri2iloOBD1ELJBUT2zsaZ8sdjW02AXPnRuC3396C2ezZMpYyWNHKFG3cOBGCAJ338u12b+Xn27QpW3H9pvOZGWD48DxcddVVCA8PxxtvfKTKCCmvR6/54dSp6zBgwD755+HD8zBgQIFTpspsNuvutvI0S8OdW0QtHwMfIg9JmRg9vvjbvXR+vYLevXv7oV+/vV7X3GgHC6bzYyDU7+WqG7K3XH0+QMDWrRkYMKAAH330EQIC7jgf/GnXMJWUxELaoi6fQbAhIaHY6X21MlVlZWW6/86kwuySkhLDRdhE1LIx8CHygJSpcMdI92MjtDsai/jkk/HYsmWsHAR4WnOjfV7b+YxPw4ugjerUyQLABq0t6OXlnQAAy5bFylkoxxomqzUcW7dmwLF2JyNDv3bHkRTQ6P078+TfI+t4iFo+Bj5EHjBa7Oqroljnjsb1mQ1peSom5hji40s9ysroZYoAeJw9UvL0i99srsKYMVudCo+lgKu8PEruPyRRZqH0MkZxcSUeXQfg+t+Zqy35EtbxELUODHyIGsDIbqSGkjI6e/f2wyefjHd41oQVK273qgOxXqZI+RgAFBYmolMnC667Lh0dOnRAYGAgIiMjnc7n7Re/VHisNX4CgMsaJl9u1XeHQQ1R28DAh8hLrvrnlJWVAfBdFsBsrkK/fnuxZctYzZofvQ7EjjuWHBsGatW8SI85fr5DhzYjJWULAGDatGlOwU91dTUsFovXwY924bHrGiZva5yIyH8x8CHygrv+Ocrgwlf1PtKXvL1fjfst7PbX6O9YcsXd51u7dq1utsvbz6u3Rd5dDVNj9BUioraLgQ+RF4zstpL4sgleSsouxMQcw4oVtxta3vG22NbdeAhX2S5Xn1dqxBgRoT1CQo+7vkFNNWGdiFo/Bj5EXjC628pT0lZ5qSuzVkYlPr7U5fKO1ERPa5nNaCCkt+srKKjaULdorc+kbMQoCDFIT89AWlo+AxYialKGA5/KykrDJ42IiPDqYohaC3e7rVwFAnqUW+XtGRX93jVGlneqq6tVnYelQEhrd5LVakVNTQ1OnjyJbdu26Xw+eyF1enqeoW7Rys/k2IhRFAXk5g5Hbm46Jk/2LkgkIvKG4cAnMjISgiC4PEYURQiCgLq6ugZfGFFLpMyYuNpt5U3jPykYMZpR0Vve2bBhg8f1N1IdUGlpKbZt2yZ/PsdlNVE0nZ+Aru69o1xqU876Cghw1agQAOxb8q++Oh4jR4bJhdieTG33FfbgIfIPhgMf6X+GRP5MmTGRvpy1dls1ZEu1q/oa6XlX2+e9rb/RUlMTonkt/fv/iL17+2sOC1XO+lq40P6XIO2lM4kJd911ESZP3oy33hreLNvGb7rpJm5XJ/IThgOfkSNHNuZ1ELUajl+Q7rZUS1vbtWjV4ej1pikpicM//3mL7vIXYDxbZJReLdNPPw0EYMOwYdvlOp36966f9bVoURyys8Nd7kizs19nUdEJREX5Nvty0003ISwsTPd5Nh4k8i9eFzdXVFRgxYoV2LfPPgSwf//+uO2225ymHBP5A1c1N9KSjdHlJ61AKiNjq9zgD3AOaEaPHo1t27a53Y3ljmPA4aqWCbAve6Wl5QNwvxNMWjp78807oB4xUX9sUVEgUlPdd0o2uhQ2bdo0JCcnuz2OiPyHV4HP999/j3HjxiE0NBSXXHIJAGDJkiV44oknsGXLFqSkpPj0IolaIq0gwVfLT46BVH5+msugomPHjgAa3slYayinkVomADh9OgxatT9BQdVy9+f4+FJMnrxJlZVSHhsVdRJAlHwtDaXVYZqI/JtXgc99992HyZMn44033kBgoP0UtbW1uP3225GdnY0vv/zSpxdJ1BIZzUq4W34qLCyUj1eSAimrNRy5uelO59cKaHzRyTgqKsrpM+l1jnZcgrNnhOzBjyDYMGjQHrk4WhnwJScfRH5+2vnPVf9cVFSS4eskIvKG1xkfZdADAIGBgZg3bx6GDh3qs4sjaumMZCXcLQHl5OS4fb1WXUx6ep5XnY7dsVgssFqtTo8bWYIDBAiCiKlT1yIoqAb/+tf18rU7Bnxjx25FWlq+w3UaC3yM1gBxpxYROfIq8ImIiMCRI0fQt29f1eNHjx5FeHi4Ty6MqK0wuvykVwOk10xQqq3R4m0nY2UvIa1rcgyq9IK6kpJu57e9u643crzOkydPGpr3xWnpROQtrwKfzMxMZGVl4bnnnsOwYcMAANu3b8fcuXNx/fXX+/QCiVo75wJhQBSBQ4d6yXU+rmqAjCxfKbOvrlRUVADQDwqUgYTeNTkGK1pBnbSE5chdvdG2bduwbds2Q/O+GNQQkTe8Cnyee+45CIKAW265BbW1tQCAoKAg/OUvf8FTTz3l0wskam2kEQ3Kmp3k5IMQReVR9cs+ANxuQXe3fBUTEyNnQKxWK9asWaN5bWvXrpV/rwwuHK/ZkyaKjkFZenoecnOHa7y78XojX843IyJS8irwCQ4OxrJly7B48WIcOnQIAJCcnOyyV0ZDFBUV4fHHH8dnn32GY8eOIS4uDjfddBMWLFigWsPfs2cPZs2ahe+++w6dO3fG3XffjXnz5jXKNRFpcVwqkmjV6dTviBIMbUHXW77KzMx0mf3QW0KTggut5a2ffurv8pqk7fOAc1AGAHl56U5ZoKysNxEfXwoioubUoCGlYWFhGDhwoK+uRdfPP/8Mm82G119/Hb169UJBQQHuuOMOnD59Gs899xwA+yyxsWPHIiMjA6+99hp+/PFH3HbbbYiMjMTMmTMb/RqJAP1Mhbs6n4ZsQe/cubPuc66W0LSuWXm8umeP+pqk7fMSx6BMa2mOQQ8RtQReBT5//PEHXnrpJWzbtg3Hjx+HzWZTPb9z506fXJxk/PjxGD++vn9Iz549sX//frz66qty4PPee++huroab731FoKDg9G/f3/s3r0bS5YsYeBDhigno0tLuEpBQUEwm80eFc0qsy16dTpWazjS0/PkLInetHUtrq7F0y7Ojsfbgx578ON4vd9+2x5Wa7juslVDd5YRETUWrwKfrKwsbNmyBddeey0uueQSt8NLG4PVakWnTp3kn/Py8jBixAjV0te4cePw9NNP4+TJk05/QyVS0lui0mOk+FYr25KdvVQVDKgzLOoREJLo6Gh5iKjetSuzNlKdjqddnLUHiQoYN+5j9Ou31+l6BSFbM4Mk8XZnGRFRY/Iq8Nm8eTP+97//YfhwrQLGxnfw4EG89NJLcrYHAI4dO4akJHUPkC5dusjP6QU+586dw7lz5+SfKysrG+GKqaXztJjW3fF62Zbs7KVISjqseYzjCAiJq140rgI2T7s46x0vBT2+ngNGRNQctKYFutWtWzef9Ot58MEHIQiCy18///yz6jW//fYbxo8fj+uuuw533HFHg69h8eLFMJvN8q+EhIQGn5N8x2KxoLS0VPeXxWJp7kvU5G7CupFjpkyZoplZUt6TkpIS3WuQdlwJgn0p2l0XZ63j09Pz3F7v8OHTMXPmTEyZMkX3WoiIWgqvMj7PP/885s+fj9deew09evTw+s3nzJmDGTNmuDymZ8+e8u9LSkowevRoDBs2DMuXL1cd17VrV/z++++qx6Sfu3btqnv+hx56CPfff7/8c2VlJYOfFsLo8pNjcOC49OPISI2OVJsTFHQONTUhTjuiHMdLOJ7TSLbFyDHV1dWqhn7u7om7hoNan0HZpVk6/ssvL8eOHanIzR2OvLx0TJq0GVOmtMc774iw2eqXtgMCRCQm1iI4OFi3Dskb7LhMRI3Fq8Bn6NCh+OOPP9CzZ0+EhYUhKChI9Xx5ufHdKK52pCj99ttvGD16NFJTU/H222/DZFL/zTM9PR0LFixATU2NfD05OTno06ePy/qekJAQhISEGLoGalpGl5+Ux3kbLClp7Wxy3BGlNRl89uzZ8u+NNB10d4zyPaTrdXVPjDQcVAZGetPNCwoGYMeOoZB2dNUvaS3FxImnVe8xYcJmbN5svyeZmZm616ZFr2ibHZeJqDF5Ffhcf/31+O233/Dkk0+iS5cujV7c/Ntvv2HUqFHo0aMHnnvuOZw4cUJ+Tsrm3HDDDVi0aBGysrIwf/58FBQUYNmyZXjhhRca9dqoZfEmWFLS3tlkrJ6lurpalakwsrPJ6O4nV0taWtetdb1GtrZbreHIycmAchu7dL7y8k4ur7empsblNTqKi4tjgENETc6rwCc3Nxd5eXkYPHiwr69HU05ODg4ePIiDBw8iPj5e9Zx4vh2u2WzGli1bMGvWLKSmpiI6OhoLFy7kVvZWSKvzcVPR3tlk52pHlMToDCkA8mfcsGGD2+JgKTszbdo01eNSBuf06TCXO7jcBUbTpk3D/v2n8eqrFrgbNdHQ3VpTpkxh0ENEzcarwKdv3744e/asr69F14wZM9zWAgHAoEGD8NVXXzX+BVGj8XRbua9pDwS1k7789TohSxrzC12Z7XTM4AA2KIMWZbDibmv7+vVmLFzYR7NxISAiI2Or22DH6LwwBj1E1Jy8CnyeeuopzJkzB0888QQGDhzoVOMTERHhk4sj/9NcM5qkLIzzQFF1jc+hQ73cLhc1hLugShoToZXBsQds9uDHsV7IVSG11RqOZctiIYpSsFPfuBCwYcyYrRg+vH53l57IyEhOTCeiFs+rwEfqonzllVeqHhdFEYIgoK6uruFXRtSElEtUFRUVmD17P44eDUG7dnU4ceIMDh/+FACwdGl2g/vYKHedKZfzHDM46el5Ts0MJXoZnGuvXYv27c9o1gspu0MD9VvVy8ujVDu17NSNC41iUENELZ1XgY/0t06ils5dBkVJ+tKOjY3FhRfWP15aWorly6tQWJjoUSdkLXpLeVoZnNzc4cjNTcfkyc5ZJb0MTkJCMczmKowZMwY5OTkAHHep2dCjRyEOH06Ut6pnZGx12biQiKgt8SrwGTlypKHj/vrXv+Kxxx7zaX8PIkd6PV+M7GLyhKedkLXoLQPpF1Xbs0rBweeQkHBUDkTcbYWXxrlodYc+fDgRyt1qW7dmICNjK7ZuzdDdek9E1FY0aDq7O++++y4eeOABBj5kWEVFhe5zyuzNrbeOQXR0tFPNiBQEudvF5EmDPL36H8cAoSFN91wVVYuiCevWXecUvM2ZEykvySUknEPXrkkAkhAUFISqKvs16c3fcjx/XFyJ0xwxT7HpIBG1Bo0a+EhbzYmMsFgsWLt2reZzjtmbbt0qMWdOpNNxUq3Otm3ACy9oj1cYNcqzWhTHLeoLF55AUVEgEhNrERd3MYCL5QDMXddovcDOuajamWPwplxyPnpU+/2Cgs7BeZeWmpS1MrJNfcyYMU4z8QAWLRNR69GogQ+RJ4w2FRRFE+bPNyMzE3Bo6wTAHqhceilgMgE2W/3jAQFAWloUvPl+Vn6px8YCqanOx3izFV+ZxZKaA+bnpyE3Nx1a/XS0aopc1THV1ITAVdBjdKu6JCkpyeWkeCKilo6BDzUZb7MhWss1dXUCDh7UDnwA++PLlwN33gnU1dmDntdf1z/eFzzdiq9XgzR27FakpeXj6NF4rFt3LfR687g6h0R7Cc3zreoSLmcRUWvHwIeaREMaE2p9eQcEAL16uX5dVhYwbhxw8KD92MYMejzlrgbJ/msfqqv1a4r0zhETcwwVFfb5dAkJR53qkjIytiIursRwLY80U4vLWUTUFjDwIZ/Tyux4O35CWsZR7joKCBDx+uuCoUAmPr5lBTwSd52UJSkpuzB7dm9UVsbINUVlZUnYsGGD7jnefPN21GeJREyevKlBhcvR0dFc3iKiNsPjwKe2thZPPvkkbrvtNqe5WY5uuukmdnH2M74cOeG4jCNlKu6++yqkpnbxyXs0F72t8adPt4fVGq4KTgYMiERsrHOmRX8ZS/mzgE2bJiI7eymSkg57da1c3iKitsTjwCcwMBDPPvssbrnlFrfHvvrqq15dFLVevho5obWMs3VrBrKzlyIuzubm1S2Dq6Jjra3xogjNbevO57XK5xg0aA9++GEw1GMm1Dxpsigta0m4vEVEbY1XS11XXHEFvvjiCyQmJvr4cojsXC0FecNdYbWvv+D1io61dnE5FjG7G4VRU1MDwB5Y7dkzCPXBjnbw40mTRS5rEVFb51Xgc9VVV+HBBx/Ejz/+iNTUVLRv3171/OTJk31yceS/XHVJlpZejAYzRpffZs+e7ZPgR6/o+OzZdk7dkVNSdqG8/Cwct667ytJIU9D1mxPWT2n3tAszl7WIqK3zKvD561//CgBYsmSJ03McUkoNkZmZCbPZDADo1q0S8+ebUVcnICBAxNNPV2LGjOkeBzNGl9+qq6vlYMpqtcqZFaXAwEBERkZqZoikoEEvWyUFPdLPUlbH3SgMx2AkMjISgH5wmJX1Jioq7MdIs7uMyMzM5LIWEbV5XgU+NlvrqLGg1sdsNstLLZmZQEICIAhAerqA+PhI+ThPghmjrFYr1qxZY/h4xwyR1OG5qKgW77wjOkw8dx5HIWV1kpIOq+p9pCDvhhuud7kEpzdCIz6+FPHxpYY/h6Rz584ev4aIqLXxKvD55z//iczMTISEhKger66uxvvvv2+o8Jlal6aqkZGyGytWADNn2jsvm0z2ZoRZWQ0+vUtaGR5XpPvheG/CwiowcWK+0440ZcYHUGd1pHqf4cOnIy0t6nyQF+n2GqTXGd2q7li8LGERMxH5C68Cn1tvvRXjx49HTEyM6vGqqirceuutDHzaGE+WlYzS+gKWvnyLi+uDHsD+zzvvtDcjbGk9efTuTUoKnAKS0NA/dJsRAvYMzrBh1YiNVQdTJSUmFBYGIimpFnFxNqeeSEZmbElYvExE/s6rwEcURQiC87bZ4uJiuT6D2g5PlpWMFsfGxcXpZhgOHFDP2ALsYydcjahoLq7ujWNAoszOBAVVo6YmxKlnD6AOptyNpCAiIs94FPgMGTIEgiBAEARceeWV8u4SAKirq0NhYSHGjx/v84uk1sNxkrkWd8sqvXtrDxh1N6LCKFf9dRqb2VyFQ4d6uQxmpHvnbqwFERF5zqPA55prrgEA7N69G+PGjUOHDh3k54KDg5GYmIipU6f69AKp9XEV1EhLODt2/K5avpEEBwcjPj6q0QaMNncGxUgwY1/aSsTp02Eux1oolwsLCwuRk5PTZJ+DiKi18ijweeSRRwAAiYmJyMzMRLt27RrloqhtkpZw3AUfs2fPRlZWlM8GjErLb+6CDmUG05eUGSZ3M7pWrAAeeSQGNtt02PvxqBsSKguilfU6wcHBhgIf9ukhIn/n1f/pp0+fDsCekj9+/LjT9vbu3bs3/MqozbH3x3Gf8ZCWelwNGHX8AtdbvpKW1WbPno1t24AXXnAOOoYPn45Ro5zrdaRzBgWdQ01NiObSWEVFhcvr0Jo35th7x2QSERRUjYKCfli3rgvqAx0TABsEQdQtiJb4YomRiMgfeBX4HDhwALfddhtyc3NVj0tFz2xgSHqMTiV3R/qiP378ONauDceyZd1gswkwmUQsXPgbpkw5iaCgINXxl16qXTuUlhaFqCh7RkqiDFikrItjdurMmTNYu3at5mu0trBLTQyVjwuCDQMH7sGKFbdrdGEGABOmTl2L9u3PuN2uzqCGiMg9rwKfGTNmIDAwEJs3b0ZsbKzmDi8iACgutu/S6t3bHmS461LsqTfe+AhLl2ZDFO1/Bm02AYsWxcFqXSsHCVKjwfh4uKwdUjYgfOyxGPmcUgZGFE3YvHkSFi5MQ2JioCq7ojdUVSvIi4srQXb2Unl3l37QY783nnRfJiIi17wKfHbv3o0dO3agb9++vr4eaoGM1oU4HufYhPCZZ0J1uw1788VeXV1tKIOkDFCysuCydigqKgp79jhvp5fYbAKqqrogKgooLa3vjqx3Hcq5WYA9kAkKqpaX0I4c6eEy6PH23hARkTavAp9+/fo5NVGjtknahZWZmanqbHzq1CnU1NQgMDAQ4eHhCAy0Z0CkYKCsrB1mzuyoakI4f74Z99wT7nG3YVe8ySC5qh0CtLfTS/S21etdR0bGVnz66RjYbPalskGDlMtaUuGycwHz1Knr3GZ6WKhMROQ5rwKfp59+GvPmzcOTTz6JgQMHqmopACAiIsInF0fNy2jHZi2FhYnndybVq6sT5EyMJ92GXfFlBkniuCQmcbWtXu86UlJ2Ye7cBLz3Xr7GspZyKU1dRzRgwD6n91BuX2ehMhGRd7wKfDIyMgAAV1xxhaq+h8XNbYsnAz4d+bqWxxVfZpAkyiWx9u2B06fdb6tPTj6IqVPXAxBV2ZquXWuRlHQYhYWJustagIBx4z5Gv357da+f4yaIiBrOq8Bn27Ztvr4OamMakomxWq3y741mNtxlkLwZsqq1JKY8j3K510hjRK1gUCIINpdBDxER+YZXgc/IkSPx1Vdf4fXXX8ehQ4ewbt06dOvWDe+88w6SkpJ8fY3UTBx71HhKKxOTmZkpz3OrqKhQbQeXrFmzRvWz8jWA58s8eu/jSNr9pUdv6c/oaAnHYNBxectd0MOaHiKihvMq8Fm/fj1uvvlm3Hjjjdi1axfOnTsHwP439SeffBL/+9//fHqR1PQsFouhYMEdx0yM2Wz2eLnGMRACPJsEX1tba+g4d0t7es+721l29uxZ+XHnQaXBLpfnpLoe1vQQEfmGXsGBS3//+9/x2muv4Y033lAVNg8fPhw7d+702cVR82lIfY9R0kwqqzXc49d6Mgnesfje16QlLCVBsOH06fawWsPx0UcfqZ4zm6uQlHQY8fGlSEo67DLTExcXh9jYWAY9REQ+4lXGZ//+/RgxYoTT42azucHLI9Ty+WK6ub3Hj30mlbfDQh3HNNgDKfXg0+DgYK+COK2aIL0WDlr1TKIIrFt3nVefjVkeIqLG41Xg07VrVxw8eBCJiYmqx7/++mv07NnTF9dFLZQvppsXF0uNDes7ImvVxBghBQaOzRKXL7fvzALUjQaNMLqNXxkASktYR4/GY926ayElU735bHFxcQx4iIgaiVeBzx133IF7770Xb731FgRBQElJCfLy8vDAAw/g4Ycf9vU1UgthtIjXnQMHnJsDejOvS1IfSNl/ttnsPXjGjQNCQy2Gm21Kxxk5Xi8ALC8/C8cVZOVncyzUdsQsDxFR4/Iq8HnwwQdhs9lw5ZVX4syZMxgxYgRCQkLwwAMP4O677/b1NVILoVfEO3z4dPTvf0KzCNlRcHCwZmfkhvT40Qqk6uqAHTus2L3beAPGDRs2GDrOVQDorn+RN8XdRETkO14FPoIgYMGCBZg7dy4OHjyIU6dOoV+/fujQoYOvr49aEK0vdZNJRGJiLcxmMzIzMyGKIiIjIzVfr8xm2Dsji6irM76dW49WIBUQICIsrMSr87njahdXUtJhn3eSJiIi3/Eq8JEEBwejX79+vroWauG0ingnTtyMzZvVNT7u+uEA9vqbSy6pwPPP/7fB3Zbj44ElS07hvvvC5OuaMGEzcnM9qz0yyl1WpzE6SRMRkW80KPChtktvq7iRL3Wju6gGDuyI55+fpDreaLNBx+ubNq0Khw8vNxRseLorzfH4Q4d6QRTrn9fK6vhqFhkREfkWAx/S5LhVHLAX/W7YsMHpS10rkCguttfe9O7ter6VMjNUXAwcOxaLKVPuQXT0HwD0t6hrZZSMBBue7kpzPP6yy77CV19dDmUBsyja53QZwe7LRETNi4EP6TKyu0grkFi9OhTz5mlvLdej3o7eEcuX2x/X26LuDU93pWkd/9VXI1A/VV1Sv2tLOUHdEXdsERE1PwY+ZJjUaVnK7GgFBhs3TsTmzYLm1nK9zI/ednSbDfKSkv0xERdddFzO/EiMZlHcjZaQSBms06fDNAaKOgY96voeTlAnImrZGPiQIVqdljt2PKkRGJg0t5YfPKgf+OhtR3dUVyfgpZc+QlLSYafnMjMz3X4Gd0XJgHMGC7DB9WQX7toiImpNvJrVRf5Fr9NyUNA5pxlVgA0mk6h6JCAA6NVL//zSdnTH1wgOyRVXvX5qamqMfBSkp+fJ1+xYlKyVwbJfg+NnrL+e229/U1UjxBoeIqKWjRmfNkZrxpSSN3Umep2Wa2qCNXvWjBgxAvPnR6Kuzh7AvP66frbHYrEgIKAazzwTivnzzairExAQIOLpp60AgHnzzLDZGt7rR53JETFs2HakpeWrznf0aILmUtiIEZ/jyy9HOZ0zPT0P8fH2cRiZmZno3Lkza3iIiFo4Bj5tiNEZU0b67Ci56rSclHTYaXv7jBnDkZlpX97q1ct10KO83nvuCZfPc+qUPSC5995wQ1vUAwP1/yg7Z3IE5OWlIy0tXz5m584h2LhxotNrBcGGCy74BV99NcIhKLLJr8/MzETfvn1135+IiFoOBj5tiNH+OZ5OK//kEzj0rRHx7LOVuOGG652OlZZ6AgJK0aeP/THHGaFS1snxOrS2oxvthxMZGSlvvy8rK8Pbb+fIW+zdFTVLgZHjyq+UZYqPL3XZjdnV7C0iImpZGPiQS1J9jzrwEZCZGYnY2Ein4z3JOvlaVFQULBYLNmzoiKVLs+UgJSNjq8uiZq0lLgBYtOgX2Gz2+h1XjRtZ10NE1How8CGXtOp7bDYgP99em+PIarUaOq+nWScjLBYLnnhilRz0APbMztatGcjI2IqtWzOcMjZS7Y8jQbDh6qs7IjFxts9rpoiIqPkw8CGX9Op7tm9fhYKClrWFu7q6WndZKy6uBNnZS1UZG8fan3r2wKh9+wsQFcXaHSKitoTb2dsAi8WC0tJSlJWV+fzc8fH2jskBAfafldkSqzUchYWJsFrDff6+njpz5gzKysrkXj1K0rKW2VyFpKTD8jKVVpAEANdeuw4pKbuwZs0aWCyWJrl+IiJqGsz4tHJGa2oaIivL3nk5P9+C7dtXqZaIjM68aohp06YhMjJS9VhFRQVqa2sBAGfPnsW7774LADCb4bIQWUmroSEgwmqtf6/GWJIjIqLmw8CnlWuqL+b4eCAgoBoFBdqjKlzNvGqoyMhIeQxEcTHw/fdWfPnlv3Tfy7EQGYBq1IbEbK46P3RUOX9LwNatGRgwoIDdmImI2iAGPn6oobuQjM688tV1SMfVDzI1QxCyXWaZpG3wrjJTO3cOwddfXw7H+VvefBYiImodGPj4CWlquC92IRmZeWVEVFSU3HtHj3S9joNMjWSZXGWmAOgUNnv3WYiIqHVg4OMnfDk13GyuMlxHo0fK4iiDsOJi+/b53r2duz3rjc1wlZnRG0FRXt4JgKAZ9HDoKBFR28bAh7ziqqGfVjGyklbWqX4Zy759fvlye1G1xL6tXpQHpQKuMzOuRlBIr9HKWmVlvSnP3yIioraHgQ8Z5liTozdOIiYmxqPlNMdlLJsNuPNO+04yKfMTGmrBxInbDWWZ9EZQmEwiFi06hro6+2u0slYMeoiI2jYGPmSYJzU5ElfLVxKtZay6OvuQU+k11dXVLrNMSnojKF59tQKTJglYvtz+s5HzcRwFEVHbwsCnlfN0Z1RDeZLJcbd8JdHqDh0QYJ/s7sjd0FJXS1zp6cazVpmZmejcuTPHURARtTGCKCrHT1JlZSXMZjOsVisiIiKa+3IMsVgszTJPylU2p7gY6NHDOZgpKtLO/KxYYV/eqquzH/f66+ogqbS0FMulVI0DqzUc5eVRCAo6hxUrbnfK9phMIpYsOY177+0AoPnuFxERNR6j39/M+LRSzf3l7S6bY2T5SmKxWHD11dXIzzehqCgQiYm1iIuzobS0/rPocezTo7fENXNmR/lnBjVERP6LgU8rZHRMxezZs+UveV8GSkaKkY0uX2l9loIC5/fMzMx0ekyrTw8gQtmQUBBsSE3l2AkiIrJj4NMKGR1TUVRUiz17gOjok9iwwbNAyRUj2RxpuKnj8pVjtsfoZ6mpqXF6THvIqAAp+JF2asXFXWzoPYiIqO3jdPY2aufOIbjkkhhccQVw0UWR2LlziNvXGA1CpGyOklY2JyvLXtOzbZv9n1qFzd6yWsNx+nQYAJvGs4Lck6exBqcSEVHr1OoCn3PnzuGiiy6CIAjYvXu36rk9e/bg8ssvR7t27ZCQkIBnnnmmeS6ymUlLQFKzP5tNwKZNE2G1hvvk/FI2JyDA/rNeNgew99/p06cUAQGlKC11/lVRUWHoPQMD65OTO3cOwdKl2Vi37jo4ztmSiKIJNTXcik5ERGqtbqlr3rx5iIuLww8//KB6vLKyEmPHjkVGRgZee+01/Pjjj7jtttsQGRmJmTNnNtPVNg9vh4harVaX51XWAWVl2Wt6Dh60Z3q0gh6jtUhGREZGYvbs2fjxx5NYtKin4vNpBz7KDs3sxUNERJJWFfh89NFH2LJlC9avX4+PPvpI9dx7772H6upqvPXWWwgODkb//v2xe/duLFmyxO8CH2+HiK5Zs8btuZV1QPHx+k0JAeDEiRPGLtigqKgoiGIU3DVgMJlEPPNMJW644XpuTSciIpVWs9T1+++/44477sA777yDsLAwp+fz8vIwYsQI1d/ux40bh/379+PkyZO65z137hwqKytVv1ozqadNRsZWBATYI4SAANFngzeN1gEB2gXJ3iorK0NpaSkiIn6HyaQd+QQEAGvXAocPC5gzJxKxsbEMeoiISKVVZHxEUcSMGTNw1113YejQoSgqKnI65tixY0hKSlI91qVLF/m5jh07Or0GABYvXoxFixb5/Jqbg2NPmzlzjuPSSwMREXEcubmtu8h3w4YN8u8nThyi2MZu38El1Rldd12zXSIREbUCzRr4PPjgg3j66addHrNv3z5s2bIFVVVVeOihh3x+DQ899BDuv/9++efKykokJCT4/H18SatmRaunzfPPd0Z29lKfZHqaipSx6tTJonvdyhlb06ZNQFhYZ906IyIiIqVmDXzmzJmDGTNmuDymZ8+e+Oyzz5CXl4eQkBDVc0OHDsWNN96IVatWoWvXrvj9999Vz0s/d+3aVff8ISEhTudt6bSGhW7fHowXXvC8oLkl+eOPG7FsWTJsNgEmk4iJEzfpbkeXZmwNGVKL2NgmvlAiImq1mjXw6dy5Mzp37uz2uBdffBF///vf5Z9LSkowbtw4rFmzBmlpaQCA9PR0LFiwADU1NQgKCgIA5OTkoE+fPrrLXK2ZY+3KpZc6d0o2UtDcUlit4Vi6tCdEUb0FPzn5IMzmKkOZICIiIndaRY1P9+7dVT936GAfNpmcnIz48+sbN9xwAxYtWoSsrCzMnz8fBQUFWLZsGV544YUmv97m4NwpWcSECdoFzS0xiHC1Bf/QoV6q2qVJkzazMSEREXmlVQQ+RpjNZmzZsgWzZs1CamoqoqOjsXDhQr/ayq7srRMefhybNzsHB44F0L4MIhzngVVVGQ+q9LbgBwVVO9UuKTNBREREnmiVgU9iYiJEjWYugwYNwldffdUMV9S8iovt87N6967vrVNa6jzKQasA2tMgQq8ZoJFmha4yTWZzFSZN2ixfn73GZzNqakK8asZIRESkpVUGPlRvxYr6Sekmk325S28mlicdnfv3HwerNQbdu59D1661AOxjI6qrq2GxWJxqjNz199m5cwg2bpwIe+soGyZPds40KXdr3XhjGnJzd8FqDfeqGSMREZEWBj6tWHFxfdAD2P9555325S5pjpaS0Y7OO3cOwaJFl7hcDjM6yR2wZ3rqgx4AMGHjRu1Mk7RbKz4+Tf5ZmQmSrkd6HcdREBGRJxj4tGIHDqh3cQH2wuaDB4GBA9UBgbKj89atGXIQkZGxFeXl9gBG2j1lZDnMkw7OR48mwLlJuAlHj8YDKNZc/pJmc0nvs3DhCRQVBSIxsRZxcRcDuJjjKIiIyGMMfFqx3r2dt7AHBNiHhip7/axeHYrHHjPL/XEyMnIQF1eCkpI4VRA0adJmdOx4sslqavbvvwDr11+rm1lSBjWxsUBqqk/fnoiI/FCrmdVFzqQt7NKyljS2QepgHBUVhbq6WMybFwmbrb4/zmefjcG0aRPw6adjVJmdDz+chLFjh0EQ1GkkIzU1FRUVus8lJByFfbSE2o8/DnbKLFmt4QC4hEVERI2DgU8rl5UFFBUB27bZ/+lY2Ky9HCZg//7OcjCkfPyPPwIwadJmOfhxrKnRYrFYsHbtWt3nzeYqTJ68CYDjTjP1+4uiCcOHT/eofoiIiMgTXOpqA6Qt7Fr0lsMuuwwwmURV8CMINhQUfICUlCp5d1WnTuVul7hOnDjh9Jjj1vWUlF0IDj6Hdev0p4gGBABpaVFgzENERI2FGZ82Tm85rGdPCxYu/E03s2M2VyEp6TAAoLAwUV6CcmSxWLBmzRrVYzt3DsHSpdlYtWo6li7Nxs6dQwDYl7wcl9GkJbCAAFG1TEdERNQYmPHxA8qOzr16AaGh9c0Gs7PDdTM7Rro8O+7ucrcrzHFrekbGVsTFleDuu69CamqXRrwLREREDHz8hnI5rLS0PliR+uY4chfABAcHw2KxoKysTPU6vSaJP/3UD/3771U1KVQGW3Fxzp2miYiIfI2BD2nSC2CGD5+OUaPsP2uNqNBqkgiI2LJlPHJyxp7P8JS2qAGpRETkPxj4kExZkKwVwCiLj0tLSzXPYTZXqZok2mt47AXUomhCTs4YAAKnrBMRUbNg4EMAtOt5lPU4jj2CXJ2nPuixwbl+vj4I4pR1IiJqatzVRSgujsXGjdr1PNnZS7FunUWzR5AjaSZXfZZIyvhokzpCExERNRVmfPyclOlxjIGloCQp6TBGjYKh3jr5+WlO55GWtRyXvQBOWScioqbHwMePOe7cUhIEG268MQ0pKZMMdVG2WsORl5eu8YwNWVlvoqYmWHM2GKesExFRU2Lg44ekIENr55adVHg83PDoiKNHEzTPNX26BQ8+eCXCwsIAACUlnLJORETNh4GPH5ImtxcV1eKdd9RjK0wmEZs2WZCWZjzo2blzCDZunOj0uCDYcPfdNiQnJ8uPcco6ERE1JxY3+6moqCikpnbB8uWCapzF8uUCrr66s9ugR8oaSctljn+UpKWsyMhTjXH5REREXhFEUdTfduOHKisrYTabYbVaERER0dyX0+gsFguKimoVy0/qDsqulqEOHTqExx//GqtWTXd67tpr12LAgH0AwGnrRETU6Ix+f3Opy49ZLBZV9+WCAu3jpk2bhsjISNVjwcHBCAsL0+nUbENCQrH8k+M8LyIioubCwKeFKy4GDhwAevd23TzQ8TgjrzMakKxdu1bz8czMTHnwqL3GRwp+BBw61ItdmYmIqMVhjU8LtmIF0KMHcMUV9n+uWGHsuBkzjL2uoWpqagAAyckHIQjKZwRs2jQRVmt447wxERGRlxj4NKHiYmDbNvs/jRw7cyZgO19yY7MBd97p/Fqt41atcv86LVZrOAoLEz0OWLS2srMrMxERtURc6moiK1bUBygmE7B8uesREAcO1Acvkro64OBB9dKV1nGO6uqA/HwLQkOhW2SsNavL3VLVyZMnXW5lZ1dmIiJqaZjxaQJGszdKvXvbAySlgACgVy/3xznOxxIEG7ZvX4WXX34ZFovF6b0cOzhLs7qkzI9eJuiDD753uZWdw0eJiKilYcanCRjN3ijFx9uzQnfeaT9Wazq6xWJBQEA1nnkmFPPmmc83IrTBcR6WMgg5fvy4XNRcVlYGQLuDs7RUdehQL91MkF7n56lT18lb2YmIiFoSBj5NQMrKKIMfreyNo6wsYNw4e4DUq5dz0KPcin7vveE4ejQe69dfC1GsD3xE0V58LNHaoaW1JV0QbAgKqtbMBCUnH4TZXKX7OuVWdoBzuIiIqOXgUlcTkLI3yg7JjtkbV68dNcr5WMet6GZzFdq3P6uRgdEvMpaWsABg0qTNEAR7ZCZldmpqQlwWLUtb2R1fJ2WXpkyZwuaFRETUojDj00RcZW88VVwMfPNNMKzWcFUdjV4GRqvIWKuYOTt7KcrLO+H//b+B2L17F6zWcLfnS0nZheTkgygv74ROncpV1xMdHc2gh4iIWhRmfJqQXvbGE1LPnuuui8LSpdnYuXOI/Jy7DIxEr5gZAJKSDuOSS+Iwe/ZszJ17PZ59thIBAfZi6YAAEc8+W4lbbx2jOp/ZXIWkpMMsZiYiohaPGZ9WxHF3mGPNDeA6AyNxVcwsHS9laubMATIzpUyVgPj4SJSWnm3ET0lERNR4GPi0MK5GTWjtDnMMWAB7BsZV9sWTJTHAfh0NyVIRERG1FFzqakH0RlRIHZ87dHDu2eNNo0CjS2J6jO7S4m4uIiJqaZjxaSG0mxyKKCqqxJNPRsBmE2AyiZg69Sw2bAhFXZ3gccAyduxYbNmyBYCxJTE9UVFRmD17tsshp8HBwSxsJiKiFoeBTwuh3eRQwBNPhMt9eWw2AevWtUNW1hsYNWoCduxY41HA0rFjR9XPektiRjI1DGqIiKg1YuDTQmg1OQRsmkXIFRWRuOIKE8aPn47q6mqUlZVhw4YNbt9DFEVkZmbKU9UdBQYGIiYmBlFRUS5rjYiIiForBj4tRP2IClFexsrI2IqtWzOcgp91667FpZdWYs6cSI/eQ9m12WoNR3l5FDp1sqiyPpmZmVi5MkAegWEyiXjmGStuuOEsl6+IiKjVE0RRFN0f5j8qKythNpthtVoRERHR5O+/Y8fveOmlj+S6G2WjQaWAABFFRQLi453HV7jjahK71RqOpUuznXZ8ZWcvhdlcxU7MRETUIhn9/mbGp4WJi7MhKemw/HNKyi4EB5/DunXXqY6rqxPkIadGio2tVivWrFmj27xQ6gXkrsePq/cgIiJq6Rj4tAIJCUed+u4EBIjo1at+GKnRLIy7wMbTHj9EREStCfv4tAJafXcefvg3BASUorTU/stisRg6lxTYKCkDm4b2+CEiImrJmPFpJRz77gBVWL5cfYxW/Y3FYkFRUS127z6L4uJY1NSEqIqmpSLq8nL768zmqgb1+CEiImrJGPi0MK566LgbRaGsv7FYLDh+/DgefPDA+ZqeLgDuAFC/YywurgQlJXGqIEgqdHb3XkRERK0RA58WRlmoXFJiQmFhIMzm48jNXev2tSUlJvz8MxAdfRIbNrx8vpBZuUPLXhMkiibk5GTg9tvfVG2X1xp6SkRE1JYw8GmBoqKisGJF/QgLk6kTJk4cIm8517Jz5xA89ljM+eMjMXHiEHTseNKpkLmeCXl56W6ntBMREbUlLG5uYtLA0eJi18eo53YJ2LRpIqzWcM3jpS3qNpugOj4o6JxTIbPS3r39AegXOmvh4FEiImrNmPFpQuosjr1Tc1aW83Fac7u0MjFS9+XTp8M0Mzc1NcGYNGmzZgNE6Zhhw7bLmR/HHVyZmZkwm83y8ezcTERErR0DnyaiPX0dGDfOeRaW1twux0yMY/dle+bGufdOUtJhxMQcw5tv3g7HBJ8g2JCWlo+0tHzNHVxmsxmxsbE+ugNERETNj0tdTUR7+jpw8KDzsdLcroAA+88BAaIqE6PVfdm+W8s+fcRkUh9fUxMCrX/V6el58u6tpKTDrOshIqI2jxmfJqKVxQkIAHr10j4+K8ueDTp4EIiKqsCGDfWFzVrdlwEBomjDsGG5SEvLVwUxWt2YAXu2h4iIyJ8w49NEnLM4wOuvOy9zSSwWCwICStGlyz4AxRgzZgxGjx6N0aNHY+LEPjCZtGbL2ndqOdLqxjx5MrsxExGR/2HGpwllZQGDBgFffw1cdhlw8cXaxxmZtj5x4nHNomW97ejsxkxERMTAp0kZ3dVlZAJ6SsouzaJlV9vR2Y2ZiIj8HZe6mojeri69fj5WazgKCxN1e/cAQHx8KSZP5kBRIiIio5jxaSKudnU51vmsXh2KpUuzneZnaWnMJSw2KyQioraGgU8TcbWry2KxyMtbJSUmzJsXA1Gsn6vlbn5WQ5ewpk2bhsjISNVjbFZIRERtEQOfJiLt6rrzTnumR9rVFRqqLmQuLEyEzTZd9dqGzs+aMmUKoqOjNZ9jgENERP6EgU8TUvbm6dXLHgyVlqoLmbV67ribn+VOXFwcgxsiIiIw8Gly8fH6vXuA+p47ynEU3hYsT5kyhUEPERGRAgOfFshXBcvR0dEMeoiIiBQY+LRQjgXLQ4cOBQB8//33zXVJRERErR4Dn1aCAQ8REVHDsYFhEykuBrZtc25YWFJictuo0Fvsw0NERKTGjE8T0BtVYX88BjbbdLeNCpWmTJmCwMBA1NbWOj0XFBQEs9nMbepEREQaWlXG58MPP0RaWhpCQ0PRsWNHXHPNNarnjxw5ggkTJiAsLAwxMTGYO3euZnDQlH788SRmzhQdRlWI+N//Tpx/vL5R4caNE91mfqzWcOzfH4fw8AvRseNAlJUNRMeOAzFwoP1X3759ERsby6CHiIhIQ6vJ+Kxfvx533HEHnnzySVxxxRWora1FQUGB/HxdXR0mTJiArl27Ijc3F6WlpbjlllsQFBSEJ598slmu2WKx4PnnNzs1JKyrE/Diiztgs413eIUJ+flpGDt2q+b5du4cgk2bJuKFF0wQ7PESRNH1wFMiIiKq1yoyPrW1tbj33nvx7LPP4q677sIFF1yAfv36Ydq0afIxW7Zswd69e/Huu+/ioosuwlVXXYXHH38c//jHPwxNO28M1dXVckNCJUGwISHhCACb02vy8tI1sz5Wa7jc2wewBzyiaH/O3cBTIiIismsVgc/OnTvx22+/wWQyYciQIYiNjcVVV12lyvjk5eVh4MCB6NKli/zYuHHjUFlZiZ9++kn33OfOnUNlZaXqly9JDQkdJ6jHx5di2LA8p+Ol8RSOysujVN2cHUkDT4mIiEhfq1jq+vXXXwEAjz76KJYsWYLExEQ8//zzGDVqFH755Rd06tQJx44dUwU9AOSfjx07pnvuxYsXY9GiRY138dBvSJiWlo+8vHRD4ym0RlkoSQNPiYiISF+zZnwefPBBCILg8tfPP/8M2/nK4AULFmDq1KlITU3F22+/DUEQ8O9//7tB1/DQQw/BarXKv44ePeqLj+bEbK5CUtJhVVNCKRsUEGBfs3I1nsI5cyRCEOyvCwgQ8frrrkdhEBERUTNnfObMmYMZM2a4PKZnz54oLS0FAPTr109+PCQkBD179sSRI0cAAF27dsW3336reu3vv/8uP6cnJCQEISEh3ly+T6Sk7MLChWkoKgrExx+vR01NCKzWcM3gxzFzBED+/TXXTAfAnVxERESuNGvg07lzZ3Tu3NntcampqQgJCcH+/ftx2WWXAQBqampQVFSEHj16AADS09PxxBNP4Pjx44iJiQEA5OTkICIiQhUwtURxcTZ8/nkAVqy4XTWYVKunj+MoC+n3JSUlqK6uZv8eIiIiF1pFjU9ERATuuusuPPLII0hISECPHj3w7LPPAgCuu+46AMDYsWPRr18/3HzzzXjmmWdw7Ngx/N///R9mzZrVrBkdI0pKTJg3zwxRrO/ps2nTRCQnHzQ8oHTDhg3y72fPns3gh4iISEOrCHwA4Nlnn0VgYCBuvvlmnD17Fmlpafjss8/QsWNHAEBAQAA2b96Mv/zlL0hPT0f79u0xffp0PPbYY812zUZHRhw92k5uZCiRdnd5M5m9ubbvExERtXSCKErdYAgAKisrYTabYbVaERER0eDzWSwWzUDEPqMrEH36mNCxY0f06CGqgh9BsCE7e6lXgc/MmTMRGxvboOsmIiJqTYx+f7eajE9rpbXkpDW765lnrJg7N0JV4+NN0ENERET6GPg0seLi+qAHqO+6vGuXiN9+W+rU64eIiIh8h4FPEztwoD7okdTVARZLRyxYMN1pWaysrExVuExERETeY+DTBJR1PhERJphMMap6noAAEb16CZrLYkYLpImIiMg9Bj6NzGKx4OWXX1Y9NnHiEHngqCDYMGHCZhw5Eo2TJ+3DSQMDAxETE4OoqChERUVh9uzZqK6uZvaHiIiogRj4NDKtHV1as7tycpxfm5mZic6dO8uZIKPZH2aJiIiItDHwaWQVFRVOj1mt4Sgvj0KnThaXRcxr1qwBUN+QUJn90cPOzURERPoY+DSy2tpa1c87d6qXufRGUygpAx0GNURERN5r1uns/sZqDZeDHqB+NIXVGt7MV0ZEROQfGPg0ofLyKDnokUijKYiIiKjxMfBpQp06WSAI6iY+gmBDp07lzXRFRERE/oWBTxMym6swadJmOfjhaAoiIqKmxeLmRmSxWHDq1CnVY1pb2YmIiKhpMPBpJFqNCyVmcxUDHiIiombApa5G4qrXjqfYkJCIiMg3mPFpYaZMmYLo6Gj5ZzYkJCIi8h0GPi1MdHQ0YmNjm/syiIiI2iQudREREZHfYOBDREREfoOBDxEREfkNBj4tDHdwERERNR4GPo3EmwAmMzOTO7iIiIgaEXd1NZKoqCjMnj0b1dXVKCsrw4YNG9y+xmw2N8GVERER+S8GPo2I2RsiIqKWhUtdRERE5DcY+DQBo/U+LGwmIiJqXFzqagLKeh89HE1BRETU+Bj4NBEGNURERM2PS11ERETkNxj4EBERkd9g4ENERER+g4EPERER+Q0GPkREROQ3GPgQERGR32DgQ0RERH6DgQ8RERH5DQY+RERE5DfYudmBKIoAgMrKyma+EiIiIjJK+t6Wvsf1MPBxUFVVBQBISEho5ishIiIiT1VVVcFsNus+L4juQiM/Y7PZUFJSgvDwcAiC4PV5KisrkZCQgKNHjyIiIsKHV9h68B7Y8T7wHgC8BwDvgYT3oXHugSiKqKqqQlxcHEwm/UoeZnwcmEwmxMfH++x8ERERfvsHW8J7YMf7wHsA8B4AvAcS3gff3wNXmR4Ji5uJiIjIbzDwISIiIr/BwKeRhISE4JFHHkFISEhzX0qz4T2w433gPQB4DwDeAwnvQ/PeAxY3ExERkd9gxoeIiIj8BgMfIiIi8hsMfIiIiMhvMPAhIiIiv8HAxwOvvvoqBg0aJDdcSk9Px0cffSQ//8cff2DWrFmIiopChw4dMHXqVPz++++qcxw5cgQTJkxAWFgYYmJiMHfuXNTW1jb1R/GZp556CoIgIDs7W37MH+7Do48+CkEQVL/69u0rP+8P9wAAfvvtN9x0002IiopCaGgoBg4ciO+//15+XhRFLFy4ELGxsQgNDUVGRgYOHDigOkd5eTluvPFGREREIDIyEllZWTh16lRTfxSvJCYmOv05EAQBs2bNAuAffw7q6urw8MMPIykpCaGhoUhOTsbjjz+umpfU1v8cAPYxCdnZ2ejRowdCQ0MxbNgwfPfdd/LzbfEefPnll5g0aRLi4uIgCAI++OAD1fO++sx79uzB5Zdfjnbt2iEhIQHPPPNMwy5cJMM2btwofvjhh+Ivv/wi7t+/X/zb3/4mBgUFiQUFBaIoiuJdd90lJiQkiJ9++qn4/fffi5deeqk4bNgw+fW1tbXigAEDxIyMDHHXrl3i//73PzE6Olp86KGHmusjNci3334rJiYmioMGDRLvvfde+XF/uA+PPPKI2L9/f7G0tFT+deLECfl5f7gH5eXlYo8ePcQZM2aI+fn54q+//ip+8skn4sGDB+VjnnrqKdFsNosffPCB+MMPP4iTJ08Wk5KSxLNnz8rHjB8/Xhw8eLD4zTffiF999ZXYq1cv8frrr2+Oj+Sx48ePq/4M5OTkiADEbdu2iaLoH38OnnjiCTEqKkrcvHmzWFhYKP773/8WO3ToIC5btkw+pq3/ORBFUZw2bZrYr18/8YsvvhAPHDggPvLII2JERIRYXFwsimLbvAf/+9//xAULFogbNmwQAYj/+c9/VM/74jNbrVaxS5cu4o033igWFBSI//rXv8TQ0FDx9ddf9/q6Gfg0UMeOHcU333xTrKioEIOCgsR///vf8nP79u0TAYh5eXmiKNr/kJhMJvHYsWPyMa+++qoYEREhnjt3rsmvvSGqqqrE3r17izk5OeLIkSPlwMdf7sMjjzwiDh48WPM5f7kH8+fPFy+77DLd5202m9i1a1fx2WeflR+rqKgQQ0JCxH/961+iKIri3r17RQDid999Jx/z0UcfiYIgiL/99lvjXXwjuffee8Xk5GTRZrP5zZ+DCRMmiLfddpvqsSlTpog33nijKIr+8efgzJkzYkBAgLh582bV4ykpKeKCBQv84h44Bj6++syvvPKK2LFjR9V/D/Pnzxf79Onj9bVyqctLdXV1eP/993H69Gmkp6djx44dqKmpQUZGhnxM37590b17d+Tl5QEA8vLyMHDgQHTp0kU+Zty4caisrMRPP/3U5J+hIWbNmoUJEyaoPi8Av7oPBw4cQFxcHHr27Ikbb7wRR44cAeA/92Djxo0YOnQorrvuOsTExGDIkCF444035OcLCwtx7Ngx1X0wm81IS0tT3YfIyEgMHTpUPiYjIwMmkwn5+flN92F8oLq6Gu+++y5uu+02CILgN38Ohg0bhk8//RS//PILAOCHH37A119/jauuugqAf/w5qK2tRV1dHdq1a6d6PDQ0FF9//bVf3ANHvvrMeXl5GDFiBIKDg+Vjxo0bh/379+PkyZNeXRuHlHroxx9/RHp6Ov744w906NAB//nPf9CvXz/s3r0bwcHBiIyMVB3fpUsXHDt2DABw7Ngx1f/gpOel51qL999/Hzt37lStX0uOHTvmF/chLS0NK1euRJ8+fVBaWopFixbh8ssvR0FBgd/cg19//RWvvvoq7r//fvztb3/Dd999h3vuuQfBwcGYPn26/Dm0PqfyPsTExKieDwwMRKdOnVrNfZB88MEHqKiowIwZMwD4z38LDz74ICorK9G3b18EBASgrq4OTzzxBG688UYA8Is/B+Hh4UhPT8fjjz+OCy+8EF26dMG//vUv5OXloVevXn5xDxz56jMfO3YMSUlJTueQnuvYsaPH18bAx0N9+vTB7t27YbVasW7dOkyfPh1ffPFFc19Wkzl69Cjuvfde5OTkOP3txp9If5sFgEGDBiEtLQ09evTA2rVrERoa2oxX1nRsNhuGDh2KJ598EgAwZMgQFBQU4LXXXsP06dOb+eqa3ooVK3DVVVchLi6uuS+lSa1duxbvvfceVq9ejf79+2P37t3Izs5GXFycX/05eOedd3DbbbehW7duCAgIQEpKCq6//nrs2LGjuS+NHHCpy0PBwcHo1asXUlNTsXjxYgwePBjLli1D165dUV1djYqKCtXxv//+O7p27QoA6Nq1q9OODuln6ZiWbseOHTh+/DhSUlIQGBiIwMBAfPHFF3jxxRcRGBiILl26+MV9cBQZGYkLLrgABw8e9Js/C7GxsejXr5/qsQsvvFBe8pM+h9bnVN6H48ePq56vra1FeXl5q7kPAHD48GFs3boVt99+u/yYv/w5mDt3Lh588EH8+c9/xsCBA3HzzTfjvvvuw+LFiwH4z5+D5ORkfPHFFzh16hSOHj2Kb7/9FjU1NejZs6ff3AMlX33mxvhvhIFPA9lsNpw7dw6pqakICgrCp59+Kj+3f/9+HDlyBOnp6QCA9PR0/Pjjj6p/0Tk5OYiIiHD6AmmprrzySvz444/YvXu3/Gvo0KG48cYb5d/7w31wdOrUKRw6dAixsbF+82dh+PDh2L9/v+qxX375BT169AAAJCUloWvXrqr7UFlZifz8fNV9qKioUP2t+LPPPoPNZkNaWloTfArfePvttxETE4MJEybIj/nLn4MzZ87AZFJ/lQQEBMBmswHwrz8HANC+fXvExsbi5MmT+OSTT/CnP/3J7+4B4Lt/7+np6fjyyy9RU1MjH5OTk4M+ffp4tcwFgNvZPfHggw+KX3zxhVhYWCju2bNHfPDBB0VBEMQtW7aIomjfutq9e3fxs88+E7///nsxPT1dTE9Pl18vbV0dO3asuHv3bvHjjz8WO3fu3Kq2rmpR7uoSRf+4D3PmzBE///xzsbCwUNy+fbuYkZEhRkdHi8ePHxdF0T/uwbfffisGBgaKTzzxhHjgwAHxvffeE8PCwsR3331XPuapp54SIyMjxf/+97/inj17xD/96U+a21mHDBki5ufni19//bXYu3fvFr2F11FdXZ3YvXt3cf78+U7P+cOfg+nTp4vdunWTt7Nv2LBBjI6OFufNmycf4w9/Dj7++GPxo48+En/99Vdxy5Yt4uDBg8W0tDSxurpaFMW2eQ+qqqrEXbt2ibt27RIBiEuWLBF37dolHj58WBRF33zmiooKsUuXLuLNN98sFhQUiO+//74YFhbG7exN5bbbbhN79OghBgcHi507dxavvPJKOegRRVE8e/as+Ne//lXs2LGjGBYWJv6///f/xNLSUtU5ioqKxKuuukoMDQ0Vo6OjxTlz5og1NTVN/VF8yjHw8Yf7kJmZKcbGxorBwcFit27dxMzMTFX/Gn+4B6Ioips2bRIHDBgghoSEiH379hWXL1+uet5ms4kPP/yw2KVLFzEkJES88sorxf3796uOsVgs4vXXXy926NBBjIiIEG+99VaxqqqqKT9Gg3zyySciAKfPJYr+8eegsrJSvPfee8Xu3buL7dq1E3v27CkuWLBAtf3YH/4crFmzRuzZs6cYHBwsdu3aVZw1a5ZYUVEhP98W78G2bdtEAE6/pk+fLoqi7z7zDz/8IF522WViSEiI2K1bN/Gpp55q0HULoqhor0lERETUhrHGh4iIiPwGAx8iIiLyGwx8iIiIyG8w8CEiIiK/wcCHiIiI/AYDHyIiIvIbDHyIiIjIbzDwISIiIr/BwIeIGmzUqFHIzs5u7stodI8++iguuuii5r4MImoABj5E5Peqq6ub9P1EUURtbW2TvicR2THwIaIGmTFjBr744gssW7YMgiBAEAQUFRWhoKAAV111FTp06IAuXbrg5ptvRllZmfy6UaNG4e6770Z2djY6duyILl264I033sDp06dx6623Ijw8HL169cJHH30kv+bzzz+HIAj48MMPMWjQILRr1w6XXnopCgoKVNf09ddf4/LLL0doaCgSEhJwzz334PTp0/LziYmJePzxx3HLLbcgIiICM2fOBADMnz8fF1xwAcLCwtCzZ088/PDD8lTolStXYtGiRfjhhx/kz7ly5UoUFRVBEATs3r1bPn9FRQUEQcDnn3+uuu6PPvoIqampCAkJwddffw2bzYbFixcjKSkJoaGhGDx4MNatW+frf0VEpMDAh4gaZNmyZUhPT8cdd9yB0tJSlJaWIjw8HFdccQWGDBmC77//Hh9//DF+//13TJs2TfXaVatWITo6Gt9++y3uvvtu/OUvf8F1112HYcOGYefOnRg7dixuvvlmnDlzRvW6uXPn4vnnn8d3332Hzp07Y9KkSXKAcujQIYwfPx5Tp07Fnj17sGbNGnz99deYPXu26hzPPfccBg8ejF27duHhhx8GAISHh2PlypXYu3cvli1bhjfeeAMvvPACACAzMxNz5sxB//795c+ZmZnp0b168MEH8dRTT2Hfvn0YNGgQFi9ejH/+85947bXX8NNPP+G+++7DTTfdhC+++MKj8xKRBxo04pSISBTFkSNHivfee6/88+OPPy6OHTtWdczRo0dVU8xHjhwpXnbZZfLztbW1Yvv27cWbb75Zfqy0tFQEIObl5YmiWD8N+v3335ePsVgsYmhoqLhmzRpRFEUxKytLnDlzpuq9v/rqK9FkMolnz54VRVEUe/ToIV5zzTVuP9ezzz4rpqamyj8/8sgj4uDBg1XHFBYWigDEXbt2yY+dPHlSBCBu27ZNdd0ffPCBfMwff/whhoWFibm5uarzZWVliddff73bayMi7wQ2Z9BFRG3TDz/8gG3btqFDhw5Ozx06dAgXXHABAGDQoEHy4wEBAYiKisLAgQPlx7p06QIAOH78uOoc6enp8u87deqEPn36YN++ffJ779mzB++99558jCiKsNlsKCwsxIUXXggAGDp0qNO1rVmzBi+++CIOHTqEU6dOoba2FhERER5/fj3K9zx48CDOnDmDMWPGqI6prq7GkCFDfPaeRKTGwIeIfO7UqVOYNGkSnn76aafnYmNj5d8HBQWpnhMEQfWYIAgAAJvN5tF733nnnbjnnnucnuvevbv8+/bt26uey8vLw4033ohFixZh3LhxMJvNeP/99/H888+7fD+TyV4xIIqi/Ji07OZI+Z6nTp0CAHz44Yfo1q2b6riQkBCX70lE3mPgQ0QNFhwcjLq6OvnnlJQUrF+/HomJiQgM9P3/Zr755hs5iDl58iR++eUXOZOTkpKCvXv3olevXh6dMzc3Fz169MCCBQvkxw4fPqw6xvFzAkDnzp0BAKWlpXKmRlnorKdfv34ICQnBkSNHMHLkSI+ulYi8x+JmImqwxMRE5Ofno6ioCGVlZZg1axbKy8tx/fXX47vvvsOhQ4fwySef4NZbb3UKHLzx2GOP4dNPP0VBQQFmzJiB6OhoXHPNNQDsO7Nyc3Mxe/Zs7N69GwcOHMB///tfp+JmR71798aRI0fw/vvv49ChQ3jxxRfxn//8x+lzFhYWYvfu3SgrK8O5c+cQGhqKSy+9VC5a/uKLL/B///d/bj9DeHg4HnjgAdx3331YtWoVDh06hJ07d+Kll17CqlWrvL43ROQaAx8iarAHHngAAQEB6NevHzp37ozq6mps374ddXV1GDt2LAYOHIjs7GxERkbKS0MN8dRTT+Hee+9Famoqjh07hk2bNiE4OBiAvW7oiy++wC+//ILLL78cQ4YMwcKFCxEXF+fynJMnT8Z9992H2bNn46KLLkJubq6820sydepUjB8/HqNHj0bnzp3xr3/9CwDw1ltvoba2FqmpqcjOzsbf//53Q5/j8ccfx8MPP4zFixfjwgsvxPjx4/Hhhx8iKSnJi7tCREYIonJhmoioBfv8888xevRonDx5EpGRkc19OUTUCjHjQ0RERH6DgQ8RERH5DS51ERERkd9gxoeIiIj8BgMfIiIi8hsMfIiIiMhvMPAhIiIiv8HAh4iIiPwGAx8iIiLyGwx8iIiIyG8w8CEiIiK/wcCHiIiI/Mb/ByivaIjw4Gq5AAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "surrogate_scatter2D(keras_surrogate, data_training)\n", + "surrogate_parity(keras_surrogate, data_training)\n", + "surrogate_residual(keras_surrogate, data_training)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation\n", + "\n", + "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4/4 [==============================] - 0s 5ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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cAABQibKmyIdW71GJYfi9KbJJkya67777dO+996q0tFRXX321XC6XPvvsM0VHR7sThuokJSXpwIEDysrKUuvWrdWkSRNFRkb6Jd5LNWrUSNOmTdP999+v5s2bq02bNnr66ad1+vRpTZo0KSAxlCG5AQCgCoFuinz88ccVExOjzMxM/etf/1LTpk115ZVX6qGHHqpV88+tt96q1atXa8iQITpx4oSWLVum8ePH+zXmiy1cuFClpaW64447dPLkSfXt21cff/yxmjVrFrAYJMlhGIZR8272UVBQIKfTKZfLpejoaLPDAQD42JkzZ3TgwAG1a9dODRo0MDsceKC6v50n129GSwEAAFshuQEAIMRMnTq13HDzi29Tp041O7w6o88NAAAhZsGCBbrvvvsqvc8OXTZIbgAACDGxsbGKjY01Owy/oVkKAADYCskNAMCWrDhzLqrnqwHcNEsBAGwlIiJCYWFhOnz4sGJiYhQREeFepgDWZRiGjh07VukyD54iuQEA2EpYWJjatWunvLw8HT582Oxw4AGHw6HWrVsrPDy8Ts9DcgMAsJ2IiAi1adNG58+fN22tJXiufv36dU5sJJIbAIBNlTVv1LWJA8GHDsUAAMBWSG4AAICtkNwAAABbIbkBAAC2QnIDAABsheQGAADYCskNAACwFZIbAABgKyQ3AADAVkxNbhYvXqyePXsqOjpa0dHRSklJ0YcffljtY1atWqWuXbuqQYMG6tGjh9auXRugaAEAQDAwNblp3bq1Fi5cqF27dmnnzp269tprdfPNN2vv3r2V7r9161aNHj1akyZN0hdffKGMjAxlZGRoz549AY4cAABYlcMwDMPsIC7WvHlzPfPMM5o0aVKF+0aNGqXCwkK9//777m0DBgxQr169tGTJklo9f0FBgZxOp1wul6Kjo30WNwAA8B9Prt+W6XNTUlKit956S4WFhUpJSal0n23btmnYsGHltqWnp2vbtm1VPm9xcbEKCgrK3QAAgH2Zntx8/fXXaty4sSIjIzV16lStWbNG3bp1q3Tf/Px8xcXFldsWFxen/Pz8Kp8/MzNTTqfTfUtMTPRp/AAAwFpMT266dOmirKwsbd++XdOmTdO4ceP0z3/+02fPP3fuXLlcLvctNzfXZ88NAACsp57ZAURERKhjx46SpD59+mjHjh167rnn9NJLL1XYNz4+XkeOHCm37ciRI4qPj6/y+SMjIxUZGenboAEAgGWZXrm5VGlpqYqLiyu9LyUlRevXry+3bd26dVX20QEAAKHH1MrN3LlzNWLECLVp00YnT57UihUrtGnTJn388ceSpLFjx6pVq1bKzMyUJM2cOVODBw/Ws88+qxtuuEFvvfWWdu7cqT/+8Y9mvg0AAGAhpiY3R48e1dixY5WXlyen06mePXvq448/1nXXXSdJysnJUVjY/4pLAwcO1IoVK/Twww/roYceUqdOnfTOO++oe/fuZr0FAABgMZab58bfmOcGAIDgE5Tz3AAAAPgCyQ0AALAVkhsAAGArJDcAAMBWSG4AAICtkNwAAABbIbkBAAC2QnIDAABsheQGAADYCskNAACVyHMVaWv2ceW5iswOBR4ydW0pAACsaOWOHM1d/bVKDSnMIWWO7KFR/dqYHRZqicoNAAAXyXMVuRMbSSo1pIdW76GCE0RIbgAAuMiB44XuxKZMiWHo4PHT5gQEj5HcAABwkXYtGynMUX5buMOhpJYNzQkIHiO5AQDgIgnOKGWO7KFwx4UMJ9zh0JMjuyvBGWVyZNZi5Q7XdCgGAJvJcxXpwPFCtWvZiAuyl0b1a6NBnWN08PhpJbVsyHG8hNU7XJPcAICNWP2iE0wSnFEkNZWoqsP1oM4xljleNEsBgE0wygeBEAwdrkluAMAmguGig+AXDB2uSW4AwCaC4aKD4BcMHa7pcwMANlF20Xlo9R6VGIYlLzqwB6t3uCa5AQAbsfpFB/Zh5Q7XJDcAYDNWvugAgUCfGwAAYCskNwAAwFZIbgAAgK2Q3AAAAFshuQEAALZCcgMAAGyF5AYAANgKyQ0AIGTluYq0Nfs4i4vaDJP4AQBC0sodOe5V1MMcUubIHhrVr43ZYcEHqNwAAEJOnqvIndhIUqkhPbR6DxUcmyC5AQCEnAPHC92JTZkSw9DB46fNCQg+RXIDAAg57Vo2Upij/LZwh0NJLRuaExB8iuQGABByEpxRyhzZQ+GOCxlOuMOhJ0d2Z8FRm6BDMQAgJI3q10aDOsfo4PHTSmrZkMTGRkhuAAAhK8EZRVJjQzRLAQAAWyG5AQAAtmJqcpOZmal+/fqpSZMmio2NVUZGhvbt21ftY5YvXy6Hw1Hu1qBBgwBFDAAArM7U5Gbz5s2aPn26Pv/8c61bt07nzp3T9ddfr8LCwmofFx0drby8PPft0KFDAYoYAABYnakdij/66KNy/1++fLliY2O1a9cuDRo0qMrHORwOxcfH+zs8AAAQhCzV58blckmSmjdvXu1+p06dUtu2bZWYmKibb75Ze/furXLf4uJiFRQUlLsBAAD7skxyU1paqlmzZik1NVXdu3evcr8uXbrolVde0bvvvqvXX39dpaWlGjhwoL7//vtK98/MzJTT6XTfEhMT/fUWAACABTgMwzBq3s3/pk2bpg8//FBbtmxR69ata/24c+fO6fLLL9fo0aP1+OOPV7i/uLhYxcXF7v8XFBQoMTFRLpdL0dHRPokdAAD4V0FBgZxOZ62u35aYxG/GjBl6//339emnn3qU2EhS/fr11bt3b+3fv7/S+yMjIxUZGemLMAEAQBAwtVnKMAzNmDFDa9as0YYNG9SuXTuPn6OkpERff/21EhIS/BAhAAAINqZWbqZPn64VK1bo3XffVZMmTZSfny9Jcjqdioq6MB322LFj1apVK2VmZkqSFixYoAEDBqhjx446ceKEnnnmGR06dEiTJ0827X0AAADrMDW5Wbx4sSQpLS2t3PZly5Zp/PjxkqScnByFhf2vwPSf//xHU6ZMUX5+vpo1a6Y+ffpo69at6tatW6DCBgAAFmaZDsWB4kmHJAAAYA2eXL8tMxQcAADAF0huAACArZDcAAAAWyG5AQAAtkJyAwAAbIXkBgAA2ArJDQAAsBWSGwAAYCskNwAAwFZIboD/ynMVaWv2ceW5iswOBQBQB6auLQVYxcodOZq7+muVGlKYQ8oc2UOj+rUxOywAgBeo3CDk5bmK3ImNJJUa0kOr91DBAYAgRXKDkHfgeKE7sSlTYhg6ePy0OQEBAOqE5AYhr13LRgpzlN8W7nAoqWVDcwICANQJyQ1CXoIzSpkjeyjccSHDCXc49OTI7kpwRpkcGQDAG3QoBiSN6tdGgzrH6ODx00pq2ZDEBgCCGMkN8F8JziiSGgCwAZqlAAAIUszPVTkqNwAABCHm56oalRsAsAl+xYcO5ueqHpUbALABfsWHlurm56LvIJUbAAh6/IoPPczPVT2SmwCjbAwEj2D5vDLLduhhfq7q0SwVQJSNgeARTJ/Xsl/xFyc4/Iq3P+bnqhqVmwChbAwEj2D7vPIrPnQlOKOU0qEFf+tLULkJEDp/AbWX5yrSgeOFateykSmfj2D8vPIrHvgfkpsAoWwM1I4VmoOC9fPKLNvABTRLBQhlY6BmVmkO4vMKBDcqNwFE2RionpWag/i8AsGL5CbAKBsDVbNacxCfVyA40SwFwDJoDgLgC1RuAFgKzUEA6orkBoDl0BwEoC5olgIAALZCcgMAAGyF5AYAANSZlRaapc8NAACoEyvMLH4xKjcAAMBrVplZ/GIkN/A5K5UmgWDH5wlWV93M4mahWQo+ZbXSJIKH2SuBWxGfJwQDq80sLplcucnMzFS/fv3UpEkTxcbGKiMjQ/v27avxcatWrVLXrl3VoEED9ejRQ2vXrg1AtKiJFUuTCA4rd+QodeEGjVm6XakLN2jljhyzQzIdnycECyvOLG5qcrN582ZNnz5dn3/+udatW6dz587p+uuvV2FhYZWP2bp1q0aPHq1Jkybpiy++UEZGhjIyMrRnz54ARo7KWLE0CevjIl45Pk8IJqP6tdGWOUP05pQB2jJniOkVRlObpT766KNy/1++fLliY2O1a9cuDRo0qNLHPPfccxo+fLjuv/9+SdLjjz+udevW6YUXXtCSJUv8HjOqZsXSJKzPSiuBWwmfJ/uzW1OslWYWt1SHYpfLJUlq3rx5lfts27ZNw4YNK7ctPT1d27Zt82tsduHPzolWLE3C+sou4hfjIs7nye5oivUvy3QoLi0t1axZs5Samqru3btXuV9+fr7i4uLKbYuLi1N+fn6l+xcXF6u4uNj9/4KCAt8EHIQC0TmRRQ/hqbKL+EOr96jEMLiIX4TPkz1V1RQ7qHOMV39ju1WAfMEyyc306dO1Z88ebdmyxafPm5mZqfnz5/v0OYORrz9M1bFSaRLBgYt41fg82Y8vm2IZUVc5SzRLzZgxQ++//742btyo1q1bV7tvfHy8jhw5Um7bkSNHFB8fX+n+c+fOlcvlct9yc3N9FncwoXMirC7BGaWUDi24kMP2fNUUS2f8qpma3BiGoRkzZmjNmjXasGGD2rVrV+NjUlJStH79+nLb1q1bp5SUlEr3j4yMVHR0dLlbKKJfAwBYg6/6U/GjtWqmNktNnz5dK1as0LvvvqsmTZq4+804nU5FRV34I48dO1atWrVSZmamJGnmzJkaPHiwnn32Wd1www166623tHPnTv3xj3807X1cyortn/RrAIC689X3uy+aYhlRVzWHYRhGzbv56cUdjkq3L1u2TOPHj5ckpaWlKSkpScuXL3ffv2rVKj388MM6ePCgOnXqpKefflo/+clPavWaBQUFcjqdcrlcfqniWL39M89VRL8GAPCCFb/fV+7IqfCj1eyY/MWT67epyY0Z/Jnc5LmKlLpwQ4UsesucISQSABDErPz9Hio/Wj25fte6WcqTIdSh2q+FycgAwJ6s/P3OiLqKap3cNG3atMpmpDKGYcjhcKikpKTOgQUj2j8BwJ74fg8utU5uNm7c6M84bIFOuwBgT3y/Bxf63PhBqLR/AkCo4fvdPH7pc3OpEydO6E9/+pO++eYbSdIVV1yhiRMnyul0evuUtmHH9k8rDm8HgECz4/e7HXlVudm5c6fS09MVFRWl/v37S5J27NihoqIiffLJJ7ryyit9HqivBKJyYzdWHP4IAAgtfh8Kfs0116hjx45aunSp6tW7UPw5f/68Jk+erH/961/69NNPvYs8AEhuPGPl4Y8AgNDh92apnTt3lktsJKlevXp64IEH1LdvX2+eEhZl5eGPAABUxqu1paKjo5WTk1Nhe25urpo0aVLnoGCePFeRtmYfdy+8xppUAIBg41VyM2rUKE2aNEkrV65Ubm6ucnNz9dZbb2ny5MkaPXq0r2NEgKzckaPUhRs0Zul2pS7coJU7cny2wBsAAIHiVbPUb37zGzkcDo0dO1bnz5+XJNWvX1/Tpk3TwoULfRogAiPPVeTuNCxdmKjqodV7NKhzjE8WeIPnGKEGAN7xKrmJiIjQc889p8zMTGVnZ0uSOnTooIYNaaoIVjX1rWH4Y2AxQg0AvOf1PDeS1LBhQ/Xo0cNXscBETC1uHdVV0UgwAaBmXiU3Z86c0fPPP6+NGzfq6NGjKi0tLXf/7t27fRIcAoepxa2DEWrwFE2YQHleJTeTJk3SJ598ottuu039+/evcUFNBAf61lhDIKtoXBSDH02YNeM8Dz1eTeLndDq1du1apaam+iMmv2ISPwSDlTtyKlTRfH3B4qIY/Jhks2ac5/bh90n8WrVqxXw2gB/5u4pGvx57oAmzepznocureW6effZZPfjggzp06JCv4wHwXwnOKKV0aOGXL+HqLooIHoGeZPPSST6tzu7nebD9PQLJq8pN3759debMGbVv314NGzZU/fr1y93/73//2yfBAfAPRsfZQyAHAgRj846dz/Ng/HsEklfJzejRo/XDDz/oySefVFxcHB2KgSDD6Dj78HUTZmWdb4O1eccu5/mlf5Ng/XsEklfJzdatW7Vt2zYlJyf7Oh4AAcLoOPvw1SSbVVUDgrlvT7Cf55X9TRKbNwzav0egeNXnpmvXrioqoo0PCHb+7NeD4FJVNSDPVRT0C+hWdp4HQ3+Vqv4mjSLCg/rvEQheJTcLFy7UL3/5S23atEk//vijCgoKyt0AAMGlpuqMnRbQrWyRYCuq6m9y+myprf4e/uBVs9Tw4cMlSUOHDi233TAMORwOlZSU1D0yAEDA1NT5Ntibd8oEU3+V6v4mKR1a2OLv4S9eJTcbN270dRwAABPVpvOtHRbQDab+QzX9Tezw9/AXr5KbwYMH12q/u+66SwsWLFDLli29eRkAQADZpTpTnWAbHh4KfxN/8KrPTW29/vrr9MEBgCBi907mwdh/yO5/E3/wqnJTW14sWwUAgF9RDbE/vyY3AABYkZn9VVil3P9IbgAACBCWTQgMv/a5AQAAF1Q3USJ8i+QGMEEwzI4KwLfsvkq5lXic3Jw/f14LFizQ999/X+O+P//5zxUdHe1VYIBdBcvsqAB8K9iXsQgmHic39erV0zPPPKPz58/XuO/ixYuZ4wa4CGVpIHQF4zD0YOVVh+Jrr71WmzdvVlJSko/DAeytprI0IygAe2MYemB4ldyMGDFCc+bM0ddff60+ffqoUaNG5e6/6aabfBIcYDdVzY761fcn9H8vf84ICiAEsGyC/zkML2baCwurujXL6gtnFhQUyOl0yuVy0R8Ipli5I6fcWjEPjOiipz78tkLCs2XOEL4AAeC/PLl+e1W5KS0t9SowABXL0sG0kB8ABAOvhoK/+uqrKi4urrD97NmzevXVV+scFGB3F68VwwgKAPAtr5KbCRMmyOVyVdh+8uRJTZgwoc5BAaGEERQA4FteNUsZhiGHw1Fh+/fffy+n01nnoIBQwwgKAPAdj5Kb3r17y+FwyOFwaOjQoapX738PLykp0YEDBzR8+PBaP9+nn36qZ555Rrt27VJeXp7WrFmjjIyMKvfftGmThgwZUmF7Xl6e4uPjPXkrgOXUZgQFC+4BQM08Sm7KEo+srCylp6ercePG7vsiIiKUlJSkW2+9tdbPV1hYqOTkZE2cOFEjR46s9eP27dtXrqd0bGxsrR8LBCsW3AOA2vEouXnsscckSUlJSRo1apQaNGhQpxcfMWKERowY4fHjYmNj1bRp0zq9NhBMqprZeFDnGCo4AHAJr/rcjBs3TtKF0VFHjx6tMDS8TRv//prs1auXiouL1b17d82bN0+pqalV7ltcXFxuZFdBQYFfYwP8oarh4rsP/UfNGtFMBQAX8yq5+e677zRx4kRt3bq13Payjsb+msQvISFBS5YsUd++fVVcXKyXX35ZaWlp2r59u6688spKH5OZman58+f7JR6grmrbh6aymY0dDmnGii9kiGYqALiYVzMUp6amql69epozZ44SEhIqjJxKTk72PBCHo8YOxZUZPHiw2rRpo9dee63S+yur3CQmJjJDMXyiLh18Pe1Dc/HMxmEOyTCkiz+8zGoMwM78PkNxVlaWdu3apa5du3oVoC/1799fW7ZsqfL+yMhIRUZGBjAimMGMUUR16eDrTR+aQZ1jtOj2ZIU5HCo1DN39Zla5+y+e1ZhRVQBCmVfJTbdu3XT8+HFfx+KVrKwsJSQkmB0GTGTGKKK6dvD1dMmFS9/jg8O7VroAZ1LLhoyqAhDyvJqh+KmnntIDDzygTZs26ccff1RBQUG5W22dOnVKWVlZysrKkiQdOHBAWVlZysnJkSTNnTtXY8eOde+/aNEivfvuu9q/f7/27NmjWbNmacOGDZo+fbo3bwM2UFWSkecq8uvrVpec1IYnSy5U9h6f/mifHhzRtcKsxpJMOR4AYCVeVW6GDRsmSbr22mvL9bfxtEPxzp07y03KN3v2bEkXRmMtX75ceXl57kRHujA665e//KV++OEHNWzYUD179tTf//73Sif2Q2jw9aKTdeng68l6UGVLLly8OnhVSy5U9R57tmqqLXOGlJvVeGv2cRbhBBDyvEpuNm7c6JMXT0tLU3X9mZcvX17u/w888IAeeOABn7w27KGuScbFPGnO8SQ5qUptl1yo7j1eOquxL48HAAQrr5qlBg8erLCwMC1dulRz5sxRx44dNXjwYOXk5Cg8PNzXMQJV8tWik940b43q10Zb5gzRm1MGaMucIV71a7l4dfDq9qnte2QRTgDwsnLz17/+VXfccYf+7//+T1988YV7qLXL5dKTTz6ptWvX+jRIoDq+WHTS2+at2qwH5QuevEcW4QQQ6ryq3DzxxBNasmSJli5dqvr167u3p6amavfu3T4LDqit2lRAquNJB1+zePIe63o8ACCYeZXc7Nu3T4MGDaqw3el06sSJE3WNCQg4mnMAc+S5irQ1+zgj+uBTXjVLxcfHa//+/UpKSiq3fcuWLWrfvr0v4gICjuYcILCYkwn+4lXlZsqUKZo5c6a2b98uh8Ohw4cP64033tB9992nadOm+TpG+Am/mCqiOQcIDLPmqEJo8KpyM2fOHJWWlmro0KE6ffq0Bg0apMjISN133326++67fR0j/IBfTADMXKbD13NUWQnLn5jPq4Uzy5w9e1b79+/XqVOn1K1bNzVu3NiXsfmFJwtv2VWeq0ipCzdUmAuFRRfhK3y5W5/ZP3Ds+j1k9nG1M0+u3141S5WJiIhQt27d1L9//6BIbHBBXZcOAKqzckeOUhdu0Jil25W6cINW7sip+UEIKCs0CdmxE78Vjisu8KpZCsGNWWzhL1/m/kdzVn8t45Iv99ouKIrAsEqTkN068VvluKKOlRsEJzv+YoL5Vu7IUcaLW3VpQzdVQeux0rxOdurEb6XjGuqo3IQou/1igrnKyvGVdeDjy916fLE2GiriuFoHyU0IC9TSAbC/ysrx0oUOlXy5WxM/cPyD42oNJDcA6qyyflxhktbcNVDJic1MiwvV4weOf3BczUefGwB1Vlk/rsxbe5DYADAFlRsAPkE5HpVhziOYgeQGgM9QjsfFmNAOZqFZCkDAsa6Z/TGhHee5majcAAgofs2HhlCf0I7z3FxUbgAEDL/mQ0coT2jHeW4+khsAAcO6ZqEjlGdC5zw3H81SAAKGdc1CS6iOoOM8Nx+VGwABE8q/5kOVndaOqi3Oc/M5DOPSZe7sraCgQE6nUy6XS9HR0WaHA4SkPFdRyP2aR+jhPPctT67fNEsBCDi7zIfDBHWojl3O82BEcgMAXmCoL2Bd9LkBAA8x1BewNpIbAPAQQ30vYAZeWBXNUgDgIYb60iwHa6NyAwAeCvWhvjTLweqo3ACAF0J1gjqJdaNgfSQ3AOClUB3qS7McrI5mKQCwIX929g31ZjlYH5UbAF5hAjvrCkRnX6s1y3E+4mIkNwA8ZoeRMna9GFbV2XdQ5xifv0+rNMvZ4XyEb9EsBcAjdhgps3JHjlIXbtCYpduVunCDVu7IMTsknwm1OXjscD7ajRXmP6JyA8AjwT5SJpCVDTOEWmffYD8f7cYqVTQqNwA8UnbxvFgwXTztVNmo7BdyqHX2Dfbz0U6sVEWjcgPAI2UXz4dW71GJYQTdxdMulY3qfiFbrbOvPwX7+WgnVqqikdwA8FgwXzztcDGsTdOaVTr7BkIwn492YqUfDqY2S3366ae68cYbddlll8nhcOidd96p8TGbNm3SlVdeqcjISHXs2FHLly/3e5wAKkpwRimlQ4ugvJCM6tdGW+YM0ZtTBmjLnCFBN7LGTk1rvhLM56NdWKlJ1NTKTWFhoZKTkzVx4kSNHDmyxv0PHDigG264QVOnTtUbb7yh9evXa/LkyUpISFB6enoAIgZgF8Fc2bDSL2TgYlapojkMwzBq3s3/HA6H1qxZo4yMjCr3efDBB/XBBx9oz5497m233367Tpw4oY8++qhWr1NQUCCn0ymXy6Xo6Oi6hg0AAXHpvDwrd+RUaFoLtgoU4AlPrt9B1edm27ZtGjZsWLlt6enpmjVrVpWPKS4uVnFxsfv/BQUF/goPAPyiqs7DVviFDFhRUA0Fz8/PV1xcXLltcXFxKigoUFFR5UPNMjMz5XQ63bfExMRAhGobVpiMCQhl1Q2vpZ8JULmgSm68MXfuXLlcLvctNzfX7JCChp1ncQWCBZ2HAc8FVXITHx+vI0eOlNt25MgRRUdHKyqq8l8ukZGRio6OLndDzaw0GRPshWqgZ5ikznycs8EnqPrcpKSkaO3ateW2rVu3TikpKSZFZF9WmowJ9mGVqdmDiR3m5bGq2iyeyjkbnExNbk6dOqX9+/e7/3/gwAFlZWWpefPmatOmjebOnasffvhBr776qiRp6tSpeuGFF/TAAw9o4sSJ2rBhg/7yl7/ogw8+MOst2BZDTeFrdl/TyZ/oPOx7tUlaOGeDl6nNUjt37lTv3r3Vu3dvSdLs2bPVu3dvPfroo5KkvLw85eT8r59Hu3bt9MEHH2jdunVKTk7Ws88+q5dffpk5bvzASpMxwR7oO1I3dB72ndo2u3POBi9TKzdpaWmqbpqdymYfTktL0xdffOHHqFCGX4vwJaqBsIraNrtzzgavoOpQjMDj1yJ8hWogrKK2nbQ5Z4OXZWYoDhRmKAbMlecqohpYhdp0cIVveDLDM+esNXhy/Sa5AQALYFRO4JG0BBdPrt80SwGAycycVyqU53Ch2d2+gmqeGwCwI7PmlaJaBLuicgMAJjNjFmJmIYedkdwAgMnMGJXDHC7WE8pNhL5GsxQAWECg55ViDhdroYnQt6jcAIBFBLKDK3O4WAdNhL5H5QYAQhSzkFsDCxX7HskNAISwBGcUF1CT0UToezRLAQBgIpoIfY/KDQAAJqOJ0LdIbhAyWLcHgJXRROg7JDcICQyzBIDQQZ8b2B7DLIHQxKR4oYvKDWyPYZZA6KFaG9qo3MD2zFi3B4B5qNaC5Aa2xzBLWBXNJv7BulmgWQohgWGWsBqaTfyHSfFA5QYhI5Dr9gDVodnEv6jWgsoNAAQYndz9j2ptaCO5ga0wUR+CAc0mgcGkeKGLZinYxsodOUpduEFjlm5X6sINWrkjx+yQvEZHU3uj2QTwL4dhGEbNu9lHQUGBnE6nXC6XoqOjzQ4HPpLnKlLqwg0VfglvmTMk6C4YdDQNHXmuIppNgFry5PpN5Qa2YJehn3Q0DS10cgf8g+QGtmCXifrskqQBgJlIbmALdunDYJckDUB59KMLLEZLwTbsMPSzLEl7aPUelRhG0CZpgcYoOVgZ/egCjw7FqBEXjsCjo2ntceGAldlpsIPZPLl+U7lBtbhwmIP5OWqnqg7YgzrHcPxgCUzYaA763KBKoTxyh/bx4EAHbFgd/ejMQXKDKoXqhcNOkwHaHRcOWJ1dBjsEG5qlUKVQnCKeZo7gQgdsBAM7DHYItr6XJDeoUiheOGgfr8jqX2p2uHDAXIE4x4O5H10w9r0kuUG1Qu3CEYrVquoEy5daMF84YK5gOcfNEqzVbPrcoEahNEU87eP/E8odyhEaOMdrFqx9L6ncAJcItWpVVWiig91xjtcsWKvZVG58iOHD9hFK1aqqzltGIsHuOMdrFqzVbCo3PkK7LcxQ146Q1Z23odihHKGFc7x2grGabYnlF1588UU988wzys/PV3Jysp5//nn179+/0n2XL1+uCRMmlNsWGRmpM2fO1Oq1/LH8AtNrwwx1Tahre96yFATsjnM8OHhy/Ta9WWrlypWaPXu2HnvsMe3evVvJyclKT0/X0aNHq3xMdHS08vLy3LdDhw4FMOKKgrXDFYKXLzpC1va8NbuJjuZe+JvZ5zh8z/Tk5re//a2mTJmiCRMmqFu3blqyZIkaNmyoV155pcrHOBwOxcfHu29xcXEBjLgi2m2tzY4XR18k1MFw3jJbNABvmJrcnD17Vrt27dKwYcPc28LCwjRs2DBt27atysedOnVKbdu2VWJiom6++Wbt3bs3EOFWKVg7XIUCu14cfZGYWP28ZZguAG+Z2qH4+PHjKikpqVB5iYuL07ffflvpY7p06aJXXnlFPXv2lMvl0m9+8xsNHDhQe/fuVevWrSvsX1xcrOLiYvf/CwoKfPsm/isYO1zZXbBOPlUbvuoIaeXzlmG6ALwVdKOlUlJSlJKS4v7/wIEDdfnll+ull17S448/XmH/zMxMzZ8/PyCxMUuqtdj94uirxMSq522wzq8B37L68h+wJlObpVq2bKnw8HAdOXKk3PYjR44oPj6+Vs9Rv3599e7dW/v376/0/rlz58rlcrlvubm5dY4bwSEY+pTUlZ07Qlq92Qz+Z9dmZfifqclNRESE+vTpo/Xr17u3lZaWav369eWqM9UpKSnR119/rYSEhErvj4yMVHR0dLkbQgMXx+A3ql8bbZkzRG9OGaAtc4Ywd1QIoc8V6sL0ZqnZs2dr3Lhx6tu3r/r3769FixapsLDQPZfN2LFj1apVK2VmZkqSFixYoAEDBqhjx446ceKEnnnmGR06dEiTJ082823AoqzcpyQQ7FDSt2qzGfzL7s3K8C/Tk5tRo0bp2LFjevTRR5Wfn69evXrpo48+cncyzsnJUVjY/wpM//nPfzRlyhTl5+erWbNm6tOnj7Zu3apu3bqZ9RZgcaF6cWTWbAQz+lyhLiwxQ3Eg+WOGYsBqmDUbdrByR06FEYEk6KHLk+u36ZUb+J8dmibgGUr6sINQb1aG90hubI6midBESR92EarNyqgb05dfgP8w2iB0MVIMQCijcmNjNE2ENkr6AEIVyY2N0TQBSvoAQhHNUjZG0wQAIBRRubE5miYAAKGG5CYE0DQBAAglNEshKOW5irQ1+zgjvwAAFVC5QdBh7h4AQHWo3KBOAl1BYe4eAEBNqNzAa2ZUUJi7BwBQEyo3FmblfiVmVVDK5u65GHP3AAAuRnJjUSt35Ch14QaNWbpdqQs3aOWOHLNDKqe6Coo/MXcPAKAmNEtZUFVVkUGdYyxzETdz9mPm7gEAVIfKjQWZVRXxhNkVlARnlFI6tCCxAQBUQOXGgoJlTSgqKAAAK6JyY0H+ror4sqMyFRQAgNVQubEof1VFmAAPAGB3VG4szNdVESbAAwCEApKbEBIMHZUBAKgrkpsQwgR4AIBQQHITQswevg0AQCDQoTjEMHwbAGB3JDchKMEZRVIDALAtmqUAAICtkNwAAABbIbkBAAC2QnIDAABsheQGgGX5ch00AKGD0VIALIl10AB4i8oNJPELGdbCOmi1x2e3djhOoYXKDfiFbGF5riIdOF6odi0bhdTcRNWtgxZKx6EmfHZrh+MUeqjchDh+IVvXyh05Sl24QWOWblfqwg1auSPH7JD8orJf1KyDVjM+u7XDcQpNJDchjpXCrcnTL+RgLblXlcCxDlrN+OzWDscpNNEsFeLKfiFf/OHnF7L5PGmWCWTJ3ZfNZFUlcIM6xyjBGcU6aDXgs1s7HKfQROUmxPEL2Zpq2ywTyJK7r5vJavOLOsEZpZQOLTgfK8Fnt3Y4TqGJyg34hWxBZV/ID63eoxLDqPILOVAdb2uqsniDX9R1x2e3djhOoYfkBpJYKdyKavOFHKgEwR9JVG0TOFSPz27tcJxCC8kNEECe9lmp6Qs5UAmCv5IoflED8AeSGyBA/NXxNxAJgj+TKH5RA/A1S3QofvHFF5WUlKQGDRroqquu0j/+8Y9q91+1apW6du2qBg0aqEePHlq7dm2AIgW84++Ov4HoeDuqXxttmTNEb04ZoC1zhjAJGgDLMj25WblypWbPnq3HHntMu3fvVnJystLT03X06NFK99+6datGjx6tSZMm6YsvvlBGRoYyMjK0Z8+eAEcO1J5d5tpg9BKAYOAwDMOoeTf/ueqqq9SvXz+98MILkqTS0lIlJibq7rvv1pw5cyrsP2rUKBUWFur99993bxswYIB69eqlJUuW1Ph6BQUFcjqdcrlcio6O9t0bAaqR5ypS6sINFfqsbJkzhEQBAGrBk+u3qZWbs2fPateuXRo2bJh7W1hYmIYNG6Zt27ZV+pht27aV21+S0tPTq9y/uLhYBQUF5W5AoDHXBgAEjqkdio8fP66SkhLFxcWV2x4XF6dvv/220sfk5+dXun9+fn6l+2dmZmr+/Pm+CRioA0YGAUBgmN7nxt/mzp0rl8vlvuXm5podEkIYfVYAwP9Mrdy0bNlS4eHhOnLkSLntR44cUXx8fKWPiY+P92j/yMhIRUZG+iZgAABgeaZWbiIiItSnTx+tX7/eva20tFTr169XSkpKpY9JSUkpt78krVu3rsr9AQBAaDF9Er/Zs2dr3Lhx6tu3r/r3769FixapsLBQEyZMkCSNHTtWrVq1UmZmpiRp5syZGjx4sJ599lndcMMNeuutt7Rz50798Y9/NPNtAAAAizA9uRk1apSOHTumRx99VPn5+erVq5c++ugjd6fhnJwchYX9r8A0cOBArVixQg8//LAeeughderUSe+88466d+9u1lsAAAAWYvo8N4HGPDcAAASfoJnnBgAAwNdIbgAAgK2Q3AAAAFshuQEAALZCcgMAAGyF5AYAANiK6fPcBFrZyHdWBwcAIHiUXbdrM4NNyCU3J0+elCQlJiaaHAkAAPDUyZMn5XQ6q90n5CbxKy0t1eHDh9WkSRM5HA6zwwmogoICJSYmKjc3lwkM64hj6RscR9/hWPoGx9F3fH0sDcPQyZMnddlll5VbuaAyIVe5CQsLU+vWrc0Ow1TR0dF8aH2EY+kbHEff4Vj6BsfRd3x5LGuq2JShQzEAALAVkhsAAGArJDchJDIyUo899pgiIyPNDiXocSx9g+PoOxxL3+A4+o6ZxzLkOhQDAAB7o3IDAABsheQGAADYCskNAACwFZIbAABgKyQ3NvTpp5/qxhtv1GWXXSaHw6F33nmn3P2GYejRRx9VQkKCoqKiNGzYMH333XfmBGthNR3H8ePHy+FwlLsNHz7cnGAtLDMzU/369VOTJk0UGxurjIwM7du3r9w+Z86c0fTp09WiRQs1btxYt956q44cOWJSxNZVm2OZlpZW4bycOnWqSRFb1+LFi9WzZ0/3BHMpKSn68MMP3fdzTtZOTcfRrPOR5MaGCgsLlZycrBdffLHS+59++mn9/ve/15IlS7R9+3Y1atRI6enpOnPmTIAjtbaajqMkDR8+XHl5ee7bm2++GcAIg8PmzZs1ffp0ff7551q3bp3OnTun66+/XoWFhe597r33Xv3tb3/TqlWrtHnzZh0+fFgjR440MWprqs2xlKQpU6aUOy+ffvppkyK2rtatW2vhwoXatWuXdu7cqWuvvVY333yz9u7dK4lzsrZqOo6SSeejAVuTZKxZs8b9/9LSUiM+Pt545pln3NtOnDhhREZGGm+++aYJEQaHS4+jYRjGuHHjjJtvvtmUeILZ0aNHDUnG5s2bDcO4cP7Vr1/fWLVqlXufb775xpBkbNu2zawwg8Klx9IwDGPw4MHGzJkzzQsqiDVr1sx4+eWXOSfrqOw4GoZ55yOVmxBz4MAB5efna9iwYe5tTqdTV111lbZt22ZiZMFp06ZNio2NVZcuXTRt2jT9+OOPZodkeS6XS5LUvHlzSdKuXbt07ty5cudk165d1aZNG87JGlx6LMu88cYbatmypbp37665c+fq9OnTZoQXNEpKSvTWW2+psLBQKSkpnJNeuvQ4ljHjfAy5hTNDXX5+viQpLi6u3Pa4uDj3faid4cOHa+TIkWrXrp2ys7P10EMPacSIEdq2bZvCw8PNDs+SSktLNWvWLKWmpqp79+6SLpyTERERatq0abl9OSerV9mxlKQxY8aobdu2uuyyy/TVV1/pwQcf1L59+7R69WoTo7Wmr7/+WikpKTpz5owaN26sNWvWqFu3bsrKyuKc9EBVx1Ey73wkuQG8dPvtt7v/3aNHD/Xs2VMdOnTQpk2bNHToUBMjs67p06drz5492rJli9mhBL2qjuWdd97p/nePHj2UkJCgoUOHKjs7Wx06dAh0mJbWpUsXZWVlyeVy6e2339a4ceO0efNms8MKOlUdx27dupl2PtIsFWLi4+MlqUKv/yNHjrjvg3fat2+vli1bav/+/WaHYkkzZszQ+++/r40bN6p169bu7fHx8Tp79qxOnDhRbn/OyapVdSwrc9VVV0kS52UlIiIi1LFjR/Xp00eZmZlKTk7Wc889xznpoaqOY2UCdT6S3ISYdu3aKT4+XuvXr3dvKygo0Pbt28u1kcJz33//vX788UclJCSYHYqlGIahGTNmaM2aNdqwYYPatWtX7v4+ffqofv365c7Jffv2KScnh3PyEjUdy8pkZWVJEudlLZSWlqq4uJhzso7KjmNlAnU+0ixlQ6dOnSqXFR84cEBZWVlq3ry52rRpo1mzZumJJ55Qp06d1K5dOz3yyCO67LLLlJGRYV7QFlTdcWzevLnmz5+vW2+9VfHx8crOztYDDzygjh07Kj093cSorWf69OlasWKF3n33XTVp0sTdZ8HpdCoqKkpOp1OTJk3S7Nmz1bx5c0VHR+vuu+9WSkqKBgwYYHL01lLTsczOztaKFSv0k5/8RC1atNBXX32le++9V4MGDVLPnj1Njt5a5s6dqxEjRqhNmzY6efKkVqxYoU2bNunjjz/mnPRAdcfR1PMx4OOz4HcbN240JFW4jRs3zjCMC8PBH3nkESMuLs6IjIw0hg4dauzbt8/coC2ouuN4+vRp4/rrrzdiYmKM+vXrG23btjWmTJli5Ofnmx225VR2DCUZy5Ytc+9TVFRk3HXXXUazZs2Mhg0bGrfccouRl5dnXtAWVdOxzMnJMQYNGmQ0b97ciIyMNDp27Gjcf//9hsvlMjdwC5o4caLRtm1bIyIiwoiJiTGGDh1qfPLJJ+77OSdrp7rjaOb56DAMw/Bv+gQAABA49LkBAAC2QnIDAABsheQGAADYCskNAACwFZIbAABgKyQ3AADAVkhuAACArZDcAAAAWyG5AQAAtkJyA8BSzp49a3YIFVgxJgBVI7kB4FdpaWmaMWOGZsyYIafTqZYtW+qRRx5R2covSUlJevzxxzV27FhFR0frzjvvlCRt2bJF11xzjaKiopSYmKh77rlHhYWF7uf9wx/+oE6dOqlBgwaKi4vTbbfd5r7v7bffVo8ePRQVFaUWLVpo2LBh7sempaVp1qxZ5WLMyMjQ+PHj3f/3NiYA1kByA8Dv/vznP6tevXr6xz/+oeeee06//e1v9fLLL7vv/81vfqPk5GR98cUXeuSRR5Sdna3hw4fr1ltv1VdffaWVK1dqy5YtmjFjhiRp586duueee7RgwQLt27dPH330kQYNGiRJysvL0+jRozVx4kR988032rRpk0aOHClPl9HzNCYA1sHCmQD8Ki0tTUePHtXevXvlcDgkSXPmzNF7772nf/7zn0pKSlLv3r21Zs0a92MmT56s8PBwvfTSS+5tW7Zs0eDBg1VYWKi1a9dqwoQJ+v7779WkSZNyr7d792716dNHBw8eVNu2bSuNp1evXlq0aJF7W0ZGhpo2barly5dLklcxNWjQoE7HCYDvULkB4HcDBgxwJzaSlJKSou+++04lJSWSpL59+5bb/8svv9Ty5cvVuHFj9y09PV2lpaU6cOCArrvuOrVt21bt27fXHXfcoTfeeEOnT5+WJCUnJ2vo0KHq0aOHfvrTn2rp0qX6z3/+43HMnsYEwDpIbgCYrlGjRuX+f+rUKf3iF79QVlaW+/bll1/qu+++U4cOHdSkSRPt3r1bb775phISEvToo48qOTlZJ06cUHh4uNatW6cPP/xQ3bp10/PPP68uXbq4E5CwsLAKTVTnzp2rc0wArIPkBoDfbd++vdz/P//8c3Xq1Enh4eGV7n/llVfqn//8pzp27FjhFhERIUmqV6+ehg0bpqefflpfffWVDh48qA0bNkiSHA6HUlNTNX/+fH3xxReKiIhwNzHFxMQoLy/P/VolJSXas2dPje+hNjEBsAaSGwB+l5OTo9mzZ2vfvn1688039fzzz2vmzJlV7v/ggw9q69atmjFjhrKysvTdd9/p3XffdXfeff/99/X73/9eWVlZOnTokF599VWVlpaqS5cu2r59u5588knt3LlTOTk5Wr16tY4dO6bLL79cknTttdfqgw8+0AcffKBvv/1W06ZN04kTJ2p8DzXFBMA66pkdAAD7Gzt2rIqKitS/f3+Fh4dr5syZ7uHVlenZs6c2b96sX/3qV7rmmmtkGIY6dOigUaNGSZKaNm2q1atXa968eTpz5ow6deqkN998U1dccYW++eYbffrpp1q0aJEKCgrUtm1bPfvssxoxYoQkaeLEifryyy81duxY1atXT/fee6+GDBlS43uoKSYA1sFoKQB+VdnoJADwJ5qlAACArZDcAAAAW6FZCgAA2AqVGwAAYCskNwAAwFZIbgAAgK2Q3AAAAFshuQEAALZCcgMAAGyF5AYAANgKyQ0AALAVkhsAAGAr/x+FoOgGs8heLQAAAABJRU5ErkJggg==", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(keras_surrogate, data_validation)\n", + "surrogate_parity(keras_surrogate, data_validation)\n", + "surrogate_residual(keras_surrogate, data_validation)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_keras_surrogate_embedding_usr.ipynb](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb_usr.ipynb) file." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb new file mode 100644 index 00000000..29b68cbd --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_keras_surrogate.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Changes the divide behavior to not do integer division\n", + "from __future__ import division\n", + "\n", + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, Param, \\\n", + " Reals, Set, value, Var, NonNegativeReals, units\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component)\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occuring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + " def build(self):\n", + " '''\n", + " Callable method for Block construction.\n", + " '''\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties({\n", + " 'flow_mol': {'method': None, 'units': 'kmol/s'},\n", + " 'pressure': {'method': None, 'units': 'MPa'},\n", + " 'temperature': {'method': None, 'units': 'K'},\n", + " 'enth_mol': {'method': None, 'units': 'kJ/kmol'},\n", + " 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}})\n", + "\n", + " obj.add_default_units({'time': units.s,\n", + " 'length': units.m,\n", + " 'mass': units.kg,\n", + " 'amount': units.mol,\n", + " 'temperature': units.K})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the SCO2_keras_surrogate_doc.md file) using the keras Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\",\n", + " block_class=_StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self): \n", + " self.flow_mol = Var(domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol/units.s,\n", + " doc='Total molar flowrate [kmol/s]')\n", + " self.pressure = Var(domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc='State pressure [MPa]')\n", + " self.temperature = Var(domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760+273.15),\n", + " units=units.K,\n", + " doc='State temperature [K]')\n", + " self.entr_mol = Var(domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ/units.kmol/units.K,\n", + " doc='Entropy [kJ/kmol/K]')\n", + " self.enth_mol = Var(domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ/units.kmol,\n", + " doc='Enthalpy [kJ/ kmol]')\n", + " \n", + " inputs=[self.pressure,self.temperature]\n", + " outputs=[self.enth_mol,self.entr_mol]\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + " \n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol*self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure}\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error('{} Temperature set below lower bound.'\n", + " .format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error('{} Temperature set above upper bound.'\n", + " .format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error('{} Pressure set below lower bound.'.format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error('{} Pressure set above upper bound.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would interm fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the varible with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + " def initialize(blk, state_args=None, hold_state=False, outlvl=1,\n", + " state_vars_fixed=False, solver='ipopt',\n", + " optarg={'tol': 1e-8}):\n", + "\n", + " '''\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output infomation (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating whcih solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states varaibles are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " relase_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " '''\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag,\n", + " \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\")\n", + " \n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " '''\n", + " Method to relase state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " '''\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags['Fcflag'][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags['Pflag'][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags['Tflag'][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info('{} State Released.'.format(blk.name))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_keras_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.md). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb new file mode 100644 index 00000000..6e566a20 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_keras_surrogate.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Changes the divide behavior to not do integer division\n", + "from __future__ import division\n", + "\n", + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, Param, \\\n", + " Reals, Set, value, Var, NonNegativeReals, units\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component)\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occuring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + " def build(self):\n", + " '''\n", + " Callable method for Block construction.\n", + " '''\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties({\n", + " 'flow_mol': {'method': None, 'units': 'kmol/s'},\n", + " 'pressure': {'method': None, 'units': 'MPa'},\n", + " 'temperature': {'method': None, 'units': 'K'},\n", + " 'enth_mol': {'method': None, 'units': 'kJ/kmol'},\n", + " 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}})\n", + "\n", + " obj.add_default_units({'time': units.s,\n", + " 'length': units.m,\n", + " 'mass': units.kg,\n", + " 'amount': units.mol,\n", + " 'temperature': units.K})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the SCO2_keras_surrogate_test.ipynb file) using the keras Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\",\n", + " block_class=_StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self): \n", + " self.flow_mol = Var(domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol/units.s,\n", + " doc='Total molar flowrate [kmol/s]')\n", + " self.pressure = Var(domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc='State pressure [MPa]')\n", + " self.temperature = Var(domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760+273.15),\n", + " units=units.K,\n", + " doc='State temperature [K]')\n", + " self.entr_mol = Var(domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ/units.kmol/units.K,\n", + " doc='Entropy [kJ/kmol/K]')\n", + " self.enth_mol = Var(domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ/units.kmol,\n", + " doc='Enthalpy [kJ/ kmol]')\n", + " \n", + " inputs=[self.pressure,self.temperature]\n", + " outputs=[self.enth_mol,self.entr_mol]\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + " \n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol*self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure}\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error('{} Temperature set below lower bound.'\n", + " .format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error('{} Temperature set above upper bound.'\n", + " .format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error('{} Pressure set below lower bound.'.format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error('{} Pressure set above upper bound.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would interm fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the varible with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + " def initialize(blk, state_args=None, hold_state=False, outlvl=1,\n", + " state_vars_fixed=False, solver='ipopt',\n", + " optarg={'tol': 1e-8}):\n", + "\n", + " '''\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output infomation (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating whcih solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states varaibles are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " relase_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " '''\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag,\n", + " \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\")\n", + " \n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " '''\n", + " Method to relase state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " '''\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags['Fcflag'][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags['Pflag'][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags['Tflag'][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info('{} State Released.'.format(blk.name))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_keras_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb new file mode 100644 index 00000000..8f30874e --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_keras_surrogate.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Changes the divide behavior to not do integer division\n", + "from __future__ import division\n", + "\n", + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, Param, \\\n", + " Reals, Set, value, Var, NonNegativeReals, units\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component)\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occuring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + " def build(self):\n", + " '''\n", + " Callable method for Block construction.\n", + " '''\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties({\n", + " 'flow_mol': {'method': None, 'units': 'kmol/s'},\n", + " 'pressure': {'method': None, 'units': 'MPa'},\n", + " 'temperature': {'method': None, 'units': 'K'},\n", + " 'enth_mol': {'method': None, 'units': 'kJ/kmol'},\n", + " 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}})\n", + "\n", + " obj.add_default_units({'time': units.s,\n", + " 'length': units.m,\n", + " 'mass': units.kg,\n", + " 'amount': units.mol,\n", + " 'temperature': units.K})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the SCO2_keras_surrogate_usr.ipynb file) using the keras Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\",\n", + " block_class=_StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self): \n", + " self.flow_mol = Var(domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol/units.s,\n", + " doc='Total molar flowrate [kmol/s]')\n", + " self.pressure = Var(domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc='State pressure [MPa]')\n", + " self.temperature = Var(domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760+273.15),\n", + " units=units.K,\n", + " doc='State temperature [K]')\n", + " self.entr_mol = Var(domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ/units.kmol/units.K,\n", + " doc='Entropy [kJ/kmol/K]')\n", + " self.enth_mol = Var(domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ/units.kmol,\n", + " doc='Enthalpy [kJ/ kmol]')\n", + " \n", + " inputs=[self.pressure,self.temperature]\n", + " outputs=[self.enth_mol,self.entr_mol]\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.keras_surrogate,\n", + " formulation=KerasSurrogate.Formulation.FULL_SPACE,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + " \n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol*self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure}\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error('{} Temperature set below lower bound.'\n", + " .format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error('{} Temperature set above upper bound.'\n", + " .format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error('{} Pressure set below lower bound.'.format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error('{} Pressure set above upper bound.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would interm fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the varible with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + " def initialize(blk, state_args=None, hold_state=False, outlvl=1,\n", + " state_vars_fixed=False, solver='ipopt',\n", + " optarg={'tol': 1e-8}):\n", + "\n", + " '''\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output infomation (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating whcih solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states varaibles are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " relase_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " '''\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag,\n", + " \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\")\n", + " \n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " '''\n", + " Method to relase state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " '''\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags['Fcflag'][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags['Pflag'][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags['Tflag'][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info('{} State Released.'.format(blk.name))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_keras_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc.ipynb new file mode 100644 index 00000000..5d28682d --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc.ipynb @@ -0,0 +1,1426 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - SCO2_flowsheet_optimization_pysmo_surrogate (Part 3)\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory, TerminationCondition,\n", + " value, Expression, minimize, units)\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (Mixer, MomentumMixingType,\n", + " PressureChanger, Heater,\n", + " Separator, HeatExchanger)\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from SCO2_properties_pysmo_surrogate import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:45:31 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 452\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 118\n", + "\n", + "Total number of variables............................: 178\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 59\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 178\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.12e+02 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 3.28e-01 1.12e-02 -1.0 1.32e+01 - 9.89e-01 1.00e+00h 1\n", + " 2 0.0000000e+00 5.45e-06 1.05e-06 -1.0 1.32e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 1.37e-08 2.83e-08 -2.5 2.87e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 3.4924596548080444e-10 1.3737007975578308e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 3.4924596548080444e-10 1.3737007975578308e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.4382e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -9.9927e+05 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 729.38\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.4056e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 729.38 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.4056e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 535.47 736.02\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.0929e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.0929e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 378.99 566.32\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -4.4513e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 2.2092e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 378.99\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.1041e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 460.04\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 378.99 378.99 378.99\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 31903. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 378.99 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 566.32 460.04 535.47\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "667.9424945058901 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory('ipopt')\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True)\n", + "\n", + " m.fs.turbine = PressureChanger(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.splitter_1 = Separator(property_package= m.fs.properties,\n", + " outlet_list= [\"bypass\", \"to_cooler\"])\n", + "\n", + " m.fs.co2_cooler = Heater(dynamic= False,\n", + " property_package=m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.main_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.splitter_2 = Separator(property_package= m.fs.properties,\n", + " ideal_separation= False,\n", + " outlet_list= [\"to_FG_cooler\",\n", + " \"to_LTR\"])\n", + "\n", + " m.fs.FG_cooler = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.mixer = Mixer(property_package= m.fs.properties,\n", + " inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"])\n", + "\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet,\n", + " destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet,\n", + " destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet,\n", + " destination=m.fs.LTR_pseudo_shell.inlet)\n", + " m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet,\n", + " destination=m.fs.splitter_1.inlet)\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler,\n", + " destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(source=m.fs.splitter_1.bypass,\n", + " destination=m.fs.bypass_compressor.inlet)\n", + " m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet,\n", + " destination=m.fs.main_compressor.inlet)\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet,\n", + " destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(source=m.fs.main_compressor.outlet,\n", + " destination=m.fs.splitter_2.inlet)\n", + " m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler,\n", + " destination=m.fs.FG_cooler.inlet)\n", + " m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR,\n", + " destination=m.fs.LTR_pseudo_tube.inlet)\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet,\n", + " destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", + " destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline \n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1/3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0) \n", + " m.fs.LTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.LTR_pseudo_tube.heat_duty[0])\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.HTR_pseudo_tube.heat_duty[0])\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW)\n", + " return m\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_test.ipynb new file mode 100644 index 00000000..5d28682d --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_test.ipynb @@ -0,0 +1,1426 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - SCO2_flowsheet_optimization_pysmo_surrogate (Part 3)\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory, TerminationCondition,\n", + " value, Expression, minimize, units)\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (Mixer, MomentumMixingType,\n", + " PressureChanger, Heater,\n", + " Separator, HeatExchanger)\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from SCO2_properties_pysmo_surrogate import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:45:31 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 452\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 118\n", + "\n", + "Total number of variables............................: 178\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 59\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 178\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.12e+02 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 3.28e-01 1.12e-02 -1.0 1.32e+01 - 9.89e-01 1.00e+00h 1\n", + " 2 0.0000000e+00 5.45e-06 1.05e-06 -1.0 1.32e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 1.37e-08 2.83e-08 -2.5 2.87e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 3.4924596548080444e-10 1.3737007975578308e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 3.4924596548080444e-10 1.3737007975578308e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.4382e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -9.9927e+05 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 729.38\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.4056e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 729.38 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.4056e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 535.47 736.02\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.0929e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.0929e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 378.99 566.32\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -4.4513e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 2.2092e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 378.99\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.1041e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 460.04\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 378.99 378.99 378.99\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 31903. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 378.99 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 566.32 460.04 535.47\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "667.9424945058901 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory('ipopt')\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True)\n", + "\n", + " m.fs.turbine = PressureChanger(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.splitter_1 = Separator(property_package= m.fs.properties,\n", + " outlet_list= [\"bypass\", \"to_cooler\"])\n", + "\n", + " m.fs.co2_cooler = Heater(dynamic= False,\n", + " property_package=m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.main_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.splitter_2 = Separator(property_package= m.fs.properties,\n", + " ideal_separation= False,\n", + " outlet_list= [\"to_FG_cooler\",\n", + " \"to_LTR\"])\n", + "\n", + " m.fs.FG_cooler = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.mixer = Mixer(property_package= m.fs.properties,\n", + " inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"])\n", + "\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet,\n", + " destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet,\n", + " destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet,\n", + " destination=m.fs.LTR_pseudo_shell.inlet)\n", + " m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet,\n", + " destination=m.fs.splitter_1.inlet)\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler,\n", + " destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(source=m.fs.splitter_1.bypass,\n", + " destination=m.fs.bypass_compressor.inlet)\n", + " m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet,\n", + " destination=m.fs.main_compressor.inlet)\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet,\n", + " destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(source=m.fs.main_compressor.outlet,\n", + " destination=m.fs.splitter_2.inlet)\n", + " m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler,\n", + " destination=m.fs.FG_cooler.inlet)\n", + " m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR,\n", + " destination=m.fs.LTR_pseudo_tube.inlet)\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet,\n", + " destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", + " destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline \n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1/3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0) \n", + " m.fs.LTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.LTR_pseudo_tube.heat_duty[0])\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.HTR_pseudo_tube.heat_duty[0])\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW)\n", + " return m\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_usr.ipynb new file mode 100644 index 00000000..5d28682d --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_usr.ipynb @@ -0,0 +1,1426 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - SCO2_flowsheet_optimization_pysmo_surrogate (Part 3)\n", + "\n", + "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Importing libraries\n", + "\n", + "We will be using the unit models from the `IDAES` package along with components from `pyomo.environ` and `pyomo.network`. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyomo.environ import (ConcreteModel,\n", + " Block,\n", + " Var,\n", + " Param,\n", + " Constraint,\n", + " SolverFactory,\n", + " TransformationFactory, TerminationCondition,\n", + " value, Expression, minimize, units)\n", + "from pyomo.network import Arc, SequentialDecomposition\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core import FlowsheetBlock, UnitModelBlockData\n", + "from idaes.models.unit_models import (Mixer, MomentumMixingType,\n", + " PressureChanger, Heater,\n", + " Separator, HeatExchanger)\n", + "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.initialization import propagate_state\n", + "from SCO2_properties_pysmo_surrogate import SCO2ParameterBlock\n", + "\n", + "import idaes.logger as idaeslog\n", + "\n", + "_log = idaeslog.getModelLogger(\"my_model\", level=idaeslog.DEBUG, tag=\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Constructing the flowsheet\n", + "\n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "\n", + "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:27 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234527.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.core.surrogate.pysmo_surrogate: Decode surrogate. type=poly\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; results will be saved to \" solution_v_08-19-23_234528.pickle \".\n", + "\n", + "The number of cross-validation cases (3) is used.\n", + "The default training/cross-validation split of 0.75 is used.\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:28 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:29 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2023-08-19 23:45:30 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2023-08-19 23:45:31 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "--------------------------------------------------------------------\n", + "The degrees of freedom for the flowsheet is 0\n", + "--------------------------------------------------------------------\n", + "Ipopt 3.13.2: \n", + "\n", + "******************************************************************************\n", + "This program contains Ipopt, a library for large-scale nonlinear optimization.\n", + " Ipopt is released as open source code under the Eclipse Public License (EPL).\n", + " For more information visit http://projects.coin-or.org/Ipopt\n", + "\n", + "This version of Ipopt was compiled from source code available at\n", + " https://github.com/IDAES/Ipopt as part of the Institute for the Design of\n", + " Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE\n", + " Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.\n", + "\n", + "This version of Ipopt was compiled using HSL, a collection of Fortran codes\n", + " for large-scale scientific computation. All technical papers, sales and\n", + " publicity material resulting from use of the HSL codes within IPOPT must\n", + " contain the following acknowledgement:\n", + " HSL, a collection of Fortran codes for large-scale scientific\n", + " computation. See http://www.hsl.rl.ac.uk.\n", + "******************************************************************************\n", + "\n", + "This is Ipopt version 3.13.2, running with linear solver ma27.\n", + "\n", + "Number of nonzeros in equality constraint Jacobian...: 452\n", + "Number of nonzeros in inequality constraint Jacobian.: 0\n", + "Number of nonzeros in Lagrangian Hessian.............: 118\n", + "\n", + "Total number of variables............................: 178\n", + " variables with only lower bounds: 32\n", + " variables with lower and upper bounds: 59\n", + " variables with only upper bounds: 0\n", + "Total number of equality constraints.................: 178\n", + "Total number of inequality constraints...............: 0\n", + " inequality constraints with only lower bounds: 0\n", + " inequality constraints with lower and upper bounds: 0\n", + " inequality constraints with only upper bounds: 0\n", + "\n", + "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", + " 0 0.0000000e+00 1.12e+02 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 0.0000000e+00 3.28e-01 1.12e-02 -1.0 1.32e+01 - 9.89e-01 1.00e+00h 1\n", + " 2 0.0000000e+00 5.45e-06 1.05e-06 -1.0 1.32e+01 - 1.00e+00 1.00e+00h 1\n", + " 3 0.0000000e+00 1.37e-08 2.83e-08 -2.5 2.87e-04 - 1.00e+00 1.00e+00h 1\n", + "\n", + "Number of Iterations....: 3\n", + "\n", + " (scaled) (unscaled)\n", + "Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Constraint violation....: 3.4924596548080444e-10 1.3737007975578308e-08\n", + "Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00\n", + "Overall NLP error.......: 3.4924596548080444e-10 1.3737007975578308e-08\n", + "\n", + "\n", + "Number of objective function evaluations = 4\n", + "Number of objective gradient evaluations = 4\n", + "Number of equality constraint evaluations = 4\n", + "Number of inequality constraint evaluations = 0\n", + "Number of equality constraint Jacobian evaluations = 4\n", + "Number of inequality constraint Jacobian evaluations = 0\n", + "Number of Lagrangian Hessian evaluations = 3\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.004\n", + "Total CPU secs in NLP function evaluations = 0.002\n", + "\n", + "EXIT: Optimal Solution Found.\n", + "\n", + "====================================================================================\n", + "Unit : fs.boiler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.4382e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 685.15 893.15\n", + " pressure pascal 3.4510e+07 3.4300e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.turbine Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.92700 : dimensionless : True : (None, None)\n", + " Mechanical Work : -9.9927e+05 : watt : False : (None, None)\n", + " Pressure Change : -24.979 : pascal : False : (None, None)\n", + " Pressure Ratio : 0.27174 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 893.15 729.38\n", + " pressure pascal 3.4300e+07 9.3207e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.4056e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 729.38 489.15\n", + " pressure pascal 9.3207e+06 9.2507e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.HTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.4056e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 535.47 736.02\n", + " pressure pascal 3.4560e+07 3.4490e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_shell Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -1.0929e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 1.2110e+05 1.2110e+05\n", + " temperature kelvin 489.15 354.15\n", + " pressure pascal 9.2507e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.LTR_pseudo_tube Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 1.0929e+06 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 86647. 86647.\n", + " temperature kelvin 378.99 566.32\n", + " pressure pascal 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_1 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('bypass',)] : 0.25000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_cooler',)] : 0.75000 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet bypass to_cooler\n", + " flow_mol mole / second 1.2110e+05 30275. 90825.\n", + " temperature kelvin 354.15 354.15 354.15\n", + " pressure pascal 9.1807e+06 9.1807e+06 9.1807e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.co2_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : -4.4513e+05 : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 354.15 308.15\n", + " pressure pascal 9.1807e+06 9.1107e+06\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.main_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 2.2092e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.510 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 90825. 90825.\n", + " temperature kelvin 308.15 378.99\n", + " pressure pascal 9.1107e+06 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.bypass_compressor Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Isentropic Efficiency : 0.85000 : dimensionless : True : (None, None)\n", + " Mechanical Work : 1.1041e+05 : watt : False : (None, None)\n", + " Pressure Change : 25.706 : pascal : False : (None, None)\n", + " Pressure Ratio : 3.8000 : dimensionless : True : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 30275. 30275.\n", + " temperature kelvin 354.15 460.04\n", + " pressure pascal 9.1807e+06 3.4886e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.splitter_2 Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Split Fraction [('to_FG_cooler',)] : 0.046000 : dimensionless : True : (None, None)\n", + " Split Fraction [('to_LTR',)] : 0.95400 : dimensionless : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet to_FG_cooler to_LTR \n", + " flow_mol mole / second 90825. 4177.9 86647.\n", + " temperature kelvin 378.99 378.99 378.99\n", + " pressure pascal 3.4620e+07 3.4620e+07 3.4620e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.FG_cooler Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Unit Performance\n", + "\n", + " Variables: \n", + "\n", + " Key : Value : Units : Fixed : Bounds\n", + " Heat Duty : 31903. : watt : False : (None, None)\n", + "\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units Inlet Outlet \n", + " flow_mol mole / second 4177.9 4177.9\n", + " temperature kelvin 378.99 483.15\n", + " pressure pascal 3.4620e+07 3.4560e+07\n", + "====================================================================================\n", + "\n", + "====================================================================================\n", + "Unit : fs.mixer Time: 0.0\n", + "------------------------------------------------------------------------------------\n", + " Stream Table\n", + " Units FG_out LTR_out bypass Outlet \n", + " flow_mol mole / second 4177.9 86647. 30275. 1.2110e+05\n", + " temperature kelvin 483.15 566.32 460.04 535.47\n", + " pressure pascal 3.4560e+07 3.4620e+07 3.4886e+07 3.4560e+07\n", + "====================================================================================\n", + "667.9424945058901 kW\n" + ] + } + ], + "source": [ + "def main():\n", + " # Setup solver and options\n", + " solver = SolverFactory('ipopt')\n", + " outlvl = 0\n", + " tee = True\n", + "\n", + " # Set up concrete model\n", + " m = ConcreteModel()\n", + "\n", + " # Create a flowsheet block\n", + " m.fs = FlowsheetBlock(dynamic=False)\n", + "\n", + " # Create the properties param block\n", + " m.fs.properties = SCO2ParameterBlock()\n", + "\n", + " # Add unit models to the flowsheet\n", + " m.fs.boiler = Heater(dynamic=False,property_package= m.fs.properties,has_pressure_change=True)\n", + "\n", + " m.fs.turbine = PressureChanger(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " compressor=False,\n", + " thermodynamic_assumption=ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.HTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.HTR_pseudo_tube = Heater(dynamic=False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.LTR_pseudo_shell = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.LTR_pseudo_tube = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change=True)\n", + "\n", + " m.fs.splitter_1 = Separator(property_package= m.fs.properties,\n", + " outlet_list= [\"bypass\", \"to_cooler\"])\n", + "\n", + " m.fs.co2_cooler = Heater(dynamic= False,\n", + " property_package=m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.main_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.bypass_compressor = PressureChanger(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " compressor= True,\n", + " thermodynamic_assumption= ThermodynamicAssumption.isentropic)\n", + "\n", + " m.fs.splitter_2 = Separator(property_package= m.fs.properties,\n", + " ideal_separation= False,\n", + " outlet_list= [\"to_FG_cooler\",\n", + " \"to_LTR\"])\n", + "\n", + " m.fs.FG_cooler = Heater(dynamic= False,\n", + " property_package= m.fs.properties,\n", + " has_pressure_change= True)\n", + "\n", + " m.fs.mixer = Mixer(property_package= m.fs.properties,\n", + " inlet_list=[\"FG_out\", \"LTR_out\", \"bypass\"])\n", + "\n", + "\n", + " # # Connect the flowsheet\n", + " m.fs.s01 = Arc(source=m.fs.boiler.outlet,\n", + " destination=m.fs.turbine.inlet)\n", + " m.fs.s02 = Arc(source=m.fs.turbine.outlet,\n", + " destination=m.fs.HTR_pseudo_shell.inlet)\n", + " m.fs.s03 = Arc(source=m.fs.HTR_pseudo_shell.outlet,\n", + " destination=m.fs.LTR_pseudo_shell.inlet)\n", + " m.fs.s04 = Arc(source=m.fs.LTR_pseudo_shell.outlet,\n", + " destination=m.fs.splitter_1.inlet)\n", + " m.fs.s05 = Arc(source=m.fs.splitter_1.to_cooler,\n", + " destination=m.fs.co2_cooler.inlet)\n", + " m.fs.s06 = Arc(source=m.fs.splitter_1.bypass,\n", + " destination=m.fs.bypass_compressor.inlet)\n", + " m.fs.s07 = Arc(source=m.fs.co2_cooler.outlet,\n", + " destination=m.fs.main_compressor.inlet)\n", + " m.fs.s08 = Arc(source=m.fs.bypass_compressor.outlet,\n", + " destination=m.fs.mixer.bypass)\n", + " m.fs.s09 = Arc(source=m.fs.main_compressor.outlet,\n", + " destination=m.fs.splitter_2.inlet)\n", + " m.fs.s10 = Arc(source=m.fs.splitter_2.to_FG_cooler,\n", + " destination=m.fs.FG_cooler.inlet)\n", + " m.fs.s11 = Arc(source=m.fs.splitter_2.to_LTR,\n", + " destination=m.fs.LTR_pseudo_tube.inlet)\n", + " m.fs.s12 = Arc(source=m.fs.LTR_pseudo_tube.outlet,\n", + " destination=m.fs.mixer.LTR_out)\n", + " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", + " destination=m.fs.mixer.FG_out)\n", + " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # NETL Baseline \n", + " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", + " m.fs.boiler.inlet.temperature.fix(685.15)\n", + " m.fs.boiler.inlet.pressure.fix(34.51)\n", + "\n", + " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", + " m.fs.boiler.deltaP.fix(-0.21)\n", + "\n", + " m.fs.boiler.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s01)\n", + "\n", + " m.fs.turbine.ratioP.fix(1/3.68)\n", + " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", + " m.fs.turbine.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s02)\n", + " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", + " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", + "\n", + " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s03)\n", + "\n", + " m.fs.LTR_pseudo_shell.outlet.temperature.fix(354.15)\n", + " m.fs.LTR_pseudo_shell.deltaP.fix(-0.07)\n", + " m.fs.LTR_pseudo_shell.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s04)\n", + " m.fs.splitter_1.split_fraction[0, \"bypass\"].fix(0.25)\n", + "\n", + " m.fs.splitter_1.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s05)\n", + " m.fs.co2_cooler.outlet.temperature.fix(308.15)\n", + " m.fs.co2_cooler.deltaP.fix(-0.07)\n", + " m.fs.co2_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s06)\n", + " m.fs.bypass_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.bypass_compressor.ratioP.fix(3.8)\n", + " m.fs.bypass_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s07)\n", + " m.fs.main_compressor.efficiency_isentropic.fix(0.85)\n", + " m.fs.main_compressor.ratioP.fix(3.8)\n", + " m.fs.main_compressor.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s09)\n", + "\n", + " m.fs.splitter_2.split_fraction[0, \"to_FG_cooler\"].fix(0.046)\n", + " m.fs.splitter_2.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s10)\n", + " m.fs.FG_cooler.outlet.temperature.fix(483.15)\n", + " m.fs.FG_cooler.deltaP.fix(-0.06)\n", + " m.fs.FG_cooler.initialize(outlvl=outlvl)\n", + "\n", + "\n", + " propagate_state(m.fs.s11)\n", + "\n", + " m.fs.LTR_pseudo_tube.deltaP.fix(0) \n", + " m.fs.LTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.LTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.LTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " # Add constraint heats of the LTR_pseudo shell and tube\n", + " m.fs.LTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c1 = Constraint(expr=m.fs.LTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.LTR_pseudo_tube.heat_duty[0])\n", + "\n", + " propagate_state(m.fs.s08)\n", + " propagate_state(m.fs.s12)\n", + " propagate_state(m.fs.s13)\n", + "\n", + " m.fs.mixer.initialize(outlvl=outlvl)\n", + "\n", + " propagate_state(m.fs.s14)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].\\\n", + " fix(-value(m.fs.HTR_pseudo_shell.heat_duty[0]))\n", + " m.fs.HTR_pseudo_tube.deltaP.fix(-0.07)\n", + " m.fs.HTR_pseudo_tube.initialize(outlvl=outlvl)\n", + "\n", + " m.fs.HTR_pseudo_tube.heat_duty[0].unfix()\n", + " m.fs.c2 = Constraint(expr=m.fs.HTR_pseudo_shell.heat_duty[0] ==\n", + " -m.fs.HTR_pseudo_tube.heat_duty[0])\n", + "\n", + " TransformationFactory(\"network.expand_arcs\").apply_to(m.fs)\n", + "\n", + " print(\"--------------------------------------------------------------------\")\n", + " print(\"The degrees of freedom for the flowsheet is \", degrees_of_freedom(m))\n", + " print(\"--------------------------------------------------------------------\")\n", + "\n", + " solver.solve(m, tee=tee)\n", + "\n", + " #\n", + " from idaes.core.util.units_of_measurement import convert_quantity_to_reporting_units,report_quantity\n", + " # Print reports\n", + " for i in m.fs.component_objects(Block):\n", + " if isinstance(i, UnitModelBlockData):\n", + " i.report()\n", + "\n", + " # Converting units for readability\n", + " print(-1*value(units.convert(m.fs.turbine.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.main_compressor.work_mechanical[0],units.kW))\\\n", + " -1*value(units.convert(m.fs.bypass_compressor.work_mechanical[0],units.kW)),units.kW)\n", + " return m\n", + "\n", + "if __name__ == \"__main__\":\n", + " m = main()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb new file mode 100644 index 00000000..eec9bfe0 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_pysmo_surrogate.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Changes the divide behavior to not do integer division\n", + "from __future__ import division\n", + "\n", + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, Param, \\\n", + " Reals, Set, value, Var, NonNegativeReals, units\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component)\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occuring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + " def build(self):\n", + " '''\n", + " Callable method for Block construction.\n", + " '''\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties({\n", + " 'flow_mol': {'method': None, 'units': 'kmol/s'},\n", + " 'pressure': {'method': None, 'units': 'MPa'},\n", + " 'temperature': {'method': None, 'units': 'K'},\n", + " 'enth_mol': {'method': None, 'units': 'kJ/kmol'},\n", + " 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}})\n", + "\n", + " obj.add_default_units({'time': units.s,\n", + " 'length': units.m,\n", + " 'mass': units.kg,\n", + " 'amount': units.mol,\n", + " 'temperature': units.K})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the SCO2_pysmo_surrogate_doc.md file) using the PySMO Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\",\n", + " block_class=_StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " \n", + " self.flow_mol = Var(domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol/units.s,\n", + " doc='Total molar flowrate [kmol/s]')\n", + " self.pressure = Var(domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc='State pressure [MPa]')\n", + "\n", + " self.temperature = Var(domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760+273.15),\n", + " units=units.K,\n", + " doc='State temperature [K]')\n", + "\n", + " self.entr_mol = Var(domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ/units.kmol/units.K,\n", + " doc='Entropy [kJ/ kmol / K]')\n", + " \n", + " self.enth_mol = Var(domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ/units.kmol,\n", + " doc='Enthalpy [kJ/ kmol]')\n", + " \n", + " inputs=[self.pressure,self.temperature]\n", + " outputs=[self.enth_mol,self.entr_mol]\n", + " self.pysmo_surrogate = PysmoSurrogate.load_from_file(\"pysmo_poly_surrogate.json\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.pysmo_surrogate,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol*self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure}\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error('{} Temperature set below lower bound.'\n", + " .format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error('{} Temperature set above upper bound.'\n", + " .format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error('{} Pressure set below lower bound.'.format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error('{} Pressure set above upper bound.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would interm fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the varible with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + " def initialize(blk, state_args=None, hold_state=False, outlvl=1,\n", + " state_vars_fixed=False, solver='ipopt',\n", + " optarg={'tol': 1e-8}):\n", + "\n", + " '''\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output infomation (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating whcih solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states varaibles are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " relase_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " '''\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag,\n", + " \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\")\n", + " \n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " '''\n", + " Method to relase state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " '''\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags['Fcflag'][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags['Pflag'][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags['Tflag'][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info('{} State Released.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_pysmo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.md). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb new file mode 100644 index 00000000..f454ab75 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_pysmo_surrogate.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Changes the divide behavior to not do integer division\n", + "from __future__ import division\n", + "\n", + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, Param, \\\n", + " Reals, Set, value, Var, NonNegativeReals, units\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component)\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occuring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + " def build(self):\n", + " '''\n", + " Callable method for Block construction.\n", + " '''\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties({\n", + " 'flow_mol': {'method': None, 'units': 'kmol/s'},\n", + " 'pressure': {'method': None, 'units': 'MPa'},\n", + " 'temperature': {'method': None, 'units': 'K'},\n", + " 'enth_mol': {'method': None, 'units': 'kJ/kmol'},\n", + " 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}})\n", + "\n", + " obj.add_default_units({'time': units.s,\n", + " 'length': units.m,\n", + " 'mass': units.kg,\n", + " 'amount': units.mol,\n", + " 'temperature': units.K})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the SCO2_pysmo_surrogate_test.ipynb file) using the PySMO Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\",\n", + " block_class=_StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " \n", + " self.flow_mol = Var(domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol/units.s,\n", + " doc='Total molar flowrate [kmol/s]')\n", + " self.pressure = Var(domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc='State pressure [MPa]')\n", + "\n", + " self.temperature = Var(domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760+273.15),\n", + " units=units.K,\n", + " doc='State temperature [K]')\n", + "\n", + " self.entr_mol = Var(domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ/units.kmol/units.K,\n", + " doc='Entropy [kJ/ kmol / K]')\n", + " \n", + " self.enth_mol = Var(domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ/units.kmol,\n", + " doc='Enthalpy [kJ/ kmol]')\n", + " \n", + " inputs=[self.pressure,self.temperature]\n", + " outputs=[self.enth_mol,self.entr_mol]\n", + " self.pysmo_surrogate = PysmoSurrogate.load_from_file(\"pysmo_poly_surrogate.json\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.pysmo_surrogate,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol*self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure}\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error('{} Temperature set below lower bound.'\n", + " .format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error('{} Temperature set above upper bound.'\n", + " .format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error('{} Pressure set below lower bound.'.format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error('{} Pressure set above upper bound.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would interm fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the varible with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + " def initialize(blk, state_args=None, hold_state=False, outlvl=1,\n", + " state_vars_fixed=False, solver='ipopt',\n", + " optarg={'tol': 1e-8}):\n", + "\n", + " '''\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output infomation (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating whcih solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states varaibles are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " relase_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " '''\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag,\n", + " \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\")\n", + " \n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " '''\n", + " Method to relase state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " '''\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags['Fcflag'][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags['Pflag'][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags['Tflag'][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info('{} State Released.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_pysmo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb new file mode 100644 index 00000000..81f5dd2e --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##############################################################################\n", + "# Institute for the Design of Advanced Energy Systems Process Systems\n", + "# Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the\n", + "# software owners: The Regents of the University of California, through\n", + "# Lawrence Berkeley National Laboratory, National Technology & Engineering\n", + "# Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia\n", + "# University Research Corporation, et al. All rights reserved.\n", + "#\n", + "# Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and\n", + "# license information, respectively. Both files are also available online\n", + "# at the URL \"https://github.com/IDAES/idaes-pse\".\n", + "##############################################################################" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "\n", + "## 1. Integration of Surrogate into Custom Property Package\n", + "\n", + "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", + "\n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_pysmo_surrogate.py\" file which is how embedding file should look like. \n", + "\n", + "### 1.1 Steps in Creating a Property Package\n", + "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", + "\n", + "1. Defining the **units of measurement** for the property package.\n", + "2. Defining the **properties supported** by the property package and the associated metadata.\n", + "3. Defining the **phases and components** of interest.\n", + "4. Defining the necessary **parameters** required to calculate the properties of interest.\n", + "5. Declaring the **state variables** to be used for the property package.\n", + "6. Creating **variables and constraints** to describe the properties of interest.\n", + "7. Creating an **initialization routine** for the property package.\n", + "8. Defining **interface methods** used to couple the property package with unit models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Importing libraries for making Property Package\n", + "\n", + "To begin with, we are going to need a number of components from the Pyomo modeling environment to construct the variables, constraints and parameters that will make up the property package, and we will also make use of the Pyomo units of measurement tools to define the units of our properties. We will also make use of a number of components and supporting methods from the IDAES modeling framework and libraries. We shall also use the Surrogate API in the IDAES framework to embed the trained surrogate in the property package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Changes the divide behavior to not do integer division\n", + "from __future__ import division\n", + "\n", + "# Import Python libraries\n", + "import logging\n", + "\n", + "# Import Pyomo libraries\n", + "from pyomo.environ import Constraint, Param, \\\n", + " Reals, Set, value, Var, NonNegativeReals, units\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "# Import IDAES cores\n", + "from idaes.core import (declare_process_block_class,\n", + " PhysicalParameterBlock,\n", + " StateBlockData,\n", + " StateBlock,\n", + " MaterialBalanceType,\n", + " EnergyBalanceType,\n", + " LiquidPhase,\n", + " Component)\n", + "from idaes.core.util.initialization import solve_indexed_blocks\n", + "from idaes.core.util.model_statistics import degrees_of_freedom\n", + "from idaes.core.util.misc import extract_data\n", + "from idaes.core.solvers import get_solver\n", + "from pyomo.util.check_units import assert_units_consistent\n", + "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", + "\n", + "from pyomo.util.model_size import build_model_size_report\n", + "\n", + "# Some more information about this module\n", + "__author__ = \"Javal Vyas\"\n", + "\n", + "\n", + "# Set up logger\n", + "_log = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 Defining Classes\n", + "\n", + "We shall be going through each class of the property package in detail. Since there are not reactions occuring in the flowsheet we shall only write the Physical Parameter Block.\n", + "\n", + "## 3.1 Physical Parameter Block\n", + "\n", + "The Physical Parameter Block serves as the central point of reference for all aspects of the property package, and needs to define a number of things about the package. These are summarized below:\n", + "\n", + "* Units of measurement\n", + "* What properties are supported and how they are implemented\n", + "* What components and phases are included in the packages\n", + "* All the global parameters necessary for calculating properties\n", + "* A reference to the associated State Block class, so that construction of the State Block components can be automated from the Physical Parameter Block\n", + "\n", + "To assemble the above mentioned things in a class we need to follow the following steps:\n", + "\n", + "* Declaring the new class and inheriting from the PhysicalParameterBlock base class\n", + "* Declaring any necessary configuration arguments\n", + "* Writing the build method for our class\n", + "* Creating a define_metadata method for the class.\n", + "\n", + "The code below follows the above mentioned steps. \n", + "\n", + "*NOTE*: The SCO2StateBlock will be discussed in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2ParameterBlock\")\n", + "class PhysicalParameterData(PhysicalParameterBlock):\n", + " \"\"\"\n", + " Property Parameter Block Class\n", + "\n", + " Contains parameters and indexing sets associated with properties for\n", + " supercritical CO2.\n", + "\n", + " \"\"\"\n", + " def build(self):\n", + " '''\n", + " Callable method for Block construction.\n", + " '''\n", + " super(PhysicalParameterData, self).build()\n", + "\n", + " self._state_block_class = SCO2StateBlock\n", + "\n", + " # List of valid phases in property package\n", + " self.Liq = LiquidPhase()\n", + "\n", + " # Component list - a list of component identifiers\n", + " self.CO2 = Component()\n", + "\n", + "\n", + " @classmethod\n", + " def define_metadata(cls, obj):\n", + " obj.add_properties({\n", + " 'flow_mol': {'method': None, 'units': 'kmol/s'},\n", + " 'pressure': {'method': None, 'units': 'MPa'},\n", + " 'temperature': {'method': None, 'units': 'K'},\n", + " 'enth_mol': {'method': None, 'units': 'kJ/kmol'},\n", + " 'entr_mol': {'method': None, 'units': 'kJ/kmol/K'}})\n", + "\n", + " obj.add_default_units({'time': units.s,\n", + " 'length': units.m,\n", + " 'mass': units.kg,\n", + " 'amount': units.mol,\n", + " 'temperature': units.K})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 State Block\n", + "\n", + "After the Physical Parameter Block class has been created, the next step is to write the code necessary to create the State Blocks that will be used through out the flowsheet.\n", + "\n", + "For this example, we will begin by describing the content of the StateBlockData objects, as this is where we create the variables and constraints that describe how to calculate the thermophysical properties of the material. \n", + "\n", + "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", + "\n", + "1. Define the input and output variables to the trained surrogate\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the SCO2_pysmo_surrogate_usr.ipynb file) using the PySMO Surrogate API of IDAES package\n", + "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", + "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@declare_process_block_class(\"SCO2StateBlock\",\n", + " block_class=_StateBlock)\n", + "class SCO2StateBlockData(StateBlockData):\n", + " \"\"\"\n", + " An example property package for ideal gas properties with Gibbs energy\n", + " \"\"\"\n", + "\n", + " def build(self):\n", + " \"\"\"\n", + " Callable method for Block construction\n", + " \"\"\"\n", + " super(SCO2StateBlockData, self).build()\n", + " self._make_state_vars()\n", + "\n", + " def _make_state_vars(self):\n", + " \n", + " self.flow_mol = Var(domain=NonNegativeReals,\n", + " initialize=1.0,\n", + " units=units.kmol/units.s,\n", + " doc='Total molar flowrate [kmol/s]')\n", + " self.pressure = Var(domain=NonNegativeReals,\n", + " initialize=8,\n", + " bounds=(7.38, 40),\n", + " units=units.MPa,\n", + " doc='State pressure [MPa]')\n", + "\n", + " self.temperature = Var(domain=NonNegativeReals,\n", + " initialize=350,\n", + " bounds=(304.2, 760+273.15),\n", + " units=units.K,\n", + " doc='State temperature [K]')\n", + "\n", + " self.entr_mol = Var(domain=Reals,\n", + " initialize=10,\n", + " units=units.kJ/units.kmol/units.K,\n", + " doc='Entropy [kJ/ kmol / K]')\n", + " \n", + " self.enth_mol = Var(domain=Reals,\n", + " initialize=1,\n", + " units=units.kJ/units.kmol,\n", + " doc='Enthalpy [kJ/ kmol]')\n", + " \n", + " inputs=[self.pressure,self.temperature]\n", + " outputs=[self.enth_mol,self.entr_mol]\n", + " self.pysmo_surrogate = PysmoSurrogate.load_from_file(\"pysmo_poly_surrogate.json\")\n", + " self.surrogate_enth = SurrogateBlock()\n", + " self.surrogate_enth.build_model(\n", + " self.pysmo_surrogate,\n", + " input_vars=inputs,\n", + " output_vars=outputs,\n", + " )\n", + "\n", + " def get_material_flow_terms(self, p, j):\n", + " return self.flow_mol\n", + "\n", + " def get_enthalpy_flow_terms(self, p):\n", + " return self.flow_mol*self.enth_mol\n", + "\n", + " def default_material_balance_type(self):\n", + " return MaterialBalanceType.componentTotal\n", + "\n", + " def default_energy_balance_type(self):\n", + " return EnergyBalanceType.enthalpyTotal\n", + "\n", + " def define_state_vars(self):\n", + " return {\"flow_mol\": self.flow_mol,\n", + " \"temperature\": self.temperature,\n", + " \"pressure\": self.pressure}\n", + "\n", + " def model_check(blk):\n", + " \"\"\"\n", + " Model checks for property block\n", + " \"\"\"\n", + " # Check temperature bounds\n", + " if value(blk.temperature) < blk.temperature.lb:\n", + " _log.error('{} Temperature set below lower bound.'\n", + " .format(blk.name))\n", + " if value(blk.temperature) > blk.temperature.ub:\n", + " _log.error('{} Temperature set above upper bound.'\n", + " .format(blk.name))\n", + "\n", + " # Check pressure bounds\n", + " if value(blk.pressure) < blk.pressure.lb:\n", + " _log.error('{} Pressure set below lower bound.'.format(blk.name))\n", + " if value(blk.pressure) > blk.pressure.ub:\n", + " _log.error('{} Pressure set above upper bound.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 Define Initialization Routine\n", + "\n", + "After defining the variables and constraints required to describe the properties of interest for S-CO2, we need to provide them with a good initial guess. It is often the case that the default values provided to the variables while creating the model are not likely the actual conditions the user would simulate. Given the highly non-linear nature of the physical property calculations, it is more often than not impossible to solve a State Block without providing a set of good initial values for all the variables we have declared.\n", + "\n", + "Any initialization routine can be written by following a 3 step process:\n", + "1. `Fix the state` of the model such that there are no degrees of freedom. For State Blocks, it should only be necessary to fix the state variables to a set of initial guesses provided by the user or unit model, as well as deactivating any constraints like the sum of mole fractions.\n", + "\n", + "2. `Iteratively build up a solution` for the full model. This often involves multiple steps and can involve deactivating constraints and fixing some variables to reduce complexity, as well as analytically calculating values for variables based on the known state (and any previously calculated variables). Solvers can be called as part of any step to efficiently initialize large numbers of variables simultaneously.\n", + "\n", + "3. `Return the state of the model` to where it originally started (with the exception of variable values). Any variable that was fixed or constraint that was deactivated during initialization should be unfixed or reactivated, so that the degrees of freedom are restored to what they were before the initialization began.\n", + "\n", + "\n", + "Thus, we start with fixing the state variables. Here since enth_mol and entr_mol are a function of pressure and temperature, we do not fix them as fixing pressure and temperature would interm fix them. So, we check if a state variable if fixed or not, if it is fixed then we do not change them, if they are not fixed then we check for an initial guess from the `state_args`, if we get a value then we fix the varible with state_args, else we fix it with the value provided by the user. This should bring the degrees of freedom to 0. Here since we do not have any variable/constrained that we have unfixed/deactivated we can skip step 2 and move to step 3. We unfix the variables that were fixed in step 1 using the `release_state` function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class _StateBlock(StateBlock):\n", + " \"\"\"\n", + " This Class contains methods which should be applied to Property Blocks as a\n", + " whole, rather than individual elements of indexed Property Blocks.\n", + " \"\"\"\n", + " def initialize(blk, state_args=None, hold_state=False, outlvl=1,\n", + " state_vars_fixed=False, solver='ipopt',\n", + " optarg={'tol': 1e-8}):\n", + "\n", + " '''\n", + " Initialisation routine for property package.\n", + "\n", + " Keyword Arguments:\n", + " flow_mol : value at which to initialize component flows\n", + " (default=None)\n", + " pressure : value at which to initialize pressure (default=None)\n", + " temperature : value at which to initialize temperature\n", + " (default=None)\n", + " outlvl : sets output level of initialisation routine\n", + "\n", + " * 0 = no output (default)\n", + " * 1 = return solver state for each step in routine\n", + " * 2 = include solver output infomation (tee=True)\n", + " state_vars_fixed: Flag to denote if state vars have already been\n", + " fixed.\n", + " - True - states have already been fixed by the\n", + " control volume 1D. Control volume 0D\n", + " does not fix the state vars, so will\n", + " be False if this state block is used\n", + " with 0D blocks.\n", + " - False - states have not been fixed. The state\n", + " block will deal with fixing/unfixing.\n", + " optarg : solver options dictionary object (default=None)\n", + " solver : str indicating whcih solver to use during\n", + " initialization (default = 'ipopt')\n", + " hold_state : flag indicating whether the initialization routine\n", + " should unfix any state variables fixed during\n", + " initialization (default=False).\n", + " - True - states varaibles are not unfixed, and\n", + " a dict of returned containing flags for\n", + " which states were fixed during\n", + " initialization.\n", + " - False - state variables are unfixed after\n", + " initialization by calling the\n", + " relase_state method\n", + "\n", + " Returns:\n", + " If hold_states is True, returns a dict containing flags for\n", + " which states were fixed during initialization.\n", + " '''\n", + " if state_vars_fixed is False:\n", + " # Fix state variables if not already fixed\n", + " Fcflag = {}\n", + " Pflag = {}\n", + " Tflag = {}\n", + "\n", + " for k in blk.keys():\n", + " if blk[k].flow_mol.fixed is True:\n", + " Fcflag[k] = True\n", + " else:\n", + " Fcflag[k] = False\n", + " if state_args is None:\n", + " blk[k].flow_mol.fix()\n", + " else:\n", + " blk[k].flow_mol.fix(state_args[\"flow_mol\"])\n", + "\n", + " if blk[k].pressure.fixed is True:\n", + " Pflag[k] = True\n", + " else:\n", + " Pflag[k] = False\n", + " if state_args is None:\n", + " blk[k].pressure.fix()\n", + " else:\n", + " blk[k].pressure.fix(state_args[\"pressure\"])\n", + "\n", + " if blk[k].temperature.fixed is True:\n", + " Tflag[k] = True\n", + " else:\n", + " Tflag[k] = False\n", + " if state_args is None:\n", + " blk[k].temperature.fix()\n", + " else:\n", + " blk[k].temperature.fix(state_args[\"temperature\"])\n", + "\n", + " # If input block, return flags, else release state\n", + " flags = {\"Fcflag\": Fcflag, \"Pflag\": Pflag,\n", + " \"Tflag\": Tflag}\n", + "\n", + " else:\n", + " # Check when the state vars are fixed already result in dof 0\n", + " for k in blk.keys():\n", + " if degrees_of_freedom(blk[k]) != 0:\n", + " raise Exception(\"State vars fixed but degrees of freedom \"\n", + " \"for state block is not zero during \"\n", + " \"initialization.\")\n", + " \n", + " if state_vars_fixed is False:\n", + " if hold_state is True:\n", + " return flags\n", + " else:\n", + " blk.release_state(flags)\n", + "\n", + " def release_state(blk, flags, outlvl=0):\n", + " '''\n", + " Method to relase state variables fixed during initialisation.\n", + "\n", + " Keyword Arguments:\n", + " flags : dict containing information of which state variables\n", + " were fixed during initialization, and should now be\n", + " unfixed. This dict is returned by initialize if\n", + " hold_state=True.\n", + " outlvl : sets output level of of logging\n", + " '''\n", + " if flags is None:\n", + " return\n", + "\n", + " # Unfix state variables\n", + " for k in blk.keys():\n", + " if flags['Fcflag'][k] is False:\n", + " blk[k].flow_mol.unfix()\n", + " if flags['Pflag'][k] is False:\n", + " blk[k].pressure.unfix()\n", + " if flags['Tflag'][k] is False:\n", + " blk[k].temperature.unfix()\n", + "\n", + " if outlvl > 0:\n", + " if outlvl > 0:\n", + " _log.info('{} State Released.'.format(blk.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_pysmo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb). " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc.ipynb new file mode 100644 index 00000000..cb8b3c89 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc.ipynb @@ -0,0 +1,632 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part -1)\n", + "\n", + "## 1. Introduction\n", + "This notebook illustrates the use of the PySMO Polynomial surrogate trainer to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package. PySMO also has other training methods like Radial Basis Function and Kriging surrogate models, but we focus on Polynomial surrogate model. \n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "\n", + "### 1.1 Need for ML Surrogates\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebric, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebric equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train the model using polynomial regression for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoPolyTrainer, PysmoSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsquent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "csv_data.columns.values[0:6] =[\"pressure\", \"temperature\",\"enth_mol\",\"entr_mol\",\"CO2_enthalpy\",\"CO2_entropy\"]\n", + "data = csv_data.sample(n=500)\n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# # Define labels, and split training and validation data\n", + "input_labels = list(input_data.columns)\n", + "output_labels = list(output_data.columns) \n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogates with PySMO\n", + "\n", + "IDAES builds a model class for each type of PySMO surrogate model. In this case, we will call and build the Polynomial Regression class. Regression settings can be directly passed as class arguments, as shown below. In this example, allowed basis terms span a 5th order polynomial, a variable product as well as a extra features are defined, and data is internally cross-validated using 10 iterations of 80/20 splits to ensure a robust surrogate fit. Note that PySMO uses cross-validation of training data to adjust model coefficients and ensure a more accurate fit, while we separate the validation dataset pre-training in order to visualize the surrogate fits.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a folder which contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "No iterations will be run.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 20466.657669\n", + "\n", + "------------------------------------------------------------\n", + "The final coefficients of the regression terms are: \n", + "\n", + "k | -534397.59515\n", + "(x_ 1 )^ 1 | -2733.579691\n", + "(x_ 2 )^ 1 | 1036.106357\n", + "(x_ 1 )^ 2 | 32.409203\n", + "(x_ 2 )^ 2 | -2.852387\n", + "(x_ 1 )^ 3 | 0.893563\n", + "(x_ 2 )^ 3 | 0.004018\n", + "(x_ 1 )^ 4 | -0.045284\n", + "(x_ 2 )^ 4 | -3e-06\n", + "(x_ 1 )^ 5 | 0.000564\n", + "(x_ 2 )^ 5 | 0.0\n", + "x_ 1 .x_ 2 | 4.372684\n", + "\n", + "The coefficients of the extra terms in additional_regression_features are:\n", + "\n", + "Coeff. additional_regression_features[ 1 ]: -0.002723\n", + "Coeff. additional_regression_features[ 2 ]: 3.6e-05\n", + "Coeff. additional_regression_features[ 3 ]: -0.050607\n", + "Coeff. additional_regression_features[ 4 ]: 169668.814595\n", + "Coeff. additional_regression_features[ 5 ]: -44.726026\n", + "\n", + "Regression model performance on training data:\n", + "Order: 5 / MAE: 134.972465 / MSE: 54613.278159 / R^2: 0.999601\n", + "\n", + "Results saved in solution.pickle\n", + "2023-08-19 23:48:46 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "No iterations will be run.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.156437\n", + "\n", + "------------------------------------------------------------\n", + "The final coefficients of the regression terms are: \n", + "\n", + "k | -519.862457\n", + "(x_ 1 )^ 1 | -8.820865\n", + "(x_ 2 )^ 1 | 3.676641\n", + "(x_ 1 )^ 2 | 0.18002\n", + "(x_ 2 )^ 2 | -0.010217\n", + "(x_ 1 )^ 3 | -0.000783\n", + "(x_ 2 )^ 3 | 1.4e-05\n", + "(x_ 1 )^ 4 | -6.9e-05\n", + "(x_ 2 )^ 4 | -0.0\n", + "(x_ 1 )^ 5 | 1e-06\n", + "(x_ 2 )^ 5 | 0.0\n", + "x_ 1 .x_ 2 | 0.010367\n", + "\n", + "The coefficients of the extra terms in additional_regression_features are:\n", + "\n", + "Coeff. additional_regression_features[ 1 ]: -7e-06\n", + "Coeff. additional_regression_features[ 2 ]: 0.0\n", + "Coeff. additional_regression_features[ 3 ]: -0.000112\n", + "Coeff. additional_regression_features[ 4 ]: 484.312223\n", + "Coeff. additional_regression_features[ 5 ]: -0.1166\n", + "\n", + "Regression model performance on training data:\n", + "Order: 5 / MAE: 0.398072 / MSE: 0.495330 / R^2: 0.998873\n", + "\n", + "Results saved in solution.pickle\n", + "2023-08-19 23:49:20 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" + ] + } + ], + "source": [ + "# Create PySMO trainer object\n", + "trainer = PysmoPolyTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + ")\n", + "\n", + "var = output_labels\n", + "trainer.config.extra_features=['pressure*temperature*temperature','pressure*pressure*temperature*temperature','pressure*pressure*temperature','pressure/temperature','temperature/pressure']\n", + "# Set PySMO options\n", + "trainer.config.maximum_polynomial_order = 5\n", + "trainer.config.multinomials = True\n", + "trainer.config.training_split = 0.8\n", + "trainer.config.number_of_crossvalidations = 10\n", + "\n", + "# Train surrogate (calls PySMO through IDAES Python wrapper)\n", + "poly_train = trainer.train_surrogate()\n", + "\n", + "# create callable surrogate object\n", + "xmin, xmax = [7,306], [40,1000]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "poly_surr = PysmoSurrogate(poly_train, input_labels, output_labels, input_bounds)\n", + "# save model to JSON\n", + "model = poly_surr.save_to_file(\"pysmo_poly_surrogate.json\", overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing surrogates\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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SnD5R0gr7/H6d1lzqEgQB33//PX777TeEh4dLQg8ANGnSBIaGhjh69Ch69uwJALhx4wbu378PNzc3OVomIiIqs/LW5Dl/PgsTJszCq7NKunTpgkaNGsnX3BtoTfDx9vbGpk2bsGfPHlhaWorzdqytrWFqagpra2sMHDgQY8aMQbly5WBlZYXvv/8ebm5uJXJHFxERkS7z9QUmTLiNXr02SOo//vgjzM3NZerq7bQm+CxfvhwA0KZNG0k9ODhYXCV4wYIF0NPTQ8+ePSULGBIREZF6paRsQK9et8Xt999/H59//rmMHalGa4KPKlORTExMsHTpUixdulQDHREREemelJQULFiwQFL79ttv4ezsLFNHRaM1wYeIiIjkdebMGcmCwnp6evDz84OBgfbECe3plIiIiGSRk5ODoKAgyYr8N254YNOmD2XsqngYfIiIiKhQDx48wOrVqyW1Eyd+wKhRNvI09I4YfIiIiKhAe/bswaVLl8TtqlWron///rI+a+tdMfgQERGRRFpaGubMmSOpVa/eG337vidTR+qjlY+soNLJy8sL3bp1E7fbtGmDUaNGvdM51XEOIiJSTWgo0LPn5XyhZ9YsPyxfrv2hB+CIj07w8vLCunXrAACGhoZwdnZGv379MGHChBKdib9r1y4YGhqqdGx4eDjatm2b75EjRTkHEREVT+7jJwS4uS3FBx88Eevu7u5IT/8/NGmSu2BhWcDgoyM6dOiA4OBgZGRk4Pfff4e3tzcMDQ3h5+cnOS4zMxNGRkZqec1y5cqVinMQEdGbLV78GJ6e0gV/hw8fDjs7OwBAly5ydFUyeKlLRxgbG8PBwQFVq1bFsGHD4OHhgdDQUPHy1MyZM+Ho6AhXV1cAQFxcHL744gvY2NigXLly6Nq1K2JjY8Xz5eTkYMyYMbCxsUH58uUxbty4fItMvn6ZKiMjA+PHj4eTkxOMjY1Rs2ZNrFmzBrGxsWjbti0AwNbWFgqFQlyN+/VzPH36FP369YOtrS3MzMzQsWNH3Lx5U9wfEhICGxsbhIWFoU6dOrCwsECHDh0QHx8vHhMeHo7mzZvD3NwcNjY2+PDDD3Hv3j01/U0TEWmXw4cP46OPXoae1NRymDx5shh6yhoGHx1lamoqrsdw9OhR3LhxA4cPH8a+ffuQlZUFT09PWFpa4tSpUzh9+rQYIPJ+Zt68eQgJCcHatWvxxx9/4L///sNvv/32xtfs168fNm/ejEWLFuH69etYuXIlLCws4OTkhJ07dwLIfbBsfHw8Fi5cWOA5vLy8cO7cOYSGhiIyMhKCIODTTz9FVlaWeExaWhrmzp2L9evX4+TJk7h//z5+/PFHAEB2dja6deuG1q1b4/Lly4iMjMSQIUO0+g4FIqLiyMzMxNSpUxERESHWLl/ujo8++r5M/zeRl7p0jCAIOHr0KMLCwvD999/j8ePHMDc3x+rVq8VLXBs2bIBSqcTq1avFf/iDg4NhY2OD8PBwtG/fHj///DP8/PzQo0cPAMCKFSsQFhZW6Ov+/fff2LZtGw4fPgwPDw8AQPXq1cX9eZe07O3tJXN8XnXz5k2Ehobi9OnTcHd3BwBs3LgRTk5O2L17N3r16gUAyMrKwooVK1CjRg0AwIgRIzBt2jQAuUutJycno1OnTuL+OnXqFP0vkohIi928eRObNm2S1OrXHwt/fzOZOtIcjvjIJDQUcHfP/a4J+/btg4WFBUxMTNCxY0d8+eWXmDJlCgCgfv36knk9UVFRuHXrFiwtLWFhYQELCwuUK1cOL168wO3bt5GcnIz4+Hi0aNFC/BkDAwM0bdq00Ne/dOkS9PX10bp162L/DtevX4eBgYHkdcuXLw9XV1dcv35drJmZmYmhBgAqVaqExMREALkBy8vLC56enujcuTMWLlwouQxGRFSWCYKAdevWSULPlSsNMGWKPyZMKPuhB+CIj2wCA4HIyNzvmpg01rZtWyxfvhxGRkZwdHSU3M1lbm4uOfb58+do0qQJNm7cmO88xb3ma2pqWqyfK47X7wJTKBSS+UfBwcEYOXIkDh48iK1bt2LSpEk4fPgwWrZsqbEeiYg0LSkpKd80gg0bBiI9vQoAoAxf3ZLgiI9MfH0BNzfN3R5obm6OmjVrwtnZ+a23sDdu3Bg3b96Evb09atasKfmytraGtbU1KlWqhDNnzog/k52djfPnzxd6zvr160OpVOLEiRMF7s8bccrJySn0HHXq1EF2drbkdZ88eYIbN26gbt26b/ydXteoUSP4+fkhIiIC9erVyzfkS0RUlkREREhCjyAY4dChSZg3rwqWLcv9PJo9W8YGNYgjPjLp0qX03h749ddfY86cOejatSumTZuGKlWq4N69e9i1axfGjRuHKlWq4IcffkBgYCBq1aqF2rVrY/78+UhKSir0nC4uLujfvz8GDBiARYsWoUGDBrh37x4SExPxxRdfoGrVqlAoFNi3bx8+/fRTmJqawsLCQnKOWrVqoWvXrhg8eDBWrlwJS0tL+Pr6onLlyujatatKv9vdu3exatUqdOnSBY6Ojrhx4wZu3ryJfv36vctfGRFRqbR7dw4uXpwFPT2lWDtwoAOuXm2BjRtffg6V1s+jksARH8rHzMwMJ0+ehLOzM3r06IE6depg4MCBePHiBaysrAAAPj4+6Nu3L/r37w83NzdYWlqie/fubzzv8uXL8fnnn2P48OGoXbs2Bg8ejNTUVABA5cqVMXXqVPj6+qJixYoYMWJEgecIDg5GkyZN0KlTJ7i5uUEQBPz+++8qL3JoZmaGmJgY9OzZE++99x6GDBkCb29vfPfdd0X4GyIiKv3u37+PqKgZktDz/vujcfVqCzx/njvVQhcphNcXX9FxKSkpsLa2RnJysvghDwAvXrzA3bt3Ua1aNZiYmMjYIWka33si0jY7duzA1atXxe1bt2rg9u1vEBGRt0pz7lSLsjTSU9jn9+t4qYuIiKiMSE1Nxdy5cyW1rKxvcPt2DXFOaWmeaqEJDD5ERERlwKVLl7Bnzx5JbebMCWja1BCvrFGo8xh8iIiItJhSqcSiRYuQnJws1i5caIUDB9rB3r7sPFxUXRh8iIiItNSjR4+wYsUKSe3UKW8cPVoBAODsrNuXtQrC4FNEnAuue/ieE1FpdPDgQcm6ZgkJFREe/h1mz1bgwQNAEDjaUxAGHxXl3S6dlpam0VWISX5paWkA8q8ITUQkh4yMDAS+di/6gQM9ceZMPbi6cvLy2zD4qEhfXx82NjbiM5/MzMzK9NNrKXekJy0tDYmJibCxsYG+vr7cLRGRjouJicHWrVsltcDAcQBMNfo0AG3G4FMEDg4OACCGH9INNjY24ntPRCQHQRCwZs0aPHjwQKw1btwYf/7ZGQYGwMiRwMyZMjaoRbiA4WtUWQApJycHWVlZGu6M5GBoaMiRHiKS1dOnT7Fo0SJJbfDgwXB0dJSpo9KJCxiWIH19fX4YEhFRiTt16hSOHTsmbqemmmHfPh/4+/OJU8XF4ENERFTKZGdnY+Zr16727fsU5841g6urTE2VEQw+REREpUhsbCzWrVsnqc2dOwbPn1vCxAQICpKpsTKCwYeIiKiU2Lp1K2JiYsTtGzfew+bNvQEAlSsDy5bxVvV3xeBDREQks+fPn2PevHmSWkhIP2RlVYOFBe/aUicGHyIiIhmdO3cO+/fvl9RmzJiI7GwD7NnDER51Y/AhIiKSgVKpxPz585GamirWjh9vg9OnWyMnB/j8c4aeksDgQ0REpGHx8fFYtWqVpLZly/ewtS2HnTsZeEoSgw8REZEG7d+/H+fOnRO3HzxwxC+/DELt2gpERMjYmI5g8CEiItKAFy9eYPbs2ZLa1q1f4Pr1OgCA13ZRCWHwISIiKmHXrl3D9u3bJbWAgPHIyDCBqSkwejQvb2kKgw8REVEJEQQBq1atQkJCglg7c6YZjhz5FAYGwIQJvE1d0xh8iIiISsCTJ0+wZMkSSS0r6zskJTlgxw6O8MiFwYeIiEjNjh8/jpMnT4rbyclW+PnnH9CypR4nMMuMwYeIiEhNsrKyMGvWLEktNLQzLlxoDADw9ZWjK3oVgw8REZEa3LlzB+vXr5fU5szxQVqaBfT0gB49eHmrNGDwISIiekebNm3CzZs3xe2rV+ti+/ZeADiBubRh8CEiIiqmlJQULFiwQFJbu9YL9+9XBcDQUxox+BARERXD2bNnceDAAXFbqVRg5swJsLY24BPVSzEGHyIioiLIycnBrFlzoFRmiLXDhz1w+vSHMDEBgoM5l6c005O7ASIiIm3x4MEDzJgxQxJ6Vq36AZUqfQg3N2DrVoae0o4jPkRERCrYs2cPLl26JG7fu+eMyEgvPHigkK8pKjIGHyIiojdIT09HUFCQpLZp01eIi3PFli0yNUXFxuBDRERUiJ9+ugIDg12S2gcf+CIszBhz5vCyljZi8CEiInqNIAiYMGEZTEz+FWuRkW4YPrw9unQBuneXsTl6Jww+REREr/j333+xdOlSmJi8rC1bNgyJifZISeEoj7Zj8CEiIvqfI0eO4PTp0+L2f//ZYu3a77FxowKBgXzWVlnA4ENERDrvt98ycflywGu1boiKaoDatXNHeTjSUzYw+BARkU67efMmLl/eJKnNmTMWHTuawcyMozxlDYMPERHppD17BBw9+ivKl48VazdvfoA7d7pj0yaO8JRVDD5ERKRzkpOTcenSzyhf/mVt48YBmDvXiYGnjGPwISIinREaCmzeHInatQ+JtexsAxw75ou5c/UZenQAgw8REemE3btzcP58AGrXzhFrMTHt0bu3G6ZPl7Ex0igGHyIiKvM2bYrDzZtrYfDKp96oUaNgbW0tX1MkCwYfIiIqk0JDgcBAwMNjF/T1r4j1f/6pjqtXv0GjRgpe2tJBDD5ERFQm/fRTKnr0mCupHT/+NRISauLGjdxQxOCje/TkboCIiEjdfvrpUr7Q88EHfggPr4mgIMDNjevz6KoyGXyWLl0KFxcXmJiYoEWLFjh79qzcLRERkQYIgoCFCxfCwGCPWDt58iNs2eKP7t2NAOSO8kREcLRHV5W54LN161aMGTMG/v7+uHDhAho0aABPT08kJibK3RoREZWQiRMBZ+dHmDZtGpKSksT62rXeePjwE8yeLV9vVLooBEEQ5G5CnVq0aIFmzZphyZIlAAClUgknJyd8//338FVhXDMlJQXW1tZITk6GlZVVSbdLRETvKDQUWL48DC1b/inWnj+3w7x5w9CypQIRETI2Rxqj6ud3mRrxyczMxPnz5+Hh4SHW9PT04OHhgcjIyAJ/JiMjAykpKZIvIiIq/UJDgVatMnDx4lRJ6ImI6IlWrYajZUsF5/FQPmUq+Pz777/IyclBxYoVJfWKFSsiISGhwJ8JCAiAtbW1+OXk5KSJVomI6B2EhgITJ96Ah0egpL5kyTh4e9fjPB4qVJkKPsXh5+eH5ORk8SsuLk7uloiIqBATJwKWlgIOHFiLzz/fItbLl2+EsDB/rFljyrBDb1Sm1vGpUKEC9PX18ejRI0n90aNHcHBwKPBnjI2NYWxsrIn2iIjoHYSGAitWPMWPPy6S1N97bxB6966MESNkaoy0Spka8TEyMkKTJk1w9OhRsaZUKnH06FG4ubnJ2BkREb2LiROBuXP/wMiRL0NPZqYpfvrpJ/TuXVnGzkjblKkRHwAYM2YM+vfvj6ZNm6J58+b4+eefkZqaim+//Vbu1oiIqBiys7NhZDQTn3zysnbkyKcYO7YZ9MrU/76TJpS54PPll1/i8ePHmDx5MhISEtCwYUMcPHgw34RnIiIq/TZtuoebN0MktfDwMRg71pJzeahYytw6Pu+K6/gQEZUOCxduQ1LSdXG7Vq1a6NOnj4wdUWmm6ud3mRvxISIi7bZz53NER8+T1Pbv74uzZ6vL1BGVJbw6SkREsgsNBdzdgbVrL+QLPTNmTMCzZww9pB4c8SEiItkFBirh7r4AcXHPxdrx421gZ9cazZrxSeqkPgw+REQki9BQIDAQsLJKgKfnSsm+U6e+x5gx5TiBmdSOwYeIiGQxfjxQvfp+NG9+Tqw9fFgJ69cPRlqaQsbOqCxj8CEiIo2aOBFYuvQFRo+eLalv3doLt27VxdixMjVGOoHBh4iINCY0FNi9+xpGj94uqe/cOR6BgSa8tEUljsGHiIg0QhAEnDixCl98kSDWzp5tiqdPP8PlyzI2RjqFwYeIiErc9u3/4dq1xXh1XbmsrCF4+rQS79gijWLwISKiEvXTT+EwMDghbr94YYGZM0dDjw/aIhkw+BARUYnYvTsLUVGzYPDKJ82BA50wYUITPlyUZMPgQ0REanfnzh1ERa2X1DZs8MG8eRacwEyyYvAhIiK1WrBgE1JSborbd+7Uwbp1X8DfX8amiP6HwYeIiNRi585niI6eL6kFB3vBxKSqTB0R5cerrERE9E5CQ4G2bc/mCz1r1kyEqWlVBAXJ1BhRATjiQ0RExaZUKnHmzBy0afNCrB050g45Oa1w/76MjREVgsGHiIiK5eHDh/jll19gZPSy9ssvI+HsbMu1eajUYvAhIqIiCw0NxcWLF8XthAQnhId/i2XLFLxri0o1Bh8iIlJZeno6gl6btLN581coV84V16/L1BRRETD4EBGRSqKjo7Fz505JbdYsXxgZGXMCM2kNBh8iInojQRAQFLQcL148FmvnzrXEvn2e0NMDtm8HL2+R1uDt7EREVKh///0X06ZNk4SeYcOG4YMPPGFhAfj6MvSQdlEIgiDI3URpkpKSAmtrayQnJ8Pq1ccIExHpmMmTj0Jf/w9xOy3NFu7u36NrV4WMXREVTNXPb17qIiIiUWgoMGdOJjw8AqCv/7J+5UpX7NjRULa+iNSFwYeIiES//HILHh4bJbX163/E/PnmMnVEpF4MPkREBEEQsGDBBjRtekesxcbWR/fuPfhwUSpTGHyIiHRccnIyfv75Z0ltzZoBqFLFiROXqcxh8CEi0mF//vknwsLCxO3sbAOsW+cLc3N9PnaCyiQGHyIiHbR7dw4uXAiEvn62WGvfvj3c3NwwfbqMjRGVMK7jQ0SkY+Li4hAVNUMSelauHAU3NzcZuyLSDI74EBHpkF27duHKlSvi9p071bBhQ1/4+nJtHtINDD5ERDogLS0Nc+bMkdQ2bOiD9PRa+O03rr5MuoPBh4iojIuKisLu3bsltZkz/ZCVZQQ3N4Ye0i0MPkREZZQgCFi8eDGePn0q1k6d+hBHj3rAxASoUQO8c4t0Dic3ExGVAaGhgLt77ncASExMxLRp0yShZ/ny4Th2zAPlygFbtwLXr3O0h3QPR3yIiMqAwEAgMjL3u4nJIURGRor7/vuvAlatGo4xYxSYOVPGJolKgSKP+Ojr6yMxMTFf/cmTJ9B/9Yl2RESkMb6+wEcfZcLTc6ok9OzY0QOLFnmjUSOGHiKgGCM+giAUWM/IyICRkdE7N0REREUTGgqsWXMDHh5bJPWFC8fh6VNTmJpyLg9RHpWDz6JFiwAACoUCq1evhoWFhbgvJycHJ0+eRO3atdXfIRERFUoQBBw7FozGjePE2oULDfH3310REpJ76cvXl3N5iPKoHHwWLFgAIPdfshUrVkguaxkZGcHFxQUrVqxQf4dERFSgpKQkLFy4ELa2L2u//DII8fGVxbV5GHiIpFQOPnfv3gUAtG3bFrt27YLtq/+mERGRRp0+fRpHjhwRt7OyjBEQMBaAPkd4iN6gyHN8jh8/XhJ9EBGRCnbvzsalS7OgULycb3n9ekf06dMcLVrwshbR2xQ5+AwYMOCN+9euXVvsZoiIdF1o6Mt5OYB0js69e/cQFRUCxSuP1QoPH43Ro614WYtIRUUOPq8uhgUAWVlZiI6ORlJSEtq1a6e2xoiIdNGr6/EAL/+ckbEd165dE4+7ebMm9uz5Gs+eydQokZYqcvD57bff8tWUSiWGDRuGGjVqqKUpIiJd8uooj6+vdMRn/vxUtG07F69kHvz6a1/ExlbnLepExaAQCluYp4hu3LiBNm3aID4+Xh2nk01KSgqsra2RnJwMKysrudshIh3g7p47suPmBkREvKxfuHABe/fulRybmTkBx48bci4P0WtU/fxW2yMrbt++jezsbHWdjohIZ7w+yqNUKhEY+DOysl5exwoP/xjh4W3zhSMiKpoiB58xY8ZItgVBQHx8PPbv34/+/furrTEiorLs1ctbeROTQ0MBT88EuLuvlBy7b98InDtXniswE6lBkYPPxYsXJdt6enqws7PDvHnz3nrHFxER5Qacr78Gnj/PDT95l6x27/4d7u5/icfFxzvg11+HYMsWBVdgJlITtc3xKSs4x4eISlrenB4LC2DjRiA7+wWuXJktOWbbtl6IiakLX1/w4aJEKijxOT6JiYm4ceMGAMDV1RX29vbFPRURkU55dU6Pre0pHDt2TLI/MHA8DAxMxMdOEJH6FDn4pKSkwNvbG5s3b4ZSqQQA6Ovr48svv8TSpUthbW2t9iaJiMqSLl2Azp0FTJs2TVK/cKEJzp/vBBcXYPZshh6ikqBX1B8YPHgwzpw5g/379yMpKQlJSUnYt28fzp07h++++64keiQiKlMePHiQL/Rs3NgHoaGd4OwMXL/O0ENUUoo8x8fc3BxhYWH46KOPJPVTp06hQ4cOSE1NVWuDmsY5PkRUkn799Vfxoc95liyZhCdP9GFrCwQHM/QQFUeJzfEpX758gZezrK2t+cR2IiLkv1UdADIyMhCY9xyK/zl3rjGePOmMx49laJJIRxX5UtekSZMwZswYJCQkiLWEhASMHTsWP/30k1qbIyLSRq8/b+vixYv5Qk9m5vd48qQz1+Uh0rAiX+pq1KgRbt26hYyMDDg7OwMA7t+/D2NjY9SqVUty7IULF9TXqYbwUhcRvauJE4FFi4CRIwEjo6n59k+Z4s8VmInUrMQudXXt2hUKheKdmiMiKsuOHweMjZ/AyGiJpH7lSlfs3NkQFhZcgZlILkUOPlOmTCmBNoiIyo7evffgv/8uSWrLl/th4EAjuLlxBWYiORU5+FSvXh1//fUXypcvL6knJSWhcePGuHPnjtqaIyLSFqGhwOzZOWjffoakfvNmTWzc+DWA3JEgXt4ikleRg09sbCxycnLy1TMyMvDPP/+opSkiIm2zZs0NtG+/RVL79dchuHOnEhQKwNGRl7eISgOVg09oaKj457CwMMkt7Tk5OTh69CiqVaum3u6IiLTAzJnz0Ljxc0mtYcPJaNSIDxclKm1UvqtLTy/3zneFQoHXf8TQ0BAuLi6YN28eOnXqpP4uNYh3dRGRqp49e4b58+dLaidOeOD48Q951xaRhqn6+a3yOj5KpRJKpRLOzs5ITEwUt5VKJTIyMnDjxo0SCz2xsbEYOHAgqlWrBlNTU9SoUQP+/v7IzMyUHHf58mW0atUKJiYmcHJyQlBQUIn0Q0R07NixfKEnM3MsRo36UJzATESlT5Hn+Ly+1LomxMTEQKlUYuXKlahZsyaio6MxePBgpKamYu7cuQByk1779u3h4eGBFStW4MqVKxgwYABsbGwwZMgQjfdMRGVP7orMAjw9pc/Zevy4ApYu9YabGzBzJi9rEZVmRV7A8PUH671u8uTJ79SQqubMmYPly5eLd5EtX74cEydOREJCAoyMjAAAvr6+2L17N2JiYlQ+Ly91EdGrXn38xIoVcWjRYq1k//79ffHwYXVYWABBQQw9RHIpsQUMf/vtN8l2VlYW7t69CwMDA9SoUUNjwSc5ORnlypUTtyMjI/Hxxx+LoQcAPD09MXv2bDx9+rTQ54hlZGQgIyND3E5JSSm5polI6+Q9fuLYsbVo0SJOsm/atJ+gVOpxPg+RFily8Ll48WK+WkpKCry8vNC9e3e1NPU2t27dwuLFi8XLXEDu88Jev6usYsWK4r7Cgk9AQACmTs2/pDwREQCMHfsCly/PltTOn2+O06c7QqkETEw4n4dImxT5IaUFsbKywtSpU4v8kFJfX18oFIo3fr1+merBgwfo0KEDevXqhcGDB79z735+fkhOTha/4uLi3v5DRFQmhYYC7u653wHgr7/+yhd69u79AXv3doS9PeDmBmzdystbRNqkyCM+hckLDkXh4+MDLy+vNx5TvXp18c8PHz5E27Zt4e7ujlWrVkmOc3BwwKNHjyS1vG0HB4dCz29sbAxjY+Mi9U1EZdOrT1W/eFE6EpyTo4fp039CuXLgYyeItFiRg8+iRYsk24IgID4+HuvXr0fHjh2LdC47OzvY2dmpdOyDBw/Qtm1bNGnSBMHBweK6Qnnc3NwwceJEZGVlwdDQEABw+PBhuLq6FnqZi4gIeDmBuXJloGrVx/D0XCbZHxXVEwcP1gMApKdzPg+RNivyXV2vz6PR09ODnZ0d2rVrBz8/P1haWqq1QSA39LRp0wZVq1bFunXroK+vL+7LG81JTk6Gq6sr2rdvj/HjxyM6OhoDBgzAggULinQ7O+/qItI97u65Iz1ffrkTdepES/YdPjwB48YZ4swZYNEiYOTI3FvWiah0UfXzu8jBRw4hISH49ttvC9z3avuXL1+Gt7c3/vrrL1SoUAHff/89xo8fX6TXYvAh0g2v3qauVGYjKkqaZh49qoMOHb7g5SwiLVGiwScpKQm3bt0CANSsWRM2NjbFbrS0YfAh0g15ozzdul1Dw4bbJfuWLx+KR48q8jZ1Ii1SIuv4xMbGwtvbG2FhYeJIi0KhQIcOHbBkyRK4uLi8U9NERJri6wucPRsIQ8MMSX3KlMkAFLCw4G3qRGWRysEnLi4OLVu2hKGhIaZPn446deoAAK5du4bly5fDzc0Nf/31F6pUqVJizRIRqUNycjIuXvwZ/7sPAgBw/bon+vRpCVdXQKEAZs/mXVtEZZHKl7oGDhyIW7duISwsDCYmJpJ96enp6NChA2rVqoXVq1eXSKOawktdRGXb4cOHEfHa9aujR8fhxx9NGXSItJjaL3UdPHgQW7duzRd6AMDU1BTTp0/HV199VbxuiYhKmFKpxPTp0yU1U9NK2L17CNfkIdIhKq/c/O+//75xDk/16tXx33//qaMnIiK1io2NzRd6vLy8sHv3EHHBQiLSDSoHn0qVKuHatWuF7o+Ojn7jCslERJqU9/iJoKAVWLdunWRfWNhkVK1aFb6+L1dhJiLdoPKlrm7duuHHH3/E0aNH8622nJiYiPHjx6Nbt27q7o+IqFjmzUuHp2cQ0tNf1q5d+xCXL3tg9v8ev9WlCy9xEekalSc3P336FC1atEBCQgK++eYb1K5dG4Ig4Pr169i0aRMcHBzw559/oly5ciXdc4ni5GYi7RcZGYlDhw5JauHhoxEebsW1eYjKKLVPbra1tcWZM2cwYcIEbNmyBUlJSQAAGxsb9OnTB7NmzdL60ENE2k0QBEybNk1Se/HCBHp64zF6NJCRwctaRLquWCs3C4KAx48fA8h90KhCoVB7Y3LhiA+RdkpISMDKlSsltW3bvsC1a3VgYQE8eyZTY0SkESWycnMehUIBe3v7YjdHRKROW7duRUxMjKQ2d+5EdOhggPv3cx8sSkQEFDP4EBGVBllZWZg1a5akdvlyfRw40ANbtnDiMhHlx+BDRFrpypUr2LVrl6RWp85whIXZMfQQUaEYfIhI60ybNg2vT0+cO9cfz54BX3whU1NEpBUYfIhIazx9+hSLFi2S1I4f/wxnzjTFmDEyNUVEWkWl4PP6f2jeZCRnERJRCVi27AAePz4rqQUE+CIjwxhubsDMmTI1RkRaRaXgs2DBApVOplAoGHyISK0KerjovXvO2LDhW9jbA5aWXJuHiFSnUvC5e/duSfdBRJTP7du3sWHDBkltz54BSEtzwo4dnMBMREVX7Dk+mZmZuHv3LmrUqAEDA04VIiL1WrJkCZ48eSKpNWw4Gf7+ZWfBVCLSPJWfzp4nLS0NAwcOhJmZGd5//33cv38fAPD9998jMDBQ7Q0SkW5JTU3F1KlTJaEnIqI1GjXyR9euDD1E9G6KHHz8/PwQFRWF8PBwmJiYiHUPDw9s3bpVrc0RkW45deoU5s6dK6kdP+4Db+82vKxFRGpR5GtUu3fvxtatW9GyZUvJM7ref/993L59W63NEZFuKOjhoikploiMHMMnqRORWhV5xOfx48cFPqcrNTW1TD2slIg04+HDh/lCT/XqvREZOQZt2wLu7kBoqEzNEVGZU+Tg07RpU+zfv1/czgs7q1evhpubm/o6I6Iyb8OGDfjll18ktUOHJqFv3/cQEQEcPw5ERgKcPkhE6lLkS12zZs1Cx44dce3aNWRnZ2PhwoW4du0aIiIicOLEiZLokYjKmMzMTAQEBEhqt241wrlzXTB79suar29u6OE6PUSkLgrh9QfeqOD27dsIDAxEVFQUnj9/jsaNG2P8+PGoX79+SfSoUSkpKbC2tkZycjKsrKzkboeozLl48SJCX7t2tXr1CPzzT3m4uYFzeoioWFT9/C7WAjw1atTINzxNRPQ2U6dOzVcLDPTHmDG5l7U4skNEJU2l4JOSkqLyCTlKQkSve/LkCZYsWSKp7d7dFbduNcTWrVyBmYg0R6XgY2Njo/IdWzk5Oe/UEBGVLaGhobh48aKk9sEHfggLM8LGjQw9RKRZKgWf48ePi3+OjY2Fr68vvLy8xLu4IiMjsW7dunyTFYlId+Xk5GDGjBmS2q1bNXD79jfw9we6d5epMSLSaUWe3PzJJ59g0KBB6N27t6S+adMmrFq1CuHh4ersT+M4uZno3f3999/YvHmzpObqOhiLFjnC15ejPESkfqp+fhc5+JiZmSEqKgq1atWS1P/++280bNgQaWlpxeu4lGDwIXo38+fPx7NnzyS1hg0n8zlbRFSiVP38LvIChk5OTgXe0bV69Wo4OTkV9XREVEY8f/4cU6dOlYSeo0c/wZQp/pg9m6GHiEqHIt/OvmDBAvTs2RMHDhxAixYtAABnz57FzZs3sXPnTrU3SESlX3h4eL4FTIOCfkTDhuZwc+Nt6kRUehQ5+Hz66ae4efMmli1bhpiYGABA586dMXToUI74EOmYgh8uWh7z54/4334uSEhEpUuxFjCsUqUKZs2ape5eiEiL/PPPP1izZo2k9s033+Dq1Rr4/ffc0MORHiIqbYoVfJKSkrBmzRpcv34dAPD+++9jwIABsLa2VmtzRFQ6jRsXDHPz+5LaTz/9BD09PdSowbu2iKj0KvJdXefOnYOnpydMTU3RvHlzAMBff/2F9PR0HDp0CI0bNy6RRjWFd3URFe7FixeY/epTRAGcOdMMd+58iv9d+SYikkWJ3c7eqlUr1KxZE7/88gsMDHIHjLKzszFo0CDcuXMHJ0+efLfOZcbgQ1Swc+fOYf/+/ZLaqVMjkZZmy7V5iEh2JRZ8TE1NcfHiRdSuXVtSv3btGpo2bcp1fIjKmNBQ4OJF6cNFlUoFpk6dLFNHRET5ldg6PlZWVrh//36+elxcHCwtLYt6OiIqxR4/fpwv9Ozb1wNNmjD0EJF2KvLk5i+//BIDBw7E3Llz4e7uDgA4ffo0xo4dm+8xFkSkvXbt2oUrV65Iatu2TUBAgCEvaxGR1ipy8Jk7dy4UCgX69euH7OxsAIChoSGGDRuGwMBAtTdIRJqVnZ2NmTNnSmqPHrmiQ4ev4O8vU1NERGpS5Dk+edLS0nD79m0AQI0aNWBmZqbWxuTCOT6ky3799Tru3t0mqX333XdwcHCQqSMiItWo+vldrHV8gNyHldavX7+4P05EpcyMGbORk/NCUps8eTIUitznbIWGAoGB4B1cRKTVVA4+AwYMUOm4tWvXFrsZItK8HTtScPXqAkntwgVP7NnTUlILDAQiI3O/M/gQkbZSOfiEhISgatWqaNSoEYp5dYyISpnDhw/j6lXpw7R27RqH6dNN8x3r6/tyxIeISFupPMfH29sbmzdvRtWqVfHtt9/im2++Qbly5Uq6P43jHB/SBQU9XNTUtCLGjRsqU0dERO9G7ev4LF26FPHx8Rg3bhz27t0LJycnfPHFFwgLC+MIEJEWuXfvXr7QU7Nmf4YeItIJxb6r6969ewgJCcGvv/6K7OxsXL16FRYWFuruT+M44kNlWVDQKqSnx0tqU6f+hJYt9RARUcgPERFpgRK/q0tPTw8KhQKCICAnJ6e4pyEiDUhPT0dQUJCk5u7ujvT0/0PLlpy3Q0S6o0jBJyMjA7t27cLatWvxxx9/oFOnTliyZAk6dOgAPb0iP/2CiEpYaCiwadOfqFMnTFJ///1R+L//swbAO7SISLeoHHyGDx+OLVu2wMnJCQMGDMDmzZtRoUKFkuyNiN7Bnj0CLl2ahjp1XtaMjY1Rt64vAgMBIyOGHiLSPSrP8dHT04OzszMaNWokLmhWkF27dqmtOTlwjg+VBY8ePcKKFSsktV69eqFu3bpwd89dj8fNDZzXQ0Rlhtrn+PTr1++NgYeISodt27bh+vXrktrEiRNhYJD7rzvX4yEiXVakBQyJqPTKysrCrFmzJLV69eqhZ8+eAKSPnOBIDxHpqmLf1UVEpUd0dDR27twpqQ0fPhx2dnbiNh85QUTE4EOktUJDgXHjgC++mAF9femSEv7+/vmO5yUuIqJ3WMCwrOLkZtIWjRsnoWvXhZLap59+imbNmsnUERGRfEp8AUMiks/BgwfRtesZSW38+PEwMTGRqSMiIu3A4EOkRZRKJaZPny6pmZs74ccfB8jUERGRduFyy0Ra4s6dO/lCz4ABA9459ISGAu7uud+JiMo6jvgQaYGlS5fi33//ldQmT56slrW1eLcXEekSrRvxycjIQMOGDaFQKHDp0iXJvsuXL6NVq1YwMTGBk5NTvocyEmmbtLQ0TJ06VRJ6bt/+GI0a+attQVFf39xVnHm3FxHpAq0b8Rk3bhwcHR0RFRUlqaekpKB9+/bw8PDAihUrcOXKFQwYMAA2NjYYMmSITN0SFd/p06dx5MgRSW3MmDGwtLRU6+t06cKRHiLSHVoVfA4cOIBDhw5h586dOHDggGTfxo0bkZmZibVr18LIyAjvv/8+Ll26hPnz5zP4kFYRBAHTpk2T1CwsLODj4yNTR0REZYfWBJ9Hjx5h8ODB2L17N8zMzPLtj4yMxMcffwwjIyOx5unpidmzZ+Pp06ewtbUt8LwZGRnIyMgQt1NSUtTfPJEKch8pEQ9Pz1WS+ldffQVXV1eZuiIiKlu0Yo6PIAjw8vLC0KFD0bRp0wKPSUhIQMWKFSW1vO2EhIRCzx0QEABra2vxy8nJSX2NExXBoUMb84WeSZMmMfQQEamRrMHH19cXCoXijV8xMTFYvHgxnj17Bj8/P7X34Ofnh+TkZPErLi5O7a9BVJjQUKBVq0xMnToVdna3xHq5cg3h7+8PfX39dz4/b1UnInpJ1ktdPj4+8PLyeuMx1atXx7FjxxAZGQljY2PJvqZNm+Lrr7/GunXr4ODggEePHkn25207ODgUen5jY+N85yXSlF9/vQQPjz2S2ogRI1C+fHm1nJ+3qhMRSckafOzs7CRPjy7MokWLMGPGDHH74cOH8PT0xNatW9GiRQsAgJubGyZOnIisrCwYGhoCAA4fPgxXV9dC5/cQySF3Lg/g6TkV9etL9xX0cNF3wQeTEhFJaeVDSmNjY1GtWjVcvHgRDRs2BAAkJyfD1dUV7du3x/jx4xEdHY0BAwZgwYIFRbqriw8ppZIUGgp4e/+HQYMWS+qdO3dG48aNZeqKiEj7qfr5rRWTm1VhbW2NQ4cO4e7du2jSpAl8fHwwefJk3spOpUpo6N58ocfPz6/Q0MM5OkRE6qWVIz4liSM+VBJycnIkl2sBoFq1aujXrx+Al5e/fH2lc3Hc3XPn6Li5ARERmuyYiEi76NyID1FptX79zXyhZ9CgQbCx6SeO5rw6CflVfJwEEZF6ac0ChkTaaP/+/bhz55yklvdw0V69XoadwiYh83ESRETqxeBDVAJevHiB2bNnS2qVKrXDkCGtxO1Xww4DDhGRZjD4EKnZtWvXsH37dklt/PjxMDExkdQYdoiINI9zfIjUIPfuKwFBQSsloadZs2bw9/fHoUMmxb47i3d2ERGpD4MP0TvICyXTpj2Bp+c0pKe/fC5cRMR3yM7+FEDBk5dVDTSFTXwmIqKiY/AhegeBgYCxcTg6d14i1iwtLREW9hMOHXIQw0pBd2epGmh4ZxcRkfpwHZ/XcB0fepu828/HjctCVNQsyb68FZgLW5enoPO86RgiIlKNqp/fDD6vYfCht6ldG8jKuoN+/dZL6j4+PrCwsJCpKyIi3abq5zfv6iIqotatN8HR8aa4XbduXfTq1UvGjoiISFUMPkRvkXdJasyYFFy9ugCOji/3nTnjhUaNqsrXHBERFQmDD9FbBAYCOTlncfXqAbGmUCgQFjYBEREGSEriHB0iIm3B4EP0Bjk5OejYcQ6Uygyx9vffn+DLLz9Cw4YFP2aCiIhKLwYfokI8ePAAq1evltROnPgBx4/b4O7d3Kelc6SHiEi7MPgQFWDPnj24dOmSuO3s7AwvLy/s3avAixcc5SEi0lYMPkSvSE9PR1BQkKT21VdfwdXVFQCfr0VEpO0YfIj+58qVK9i1a5ek5uvrC2NjY5k6IiIidWPwIZ2We6u6gC5dliEj41+x7ubmhvbt28vYGRERlQQGH9Jpixf/C0/Ppch4edMWhg0bBnt7e/maIiKiEsOHlJLOOnLkCD76aKm4bWtri8mTJxcaelR9mjoREZVeHPEhnZOZmYmAgABJrVu3bmjQoMEbf+7Vp6lzgjMRkXZi8CGdkPfYiWHDbuLOnU2SfWPHjoWZmdlbz+HrywULiYi0HZ/O/ho+nb1scncX8N57v6JatVix9sEHH6B79+7yNUVERGqj6uc35/hQmfTqfJzk5GR4ek6ThJ4BAwYw9BAR6SBe6qIyKW8+zubNkbh48ZBYNzAwgK+vL/T19WXsjoiI5MLgQ2XSuHE5uHAhAPr6OWItJqY9Nm92k7ErIiKSG4MPlTmbNsXh5s21eHVQ58SJURg1ylq+poiIqFRg8KEyIzQU2L9/Fxwdr4i16tWr45tvvoFCoZCxMyIiKi0YfKhMSE1NxcWLc+Ho+LJWo8bX+OabmvI1RUREpQ6DD2m9S5cuYc+ePZKan58fjIyMZOqIiIhKKwYf0kp5Dxft3HkRMjOTxPqdOx+hZ89PwMxDREQFYfAhrbRkSSI8PZcjM/NlzdvbGxUqVJCvKSIiKvUYfEjrhIWF4cMP/xS37ezsMGzYME5gJiKit2LwIa2RkZGBwMBASa1nz56oV6+eTB0REZG24SMrqNR69bETN27cyBd6xo0bx9BDRERFwhEfKpVCQ4GvvwaePxdw7FgwLl6ME/c1atQIXbp0kbE7IiLSVgw+VCoFBgIGBk8xZcoiSX3QoEGoXLmyTF0REZG2Y/ChUiP3FnXA1xfw8voD8fFHxX0mJiYYO3Ys9PR4dZaIiIqPwYdKjcBA4OzZbFy8OFNS79ixI5o3by5TV0REVJbwf59JFq9OXM4zYsQ9/PSTNPSMGTOGoYeIiNSGIz4ki8BAIDIy93uXLsC2bdtw8+Z1cX+tWrXQp08fGTskIqKyiMGHZOHrmxt6fHyeY+rUeZJ9ffv2RfXq1WXqjIiIyjIGH9K4vEnMgwZdQHT0Xsm+CRMmwNDQUKbOiIiorGPwIY0KDQW++UaJIUMWIC7uuVhv06YNWrduLWNnRESkCzi5WYcVNMG4pF9v1KgE+PhMh6Xly9Dz/fffM/QQEZFGMPjosFcnGJeUV8PVnj370b//SnFfpUqVMHnyZJQrV67kGiAiInoFg48O8/UF3Nxyv7+rwkaPAgOBCxde4OLFqXB2PifWe/XqhSFDhvCJ6kREpFEKQRAEuZsoTVJSUmBtbY3k5GRYWVnJ3Y7WcHfPHT1ycwMiInJroaHAokXX0arVNsmx48ePh4mJiQxdEhFRWaXq5zdHfEglb5sP9ProkSAICA9fJQk9TZs2hb+/P0MPERHJhiM+r+GIT8EKGtEpzH///YfFixdLakOGDEGlSpVKsEMiItJlqn5+83Z2UknegoNvmw8UHh6OEydOiNsWFhYYPXo0Hy5KRESlAoMPqaRLl9yvwmRlZWHWrFmSWqdOndCkSZMS7oyIiEh1DD70zu7cuYP169dLaj4+PrCwsJCpIyIiooIx+FCR5T1ywtcXeP58E27evCnuq1OnDr744gsZuyMiIiocgw8VWWAgEB39DBcvzpfU+/fvDxcXF3maIiIiUgGDDxXZwIF/4Z9/fpfUJk6cCAMD/uNERESlGz+pSGVKpRJz585Fenq6WGvXrh1atWolY1dERESqY/AhlTx8+BC//PKLpDZy5EjY2trK1BEREVHRcXEVkihohebQ0FBJ6HFycsLkyZMZeoiISOtwxIckXn1i+//9XzqCgoIk+7/66iu4urrK1B0REdG7YfAhibwVmr/7LhpBQTtf2+cLY2NjmTojIiJ6dww+JNG5s4B//lmO2NjHYq1ly5bw9PSUsSsiIiL1YPAh0b///oulS5dKakOHDkXFihVl6oiIiEi9tGpy8/79+9GiRQuYmprC1tYW3bp1k+y/f/8+PvvsM5iZmcHe3h5jx45Fdna2PM1qmaNHj0pCj42NDX766SeGHiIiKlO0ZsRn586dGDx4MGbNmoV27dohOzsb0dHR4v6cnBx89tlncHBwQEREBOLj49GvXz8YGhrme3gmvVTQw0W7du2Khg0bytMQERFRCVIIgiDI3cTbZGdnw8XFBVOnTsXAgQMLPObAgQPo1KkTHj58KI5SrFixAuPHj8fjx49hZGSk0mulpKTA2toaycnJsLKyUtvvUBrdunULGzdulNR+/PFHmJuby9QRERFR8aj6+a0Vl7ouXLiABw8eQE9PD40aNUKlSpXQsWNHyYhPZGQk6tevL7k04+npiZSUFFy9erXQc2dkZCAlJUXyVdYJgoD169dLQk/9+vXh7+/P0ENERGWaVgSfO3fuAACmTJmCSZMmYd++fbC1tUWbNm3w33//AQASEhLyzUfJ205ISCj03AEBAbC2tha/nJycSui3kFfewoQ7diRj2rRp4t8pAAwYMAA9evSQsTsiIiLNkDX4+Pr6QqFQvPErJiYGSqUSQO6DMHv27IkmTZogODgYCoUC27dvf6ce/Pz8kJycLH7FxcWp41crdQIDAUH4E1ev/izWDAwMMGnSpDIb9oiIiF4n6+RmHx8feHl5vfGY6tWrIz4+HgBQt25dsW5sbIzq1avj/v37AAAHBwecPXtW8rOPHj0S9xXG2Ni4zC/Kl5OTgw4dAiEIL+9wa9++Pdzc3GTsioiISPNkDT52dnaws7N763FNmjSBsbExbty4gY8++ghA7t1IsbGxqFq1KgDAzc0NM2fORGJiIuzt7QEAhw8fhpWVlSQwabvQ0NzRG19foEuXtx8fFxeHtWvXSmonToyCv791CXVIRERUemnF7exWVlYYOnQo/P394eTkhKpVq2LOnDkAgF69egHIHcGoW7cu+vbti6CgICQkJGDSpEnw9vYuUyM6rz5L623B57fffsPly5fFbQuLati5sy98fRUl3CUREVHppBXBBwDmzJkDAwMD9O3bF+np6WjRogWOHTsmPiFcX18f+/btw7Bhw+Dm5gZzc3P0798f06ZNk7lz9cp7lpavb+HHpKWlicEwT58+fVCrVi34+JRwg0RERKWYVqzjo0navo5PVFQUdu/eLan5+fmpvI4RERGRNlL181trRnzozQRBwOLFi/H06VOxlpPzIY4c8UCjRqrNByIiIirrtGIdH12XtwZPaGjB+xMTEzFt2jRJ6FmyZDgWLPAQ5wMRERERR3y0wpsmNB86dAiRkZHitolJBdSuPRy1ainQti1w/Pib5wMRERHpEgYfLVDQhObMzEwEBARIjtuxowcsLetj/Higa1cNN0lERKQFGHy0QJcu0pGeGzduYMuWLZJj6tcfh7AwU47uEBERvQGDjxYRBAEhISHiatUA0LBhQ3T93/AOH7dFRET0Zgw+WiIpKQkLFy6U1AYNGoTKlSvL1BEREZH2YfDRAqdPn8aRI0fEbX19Yxw4MBaNGumDuYeIiEh1DD6lWHZ2NmbNmoVX15g8f74j/v23ucqPrSAiIqKXGHxKqXv37iEkJERSmzdvNBwdrRAU9PbHVhAREVF+DD6l0Pbt23Ht2jVxu2bNmrC0/Br16r18KjtHeoiIiIqOwacUSU1Nxdy5cyW1vn37Ijq6ujjCw8BDRERUfAw+pcSFCxewd+9eSW3ChAkwNDTEN98UvnIzERERqY7BR2ZKpRI///wznj17JtY+/vhjtG3bVtwuaOVmIiIiKjoGHxlt2ZKAGzdWSmojRoxA+fLlJTXO6SEiIlIPBh+ZHDhwADdunBW3HRwcMGTIECgUChm7IiIiKtsYfDQsIyMDgYGBkpqLSy/0719Xpo6IiIh0B4OPBl2/fh3btm2T1MaPHw8TExOZOiIiItItDD4asnfvXly4cEHcbtq0KT777DMZOyIiItI9enI3oCuuX78u/nnIkCFi6AkNBdzdc78TERFRyVIIrz4IipCSkgJra2skJyfDyspKbeeNjY3Fw4cP0bJlS+jpvcyb7u65a/S4uQEREWp7OSIiIp2i6uc3L3VpiIuLC1xcXPLV27YFrlzJ/U5EREQli5e6ZHb8OPD8ee53IiIiKlkMPjLz9c29zMVVmYmIiEoeL3XJjKsyExERaQ5HfIiIiEhnMPgQERGRzmDw0RCu10NERCQ/Bh8NCQzMXa/ntcd0ERERkQYx+GgI794iIiKSH+/q0hDevUVERCQ/jvgQERGRzmDwISIiIp3B4ENEREQ6g8GHiIiIdAaDDxEREekMBh8iIiLSGQw+REREpDMYfIiIiEhnMPgQERGRzmDwISIiIp3B4ENEREQ6g8GHiIiIdAaDDxEREekMPp39NYIgAABSUlJk7oSIiIhUlfe5nfc5XhgGn9c8e/YMAODk5CRzJ0RERFRUz549g7W1daH7FcLbopGOUSqVePjwISwtLaFQKORup0SlpKTAyckJcXFxsLKykrsdegXfm9KJ70vpxPel9NLkeyMIAp49ewZHR0fo6RU+k4cjPq/R09NDlSpV5G5Do6ysrPgfi1KK703pxPeldOL7Unpp6r1500hPHk5uJiIiIp3B4ENEREQ6g8FHhxkbG8Pf3x/GxsZyt0Kv4XtTOvF9KZ34vpRepfG94eRmIiIi0hkc8SEiIiKdweBDREREOoPBh4iIiHQGgw8RERHpDAYfHbZ//360aNECpqamsLW1Rbdu3ST779+/j88++wxmZmawt7fH2LFjkZ2dLU+zOiYjIwMNGzaEQqHApUuXJPsuX76MVq1awcTEBE5OTggKCpKnSR0SGxuLgQMHolq1ajA1NUWNGjXg7++PzMxMyXF8b+SxdOlSuLi4wMTEBC1atMDZs2flbkmnBAQEoFmzZrC0tIS9vT26deuGGzduSI558eIFvL29Ub58eVhYWKBnz5549OiRLP0y+OionTt3om/fvvj2228RFRWF06dPo0+fPuL+nJwcfPbZZ8jMzERERATWrVuHkJAQTJ48Wcaudce4cePg6OiYr56SkoL27dujatWqOH/+PObMmYMpU6Zg1apVMnSpO2JiYqBUKrFy5UpcvXoVCxYswIoVKzBhwgTxGL438ti6dSvGjBkDf39/XLhwAQ0aNICnpycSExPlbk1nnDhxAt7e3vjzzz9x+PBhZGVloX379khNTRWPGT16NPbu3Yvt27fjxIkTePjwIXr06CFPwwLpnKysLKFy5crC6tWrCz3m999/F/T09ISEhASxtnz5csHKykrIyMjQRJs66/fffxdq164tXL16VQAgXLx4Udy3bNkywdbWVvIejB8/XnB1dZWhU90WFBQkVKtWTdzmeyOP5s2bC97e3uJ2Tk6O4OjoKAQEBMjYlW5LTEwUAAgnTpwQBEEQkpKSBENDQ2H79u3iMdevXxcACJGRkRrvjyM+OujChQt48OAB9PT00KhRI1SqVAkdO3ZEdHS0eExkZCTq16+PihUrijVPT0+kpKTg6tWrcrStEx49eoTBgwdj/fr1MDMzy7c/MjISH3/8MYyMjMSap6cnbty4gadPn2qyVZ2XnJyMcuXKidt8bzQvMzMT58+fh4eHh1jT09ODh4cHIiMjZexMtyUnJwOA+O/H+fPnkZWVJXmfateuDWdnZ1neJwYfHXTnzh0AwJQpUzBp0iTs27cPtra2aNOmDf777z8AQEJCgiT0ABC3ExISNNuwjhAEAV5eXhg6dCiaNm1a4DF8X0qHW7duYfHixfjuu+/EGt8bzfv333+Rk5NT4N87/87loVQqMWrUKHz44YeoV68egNx//o2MjGBjYyM5Vq73icGnDPH19YVCoXjjV95cBQCYOHEievbsiSZNmiA4OBgKhQLbt2+X+bcoe1R9XxYvXoxnz57Bz89P7pZ1hqrvzasePHiADh06oFevXhg8eLBMnROVTt7e3oiOjsaWLVvkbqVQBnI3QOrj4+MDLy+vNx5TvXp1xMfHAwDq1q0r1o2NjVG9enXcv38fAODg4JDvzoi8GfgODg5q7LrsU/V9OXbsGCIjI/M906Zp06b4+uuvsW7dOjg4OOS7E4LvS/Gp+t7kefjwIdq2bQt3d/d8k5b53mhehQoVoK+vX+DfO//ONW/EiBHYt28fTp48iSpVqoh1BwcHZGZmIikpSTLqI9v7pPFZRSS75ORkwdjYWDK5OTMzU7C3txdWrlwpCMLLyc2PHj0Sj1m5cqVgZWUlvHjxQuM964J79+4JV65cEb/CwsIEAMKOHTuEuLg4QRBeTqDNzMwUf87Pz48TaDXgn3/+EWrVqiV89dVXQnZ2dr79fG/k0bx5c2HEiBHidk5OjlC5cmVObtYgpVIpeHt7C46OjsLff/+db3/e5OYdO3aItZiYGNkmNzP46KgffvhBqFy5shAWFibExMQIAwcOFOzt7YX//vtPEARByM7OFurVqye0b99euHTpknDw4EHBzs5O8PPzk7lz3XH37t18d3UlJSUJFStWFPr27StER0cLW7ZsEczMzMTASiXjn3/+EWrWrCl88sknwj///CPEx8eLX3n43shjy5YtgrGxsRASEiJcu3ZNGDJkiGBjYyO5I5VK1rBhwwRra2shPDxc8u9GWlqaeMzQoUMFZ2dn4dixY8K5c+cENzc3wc3NTZZ+GXx0VGZmpuDj4yPY29sLlpaWgoeHhxAdHS05JjY2VujYsaNgamoqVKhQQfDx8RGysrJk6lj3FBR8BEEQoqKihI8++kgwNjYWKleuLAQGBsrToA4JDg4WABT49Sq+N/JYvHix4OzsLBgZGQnNmzcX/vzzT7lb0imF/bsRHBwsHpOeni4MHz5csLW1FczMzITu3btL/sdBkxT/a5qIiIiozONdXURERKQzGHyIiIhIZzD4EBERkc5g8CEiIiKdweBDREREOoPBh4iIiHQGgw8RERHpDAYfIqISEh4eDoVCgaSkJLlbIaL/YfAhIq01ZcoUNGzYUO42iEiLMPgQUZmXlZUldwtEVEow+BCRbJRKJQICAlCtWjWYmpqiQYMG2LFjB4CXl4mOHj2Kpk2bwszMDO7u7rhx4wYAICQkBFOnTkVUVBQUCgUUCgVCQkIAAAqFAsuXL0eXLl1gbm6OmTNnvrGPvNcKCwtDo0aNYGpqinbt2iExMREHDhxAnTp1YGVlhT59+iAtLU38uYyMDIwcORL29vYwMTHBRx99hL/++qtk/rKISD1keUIYEZEgCDNmzBBq164tHDx4ULh9+7YQHBwsGBsbC+Hh4cLx48cFAEKLFi2E8PBw4erVq0KrVq0Ed3d3QRAEIS0tTfDx8RHef//9fE+DBiDY29sLa9euFW7fvi3cu3fvjX3kvVbLli2FP/74Q7hw4YJQs2ZNoXXr1kL79u2FCxcuCCdPnhTKly8vefDoyJEjBUdHR+H3338Xrl69KvTv31+wtbUVnjx5Ijnv06dPS+YvkIiKjMGHiGTx4sULwczMTIiIiJDUBw4cKPTu3VsMDUeOHBH37d+/XwAgpKenC4IgCP7+/kKDBg3ynRuAMGrUKJV7Kei1AgICBADC7du3xdp3330neHp6CoIgCM+fPxcMDQ2FjRs3ivszMzMFR0dHISgoSHJeBh+i0sNArpEmItJtt27dQlpaGv7v//5PUs/MzESjRo3E7Q8++ED8c6VKlQAAiYmJcHZ2fuP5mzZtWuSeXn2tihUrwszMDNWrV5fUzp49CwC4ffs2srKy8OGHH4r7DQ0N0bx5c1y/fr3Ir01EmsHgQ0SyeP78OQBg//79qFy5smSfsbExbt++DSA3TORRKBQAcucGvY25uXmRe3r9tV7dzqup8tpEVHpxcjMRyaJu3bowNjbG/fv3UbNmTcmXk5OTSucwMjJCTk5OCXdasBo1asDIyAinT58Wa1lZWfjrr79Qt25dWXoiorfjiA8RycLS0hI//vgjRo8eDaVSiY8++gjJyck4ffo0rKysULVq1beew8XFBXfv3sWlS5dQpUoVWFpawtjYWAPd544oDRs2DGPHjkW5cuXg7OyMoKAgpKWlYeDAgRrpgYiKjsGHiGQzffp02NnZISAgAHfu3IGNjQ0aN26MCRMmqHRJqWfPnti1axfatm2LpKQkBAcHw8vLq+Qb/5/AwEAolUr07dsXz549Q9OmTREWFgZbW1uN9UBERaMQBEGQuwkiIiIiTeAcHyIiItIZDD5EVOYNHToUFhYWBX4NHTpU7vaISIN4qYuIyrzExESkpKQUuM/Kygr29vYa7oiI5MLgQ0RERDqDl7qIiIhIZzD4EBERkc5g8CEiIiKdweBDREREOoPBh4iIiHQGgw8RERHpDAYfIiIi0hkMPkRERKQz/h8ZE1xgsXhGfQAAAABJRU5ErkJggg==", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_training, filename=\"pysmo_poly_train_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_training, filename=\"pysmo_poly_train_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_training, filename=\"pysmo_poly_train_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation\n", + "\n", + "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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YtmwZ3N3dAQAJCQmIiIhQXCsyMhKxsbEAilYbnjhxArNnz5bP29jYICIiAgkJCQCAEydOoKCgQHGd5s2bIyAgAAkJCejQoQMSEhLQunVreHt7K77npZdewh9//IF27dpVyHMhIiIi4zIzM7FmzZpi28XExMDDw6MSemRF05EAMHPmTNSuXRseHh64cuUK/ve//8nn/v77b1y+fBnbtm3DZ599hri4OJw4cUIxZZmenq4IjADA29sbOTk5uHfvHjIyMlBYWGi0TXp6unwNe3t7uLm5mW1j7Brac6bk5eUhJydH8apuRo0aBUmSIEkS7Ozs4O3tjR49euCTTz4pVamNuLg4g38HREREpujX1FSrXZCSEgi12sVsu4pk0SBs1qxZ8h9kU6/z58/L7adPn45Tp07hu+++g62tLUaMGAEhBICiUgJ5eXn47LPP0LlzZzz99NP4+OOPcfjwYSQlJVnqFktlyZIlUKlU8svf37/CviszMxNpaWkmX5mZmRX23b169UJaWhouXbqEffv2oVu3bpg8eTL69u1brosXiIiIjDl5sh1WrozFhg0jsXJlLE6etMwMlUWnI6dNm4ZRo0aZbdOoUSP5Z09PT3h6eqJp06Zo0aIF/P398csvvyA8PBy+vr6oVasWmjZtKrdv0aIFgKKVis2aNYOPj4/BKsbr16/D1dUVTk5OsLW1ha2trdE2Pj4+AAAfHx/k5+cjOztbMRKj30Z/RaX2mto2xsyePRtTp06V3+fk5FRIIGbpIVkHBwf5OdSvXx8hISHo0KEDunfvjri4OIwdOxbvvPMOPv30U/z9999wd3dHVFQUli1bhjp16uD777/H888/D+Bh0vz8+fOxYMECfP7551i1ahWSkpJQu3ZtPPPMM1i5ciXq1atX7vdBRETWR612wa5dfSFE0TiUEDbYtasvgoMvQqXKrdS+WHQkzMvLC82bNzf7MlWCQTt1lZeXBwB46qmn8ODBAyQnJ8tt/vrrLwBAw4YNAQDh4eE4ePCg4jrx8fEIDw8HUFSKIDQ0VNFGo9Hg4MGDcpvQ0FDY2dkp2iQlJeHKlStym/DwcPz++++KFZXx8fFwdXVFy5YtTT4PBwcHuLq6Kl4VoaRDrZU5JPvMM8+gTZs22LFjB4CiXLzVq1fjjz/+wIYNG3Do0CHMmDEDANCxY0esXLkSrq6u8sjdyy+/DAAoKCjAokWL8Ntvv+Gbb77BpUuXig30iYioesjMzMT58+fx+++/G7wuXLgAAMjK8pADMC0hbJCV5V7p/bWKxPzExEQcP34cnTp1Qt26dZGcnIy5c+ciODhYDnwiIiIQEhKC0aNHY+XKldBoNJgwYQJ69Oghj46NHz8ea9aswYwZMzB69GgcOnQIW7duxZ49e+Tvmjp1KkaOHIn27dvjySefxMqVK3Hnzh155EWlUmHMmDGYOnUq3N3d4erqiokTJyI8PBwdOnQAAPTs2RMtW7bE8OHDsWzZMqSnp+PVV1/FhAkT4ODgUMlPz3o0b94cZ86cAQB5sQQABAYG4vXXX8f48ePx/vvvw97eHiqVCpIkGYwsjh49Wv65UaNGWL16NZ544gncvn0bderUqZT7ICKiypWZmYmbN29iy5YtxbZ1d8+EJGkUgZgkaeDunlWRXTTKKoIwZ2dn7NixA/Pnz8edO3fg6+uLXr164dVXX5WDGhsbG+zatQsTJ05Ely5dULt2bfTu3RvLly+XrxMUFIQ9e/ZgypQpWLVqFRo0aICPPvoIkZGRcpvBgwfj5s2bmDdvHtLT09G2bVvs379fkWi/YsUK2NjYIDo6Gnl5eYiMjMT7778vn7e1tcXu3bvx0ksvITw8HLVr18bIkSPx2muvVcLTsl5CCHl68cCBA1iyZAnOnz+PnJwcPHjwAPfv38fdu3fN7hBw4sQJLFiwAL/99htu3bolj5heuXLF7CgkERFZJ/0UG7XaBVlZHrCzy0N2dl0AgL//VXmqUaXKRVTUbnlKUpI0iIraXelTkYCVBGGtW7fGoUOHim3n5+eH7du3m23z9NNP49SpU2bbxMTEICYmxuR5R0dHvPfee3jvvfdMtmnYsCH27t1rvsOkcO7cOQQFBeHSpUvo27cvXnrpJSxevBju7u746aefMGbMGOTn55sMwu7cuYPIyEhERkZi48aN8PLywpUrVxAZGVmpU6tERFSxMjMzkZ+fD7VarUj9OXmynU6+lwCgLbYt0K/fLoSEFP39Dwk5heDgi8jKcoe7e5ZFAjDASoIwqv4OHTqE33//HVOmTMGJEyeg0WiwfPlyeWPyrVu3Ktrb29sbbCN0/vx5ZGZmYunSpfKChl9//bVyboCIiCqF8ZGvQNjZ5SkS7h8GYEU/6yffq1S5RoOvytwOkEEYVbq8vDykp6ejsLAQ169fx/79+7FkyRL07dsXI0aMwNmzZ1FQUIB3330XUVFR+Pnnn7Fu3TrFNQIDA3H79m0cPHgQbdq0gbOzMwICAmBvb493330X48ePx9mzZ7Fo0SIL3SUREVUEUyNf+nle+rTJ9ypVLgYMGABPT0+DNvb29pVWqBWwsmKtVD3s378fvr6+CAwMRK9evXD48GGsXr0a//vf/2Bra4s2bdrgnXfewZtvvolWrVph48aNWLJkieIaHTt2xPjx4zF48GB4eXlh2bJl8PLyQlxcHLZt24aWLVti6dKlePvtty10l0REVN4yMzPlmRFjpSaKpiCN002+9/T0hK+vr8GrMgMwgCNhNVJJh1orYkg2Li4OcXFxxbabMmUKpkyZojg2fPhwxfu1a9di7dq1imNDhw7F0KFDFce0BX2JiMi66eb3Gis1UTQFKfT+CYsm35vDIKwG8vDwQExMjNlk9coekiUiIioNY6UmikiQJA2GDPkSBQVF+0v7+1+rcgEYwCCsxmKARURE1iw5uTFMTXQIYQN7+wI0a3bR6PnKTL43h0EYERERWRVtPpip1HZjxVd79OiBunXrol69elVmIIJBGBEREVmUtu6XKfopMsbzwYro538NGDAAfn5+VSbw0sUgjIiIiCxGv+6XKbpF1I3ng2kwcOBXBvlfVTUAAxiEERERkYVkZmYiNTVVcUy77ZC7e6YimNIdKTO19VCrVucAFE09BgUFVflFZgzCiIiIqFKZ2nBbv/hqVNRueashQJlQb27roWbNmlXp4EuLQRgRERFVGlPTj9eu+WLnzofJ9kLYGGw1VN1KLDEIIyIiokqjH0Cp1S5ITAzD0aMdodzvUbnVkJa1BFglwW2LqFr5/vvvIUkSsrOzS/yZwMBArFy5ssL6RERUk2VmZiItLU1+ZWRkyOdOnmyHFSticfToU9APwADjpSaqEwZhVKlGjRoFSZIwfvx4g3MTJkyAJEkYNWpU5XeMiIjKnXbqcf369fJrx44dAIqv9QVUza2GyhODMKp0/v7+2Lx5M+7duycfu3//PjZt2oSAgAAL9oyIiMqTsanHlJRAeQWkuVpfY8d+pEjKr44YhFGlCwkJgb+/v/xfQwCwY8cOBAQEoF27dvKxvLw8TJo0CfXq1YOjoyM6deqE48ePK661d+9eNG3aFE5OTujWrRsuXbpk8H0//fQTOnfuDCcnJ/j7+2PSpEm4c+dOhd0fEREZOnmyHVaujMWGDSOxYkUs/v47EJKkMWinXRXZoEGa4nhV2WqoPDEII1y7Bhw+XPTPyjJ69Gh8+umn8vtPPvkEzz//vKLNjBkzsH37dmzYsAEnT55E48aNERkZiaysovyAq1evYsCAAYiKisLp06cxduxYzJo1S3GN5ORk9OrVC9HR0Thz5gy2bNmCn376SVH0j4iIKpZ26vHhyJcNfvyxC1q0OKcTiGnQsePPiI1dqRgBGzBgAGJiYqpVQr4WV0fWcB9/DIwbB2g0gI0NsH49MGZMxX/vc889h9mzZ+Py5csAgJ9//hmbN2/G999/DwC4c+cO1q5di7i4OPTu3RsA8OGHHyI+Ph4ff/wxpk+fjrVr1yI4OBjLly8HUFQX5vfff8ebb74pf8+SJUswbNgwxMbGAgCaNGmC1atXo2vXrli7di0cHR0r/maJiGo441OPEs6da4ExYz5CQYG9Qa0vrapc8f5RMQirwa5dexiAAUX/fPFFIDISaNCgYr/by8sLffr0QVxcHIQQ6NOnDzw9PeXzycnJKCgowFNPPSUfs7Ozw5NPPolz54oqIp87dw5hYWGK64aHhyve//bbbzhz5gw2btwoHxNCQKPRICUlBS1atKiI2yMiqlFM7f2oXQnp7p4JQAP9CTghbFBQYI+goMsGnx00aFCV2my7IjAIq8EuXHgYgGkVFgIXL1Z8EAYUTUlqpwXfe++9CvmO27dv48UXX8SkSZMMznERABHRoyvJ3o8qVS569DiA+Pge0C1FoVuCYsCAAfJ/jFtTwdVHwSCsBmvSpGgKUjcQs7UFGjeunO/v1asX8vPzIUkSIiMjFeeCg4Nhb2+Pn3/+GQ0bNgQAFBQU4Pjx4/LUYosWLbBz507F53755RfF+5CQEPz5559oXFk3RURUjRkb8dKt+wWY3vvxqacSAAAHDkQotiXStvH09ISvr28F30HVwiCsBmvQoCgH7MUXi0bAbG2BDz6onFEwALC1tZWnFm1tbRXnateujZdeegnTp0+Hu7s7AgICsGzZMty9exdj/i9pbfz48Vi+fDmmT5+OsWPH4sSJE4iLi1NcZ+bMmejQoQNiYmIwduxY1K5dG3/++Sfi4+OL/S83IiJ6GHip1WqDvR71Fbf341NPJaBVq7NG93usiRiE1XBjxhTlgF28WDQCVlkBmJarq6vJc0uXLoVGo8Hw4cORm5uL9u3b49tvv0XdunUBFE0nbt++HVOmTMG7776LJ598Em+88QZGjx4tX+Pxxx/HkSNHMGfOHHTu3BlCCAQHB2Pw4MEVfm9ERNbO3FSj/oiX/gpIY3s/AkVTk8aCr+pYgqI4khBCWLoTZFxOTg5UKhXUarVBsHL//n2kpKQgKCiIK/wqGJ81EdVUaWlpWL9+vcFxYyNedevewoYNIw3ajhwZh6Cgy4qcL33VLQfM3N9vXRwJIyIiohJRq11w9aq/0RGvMWM+giRpFKUodBPva2LOV3EYhBEREVGxdEe/9GlLTURF7TYYIavpeV/mMAgjIiIime4KSO3KR8OK90raEa+goMsIDr7IxPsSYhBGREREAEwn4he32bbuiBcT70uOQZiV47qKisdnTETVUXJyMu7evas4duvWLcV77QpIO7s8g3wvQIOBA7+Cv/81RdBlLAG/uiXelxcGYVZKW1crPz8fTk5OFu5N9aYdltevZUZEZK2Sk5PxxRdfmG2jvwLy8cfP4MyZxxX5Xq1anTP4XHXe67G8MQizUrVq1YKzszNu3rwJOzs72NgYHyamR6PRaHDz5k04OzujVi3+z4WIqgf9ETB9xmp+nTnzuMnNtgcNGgQ3NzeOeJUS/6pYKUmS4Ovri5SUFFy+bLjxKZUfGxsbBAQEQJKk4hsTEVmha9d8ceVKQwQEXEaDBmlGc8B0N9uuifs8VgQGYVbM3t4eTZo0MbpzPZUfe3t7jjQSUbX19df/xm+/tUHRxtoCbdr8hmeeOcSaX5WAQZiVs7GxYRV3IiIqk2vXfHUCMACQ8NtvbfDEE8dY86sSMAgjIiKq5nRrfwEPV0FeudIQDwMwLQlXrwYgPDyRNb8qGIMwIiKiaszcJtwBAZcBCCgDMQF//ysAWPOrojEIIyIismL6o1z61Gq13vui2l/u7plo0CANbdr8ZpAT1qBBGgCgW7duaNKkieLzTMQvPwzCiIiIrJS5US5j9Gt/RUXtxv/7f//DE08cw9WrAfD3vyIHYABQv359JuBXIAZhREREVkJ/1Eu7t2NJGKv9tWtXXwQHX0SDBmkYPrw56tYNl9s7OzsjODi4/DpPBhiEERERWYGSjHrpTjXq53KZqv2VleUOlSoXTZo04ahXJWMQRkREZAWKqwlpbKoxJOSUfN7dPdNs7S+qfKxASUREZOVMTTWq1S5yG5UqF1FRuyFJGgBg7a8qgCNhREREVq64qUatkJBTrP1VhTAIIyIiskK6+V+pqb7Qr/dlaqqRtb+qDgZhREREVVxmZqZiJaRu/hegQVHwpSy4GhFxQA62Bg0aBDc3N5PXZ+0vy2AQRkREVIXpr4rUz/8ynt4twc8vVX5Xr149BllVEBPziYiIqjD9VZFXr/ob5H/p052KHDx4MAOwKoojYURERBZQ3HZDxqYItdOQhjSQJCjKU2inIlUqVXl2m8oRgzAiIqJKVtLthmJiYuSfDachtTTo1283Vz1aIQZhRERElUx/BMxUpXvddsbKUADAwIFfoVWrcwDAVY9WhkEYERGRBZmrdJ+dnS2vajRV8d7f/xoAYMCAAfD09FRcm6seqzYm5hMREVlIcZXut27dCrVaDaD4iveenp7w9fVVvBiAVW0cCSMiIqogppLvtTW/TFW6T0wMQ8+eBwAABQUF8jlWvK9eGIQRERFVgJIk3xubYgSAo0fDERaWCJUqF7VqKf9Us+J99cEgjIiIqAKYKz+hpVLlIjw8AUePPqV35uG+j25uboiJiSl1OQuq+hiEERERVQJTKyDDwhJx9Gg4dNO09fd9ZIBVPTEIIyIiqmD6KyAjIg7gqacSABSNhvXrt9tghSTzvao/BmFEREQVyNgKyPj4HgAgB2JMuK+ZWKKCiIioAhkvsiohPj5CLkUBFI2IBQVdZgBWgzAIIyIiqkDaFZCGipLvi8NVj9UXpyOJiIgqkEqVi4iIA/83BSnJx3WT741Vuwe46rG6YxBGRERkgqliq1rmgiTdESxt7ld8fAQAw+R7bbV7qlkYhBERERlRkmKrADB48GCoVCrFMW1wNnjwYGzZsgVAUSDWqtVZJt+TjEEYERGRESUptgoA69fvNVr/KyYmxiA4M1Xtnmomq0nM79evHwICAuDo6AhfX18MHz4cqamp8vkFCxZAkiSDV+3atRXX2bZtG5o3bw5HR0e0bt0ae/fuVZwXQmDevHnw9fWFk5MTIiIicOHCBUWbrKwsDBs2DK6urnBzc8OYMWNw+/ZtRZszZ86gc+fOcHR0hL+/P5YtW1bOT4SIiMpTZmYm0tLS5Jd2f0cttdoFKSmBihWNJ0+2w8qVsdiwYSRWrozFyZPt5HP5+fklTqpn8n3NZDUjYd26dcMrr7wCX19f/PPPP3j55ZcxcOBAHD16FADw8ssvY/z48YrPdO/eHU888YT8/ujRoxg6dCiWLFmCvn37YtOmTejfvz9OnjyJVq1aAQCWLVuG1atXY8OGDQgKCsLcuXMRGRmJP//8E46OjgCAYcOGIS0tDfHx8SgoKMDzzz+PcePGYdOmTQCAnJwc9OzZExEREVi3bh1+//13jB49Gm5ubhg3blxlPC4iIiqF4qYe9YutRkXtRnDwRYP6X7t29UVw8EV5tMvDw4NbDpFJkhBCWLoTZbFz5070798feXl5sLOzMzj/22+/oW3btvjhhx/QuXNnAEXz9nfu3MHu3bvldh06dEDbtm2xbt06CCHg5+eHadOm4eWXXwYAqNVqeHt7Iy4uDkOGDMG5c+fQsmVLHD9+HO3btwcA7N+/H//6179w7do1+Pn5Ye3atZgzZw7S09Pl/7qZNWsWvvnmG5w/f77E95iTkwOVSgW1Wg1XV9cyPysiIjIvLS0N69evN3pOrXbBypWxilpfkqRBdPR2fPXVfwzajxwZh6Cgyxg3bhyT7Wuokv79tprpSF1ZWVnYuHEjOnbsaDQAA4CPPvoITZs2lQMwAEhISEBERISiXWRkJBISilatpKSkID09XdFGpVIhLCxMbpOQkAA3Nzc5AAOAiIgI2NjYIDExUW7TpUsXxfByZGQkkpKScOvWrUe8eyIiqmhqtQvOnm2JY8dCceJEiEGx1aL3wqD+l/6ej0TmWM10JADMnDkTa9aswd27d9GhQwfFiJau+/fvY+PGjZg1a5bieHp6Ory9vRXHvL29kZ6eLp/XHjPXpl69eorztWrVgru7u6JNUFCQwTW05+rWrWu033l5ecjLy5Pf5+TkGG1HRETlS61Wyz+fPNkOO3dGQbemFyCgX+PL3/8aoqK45yOVnUVHwmbNmmU0mV73pTt9N336dJw6dQrfffcdbG1tMWLECBibTf3666+Rm5uLkSNHVubtPLIlS5ZApVLJL39/f0t3iYio2svMzJTLSKjVLti5sy+UARj+733R3xvdYCsk5BRiY1di5Mg4xMauREjIqUrtO1k3i46ETZs2DaNGjTLbplGjRvLPnp6e8PT0RNOmTdGiRQv4+/vjl19+QXh4uOIzH330Efr27WswouXj44Pr168rjl2/fh0+Pj7yee0x3Xn869evo23btnKbGzduKK7x4MEDZGVlKa5j7Ht0v8OY2bNnY+rUqfL7nJwcBmJERBVMN2k+MTEMpscnJERG7kfLln8qRrtYdoLKyqJBmJeXF7y8vMr0WY2maB5ed/oOKMrrOnz4MHbu3GnwmfDwcBw8eBCxsbHysfj4eDmICwoKgo+PDw4ePCgHXTk5OUhMTMRLL70kXyM7OxsnTpxAaGgoAODQoUPQaDQICwuT28yZMwcFBQVyzlp8fDyaNWtmcioSABwcHODg4FCGp0FERI9KrXZBQkK4yfOSpDEIwMxh2QkqjlXkhCUmJuL48ePo1KkT6tati+TkZMydOxfBwcEGo2CffPIJfH190bt3b4PrTJ48GV27dsXy5cvRp08fbN68Gb/++qu8IkaSJMTGxuL1119HkyZN5BIVfn5+6N+/PwCgRYsW6NWrF1544QWsW7cOBQUFiImJwZAhQ+Dn5wcAePbZZ7Fw4UKMGTMGM2fOxNmzZ7Fq1SqsWLGiYh8UERGVWVaWh0ECvpZ+vpexKvm6WHaCSsIqgjBnZ2fs2LED8+fPx507d+Dr64tevXrh1VdfVYwcaTQaxMXFYdSoUbC1tTW4TseOHbFp0ya8+uqreOWVV9CkSRN88803co0wAJgxYwbu3LmDcePGITs7G506dcL+/fvlGmEAsHHjRsTExKB79+6wsbFBdHQ0Vq9eLZ9XqVT47rvvMGHCBISGhsLT0xPz5s1jjTAiIgsobv9HbVK+u3smJEmjF4hp8K9/7UGzZhf0piBVLD9Bj8xq64TVBKwTRkT0aPSLsKrVLka3GNIyVpTVWLI9a4CROSX9+20VI2FERERloTsCVpIAKyTkFIKDLxa7yTbzvag8MAgjIqJqT612KXaLIS1Tqx0HDBgAT09P5ntRuWEQRkRE1YpuDph2E25jSfdC2CAry73Eqx39/PwYfFG5YhBGRETVhvEcsEDk59cySLrX3WJo0KBBcHNzM3ldjn5RRWAQRkRE1YapHLCH2w4V/VO/5ISbmxsT7anSMQgjIiKrUFypCd1kef0csIfbEEkANBgz5iM0aJBWYX0lKgkGYUREVOXpTzOa0qNHDwDmC68CNigo4OpGsjwGYUREVGVpR7+0CfZa2npfdnZ5KChwkOt+xcfHAzBVeLWIbi6YFktOkCUwCCMioirJ1OiXsVwv/bpfKlUuoqJ2m2ynzQUbMGAAVz2SxTAIIyKiKkk//0utdsHVq/5Gc72M1f3SLbxqZ5ePggJ7gwKsnp6eDMDIYhiEERGRRZlKuNedglSOfhlnrO6XqcKrRFUBgzAiIrKYkiTcG650NM5YrhdRVWb+N5qIiKgCGZtyTEkJhFrtIh8zv9JRAIBBrlfPnj1L9P1MyCdL4kgYERFVCaY22HZ3zwSggXLcQIOBA7+Cm1u20VyvwMBAxMTEFFtXjPlgZEkMwoiIyOLMbbBdRFK0lyTA3/+a2XwvBlhU1TEIIyIiizO3wXZRACYZPWcqCOM0I1kDBmFERGRxxoqr6ibamzvXo0cPBAUFyec4zUjWgon5RERkcdriqpKkAaBMtDd3DgCCgoLg6+srvxiAkbXgSBgREZW7kmy2rR8s6RZX1U+0N3eOyFoxCCMionJV0s22Y2JiDHK3zBVXZeFVqm4YhBERUbkyVvsrK8tD3mRb648//kDdunXRu3dvODk5oVatWnBzc5PPZ2dnY+vWrcV+H5PwyVoxCCMiogqjX/srIuIA/PzS4O6eicOHDxu0j4mJkacpfX19WeuLqjUGYUREVCGM1f6Kj++BonITGnTsmICwsETF6Jh+wMUAi6ozro4kIqIKYXy7IW29LxscPfoUVq6MxcmT7Sq7a0RVAoMwIiIqF5mZmUhLS0NGRgaAh7W/zNFWxtfdK5KopuB0JBERPTJjKyK19b0eTkkK6Fe+B4qvfk9UXTEIIyKiR2ZqRWRw8EXExq5EVpY7UlP9EB8fAf1JGN3q90Q1CYMwIiIqE92CrNopSMBwRWRU1G6EhJxCUNBltGp1FomJYUhICFec5ygY1UQMwoiIqNSSk5PxxRdfyO+LRr4CYWeXZ7AicteuvggOvigXW+3Z8wDCwhJZ/Z5qPAZhRERUKpmZmYoATHfkC9BAf7rRWM6Xqer3LLxKNQmDMCIiKpUbN27IP6vVLti5sy8eBl6GCfi6OV/dunVD3bp1FdfTVspn4VWqaRiEERFRiWVmZiq2Evrhh84wrHYkQZI0RnO+mjRpAl9f38rrMFEVxiCMiKiG0E2kN6YkI1G6n//553CcONHeSCsNxoz5CAUF9sz5IjKDQRgRUQ1grI6XMTExMQAMS05oaVdBqtUu/1duwrDuV8eOCWjQIM3o55nzRfQQgzAiohrA3AiYrqSkJMTHx8vvtfW+3N0zFSNaWVkeMLbpiiRpEBaWKL8fMGAAPD09AXCzbSJ9DMKIiGogU8GVbgBmqt4X8HBLIuXekAIREQcU1/P09GQOGJEJDMKIiKqp0hZT1Q3MABRb70u5JZEGPXocwFNPJSj6wOlHItMYhBERVUOmcsDUahejwdW9e444cCBCDszCwxP0RrkM632FhJxCcPBFk0VXn3vuOU4/EpnBIIyIqBoytZfjnTvORoMrbQCmfZ+QEA79wqvG9ng0VXR18ODBCA4OLp+bIaqmGIQREVVzhhXtlcVUAf3crqJArGPHn8u8x6OXl1d5dZ+o2mIQRkRUjelPPxaNbGkgSUIOriIiDihGwoCHqxxN7fGou+pRH1dBEpUMgzAiomosK8vDYJQLsEF09FbUrn1XDq6cnO4bJOtrgy5jo19c9Uj06BiEERFVI9oVkdrVkMZKSUiSBv7+1xTBVXFJ9vq46pHo0TEIIyKyYrplKNRqNbZs2SKf0ybj6043msvtMpdkr1Kp5PecbiQqHwzCiIislLmtiPRrgUVEHICfX6rBKJd+gKWPARdRxWEQRkRkpUyVobCzyzOoBXbgQARiY1cajHR5eXkxyCKyEAZhRETVgGEZCtOFVrUrGznKRWRZDMKIiKxQZmamnHxvvAyFshaYbqFVrmwkqhoYhBERWRn9XDDjZSgkeVVkaQutElHlYBBGRGRl9HPB7OzyjJahGDPmIxQU2Jeo5AQRVT4GYURElUi3pIQxpc3TUuaCFU1Bake+GjRIe/QOE1GFYRBGRFRJzJWU0BUTE1OiQMwwF0wCUDQCZi4AY6FVoqqBQRgRUSUxVVLC3T1TMV1obqRMl6ktiQoKlEHWoEGD4ObmBoB1v4iqEgZhREQWoF9MNSpqN0JCTpXqGqa2JNKughwwYAD8/PwYdBFVUfr/CUVERBVMfxpRCBvs2tUXarVLqa6jUuUiKmo3JEkDAAarID09PRmAEVVhHAkjIqpkxqYRdYuplkZpN94moqqDQRgRUTkztQJSW1y1uGnE4ugn1pvaeJsJ+ERVG4MwIqJyVJIVkNppRP2csJKOYnl4eCAmJqZcS10QUeUrcRCWk5NT4ou6urqWqTNERNaupCsgH3UakQEWkfUrcRDm5uYGSZLMthFCQJIkFBYWPnLHiIisXXErIE1NI969e7cyu0lEFlLiIOzw4cMV2Q8iomrF1ArI4OCLxY56ffHFFyUu2EpE1qvEQVjXrl0rsh9ERNVKSVdAPmrBViKyXmVOzM/OzsbHH3+Mc+fOAQAee+wxjB49GiqVqtw6R0RkrUqyArI8CrYSkfUqU7HWX3/9FcHBwVixYgWysrKQlZWFd955B8HBwTh58mR595GIyOoUV0i1vAq2EpH1KtNI2JQpU9CvXz98+OGHqFWr6BIPHjzA2LFjERsbix9++KFcO0lEZI3MrYAsz4KtRGSdyjwSNnPmTDkAA4BatWphxowZ+PXXX8utc7r69euHgIAAODo6wtfXF8OHD0dqaqqizbfffosOHTrAxcUFXl5eiI6OxqVLlxRtvv/+e4SEhMDBwQGNGzdGXFycwXe99957CAwMhKOjI8LCwnDs2DHF+fv372PChAnw8PBAnTp1EB0djevXryvaXLlyBX369IGzszPq1auH6dOn48GDB+XyLIio6jJWSDUo6LJBYKWdrtRVmoKtRGT9yjQS5urqiitXrqB58+aK41evXoWLS8UMpXfr1g2vvPIKfH198c8//+Dll1/GwIEDcfToUQBASkoK/v3vf2Pq1KnYuHEj1Go1pkyZggEDBshTpCkpKejTpw/Gjx+PjRs34uDBgxg7dix8fX0RGRkJANiyZQumTp2KdevWISwsDCtXrkRkZCSSkpJQr149AEUjgXv27MG2bdugUqkQExODAQMG4OeffwYAFBYWok+fPvDx8cHRo0eRlpaGESNGwM7ODm+88UaFPB8iqhqKK6SakZGBHTt2PHLBViKyfpIQQpT2Q5MmTcLXX3+Nt99+Gx07dgQA/Pzzz5g+fTqio6OxcuXK8u6ngZ07d6J///7Iy8uDnZ0dvvrqKwwdOhR5eXmwsSka4Nu1axf+/e9/y21mzpyJPXv24OzZs/J1hgwZguzsbOzfvx8AEBYWhieeeEKueK3RaODv74+JEydi1qxZUKvV8PLywqZNmzBw4EAAwPnz59GiRQskJCSgQ4cO2LdvH/r27YvU1FR4e3sDANatW4eZM2fi5s2bJd5KJCcnByqVCmq1mgVwiaqJtLQ0rF+/Xn5ftDrScLpy3Lhx8PX1tUQXiegRlfTvd5mmI99++20MGDAAI0aMQGBgIAIDAzFq1CgMHDgQb775Zpk7XVJZWVnYuHEjOnbsCDs7OwBAaGgobGxs8Omnn6KwsBBqtRqff/45IiIi5DYJCQmIiIhQXCsyMhIJCQkAipaEnzhxQtHGxsYGERERcpsTJ06goKBA0aZ58+YICAiQ2yQkJKB169ZyAKb9npycHPzxxx8m7ysvLw85OTmKFxFVLyWdruS+j0TVX5mmI+3t7bFq1SosWbIEycnJAIDg4GA4OzuXa+f0zZw5E2vWrMHdu3fRoUMH7N69Wz4XFBSE7777DoMGDcKLL76IwsJChIeHY+/evXKb9PR0RWAEAN7e3sjJycG9e/dw69YtFBYWGm1z/vx5+Rr29vZwc3MzaJOenm72e7TnTFmyZAkWLlxYwqdBRNaI+z4SkVaZRsK0nJ2d0bp1a7Ru3bpMAdisWbMgSZLZlzb4AYDp06fj1KlT+O6772Bra4sRI0ZAO5uanp6OF154ASNHjsTx48dx5MgR2NvbY+DAgSjDjKtFzJ49G2q1Wn5dvXrV0l0iogrg4eEBX19fky8GYEQ1Q5lGwu7fv493330Xhw8fxo0bN6DRKFf4lLRW2LRp0zBq1CizbRo1aiT/7OnpCU9PTzRt2hQtWrSAv78/fvnlF4SHh+O9996DSqXCsmXL5PZffPEF/P39kZiYiA4dOsDHx8dgFeP169fh6uoKJycn2NrawtbW1mgbHx8fAICPjw/y8/ORnZ2tGA3Tb6O/olJ7TW0bYxwcHODg4GD2eRAREVH1UKYgbMyYMfjuu+8wcOBAPPnkk8Vu7G2Kl5cXvLy8yvRZbeCXl5cHoGjDW21Cvpatra2irf70JADEx8cjPDwcQNEUQGhoKA4ePIj+/fvLnz148CBiYmIAFOWe2dnZ4eDBg4iOjgYAJCUl4cqVK/J1wsPDsXjxYty4cUNeURkfHw9XV1e0bNmyTPdLRBUnMzOT04NEVOnKtDpSpVJh7969eOqppyqiTwYSExNx/PhxdOrUCXXr1kVycjLmzp2L69ev448//oCDgwMOHTqEiIgILFiwAEOHDkVubi5eeeUVnD9/HufOnYOTkxNSUlLQqlUrTJgwAaNHj8ahQ4cwadIk7NmzR1GiYuTIkfjggw/w5JNPYuXKldi6dSvOnz8v53W99NJL2Lt3L+Li4uDq6oqJEycCgFwuo7CwEG3btoWfnx+WLVuG9PR0DB8+HGPHji1ViQqujiSqeJmZmfJqaHO4oTYRlVSFro6sX79+hdUDM8bZ2Rk7duxA9+7d0axZM4wZMwaPP/44jhw5Ik/fPfPMM9i0aRO++eYbtGvXDr169YKDgwP2798PJycnAEXJ+3v27EF8fDzatGmD5cuX46OPPpIDMAAYPHgw3n77bcybNw9t27bF6dOnsX//fkWi/YoVK9C3b19ER0ejS5cu8PHxwY4dO+Tztra22L17N2xtbREeHo7nnnsOI0aMwGuvvVZJT4yISqqkG2VzQ20iKm9lGgnbt28fVq9ejXXr1qFhw4YV0S8CR8KIKoPxul0ecHfPZN0uIiqTkv79LlNOWPv27XH//n00atQIzs7Och0urawsbrtBRNbn5Ml2BhXsQ0JOWbpbRFRNlSkIGzp0KP755x+88cYb8Pb2LnNiPhFRVaFWu8gBGFC0mfauXX0RHHyRWwkRUYUoUxB29OhRJCQkoE2bNuXdHyIii8jK8pADMC0hbJCV5c4gjIgqRJkS85s3b4579+6Vd1+IiCzG3T0TkqSseShJGri7M72CiCpGmUbCli5dimnTpmHx4sVo3bq1QU4Yk8iJqDKUZ30vlSoXUVG7DXLCOApGRBWlTEFYr169AADdu3dXHBdCQJIkFBYWPnrPiIjMKK/6XrobZYeEnEJw8EVkZbnD3T1LEYBxQ20iKm9lCsIOHz5c3v0gIioV/REwU6UlUlNTFW31R8e4oTYRWUqZgrCuXbuWqN1///tfvPbaa/D09CzL1xARlYi50hK6hZS19EfHGGARkSWUKTG/pL744gvk5ORU5FcQUQ1nqrSEWm16Vw9WvyeiqqBMI2ElVYZi/EREpWKutIT2vP4UJRFRVVChQRgRUUXTlpbQDcQkSYPUVD989tkIVr8noiqrQqcjiYgqmra0hLbGlyRpEBFxAAcORJRqipKIqLJxJIyIqixzdcAyMjLkn/VLS7D6PRFZAwZhRFQllbQOmJZKlasIsIxNUbL6PRFVJRUahD333HOsnk9EZVLSFYwDBgyQy+BkZGRgx44drH5PRFahzEFYdnY2jh07hhs3bkCjUe63NmLECADA2rVrH613RFQjZWZmKqYbAdPFWD09PeHr6wuA1e+JyLqUKQjbtWsXhg0bhtu3b8PV1RWSJMnnJEmSgzAiotIyNg1prhirLla/JyJrUqYgbNq0aRg9ejTeeOMNODs7l3efiKgGM7YdkbFirMHBF41OLzLAIiJrUaYSFf/88w8mTZrEAIyIKtzVq/5mi7ESEVmrMgVhkZGR+PXXX8u7L0RECidPtsP27dEGx7nSkYiqgxJPR+7cuVP+uU+fPpg+fTr+/PNPtG7dGnZ2doq2/fr1K78eElGNpD8N+ZBypaNarZYT84mIrEmJg7D+/fsbHHvttdcMjkmShMLCwkfqFBHVLLpFWbWrIo0VXAWAgQO/QqtW5+T3W7ZsQUxMDHPBiMjqlDgI0y9DQURUHkwVZTW1J6S//zWDtiWtKUZEVJWUKSfss88+Q15ensHx/Px8fPbZZ4/cKSKq2jIzM5GWlmbylZmZWeJrGVsNmZISCAAGe0J26vQjsrI8uAckEVULkhBClPZDtra2SEtLQ7169RTHMzMzUa9ePU5HlpOcnByoVCqo1WruPEBVhv7IlakiqoMGDYKbm5v83lR9rrS0NKxfvx6A8Xpg9+45Ij4+AkX/zSgASAa1wsaNG8e8MCKqMkr697tMdcKEEIoCrVrXrl2DSqUqyyWJyErojlyZK6K6detWg8+ay90yVg9s586+KPq/Gu2gvSSfM1crjIjIGpQqCGvXrh0kSYIkSejevTtq1Xr48cLCQqSkpKBXr17l3kkiqnpKW0QVMJ+7ZTwR3wamxuq1tcIYhBGRtSpVEKZdIXn69GlERkaiTp068jl7e3sEBgYiOtqwpg8RVT/GgibdIqrGpijNMZaIXzT9CGhHwHSxVhgRWbtSBWHz588HAAQGBmLw4MFwdHSskE4RUdVnavViaqofPvtsRLH7POpTqXIREXEA8fE98DDokgBoIEni/75HmRPGUTAismZlygkbOXIkgKKphRs3bhiUrwgICHj0nhFRlaZS5SIqarciJywi4gAOHIgo1RSlLj+/NBiOetkgOnorate+Czu7fBQU2MPdPUtxPXt7+3K8MyKiylGmIOzChQsYPXo0jh49qjiuTdjn6kiimiEk5BSCgy8iK8sd7u5ZZqcoVapcuRAr8HC1pG4AZa42mEqVi27duqFu3boAgFq1asHNzc3kqksioqquTEHYqFGjUKtWLezevRu+vr5GV0oSkXXRrVpvjKlgR6XKVYxKGQuitLlbO3bsUHxWu1py0KBB2Lp1q9HRNd1pxyZNmrAUBRFVG2UKwk6fPo0TJ06gefPm5d0fIrKAktb+iomJMTv1V1wQpU8b9OnWHNQfXeO0IxFVV2UKwlq2bKmYViAi61bS2l/5+fnw9fVFTEyM4jNqtRpbtmwBYDyIMhXUaXl4eBhcUx+nHYmouilTEPbmm29ixowZeOONN9C6dWvY2dkpzrO6O5F1KmntL/1gSDcwy8jIwI4dO+T25oI6XQywiKimKVMQFhERAQB45plnFPlgTMwnsm7FJdabYyyIUqtdsHNnX2gr3rPSPRHRQ2UKwg4fPlze/SCiKsDU6sSyFkVNTAzDwy2HirDSPRFREf09Qkqka9eusLGxwYcffohZs2ahcePG6Nq1K65cuQJbW9vy7iMRVRJtYr0kFdX+00+sz8jIQFpaGjIzM4u9llrtgoSEcCNnWOmeiAgo40jY9u3bMXz4cAwbNgynTp1CXl4egKLk3DfeeAN79+4t104SUcXQlqXQXWhjbnWibokJc5txA6b2ggQ6dkzgKBgREcoYhL3++utYt24dRowYgc2bN8vHn3rqKbz++uvl1jkiqjj6ZSl06db+MrWy0dxKRsD01GZYWKL8niUniKgmK1MQlpSUhC5duhgcV6lUyM7OftQ+EVEl0A+ijAVbJV3ZaExxNcMGDRrEFZFEVKOVKQjz8fHBxYsXERgYqDj+008/oVGjRuXRLyKqRMaCreDgiyUqV6FPd3TL3NSmboFWIqKaqExB2AsvvIDJkyfjk08+gSRJSE1NRUJCAl5++WXMnTu3vPtIRBXIVG2w6OjtZSpXwcKrREQlU6YgbNasWdBoNOjevTvu3r2LLl26wMHBAS+//DImTpxY3n0kogpkqjYYIMpcroIBFhFR8cpUokKSJMyZMwdZWVk4e/YsfvnlF9y8eROLFi0q7/4RUQXTJtDrkiQN/P2vmS1XQUREj6ZMI2Fa9vb2aNmyZXn1hYgswFwCvbmcLiIiejSPFIQRUfVgLtjSLVehi+UliIgeDYMwohpKP4gyFWwNGjQIbm5uBp9l3hcR0aNhEEZUQ3EVIxGRZTEII6oCtNsHAUBqqg1SUmohKOgB/PyKkuIrKhhigEVEZDkMwogsTHf7IHMV6ovbq5GIiKxLmUpUEFH50Y6AmSqaqla7KNoREVH1wCCMqIowVTQ1K8vdQj0iIqKKxCCMqIowVTS1JBXqiYjI+jAII6oitEVTWaGeiKhmYGI+URXCCvVERDUHgzCiKsZU0VQiIqpeGIQRWYmMjAwALKBKRFRdMAgjsrCS7sG4Y8cO+WfWDCMisn4MwogsTHf7oIyMDEWwpVa7ICvLA+7umYopSv2aYboV943h6BkRUdXDIIyoBK5dAy5cAJo0ARo0KP/rGwuQzFXP105NAoBarcaWLVuK/Q6OnhERVS0MwohM0I4ubdrkhBkzVNBoJNjYCCxbpsazz96r0NElU9Xzg4MvQqXKVYyWGftsSUbPiIjIshiEERmh3c9RrXbBypWxEEICAGg0EqZPd8U//3wClSq3wkaXzFXPV6lyTQZa5kbPiIioamEQRmSEdtSouGCookaXtNXzdb9bWz3fVKBV3OgZERFVLayYT2SGpbYSMlU9H4DJTb659yQRkXWxmiCsX79+CAgIgKOjI3x9fTF8+HCkpqYq2mzduhVt27aFs7MzGjZsiLfeesvgOt9//z1CQkLg4OCAxo0bIy4uzqDNe++9h8DAQDg6OiIsLAzHjh1TnL9//z4mTJgADw8P1KlTB9HR0bh+/bqizZUrV9CnTx84OzujXr16mD59Oh48ePDoD4IqVWVvJZSdnS3/HBJyCrGxKzFyZBxiY1ciJOSU2UCLe08SEVkXqwnCunXrhq1btyIpKQnbt29HcnIyBg4cKJ/ft28fhg0bhvHjx+Ps2bN4//33sWLFCqxZs0Zuk5KSgj59+qBbt244ffo0YmNjMXbsWHz77bdymy1btmDq1KmYP38+Tp48iTZt2iAyMhI3btyQ20yZMgW7du3Ctm3bcOTIEaSmpmLAgAHy+cLCQvTp0wf5+fk4evQoNmzYgLi4OMybN6+CnxJVBGPBUEWRJMnYUfknc4EW954kIrIukhBCWLoTZbFz5070798feXl5sLOzw7PPPouCggJs27ZNbvPuu+9i2bJluHLlCiRJwsyZM7Fnzx6cPXtWbjNkyBBkZ2dj//79AICwsDA88cQTcvCm0Wjg7++PiRMnYtasWVCr1fDy8sKmTZvkIPD8+fNo0aIFEhIS0KFDB+zbtw99+/ZFamoqvL29AQDr1q3DzJkzcfPmzRIX58zJyYFKpYJarYarq2u5PDcqmbS0NKxfv77YduPGjYOvr2+FfK+p3K/iku+LpiYN954s774SEZFxJf37bTUjYbqysrKwceNGdOzYEXZ2dgCAvLw8ODo6Kto5OTnh2rVruHz5MgAgISEBERERijaRkZFISEgAUJSMfeLECUUbGxsbREREyG1OnDiBgoICRZvmzZsjICBAbpOQkIDWrVvLAZj2e3JycvDHH3+YvK+8vDzk5OQoXlQzmUqyV6tdih2ZU6lyERR02WAErKTBPxERVQ6rWh05c+ZMrFmzBnfv3kWHDh2we/du+VxkZCSmTJmCUaNGoVu3brh48SKWL18OoGh0ITAwEOnp6YrACAC8vb2Rk5ODe/fu4datWygsLDTa5vz58wCA9PR02Nvbw83NzaBNenq63MbYNbTnTFmyZAkWLlxYiidClqZbNFXXo9YQK25VpqlNvgcNGmTwu1ke/SEiovJn0SBs1qxZePPNN822OXfuHJo3bw4AmD59OsaMGYPLly9j4cKFGDFiBHbv3g1JkvDCCy8gOTkZffv2RUFBAVxdXTF58mQsWLAANjbWMeA3e/ZsTJ06VX6fk5MDf39/C/ao5irLfo76HqWGmLkSFQAwYMAAeHp6Kj7DQIuIyLpYNAibNm0aRo0aZbZNo0aN5J89PT3h6emJpk2bokWLFvD398cvv/yC8PBwSJKEN998E2+88QbS09Ph5eWFgwcPKq7h4+NjsIrx+vXrcHV1hZOTE2xtbWFra2u0jY+Pj3yN/Px8ZGdnK0Yc9Nvor6jUXlPbxhgHBwc4ODiYfR5UOXT3c9Snv7+jKY9SQ0ybZK+f+6Ud/fL09GR+FxGRlbNoEObl5QUvL68yfVajKVoBlpeXpzhua2uL+vXrAwC+/PJLhIeHy98RHh6OvXv3KtrHx8cjPDwcQNFIQmhoKA4ePIj+/fvL33Pw4EHExMQAAEJDQ2FnZ4eDBw8iOjoaAJCUlIQrV67I1wkPD8fixYtx48YN1KtXT/4eV1dXtGzZskz3S5WvpKNKpqrXP6qQkFMIDr5oNMmeiIisn1XkhCUmJuL48ePo1KkT6tati+TkZMydOxfBwcFy4JORkYGvvvoKTz/9NO7fv49PP/1ULiGhNX78eKxZswYzZszA6NGjcejQIWzduhV79uyR20ydOhUjR45E+/bt8eSTT2LlypW4c+cOnn/+eQCASqXCmDFjMHXqVLi7u8PV1RUTJ05EeHg4OnToAADo2bMnWrZsieHDh2PZsmVIT0/Hq6++igkTJnCkq5op722C9KdBTeV+McmeiMj6WUUQ5uzsjB07dmD+/Pm4c+cOfH190atXL7z66quKoGbDhg14+eWXIYRAeHg4vv/+ezz55JPy+aCgIOzZswdTpkzBqlWr0KBBA3z00UeIjIyU2wwePBg3b97EvHnzkJ6ejrZt22L//v2KRPsVK1bAxsYG0dHRyMvLQ2RkJN5//335vK2tLXbv3o2XXnoJ4eHhqF27NkaOHInXXnutgp8UVaaK2CbI3DSoFnO/iIiqB6utE1YTsE5Y1aSt5ZWSEogNG0YanB85Mg5BQZfN1uXKzMxkoEVEVE2V9O+3VYyEEVVFxa1gNCUzM1Oxk4OpnLJHWV1JRERVH4MwojIqbgWjKbojYOZyyh5ldSUREVV9DMKISkk3Kd7cCsbikucrIqeMiIisB4MwolIqr+T54qriExFR9cYgjKgMyiNXq6w5ZUREVD0wCCOrZ+mVhmX9/rLmlBERUfXAIIysin7Ao1arsWXLlmI/Vx4rDY0FW/rfX9qVjqyKT0RUczEIoypLP+jJzs7G1q1by3StR11pqF9WwpiyrnQ0VRWfiIiqNwZhVCWVJOipTIYjYMoRr9KsdCzplkPcmoiIqHpjEEZVTmZmJlJTU8v02YraTFuXsRGvunVvlXilI7cmIiIigEEYVTGPMgJW3ptp6/YpIyMDgOnaXmPGfFSqlY4MsIiIyKb4JkSVpzS5W2q1C1JSAqFWu5gMjtRql0fqjzYo3LFjBwDg6lV/oyNeBQX2iIraDUnSAABXOhIRUbE4EkZWSX/UKzw8oUIKnxrbYkifdsQrKOgyVzoSEVGJMQgjq6Cb6wXAYNTr6NHwCi18qj/S9pByxIsrHYmIqKQYhFGVV5JRL8AG4eE/IyEh3Gjh00ddaWhsiyEAGDjwK7Rqda7Yz3OlIxER6WMQRlWasVyvhIRwABropjRKkgZhYYkIC0tEmzbRaNXKEX5+TwB4olxWGpraYsjf/5qi3aBBg+Dm5qY4xpWORERkDIMwqtJMJcJ37Phw1MvWVuDNN3Pw7LNDKyzgKW6LoQEDBsDPz4/BFhERlRiDMKqyTp5sh507jSfCL1jgDh8fNTIz66JxYwkNGrgBcCv1dxS376NarZZ/NrfFkKenJwMwIiIqFQZhVKVoc6e005D6VVQe1v96Ch4edR/pu/RrkpWk0CsT74mIqLwwCKMqRVtN/vBhYMUKw0T4devUiI5+qlxGnYyVnyhroVcm3hMRUWkxCKMqx8PDAx06ADY2gEbz8LitLfCvf9VFec/6Fbfvo7Fke11MvCciorJgEEZVUoMGwPr1wIsvAoWFRQHYBx8UHS9vxspP6BZ6dXNzg6+vb/l/MRER1WgMwqjKGjMGiIwELl4EGjcufQBWXNJ9dnY2ANPlJ8qr0CsREZExDMKoSmvQoGyjX6XZCLy48hNEREQVgUEYVUv6I2DGVj7qHjNXfoKIiKgiMAijas/YykcARldDMvgiIqLKwiCMqjVjKx937uwLSYLJ1ZD6WH6CiIgqAoMwqtaMb7xtAyGUR3RXQw4YMACenp4AWH6CiIgqDoMwqtaMrXwENIqRMEC5GtLT05MlKYiIqMIZliQnqka0Kx8lqajqqyRp0K/fboNjXA1JRESVjSNhVO2ZWvnI1ZBERGRJDMKoWtJPpje28TY34yYiIktiEEbVknYjcN16YWq1Glu2bCn2s1wNSURElUESQn+dGFUVOTk5UKlUUKvVcHV1tXR3Hllx2whVxkrEqtAHIiKq3kr695sjYVQpSrqNUExMTIUGQQywiIioquDqSKoU5kafytKOiIjI2jEIIyIiIrIABmFEREREFsAgjIiIiMgCmJhPFqFWuyArywPu7pllrtWlu9IxNdUGKSm1EBT0AH5+RZXwudKRiIiqMgZhVOlOnmyHXbv6QggbecugkJBTpbqG7mpLc9er6NWWREREZcXpSKpUarWLHDABRZto79rVF2q1S6muox0BK+56XG1JRERVFYMwqhTaKvRZWR5ywKQlhA2ystwV7UqquOsRERFVVZyOpEqh3Ubo0qUH+PxzAY1Gks/Z2gpMnNgbgYG1Sj116O6eCUnSKAIxSdLA3T2r3PpORERUETgSRpXGw8MDoaHeWL9egq1t0TFbW+CDDySEhnqXKXdLpcpFVNRuSFJRMr42J4wbcxMRUVXHkTCqdGPGAJGRwMWLQOPGQIMGj3a9kJBTCA6+iKwsd7i7ZzEAIyIiq8AgjCyiQQPAyamoxERa2qOXmFCpchl8ERGRVWEQRhZR2hIT164BFy4ATZo8+sgZERFRVcCcMLKIkpaYSE1NxfLl2WjYUOCZZ4CGDQWWL8+GWq0u0feUdrUlERFRZeFIGFmUuRITKlUuPv00HitXxkKIotWUGo2E6dNd8c8/n0ClAgYNGgQ3Nzej12bFfCIiqsoYhJFFFVdiorggzc3NDb6+vpXaZyIiovLA6UiyqOJKTGiDNF2sA0ZERNUBR8LI4syVmNAGafqJ+1wJSURE1o5BGFUJ5kpMsA4YERFVRwzCyCqwDhgREVU3zAkji2DpCCIiquk4EkYWod3QW1svTF9GRgZ27NhRyb0iIiKqPAzCyGLM1fAq6UgZR9SIiMhaMQijKqm4kTKAxViJiMi6MQijKosBFhERVWdMzCciIiKyAAZhRERERBbAIIyIiIjIAhiEEREREVkAgzAiIiIiC7C6ICwvLw9t27aFJEk4ffq04tyZM2fQuXNnODo6wt/fH8uWLTP4/LZt29C8eXM4OjqidevW2Lt3r+K8EALz5s2Dr68vnJycEBERgQsXLijaZGVlYdiwYXB1dYWbmxvGjBmD27dvl7ovREREVHNZXRA2Y8YM+Pn5GRzPyclBz5490bBhQ5w4cQJvvfUWFixYgPXr18ttjh49iqFDh2LMmDE4deoU+vfvj/79++Ps2bNym2XLlmH16tVYt24dEhMTUbt2bURGRuL+/ftym2HDhuGPP/5AfHw8du/ejR9++AHjxo0rVV+qk2vXgMOHi/5JREREJSSsyN69e0Xz5s3FH3/8IQCIU6dOyefef/99UbduXZGXlycfmzlzpmjWrJn8ftCgQaJPnz6Ka4aFhYkXX3xRCCGERqMRPj4+4q233pLPZ2dnCwcHB/Hll18KIYT4888/BQBx/Phxuc2+ffuEJEnin3/+KXFfSkKtVgsAQq1Wl+pzlSEjI0OkpqaKt9++JWxsNAIQwsZGI95++5ZITU0VGRkZlu4iERGRRZT077fVjIRdv34dL7zwAj7//HM4OzsbnE9ISECXLl0U29hERkYiKSkJt27dkttEREQoPhcZGYmEhAQAQEpKCtLT0xVtVCoVwsLC5DYJCQlwc3ND+/bt5TYRERGwsbFBYmJiiftiTF5eHnJychSvqigzMxNr1qzBW299ienTXaHRSAAAjUbC9OmueOutL7FmzRpkZmZauKdERERVl1UEYUIIjBo1CuPHj1cEP7rS09Ph7e2tOKZ9n56ebraN7nndz5lqU69ePcX5WrVqwd3dvdjv0f0OY5YsWQKVSiW//P39TbatTPrTjdqthLKyPKAfxwthg6wsd0U7IiIiMmTRIGzWrFmQJMns6/z583j33XeRm5uL2bNnW7K7FW727NlQq9Xy6+rVq5buEj7+GGjYEHjmmaJ/fvwxkJ2dDQBwd8+EJGkU7SVJA3f3LAv0lIiIyLpYdO/IadOmYdSoUWbbNGrUCIcOHUJCQgIcHBwU59q3b49hw4Zhw4YN8PHxwfXr1xXnte99fHzkfxpro3tee8zX11fRpm3btnKbGzduKK7x4MEDZGVlFfs9ut9hjIODg8E9WkpmZiYuXXqAcePq6Uw3AuPGCUyevA8qFZCc3BhCPPyMJGkQFbUbKlWuhXpNRERkPSwahHl5ecHLy6vYdqtXr8brr78uv09NTUVkZCS2bNmCsLAwAEB4eDjmzJmDgoIC2NnZAQDi4+PRrFkz1K1bV25z8OBBxMbGyteKj49HeHg4ACAoKAg+Pj44ePCgHHTl5OQgMTERL730knyN7OxsnDhxAqGhoQCAQ4cOQaPRlKovVZk25yslJRAazUjFOY1Gkqcbd+3qC93BVCGA4OCLldlVIiIiq2UVOWEBAQFo1aqV/GratCkAIDg4GA0aNAAAPPvss7C3t8eYMWPwxx9/YMuWLVi1ahWmTp0qX2fy5MnYv38/li9fjvPnz2PBggX49ddfERMTAwCQJAmxsbF4/fXXsXPnTvz+++8YMWIE/Pz80L9/fwBAixYt0KtXL7zwwgs4duwYfv75Z8TExGDIkCFy6YyS9KUq0+ZymZtuNJYPBjzMByMiIiLzrCIIKwmVSoXvvvsOKSkpCA0NxbRp0zBv3jxF/a6OHTti06ZNWL9+Pdq0aYOvvvoK33zzDVq1aiW3mTFjBiZOnIhx48bhiSeewO3bt7F//344OjrKbTZu3IjmzZuje/fu+Ne//oVOnTopaoCVpC/WQKXKRVTUbjkQ051uZD4YERHRo5GE0M3qoaokJycHKpUKarUarq6ulfa9aWlpiqBSrXZBVpY73N2zFPleJ0+2w65dfSGEjRyghYScks+PGzdOkVtHRERUE5T077dFc8LIOqhUuUaT7UNCTiE4+KLRAA2Aok4aERERKTEIo0diKkAbPHgwPDw8LNAjIiIi61BtcsKoalGpVJbuAhERUZXGIIwqBKciiYiIzON0JBkoaQA1ePBgoyNe9vb2nIokIiIqBoMwMuDh4YGYmBizez8y0CIiIno0DMLIKAZYREREFYs5YUREREQWwCCMiIiIyAIYhBERERFZAIMwIiIiIgtgEEZERERkAQzCiIiIiCyAQRgRERGRBTAIIyIiIrIABmFEREREFsCK+TVEZmYmtyEiIiKqQhiE1QCZmZlYs2ZNse1iYmIYiBEREVUSTkfWAOZGwMrSjoiIiB4dgzAiIiIiC2AQRkRERGQBDMKIiIiILIBBGBEREZEFMAgjIiIisgAGYUREREQWwCCsBrC3t1e8V6tdkJISCLXaxWw7IiIiqjgs1loDeHh4ICYmBvn5+di0yQmvvaaCRiPBxkZg2TI1nn32HivmExERVTJJCCEs3QkyLicnByqVCmq1Gq6uro98vWvXgIYNAY3m4TFbW+DSJaBBg0e+PBEREaHkf785HVmDXLigDMAAoLAQuHjRMv0hIiKqyRiE1SBNmgA2ev/GbW2Bxo0t0x8iIqKajEFYDdKgAbB+fVHgBRT984MPOBVJRERkCUzMr2HGjAEiI4umIBs3ZgBGRERkKQzCaqAGDRh8ERERWRqnI4mIiIgsgEEYERERkQUwCCMiIiKyAAZhRERERBbAIIyIiIjIAhiEEREREVkAgzAiIiIiC2AQRkRERGQBDMKIiIiILIBBGBEREZEFMAgjIiIisgDuHVmFCSEAADk5ORbuCREREZWU9u+29u+4KQzCqrDc3FwAgL+/v4V7QkRERKWVm5sLlUpl8rwkigvTyGI0Gg1SU1Ph4uICSZJK9JmcnBz4+/vj6tWrcHV1reAeVl18DkX4HPgMtPgcivA58BloVeRzEEIgNzcXfn5+sLExnfnFkbAqzMbGBg0aNCjTZ11dXWv0/7i0+ByK8DnwGWjxORThc+Az0Kqo52BuBEyLiflEREREFsAgjIiIiMgCGIRVMw4ODpg/fz4cHBws3RWL4nMowufAZ6DF51CEz4HPQKsqPAcm5hMRERFZAEfCiIiIiCyAQRgRERGRBTAIIyIiIrIABmFEREREFsAgzAqsXbsWjz/+uFxQLjw8HPv27ZPP379/HxMmTICHhwfq1KmD6OhoXL9+XXGNK1euoE+fPnB2dka9evUwffp0PHjwoLJvpdwsXboUkiQhNjZWPlZTnsOCBQsgSZLi1bx5c/l8TXkO//zzD5577jl4eHjAyckJrVu3xq+//iqfF0Jg3rx58PX1hZOTEyIiInDhwgXFNbKysjBs2DC4urrCzc0NY8aMwe3btyv7VsosMDDQ4HdBkiRMmDABQM35XSgsLMTcuXMRFBQEJycnBAcHY9GiRYp9+2rC70Nubi5iY2PRsGFDODk5oWPHjjh+/Lh8vjo+gx9++AFRUVHw8/ODJEn45ptvFOfL657PnDmDzp07w9HREf7+/li2bFn53ICgKm/nzp1iz5494q+//hJJSUnilVdeEXZ2duLs2bNCCCHGjx8v/P39xcGDB8Wvv/4qOnToIDp27Ch//sGDB6JVq1YiIiJCnDp1Suzdu1d4enqK2bNnW+qWHsmxY8dEYGCgePzxx8XkyZPl4zXlOcyfP1889thjIi0tTX7dvHlTPl8TnkNWVpZo2LChGDVqlEhMTBR///23+Pbbb8XFixflNkuXLhUqlUp888034rfffhP9+vUTQUFB4t69e3KbXr16iTZt2ohffvlF/Pjjj6Jx48Zi6NChlrilMrlx44bi9yA+Pl4AEIcPHxZC1IzfBSGEWLx4sfDw8BC7d+8WKSkpYtu2baJOnTpi1apVcpua8PswaNAg0bJlS3HkyBFx4cIFMX/+fOHq6iquXbsmhKiez2Dv3r1izpw5YseOHQKA+PrrrxXny+Oe1Wq18Pb2FsOGDRNnz54VX375pXBychIffPDBI/efQZiVqlu3rvjoo49Edna2sLOzE9u2bZPPnTt3TgAQCQkJQoiiX1IbGxuRnp4ut1m7dq1wdXUVeXl5ld73R5GbmyuaNGki4uPjRdeuXeUgrCY9h/nz54s2bdoYPVdTnsPMmTNFp06dTJ7XaDTCx8dHvPXWW/Kx7Oxs4eDgIL788kshhBB//vmnACCOHz8ut9m3b5+QJEn8888/Fdf5CjR58mQRHBwsNBpNjfldEEKIPn36iNGjRyuODRgwQAwbNkwIUTN+H+7evStsbW3F7t27FcdDQkLEnDlzasQz0A/Cyuue33//fVG3bl3F/yZmzpwpmjVr9sh95nSklSksLMTmzZtx584dhIeH48SJEygoKEBERITcpnnz5ggICEBCQgIAICEhAa1bt4a3t7fcJjIyEjk5Ofjjjz8q/R4exYQJE9CnTx/F/QKocc/hwoUL8PPzQ6NGjTBs2DBcuXIFQM15Djt37kT79u3xn//8B/Xq1UO7du3w4YcfyudTUlKQnp6ueA4qlQphYWGK5+Dm5ob27dvLbSIiImBjY4PExMTKu5lykp+fjy+++AKjR4+GJEk15ncBADp27IiDBw/ir7/+AgD89ttv+Omnn9C7d28ANeP34cGDBygsLISjo6PiuJOTE3766aca8Qz0ldc9JyQkoEuXLrC3t5fbREZGIikpCbdu3XqkPnIDbyvx+++/Izw8HPfv30edOnXw9ddfo2XLljh9+jTs7e3h5uamaO/t7Y309HQAQHp6uuL/ZLXnteesxebNm3Hy5ElFjoNWenp6jXkOYWFhiIuLQ7NmzZCWloaFCxeic+fOOHv2bI15Dn///TfWrl2LqVOn4pVXXsHx48cxadIk2NvbY+TIkfJ9GLtP3edQr149xflatWrB3d3dap6Drm+++QbZ2dkYNWoUgJr1v4lZs2YhJycHzZs3h62tLQoLC7F48WIMGzYMAGrE74OLiwvCw8OxaNEitGjRAt7e3vjyyy+RkJCAxo0b14hnoK+87jk9PR1BQUEG19Ceq1u3bpn7yCDMSjRr1gynT5+GWq3GV199hZEjR+LIkSOW7laluXr1KiZPnoz4+HiD/9KrabT/dQ8Ajz/+OMLCwtCwYUNs3boVTk5OFuxZ5dFoNGjfvj3eeOMNAEC7du1w9uxZrFu3DiNHjrRw7yzj448/Ru/eveHn52fprlS6rVu3YuPGjdi0aRMee+wxnD59GrGxsfDz86tRvw+ff/45Ro8ejfr168PW1hYhISEYOnQoTpw4YemukQmcjrQS9vb2aNy4MUJDQ7FkyRK0adMGq1atgo+PD/Lz85Gdna1of/36dfj4+AAAfHx8DFZEad9r21R1J06cwI0bNxASEoJatWqhVq1aOHLkCFavXo1atWrB29u7RjwHY9zc3NC0aVNcvHixxvw++Pr6omXLlopjLVq0kKdltfdh7D51n8ONGzcU5x88eICsrCyreQ5aly9fxoEDBzB27Fj5WE35XQCA6dOnY9asWRgyZAhat26N4cOHY8qUKViyZAmAmvP7EBwcjCNHjuD27du4evUqjh07hoKCAjRq1KjGPANd5XXPFfm/EwZhVkqj0SAvLw+hoaGws7PDwYMH5XNJSUm4cuUKwsPDAQDh4eH4/fffFb9o8fHxcHV1NfhDVlV1794dv//+O06fPi2/2rdvj2HDhsk/14TnYMzt27eRnJwMX1/fGvP78NRTTyEpKUlx7K+//kLDhg0BAEFBQfDx8VE8h5ycHCQmJiqeQ3Z2tmKU4NChQ9BoNAgLC6uEuyg/n376KerVq4c+ffrIx2rK7wIA3L17FzY2yj9ntra20Gg0AGre70Pt2rXh6+uLW7du4dtvv8W///3vGvcMgPL79x4eHo4ffvgBBQUFcpv4+Hg0a9bskaYiAbBEhTWYNWuWOHLkiEhJSRFnzpwRs2bNEpIkie+++04IUbQMPSAgQBw6dEj8+uuvIjw8XISHh8uf1y5D79mzpzh9+rTYv3+/8PLysrpl6Pp0V0cKUXOew7Rp08T3338vUlJSxM8//ywiIiKEp6enuHHjhhCiZjyHY8eOiVq1aonFixeLCxcuiI0bNwpnZ2fxxRdfyG2WLl0q3NzcxP/+9z9x5swZ8e9//9vo0vR27dqJxMRE8dNPP4kmTZpU6eX4xhQWFoqAgAAxc+ZMg3M14XdBCCFGjhwp6tevL5eo2LFjh/D09BQzZsyQ29SE34f9+/eLffv2ib///lt89913ok2bNiIsLEzk5+cLIarnM8jNzRWnTp0Sp06dEgDEO++8I06dOiUuX74shCife87Ozhbe3t5i+PDh4uzZs2Lz5s3C2dmZJSpqitGjR4uGDRsKe3t74eXlJbp37y4HYEIIce/ePfHf//5X1K1bVzg7O4v/9//+n0hLS1Nc49KlS6J3797CyclJeHp6imnTpomCgoLKvpVypR+E1ZTnMHjwYOHr6yvs7e1F/fr1xeDBgxX1sWrKc9i1a5do1aqVcHBwEM2bNxfr169XnNdoNGLu3LnC29tbODg4iO7du4ukpCRFm8zMTDF06FBRp04d4erqKp5//nmRm5tbmbfxyL799lsBwODehKg5vws5OTli8uTJIiAgQDg6OopGjRqJOXPmKEoK1ITfhy1btohGjRoJe3t74ePjIyZMmCCys7Pl89XxGRw+fFgAMHiNHDlSCFF+9/zbb7+JTp06CQcHB1G/fn2xdOnScum/JIROSWEiIiIiqhTMCSMiIiKyAAZhRERERBbAIIyIiIjIAhiEEREREVkAgzAiIiIiC2AQRkRERGQBDMKIiIiILIBBGBEREZEFMAgjomrl6aefRmxsrKW7UeEWLFiAtm3bWrobRPQIGIQREVUh+fn5lfp9Qgg8ePCgUr+TiIowCCOiamPUqFE4cuQIVq1aBUmSIEkSLl26hLNnz6J3796oU6cOvL29MXz4cGRkZMife/rppzFx4kTExsaibt268Pb2xocffog7d+7g+eefh4uLCxo3box9+/bJn/n+++8hSRL27NmDxx9/HI6OjujQoQPOnj2r6NNPP/2Ezp07w8nJCf7+/pg0aRLu3Lkjnw8MDMSiRYswYsQIuLq6Yty4cQCAmTNnomnTpnB2dkajRo0wd+5cFBQUAADi4uKwcOFC/Pbbb/J9xsXF4dKlS5AkCadPn5avn52dDUmS8P333yv6vW/fPoSGhsLBwQE//fQTNBoNlixZgqCgIDg5OaFNmzb46quvyvtfERHpYBBGRNXGqlWrEB4ejhdeeAFpaWlIS0uDi4sLnnnmGbRr1w6//vor9u/fj+vXr2PQoEGKz27YsAGenp44duwYJk6ciJdeegn/+c9/0LFjR5w8eRI9e/bE8OHDcffuXcXnpk+fjuXLl+P48ePw8vJCVFSUHCwlJyejV69eiI6OxpkzZ7Blyxb89NNPiImJUVzj7bffRps2bXDq1CnMnTsXAODi4oK4uDj8+eefWLVqFT788EOsWLECADB48GBMmzYNjz32mHyfgwcPLtWzmjVrFpYuXYpz587h8ccfx5IlS/DZZ59h3bp1+OOPPzBlyhQ899xzOHLkSKmuS0SlUC7bgBMRVRFdu3YVkydPlt8vWrRI9OzZU9Hm6tWrAoBISkqSP9OpUyf5/IMHD0Tt2rXF8OHD5WNpaWkCgEhISBBCCHH48GEBQGzevFluk5mZKZycnMSWLVuEEEKMGTNGjBs3TvHdP/74o7CxsRH37t0TQgjRsGFD0b9//2Lv66233hKhoaHy+/nz54s2bdoo2qSkpAgA4tSpU/KxW7duCQDi8OHDin5/8803cpv79+8LZ2dncfToUcX1xowZI4YOHVps34iobGpZMgAkIqpov/32Gw4fPow6deoYnEtOTkbTpk0BAI8//rh83NbWFh4eHmjdurV8zNvbGwBw48YNxTXCw8Pln93d3dGsWTOcO3dO/u4zZ85g48aNchshBDQaDVJSUtCiRQsAQPv27Q36tmXLFqxevRrJycm4ffs2Hjx4AFdX11Lfvym633nx4kXcvXsXPXr0ULTJz89Hu3btyu07iUiJQRgRVWu3b99GVFQU3nzzTYNzvr6+8s92dnaKc5IkKY5JkgQA0Gg0pfruF198EZMmTTI4FxAQIP9cu3ZtxbmEhAQMGzYMCxcuRGRkJFQqFTZv3ozly5eb/T4bm6IMEyGEfEw7NapP9ztv374NANizZw/q16+vaOfg4GD2O4mo7BiEEVG1Ym9vj8LCQvl9SEgItm/fjsDAQNSqVf7/l/fLL7/IAdWtW7fw119/ySNcISEh+PPPP9G4ceNSXfPo0aNo2LAh5syZIx+7fPmyoo3+fQKAl5cXACAtLU0ewdJN0jelZcuWcHBwwJUrV9C1a9dS9ZWIyo6J+URUrQQGBiIxMRGXLl1CRkYGJkyYgKysLAwdOhTHjx9HcnIyvv32Wzz//PMGQUxZvPbaazh48CDOnj2LUaNGwdPTE/379wdQtMLx6NGjiImJwenTp3HhwgX873//M0jM19ekSRNcuXIFmzdvRnJyMlavXo2vv/7a4D5TUlJw+vRpZGRkIC8vD05OTujQoYOccH/kyBG8+uqrxd6Di4sLXn75ZUyZMgUbNmxAcnIyTp48iXfffRcbNmwo87MhIvMYhBFRtfLyyy/D1tYWLVu2hJeXF/Lz8/Hzzz+jsLAQPXv2ROvWrREbGws3Nzd5+u5RLF26FJMnT0ZoaCjS09Oxa9cu2NvbAyjKMzty5Aj++usvdO7cGe3atcO8efPg5+dn9pr9+vXDlClTEBMTg7Zt2+Lo0aPyqkmt6Oho9OrVC926dYOXlxe+/PJLAMAnn3yCBw8eIDQ0FLGxsXj99ddLdB+LFi3C3LlzsWTJErRo0QK9evXCnj17EBQUVIanQkQlIQnd5AEiIiqR77//Ht26dcOtW7fg5uZm6e4QkRXiSBgRERGRBTAIIyIiIrIATkcSERERWQBHwoiIiIgsgEEYERERkQUwCCMiIiKyAAZhRERERBbAIIyIiIjIAhiEEREREVkAgzAiIiIiC2AQRkRERGQBDMKIiIiILOD/A1M3HSEaHErLAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_validation, filename=\"pysmo_poly_val_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_validation, filename=\"pysmo_poly_val_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_validation, filename=\"pysmo_poly_val_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_pysmo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.md) file." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_test.ipynb new file mode 100644 index 00000000..5070b413 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_test.ipynb @@ -0,0 +1,632 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part -1)\n", + "\n", + "## 1. Introduction\n", + "This notebook illustrates the use of the PySMO Polynomial surrogate trainer to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package. PySMO also has other training methods like Radial Basis Function and Kriging surrogate models, but we focus on Polynomial surrogate model. \n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "\n", + "### 1.1 Need for ML Surrogates\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebric, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebric equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train the model using polynomial regression for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoPolyTrainer, PysmoSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsquent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "csv_data.columns.values[0:6] =[\"pressure\", \"temperature\",\"enth_mol\",\"entr_mol\",\"CO2_enthalpy\",\"CO2_entropy\"]\n", + "data = csv_data.sample(n=500)\n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# # Define labels, and split training and validation data\n", + "input_labels = list(input_data.columns)\n", + "output_labels = list(output_data.columns) \n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogates with PySMO\n", + "\n", + "IDAES builds a model class for each type of PySMO surrogate model. In this case, we will call and build the Polynomial Regression class. Regression settings can be directly passed as class arguments, as shown below. In this example, allowed basis terms span a 5th order polynomial, a variable product as well as a extra features are defined, and data is internally cross-validated using 10 iterations of 80/20 splits to ensure a robust surrogate fit. Note that PySMO uses cross-validation of training data to adjust model coefficients and ensure a more accurate fit, while we separate the validation dataset pre-training in order to visualize the surrogate fits.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a folder which contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "No iterations will be run.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 20466.657669\n", + "\n", + "------------------------------------------------------------\n", + "The final coefficients of the regression terms are: \n", + "\n", + "k | -534397.59515\n", + "(x_ 1 )^ 1 | -2733.579691\n", + "(x_ 2 )^ 1 | 1036.106357\n", + "(x_ 1 )^ 2 | 32.409203\n", + "(x_ 2 )^ 2 | -2.852387\n", + "(x_ 1 )^ 3 | 0.893563\n", + "(x_ 2 )^ 3 | 0.004018\n", + "(x_ 1 )^ 4 | -0.045284\n", + "(x_ 2 )^ 4 | -3e-06\n", + "(x_ 1 )^ 5 | 0.000564\n", + "(x_ 2 )^ 5 | 0.0\n", + "x_ 1 .x_ 2 | 4.372684\n", + "\n", + "The coefficients of the extra terms in additional_regression_features are:\n", + "\n", + "Coeff. additional_regression_features[ 1 ]: -0.002723\n", + "Coeff. additional_regression_features[ 2 ]: 3.6e-05\n", + "Coeff. additional_regression_features[ 3 ]: -0.050607\n", + "Coeff. additional_regression_features[ 4 ]: 169668.814595\n", + "Coeff. additional_regression_features[ 5 ]: -44.726026\n", + "\n", + "Regression model performance on training data:\n", + "Order: 5 / MAE: 134.972465 / MSE: 54613.278159 / R^2: 0.999601\n", + "\n", + "Results saved in solution.pickle\n", + "2023-08-19 23:48:46 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "No iterations will be run.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.156437\n", + "\n", + "------------------------------------------------------------\n", + "The final coefficients of the regression terms are: \n", + "\n", + "k | -519.862457\n", + "(x_ 1 )^ 1 | -8.820865\n", + "(x_ 2 )^ 1 | 3.676641\n", + "(x_ 1 )^ 2 | 0.18002\n", + "(x_ 2 )^ 2 | -0.010217\n", + "(x_ 1 )^ 3 | -0.000783\n", + "(x_ 2 )^ 3 | 1.4e-05\n", + "(x_ 1 )^ 4 | -6.9e-05\n", + "(x_ 2 )^ 4 | -0.0\n", + "(x_ 1 )^ 5 | 1e-06\n", + "(x_ 2 )^ 5 | 0.0\n", + "x_ 1 .x_ 2 | 0.010367\n", + "\n", + "The coefficients of the extra terms in additional_regression_features are:\n", + "\n", + "Coeff. additional_regression_features[ 1 ]: -7e-06\n", + "Coeff. additional_regression_features[ 2 ]: 0.0\n", + "Coeff. additional_regression_features[ 3 ]: -0.000112\n", + "Coeff. additional_regression_features[ 4 ]: 484.312223\n", + "Coeff. additional_regression_features[ 5 ]: -0.1166\n", + "\n", + "Regression model performance on training data:\n", + "Order: 5 / MAE: 0.398072 / MSE: 0.495330 / R^2: 0.998873\n", + "\n", + "Results saved in solution.pickle\n", + "2023-08-19 23:49:20 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" + ] + } + ], + "source": [ + "# Create PySMO trainer object\n", + "trainer = PysmoPolyTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + ")\n", + "\n", + "var = output_labels\n", + "trainer.config.extra_features=['pressure*temperature*temperature','pressure*pressure*temperature*temperature','pressure*pressure*temperature','pressure/temperature','temperature/pressure']\n", + "# Set PySMO options\n", + "trainer.config.maximum_polynomial_order = 5\n", + "trainer.config.multinomials = True\n", + "trainer.config.training_split = 0.8\n", + "trainer.config.number_of_crossvalidations = 10\n", + "\n", + "# Train surrogate (calls PySMO through IDAES Python wrapper)\n", + "poly_train = trainer.train_surrogate()\n", + "\n", + "# create callable surrogate object\n", + "xmin, xmax = [7,306], [40,1000]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "poly_surr = PysmoSurrogate(poly_train, input_labels, output_labels, input_bounds)\n", + "# save model to JSON\n", + "model = poly_surr.save_to_file(\"pysmo_poly_surrogate.json\", overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing surrogates\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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AcXFxWLJkCX744Qe8/PLL+PjjjzFr1iwAQK9evbB48WKkpqaGwi/++Mc/AgimnD/88MP47rvv8Pbbb2P//v0YO3aspd9FCMcIn3nz5qFHjx5ISUlBZmYmhg8fjt27d0dcc+bMGUydOhXp6elo0aIFRo4ciSNHjtjUYmNp1coHjyfS5+rxBNCq1XGbWkQQBEG4lYsuugj79+8HAEyfPh1XXXUV8vPz0b9/fzzyyCNYs2YNgKArzuv1wuPxICsrC1lZWWjRogUAYPz48RgyZAjat2+PK664AkuWLMEHH3yAX375xa6vBcBBwufTTz/F1KlT8eWXX6KsrAz19fW4+uqrcfr06dA1d999N9577z28+eab+PTTT1FZWYkRI0bY2Grj8HpPYdiwDSHxw8f4eL2nbG4ZQRAE4TY4jgu5rjZt2oQBAwbgggsuQEpKCm655Rb4fD7U1tZKvse2bdswbNgwtG3bFikpKejbty8A4MCBA6a3XwrHxPh8+OGHEX+vXLkSmZmZ2LZtG/r06QO/34/ly5dj1apV6N+/PwBgxYoV+O1vf4svv/wSV1xxhR3NNpTu3b9FYeFeHD/eCq1aHSfRQxAEQZjCrl27UFBQgP3792Po0KG4/fbb8eijj6JVq1b4/PPPMWHCBNTV1YlWrD59+jQGDRqEQYMG4bXXXkPr1q1x4MABDBo0yPaYVMcIn2j8fj8AoFWrYIzLtm3bUF9fj5KSktA1F110Edq2bYvy8nJR4XP27FmcPXs29PfJkydNbLV+vN5TJHgIgiAI0/j444/x/fff4+6778a2bdsQCATw5JNPIi4u6CTi3Vw8SUlJaGhoiHjsxx9/hM/nw+OPP468vDwAwNdff23NF5DBkcInEAhg+vTp6N27Nzp37gwgeDpuUlJSo1oCbdq0weHDh0Xfa968eZg7d66ZzXUc4emVlZVxqKhIQEHBOeTkBN1sRqZXHjoE7NkDdOwI5OYa8pYEQRCEQs6ePYvDhw9HpLPPmzcPQ4cOxa233oqdO3eivr4eS5cuxbBhw/DFF1/g+eefj3iP/Px8/PLLL/joo4/QrVs3NGvWDG3btkVSUhKWLl2K2267DTt37sTDDz9s07eMxDExPuFMnToVO3fuxBtvvKH7vWbPng2/3x/6d/DgQQNaaBxJSUmGXicHn165bNky3HbbV+jRozWuvz4dl13WGmPGfI9ly5bhmWeewb59+3R/1vLlQLt2QP/+wf8uX27AFyAIgiAU8+GHHyI7Oxv5+fkYPHgwNm/ejCVLluCdd95BfHw8unXrhkWLFmH+/Pno3LkzXnvtNcybNy/iPXr16oXbbrsNpaWlaN26NRYsWIDWrVtj5cqVePPNN9GpUyc8/vjjeOKJJ2z6lpF4OI7j7G6EGqZNm4Z33nkH//znP1FQUBB6/OOPP8aAAQNw4sSJCKtPu3btMH36dNx9992K3v/kyZPwer3w+/1ITU01uvmasLLAVVVVFZYtWwa/PwWLF0+PSqHnMHBgGXr3LgegvbaEz+fD/v3ncPnlmQgEzh84Fx/PYevWo8jPT6BijARBOIIzZ86goqICBQUFaNKkiarXslDHx2lI9bfS9dsxri6O43DHHXfgrbfewieffBIhegCgqKgIiYmJ+OijjzBy5EgAwO7du3HgwAEUFxfb0WTDUDPgjRJJQnWDAA82bSpB5847NRdN5G/0iop8BAJjIp5raPBg6dIPUFDwE93oBEG4nvT0dEybNs3Wys2xiGOEz9SpU7Fq1Sq88847SElJCcXteL1eNG3aFF6vFxMmTMCMGTPQqlUrpKam4o477kBxcbErMrqUYOTuoVUrH4AAor2hfNFErQHW/A3O1yUKF1fhdYnsjvonCIKwAhI11uOYGJ/nnnsOfr8f/fr1Q3Z2dujf6tWrQ9c89dRTGDp0KEaOHIk+ffogKysrVHkyFjCyCqjXewoDB24CEOkJNapoItUlIgiCIOzAMRYfJaFITZo0wV/+8hf85S9/saBF9hPt1qquro543u9PwfHj6WjVyqdJUPCxPJs2lUQcjGqUOKG6RARBEITVOEb4EJHIubWkTnJXQ+/e5ejceadp4oTqEhEEQRBW4hhXFxGJlLtK7CR3vz9F02d5vadQUPATCRSCIAjC8ZDwcSF6TnI3qh4QQRAEQbAIubpcQng8j1zGlBTp6ekYPXo0Xn31Vdlr3SCSrKxSTRAEQdgPCR8XIBTPM2zYhkaPKXVVFRYWmlZbQqlYqq2tRVVVleGfH054nJRUTJSS9H8SUARBEM6AhA/jiBUk5DO4xOJ5pk9fjOnTFwsGJfPiw8qK0DxKCnbV1tZGWJzEstP0Fjnk2yDWh4WFexUVajRSQBEEQbiFTz75BFdddVWjExWkyM/Px/Tp0zF9+nTT2kXCh2GUFCSUiucpKPgJ48YNREZGRug5XszYWSpd7v3CLT1SQsKoIodSfajESmaUgCIIgrCSsWPH4uWXX8aUKVMaHTw6depUPPvssxgzZgxWrlxpTwNNgoKbGUbJQsnH84QTHs+TkZERUfCRFx1GFjs0C6Oz08SQ68Pq6mr4fD7B1/p8vpD1TU9QOUEQhB3k5eXhjTfewK+//hp67MyZM1i1ahXatm1rY8vMg4SPw5GrgKw0psbvT0FFRb6kqPD5fKiqqhL9JyYOtGKVkBDqw+Li8tDz69evxzPPPNPo+/FWM746uJyAIgiCYI3u3bsjLy8v4pSD9evXo23btrj00ktDj509exZ33nknMjMz0aRJE/zud7/DV199FfFef//73/Gb3/wGTZs2xVVXXYX9+/c3+rzPP/8cV155JZo2bYq8vDzceeedOH36tGnfTwhydTkIsVgXvgLyJZdch4svTkZOTg8APRTH6CgpdmiHa0xPdppa+D7curUntmwpxpYtvVFeXizpWhOyhhUXl6O8vNiUStcEQbibQ4eAPXuAjh2B3FzrPnf8+PFYsWIFbr75ZgDASy+9hHHjxuGTTz4JXTNr1iysW7cOL7/8Mtq1a4cFCxZg0KBB2Lt3L1q1aoWDBw9ixIgRmDp1KiZPnoyvv/4a99xzT8Tn7Nu3D4MHD8YjjzyCl156CceOHcO0adMwbdo0rFixwrLvS8LHIciJk+DZWonIzm6j6n3l4lJ47HCN8ZYYrdlpWigvLwZvCBXrCz4oPPyIkPDfBwigV68v0LPnVhI9BEEoYvlyYPJkIBAA4uKAZcuACROs+ezRo0dj9uzZ+OmnnwAAX3zxBd54442Q8Dl9+jSee+45rFy5EkOGDAEA/PWvf0VZWRmWL1+OmTNn4rnnnkNhYSGefPJJAMCFF16I77//HvPnzw99zrx583DzzTeHApc7duyIJUuWoG/fvnjuuefQpEkTS74vCR8HoFScaEFrYK/ec8CUYuV5Xkr6QsjyFf37AHEoLy9Gz55bTWsrQRDu4dCh86IHCP53yhRg0CBrLD+tW7fGNddcg5UrV4LjOFxzzTURSTH79u1DfX09evfuHXosMTERl19+OXbt2gUA2LVrF3r27BnxvsXFxRF/f/fdd9ixYwdee+210GMcxyEQCKCiogK//e1vzfh6jSDh4wDEFuSDB3Ph9e7S9d5a3ElGnQOmFDPO8/L5fPD7/RGPyfWF3+9HfX19o/fSmxUWi1DdI4I4z54950UPT0MDsHevdS6v8ePHY9q0aQBg2kHfv/zyC6ZMmYI777yz0XNWBlKT8HEAQgsyAKxdex3q6s6Ljj179qC6uhrNmjVDYWGhovdW604y0/rEozQgW2vlaLF4Jbm+WL16daPX+P0pOH26GYAAwnMFhMSjGypdGwHVPSKISDp2DLq3wsVPfDzQoYN1bRg8eDDq6urg8XgwaNCgiOcKCwuRlJSEL774Au3atQMA1NfX46uvvgq5rX7729/i3XffjXjdl19+GfF39+7d8e9//xsdrPxiApDwYRh+oYxekM8TKTo2b94cemb06NGS4id8EZZyJ0Uv1lZYN5QUOdRjEZB6XzWutci4Hg68+IkWTCNGjEBOTg4t4v+F6h4RRCS5ucGYnilTgpae+HjghResDXCOj48Pua3i4+MjnmvevDluv/12zJw5E61atULbtm2xYMEC1NbWYsJ/A5Fuu+02PPnkk5g5cyYmTpyIbdu2Nar/c++99+KKK67AtGnTMHHiRDRv3hz//ve/UVZWpih5xihI+DAMLwAqKysBrEdS0lmsXXt9xDVioqO2tlbRe6sVF1ZlWlkpEqLjlZS41g4dysa77w7FeSuPBx4Ph5Ej1yAv71DE6zMyMkj0CEAuQoI4z4QJwZievXuDlh4rRQ9Pamqq6HOPP/44AoEAbrnlFpw6dQqXXXYZ/vGPf6Bly5YAgq6qdevW4e6778bSpUtx+eWX47HHHsP48eND79G1a1d8+umnmDNnDq688kpwHIfCwkKUlpaa/t3CIeHDOOnp6SFxkpd3UJfoMOKICjsyrcxES7wS/5roMlgcF4fmzWsb9QW5uM4TXvDRynIFBOEEcnOtFTxyFZnffvvt0P83adIES5YswZIlS0SvHzp0KIYOHRrx2Lhx4yL+7tGjBzZu3Cj6HkK1f4yGhI8DEHN5eTwB/O53n+GHHy5G27Y/ITdX/FBPvXV4tLrGWEbO1TJixIhQZkNNTQ3WrFkjkMF1nvBFm3+tm4N0pYR0TU0NPB4PvF5v6DG/3x8RJ+U2EU0QhDMg4eMAwt1Se/bsQWFh8PDRf/2rBz77rA8ADwAO3bp9h//5n3cE30NvHR6z427sQM7Vwh/3IfeaIJGLttBr3YRSIS2F35+Cli1PYMKEF1Ffn2R6uQKCIAiAhI9j4AVFdXU1vN5TOHWqBXbt6oSg6AEAD777rht69PiXpOWHR0sdHieJGiVocbWIvWbChBcj+t1Jli8t6A08FnIxFhT8ZFDrCIIgxCHh41AOHGiH86KHx4ODB9vKCh+r6/CwihZXi9hr+D6P1QwuKSEd/ZwVJRHUYETsG0EQzoGEj0Np2/YnBFOow8UPh7y8A5KvY23RMRuhRS28cKFUvFL4dUpfE4uiR0pICz3XsuUJRdlcVljN7DiDjnAnHMfZ3YSYwIh+JuHjMJo1awYAyM2tQrdu3+G777ohPMaHtzzw10UTSynEShe1SZOGIC0tDcD5IGZAuGCh0Gt47LYMKLVc+Hw+HDt2TLAKNQAkJCQgMzNT0XeREtIABJ+bMOFFSRejlVYzO86gI9xFYmIigGAJkaZNm9rcGvfDl2rh+10LJHwcRmFhIUaPHo3a2lqMGAF8//1ebN/eHJdcchpdusQDGCFZuVkurqWiokL0s7Us7HrdCGpeH31t+CGiUqSlpakORNbyGjNRKvJGjx6NV199VdF7KrFySAlpwCP4XH19kqSL0c66R1adQccK5ObTT3x8PNLS0nD06FEAwU2nxxMdhkDoheM41NbW4ujRo0hLS2tUZFENJHwcSLio6dIFuOkm5a+Vi2spKyuTfL0ak79eN4Ka1wOQvVbLouaUhVCpRUKusKXa95QT0mLPFRT8ZNnhs0phLfbNbFFCbj7jyMrKAoCQ+CHMIy0tLdTfWiHhEyMorcPDI7bgqzH5N46tUfeeel8fjp5ChXoXQrkFrLa2VtQ1CWhb4JQKNr8/BQcP5gEIFshUK0DkhLTUc2IVsu3IiGMp9o13RUq5Wnn0iBIj769Yx+PxIDs7G5mZmaIuZEI/iYmJuiw9PCR8YoT09HSUlpaGJlN+Yjt+PD3ib8Ccna/e91T7+vBJHBCONZFa1IxaCI2odwOoW+CU9tU331wadewGh2uvfU/R76JUSEs9N2rUKGbipFiJfVM7XrSKkvAK2gB71i4zsMKtFx8fb8jCTJgLCZ8YIryKrthEZ8bOV+97qn199HcrLi5XvagZtRAatVtW+j5K+8rvT4kSPQDgUfy7yBW0FKrcHA5rsSOsHJ8h1p9GulyjxRVL1i6zILceEQ4JnxhEaqJTsuCr3TnpFRFqXi/03crLi8GfnM6jtVCh3oVQbgHTu8Ap7augpa9xBWo1v4vUAsFS4DeP0LjlrR6sHp/h96dg69aeKC8vNswaE90HrFi7zIRVtx4Fl9sDCR8GsHrwS010cgt+9HlLYvABx4B+EaHm9WLfrVevLxotHloKFepZCOTcCUa4G5T2VdAFGCkGxa41ErsmeiU7ftbOoGvsijTHGpOYeJYJa5dVsOLWIyuUfZDwsRmzBn/0AlNTU4Njx44BkF4c5RZ8pYF74Z+tV0Soeb3Yd+vZcyt69twqu6iZdRirnDvBKHeDXF+dOHEidN21126IWFjNtnLYOdEr3cmPGzcwdDAtjx27bn486LXKyREuAviCqFrGgVMsFyy59Vi1QsUCJHxsxowCanILjNTiqPbgSKWuGSWZZFIofb3cwj98+GVo2bIodH10ESyzDmOVcyfodTcoFWybN29udN3Bg7kAgLy8Q6ZaOVgqFig2blk5XFb8MFzjrDHRIiBYCLXxuXNyOMlywapbjxUrVKxAwseFyO0kxMSNkoMjExLODxm5mzV64VSbvqz19UoX/nDCJ2UzJmc5F5Red2C0YKuursb69etlXxcUgi3/+9dvAQhXbjZjR29XjSQnLDJC4wEw1ionLK6CBSbDkRPALAlaOVgJYg+HJStUrEDChzGMXgyiJ/muXXdgx46ujcSN3M3HHyPAT15Kbla91hO1rxe6NloA2GVOlrNEGRFTJCU8xL53x44dZS0cZuzo9YgPPSLMKYtM9HgAAujVqxw9e241zConJwK0Hh3CctFPFoPYWbVCuRkSPgxh9E5UaJI/f7aXumwu/hiBqqqgCVzpzarXeqLm9XLXSvWv2PEWemMT1BSOlHq+pqZG9DOECiAaWaPF6FgEPeJDrwize5FRUsySR64GktLz1MSQEwFajg4x8x4zyuqo1+1uNCxaodwOCR9GMGMnKmzKjjxDRmk2VzRqrmch8FGuf6XcQnpiE5RYraQqN/NZdPzBqWoxelwZIc71iA+9bhUzFhk1h8MqPU/N6Ere0a/nsTKAX889plfw6nW7mwlrVigW5muzIeHDCGbsRIXjBIKZGzxKs7miUXo9K4GPSvrXLDeYlZNE9HcwclwZJaLkxIeUdSvaaiD2m9XU1Ai68IxeZNSMb6XjqFmzZqYGWNsVwC9FZWVlqD1Cn620744ePYpjx44JZp9effXV4DgOLVu2bFQxXOxzwzFTELBihWJlvjYbEj42w0/yShYDtZOh0CQvFOPD32RKdn9qd4uspGzK9a8TAl4B6fgJoe9QWLjXMAuHUSJKTnwotW5J/WZr1qyJmJzNsnLosUDZGQtjRwA/IP6do61Bcgur2PsoHTtayoMYLQhYtEKxMl+bDQkfm/F4gtYXucWAv06O6DN4hCb5/v0/Fpz0R4wY0aiGCRC5k9GzW7RTXMil8EtZMsL7004zr1T/iX2H6dMXG2bh0OsmUntQrhRKrE/hY9QsK4dQu5SIGacIbTWI3WMAUFGRj8rKbGzaVKLoO0v9Tkb0ndqF24zMNavGpFbcOEZ5SPjYTPhZRlKLgdiZR+GI7UqidxJiOwulGRxabkQWsmnE+lfOkqF2N2oGcv0n9R2MsnDodRMpmeijK4OLCQkt1iezfzOlCwUL94KRSAnaffs6YPHi6f/9rufd7Fq/s9q+M8uqZtT76hmTZrre3DZGoyHhwyTKrDvRaDU/lpaWonXr1rbH2JiBEnOymCUjMbEOFRX5TJh55fpPzhpjVEVivZYaNZ8nJSTE4tcqK3Ma1Z6yAjULhZ57gaXA0/C2lJaWhuJqTpw4gc2bN4sUSDwPx8Xh4MFcHD/+q2IBoabvvviiWLGFSQ0sWELMjsWxO/vRbEj4MIQZ6ezRhQuFdiler9f0ydKulE0pKwNf40csFmr58onMmHnl+k9JarLSGDGh407CsSIWQU5IeL2nUFKyCWVlA3F+QfVg06YSdO680/LJWc1CofVeYCnwVElbpKpPA8HvvHbtdQCU32NK++6LL4ojxoZRFgtWLCFmF410e4o9CR9GMDvtWCiomZ9ktAROq8XOlE21tT0SE+tCogfQ9lsYvTNX0n9GZIYoXVxLS0sj3K9WHqTLf6+cnCqIlWewWvioWSi03gssVUhW8hlSWaXBxwH+LDKl95iSvvP7U1BWVgIzxgarlhCjXXqspdgbDQkfRjA77ViscKHXe6pRBoxZsJKyySPmBquoyNf1Wxi5M1cbEKzXGqM0q8Pr9ZoqlpUICZZ2pUoWCqMzy1iqkCzUFqE+KSnZhJycSpw+3Rxr114f8R5S95ia++D48XQIHe5qxNhgaczxmOV6Y22+NhISPoxg5A2lpnChUAaMkbCYsskjdr6V3t/CyJRQvQUQAe3WGBaz8ABExF3ZvStVI2aMzOJhIc5ESVvE+sTvT1F1j6kJjBezNJWUbNI9NlgYc+EY7Slgeb42EhI+NsMPILkbSs1AU1O40GxYT9kU+lwjJzcjFigtfRPuaqurqwsdNcIj1+d2xTIozRAK70ujavJoQe34NmKcsxJnoqQt0SUyampqsGbNGk3znVLraPR7AwEMHLgJvXuXN7pWKWbVgeLR6ho32vWmdDwDaDSnKGkvK5DwsZnogfbAA8ewf38C8vPPISenB4AehsSDhMf4AADHAfv2dbBklyh3ynf4wszKDWOEmdeuBcoIV5vchGrW2WZiVji5vozOWjN7HNmZXcVSnImSM/7CXaLZ2dmGz3c8/Ng5duwYRoyox7Rpu3HwYDLy8s4iKysbwAgkJCRoOuPMzA2cnvvVDNeb3HdQ2t5Ro0ZpqpBtBSR8GCB8EGRnA0VF2t5HalcCAN991zXsaut3iSxlpQhhtJnXrgXKCFeb3ITKixGh99X7+2nZ2arJWtNL9Dg2qx/4zwoXgQBbcSZa2mLEfCclPL1eL5KSktCli7FziFlzktIwg/BjPfixYIfrTWl7papo233kBQkfFxG9KwkvBldRkY/ogD+rd4msl0M3elfHwgKl1dUmN6FKva8Zvx8LfckT/v3M7AdhgRWMb2IlzkTrwqvHYsb6BkovSo/14NHqejPKaikVZM/aHM9DwsdlRO6mslFaWioa8Kd04TDDrM9ScGY4Rk6UdgdC6nW1SQWmWu3Cs7svhTC7H+QE1vTpi22LbdIT86JXuLC+gdKD2nlR7JghwBrxKNVeVud4gISP6+FrrejZmRm9u2IpONNs7EwJVeNq48Wt3++PeFzI3WeXC8+oOkVGiXir+kHqHLaCgp8iFj+r4if0WEeNFC4sL65qkZoXAQj2kVYXrxG/gVx7WZ7jSfi4HL3ZCGbsrlgKzjQDVlJClVr5lIrbgQMHoqyszFK3k5F9aXRsjlX9wFJ8UzhGCCw9wkXvBoql4z8A8d9569aeKC8vNk3caf0NpMYl4GF6jifh43Ksqh2iJsuHpXgNM2AlhV+plU+puE1JSVH1vkZgZF9qic0RWhytDix16/2iV7joPfOMtTghsd95y5ZiqK1wrRQ9v4HcuGR5zJLwiQGsqB0iFngHNJ48WIzXMBpWgirVuoeU7v6sdOEZ3ZdKJ3tpC1HwMSv6wa33i17Lrx5ByGKckNDvXFxcji1bekdcZ6TlRM9vIDcuWR6zJHxMhDVTqh6U3CBqJg83l0O3G63uIbW7P7H3ZR2lk71SC5EV/eDG+0WvJcsoQWh3nJBcGRLezcVjpOVEy2+gNHyC5TFLwsckWDSl6kHuBlEyebAS++J2tLqH5ARBYmKios9n/fdTO9lrdQfI9YPcxkhJoLmTMUK46F1cWUi0ELpf+cKdgHLLiZaNtpbfQGp+CS+hwr8/i3M8CR+TYOkkZSOQukGUTh6sxL7EAlr6UE4QeL1eV/x+aid7qaDTq6/eBKBxWrFcPygNtFaC3YuIHrQIF6UbqJqaGtnfgZVEi+g2qk1K0bPR1vIbiPVpdGVuIViYI0j42Ex4UDALA0IKsRtEzeTB8veLdeQEQXV1daOaIayPWTHUTPbCZ98BW7YUo2fPrZoyq5S60cTK/vM4sf/1Wn7Dj6YIty5Ei0e+crCUVZ3VwHG1m0S1MUtmWt+dMB5J+FiE0mqcrLm+lNwgrBVHJLQjJQhWrCgz7WgGO5CyFACR2VtCQaaAfsuAnLU0LS3NllR1M1GyqNfW1goersuTlJQUqlEGaK+grcT6Z9ccpfU9lYQdxLr1nYSPBagJngs/j4WFgSd1g/B+aJaKIxLqUSJu9aR/R3+WXb+l0t1r+BlD/IalU6cfItKKAWMsA6y4WqxGzg24bNmy0N9im8bS0tLQ82ZUKA++t1/SqsQzevRoNGvWTPQzrBr3avoiludUEj4mo/amZNECpOTztfiJWUwpjUXExK3Sk9F5rDy8UwtKdrnhC1202OvWbQd27OhqaHouq64WO1HqBqyvrwegTTwqdfVwHKeoLa+++mroOjvHfawKabWQ8DEZsYF48GAujh//VTaYMdwCFA4L1iAj/cR2p5TGOkYEgFp1eKce9ByrsmNHV0yY8CLq65MMS891a40eI1AquLWeEK/E1XP+wGdlbZEa92JzOf9ZRsznRgpplq23eiHhYzLCgZEBrFt3XcTNEbSWKD+RF7BmByE3+I0w8bKQUkqIY1X6N0uIib36+iQUFPwk+3o1qeos1zuxE6WCW6t4VDJ38jFGSuuYKSnyaqZFyCghzbr1Vi+uFD5/+ctfsHDhQhw+fBjdunXD0qVLcfnll1vaBt7KET0QgQCC55h4AARvjnffHQqPB6LWDrtcQFbF4JB5lm2MSv920u+pVuyFWzW1pKqrrdETLqwqK+NQUZGAgoJzyMkJhNrjxAUpHDW/gdniUUlblIx7KyyhRvSFE6y3enCd8Fm9ejVmzJiB559/Hj179sTixYsxaNAg7N69G5mZmZa1I9qUOm3abrz22lacPt0ca9deH3V1HHhXcvQuwU4XkFUxOBTnwD5607+d9nvKiT2pE9GVLhpKEHIThwsrNanwThNDagW3mQUelbRFbtybaQmtra1t1F4j0tPdYL0VwnXCZ9GiRZg0aRLGjRsHAHj++efx/vvv46WXXsJ9991naVuiJ5mCgp/g96cI1gQJh98lAGBm0JkpwJTUjwnHaRO4W1C6sLglbkVK7Cmp2yO3aGit0aMk7gQA5s//l+NdE1K/QUKCsuXLqAKPcuJfbtwbZQmNdqOqyTxT+9u7wXorhKuET11dHbZt24bZs2eHHouLi0NJSQnKy8sFX3P27FmcPXs29PfJkydNaZuc6yv4Lwi/S2Bl0Jml+pXGOQjFOTltAnciShcMoevcEreix4ogd/+qrdHDL3jV1dXw+1Pwww8Xi1aU5s93it6kHD161HEuMrHfIC0tzfJaNHLjQWrcG2EJFXejpsh6CKRiMcVwg/VWCFcJn+rqajQ0NKBNmzYRj7dp0wY//vij4GvmzZuHuXPnmt42MddXq1bHsW9fB9FdAguDzgwB5vP5InYpgPCkQinu9qG3yJmZrgez0CP2opFbNKItmeHvHd2njd1b0//7vhzCN01AIOJQy+hNCl+jSGqBNHNToSQ2Sc1vYLZI02ItEhv3RlhCpdyoJSWbsGlTiaEbVLdYb6NxlfDRwuzZszFjxozQ3ydPnkReXp4pnxV+kyYlJWHLlmB2iNgugZVBZ4bqVxI/RCnu9qNmYTFSNNiFkRVt5e5fvkaSkmwZMfdWUPQExY/HE0D37tuwbVuPiHaozT4ya1OhNDZp2rRpzFQVVlplOryOjxRGWUKFfsOyshKEF9jkH9frIXCL9TYcVwmfjIwMxMfH48iRIxGPHzlyBFlZWYKvSU5ORnJyshXNi0BJRWSAjUFntgATmgQLC/dKTs6xHPfDan0Nt5TBN7J9UvevlmwZIesr4MGgQR8iEOAXv6hnNWQfmYHSmjh1dXVMHdOhZDxIjfvw+RwwxhIqPA7iTPMQONF6K4WrhE9SUhKKiorw0UcfYfjw4QCAQCCAjz76CNOmTbO3cQKI3VBmHiCnFbMEmNgkOHLkOsnJOVbjflg/5kPrZ/Jizu/3hyryhpOQkIC0tDRHCKdoxFy4WuLmxKyveXkHsHz5RETv+LVkH5kNK7GLRiI1Js2whIr9hr/73Wf47LMrwYsgrRtUN1hvpXCV8AGAGTNmYMyYMbjssstw+eWXY/HixTh9+nQoy4slpHbupaWl4DhONOvDDv+2GQJMbBIEONWTcyzE/ZhZYsAuS5KYmHNS0bTwvuMPOZVC6+IvZn2tr08WzBQdOXItOnfepeg9rBIddgsvqzHDEir0G3btugOff35e9JSUbNIcGuAW660YrhM+paWlOHbsGB544AEcPnwYl1xyCT788MNGAc9244TKmFYMfvEd7CHRyVlJQbhYwMgYKDstSUJiTioziTWBK3wv5wseqllfX4/169frWvyFrK9CZTL4+0jpe1iF3cLLDsyYw8N/w8TEOixfPjHCgrhpUwk6d96puV+dKmqU4DrhA5wPjmMZlitjWrnzl5oEhSZntwQ8C/VxuJuHd+2EE25Zk3OViGUM8e8T/fuxcmDsN99cinffHYpwl42YG4iVWCel97LX6w1dp3fxj7a+ank/O+M2WIhdVAor40wI/jesqMiXtSA61S1lBq4UPk6CtcqYduz8pSbB8MmZtb7SitI+FqK0tBSAvKtE6ow3QPr3k1q8wwWVkglfyaLBw/++0XEqQOR3q66uRk1NTSg1WwqjLVRC34fvE7Xj0+jFX+r9RowYgcTExEYlJISwKnHACQGzrFnmxcSLnAVx1KhRrrbgqIWEj82wFuhn1c5fafxQuHtATV+xvEtT0ndi/c5bhJS6StT+fkoPWuSRmvCVCjwpMSf03YQOe+Rfb6aFSnoR1HYvK138le7Wxd4vIyND0Xv4/Sl48slvmHK52wlrlvno8AM+Y0zO4idVIdwKDh0C9uwBOnYEcnNtbQoAEj62w3Kgn5luJTXxQ/wJyUr7ivXMp2iixYmSflfi2tDy+6ldvKV+P6UiWkrMAcKZSeHfLVjHhkN4JosZ7k+5RbCwcK/s+NSaLSN2v0SnSksh9B7hr5caL5WVlairq9NV7ZnlTCGxjZJWa56ZiPU1a+5Dvk9XrWqKWbO8CAQ8iIvjsGCBHzfd9Kutm08SPjbDaqCfFTe62kGvtK+U7r5YCJLVU31VaqIzOl06MbEOFRWNA3a1fk8lYg4IoFevcvTsuVXyu4Uf+WLFgiTWt9OnL5Ydn3oSBoQeUysmxO45oe/07rtDkZR0Fnl5B7F+/Xrd1Z5ZzRRS4s6ywjJvhJWaFfch36d+fwoWL54Ojgven4GABzNnpuLnn1+C13vKts0nCR8GYE2pA2y54MInd6m+ElsEWM0C01J9NfpgRrGJzsh06a5dd4QyRrRYVNSIMCX3gpRLTOn31INU3yppv5ETvVFiQqwg3tq11ysS5Eo3ESxYWKNRYsk7fbqZqZZ5rbFESoUvfy6iVeez8X0qNw/Ztfkk4cMIrCh1HpZccHomd5azwLRUX5U7mJF3XRiVLi2UJitnUQk/TFPse4ZPfqdORb6P3L0g5hJT+z21Ite3Su9lo+LQjFi0pPqUT41mZSNkFmICneOA4GYkEOojoy3zWmOJ5OZGPglg9erVtpzPxtI6Eg4JH5tg2d8NsOeC03JTsuSXF0JsUgjfXYu5S+RQ+/uJBZsrSZMNRyi+Sm7yKysrAxDMPPF4PKKVm8+dOycayMkvTlaMU633Rngfs5Yt1NjNGAnvegy3RrKwgKlFKitPvJgqTxw4LoDrrluDvLxDmsaYGbFESsaHXeezsbaO8JDwsQm9JmorspZYdMGpgSV3nRBik0L37t+ic+eduvtdze8nli2idscmNCaFvmdxcXmj69LS0iTPaOKD3IW+GwBLx6lc6nhGRkbE9dH3I2vZQsD573TwYC7WrbtOtSBnHbmsPDlLYpA4NG9eq1n02BVLZOdcyOI6QsLHRvSca2TWblFpmnl0WX6zgxK1CD25RVttTRqjUBKzJNbvchZAPceMCH1/vTs2fmwWFu7F9OmLsXVrT2zZUowtW3qjvLxYl+sx+rtp6S89SKWOKz1kkzWrZPA77UJdnXmC3C6UiE0xSyKPmOhXMs6UfL5ZriG7XU6shXKQ8HEgZu4WhSxRfr8/ovCZ35+C+fP/ZZlZXmt6utyiraYmjZGIWfuUVG62KnNGb0A5IJyxVl5eDH4hUbvIKxUxpaWl8Hq9hotZM9zTcjvx6upqU0W5WFvVCnInISU2o7/3vn0dROcP3rKn9veRE7tmuIZYdTnZBQkfB2PWblHqJrbDLK+0HgxfayTcGiV2rpEdxzGEI9THSq0EWt5by3voEVBCY1NvgKzd6dBmfL6weyVYPgA4L87NEuXh3ym6JpAbRI4QcmIz/HtLiX5e9NTV1UW4YcMRGg9yn2+Wa4hFl5NdkPBxMEb6baVcSXYU8QpvT7hLSkp4RVtw+J2/0iJtRCR6FlrxQFF9AbJ2p0Mb/fmNg4qDhRiXL58YMTaPHj1qqqDTC4vnQEXPafw8otbtIyYAhSzhSkIOlHy+2GfqPU7EKjHLevIOCR8HY5TfVmnMkFUBcmKuLTHhlZl5GPX1yY3a6/V6I6worMVTuBmtGWt6YfmoEjG6d/8WmZmHJcsGrFmzxnRXrFpXYvjrWOtT8TktRdbtM3DgwFCmoRRcMAAIgDpLuFa3k5OOE7HbOisHCR8HY5TfVmnMkFUBcmKurdOnmwkKL6XF9VjP8tKK0sXeSlFgdsaaEE47qiSc+vpkwbG5dWtPXH31JgDmu2JZX6zUoGROE3P7FBQUKOoH/nktGyolWYFKLdVKDwGWwgzLC8vjhISPwzHSbyt3A9sRIBd5JlMAQVeAJ+wKTvGEY3dmgxkoXeyHDBmCDz74QPa6UaNGhYKqtSxyZmasyWHVAbtGEP1dxVKpt2wpbnRkh5mwvFhpQcmcJoSSfuDjevRUSVeSFajHUu0mMWskJHxcgFF+WyU3sJUBco3PZOKrp3JhQkj5hOPGzAali70S0QMAa9asifhbrXVEbKKtqanBuXPnAACJiYkRrhLA+MmX9Vguvp8qKytDRRmLi8uxZUvvqCudb5G0Ey2iRK0AV7qh0mp90WupjjVRowQSPg7ELPOl0hvYqgA5sSMdRo5cg+bNaxsdpyDW3nDcnNmgZrFXen6Z1pII0RiVsaYEu2K51LoS09PTI67v2XMrtmw5n+4PON8iaTdyc1p0sUktAlzphkqr9cWNlmq7IeHjQMwyX+q1iBjtJxa74cPLxStpr56ifk5B7nTt8O8rdhAjiwe5asGOWC4j4ou83lO49lp3WSTtRm5OU1NsUgqlGyot1hc3WqrthoSPQzHLfKm2FD+PGX5iJTe8VHtramqQnZ0dE35uudO1eeuPmEDyeMCsW0gtduyQtcYXRYttN1sk7cKqPjXTEk7jwlhI+BCNMKIUv1EZREpueLH2hqcAO1nUKEHudG3e1SMmkPjMXDek+Bu5Q9YyjtW4HNPT01FaWhpRD8athQPtRKxPo4/eiUZqnrI6Y4rGhXGQ8CEMv4H1niWm1DUVDssZPGrRstg2LoQXCe/qUXIQoxtS/I3YIatxX/FoiS9q3bq1ovY42RVrNUr7KjqYXwixecpsSzLrRQCdDAkfwrAbmF+wlVZaFvs8Je2pqakJTVqsZ/CoQU+siNzp2rwAiBRIAQTLA3gaXes0jI7l0uK+0hJfFAuuWKtR0qdKKy/zR+FEY/ZvQuPCPEj4EAD0xwwJWXkOHszTVX9CCW6rxqzUQsVfJ7zYC5+uLVaSQOogRidh5kKhVFxrjS+ixct41PSpmqNwwjG7GKZV48KJFc/1QMKHMASxSqnRGO1GcWs1Zh65tPPwxT58Byvl6iktLQUARdc6zYxuxuSsRlxTBo7zUPL7usmVHo3e0AQnQsKHMJTGRQcjMdqNwmKNC6N2T0qtDPx7ZWdnq7J4kBldGXLiOvrgSMrAcRZyv6+bXOlCKD2yyA0ij4eED2EowllDQczY/bK2wzbqvCitLjy1VZYJeeTEtZArRCy+qLa21ryGEpqQ+n3l7sNo0RuO0zYOesMGnOQuI+FDGIpw1lAA1123NqLwoJGwtMNWG6Mjhlorg55JxUkTlh0oEddKK2G/+uqrrnIZuAGp37eiIl/yPpSK/wG0u4fsuCf1hA04zV1GwocwFLFJpHPnXYZ+jpXVmPVMQkoXxGi0WBm0TCqsnWjOqgiTEtdi7gE3x4U4GaHsU7HfV6kr3cjf2q57Uk/YgNPcZSR8CMNRaoHRI0qsSvXUMwnpiQ3Q4sLTMqkYZaEyAtZEmBJxLeYe+PXXJti0qcS1cSEsokQ0AxAdY0K/r5L70OgYIK1VwPViRNiAU7JsSfgQpiBmgeGPvTBClFix+GkVBlongPDFVkxAarUiKcHM95bDbhEmtHCWlpaivr4eAJCQkIC0tDQAQHV1NdavXy/qHigrKwF/2Cirh6S6CaWimc9olOOqq67C5s2bAUhv5Mxe6K0OrNYbNuCULFsSPoQhKLXe5OTkOHryVSoM1E4A4YvWqFGjcO7cudBzJ06cwObNPwEwdyJkLXvFShGm1dok5h5w4iGpTkaplYQXsXLXRVfTFtvImbnQ22U90XM0BotZtkKQ8CEMIRaqjKoRBmomAKWLlpkTodWTrJB1QmnFbzPQ6l4Qcg+UlGwKubl49Ez+Siw5drlHWETp2JG6Li0tDdOmTcOxY8caiSWA34xsNnWht8p6YuTRGKxl2YpBwocIcegQsGcP0LEjkJur/vVOFjVyqBUGaiYApYuRmROhlSZqOaFnd5yAWtEl5B5o2vSMYYekqnXhsGa5sxKlY0fpdeFHWghh5kJvlfUkugiqkNBLSEhAXV0dfD6f7DzPUpatGCR8CADA8uXA5MlAIADExQHLlgETJtjdKnZQKgyUxOhEXxeN2G7dzInQqPfWYp2Ixuo4AZ/PF7I2KV0Q5QKfjZr8lYpifrGyWzTajdKxo+Q6pVY0sxZ6K60n6enp8Pl8skIPUOYu1eMuswISPjGOz+fD/v3nMHlyJgKB4EGVgQAwZQqHSy45ivz8BFdbcpSiVBjodflJ7dbNnAiNeG+tAabRC4qVcQLRbVa6cAr9zuEH5wLmlFiQi3tySnCpWSgdO2rHmJwVzaxyGlZaT/QkFzjtJHkSPjEMP+lXVOQjEBgT8VxDgwdLl36AgoKfLAmIDLcUVFbGoaIiAQUF55CTEwBgf3yQGmGgtZ1KdutGn6tlhIWKR611AhBfUKza6Ua3Wc2CGP07qz0yRC1KXFhOCS41C6X3qZr7We6+5DNVo9H6W0dX97arCria5AKnxXiS8Ilh+EEqN1maHRAZvuuWmtztyEgxUhjIIbdbF5tg+c/X0jdmTlhyE6fYgpKZeRgtW57AhAkvor4+yfKDU4uLy7FlSzEA9aLLrPGp1IXllOBSM5G6TxMSEhRdF+6SlbsvMzIykJ2dbVj7mzVrZuh1WlAisp1cPoGED2H7ZMnfPHKTux0ZKWYKg+gKsnIC1OgJlseMyUnJxCm2oLz44kSEi46CgmAq/4gRI0wthxDd5uLiL9Cz51bL7gOpTDc1LiwnBJcajdJK7pmZmYrv56qqKgD2W9Gsrq2lRGQ7vXwCCR8CABuTJavxCWbcuEITh90C1CiUWieEz3XjIFb4LyMjw1KLSnl5MXr23BpxnVnWJrmFRG7xDbdkAOYe4aIGq6wCZm5Q7Lwv7cjQ0xL4LQar5RNI+BAh7I7El5vc/X5/6HEW44DUIDYhmO1OswKlAlZoQbFL+CpxM5ppbZJbIOQWX77uDEuuB6utAmZ+Nzs2hnZl6GmxcNlZ8V0LJHxsRG/dHBYw8jvITe58qiVrcUBGED1x8N/ZyCM+rEKNdSJ8QUlMrMPy5RNtcSkocTNa2f9CC4mcKGZtfBhhFbAzjsTKg5CF0GIBN6K/1Fq4nFg3ioSPTbihbo4Z30FuZ8ViHJBepCYOs+J6zEStdYI/9wqAbS4FltyMUuNh3LiBjQLcWRQ9Qqi1CtgdR6LEfVZbW4u6urpQPFA0en4btZYXvf2lJpFDaVwmq5DwsZhDh4AtW4BJkwCOCz4WrJsDDBrkDMuP2bV/pFxurMUB6d1hyU0cNTU1jhE+aibO8D6xMnNOT5v1IjVWlBZQdKIQBrRZBViII5G6d30+H5YtWyb7HlqFmVoxrre/1MRJ8UKPtflYKSR8LCTcQhJNQwOwd6+1wkdL0Sm7a//YnWERjhE7UrmJY82aNY5x32kNMLWzBohVn610rMiNh/DzzIxuo1kYZRVgLY7ELGFmlBjX0l9qxxBL87EaSPhYxKFD4qIHAOLjOaSkHIXPZ12lZC2Tvhm1f9TspllySRhxMKSSicNJ7jutY9fORduKz1b6G8qNB94lKASrAtkIq4AT4kiMEmZGiHEj+0uqzAJL87EaSPhYxJ494qLH4wngmms2YMMGYwJ01QQca/0cIwe82CF5hw8n4MCBZLRtexbNm59AWVkZADZS76PROtEI9WNJySYmvhNhHmKLpJ77ilWBrNcq4IQ4EqOFmZ7538j+UmKplJqPza4urRUSPhbg8/mQmnoOcXHnY2KA4M0/cuRa5OUdUmwhkMPKoGkjBUj0IXnCE8n568XigIRcAWa7AbRMNNHm7F9/bYKyshJwXBw2bSpB06ZnmNvRsoSTq8bKLZJKAvxZcvnIoXeTxLr7T6vQMGsMGxl3o3QtEpuPX331VSYtkSR8TCZcMQ8d2njC69x5l2GfFe1O0xM0rdRqZGTtHyMyBVasKBNcFMy8+bRMNOnp6SgtLcXq1avh96dg06YSiBXuU4vZosBu0WF3to8etJz7FJ715gSXD49RsSqsu/+0pp2bNYbNjLvRIrpZtESS8DGZ8B/dTBeNz+fDl18CgUDkTdLQAGzd6kPTpsrNp3an2mvdsUgtCmbefFonGq/XC8DYHZrZooAF0aH0t6ysrBS91i6LkJ5zn5zg8gnHqMBx1t1/Wu5/MzPWzIq7kRPdTrJEkvCxGDOqI/OLkd+fAo9neqMb8IsvXsbOnadkFyMlaepWIDeRhO+K+d2wnYuC3onGyB2aEQHXdr6/FsTaIGUJAOyxCOn5rZ2YOqynf5VajAB7F10jhIYR7TezNIPc/OokSyRAwscV8IuM3A0oVyFVSZr6qFGjzPsi/0Xuewjtiu1eFPRY8+zaoemFhclOTxvsMMHr+a2dmjqsFSmLEQvuP6OEhlHtN7M0g9T8CsBRlkiAhI/r0LoAK01T93g8gq+PRm/BN7Xfw45FQW9Jezt3aHphwe2itA0smOCN+K1ZTh02K+5L7jV2jkMjhIbR7TfLgik1v9q96dQCCR8XosedJje5er1ey4rNqfkediwKeic+u3ZoRvQJC5OdkjawYJUC9P3Wdla2VoIdcV81NTUA7B+Her+PGe03Q4TKza9Os0SS8GEQu0/hFppc+V1zZWUciorYypThsaK+j9GTih07NCe8vxFtsMsaYMYYYe3k9XCsPlrC5/NhzZo1ANgYh3owuv1Gi1Ah0X3wYC4AD/LyDgJg2xIphmLhc/LkScVvmpqaqqkxsQxrp3CHW1vCd82vvMKZluWVlJSkyC0h5w4w64aLnlTE2jpq1CikpaUJvodVv6/ZkxELk51cG+ywBphl/bBjTtAq4Mx2LYa3iYVxqAej22+0CE1PT8eoUaNCQnPfvg6CFlQWLZFSKBY+aWlpsvEdHMfB4/GgoaFBd8PcgtIfPScnhwnBE030rjkQ8Bh2GCkPP8GuWtUUTz99NwIBD+LiODzwwM8YMeIEEhMTQ6nfUgdcSmHEzRc+WUi5UPhJQgyrsonMtoDZVUFbqevHDmsACwdrGoEaAReOHa5Fpy26APvuy3D4TZycBXXcuIGNalCxspGPRrHw2bx5s5ntcC2sm6nlENo1G3kYaXgq/uLF08FxfBq9B3Pn5sDvXwOvVzwV347+1RtUa+aipzfg2qj39/v9oeuNHttyvzmf8cOCNYCFwGotaBFwdrkWxfq4tLSUaUuZFfOWkeNPTw0q1lAsfPr27WtmO1yN2TefmVYPIw8jFYJ/vdxNJfU5/HEX/DWVlXGoqEhAQcE55OQEUFdXB5/PZ9jvwHJQrdkTavj719TUiFq3+KNHAHMsXFLvx8pumpXAaiNQsoBqdS3qiYmS6mPeSmwlLBT45DF6/Dk9nioczcHNNTU1WL58OXbtCh65cPHFF2P8+PG2DLZYx4jFTmwRsGrXrOemCp9spG52oyYbVoNqecyeUNW+v9VuHRasrHaPASNRuoBquYf1CAUW+5gVV6cZfcOCBdUoNAmfr7/+GoMGDULTpk1x+eWXAwAWLVqERx99FBs3bkT38NMkCUvQO4lHLxbhBcKsiOUwoiy93M1u1GQT3VYg8kR1u1Ns7YIlt47drmOrxoDZ56apWUC13MN6hIIT7jO77gkj+0apBZV3bwu93u77MRpNwufuu+/Gtddei7/+9a9ISAi+xblz5zBx4kRMnz4d//znPw1tJGENUoPTzGwpHr0Cy8qJkD9RfdOmxiequ8kkrBRW3Dp2H6DKY8UYsMKtouSessu1yPp9JnVPhJ8ob8aYNLJvpCyo4e7ucPd2NKwdGKzZ4hMuegAgISEBs2bNwmWXXWZY44jYQ4/AsnIi5E9UF9sJS+18wyc9HhZ3RUphxeXAUnyFFW4BK85NU3JPGelaVFPOQq6P7cyIkrsnos+RM2pMqukbNZsEvW1jLYtRk/BJTU3FgQMHcNFFF0U8fvDgQaSkpBjSMIJQi9LFRioQGlBWQVduJyy18xU7PJO1XZFSWHE5sBBfYZf1wyyLm9J7yohxq/Q7RAutBx44hv37E5Cffw45OT0A9LB9I6H2njBqTCrtGwDMbBLsQJPwKS0txYQJE/DEE0+gV69eAIAvvvgCM2fOxI033mhoAwFg//79ePjhh/Hxxx/j8OHDyMnJwejRozFnzpyICWTHjh2YOnUqvvrqK7Ru3Rp33HEHZs2aZXh7CG0cOgTs2QN07Ajk5przGXLuMj2B0OHFvJTshMVTve0/0dxIWHU52BFfwWJ5BS1uFasFnFqrYfh3yM4GiooMaYZhqL0n/H6/YangSvqmqqpK0XtVVlY2ek8pWIrzk0KT8HniiSfg8Xhw66234ty5cwCAxMRE3H777Xj88ccNbSAA/PjjjwgEAnjhhRfQoUMH7Ny5E5MmTcLp06fxxBNPAAhWlr766qtRUlKC559/Ht9//z3Gjx+PtLQ0TJ482fA2uR2jUuTDixPOmuUNFSdcsMCPm276FbW1tYa2B5B2l+kNhOaLecnthPlK3AAbJ0mbCYvZHnb2s9U7ZDnrgha3itUCjhWroVGI3RMAUFGR30gYrF69mknrCj92lLTNSXObJuGTlJSEp59+GvPmzcO+ffsAAIWFhWjWrJmhjeMZPHgwBg8eHPq7ffv22L17N5577rmQ8HnttddQV1eHl156CUlJSbj44ouxfft2LFq0iISPBoyY+KSKE86cmYqff34JXu8pjB49WnLsmGG2NmKildoJCxXzYiUWxihYqZcTjZZ+ZiUoWgtqrQtqjiuwClathmqRuif27evw33lQWBjYafGVs9TItc1pc5uuQ0qbNWuGLl26GNUWVfj9frRq1Sr0d3l5Ofr06RMx8AYNGoT58+fjxIkTaNmypeD7nD17FmfPng39reZMMrcg5oIyKqBNTmQ0a9ZMl5lXjXWKb5NRE62aYGy37WpZqJcjhNp+ZikoWgtqrQss4YRAZTWIlQVhWRgYYalx2tymSficOXMGS5cuxebNm3H06FEEAoGI57/55htDGifG3r17sXTp0pC1BwAOHz6MgoKCiOvatGkTek5M+MybNw9z5841r7GMs3w5MHkyEAgAcXEw5QBSs3dzahZg3ret1T2jxwXoll1tOCwKAb0WECfGYKm1LrCC2kBlJ1jmhD6fVWEgJMjefXcoMjMPIzdXWRwQIH/PRWey2v07aRI+EyZMwMaNG3Hdddfh8ssvlz28VIz77rsP8+fPl7xm165dEdljP//8MwYPHozrr78ekyZN0vS54cyePRszZswI/X3y5Enk5eXpfl89WBEE7PP5sH//OUyenIlAgHdBwfADSAFrYkCUtNXn84VuQL8/BS1bnsCECS+ivj5Jcd0gPVYOFmNh3IiefparvWL3hB2O2LlpZloXzBAeSgOVnWyZExIGAIfKyhwUFPxkW7uEBBkQh+XLJyoSy0otdkKZrHb+TpqEz4YNG/D3v/8dvXv31vXh99xzD8aOHSt5Tfv27UP/X1lZiauuugq9evXCsmXLIq7LysrCkSNHIh7j/87KyhJ9/+TkZCQnJ6tsufHIBQEbOeHyE0hFRT4CgTERzxl5AGk4dp3kzSOXzaVm8tHTJ3b3g5vRG3OktPYKKwurmFvFLOuC3cKDhXIFagkXBiUlm1BWNhAAbyjwYNOmEnTuvNO2eUBYkCkXy0IWu+++O42dO9+W/U52/k6ahM8FF1xgSL2e1q1bo3Xr1oqu/fnnn3HVVVehqKgIK1asQFxc5A9VXFyMOXPmoL6+HomJiQCAsrIyXHjhhaJuLlaQCwL+8ssy5OUdxJw5YwyZUJTGuRg9MK2o/iyG0mwuMzD7xHQ3o7bImp6YIzHBcPBgLrzeXaHHWFpYhb6LWS5VJwoPuwkvgZGTU4XzoieIXe6uaEvNu+8OBaBNLEdb7HJyqnDw4PnXsJjirkn4PPnkk7j33nvx/PPPo127dka3qRE///wz+vXrh3bt2uGJJ57AsWPHQs/x1pybbroJc+fOxYQJE3Dvvfdi586dePrpp/HUU0+Z3j69yAUBr117PTyeAC644CTuuce4z41F14vS3bCRIoTVIGDW0WJh0NOHYrvfdeuuQ10dmzEyQlh1X9u9oNn9+UrhS2DoPYjZyPmDn5OCdXrWIzPzMJYvn2i4WGY1xV2T8Lnssstw5swZtG/fHs2aNQtZWHiOHzc2WLOsrAx79+7F3r17kRsV9MJxHADA6/Vi48aNmDp1KoqKipCRkYEHHnjAUansYhMvEFycZ83yol+/I4bG38Sa60Vu8hkxYgRycnIMFyEkatRjddCx2O6XpQwcKawsL2D3gmb356tBb+aaWS7G8Gtzc6sMz6pjOZNNk/C58cYb8fPPP+Oxxx5DmzZtNAc3K2Xs2LGysUAA0LVrV3z22WemtsVMom+MaAIBc+JvzHJBGVUE0UjkJp+MjAwSKQxi1ULXvfu3SEo6i7Vrr494nIUMHDmssizavaDZ/flq0XvEhpkuRjOP/2A1kw3QKHy2bNmC8vJydOvWzej2xDz8Tu3gwVysXXsdwneeZsbfmAGrLp5Ys3I5HbmFrqamxtA6UHl5Bx1bdsCKe8nuBc3uz9cCa0dsiLnOcnICyMmpM2ReZrl8hybhc9FFF+HXX381ui3EfwlaYHahrs758TesWk/sDLQm1CG30K1Zs0aXBTQ83mH9+vUxGfumBrsXNLs/3270xjZZlZ3H8n2kSfg8/vjjuOeee/Doo4+iS5cujWJ8UlNTDWlcrEOWCYJQttDptYCmp6dHvAfde+LYtaC5rcqzFoxw+ZqdncfqUTbhaBI+/LlZAwYMiHic4zh4PB40NDTobxkBQJtlItyMWVkZh4qKBBQUnENOTrDCthkHg7IOi/FGhDKEYt84Dti3r4OhcT5UdkAaMxY0PeUKjIxHcQJaY5ui+zi6irLRsBriEI4m4bN582aj2xHTGDmRyhXq4xcKOw4GtRMn3IyxiNTCFz5BFxbuxX8TOP+L8QGtRo8RJxyvoAYz+kdPuQIWYmWsREtsk5I+NqMsAOvjWpPw6du3r6Lr/vCHP+DPf/4zMjIytHxMzBA+ofDVV7WitFCf3oNBnQjrN2OsoXThA4KTvtYCa2qtCkZgd5VjszCyrVQQUR1aYpvk+s7IbEknCX1dp7PL8eqrr+KPf/wjCR8FqB0QcucGOTHzgYgtlNbpAbQHtNolQNxw8KnVOKUgodUYGdsU3scADCsL4DShb6rw4SJt04QCpM4RCp8U5M4NivXMB8JZyO08tQbUsiBAnFRszy6s7iMnWSeMim2K7uPi4nLDNsdOs96ZKnwIcaRuvFGjRqGmpgYbN24EID0piL0Hy6mEBBGO0qBNvZlWdggQpxXbswOr+8hp1glAf2yTUB+XlxcDCECsVpybIeFjA2piG/RMCpSSSzgBNW5ZrZlWdgkQcjnLY3UfsW6dMMMaJdbHvXp9gfLyYsPLArDutiThYwNqYhv0TgpUqM8+rDCnO8lkL4acW7a0tBRer1f09Uq+o10ChFzO8lAfnccsa5RYH/fsuRU9e27F8eOtcPPNPdG5s/6yAE5w7ZLwsRm5QUKTgjOxwpzuRJO9EHJuWa/XqzsD0a77iCWXM6si2e4+Ysk6YZY1Sv4eO4XOnQfpvs/kLKtiNYSsHnuqhc+5c+fw2GOPYfz48Y1OSo9m9OjRVMVZAiXmd7WTAhXqY2OCtyKoloXAXaMw2y1r5+LKgsuZRZHMQoVf1q0TekWZUB8fPJgLwIO8vIMGtjSInGVVqlSLlWNPtfBJSEjAwoULceutt8pe+9xzz2lqVKyg1PyuZuKM9UJ90RO82MRh5U1mxeRqxWcYLSitrpRspQCx4rup+T1YFMl2z1WsB54bcU+np6dj1KhRWLNmDYBgtXMz5wklllUWxp4mV1f//v3x6aefIj8/3+DmuJfwSYo396kxv6uJ1XGrqFFC+M2jJRvOaKyYXK34DDMsBlYsfHYdQxH93fx+P+rr6yOuSUhIQF1dHaqqqlR/Tz2/B0tWDjvnKpYDz428p9PS0gx/z2iU1hpiZexpEj5DhgzBfffdh++//x5FRUVo3rx5xPPXXnutIY1zC2KTlBHmdze7rPTAym7OisnVis8wy2Jg9sJnp1WBf0+fz4fVq1fLXq9GNGr9PVi5L1iA5fhJuXta6rwtsfFs5jwhVWsoKakA69d/y9TY0yR8/vCHPwAAFi1a1Og5OqS0MVKTrpT5fdSoUSG1LoSbXVZ6YWU3Z8XkavUEzsquTSl23yNmu5nU/B6s3Bd24oRT3uXuabljjYREtNnzhFitoaqq4OHYLI09TcInEAgY3Y6YRsz8npmZafuk7VRY2c1ZEVRrZeAuS7s2J2K0aFT7e7ByX9iJE05513tPh4toVoQeS2NPk/D529/+htLSUiQnJ0c8XldXhzfeeENR4DPRmBEjRoTONbP7xnM6dqfIhmNFUK1RnyEWMMub1lnatTkNM0Sj2t+DpfvCTlg95V1pphuPkqwvVoQeS2NPk/AZN24cBg8ejMzMzIjHT506hXHjxpHw0UhGRobjT0xnIZWcx840YiuCao3+DCUBsyzt2pyGGaJRy+/BQno9IYxcTFp1dXXIzaXGesiK0GNl7GkSPhzHwePxNHr80KFDkhVWiSAsFcwyEhZrhdhVudqKoFqjP0NJLApLuzanYYZoVPp7qBXJLG1gYg0l/WqVy9mIcWBXZqUUqoTPpZdeCo/HA4/HgwEDBiAh4fzLGxoaUFFRgcGDBxveSDfhtMBQNbBwBg5LBRytWBjM+gypccrKrs1pmCUalfweakQyixsYlrFDJOqxHiptr1HjwO56TUKoEj7Dhw8HAGzfvh2DBg1CixYtQs8lJSUhPz8fI0eONLSBbiLWAkPtsGyxeJM5DaUVxe3etTkRo0Sjll202anysYhdIlGr9VBNe40cB6zNt6qEz4MPPggAyM/PR2lpKZo0aWJKo9wGP/nIqXQ3LRp2WrZYu8mchtw4DQ/CD4cEpTBmmPqtEvhutlAbgV0iUav1UGk7KisrI/522zjQFOMzZswYAMFOPHr0aKP09rZt2+pvmYvgJ6n9+8/hlVc4BALn46Pi4znccccQ5OcnuGbRiDXLltuQ2026IQjfSswSKWbPF3Qfq8MKcWD0+WZiQi28TpAbx4Em4bNnzx6MHz8eW7ZsiXicD3qmAoaNSU9PR3o6sGwZMGUK0NAAxMcDL7zgQVFRG7ubZyiU8uxsKIDZeJy4qaH7WDlWiQMjRbRSoebGcaBJ+IwdOxYJCQnYsGEDsrOzBTO8CGEmTAAGDQL27gU6dABkDrh3JG5KeY7V7BYKYCbcdB+bjZXiwIj5RkyoZWYeRn19coQFyI3jQJPw2b59O7Zt24aLLrrI6PbEBLm57hQ8PE61GESLHL/fb/gZSyzDWtqpkaIzVgWsHpx6H9uB08SBmFB78cWJACItQG4cB5qET6dOnSQPSSNiE6P9z1aiNNtBCDWBiywvwCxlxBmZLUPp2dqJRcuflnvUaeJASKgBHABhV53bxoEm4TN//nzMmjULjz32GLp06YLExMSI51NTUw1pHOEsWFo41aJEvOhNz3fCAszKb2NktgylZ6uDNcuflei5R50kDoSEmpyrzk3jQJPwKSkpAQD0798/Ir6HgpsJVhZOozEiY4OFAo9OxMhsGbel5ZqBkzcwelErkp0mEsWs8omJdVi+fKKoq85tJSw0CZ/Nmzcb3Q6CYIrwCQ+AKRkbbj26xEiMzJZxY1quWThxMTMaJSLZaSIxur3hZ39JuercVsJCk/Dp27cvPvvsM7zwwgvYt28f1q5diwsuuACvvPIKCgoKjG4jQVhK9IRXXFxueMYGWR6UIZctEx5rKLfAuDEtlzAHNSKZFVGjlPD2aonLZDlOUSmahM+6detwyy234Oabb8a3336Ls2fPAghmwTz22GP4+9//bmgjCcIqhCa8LVuKDc3YIMuDcuSyZcILrQHS8VF2Zt64YbHQihO/u1EimfXvrtZiFR0DJWa1Zj1RQJPweeSRR/D888/j1ltvxRtvvBF6vHfv3njkkUcMaxxBWI3QhAfEobj4C5SXFxuSsUGWB+WIZcsAQEVFvqoAZbsyb9yyWGjBCQH9Qhghkvft24dXX31V9jq7v7uazw6/v6Ss1qzHKWoSPrt370afPn0aPe71elFTU6O3TYRLYX33A4hPeD17bkXPnlsNSc93Ws0Pu4k2we/b1wGLF0/X5CbUm3mjZQy7ZbHQglMz6vSKZJ/P10j0OOW7K8HpVmtNwicrKwt79+5Ffn5+xOOff/452rdvb0S7CJfB+s6PFy9yE96kSUOQlpYW8Tq17XVazQ87EMuW0TLhGpV5o3cMW7lYsLjJ0BvXZvV30iOSo9v5xRfF2LSpxDUxfU63WmsSPpMmTcJdd92Fl156CR6PB5WVlSgvL8cf//hH3H///Ua3kXABrKdyR/u6H3jgGPbvT0B+/jnk5PQA0EP3xOrkAo9WI5Z9omXCNSrzRq/1wqrFgkXXml7RZ9XGyYz09C++KEZZ2UAAwdIvWgUvS2LW6VZrTcLnvvvuQyAQwIABA1BbW4s+ffogOTkZf/zjH3HHHXcY3UaCsITwSSM7GygqMv79nZT6ajdC/aB1wjW6T7VYL6xaLFh0rekVfVa5zIy+R/3+FJSVlYAXPTxqBS9rYtbpVmtNwsfj8WDOnDmYOXMm9u7di19++QWdOnVCixYtjG4fQbgKEjX6YGHC1Wq9sLrtLMVhGCn6zC4FYeQ9evx4OvhjIMJR+91ZFLNOqlQdjSbhw5OUlIROnToZ1RaCIAhZpCZcoTMEjbai6bFeWLlYsBSHYZToM1LMWeE6EjsTq6Rkk6bfgCUxC4i7AllHl/AhCIKwAqWxF9F1fXiMdAHotV5YtViwFodhhOgzsr6OkpihUaNGhZIZjEhkAAIYOHATevcuV/U+PHaLWaWxTazHKZLwcRgsBbjpgY5rINQgFnsRXnIfsCZlWK31wq7Fwm63oM/ng9/vb9QmPcHCRok5uZgh/u+//vUD3TE0coJPze9ut5h1S5wiCR8HwXpKuFLouAb3YKUQl3sfK8eVGuuFnYuFXXEYaiwqmZmZir+7GWIuetx07boDO3Z01RVDo9RCOXr0aFW/u91iFnBHnCIJHwfBekq4FPxEIOejZt1ESpyHJSFuReyDnlRnOxcLO+IwlGZhpaWlGW5BUYPQuPnuu27Qm3pupth1clAxK5DwISyBnwg2bwaeeqqxj7p37zHo188duwkjcIJLkyUhbkXsg1PM/KzFYRhhiTOjvg4gdkSNvtRzHjPHgVODilmBhA9hGenp6bjiCiAuDggEzj8eHw/07JkO0jxBWLKkqMHOuC2rYh9Y6m8xWBJoWi1xQsJ/1KhROHfuHAAgMTERXq834nkt30ks6ypc/LBQmI81Met0SPgQlpKbCyxbBkyZAjQ0BEXPCy8EH2cNu6wuLFlSlGJ33BYLsQ8swYpA02KJs7JYn9C4EYrxsXscsSRm3QAJH8JyJkwABg0C9u4FOnRgV/Q40epiB6zUFqHYB/bQYomzulif0Ljp3/9j5sZRrM8zRkLCx4VEF3FjcSeQm8um4OFxotXFLuysLWJW7AdhDHoscWYKarlxQzE0woRbwSsr41BRkYCCgnPIyQnGLrC41ghBwseFCBVxI8uEc2G95pGdtUXIBWA/Qouh11sTel6rJc5MQR09bvx+P1avXi37ulgW0OFWcClLnBPWGhI+DkLPTUeWCWdid+yMEuyOr2F9knUz4othSwwbdmlorGqxxJktqCMPJc4mAS3DeZEobYlzwlpDwsdBCO1uoyvXEu6BldgZMcIXLaldfSzvkt2O0sVwxIgRyMjIiHitnJCwWlDHsqhRg93HZhgBCR+HQTenPdjhbmJ9giE3E8EjN1YzMjKQnZ2t+n0pYJ097D42wwhI+BCEDFa7m3gLidwEw4IlhUQNAehfDMPjhKKTMyjQmC3sdm0bAQkfgpDADndTuCXlggtO4t57vWho8CA+nsP8+Sdx0003kiWFYAo9i6Fw3Z58WeuqUuHvhCroTsPpljgSPgQhAD+pypnwzbK68BPxPfcApaV8zSMPcnPTAKSZ8pkEoQeti6HSuj3hcUJKxYqVxRDV4Ia0cCdb4kj4EIQAvNVl//5zeOUVDoHA+RL28fEc7rhjCPLzEyyZnFiveRRLkPVAGj2LoZx1VUuckNXFEJXgprRwp0LCx+HQGS7mkZ4ePD+s8REbHhQVtbG7eYTFUDVvczEzmJ+lDEmnpoW7aa0h4eNgDh0C9uxJx4gRdyIj44zodeG7UNqxqscJR2wQ5hN934i5TVhbsMzEyMXQzGwhFjMkWWyTFG7K4iTh41CWLwcmTw6ech4X1xLLlgUXaCmcumNlQayRu4kIxwmFJa3AyMXQzGwhFlOwWWyTHCytC3og4eNADh06L3qA4H+nTAlaJaQWZyeeP+VUsUa4F5bcJixg5H1nVrYQiynYLLYpViDh40D27DkvengaGoKuGLdZJZwo1gh34zQXhdMwK1uIxRRsFtsUC8TJX8IWZ8+exSWXXAKPx4Pt27dHPLdjxw5ceeWVaNKkCfLy8rBgwQJ7GmkyHTsCcVG/XHx8MP6EIAhz4V0U4YS7KKqrq1FVVYWqqir4fD47mugorAya9XpPoaDgJ6YEBottcjuOs/jMmjULOTk5+O677yIeP3nyJK6++mqUlJTg+eefx/fff4/x48cjLS0NkydPtqm15pCbK5Rp5D5rjxthIV6J0IeciyL67Dynu2HNHrNmBs26KROJMA5HCZ8PPvgAGzduxLp16/DBBx9EPPfaa6+hrq4OL730EpKSknDxxRdj+/btWLRokeuED0CZRk6E4pXcg5CLwo1ZXlYVADRrvLOYiURizH4cI3yOHDmCSZMm4e2330azZs0aPV9eXo4+ffpEDJZBgwZh/vz5OHHiBFq2bCn4vmfPnsXZs2dDf588edL4xpuEUKZRMMU96A4jMcQWFK/kbKIXovBYFLdmebFYAFAtrG0i1IoxN1R5Zg1HCB+O4zB27FjcdtttuOyyy7B///5G1xw+fBgFBQURj7Vp0yb0nJjwmTdvHubOnWt4m+0gMsUdilLc3QJ/sKGbJwFyk9lL9IJVXV2N9evXx0SWVyx8RytRep9SlWdzsFX43HfffZg/f77kNbt27cLGjRtx6tQpzJ492/A2zJ49GzNmzAj9ffLkSeTl5Rn+OWajJMXdzSbW8LgKN04C5CZjA6G+dUuWl5BlweutAeCe7+g0nFrlmXVsFT733HMPxo4dK3lN+/bt8fHHH6O8vBzJyckRz1122WW4+eab8fLLLyMrKwtHjhyJeJ7/OysrS/T9k5OTG72vE1GS4s6iv1sOLSLMjZMAucnYxYmF6KIRtyy0xLBhl6KwcK/jvyNLqHVfkfA0FluFT+vWrdG6dWvZ65YsWYJHHnkk9HdlZSUGDRqE1atXo2fPngCA4uJizJkzB/X19UhMTAQAlJWV4cILLxR1c7kJPsU9XPwIpbizJGqUEC7WqqursWJFmWBwpVvhJ0jelUewhxsK0clZFqZPX+z478gKatxXPG4Q1yzhiBiftm3bRvzdokULAEBhYSFy/2vOuOmmmzB37lxMmDAB9957L3bu3Imnn34aTz31lOXttQM3p7jzYm3VqqZYvHi6awJIxTJkeJS6twj7MaIQHQsxXFKWBSq2Zwxa3FduENcs4QjhowSv14uNGzdi6tSpKCoqQkZGBh544AFXprKL4eYU90OHgFmzvOA4DwDnB1cqyQKSWgTlRBNhPlJZXlLXCWFV2rgccpYFs6oqxyJq3VckPI3DkcInPz8fHMc1erxr16747LPPbGgRO7j1MM1gDJMn4jGn+bj5BVBupye3UNqROs2CNYI1jIyZYyVtXKtlwYkJEXajxX1FwtMYHCl8iNgjGMPERYgfp/m4+YVy82bgqaca7/R69x6Dfv2k47DsSCumjDJxjP6+LKSNS1kWRowYgYyMjIjrY1H0GgG5r+yDhA/hCHJzgQUL/Jg5M9XRk0R6ejquuEI4EL1nz3TIrR92ZHdQRpl1sJK9I2ZZyMjIQHZ2tmXtcDty7is3lyCxExI+MYzTqjyPHduAn39eLOvjZn0S0BqI7ven4PTpZpLmcda/OyFNLGXvUEXiIFLuKyeWIHECJHxiFCdWeU5PT8ecOWNcMVmqDUQPj/sAAqHF0eMJ4MEHKzF58o2WfXcKrDYPu9wftbW1iq4zSlhTRWLlxPr3NwMSPjGIkirPrMJPAk4UbtEoDUSPjvsA4sBxAVx33Rrk5R3C5Mk3yrofjApOduuZVCxhdfaOz+fDq6++GvpbTNiOHj3asEU4lisSk/vKfkj4xCBKqjyzjJOFmxr4iU8o7gOIQ/PmtYqywIwKTmYh8DZWsDJ7R2lGmdDh0HphJabJSnj31f795xpZrHmcYrl2KiR8YhClVZ5ZxenCTSnhE+Qrr0RmtMXHc7jjjiHIz0+QnSCNCk6OxUXKKliwAigRtkbH5cRSTBNwvv9WrWqKWbNaIRDwIC6Ow4IFftx0068keCyChE8MYnaVZ7NrvjhduKkhPT2Y6dX49/KgqKiNoZ9VXV0t+dvE2iJlJSwEscoJW7/fj9WrVwMwLi4nllK6ecur35/y3wr0wY1MIODBzJmp+Pnnl+D1nqK4Jgsg4ROjmFXl2YqaL24+nkMMI38vsRgO/oT76N+GtzLILVIUk6APuxc7OWFbX18PIDh+3n13KABj4nJipSIx3y9yAtONcU2sQcInhjGjyrNVNV/cfDyHGEb8XlqOyoi2RjzwwDHs35+A/PxzyMnpAaAHmehdgJyw/eWXXwAAW7f2BC96ePS6PGOpIjFZTu2HhA/hWNx6PIdZCMVwvPvuUGRmHkZubpXka8NFTXY2UFRkalMJm5CyvmzcuBF+fwrKy4sFXkkLt1Jiyb3HKiR8CCJGEMsOW758IqWlEyGkrC/CYwjo1atc1cLNQjC3ncSKe49VSPgQRIwgZGIHKC091lEjLsTcND17blX1mSwEc9tNLLn3WIOED0G4nOjg5PDAVB5KS49dlIiQmpoarFmzxlA3jZtFDcE2JHwIwuXwC1tlZSWA9cjMPIzlyydScCURQo0IITcN4XRI+BBEDJCenh7a0efmVlFwJSGKUJFCr7cm4hopN41b43L0EutxTSxBwocwFLq52SW8z6V27fTbxC7ih4e2xLBhl8oGwI8aNYpcWCJQXBM7kPAhDIVubnah34aQQ+nhoWKkpaVZ0UzHQvcWG5DwIQwn/OY+dCh4tlbHjlRzhwVo4iWUoPVcNrIWEk6AhA9hGsuXnz9FPS4ueMzEhAl2t4ogCDnkqgsPHDgQBQUFEa8hayHhFBpXoiIIAzh06LzoAYL/nTIl+DihDZ/Ph6qqKtF/Pp/P7iYSLoFPW/d4zp8EzHHAvn3Bk4DLysqQlJSE7Ozs0D8SPYRTIIsPYQp79kSeng4EDxTdu5dcXlqw4vBXgginsHAvOC78EX2HkRLOxk1hC2TxIUyhY8egeyuc+PjggaKEeqIXGb8/BRUV+fD7UySvIwitHD+eDrFCl0RswFuZn3yyBu3acejfH2jXjsOTT9Y42spMFh/CFHJzgzE9U6YELT3x8cALLzh/p2Am4fVToqmurg79v5IT1glCL3SKeGzDW5n9/hQsXjwdHOcBAAQCHsycmYqff34JXu8pR1qZSfgQpjFhAjBoUNC91aEDiR4plLqytKYZE4QSwrOy6BTx2IbfhMll+DnRykzChzCV3Fz3Cx4pSw2gLNtFyJV1/Hg6WrXyRSw0WtOMiSBCVYkLCs4hJycYkBbrmUnp6ekoLS3F6tWrAdDxFIQ7LX8kfAhCB9GWGjHBosYcLOXKcuMkZBXiVYkj+9iJpnsj8Xq9UX/TKeKxjBstfyR8CEIH4ZYaqcVUqTlYzpXlxknIKpRWJXai6Z4gzMRtlj8SPgRhAEbF3ihxZUlNQuFB0LHuthGD3IXS0Hl7hBBusvyR8CEIAzBqMVXqyoqehHgX24oVZZpdbLECuQuloTPdCLdDwocgDMCoxVTOlTVixAhkZGSguroa69evB2CMiy2WIHehPCRqCDdDwocgDMDIxVTKlZWRkYHs7OzQ35Terg23xSwQhNG42eVJwodgCieXRdezmEZPHmL+9OjrKF5FO26KWSAIo3Gzy5OED2E7fG2VVauaYtYsLwIBD+LiOCxY4MdNN/3qqJtL62KqdZKheBWCIMzCKfOuWkj4ELbi9LLoRpqDtXw/ildRjptN9+FQkUaCkIaED2ErTi+LzoI5mOJVlMHCb2U2VKSREMPJYQRGQ8KHYAInu2xYWEAoXkUZLPxWZkJFGolw3BRGYCQkfAgmiBWXjVG7rlhx2xDaoKB3wulhBGZCwodgBre7bJYvByZPBgIBIC4OWLYseIK9FmLBbUNox8kWVMIYnB5GYCYkfAimcKPLxufzYf/+c5g8OROBAL/rAqZM4XDJJUeRn5+gSaCQqCHEiBULKiEPieDGkPAhCBPhzc0VFfkIBMZEPNfQ4MHSpR+goOCnmDQ3E+ai5Ew3yvpyPySCG0PChyBMhDcjy+26YtHcTIhjVEq6mAV1/fr1lPUVQ7g9jEAtJHwciltSE2MlSJd2XYRSrEhJZy3ri2oPmY8bwwi0QsLHgRgZJGs3sRSkS7suQgl6UtKVbhBYyvqi2kPuwgmbchI+DuPQofOiB+CDZIFBg9gdZHK4cTLjb/7U1MjFhXZdhFK0iBO5jUR1dTXWr1/PVMAr1R5yPk6rF0TCx2Hs2XNe9PA0NAB79zpX+LiNSItcJoYOvTS0ayUIpWgVJ0pjf1hzvZplhYpVN5pVYQROrBdEwsdhdOwYdG+Fi5/4eKBDB/vaxDJWm10bW+Q8EbtWglCK2eKENderGVaoWHajWRVG4MR6QSR8HEZubjCmZ8qUoKUnPh544QWy9ghhRyyUkEWOKuYSWjFbnLDkejVD6MW6G81KMceS+1QOEj4OZMKEYEzP3r1BSw+JnsbYFQslZJFTcvM7PWuNMA+WxInZmCX0WArmdissuk/FIOHjUHJzSfBIYUcslM/nQ3x8HRYsaIp77/WiocGD+HgODz10GL///UAkJibC6/U2ep1bYwwI9nBC+QgzhJ6TrBFOhjX3qRgkfAhXYnUsVHgsAQDceWdK6OZvaDiF9euDj7sxloAwFjPFSSyVjwjHSdYIp+MECyUJH8KVWB0LFb2QiN38bo0lcAJOqC8CmC9OWBM1VlmhnGKNIMyHhA/hWigWinBafREeFttkFlZaoZxgjSDMh4QP4WooFip2cWJ9kViF+t+5OCFuLBoSPoTrCC9YJgSru3zCWJxYX4QwFicuyk7DiXFjJHwIVxEdZCwG7fJjB8roiV2cuCg7Eaf1HwkfwlUo3b3H6i7fKQG+RkIZPbGN0xZlwnxI+BBEjGBHJWtWoIwegiB44uQvIQhCDtZjCcQqWR86ZEtzbMHrPYWCgp9I9BBEjEMWH4IwAJZjCXw+H778EggEIj+7oQHYutWHpk3JHUAQROxAwocgDIJF8RCe0u3xTG8U4PvFFy9j505K6SYIInYgVxdBuBjeAsUH+Ho8QV9XdICvG4O9WXc/EgRhD2TxcRBUn4bQQ6wF+LLsfiQIwj4cJXzef/99/PnPf8aOHTvQpEkT9O3bF2+//Xbo+QMHDuD222/H5s2b0aJFC4wZMwbz5s1DQoKjvqYgVJ9GGbTLlybWSvbH8r1AEIQwjlEE69atw6RJk/DYY4+hf//+OHfuHHbu3Bl6vqGhAddccw2ysrKwZcsWVFVV4dZbb0ViYiIee+wxG1tuDFSfRhm0yycIgiCkcITwOXfuHO666y4sXLgQE8IKj3Tq1Cn0/xs3bsS///1vbNq0CW3atMEll1yChx9+GPfeey8eeuihmN3hxyIkapxHLBZWtAJyjxNEYxwhfL755hv8/PPPiIuLw6WXXorDhw/jkksuwcKFC9G5c2cAQHl5Obp06YI2bdqEXjdo0CDcfvvt+OGHH3DppZcKvvfZs2dx9uzZ0N8nT54098sQBAHAuSenOwVyjxOEMI7I6vq///s/AMBDDz2EP/3pT9iwYQNatmyJfv364fjx4Hk7hw8fjhA9AEJ/Hz58WPS9582bB6/XG/qXl5dn0rcgCIKHX5QXLnwdM2emIhCIPDl94cLX8cwzz8Dn89ncUudC7nGCEMZW4XPffffB4/FI/vvxxx8R+G+52Tlz5mDkyJEoKirCihUr4PF48Oabb+pqw+zZs+H3+0P/Dh48aMRXIxzKoUPA5s3uqWjMarC3kpPTw68j9OP3p6CiIh9+f4rdTTEcn8+Hqqoq0X8koIlwbHV13XPPPRg7dqzkNe3bt0dVVRWAyJie5ORktG/fHgcOHAAAZGVl4V//+lfEa48cORJ6Tozk5GQkJydraT7hMtx4lhXrwd50cro1fPPNpY0Oae3e/Vu7m2UI5NJjF1ZjzGwVPq1bt0br1q1lrysqKkJycjJ2796N3/3udwCA+vp67N+/H+3atQMAFBcX49FHH8XRo0eRmZkJACgrK0NqamqEYCIIIcTOsho0yPnBtixP9nRyuvn4/Smh/gWCFrX33huKwsK9ruhncumxCcuC1BHBzampqbjtttvw4IMPIi8vD+3atcPChQsBANdffz0A4Oqrr0anTp1wyy23YMGCBTh8+DD+9Kc/YerUqa6w6LDqsnALe/acFz08DQ3A3r3OFz6s4/TCiqzuanmk3IlO62vCObAsSB0hfABg4cKFSEhIwC233IJff/0VPXv2xMcff4yWLVsCAOLj47FhwwbcfvvtKC4uRvPmzTFmzBj8+c9/trnlxsC6y8LpdOwYdG+Fi5/4eKBDB/vaFEs4tbAiy7tanlhzJ/r9KTh+PB2tWvkcOaYI83GM8ElMTMQTTzyBJ554QvSadu3a4e9//7uFrbIWEjXmkZsbjOmZMiVo6YmPB154gaw9hDQs72p5Ysmd6OZYJqfDkiB1jPAhCLOZMCEY07N3b9DSY7TooSJ97oelyT3c7S3lTnSLe9ztsUxOhjVBSsKHIMLIzTVHlLgxY0wPboxZY21y593jx44dQ319veA1bjjHkIdimdiERUHqnlFPEBoxOzjVzRljWnFbzBqLkzvP6tWrZa9xQ6p3rMUyOQUWBSkJHyKmsSI4lTLGhOH7U0x41tXVoaqqSrEAsjO7isXJHXBGDJJeeKugXCyTk6yHboJFQUrCh4hpzF4YfD4fUlPPIS4uM3QsAwDEx3NISTkKny/B8TttPRglPO3OrrJ6cmc9hd5Koq2HDzxwDPv3JyA//xxycnoA6BFT/cEKLAtSEj4EYRLhi/HQoZHxH9dcswEbNgTjP9zgZtBK9OItFhwsJzzttmxYmTmlRuRFw1LwtZGE3z/Z2UBRkY2NIQCwLUhJ+BCESYQvslJZNU52MxiJkcHBVi3wdmROaRV5rAVfE+6HVUFKwocgLMKpRfqswMjgYCsXeKcEabMcfE0QVkPChyAI2zEqONiOBd5uUaMEVoOvCcIO4uQvIQiCMBc+ODgcLcHBUgu8W/H7U1BRkQ+/P0X0GqP6lyDcAFl8CIKwHaOCg1lMnZVCb3aWnFuP5cwaFqFsudiAhA8R07ixgrASWJzgjTil3UnnUulNwVfi1mM5s4Y17C6JQFgHCR8ipnFKcKqRRE/wYhlQVkzw0YJSLABcTng68VwqvSn4SuN2WM2sYQ27SyIQ1kHCh4h5zFrcWbUmhU/cUq4SKyZ4o4RnLAlYfrzIufVYEnkEwRIkfAjCJFhfjFlJcTbq+7tB1CghfFxdcMFJ3HuvFw0NHsTHc5g//yRuuulG14g8gjADEj4EYSIsLz6U4uxc+HF1zz1AaWnw3LcOHTzIzU0DkGZbu1iMHSOIaEj4EESM4rQMKEKY3Fw2Drul4GDCKVAdH4KIUfgMKL6+C8sZULGAkno8LEPBwYRTIIsPQcQwRqSQE/qhc7QIwjrI4kMQMY7XewoFBT+R6LEYPutKLMict/xQdpY1RPezmAWOfg/nQxYfgogxWE2zjzX47KzNm4GnnmocZN679xj068d2gLybCM+WW7WqKf78Zy8CAQ/i4jgsWODHTTf9SsHZLoGED0HEGKyn2ccS6enpuOIKIC4OCIQdpRUfD/TsmQ76CawlPT0dhw4Bs2ad/z0CAQ/uvTcNpaVp9Hu4BBI+BBGDkKhhh9xcYNkyYMoUoKEhKHpeeIGNTK1YZM+eSBEKBH+XvXvpN3ELJHwIgiBsZsIEYNAgvh4PLbB20rGjsAWuQwf72kQYCwU3EwRBMEBuLtCvn3NFj1tix3gLXHx88G+ywLkPD8dxnN2NYImTJ0/C6/XC7/cjNTXV7uYQBEE4hvDKzZWVcaioSEBBwTnk5ATNJ06KHTt0iCxwTkPp+k2uLoIgCMIQeFGzfDkweXLQXRQXF7SgTJhgc+NUwkpFbMJ4yNVFEARBGMahQ+dFDxD875QpwccJggVI+BAEQRCGIZUVRRAsQMKHIAiCMAw+KyocyooiWIKED0EQBGEYlBVFsA4FNxMEQRCGQnWJCJYh4UMQBEEYDmVFEaxCri6CIAiCIGIGEj4EQRAEQcQMJHwIgiAIgogZSPgQBEEQBBEzkPAhCIIgCCJmIOFDEARBEETMQMKHIAiCIIiYgYQPQRAEQRAxAwkfgiAIgiBiBhI+BEEQBEHEDCR8CIIgCIKIGeisrig4jgMAnDx50uaWEARBEAShFH7d5tdxMUj4RHHq1CkAQF5ens0tIQiCIAhCLadOnYLX6xV93sPJSaMYIxAIoLKyEikpKfB4PHY3x3JOnjyJvLw8HDx4EKmpqXY3x7FQPxoD9aN+qA+NgfrRGMzsR47jcOrUKeTk5CAuTjyShyw+UcTFxSE3N9fuZthOamoq3dwGQP1oDNSP+qE+NAbqR2Mwqx+lLD08FNxMEARBEETMQMKHIAiCIIiYgYQPEUFycjIefPBBJCcn290UR0P9aAzUj/qhPjQG6kdjYKEfKbiZIAiCIIiYgSw+BEEQBEHEDCR8CIIgCIKIGUj4EARBEAQRM5DwIQiCIAgiZiDhE6P885//xLBhw5CTkwOPx4O333474nmO4/DAAw8gOzsbTZs2RUlJCfbs2WNPYxlFrg/Hjh0Lj8cT8W/w4MH2NJZh5s2bhx49eiAlJQWZmZkYPnw4du/eHXHNmTNnMHXqVKSnp6NFixYYOXIkjhw5YlOL2URJP/br16/RmLzttttsajGbPPfcc+jatWuowF5xcTE++OCD0PM0FpUh1492jkUSPjHK6dOn0a1bN/zlL38RfH7BggVYsmQJnn/+eWzduhXNmzfHoEGDcObMGYtbyi5yfQgAgwcPRlVVVejf66+/bmELncGnn36KqVOn4ssvv0RZWRnq6+tx9dVX4/Tp06Fr7r77brz33nt488038emnn6KyshIjRoywsdXsoaQfAWDSpEkRY3LBggU2tZhNcnNz8fjjj2Pbtm34+uuv0b9/f/z+97/HDz/8AIDGolLk+hGwcSxyRMwDgHvrrbdCfwcCAS4rK4tbuHBh6LGamhouOTmZe/31121oIftE9yHHcdyYMWO43//+97a0x8kcPXqUA8B9+umnHMcFx15iYiL35ptvhq7ZtWsXB4ArLy+3q5nME92PHMdxffv25e666y77GuVQWrZsyb344os0FnXC9yPH2TsWyeJDNKKiogKHDx9GSUlJ6DGv14uePXuivLzcxpY5j08++QSZmZm48MILcfvtt8Pn89ndJObx+/0AgFatWgEAtm3bhvr6+ojxeNFFF6Ft27Y0HiWI7kee1157DRkZGejcuTNmz56N2tpaO5rnCBoaGvDGG2/g9OnTKC4uprGokeh+5LFrLNIhpUQjDh8+DABo06ZNxONt2rQJPUfIM3jwYIwYMQIFBQXYt28f/vd//xdDhgxBeXk54uPj7W4ekwQCAUyfPh29e/dG586dAQTHY1JSEtLS0iKupfEojlA/AsBNN92Edu3aIScnBzt27MC9996L3bt3Y/369Ta2lj2+//57FBcX48yZM2jRogXeeustdOrUCdu3b6exqAKxfgTsHYskfAjCJG644YbQ/3fp0gVdu3ZFYWEhPvnkEwwYMMDGlrHL1KlTsXPnTnz++ed2N8XRiPXj5MmTQ//fpUsXZGdnY8CAAdi3bx8KCwutbiazXHjhhdi+fTv8fj/Wrl2LMWPG4NNPP7W7WY5DrB87depk61gkVxfRiKysLABolKlw5MiR0HOEetq3b4+MjAzs3bvX7qYwybRp07BhwwZs3rwZubm5ocezsrJQV1eHmpqaiOtpPAoj1o9C9OzZEwBoTEaRlJSEDh06oKioCPPmzUO3bt3w9NNP01hUiVg/CmHlWCThQzSioKAAWVlZ+Oijj0KPnTx5Elu3bo3wzxLqOHToEHw+H7Kzs+1uClNwHIdp06bhrbfewscff4yCgoKI54uKipCYmBgxHnfv3o0DBw7QeAxDrh+F2L59OwDQmJQhEAjg7NmzNBZ1wvejEFaORXJ1xSi//PJLhLKuqKjA9u3b0apVK7Rt2xbTp0/HI488go4dO6KgoAD3338/cnJyMHz4cPsazRhSfdiqVSvMnTsXI0eORFZWFvbt24dZs2ahQ4cOGDRokI2tZo+pU6di1apVeOedd5CSkhKKlfB6vWjatCm8Xi8mTJiAGTNmoFWrVkhNTcUdd9yB4uJiXHHFFTa3nh3k+nHfvn1YtWoV/t//+39IT0/Hjh07cPfdd6NPnz7o2rWrza1nh9mzZ2PIkCFo27YtTp06hVWrVuGTTz7BP/7xDxqLKpDqR9vHoi25ZITtbN68mQPQ6N+YMWM4jgumtN9///1cmzZtuOTkZG7AgAHc7t277W00Y0j1YW1tLXf11VdzrVu35hITE7l27dpxkyZN4g4fPmx3s5lDqA8BcCtWrAhd8+uvv3J/+MMfuJYtW3LNmjXj/ud//oerqqqyr9EMItePBw4c4Pr06cO1atWKS05O5jp06MDNnDmT8/v99jacMcaPH8+1a9eOS0pK4lq3bs0NGDCA27hxY+h5GovKkOpHu8eih+M4znx5RRAEQRAEYT8U40MQBEEQRMxAwocgCIIgiJiBhA9BEARBEDEDCR+CIAiCIGIGEj4EQRAEQcQMJHwIgiAIgogZSPgQBEEQBBEzkPAhCIIgCCJmIOFDEARBEETMQMKHIAjHUFdXZ3cTGsFimwiCEIeED0EQttGvXz9MmzYN06ZNg9frRUZGBu6//37wJ+nk5+fj4Ycfxq233orU1FRMnjwZAPD555/jyiuvRNOmTZGXl4c777wTp0+fDr3vs88+i44dO6JJkyZo06YNrrvuutBza9euRZcuXdC0aVOkp6ejpKQk9Np+/fph+vTpEW0cPnw4xo4dG/pba5sIgmADEj4EQdjKyy+/jISEBPzrX//C008/jUWLFuHFF18MPf/EE0+gW7du+Pbbb3H//fdj3759GDx4MEaOHIkdO3Zg9erV+PzzzzFt2jQAwNdff40777wTf/7zn7F79258+OGH6NOnDwCgqqoKN954I8aPH49du3bhk08+wYgRI6D2yEK1bSIIgh3okFKCIGyjX79+OHr0KH744Qd4PB4AwH333Yd3330X//73v5Gfn49LL70Ub731Vug1EydORHx8PF544YXQY59//jn69u2L06dP4+9//zvGjRuHQ4cOISUlJeLzvvnmGxQVFWH//v1o166dYHsuueQSLF68OPTY8OHDkZaWhpUrVwKApjY1adJEVz8RBGEcZPEhCMJWrrjiipDoAYDi4mLs2bMHDQ0NAIDLLrss4vrvvvsOK1euRIsWLUL/Bg0ahEAggIqKCgwcOBDt2rVD+/btccstt+C1115DbW0tAKBbt24YMGAAunTpguuvvx5//etfceLECdVtVtsmgiDYgYQPQRBM07x584i/f/nlF0yZMgXbt28P/fvuu++wZ88eFBYWIiUlBd988w1ef/11ZGdn44EHHkC3bt1QU1OD+Ph4lJWV4YMPPkCnTp2wdOlSXHjhhSFxEhcX18jtVV9fr7tNBEGwAwkfgiBsZevWrRF/f/nll+jYsSPi4+MFr+/evTv+/e9/o0OHDo3+JSUlAQASEhJQUlKCBQsWYMeOHdi/fz8+/vhjAIDH40Hv3r0xd+5cfPvtt0hKSgq5rVq3bo2qqqrQZzU0NGDnzp2y30FJmwiCYAMSPgRB2MqBAwcwY8YM7N69G6+//jqWLl2Ku+66S/T6e++9F1u2bMG0adOwfft27NmzB++8804okHjDhg1YsmQJtm/fjp9++gl/+9vfEAgEcOGFF2Lr1q147LHH8PXXX+PAgQNYv349jh07ht/+9rcAgP79++P999/H+++/jx9//BG33347ampqZL+DXJsIgmCHBLsbQBBEbHPrrbfi119/xeWXX474+HjcddddoRRxIbp27YpPP/0Uc+bMwZVXXgmO41BYWIjS0lIAQFpaGtavX4+HHnoIZ86cQceOHfH666/j4osvxq5du/DPf/4TixcvxsmTJ9GuXTs8+eSTGDJkCABg/Pjx+O6773DrrbciISEBd999N6666irZ7yDXJoIg2IGyugiCsA2hLCqCIAgzIVcXQRAEQRAxAwkfgiAIgiBiBnJ1EQRBEAQRM5DFhyAIgiCImIGED0EQBEEQMQMJH4IgCIIgYgYSPgRBEARBxAwkfAiCIAiCiBlI+BAEQRAEETOQ8CEIgiAIImYg4UMQBEEQRMxAwocgCIIgiJjh/wN9s77nJ4/qUAAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_training, filename=\"pysmo_poly_train_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_training, filename=\"pysmo_poly_train_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_training, filename=\"pysmo_poly_train_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation\n", + "\n", + "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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rnh8iIlInBh+R1B5asYVDK95VUhJuCT1mguCDkpIwBh8iIg3grC6RODpkwqEV7woLM0CnM1ndptOZEBZWIlGLiIjImxh8SFP0+jL077/NEn7MNT7s7SEi0gYOdZHmpKUdRnJyPkpKwhAWVsLQQ0SkIQw+pEl6fRkDDxGRBnGoy0uMxhAUFCTCaAyRuimaFBAQ4NHtiIhImdjj4wX21o0h57myTEB4eDhycnK4vIBIuHQDESkFg4/IuG6MZ7mzTAAPvOLgqthEpCQMPiIxD5k0tG4Mh1acw2UC5IerYhORkjD4iMQ8tFJYeBPr1gkwmXSW+3x9BYwf3weJiX48A3aTrd4F8j72bhKREjD4iCg8PBzh4cBbbwFPPAFUVQG+vsCbb+qQnh4ldfMUz1u1U1qrX3H19XJVbCJSAgYfLxg1CsjMBPLzgZQUIC5O6hYpn7d6F7R26RF3Xq95Veya4YerYhOR3DD4eElcHAOPJ3mrd0HpNUXO9t6483rNq2LX7oVjbw8RyQmDDymSVL0LSqop8kRvlbOvl6tiE5HcMfiQIknRu6C09Zjc7a1y9fXaWhX78uXLNn9GbXVSRCRfDD6kKDWn/9vrXfD0MgFqmLHkTO+NM6/X0fd68+bNdu9XS52UXHmjSF9rEwFImRh8yGlS7txqrsBsNBpRWVlZZxs/Pz9UVFTAYDB4rB1Kn7HkbO+NM6+3oVWxL1++3GDoAeRbJ6UG3ijS50KWpBQMPuQUOcxyCg8Ph8FgwMaNG73WDiXPWHKlt8rZ1+vMe6ykOim1qB0qbf0O3AmfXMiSlILBh5wil1lO3tiR16TkGUuu9FaJ9XqVVielRmL/DtQwLEzqxuBDbpHD2buYO3KpaopscWWY0ZneGzFfLw+I0vPG70Dpw8Kkfgw+5DI5nL2LvSOX01XdXR1mdKb3RszXywOiY8SsofPG70DJw8KkDQw+KiBFsbFczt69sSOXSyGms8OMrvbeiPV6eUBsmNg1dN74HSh5WJi0gcFH4aQqNpbL2TsPprbJqbcK4AHREWLX0HnrdyDXhSw53Z4ABh/Fk6rYWC6BQ8sHU0fqq9zZiXvqICG3OiklEaOGzluhxNZCllKRw4xUkgcGH5URq9jYfBA0r74rp8Ah17NLMYldX+XJg4Tcep6UwpO/49qh0lYocSd8OvqzUgVcucxIJekx+KiIWAdDWwdBKc/evbEjlytv1Fd5+iDBUOMcT/+OvRE+lRZw5TAjlaTB4KMSYh4M7e3IagaOgQMHIiIiwis7N6XtZD1JivoqHiS8S4zfsTf+FpTy9yaHGalKoNaaKAYflfDmwdDWQTAiIgIxMTEefS57lPgH5wnerq/iQcL75FJDp0ZymZHqSWIEFDVfgoTBRyW8taPkQVA65mG7huqrPDm8p8aDhBLIqYZOap4+qMtlRqqniFW0reZLkDD4qIQ3dpQ8CEqr9vDezJmXUFjoh8TEm4iN7Qygs8e7ntV2kJA7zoCzJsZBXW29aWIXbatxv8/go3De3FHyIPgHqca+az5mTAyQnu7xp7CitoOE3Gm5dq0+YhzU1d6b5ul6PDXu9xl8FM6bO0oeBKtpaT0QtR8k5Ejpnxm50kJvmhilCGrc7zP4qABX3vUub18ZXgpaOEiojVpn4NTkTm+G2nvTxBqSUuN+n8GHGsSDoG1qLfZW+0FCbbTQC+mJvzWlvnZHiDkkpbZFYhl8qEE8CNZPjUV/NWnt96lkau+FVPvfmieIPSQlt0uQuIPBhxzCg2Bdaiz6I+VTYy8k/9Ya5ukhKblfgsQdDD5ELlJj0R8pm1p7Rvi3Vj+DwQCj0Wj53pOlCGru6WfwIXKRGov+SNnU1jMixaKdSmGrrqv2kNRDDz2E5s2buxRQlBhqHMHgQ+QGtRX9kbKprWdEikU7lcLReq2mTZtq8v2xh8GHyEnuXBleC1OOSTpq7IX09qKdpH4MPiQLSgoEro59a2HKMUmPvZDa5OkVm9WMwYckp8RA4Eo71D7lmKTjTi8kKZ8aZ/KJicGHPM7Z3hstBgLuqMiT1DwDh+xT60w+MTH4kEe523ujhUDAHRWJgaFGm9Q2k88bfBrehMhx9fXeFBQkwmgMsbudedv6AkHtn1U6ezsqIiJnmGfy1aTkmXzewB4fEo2zvTdaOXNR25RjIvI+rnHkOgYfmVDSrCZHuDKco5VAoMYpx0TkXa6ucaS2Y40rGHxkQImzmhriSu+N1IHAmzsETjkmUjY5BAhn1zhS47HGFQw+MuDobCUlzWpytfdGqkDgjR0CpxwTqYNSA4QWZ9DWh8GHROFM740cAoE3dgicckxaJ4deEk9QQ4DQwgxaWxh8ZEgtK3A62nsjt0Ag5g5BCTt1IjHU7iWxtZ+TWy9JQ5QYILS+pAaDj8wo8Y+oJld7b+Syo9P6DoFILDVPbOzt5+TcS1Kbt/cXnuox08oMWlsYfGREDQddufXeOEvrOwStceZAUnPb8+d9UFDgh6Skm4iNNdXZlmxTw37OzJv7C0/WFWllBq0tDD4yopaDrpx3/rYOdJcvXwbAHYJceKMWxJmhFwCWbe31VihtmEYKatnPAd7dX3iyrkjqGbRSY/CRER50xeXIGZPWdwhy4K1aEFeGXhrqrVDSMI1UvLWf80Z4lmp/4YmSCC0vqcHgIwNcgdM7HD1j0vIOQQ68XQvizNCLmnorpOKNsODNQmpv7y/cGSqUwwxaOWDwkQFXV+Ak1zV0xqTVHYKceKsWxJkww15ZzxA7LIgdnqUMEO6Eb6XXYHoKg49MOLsCJ7muoQPqwIEDERERUefntLBDkBNv9a44E2Y4FOo5tsKCJ4kVnqUMEO6Gb+7DGHxIgxo6oEZERCAmJkai1pGZt3pXnA0zHAp1jaO9H57sJREzPEsVIBi+3afK4PP666/j1VdfRXFxMTp06IBly5bhtttuk7pZJBMcrlAGb+7gnQ0z3uitUBspeknU+rfO8O0e1QWfjRs3YtKkSVi5ciW6dOmCxYsXIzMzE3l5eWjevLnUzbNJLUu5KwHPmJRDrB28wWCwLGFgxjAjPm/vw9T0t87CZM9RXfBZtGgRRo8ejREjRgAAVq5cie3bt2P16tWYNm2axK2rn1IveOcoOYY6njEph6cDiaMzfsycOZDwoCM/Svlbd2Q/ycJkz1BV8KmoqMDBgwcxffp0y20+Pj7o3bs39u/fX+/PlJeXo7y83PJ9aWmp6O2sTY1XZzeTU6jjGZMyiF0L4uiMn4EDByI2NtbyueRBR7nk3pvnzPR71h+6T1XB5/Lly6iqqkJUVJTV7VFRUThx4kS9PzN37lzMnj3bG81zmFouUgrI6yrGnMqpDN76PTU04yciIsLqOfi5UA4pCqndocbrmMmZqoKPK6ZPn45JkyZZvi8tLUV8fLxk7VH6RUrtkcNr48FLGbzxe+JihOql1JMcNV3HTM5UFXwiIiLg6+uLCxcuWN1+4cIFREdH1/szgYGBCAwM9EbzGqTmD72aXxspk1pn/FA1uYUaRzCMe4dPw5soR0BAANLT0/HFF19YbjOZTPjiiy+QkZEhYcscY+9Dr3Rqfm2kTOYZPzpd9dXVlTzjh9TBHMZrYhj3PFX1+ADApEmTMGzYMHTq1Am33XYbFi9ejGvXrllmecmZms9A1fzaSLmUMuNHa2rOcDp/3gcFBX5ISrqJ2NjqUCDHYSpPUNP0ezlTXfAZPHgwLl26hJkzZ6K4uBh/+ctf8Omnn9YpeJYjNX/o1fzaSNnkPuNHa2rOcLJXF6jU5T0awjAuPtUFH6D6DyInJ0fqZjis5swCex96ucxAcBX/oEkOlDbjR2vMPT0N1QWqeYYTw7i4VBl8lEapMxAcwbVzSG7U/PemJloq9GUY9y4GH5lQ606WBxmSI37e5E9LdYHcT3oXgw+Jjn+sROQsrdUFcj/pPQw+REQkS6wLJDEw+JDL5HjxUSJSFxb6yosalhpg8CGXyOnio0REJD61LDXA4EMukdPFR4nIu8Ts7VXLDCc19IzUppalBhh8yG1yuPgoEXlH7d5eWyc9rp71q2GGk1p6RmxR+lIDDD7kFl58lEhbagYSewd1d876lRgGalJLz4gtSl9qgMGH3KL05E/qxeJ7cfGkp2Fq3T8qfakBBh9yi9KTP6kzILD4XnxqPah7kpr3j0peaoDBh9yi9OSvdWoNCCy+F19DB/UrV64gJiZGqubJgtr3j0pdaoDBh9ym5OSvdY4e+JUcEFh8L476Duq9e39u+fv/17/+pbjALAbuH+WHwYdcwouPqpOtnhGlYh2KuNLSDuP33xth167eEAQffP55bwQF3fBIgbOr5DiNXKk9I7WpZakBBh9yiRqmnJI1JfeM1K5Tunz5MgDWoYjNaAzB55/3BiCPYKn2aeTO8nT9nlr2+ww+5DK5f7jJcUruGbFXp6Tm4lKp1Dybl1uwlMs0cjn0jIi13pIa9vsMPkQkuwOYMxoqZFZzcakUwsPDMXjwYGzcuFG2wVLqz7Mceka8sd6SUjH4EJFsD2DOsrWDZ3GpZ+n1+v/963yw9MbyCWJ+nh1tv1x6RpTcmysWBh8iUkXPSEM7eBbfi8OZYCn25S7MxPo8e6v9niR175ccMfgQaVjNA7+9A5gSAkJDO/iBAwciIiLCcp8SijCVwtFZS94cfhGjp0+Jw0dq6c31JAYfIg2TQy2CpzS0g4+IiND8gnqe4m7xrreGX8SaRq6k4SM19OZ6GoMPkcYpIdQ4gjt473E3MCt9+EVp7bfX+2Ve+sFMKSc67mDwISLVYCGz97hzcBRr+MVb08iVOHxUX++X0RiChQsPybpGSQwMPkSkaFxFXHnE6p3z1tCtEnoXG/q8K6VGSQwMPkSkaGqqU9ISsXrnvPV7lnvvYn1/F5cvX8bmzZsVVaMkBgYfIlI8hhrXeWNdHVuUfg0rubdfrTVW7mLwISLSKHuX+6jJUzUfcriUgzuU3n4zJdYoeRKDDxGRRjV0uQ9b27lK6cOSSm+/mRJqlMTE4ENERHaLXT1J7qGgIUpvv5nca5TE5NPwJkREpGa2il2NxhCJW0Zi0uvLkJT0q6ZCD8DgQ0SkefaKXUk91FKj5C4OdZFqSTlbhUhJtF7sqhVqqVFyF4MPqZISr6JMJBWtF7tqCfd3DD6kUkq8ijKRlLRc7ErawhofUjUWbRLZVt/lPuordlV7zQdpC3t8SNW0vkKpt7CeSpnEqvng54HkjMGHVI1Fm+Lz9uq/5Fme/p3w80Byx6EuUjVz0aZOZwIAFm2KwNE6KdZTaQM/DyR37PEh1WPRpnfZmkFH2sTPA8kNgw9pgtyvoqwW3rrsASkDPw8kRxzqIlXiCqXexxl0VBM/DyRX7PEhVeIKpd7HGXRUEz8PJFcMPqRaDDXexRl0VBM/DyRXHOoiIo/gDDqqiZ8Hkiv2+JDX1Vzc7Px5HxQU+CEp6SZiY6t3kByCUpaadVL2ZtCxnkob+HkgudMJgiBI3Qg5KS0thV6vh9FoRGhoqNTNUZ2ai5vZm/HBxc2UhSv1Uk38PJAUHD1+s8eHvMq8M7Q14yM5OR96fRkXN1MYHsSoJn4eSM4YfEgSnPFBYmKPAxHZwuBDkuCMD+WSe6jgtaKIyB4GH5KEecZH7Rof9vbImxJCBa8VRUT2MPiQZHgNLeVRYqjgtaKIqCYGH5IUr6GlbHIPFbxWFBHVxuBDRC6Re6hoaOYgEWkTV24mr+LFQ9VBCRegtDdzkIi0iz0+5FViXTxU7jON1EYuyxHU93u/fPkyAM4cJKL6MfiQ13k6gNSeaWSr7oTTlz1HDqGioRlmnDlIRPVh8CHFq3nGb6/uRE4zjdwldQ+XHEKFI79PXiuKiGpj8CHV0Eoxq5Rr6cj5ApS2evpGjLgHERERVtty6JNIuxh8SDXkUncitto9HbYO+GL0cIlVo+Uuez19ERERiImJ8Wp7iEi+GHxINeRQd+JtUkwpl1tPiVZ6+ojIMzidnVTDXHei05kAQPXFrEqYUu4NnLZORM5gjw+pipYug6GVob2GaLGnj4hcxx4fUh29vgxJSb+q/uBvPuDXpMUDvtZ6+ojIPezxIcXT6mrQcphSLiU5zzAjIvli8CHFk+tMI2/Q0tBebVr+vROR6xh8SBW0dHCr3YNh6wr3Wujp0NLvnYg8g8GHSGHY00GeIPXq30RSUUTwKSwsxIsvvogvv/wSxcXFiI2NxaOPPooZM2ZYndUeOXIE2dnZ+OGHHxAZGYnx48djypQpErZcW86eBU6eBFJTgbg4qVujbjwgkTukXP2bSGqKCD4nTpyAyWTCm2++iZSUFBw7dgyjR4/GtWvXsGDBAgBAaWkp7r33XvTu3RsrV67E0aNHMXLkSDRt2hRjxoyR+BWol/msccOGIEyZoofJpIOPj4BXXjFiyJDfedZIJEOOruqtpuvbEZkpIvhkZWUhKyvL8n3Lli2Rl5eHN954wxJ81q9fj4qKCqxevRoBAQFo27YtcnNzsWjRIgYfkZjPGo3GECxePBGCoAMAmEw6TJ4cinPnVkOvL+NZI5HM2brsCZEaORx8SktLHX7Q0NBQlxrjDKPRiLCwP1Zm3b9/P7p162Y19JWZmYn58+fjt99+Q7Nmzep9nPLycpSXl1u+d+Z1ukMNw0Lms8GGFtLjWSORfElx2RM1YI2UcjkcfJo2bQqdTmd3G0EQoNPpUFVV5XbD7MnPz8eyZcssvT0AUFxcjKSkJKvtoqKiLPfZCj5z587F7NmzxWtsPVatAsaMAUwmwMcHeOstYNQorzbBo7hyLpEy8TpnrmGNlLI5HHx2797t8SefNm0a5s+fb3eb48ePo3Xr1pbvz507h6ysLDz44IMYPXq0222YPn06Jk2aZPm+tLQU8fHxbj9ufQwGAwoLb2LMmOYwmczDQsATTwj4y18uIjHRT5F/JFpfSI9IqXjZE9ewRkrZHA4+3bt39/iTP/PMMxg+fLjdbVq2bGn5//nz53H33XfjjjvuwFtvvWW1XXR0NC5cuGB1m/n76Ohom48fGBiIwMBAJ1vuPPMZQkFBIkymYVb3VVXpsGzZDiQl/arYMwQtL6RHtnE4QN7YWyuuy5cv17lN7p95LfzNulzcfOXKFaxatQrHjx8HALRt2xYjR46EXq93+DEiIyMRGRnp0Lbnzp3D3XffjfT0dKxZswY+PtZnKRkZGZgxYwYqKyvh7+8PANi1axdatWplc5jLm8wfpIZ2NEo+Q7C1kB5pE4cD5I+9tZ5hqzh88+bN9W4v18+8Vv5mXQo+P/74IzIzMxEUFITbbrsNALBo0SK8/PLL2LlzJ9LS0jzayHPnzqFHjx5ISEjAggULcOnSJct95t6cIUOGYPbs2Rg1ahSmTp2KY8eOYcmSJXjttdc82hZ3cUdDWsHhAPnidc48x5XicLl+5rXyN+tS8Hn66adx33334e2334afX/VD3Lx5E48//jgmTpyIr7/+2qON3LVrF/Lz85Gfn4+4WlOgBEEAAOj1euzcuRPZ2dlIT09HREQEZs6cKcup7BwWIi3ilGn54OrfnsHicGVyucenZugBAD8/P0yZMgWdOnXyWOPMhg8f3mAtEAC0b98e33zzjcefXwxqGBbS6lXRyXlKnDKt9loHJbddLhoqDld62Fd6+21xKfiEhobi9OnTVrOtAODMmTMICQnxSMNI/njWSI5Q4lmxVmodyD32ajaVGPZrUnr77XEp+AwePBijRo3CggULcMcddwAAvv32W0yePBmPPPKIRxtI8sadPjVEiVOma4d5W2e+Sq91oGrO9u6Ze7Ft1WwCUFzYr0mJJyvOcCn4LFiwADqdDkOHDsXNmzcBAP7+/hg3bhzmzZvn0QYSkbIpfcq0ms98ybXevdq93TNnXkJhoR9CQy9i377DKChIVFzYr0mJJyvO8Gl4k7oCAgKwZMkS/Pbbb8jNzUVubi5KSkrw2muveWVNHCViPQxplfmsWKczAYCiZjLaOvM1GjmkrxauzmQKDw9HTEwMYmJikJ4ehUGDwnHrrU0B/BH2a6oZ9o1Go/sNF1FD7Vc6ty5S2rhxY7Rr185TbVE11sOQ1qhhyrTaz3zJsxoaAjN/ZjZu3CjL+jBH2y/nv1lHuBR8bty4gWXLlmH37t24ePEiTCbrZHjo0CGPNE5t5PYhJxKTGsK+0ofpyHnuzGQKDw/HQw89hH/9618NLlsix/owW0N4iYk3ERvbGUBn2f/NOsKl4DNq1Cjs3LkTDzzwAG677bYGL15KRNqk9B0kFxzVFk/UczVt2tTyfyUuW1LzbzYmBkhPl7AxInEp+Gzbtg2ffPIJunbt6un2EBHJChcc1QaxZjKpdS0cJXMp+Nxyyy1cr4eIVKt2DYOtM3el1zrQH8So5+KMQHlyKfgsXLgQU6dOxcqVK5GQkODpNhERSUoN9UnkHE/Xc6l9LRwlcyn4dOrUCTdu3EDLli3RuHFjy9XQzUpKWPhHRMrGUKMNYs1k4oxA+XIp+DzyyCM4d+4c5syZg6ioKBY3ExGRIok1k4kzAuXLpeCzb98+7N+/Hx06dPB0e4iIiLzKkzOZtLIWjpK5FHxat26N33//3dNtISIiUjStrIWjZC4Fn3nz5uGZZ57Byy+/jHbt2tWp8QkNDfVI44hImZy96CORmmhhLRwl0wmCIDj7Qz4+1WOWtWt7BEGATqdDVVWVZ1ongdLSUuj1ehiNRgY4Ihe4ctFHIiJ3OXr8dqnHZ/fu3S43jIjUrXZPj60F3OS4ZD8RqZ9Lwad79+4Obffkk0/ihRdeQEREhCtPQ0QKxwXciEhufBrexHX//Oc/UVpaKuZTEJFM2VrAzWjkqu9EJB1Rg48L5UNEpBL2FnAjIpKKqMGHiLTLvIBbTVzAjYikxuBDRKIwL+BmDj+1F3AjIpKCS8XNRESOSEs7jOTkfJSUhCEsrIShh4gkx+BDRB5Veyl+vb6s3sDDJfuJSApOB5+bN29izpw5GDlyJOLi4uxu++ijj3IRQCIXKHnl49pL9tdHzu0nInVzaeXmkJAQHD16FImJiSI0SVpcuZmkxpWPiYicJ+rKzT179sSePXtUGXzUTsk9CVrBlY+JiMTjUvDp06cPpk2bhqNHjyI9PR3BwcFW9993330eaRzZ52yIYU+C8nDlYyIiz3Ip+Dz55JMAgEWLFtW5T+kXKVUKV0KMoz0EzvYksBdJHLZWPk5OzufsKCIiF7kUfEwmU8MbkajECjHOYi+SeOytfMzgQ1rCkyvyJJeCz7vvvovBgwcjMDDQ6vaKigq8//77GDp0qEcaR+67fPlyvf/3FLkEMDUyr3xcM/xw5WPSGp5ckae5FHxGjBiBrKwsNG/e3Or2srIyjBgxgsFHRjZv3ix1E8hF5pWPa9f4sLeHtITF/uRpLgUfQRCg0+nq3H727Fno9Xq3G0VE1bjyMdEfWOzvOA4P2uZU8OnYsSN0Oh10Oh169eoFP78/fryqqgoFBQXIysryeCNJHLbOnOT2mFrDlY9Jy2ofsM1D9Cz2dxyHB+1zKvgMGDAAAJCbm4vMzEw0adLEcl9AQAASExMxaNAgjzaQHONs4BDjzIlnY57BlY9Jq+wdsFns7zgOD9rnVPB5/vnnAQCJiYkYPHgwGjVqJEqjyDnOBo6Gzpxc6Ung2ZhnMdSQFtk7YLPY3zU8Ia3LpRqfYcOGAaj+kF68eLHO9PYWLVq43zKyyxxOXAkcts6cunYdhh49XDvo8myMiDypvgM2i/2dwxPS+rkUfE6ePImRI0di3759Vrebi565gKH4zMMhu3cDr73mXOCo78zJ1xfo0iUczmYecwBr6GyM9ShE5ChbB+yJExdj4sTFLPZ3EE9I6+dS8Bk+fDj8/Pywbds2xMTE1DvDi8QXHh6O228HfHyAmp1uvr6C3e5f8zTp7dv7o6pKB19f4M03gbg419pgrke55ZZSTJ2q/99jCpg/vxRDhjzCehQicoq9A3ZS0q8s9ncQhwfr51Lwyc3NxcGDB9G6dWtPt4ecFBcHvPUW8MQTQFVVdc/NwoXXcOXKHzuG+grb0tIO46WX7oLB0AwpKa6FHjNzqHnmGWDwYCA/H0hJ0SEurimApq4/MBFpUkMH7IEDByIiIsJyH0+u6se1wOrnUvBp06aNKKsAk2tGjQIyM82BA4iLawKDoboXZsOGILzwgh4mkw4+PgJeecWIIUN+/9+OopnH2xIX516IIiJq6IAdERGBmJgYiVupDFwLrC6Xgs/8+fMxZcoUzJkzB+3atYO/v7/V/aGhoR5pHDmuduAIDw/H2bPAlCl/DIOZTDpMndoUgwc3dbqWh4jIm3jAdh3XArPPpeDTu3dvAEDPnj2t6ntY3CwvJ09a1/4A1cNh+fnslSEi+eEB2zO4Fph9LgWf3bt3e7odJILU1PoKn6uHw4iI5IYHbM/he2SbS8Gne/fu+Oabb/Dmm2/i1KlT+PDDD3HLLbdg3bp1SEpK8nQbyUX1FT67OnuLiMgbeMAmsfk0vEldmzZtQmZmJoKCgnD48GGUl5cDAIxGI+bMmePRBpJ7Ro0CCguB3bur/x01SuoWERERScel4PPSSy9h5cqVePvtt60Km7t27YpDhw55rHHkGXFxQI8e7OkhIiJyKfjk5eWhW7dudW7X6/W4cuWKu20iIiIiEoVLwSc6Ohr5+fl1bt+7dy9atmzpdqOIiIiIxOBS8Bk9ejSeeuopHDhwADqdDufPn8f69evxt7/9DePGjfN0G4mIiIg8wqVZXdOmTYPJZEKvXr1w/fp1dOvWDYGBgfjb3/6G8ePHe7qNRET0PwaDgdO9idygEwRBcPWHKyoqkJ+fj6tXr6JNmzZo0qSJJ9smidLSUuj1ehiNRq5ATUSyYjAYsHz5csv39V2HDwBycnIYfkhzHD1+u9TjYxYQEIA2bdq48xBEROSgmj09hw51rHMtq7S0w3W2I/K2mr2S58/7oKDAD0lJNxEbW72artS9km4FHyIi8j6jMcQSegBAEHywdWs/JCfna/KaVhz+k4+avZL2wrmUvZIMPkREClNSEm4JPWaC4IOSkjDNBR8O/8mLOYA2FM6l7JVk8CEiUpiwMAN0OpNV+NHpTAgLK5GwVdLg8J88yTmcuzSdnYiIpKPXl6F//23Q6aprJswHeakPKFKy1cNgNIZI3DJtMofzmuQSztnjQ0SkQGlph5GcnI+SkjCEhZVIFnrkUl8j5x4GLTKH89o9cHL4XTD4EBEplF5fJumBRE71NRz+kx+5hPPaGHyIiBQiICDAo9u5S071NXLuYdAyqcN5fRh8iIgUIjw8HDk5ObIYWqpJLtPr5drDQPLC4ENEpCBynJItp/oaOfYwaInceiXrw+BDRERukbK+RgkHWi2Ra69kTQw+REQq5o1ZV1LW1yjhQKs1cn+vGXyIiFTKm7OupKyvkfuBluSFwYeISKW8PeuK9TWkBFy5mYhI5cRa1Zj1NaREiuvxKS8vR5cuXfDTTz/h8OHD+Mtf/mK578iRI8jOzsYPP/yAyMhIjB8/HlOmTJGusUREMiDWrCvW15ASKS74TJkyBbGxsfjpp5+sbi8tLcW9996L3r17Y+XKlTh69ChGjhyJpk2bYsyYMRK1lohIemLOumKoIaVRVPDZsWMHdu7ciU2bNmHHjh1W961fvx4VFRVYvXo1AgIC0LZtW+Tm5mLRokUMPkQkCblcx4qrGhP9QTHB58KFCxg9ejQ++ugjNG7cuM79+/fvR7du3azGkjMzMzF//nz89ttvaNasmTebS0QaJ6frWAFc1ZjITBHBRxAEDB8+HGPHjkWnTp1QWFhYZ5vi4mIkJSVZ3RYVFWW5z1bwKS8vR3l5ueX70tJSzzWciDRLTtexMuOsKyKJZ3VNmzYNOp3O7teJEyewbNkylJWVYfr06R5vw9y5c6HX6y1f8fHxHn8OItIusWZUOYKzrojqkrTH55lnnsHw4cPtbtOyZUt8+eWX2L9/PwIDA63u69SpE/7617/inXfeQXR0NC5cuGB1v/n76Ohom48/ffp0TJo0yfJ9aWkpww8BkE99BimblNex4qwrorokDT6RkZGIjIxscLulS5fipZdesnx//vx5ZGZmYuPGjejSpQsAICMjAzNmzEBlZSX8/f0BALt27UKrVq3s1vcEBgbWCVREteszbPFWfQYpl5TXsQLUM+uq5onI+fM+KCjwQ1LSTcTGmgAwwJHjFFHj06JFC6vvmzRpAgBITk5GXFwcAGDIkCGYPXs2Ro0ahalTp+LYsWNYsmQJXnvtNa+3l5TP0boLb9ZnkDJxRpX7ap6I2KuX4okIOUIRwccRer0eO3fuRHZ2NtLT0xEREYGZM2dyKjsRSY4zqtxjPsGwVS+VnJwPvb6MJyLkEEUGn8TERAiCUOf29u3b45tvvpGgRURE9nFGlfukrJci9eC1uoiIRMAZVZ5nrpeqyZv1UqQOiuzxISKSO86o8jzWS5EnMPgQEYmEocbzWC9F7mLwIXKArcsNEJH3sV6K3MHgQ1SPmnUX9qbPsj6DiEhZGHyI6mGuzygsvIkXXmgOQdABqJ5Bsn17f8yc2QWJiX4cyiDyAhaKkycx+BDZEB4ejiNHAJP1JBJUVelQVhYFZh4i72ChOHkSgw+RHampgI+Pdfjx9QVSUqRrE5EWMdSQp3AdHyI74uKAt96qDjtA9b9vvll9OxERKQ97fIgaMGoUkJkJ5OdX9/Qw9BARKReDD5ED4uIYeIiI1IBDXURERKQZDD5ERESkGQw+REREpBkMPkRERKQZDD5ERESkGQw+REREpBkMPkRERKQZDD5EREQydPYssHt39b/kOQw+REREMmEwGFBUVISFC68gIUFAz55AQoKAhQuvoKioCAaDQeomKh5XbiYizTEYDLzSN8mOwWDA8uXLYTSGYPHiiRAEHQDAZNJh8uRQnDu3Gnp9GXJycvj5dAODDxFpivngYmY0hqCkJBxhYQbo9WWW23lwIW8zh/GSknAIgvWAjCD4oKQkDHp9md3QTg1j8CEiTal50Dh0qCO2bu0HQfCBTmdC//7bkJZ2uM52RN4UFmaATmeyCj86nQlhYSUStko9WONDRJpkNIZYQg9QfUa9dWs/GI0hEreMtE6vL0P//tug05kAwBLKa/ZIkuvY40NEmtTQcAKRlNLSDiM5OR8lJWEICyvhZ9KDGHyISJM4nEByp9eXMfCIgENdRKRJHE4g0ib2+BCRZnE4gUh7GHyISNM4nEByERAQ4NHtqH4MPkSkKTy4kFyFh4cjJyeHi2uKTCcIgiB1I+SktLQUer0eRqMRoaGhUjeHiETAlZuJ1MfR4zd7fIhIcxhqiKRx9ixw8iSQmgrExUnTBs7qIiIiItGtWgUkJOB/F16t/l4KDD5EREQkGoPBgIMHL2DMGAGm6tUjYDIBTzwh4ODBC16/4jyHuoiIiEgU5osCFxQkwmQaZnVfVZUOy5btQFLSr169KDB7fIiIiEgU5kkE5pXSa6q5Uro3LwrM4ENERESiktNK6RzqIiIiItHJZaV0Bh8iIiLyCjmslM6hLiIiItIMBh8iIiLSDAYfIiIi0gwGHyIiIhKFHC8KzOJmIiIiEoUcrzjP4ENERESikdtFgTnURURERJrB4ENERESaweBDREREmsHgQ0RERJrB4EMkY2fPArt3V/9LRETuY/AhkqlVq4CEBKBnz+p/V62SukXaxPBJpC4MPkQydPYsMGYMYDJVf28yAU88wYOvtxgMBhQVFWHhwitISBD+Fz4FLFx4BUVFRTAYDFI3kYhcxHV8iGTo5Mk/Qo9ZVRWQnw/ExUnTJq0wGAxYvnw5jMYQLF48EYKgAwCYTDpMnhyKc+dWQ68vQ05OjuzWJyGihrHHh0hmDAYDQkMvwMdHsLrd11dASMgF9jaIzLzCbElJOATBehcpCD4oKQmz2o6IlIU9PkQyYu5tAIB+/Tpi69Z+EAQf6HQm9O27Ddu2HQYA9jZ4QViYATqdySr86HQmhIWVSNgqInIXgw+RjNTsRUhLO4zk5HyUlIQhLKwEen1ZvduROPT6MvTvv80qfPbvv83q90BEysPgQyRjen0ZD7QSshc+HWUwGCxB9fx5HxQU+CEp6SZiY6uLuLx9gUYirWPwISKyw53wWXPo8tChjnV6j9LSOHRJ5G0sbiYiEom5p8doDLGEHqC6SHrr1n4wGkOstiMi8TH4EBGJrKEZYkTkPQw+REQ1BAQEeHQ74I8ZYjVxhhiRNFjjQ0RUQ3h4OHJycuwOPzlbkMwZYkTyweBDJCNi9DaQ88QoNPbEDDEich+DD5GMiNHbQPLB5QmIpMfgQyQzDDVEROJhcTMRkUg4dEkkP+zxISISCYcuieRHUT0+27dvR5cuXRAUFIRmzZphwIABVvefPn0affv2RePGjdG8eXNMnjwZN2/elKaxRESoDj8xMTE2vxh6iLxLMT0+mzZtwujRozFnzhz07NkTN2/exLFjxyz3V1VVoW/fvoiOjsa+fftQVFSEoUOHwt/fH3PmzJGw5URERCQXOkEQBKkb0ZCbN28iMTERs2fPxqhRo+rdZseOHejXrx/Onz+PqKgoAMDKlSsxdepUXLp0yeEx9NLSUuj1ehiNRoSGhnrsNRAREZF4HD1+K2Ko69ChQzh37hx8fHzQsWNHxMTEoE+fPlY9Pvv370e7du0soQcAMjMzUVpaiv/85z9SNJuIiIhkRhHB57///S8AYNasWfj73/+Obdu2oVmzZujRowdKSqqXfC8uLrYKPQAs3xcXF9t87PLycpSWllp9ERERkTpJGnymTZsGnU5n9+vEiRMwmaqvcTNjxgwMGjQI6enpWLNmDXQ6HT744AO32jB37lzo9XrLV3x8vCdeGhEREcmQpMXNzzzzDIYPH253m5YtW6KoqAgA0KZNG8vtgYGBaNmyJU6fPg0AiI6Oxvfff2/1sxcuXLDcZ8v06dMxadIky/elpaUNhh+TyWR3eiq5LyAgAD4+iuiQJCIiBZE0+ERGRiIyMrLB7dLT0xEYGIi8vDzceeedAIDKykoUFhYiISEBAJCRkYGXX34ZFy9eRPPmzQEAu3btQmhoqFVgqi0wMBCBgYEOt7miogIFBQWWXigSh4+PD5KSkriwGxEReZQiprOHhoZi7NixeP755xEfH4+EhAS8+uqrAIAHH3wQAHDvvfeiTZs2eOyxx/DKK6+guLgYf//735Gdne1UsLFHEAQUFRXB19cX8fHx7JEQiclkwvnz51FUVIQWLVpAp9NJ3SQiIlIJRQQfAHj11Vfh5+eHxx57DL///ju6dOmCL7/8Es2aNQMA+Pr6Ytu2bRg3bhwyMjIQHByMYcOG4YUXXvBYG27evInr168jNjYWjRs39tjjUl2RkZE4f/48bt68CX9/f6mbQ0REKqGIdXy8yd46ADdu3EBBQQESExMRFBQkUQu14ffff0dhYSGSkpLQqFEjqZtDREQyp6p1fOSGQy/i43tMRERiYPAhIiIizWDw0YDhw4db1kXy9/dHVFQU7rnnHqxevdqp2Wlr165F06ZNxWsoERGRyBh8NCIrKwtFRUUoLCzEjh07cPfdd+Opp55Cv379eAV7IiLSDAYfLzIYDCgqKrL5ZTAYRHvuwMBAREdH45ZbbkFaWhqeffZZfPzxx9ixYwfWrl0LAFi0aBHatWuH4OBgxMfH48knn8TVq1cBAF999RVGjBgBo9Fo6T2aNWsWAGDdunXo1KkTQkJCEB0djSFDhuDixYuivRYiIiJXKWY6u9IZDAYsX768we1ycnIQHh7uhRYBPXv2RIcOHbB582Y8/vjj8PHxwdKlS5GUlIT//ve/ePLJJzFlyhSsWLECd9xxBxYvXoyZM2ciLy8PANCkSRMA1YtJvvjii2jVqhUuXryISZMmYfjw4fjkk0+88jqIiIgcxeDjJY5e4sLbl8Jo3bo1jhw5AgCYOHGi5fbExES89NJLGDt2LFasWIGAgADo9XrodLo6lwAZOXKk5f8tW7bE0qVL0blzZ1y9etUSjoiIiOSAQ10aJwiCZer4559/jl69euGWW25BSEgIHnvsMRgMBly/ft3uYxw8eBD9+/dHixYtEBISgu7duwOA5TpqREREcsHgo3HHjx9HUlISCgsL0a9fP7Rv3x6bNm3CwYMH8frrrwOw3wt17do1ZGZmIjQ0FOvXr8cPP/yALVu2NPhzREREUuBQl4Z9+eWXOHr0KJ5++mkcPHgQJpMJCxcutFyD7F//+pfV9gEBAaiqqrK67cSJEzAYDJg3b57lqvY//vijd14AERGRk9jjoxHl5eUoLi7GuXPncOjQIcyZMwf3338/+vXrh6FDhyIlJQWVlZVYtmwZ/vvf/2LdunVYuXKl1WMkJibi6tWr+OKLL3D58mVcv34dLVq0QEBAgOXn/v3vf+PFF1+U6FUSERHZx+CjEZ9++iliYmKQmJiIrKws7N69G0uXLsXHH38MX19fdOjQAYsWLcL8+fNx6623Yv369Zg7d67VY9xxxx0YO3YsBg8ejMjISLzyyiuIjIzE2rVr8cEHH6BNmzaYN28eFixYINGrJCIiso8XKa3FkYuUunLhzKKiIrz11lsNbjdmzBjExMQ49dhq5M57TURE2sOLlMpMQECAR7cjIiIi57G42UvCw8ORk5Njd6ZTQECA1xYvJCIi0iIGHy9iqCEiIpIWh7qIiIhIM9jjQ0RE5ACDwcByBRVg8CEiImqAHC80Ta7hUBcREVED5HqhaXIegw8RERFpBoMPERERaQaDD7ntq6++gk6nw5UrVxz+mcTERCxevFi0NhEREdWHwUcDhg8fDp1Oh7Fjx9a5Lzs7GzqdDsOHD/d+w4iIiLyMwUcj4uPj8f777+P333+33Hbjxg1s2LABLVq0kLBlRERE3sPgoxFpaWmIj4/H5s2bLbdt3rwZLVq0QMeOHS23lZeXY8KECWjevDkaNWqEO++8Ez/88IPVY33yySf405/+hKCgINx9990oLCys83x79+7FXXfdhaCgIMTHx2PChAm4du2aaK+PiIjIEQw+GjJy5EisWbPG8v3q1asxYsQIq22mTJmCTZs24Z133sGhQ4eQkpKCzMxMlJSUAADOnDmDgQMHon///sjNzcXjjz+OadOmWT3GqVOnkJWVhUGDBuHIkSPYuHEj9u7di5ycHPFfJBGRCHihafXgAoYSOXsWOHkSSE0F4uK885yPPvoopk+fjl9//RUA8O233+L999/HV199BQC4du0a3njjDaxduxZ9+vQBALz99tvYtWsXVq1ahcmTJ+ONN95AcnIyFi5cCABo1aoVjh49ivnz51ueZ+7cufjrX/+KiRMnAgBSU1OxdOlSdO/eHW+88QYaNWrknRdMROQhvNC0ejD4SGDVKmDMGMBkAnx8gLfeAkaNEv95IyMj0bdvX6xduxaCIKBv376IiIiw3H/q1ClUVlaia9eultv8/f1x22234fjx4wCA48ePo0uXLlaPm5GRYfX9Tz/9hCNHjmD9+vWW2wRBgMlkQkFBAf785z+L8fKIiETFUKMODD5edvbsH6EHqP73iSeAzEzv9PyMHDnSMuT0+uuvi/IcV69exRNPPIEJEybUuY+F1ESkBlL02pNnsMbHy06e/CP0mFVVAfn53nn+rKwsVFRUoLKyEpmZmVb3JScnIyAgAN9++63ltsrKSvzwww9o06YNAODPf/4zvv/+e6uf++6776y+T0tLw88//4yUlJQ6Xxz/JiKlW7UKSEgAevas/nfVKqlbRM5g8PGy1NTq4a2afH2BlBTvPL+vry+OHz+On3/+Gb6+vlb3BQcHY9y4cZg8eTI+/fRT/Pzzzxg9ejSuX7+OUf8bixs7dixOnjyJyZMnIy8vDxs2bMDatWutHmfq1KnYt28fcnJykJubi5MnT+Ljjz9mcTMRKZ6tXvuzZ6VtFzmOwcfL4uKqa3rMmcPXF3jzTe92lYaGhiI0NLTe++bNm4dBgwbhscceQ1paGvLz8/HZZ5+hWbNmAKqHqjZt2oSPPvoIHTp0wMqVKzFnzhyrx2jfvj327NmDX375BXfddRc6duyImTNnIjY2VvTXRkQkJql77cl9OkEQBKkbISelpaXQ6/UwGo11wsGNGzdQUFCApKQkt2cmnT1b/YeSksLx4fp48r0mIvKUs2erh7dqhh9fX6CwkPtyqdk7ftfEHh+JxMUBPXrwD4WISEnk0GtP7uGsLiIiIieMGlU9E5e99srE4ENEROSkuDgGHqXiUBcRERFpBoMPERERaQaDjws4EU58fI+JiEgMDD5OMC/4Z+8ideQZ5ve49iKLRERE7mBxsxP8/PzQuHFjXLp0Cf7+/vCpvQQzeYTJZMKlS5fQuHFj+PnxI0pERJ7Do4oTdDodYmJiUFBQgF9//VXq5qiaj48PWrRoAZ1OJ3VTiIhIRRh8nBQQEIDU1FQOd4ksICCAPWpERORxDD4u8PHx4WUUiIiIFIin1ERERKQZDD5ERESkGQw+REREpBms8anFvHBeaWmpxC0hIiIiR5mP2w0tgMvgU0tZWRkAID4+XuKWEBERkbPKysqg1+tt3q8TeG0AKyaTCefPn0dISIjm1pApLS1FfHw8zpw5g9DQUKmbo1h8Hz2H76Vn8H30HL6XniHG+ygIAsrKyhAbG2t3ORT2+NTi4+ODuLg4qZshqdDQUP5BewDfR8/he+kZfB89h++lZ3j6fbTX02PG4mYiIiLSDAYfIiIi0gwGH7IIDAzE888/j8DAQKmbomh8Hz2H76Vn8H30HL6XniHl+8jiZiIiItIM9vgQERGRZjD4EBERkWYw+BAREZFmMPgQERGRZjD4aMzXX3+N/v37IzY2FjqdDh999JHV/YIgYObMmYiJiUFQUBB69+6NkydPStNYmWvovRw+fDh0Op3VV1ZWljSNlbG5c+eic+fOCAkJQfPmzTFgwADk5eVZbXPjxg1kZ2cjPDwcTZo0waBBg3DhwgWJWixPjryPPXr0qPOZHDt2rEQtlq833ngD7du3tyyul5GRgR07dlju5+fRcQ29l1J8Jhl8NObatWvo0KEDXn/99Xrvf+WVV7B06VKsXLkSBw4cQHBwMDIzM3Hjxg0vt1T+GnovASArKwtFRUWWr/fee8+LLVSGPXv2IDs7G9999x127dqFyspK3Hvvvbh27Zplm6effhpbt27FBx98gD179uD8+fMYOHCghK2WH0feRwAYPXq01WfylVdekajF8hUXF4d58+bh4MGD+PHHH9GzZ0/cf//9+M9//gOAn0dnNPReAhJ8JgXSLADCli1bLN+bTCYhOjpaePXVVy23XblyRQgMDBTee+89CVqoHLXfS0EQhGHDhgn333+/JO1RsosXLwoAhD179giCUP0Z9Pf3Fz744APLNsePHxcACPv375eqmbJX+30UBEHo3r278NRTT0nXKAVr1qyZ8I9//IOfRw8wv5eCIM1nkj0+ZFFQUIDi4mL07t3bcpter0eXLl2wf/9+CVumXF999RWaN2+OVq1aYdy4cTAYDFI3SfaMRiMAICwsDABw8OBBVFZWWn0uW7dujRYtWvBzaUft99Fs/fr1iIiIwK233orp06fj+vXrUjRPMaqqqvD+++/j2rVryMjI4OfRDbXfSzNvfyZ5kVKyKC4uBgBERUVZ3R4VFWW5jxyXlZWFgQMHIikpCadOncKzzz6LPn36YP/+/fD19ZW6ebJkMpkwceJEdO3aFbfeeiuA6s9lQEAAmjZtarUtP5e21fc+AsCQIUOQkJCA2NhYHDlyBFOnTkVeXh42b94sYWvl6ejRo8jIyMCNGzfQpEkTbNmyBW3atEFubi4/j06y9V4C0nwmGXyIRPLwww9b/t+uXTu0b98eycnJ+Oqrr9CrVy8JWyZf2dnZOHbsGPbu3St1UxTN1vs4ZswYy//btWuHmJgY9OrVC6dOnUJycrK3mylrrVq1Qm5uLoxGIz788EMMGzYMe/bskbpZimTrvWzTpo0kn0kOdZFFdHQ0ANSZnXDhwgXLfeS6li1bIiIiAvn5+VI3RZZycnKwbds27N69G3FxcZbbo6OjUVFRgStXrlhtz89l/Wy9j/Xp0qULAPAzWY+AgACkpKQgPT0dc+fORYcOHbBkyRJ+Hl1g672sjzc+kww+ZJGUlITo6Gh88cUXlttKS0tx4MABq/FYcs3Zs2dhMBgQExMjdVNkRRAE5OTkYMuWLfjyyy+RlJRkdX96ejr8/f2tPpd5eXk4ffo0P5c1NPQ+1ic3NxcA+Jl0gMlkQnl5OT+PHmB+L+vjjc8kh7o05urVq1ZJuqCgALm5uQgLC0OLFi0wceJEvPTSS0hNTUVSUhKee+45xMbGYsCAAdI1WqbsvZdhYWGYPXs2Bg0ahOjoaJw6dQpTpkxBSkoKMjMzJWy1/GRnZ2PDhg34+OOPERISYqmT0Ov1CAoKgl6vx6hRozBp0iSEhYUhNDQU48ePR0ZGBm6//XaJWy8fDb2Pp06dwoYNG/B///d/CA8Px5EjR/D000+jW7duaN++vcStl5fp06ejT58+aNGiBcrKyrBhwwZ89dVX+Oyzz/h5dJK991Kyz6RX55CR5Hbv3i0AqPM1bNgwQRCqp7Q/99xzQlRUlBAYGCj06tVLyMvLk7bRMmXvvbx+/bpw7733CpGRkYK/v7+QkJAgjB49WiguLpa62bJT33sIQFizZo1lm99//1148sknhWbNmgmNGzcW/t//+39CUVGRdI2WoYbex9OnTwvdunUTwsLChMDAQCElJUWYPHmyYDQapW24DI0cOVJISEgQAgIChMjISKFXr17Czp07Lffz8+g4e++lVJ9JnSAIgnixioiIiEg+WONDREREmsHgQ0RERJrB4ENERESaweBDREREmsHgQ0RERJrB4ENERESaweBDREREmsHgQ0RERJrB4ENERESaweBDRIpRUVEhdRPqkGObiMg2Bh8ikkyPHj2Qk5ODnJwc6PV6RERE4LnnnoP5SjqJiYl48cUXMXToUISGhmLMmDEAgL179+Kuu+5CUFAQ4uPjMWHCBFy7ds3yuCtWrEBqaioaNWqEqKgoPPDAA5b7PvzwQ7Rr1w5BQUEIDw9H7969LT/bo0cPTJw40aqNAwYMwPDhwy3fu9omIpIHBh8iktQ777wDPz8/fP/991iyZAkWLVqEf/zjH5b7FyxYgA4dOuDw4cN47rnncOrUKWRlZWHQoEE4cuQINm7ciL179yInJwcA8OOPP2LChAl44YUXkJeXh08//RTdunUDABQVFeGRRx7ByJEjcfz4cXz11VcYOHAgnL1kobNtIiL54EVKiUgyPXr0wMWLF/Gf//wHOp0OADBt2jT8+9//xs8//4zExER07NgRW7ZssfzM448/Dl9fX7z55puW2/bu3Yvu3bvj2rVr+OSTTzBixAicPXsWISEhVs936NAhpKeno7CwEAkJCfW25y9/+QsWL15suW3AgAFo2rQp1q5dCwAutalRo0ZuvU9E5Dns8SEiSd1+++2W0AMAGRkZOHnyJKqqqgAAnTp1str+p59+wtq1a9GkSRPLV2ZmJkwmEwoKCnDPPfcgISEBLVu2xGOPPYb169fj+vXrAIAOHTqgV69eaNeuHR588EG8/fbb+O2335xus7NtIiL5YPAhIlkLDg62+v7q1at44oknkJuba/n66aefcPLkSSQnJyMkJASHDh3Ce++9h5iYGMycORMdOnTAlStX4Ovri127dmHHjh1o06YNli1bhlatWlnCiY+PT51hr8rKSrfbRETyweBDRJI6cOCA1fffffcdUlNT4evrW+/2aWlp+Pnnn5GSklLnKyAgAADg5+eH3r1745VXXsGRI0dQWFiIL7/8EgCg0+nQtWtXzJ49G4cPH0ZAQIBl2CoyMhJFRUWW56qqqsKxY8cafA2OtImI5IHBh4gkdfr0aUyaNAl5eXl47733sGzZMjz11FM2t586dSr27duHnJwc5Obm4uTJk/j4448thcTbtm3D0qVLkZubi19//RXvvvsuTCYTWrVqhQMHDmDOnDn48ccfcfr0aWzevBmXLl3Cn//8ZwBAz549sX37dmzfvh0nTpzAuHHjcOXKlQZfQ0NtIiL58JO6AUSkbUOHDsXvv/+O2267Db6+vnjqqacsU8Tr0759e+zZswczZszAXXfdBUEQkJycjMGDBwMAmjZtis2bN2PWrFm4ceMGUlNT8d5776Ft27Y4fvw4vv76ayxevBilpaVISEjAwoUL0adPHwDAyJEj8dNPP2Ho0KHw8/PD008/jbvvvrvB19BQm4hIPjiri4gkU98sKiIiMXGoi4iIiDSDwYeIiIg0g0NdREREpBns8SEiIiLNYPAhIiIizWDwISIiIs1g8CEiIiLNYPAhIiIizWDwISIiIs1g8CEiIiLNYPAhIiIizWDwISIiIs34/2GiY/EoyBLqAAAAAElFTkSuQmCC", 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8eTMAwMvLC8HBwWjfvj0GDx6MESNGmD06bdOmTUhLS0NhYaEdS0tERK4mPz8fa9eu1T1XqwNQUBCC4OB8qFTFuu2pqakuH34YfBzImWm5V69e2LhxIyorK3H9+nV89dVXmDx5Mj799FN88cUX8PLiR4GIiAyr/t114kRH7NzZF0J4QKHQoF+/XUhIOFnrOFfFbzsHqZmWjbFXWvbx8dFN5NikSRMkJCTgoYcewhNPPIFNmzZhzJgxWL58OTZu3Ijff/8dwcHB6NevH15//XU0bNgQ3377LUaOHAngr47Hc+fOxbx587BlyxasWrUKmZmZ8Pf3R/fu3bFy5Uo0btxY8usgInK0+tLEIwW1OkAXegBACA/s3NkX8fEX9Gp+XBmDj4OYm4IdmZa7d++ODh06ID09HWPGjIGHhwdWr16NuLg4/P7773j++ecxffp0vPXWW+jSpQtWrlyJOXPmIDMzEwDQsGFDAEB5eTkWLFiAVq1a4caNG5gyZQpGjBiBL7/80mHXQkRkD87+o9XVFBSE6EKPlhAeKCgIZvAh99C6dWucPn0aAJCWlqbbHhsbi9deew3jx4/HW2+9BaVSCZVKBYVCUWsJkFGjRul+bt68OVavXo0HHngAt2/f1oUjIiJ35Ip/tDpTcHA+FAqNXvhRKDQIDi5wYqksw+HsMieE0DVd7d+/H0888QSaNGmCgIAADBs2DPn5+SgpKTF5juPHj6Nfv35o1qwZAgIC0K1bNwDApUuX7F5+IiJyHJWqGP367YJCUbVepbaPj7vU9gCs8ZG9c+fOIS4uDhcvXkTfvn0xYcIELFy4EMHBwTh8+DBGjx6NsrIyo0t03LlzB8nJyUhOTsaHH36IsLAwXLp0CcnJybL5C4iISE4SEk4iPv4CCgqCERxc4FahB2DwkbVvvvkGZ86cwYsvvojjx49Do9Fg2bJluuHtH3/8sd7xSqWy1hISv/76K/Lz87FkyRI0bdoUAPDTTz855gKIiMgpVKpitws8WmzqkonS0lLk5ubi6tWrOHHiBBYtWoS///3v6Nu3L5599lm0aNEC5eXlWLNmDX7//Xds2bIF69ev1ztHbGwsbt++jQMHDiAvLw8lJSVo1qwZlEql7nVffPEFFixY4KSrJCKyL7U6ANnZsVCrA5xdFIcyd41Kd1jLkjU+MvHVV18hMjISXl5eaNSoETp06IDVq1dj+PDh8PDwQIcOHbB8+XIsXboUs2bNQteuXbF48WI8++yzunN06dIF48ePR0pKCvLz83XD2Tdt2oSXX34Zq1evRkJCAt58803079/fiVdLRCQ9U/PXSM3VhtCHhIQgNTVVr0yFhYWoqKjQPff29kZZWRlycnKcUkZzKYQQwtmFcCVFRUVQqVRQq9UIDAzU23f37l1kZ2cjLi4ODRo0sOi8HBJpGVvuNRGRVHJycvDOO+9ArQ7AypVptUYzpaWthEpVjLFjxyIyMlKS93SH7wtXnMnZ1Pd3dazxcRBDabkmV03HRERypW26qWv+GimbeNxhCL07z+TM4ONADDVERO5F+0frxYsV2LJFQKNR6PZ5egpMmtQbsbFedv3/u7HaFFfgjjM5M/gQERGZEBISgpAQ4J13gHHjgMpKwNMTePttBRITw+363qZqU/Ly8gBY31ogRT8id5zJmcGHiIjIDKNHA8nJwIULQIsWQHS0fd+vrtqU9PR03bGW9qWRqh+RO87kzOBDRERkpuho+wceLUtqU2rW3NRVm6NWq80qQ119dLQzOdeslXLV2h6AwYeIiMglGapNATS4c8cfanWA0XBhbm1Odbb0I3K3mZwZfIiIiFxQzdoUQANAgU8/fcrkPEKWjqSSYn4id5rJmcGHiIjIhVQfGq+tTbl8ORo7dvwDQlSNKrNk9JSp2hxrR2WZO3y/sLAQJSUlRtd71J7LkaOeGXyIiEg2pJoR2Z4zK1ef9y0vLw/p6ekoKPjTqtFTddXmWDsqS1vGmzdvYvv27UaPq7nmoytMdMjgQzb79ttv8fjjj+PWrVsICgoy6zWxsbFIS0tDWlqaXctGRKQl1UgmR8ysXPN11oyeMlWbA1SFHm/vUqtHZYWEhNQKf6Zql1xlokMuUioDI0aMgEKhwPjx42vtmzhxIhQKBUaMGOH4ghEROZBUMyIb+rI3tHCplF/m2v4+CoUGAMwaPWWsNufo0c5YuTINmzcPx4YNY9C+/WmLzmvMiRMddedduTINJ0501O0zFsKcsdgra3xkomnTpti2bRtWrFgBX19fAFXrYW3duhXNmjVzcumIiBxPihmRLe0YbGkTmaH+PoZGTxnqc2NsVFhGRpJeADl9uj1Gj/5/KC9XmnVeQ+rqK+RKEx0y+MhEQkICsrKykJ6ejiFDhgAA0tPT0axZM8TFxemOKy0txbRp07Bt2zYUFRWhU6dOWLFiBR544AHdMV9++SXS0tJw+fJlPPTQQxg+fHit9zt8+DBmzZqFn376CaGhofi///s/LF68GP7+/va/WCKiOkgxksnSjsHWNJHZss6joTl2kpIycOTIw3rHCeGB8nIl4uL+wOOPP47GjRtDpVJZ1E+prmDjShMdsqnLSa5cAQ4erPrXUUaNGoWNGzfqnr/33nsYOXKk3jHTp0/Hjh07sHnzZpw4cQItWrRAcnIyCgqqPpyXL1/GgAED0K9fP5w6dQpjxozBzJkz9c6RlZWFXr16YeDAgTh9+jS2b9+Ow4cPIzU11f4XSURuJT8/Hzk5OUYf+fn5kr+nVM0upr7sDbG2qS0kJASRkZFGHzXDSc1aorS0lRg+fBPS0laic+ejumYtreoB5ODBg9i+fbvFnbO1wcbYea1pqrMX1vg4wYYNwNixgEYDeHhUrf8yerT933fo0KGYNWsW/vjjDwDADz/8gG3btuHbb78FANy5cwfr1q3Dpk2b0Lt3bwDAu+++i3379mHDhg2YNm0a1q1bh/j4eCxbtgwA0KpVK5w5cwZLly7Vvc/ixYsxZMgQXcflli1bYvXq1ejWrRvWrVuHBg0a2P9iicjlOaKTsCFSNbu4Ui1GdYZqibSjwwCYNdOyqZBWvblOu16YOTM4u8pEhww+Dnblyl+hB6j6d9y4qvVf7D0NelhYGPr06YNNmzZBCIE+ffogNDRUtz8rKwvl5eV4+OG/qkG9vb3x4IMP4ty5cwCAc+fOoXPnznrnTUpK0nv+888/4/Tp0/jwww9124QQ0Gg0yM7Oxr333muPyyMiNyNVZ2NLSRVYbF2uwZ6rrtdVC6QNIN7eZSgv9zE5E3R1psKqOcHGFSY6ZPBxsPPn/wo9WpWVVYveOWL9l1GjRumanP71r3/Z5T1u376NcePG4YUXXqi1jx2picgYY0FAW6ugZeuEd1KuL2VJLUZhYaHuZ1N9jAoLCxEZGWlxWUzR1gJdu3YN6enpUKmKkZXVwuJ+Tuas3WXNfTS3E7UUGHwcrGXLquat6uHH07NqpV9H6NWrF8rKyqBQKJCcnKy3Lz4+HkqlEj/88ANiYmIAAOXl5Th27Jiu2eree+/FF198ofe6//znP3rPExIS8Msvv6CFoy6KiNyeqSBQfRVyLWuav2wZIWXsPIDxL/uax1VUVACou1O09jipVZ93x9oZm2syFlYHDBiga1HgzM0yFx1d1adn3Liqmh5PT+Dttx232q+np6eu2crT01Nvn7+/PyZMmIBp06YhODgYzZo1w+uvv46SkhKM/l8npPHjx2PZsmWYNm0axowZg+PHj2PTpk1655kxYwYeeughpKamYsyYMfD398cvv/yCffv2WbxwHhHVf9Z8CVvT/GXLCCkpz+MKQ7ulKIO5I+P8/PwcHm5MYfBxgtGjq/r0XLhQVdPjqNCjFRgYaHTfkiVLoNFoMGzYMBQXF6NTp074+uuv0ahRIwBVTVU7duzAiy++iDVr1uDBBx/EokWLMGrUKN052rdvj0OHDmH27Nl49NFHIYRAfHw8UlJS7H5tROR+6hodJWU/GKm+fG05T3BwPgABQKHb5uhO0Zb2c9J2aNY2O9Y1K/SyZSecuiyFKQw+ThId7bjAU7NGpqbPP/9c93ODBg2wevVqrF692ujxffv2Rd++ffW21RwW/8ADD2Dv3r1Gz3Hx4kWTZSIi+TD2JXztWhTef/9Zm+bacSXe3t7VnukHHyGMHWc9Q5MlWjIKq/p5atbWm5oVWjtBojOXpTCFwYeIiJzK0Jdwjx77sX9/D5v7oLgSlUoFoCo01J5G769mJu1xtjBnqgBz+zkZCizGwuqRI0nQXpur/s4YfIiIyClMdTa2pQ+KPVdOl4Ij5v8xt3Zl5Mi/6U1rAtR9f7QdmquHU1OzQjtjWQpTGHyIiMgp6ppoz5pw4KxJES0h5XB6W4WGhlo0dL5mh+YePfYjKuqa7vdSfR0wwDUmdKyJwYeIiJzGWPiwNhwYWjndUOdoZ/Q3MXc4ffX5fqq/1pagJsVkiYY6NO/f3wNpaSt153SVQGcKg48VRPVeaGQXvMdE8iTVXDuANAuRSsnYMHi1Wo3t27frnn/88ccGX29tLZVU98Gc5kdXWZbCFAYfC2jnvSkrK4Ovr6+TS1O/af/HUHOuISKq36Saa0eqCfqkZk5wkbKWSsr7YG7fJFdYlsIUBh8LeHl5wc/PDzdv3oS3tzc8PLi4vT1oNBrcvHkTfn5+8PLiR5RIbqToe+MKkwRaQ+paKinvg619kxy5LIUp/FaxgEKhQGRkJLKzs3UrnJN9eHh4oFmzZlAoFHUfTERUg6uunG6KpbUzpkavaefrkeI+mNv8OGjQIAQFBRk9hytMXggw+FhMqVSiZcuWLjMRU32lVCpZo0ZEVnOlkVPmsqR2xtzRa3XdB3NqYaRqfnQVDD5W8PDwQIMGDZxdDCIiMsEdOtpWZ0ntjLl/fA8YMABjx4ZizpybuHjRC7GxFYiKegDAAxaFFXcJNeZg8CEiIqeScsJBa1dOdwX2qKXSztMTGQkkJkpYWDfG4ENEREbZexZkqSccdPdmGWtrqaSYp0cuGHyIiMggR8yCbI8JB1011Bhjay2Vq81X5OoYfIiIZMicmhxHz4Is1y9wW2qpXHW+IlfG4ENEJDPm1uSkpKTofrZ3KJH7F7i1tVTuOl+RM3G8MBGRzBiqycnOjoVaHaC3vby8XLffUCipebwtTH2Bk3HakWDVufp8Rc7GGh8iIhkzpybHEbUK7jjhoDNp+/tIMU+P3DD4EBHZkb1HRdnC3OYlR4QSd5xwsC72/N3X7Bdk6zw9csLgQ0RkJ44YFWWLumpybt26BcBxocRdJhw0J9AAsPvvvvrrOE+P+Rh8iIjsxNzRTs5aAqeumpyDBw/qttsrlLjbhIPmhtlBgwbpPbf3iDgyH4MPEZFMmVOTU/MLW+pQ4owJB21pgjJ3iH9FRYXuZ7kO03dVDD5ERA7iirPrmqrJMfWFPWDAAISGhkoSShzZzFezxsbY78ScJihzAo3ch+m7IgYfIiIHcKW/+s1pXqrrC1u7BpS7qV5jY+p3UlZWZrBmKC8vD4D5gYbz7LgeBh8iIjtztb/6TTUv5eXlIT093SFf2NWDRWFhoa55KDfXC5cu+SA+XoNWrfwBSN/cVdfvpLCwEB9//LHR15t7fzhM3/Uw+BAR2Zmj/uq3pO9KXSHC1i/suspSUlKCDz74QPdc2+R07Vok9u/vYbAWRsrRb3X9Tqr30alePm2TmLn3pz4O03d3DD5ERHbmiL/6pR46b8sXtrn9aLSqNzkBAoACQO1aGEtGQBkLXtqmKkt+J8aaxMy9P+4yTF8uGHyIiOzEkbPr2mNBUWu/sM3tR6MtZ/UmJ23o0bKmZsyc4GVusDPVJGbq/nh7e+udx9WH6csJgw8RkZ04a3ZdWzpSSzmvjjl9mww1OVVnTc2YucHLnGBXV5OYsfsTFhbm8GH6ZB4GHyIiO3L07Lq2dqSWcl4dc/o2GWpy0jZ32dofxpx7YSy4aNXVJKYd1l8dA41rY/AhIqpHpOhILdWXtjn9aAw1OfXosR9RUdds7g9jy73QNlXV1STmrsP65axeBp9//etfeOONN5Cbm4sOHTpgzZo1ePDBB51dLCIiu3Ol4dPm9qOxV+dfa2prgKoam+o1XuycXL/Uu+Czfft2TJkyBevXr0fnzp2xcuVKJCcnIzMzE40bN3Z28YjICVx5hXSp2Wv4tLX30NzQUFeTkzVsqa3Jz883q3zsnOx+6l3wWb58OZ577jmMHDkSALB+/Xrs3r0b7733HmbOnOnk0hGRozl6hXRXCFlS11DYusyDVKGmpKQEOTk5RvcburfW3gtnrCFGjlGvgk9ZWRmOHz+OWbNm6bZ5eHigR48eyMjIMPia0tJSlJaW6p4XFRXZvZxE5DiOXCHd0SGrOnuucm7JMg+WvMfQoUPh5+cHQH/mZi1vb2+oVCoAxic8NBS+arI2eDHU1E/1Kvjk5eWhsrIS4eHhetvDw8Px66+/GnzN4sWLMX/+fEcUj4hcgD0XCrXHXDrmckQNhbkjxqwpS10dhKvX9NQVvswNXmymkqd6FXysMWvWLEyZMkX3vKioCE2bNnViiYjIXhy5UKgzFiW1dw2FJaOk7FUWc8IXm6nIlHoVfEJDQ+Hp6Ynr16/rbb9+/ToiIiIMvsbHxwc+Pj6OKB4ROZEjFwp1tUVJpeIKI8bMDV8MNWSM8eky3ZBSqURiYiIOHDig26bRaHDgwAEkJSU5sWRE5GymvjDd+b0cSTtKSqHQAIBTFtzUhq/quNo5WaJe1fgAwJQpUzB8+HB06tQJDz74IFauXIk7d+7oRnkRkTw5srbCFWpG7MXUKCntAqDXrnkgO9sLcXEViIqqCilSNS1xtXOyVb0LPikpKbh58ybmzJmD3Nxc3H///fjqq69qdXgmInlx5Bdmff9yNjZKKj093WTfJqlGs3FCQbJFvQs+QNV/XIaGNBKR/FQfuWPqC1PqET716cvZ3HtTV98mKUez2WPCQ5KHehl8iIi0HDnCx55z6ThTXfcwLy8P6enpkqwTZgyHqJNUGHyIqN5z1Aif+jyM2pwy27NvU32+t+RYDD5ERBKS8xevvfs2yfneknQYfIiISDL1qW8T1U8MPkRUL7jC4qBURdu3Sa0OQHZ2rG7Jjry8PP4eyOkYfIjI7TlzcVAyzNCwdiAdAH8P5FwMPkTk9py5OKirsqUGrPprzZmMsOZIKkcOayeyFIMPEdUrzlgc1NXUrAEzFgQN1bxUf625kxFqR1xdu3bN7sPaiWzF4ENE9UZ9XRzUUtVrVEyFF0M1L9ptltbahISE6LbV5yU7yP0x+BBRveEuNQ2O6ohtSxC05V7W9yU7yL0x+BBRveGomgZb+884qiO2LeHF1nvJYe3kqhh8iKjecERNg63BxdaO2OaELi1bwosU95LraZErYvAhonrF3jUN5o5IMuc4Sztim9tpedCgQQBsDy+staH6iMGHiNyeMxcHNRY+zHmdpf1v6uq0XBVSQvDf/6p1x9kaXiypteFCouQOGHyIyO05awFLUzU2eXl5Jt/blv43hkLTF1/0hUJR9fP772vQr98NXVmMhRepZ1LmQqLkDhh8iKhecPSXaV01Nunp6XrH1+zzY0v/G0OhCfCAEDBYluplrl47pS1j9bLZWmvDUEOujsGHiMgKxmpsLl+ORkHBn3V2Vral/42h0FRTzdojc+fzYa0N1XcMPkREVjAcPjTYseMfZndWNtX/pnpTGVAVNtTqqr47NUMToAGg+N+jSvXaI0v7EzHUUH3G4ENEZAVj4UOIqvBhLFyY0xFbrQ7AsmUnTHaYjo+/gIEDdwAQaNr0CrKyWhitPXKXiR2JHIHBh4jIAtWDS/Uamzt3/PHpp0/pHWsoXBhqSsrLy9P1tzFniLuxY4zVHnEJCaK/MPgQEVmgZnDRhha1OsDscGGsKcmcJqm6jjFUg8MlJIj+wuBDRGQhQ8FFinBhTpOUtc1WnIyQqAqDDxGRRGwNF+Y0Sdm6DAUDD8kdgw8RkQ2knDXanFqjrKwWuvl6ANQ6ZsCAAQgNDQUAqNVqbN++3eJrIKrPGHyIiGwg9bw3pmqNtP17gL9qe4SoGuGlFRoaisjISABAZGQk5+QhqoHBh4jITOasjC5FiDBWa2RsxmZT/XsYaoj0MfgQEZmh5sroxtRcmsIc5jY1mdO/h81WRKYx+BARmaFmTY+xVdlN1QgZU1dzmXbIfF19gFJSUljDQ1QHBh8iIguZM8mgpaToA6RSqWwqA5EcGF/hjoiIajE2gaBaHeCwMqhUxYiL+4ND04mswOBDRGQBUxMI2ou5/XbYv4eobmzqIiKLOGpkk6tyxrpXUg+ZJ5IzBh8iMlvNkU3GOvhaM7LJXThr3av6ej+JHI3Bh4jMVr3GwVQHX2tGNrkTrntF5L7Yx4eILOYKHXwdzdDSFIY6GLOfDZFrY40PEVnM2hXCq3N0XyFb34/9bIjqBwYfIrKYrR187TkLsj3fj6GGyP2xqYuILKbt4KtQaADUXiG8Lub2AZKqr5Cj34+IXBdrfIjIKlJ28DU2OsxeHP1+ROQ6GHyIyGrGVhG3hD2Wf3Cl9yMi18KmLiIym9QzCDt6dJgcR6MRkT7W+BCR2aQe2STF6DBLOPr9iMj1MPgQkUWkHNnk6OUfnLHcBBG5FjZ1EZHT2Do6zNXfj4hcD2t8iNxE9Qn4rl3zQHa2F+LiKhAVVfUl7k6T51XvA2RqdJhUsyA7+v2IyHUphBDC2YVwJUVFRVCpVFCr1QgMDHR2cYgA6E/AZ2pUkjstDupuMzcTkWsz9/ubNT5EbkD7hW1sVFJ8/AWoVMVuNQGfo0MGQw0RAQw+RG7FVUclsTaFiNwFgw+RG3HEqCRLQ4yj190iIrIFgw+RG9GOSqrZx0eq2h5rQgzXwSIid8LgQ+RmpFwjqyYpQgzXwSIiV8bgQ+SGpFgjyxyWhhiug0VEro7Bh4gMsjTE1DXijIjIFXDmZiI3IPXioHWxZjFPUyPOiIhcBWt8iJzI3BFUUi8OaqwseXl5AKwbNs91sIjIHTD4EDlJzRFUxvrTaEdQ2XMoeM2yWBNi7D3ijIhICgw+RE5SvfbGVH8aU7U8Uk0cWPMcloQYroNFRO6EwYfIyaztFGzPiQPV6gA0anQLo0f/P5SXK02GGEc0wxERSYXBh8jJrF2GombQMNZUZunEgYZqn+Li/gAADBgwAFFRUbVCDEMNEbkLBh8iJ5OiU7BU8+fUVfsUGhrKkENEbo3D2YmcTNufRqHQAIDFnYKtGXpuDIekE1F9xxofIhdgyzIUUq7YziHpRFTfscaHyEWoVMWIi/vD6rBSnbVhxdbaJyIiV8caHyInkWo2Zinmz+GQdCKSCwYfIieRchi4rSu2c0g6EckFgw+RE9kSJGrWvhhbsd3cWhqGGiKSAwYfIjdlay2NVLM+ExG5E7cIPhcvXsSCBQvwzTffIDc3F1FRURg6dChmz56t99fs6dOnMXHiRBw7dgxhYWGYNGkSpk+f7sSSk7u4cgU4fx5o2RKIjnZ2acxnbTCx56zPRESuzC2Cz6+//gqNRoO3334bLVq0wNmzZ/Hcc8/hzp07ePPNNwEARUVF6NmzJ3r06IH169fjzJkzGDVqFIKCgjB27FgnXwG5Im2Nx9atvpg+XQWNRgEPD4HXX1fjmWf+rNc1HubO5mzprM9ERK7OLYJPr1690KtXL93z5s2bIzMzE+vWrdMFnw8//BBlZWV47733oFQq0bZtW5w6dQrLly9n8KFatDUeanUAVq5MgxAKAIBGo8C0aYG4evU9qFTFsqnxMLbcBRFRfWN28CkqKjL7pIGBgVYVxhJqtRrBwX/NJpuRkYGuXbvqNX0lJydj6dKluHXrFho1amTwPKWlpSgtLdU9t+Q6yb7s2fykrcmoa/I/OdR4mFruIi8vT+/Y+lwLRkTyYHbwCQoKgkKhMHmMEAIKhQKVlZU2F8yUCxcuYM2aNbraHgDIzc1FXFyc3nHh4eG6fcaCz+LFizF//nz7FZassmEDMHYsoNEAHh7AO+8Ao0dL/z5yn6m4rrW50tPTa71GLrVgRFQ/mR18Dh48KPmbz5w5E0uXLjV5zLlz59C6dWvd86tXr6JXr1546qmn8Nxzz9lchlmzZmHKlCm650VFRWjatKnN5yXr5Ofn4+LFCowd2xgajbb5CRg3TuD++28gNtZL0i9dKSb/c2fWLHchh1owIqq/zA4+3bp1k/zNp06dihEjRpg8pnnz5rqfr127hscffxxdunTBO++8o3dcREQErl+/rrdN+zwiIsLo+X18fODj42NhycketP1usrNjodEM19tXWanAmjV7EBf3h+Q1DrZO/mcpVxpGbk2NV15eHpu8iMhtWd25ubCwEBs2bMC5c+cAAG3btsWoUaOgUqnMPkdYWBjCwsLMOvbq1at4/PHHkZiYiI0bN8LDQ/+v1KSkJMyePRvl5eXw9vYGAOzbtw+tWrUy2sxFrkUbBur6MrZHjYOxyf+k5mrDyE3VeBnr8Kxt/mKTFxG5I6uCz08//YTk5GT4+vriwQcfBAAsX74cCxcuxN69e5GQkCBpIa9evYrHHnsMMTExePPNN3Hz5k3dPm1tzjPPPIP58+dj9OjRmDFjBs6ePYtVq1ZhxYoVkpaF7K8+Nz+5yjDyutbmMtXh2VFlJCKyB6uCz4svvoj+/fvj3XffhZdX1SkqKiowZswYpKWl4bvvvpO0kPv27cOFCxdw4cIFRNcY3iOEAACoVCrs3bsXEydORGJiIkJDQzFnzhwOZXdTjm5+chZnDSMPCQnBoEGD8PHHHwPQr/Gqq8MzEZE7s7rGp3roAQAvLy9Mnz4dnTp1kqxwWiNGjKizLxAAtG/fHt9//73k70/OYc/mJ6lWRreFJcPItWWRsmkpKChI77k2hN2542eww/Ply9FQqc5J9v5ERM5gVfAJDAzEpUuX9EZbAcDly5cREBAgScGI7MnZq5FbM4wcsF+/muohDNAAEAD0p6/YseMfKCur3eRFROROrAo+KSkpGD16NN5880106dIFAPDDDz9g2rRpGDx4sKQFJLIXZ3bMrWsYubEmsLr61VgzYqxmCAOqh5+/ysgmLyKqD6wKPm+++SYUCgWeffZZVFRUAAC8vb0xYcIELFmyRNICEtVHpkaumdOx2BBrR4wZCmGAB7p2PYjvvntcb2tdc/wQEbm6mv+3M4tSqcSqVatw69YtnDp1CqdOnUJBQQFWrFjBOXHIaq7Q78ZRtCPXFAoNAOgCDgCDTWBqdd1NyNVHO5pSs0ZIG8KqUyg0uOee8wa3y2VWayKqn2xapNTPzw/t2rWTqiwkc87ud+MIdQ0jz86OtXgmZaCqtmf79u1WlcXY9AHR0TkmpxWoDwGUiOTHquBz9+5drFmzBgcPHsSNGzeg0ej/VXjixAlJCkfy486hxhzGwl1eXh7S09OtXjvMUFisa6h8zbLMmXMTFy96ITa2Av7+96C8PA6DBnnpbY+KegDAA24fQIlIvqwKPqNHj8bevXvxj3/8Aw8++GCdi5cS0V9MBQapJm80t59Q9bJERgKJidpn4XrH/bWdiMi9WRV8du3ahS+//BIPP/yw1OUhkj1bJ29UqwPwxRd9oe3Cx9FYRER/sSr4NGnShPP1EEmoZn8ZY5M3mtOv5ujRzqg5boGjsYiIqlgVfJYtW4YZM2Zg/fr1iImJkbpMRLIjVcdutToAGRlJBvZwNBYREWBl8OnUqRPu3r2L5s2bw8/PT7caulZBAf8HS2QpKToLG56TB+jSJYOjsYiIYGXwGTx4MK5evYpFixYhPDycnZuJXISxUWGdOx8FAAwaNIijsYhI1qwKPkeOHEFGRgY6dOggdXmIyAp1zcmjre1p3LixM4tJROR0VgWf1q1b488//5S6LERkJVNz8nDuHSKiv1gVfJYsWYKpU6di4cKFaNeuXa0+PoGBgZIUjshRrFnc09UYn5OHiIi0FEIIYemLPDyq+g/U7NsjhIBCoUBlZaU0pXOCoqIiqFQqqNVqBjiZsHZxTyIich3mfn9bVeNz8OBBqwtG5Gpq1vQYW+rBVI0QERG5B6uCT7du3cw67vnnn8err76K0NBQa96GyOHMXeqBiIjcU+0JPyT0wQcfoKioyJ5vQSQZtTpAF3qAv5Z6UKs5SzkRUX1h1+BjRfchIqcxNPmfdqkHIiKqH+wafIjciXbyv+oUCi71QERUnzD4EP2PdvI/bfipOfkfERG5P6s6NxPVVwkJJxEffwEFBcEIDi5g6CEiqmcYfEj2ai7aqVIVGww8XNyTiMj9WRx8KioqsGjRIowaNQrR0dEmjx06dCgnASTJ2Gt25ZrLPUh5biIici1WzdwcEBCAM2fOIDY21g5Fci7O3OyaOLsyERGZYteZm7t3745Dhw7Vy+BDtbnCOlacXZmIiKRgVfDp3bs3Zs6ciTNnziAxMRH+/v56+/v37y9J4cjxaoacwsJCfPzxx3W+zpE1LZxdmYiIrGVV8Hn++ecBAMuXL6+1z90XKZUzc5uTDLl27ZpeYLJXLZCx2ZXj4y9wBBYREdXJquCj0WjqPojcji3NROnp6bW22aMWyNTsyvYIPq7QzEdERNKxKvi8//77SElJgY+Pj972srIybNu2Dc8++6wkhSPHyc/PR15enqTntEd/G+3sytXDj71mV2aHaiKi+seqmZtHjhwJtVpda3txcTFGjhxpc6HIsbRf8IZqbVyNI2dXNtShOjs7ttaipexQTUTkPqyq8RFCQKFQ1Np+5coVqFQqmwtFjuVuX9zOmF3ZVIdqYzVlbAYjInI9FgWfjh07QqFQQKFQ4IknnoCX118vr6ysRHZ2Nnr16iV5Icl1GBpGbmxouZTsPbuyob482kBTV4dqUzVlbAYjInItFgWfJ598EgBw6tQpJCcno2HDhrp9SqUSsbGxGDhwoKQFJOepGWgM1XoAcMjQcnvOrlxXXx5zOlRzXiEiIvdgUfCZO3cuACA2NhYpKSlo0KCBXQpFzlcz5PTosR/79/fQq/X44ou+UCjgsKHl9qo5qWtyxLo6VHNeISIi92FVH5/hw4cDqPrCuHHjRq3h7c2aNbO9ZOQ0hpp2qoeev3ig5oIn9hxa7gjGQky/frtqbVepijmvEBGRm7Eq+Jw/fx6jRo3CkSNH9LZrOz1zAkP3ZqxpR6EQEKJ6p3aNXo0PoF8T4m6rmZsKMcY6VDt6XiEiIrKNVcFnxIgR8PLywq5duxAZGWlwhBe5L2NNO7NnF2PxYhUqKwFPT4GlS4sAADNmqFBZqdBte+aZwW45oqmuEGOoQ7Uj5xUiIiLbWRV8Tp06hePHj6N169ZSl4ecwFDNTFJSBjIykvSadtLSHsa4ccCFC0CLFgpERwcBAFJSam4LcmTxJVNXiBkwYABCQ0MBVI34Sk9P180rZKgZjIiIXI9VwadNmzaSz/JLzlN9xNTWrb549VUVNBoFFAqB8eNvY8yYO4iNfVhXgxMdrf/66Oja29xRXSEmNDQUkZGRtV7njHmFiIjIOlYFn6VLl2L69OlYtGgR2rVrB29vb739gYGBkhSOHCckJARXrgDTpwPavupCKPDuuw0xe3ZDuFmrldXMDTH2nleIiIjsw6rg06NHDwBA9+7d9fr3sHOzezt//q/Qo1VZWdWMVR9qdIyxJsTYc14hIiKyH6uCz8GDB6UuB7mAli0BDw/98OPpCbRo4bwyOYK1IYahhojI/VgVfLp164bvv/8eb7/9NrKysvDpp5+iSZMm2LJlC+Li4qQuIzlIdDTwzjvAuHH438gt4O2363dtjxZDDBGRPFi1OvuOHTuQnJwMX19fnDx5EqWlpQAAtVqNRYsWSVpAcqzRo4GLF4GDB6v+HT3a2SUiIiKSjlXB57XXXsP69evx7rvv6nVsfvjhh3HixAnJCkfOER0NPPaYPGp6iIhIXqwKPpmZmejatWut7SqVCoWFhbaWiYiIiMgurAo+ERERuHDhQq3thw8fRvPmzW0uFBEREZE9WBV8nnvuOUyePBlHjx6FQqHAtWvX8OGHH+Kll17ChAkTpC4jERERkSSsGtU1c+ZMaDQaPPHEEygpKUHXrl3h4+ODl156CZMmTZK6jCRj+fn5nCuHiIgkoxBCCGtfXFZWhgsXLuD27dto06YNGjZsKGXZnKKoqAgqlQpqtZozUDtZfn4+1q5dq3uuVgegoCAEwcH5ehMMpqamMvwQEcmcud/fVtX4aCmVSrRp08aWUxAZVb2m58SJjrXW0EpIOFnrOFOq1x5du+aB7GwvxMVVICqqasZG1h4REdV/NgUfIkdQqwN0oQcAhPDAzp19ER9/ASpVscEFc2uGmOq1R6ZCFGuPiIjqNwYfcnkFBSG60KMlhAcKCoKhUhVj48Z9dTaBaWt66gpR5tYeERGRe2LwIZcXHJwPhUKjF34UCg2CgwssbgKrK0QREVH9ZtVwdiJHUqmK0a/fLigUVX1xtAEHgMHaG7U6wOi5tCGqOm2IIiKi+o81PuSS8vPz9fruJCScRHz8BRQUBCM4uAAqVTGys2Mtrr3RhqiatUSs7SEikgcGH3I5poaxx8X9odtuqgnMFEMhioiI5IHBh1yOucPYbam9UamKGXiIiGSIwYdcVl0jsADW3hARkWUYfMhlmTsCy5zaG6VSadZ7mnscERG5JwYfclnW9uHRqh5iQkJCkJqaynW/iIhkjsGHHK6uhUfVajWAuvvwDBgwAKGhoQbPYSjEMNQQERGDDzmUuQuPapnqwxMaGorIyEiHlJuIiOoHBh9yKHNHbFXHEVhERCQVztxMTmFsxJapWZdrYkdkIiKylNvV+JSWlqJz5874+eefcfLkSdx///26fadPn8bEiRNx7NgxhIWFYdKkSZg+fbrzCktG1TVia9CgQQgKCjL6enZEJiIia7hd8Jk+fTqioqLw888/620vKipCz5490aNHD6xfvx5nzpzBqFGjEBQUhLFjxzqptGRMXSO2goKC2H+HiIgk51bBZ8+ePdi7dy927NiBPXv26O378MMPUVZWhvfeew9KpRJt27bFqVOnsHz5cgYfB6prxFZhYSEArplFRETO4TbB5/r163juuefw+eefw8/Pr9b+jIwMdO3aVa/fR3JyMpYuXYpbt26hUaNGjiyuLEk5YouIiMge3CL4CCEwYsQIjB8/Hp06dcLFixdrHZObm4u4uDi9beHh4bp9xoJPaWkpSktLdc+LioqkK7jMcMQWERG5OqeO6po5cyYUCoXJx6+//oo1a9aguLgYs2bNkrwMixcvhkql0j2aNm0q+XvIDUdsERGRq3Jqjc/UqVMxYsQIk8c0b94c33zzDTIyMuDj46O3r1OnThgyZAg2b96MiIgIXL9+XW+/9nlERITR88+aNQtTpkzRPS8qKqpX4aeuPjf2GB1V14itlJQUqFQqh5aJiIgIcHLwCQsLQ1hYWJ3HrV69Gq+99pru+bVr15CcnIzt27ejc+fOAICkpCTMnj0b5eXl8Pb2BgDs27cPrVq1Mtm/x8fHp1agqi9q9rkxJjU1VdKgYWrEllodgB9+UOL++z0QFaUBwKBDRESO4xZ9fJo1a6b3vGHDhgCA+Ph4REdHAwCeeeYZzJ8/H6NHj8aMGTNw9uxZrFq1CitWrHB4eV2FqZoea44zl7ERW1lZLYz2+5E6fBERERniFsHHHCqVCnv37sXEiRORmJiI0NBQzJkzh0PZnaTmiC0AWLkyrVa/n/j4C1CpiiUPX0RERIa4ZfCJjY2FEKLW9vbt2+P77793QonIkOojtrKzY032+yEiInIErtVFkjE1Ekvb76e66jM1ExEROYJb1viQawoJCUFqaqpes1VeXh7S09M5UzMREbkEBh+SlKkOypypmYiInI3BR0bqWkLCEThTMxERORODTz1Wvc+NqSUkOEsyERHJBYNPPabtc3PxYgVefbUxhFAAqBpNtXt3P8yZ0xmxsV52nT/H3FDF8EVERI7A4FPPhYSE4PRpQKM/oAqVlQoUF4fD3nMGGurwXBNnbiYiIkdh8JGBli0BDw/98OPpCbRo4Zj3Z6ghIiJXwXl8ZCA6GnjnnaqwA1T9+/bbVduJiIjkhDU+MjF6NJCcDFy4UFXTw9BDRERyxOAjI9HRDDxERCRvbOoiIiIi2WDwISIiItlg8CEiIiLZYPAhIiIi2WDwISIiItlg8CEiIiLZYPAhIiIi2WDwIatduQIcPFj1LxERkTtg8CGL5OfnIycnB8uWFSImRqB7dyAmRmDZskLk5OQgPz/f2UUkIiIyijM3y0x+fr7VK6Xn5+dj7dq1UKsDsHJlGoRQAAA0GgWmTQvE1avvQaUqRmpqKhcmJSIil8TgIyPa4KKlVgegoCAEwcH5UKmKdduNBRdtYCooCIEQ+pWFQnigoCAYKlWxyWBFRETkTAw+MlI9kJw40RE7d/aFEB5QKDTo128XEhJO1jrOkODgfCgUGr3wo1BoEBxcYJ+CExERSYR9fGRIrQ7QhR6gqrZm586+UKsDzHq9SlWMfv12QaHQAIAuOFWvNSIiInJFrPGRobqaqsyRkHAS8fEXUFAQjODgAoYeIiJyCww+MiRVU5VKVczAQ0REboVNXTLEpioiIpIr1vjIFJuqiIhIjhh8ZMzSpiqlUinpcURERI7G4CMjtgaXkJAQpKamWj0BIhERkbMphBDC2YVwJUVFRVCpVFCr1QgMDHR2cSRny8zNRERErsrc72/W+MiMJaHmyhXg/HmgZUsgOtqOhSIiInIQjuoigzZsAGJi8L9FSKueExERuTsGH9KTn5+P48evY+xYAU3VaHdoNMC4cQLHj1/n6utEROTW2NRFOtpFTLOzY6HRDNfbV1mpwJo1exAX9wdXXyciIrfFGh/S0XZ61s7sXF31mZ25+joREbkrBh+qhTM7ExFRfcWmLjKIMzsTEVF9xOBDRnERUiIiqm/Y1EVERESyweBDREREssHgQ0RERLLB4EM6XH2diIjqO3ZuJh2uvk5ERPUdgw/pYaghIqL6jE1dREREJBsMPkRERCQbDD5EREQkGww+REREJBsMPi7qyhXg4MGqf4mIiEgaDD4uaMMGICYG6N696t8NG2ofw2BERERkOQYfF3PlCjB2LKDRVD3XaIBx46q25+fnIycnB8uWFSImRvwvGAksW1aInJwc5OfnO7fwRERELo7z+LiY8+f/Cj1alZXA8eNqnDq1Fmp1AFauTIMQCgCARqPAtGmBuHr1PahUxUhNTeVcPEREREawxseF5OfnIzDwOjw8hN52T08BP79rAICCghAIof9rE8IDBQXBAGBy1mUiIiK5Y42Pi8jPz8fatWsBAH37dsTOnX0hhAcUCg369NmFI0dOAgCCg/OhUGj0wo9CoUFwcIFTyk1EROROGHxcRPWamoSEk4iPv4CCgmAEBxdApSrW7VOpitGv3y69YNSv3y69Y4iIiMgwBh8XpVIVGw0z8fEXMHDgDgACTZteYeghIiIyE4OPmzlxomOt2p6EhJPOLhYREZFbYOdmN6JWB+hCD1DVqXnnzr5QqwOcXDIiIiL3wODjRuoa0UVERESmMfi4Ee2IrupqjuhSKpWOLhYREZHbYPBxI9oRXdrwU3NEV0pKCicvJCIiMoGdm12EuTU1poe6q+xVPCIionqBwcdFhISEIDU11eDMy3l5eUhPT9c9NzXUnYiIiIxj8HEhbKYiIiKyL/bxcQPmNoOxYzMREZFprPFxA6aawbSUSiVrjIiIiOrgVjU+u3fvRufOneHr64tGjRrhySef1Nt/6dIl9OnTB35+fmjcuDGmTZuGiooK5xRWYiEhIYiMjDT6YOghIiKqm9vU+OzYsQPPPfccFi1ahO7du6OiogJnz57V7a+srESfPn0QERGBI0eOICcnB88++yy8vb2xaNEiJ5aciIiIXIVCCCGcXYi6VFRUIDY2FvPnz8fo0aMNHrNnzx707dsX165dQ3h4OABg/fr1mDFjBm7evGl2/5eioiKoVCqo1WoEBgZKdg1ERERkP+Z+f7tFU9eJEydw9epVeHh4oGPHjoiMjETv3r31anwyMjLQrl07XegBgOTkZBQVFeG///2vM4pNRERELsYtgs/vv/8OAJg3bx7++c9/YteuXWjUqBEee+wxFBRULdeQm5urF3oA6J7n5uYaPXdpaSmKior0HkRERFQ/OTX4zJw5EwqFwuTj119/hUZTtUTD7NmzMXDgQCQmJmLjxo1QKBT45JNPbCrD4sWLoVKpdI+mTZtKcWlERETkgpzauXnq1KkYMWKEyWOaN2+OnJwcAECbNm102318fNC8eXNcunQJABAREYEff/xR77XXr1/X7TNm1qxZmDJliu55UVERww8REVE95dTgExYWhrCwsDqPS0xMhI+PDzIzM/HII48AAMrLy3Hx4kXExMQAAJKSkrBw4ULcuHEDjRs3BgDs27cPgYGBeoGpJh8fH/j4+EhwNUREROTq3GI4e2BgIMaPH4+5c+eiadOmiImJwRtvvAEAeOqppwAAPXv2RJs2bTBs2DC8/vrryM3NxT//+U9MnDiRwYaIiIgAuEnwAYA33ngDXl5eGDZsGP7880907twZ33zzDRo1agQA8PT0xK5duzBhwgQkJSXB398fw4cPx6uvvurkkhMREZGrcIt5fByJ8/gQERG5n3o1jw8RERGRFBh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDa8nF2A+iw/Px9lZWVG9yuVSoSEhDiwRERERPLG4GMn+fn5WLt2bZ3HpaamMvwQERE5CJu67MRUTY81xxEREZHtGHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHzsRKlUSnocERER2Y4TGNpJSEgIUlNTOXMzERGRC2HwsSOGGiIiItfCpi4HuXIFOHiw6l8iIiJyDgYfB9iwAYiJAbp3r/p3wwZnl4iIiEieGHzs7MoVYOxYQKOpeq7RAOPGseaHiIjIGRh87Oz8+b9Cj1ZlJXDhgnPKQ0REJGcMPnbWsiXgUeMue3oCLVo4pzxERERyxuBjZ9HRwDvvVIUdoOrft9+u2k5ERESOxeHsDjB6NJCcXNW81aIFQw8REZGzMPg4SHQ0Aw8REZGzsamLiIiIZIPBh4iIiGSDwYeIiIhkg8GHiIiIZIPBh4iIiGSDwYeIiIhkg8GHiIiIZIPBh4iIiGSDwYeIiIhkg8GHiIiIZIPBh4iIiGSDa3XVIIQAABQVFTm5JERERGQu7fe29nvcGAafGoqLiwEATZs2dXJJiIiIyFLFxcVQqVRG9ytEXdFIZjQaDa5du4aAgAAoFAqzXlNUVISmTZvi8uXLCAwMtHMJXRfvQxXeB94DLd6HKrwPvAda9rwPQggUFxcjKioKHh7Ge/KwxqcGDw8PREdHW/XawMBAWX+gtXgfqvA+8B5o8T5U4X3gPdCy130wVdOjxc7NREREJBsMPkRERCQbDD4S8PHxwdy5c+Hj4+PsojgV70MV3gfeAy3ehyq8D7wHWq5wH9i5mYiIiGSDNT5EREQkGww+REREJBsMPkRERCQbDD5EREQkGww+Rqxbtw7t27fXTbKUlJSEPXv26PbfvXsXEydOREhICBo2bIiBAwfi+vXreue4dOkS+vTpAz8/PzRu3BjTpk1DRUWFoy9FMkuWLIFCoUBaWppum1zuw7x586BQKPQerVu31u2Xy324evUqhg4dipCQEPj6+qJdu3b46aefdPuFEJgzZw4iIyPh6+uLHj164Pz583rnKCgowJAhQxAYGIigoCCMHj0at2/fdvSlWC02NrbWZ0GhUGDixIkA5PNZqKysxCuvvIK4uDj4+voiPj4eCxYs0FsnSQ6fh+LiYqSlpSEmJga+vr7o0qULjh07pttfH+/Bd999h379+iEqKgoKhQKff/653n6prvn06dN49NFH0aBBAzRt2hSvv/66NBcgyKAvvvhC7N69W/z2228iMzNTvPzyy8Lb21ucPXtWCCHE+PHjRdOmTcWBAwfETz/9JB566CHRpUsX3esrKirEfffdJ3r06CFOnjwpvvzySxEaGipmzZrlrEuyyY8//ihiY2NF+/btxeTJk3Xb5XIf5s6dK9q2bStycnJ0j5s3b+r2y+E+FBQUiJiYGDFixAhx9OhR8fvvv4uvv/5aXLhwQXfMkiVLhEqlEp9//rn4+eefRf/+/UVcXJz4888/dcf06tVLdOjQQfznP/8R33//vWjRooUYPHiwMy7JKjdu3ND7HOzbt08AEAcPHhRCyOOzIIQQCxcuFCEhIWLXrl0iOztbfPLJJ6Jhw4Zi1apVumPk8HkYNGiQaNOmjTh06JA4f/68mDt3rggMDBRXrlwRQtTPe/Dll1+K2bNni/T0dAFAfPbZZ3r7pbhmtVotwsPDxZAhQ8TZs2fFRx99JHx9fcXbb79tc/kZfCzQqFEj8f/+3/8ThYWFwtvbW3zyySe6fefOnRMAREZGhhCi6oPh4eEhcnNzdcesW7dOBAYGitLSUoeX3RbFxcWiZcuWYt++faJbt2664COn+zB37lzRoUMHg/vkch9mzJghHnnkEaP7NRqNiIiIEG+88YZuW2FhofDx8REfffSREEKIX375RQAQx44d0x2zZ88eoVAoxNWrV+1XeDuaPHmyiI+PFxqNRjafBSGE6NOnjxg1apTetgEDBoghQ4YIIeTxeSgpKRGenp5i165detsTEhLE7NmzZXEPagYfqa75rbfeEo0aNdL7b2LGjBmiVatWNpeZTV1mqKysxLZt23Dnzh0kJSXh+PHjKC8vR48ePXTHtG7dGs2aNUNGRgYAICMjA+3atUN4eLjumOTkZBQVFeG///2vw6/BFhMnTkSfPn30rheA7O7D+fPnERUVhebNm2PIkCG4dOkSAPnchy+++AKdOnXCU089hcaNG6Njx4549913dfuzs7ORm5urdx9UKhU6d+6sdx+CgoLQqVMn3TE9evSAh4cHjh496riLkUhZWRk++OADjBo1CgqFQjafBQDo0qULDhw4gN9++w0A8PPPP+Pw4cPo3bs3AHl8HioqKlBZWYkGDRrobff19cXhw4dlcQ9qkuqaMzIy0LVrVyiVSt0xycnJyMzMxK1bt2wqIxcpNeHMmTNISkrC3bt30bBhQ3z22Wdo06YNTp06BaVSiaCgIL3jw8PDkZubCwDIzc3V+x+bdr92n7vYtm0bTpw4oddmrZWbmyub+9C5c2ds2rQJrVq1Qk5ODubPn49HH30UZ8+elc19+P3337Fu3TpMmTIFL7/8Mo4dO4YXXngBSqUSw4cP112Hoeusfh8aN26st9/LywvBwcFucx+q+/zzz1FYWIgRI0YAkNd/EzNnzkRRURFat24NT09PVFZWYuHChRgyZAgAyOLzEBAQgKSkJCxYsAD33nsvwsPD8dFHHyEjIwMtWrSQxT2oSaprzs3NRVxcXK1zaPc1atTI6jIy+JjQqlUrnDp1Cmq1Gp9++imGDx+OQ4cOObtYDnP58mVMnjwZ+/btq/UXjdxo/4oFgPbt26Nz586IiYnBxx9/DF9fXyeWzHE0Gg06deqERYsWAQA6duyIs2fPYv369Rg+fLiTS+ccGzZsQO/evREVFeXsojjcxx9/jA8//BBbt25F27ZtcerUKaSlpSEqKkpWn4ctW7Zg1KhRaNKkCTw9PZGQkIDBgwfj+PHjzi4aGcGmLhOUSiVatGiBxMRELF68GB06dMCqVasQERGBsrIyFBYW6h1//fp1REREAAAiIiJqjeTQPtce4+qOHz+OGzduICEhAV5eXvDy8sKhQ4ewevVqeHl5ITw8XBb3wZCgoCDcc889uHDhgmw+D5GRkWjTpo3etnvvvVfX5Ke9DkPXWf0+3LhxQ29/RUUFCgoK3OY+aP3xxx/Yv38/xowZo9sml88CAEybNg0zZ87E008/jXbt2mHYsGF48cUXsXjxYgDy+TzEx8fj0KFDuH37Ni5fvowff/wR5eXlaN68uWzuQXVSXbM9/zth8LGARqNBaWkpEhMT4e3tjQMHDuj2ZWZm4tKlS0hKSgIAJCUl4cyZM3q/3H379iEwMLDWl4ereuKJJ3DmzBmcOnVK9+jUqROGDBmi+1kO98GQ27dvIysrC5GRkbL5PDz88MPIzMzU2/bbb78hJiYGABAXF4eIiAi9+1BUVISjR4/q3YfCwkK9v4a/+eYbaDQadO7c2QFXIZ2NGzeicePG6NOnj26bXD4LAFBSUgIPD/2vEE9PT2g0GgDy+zz4+/sjMjISt27dwtdff42///3vsrsHgHS/96SkJHz33XcoLy/XHbNv3z60atXKpmYuABzObszMmTPFoUOHRHZ2tjh9+rSYOXOmUCgUYu/evUKIqiGrzZo1E99884346aefRFJSkkhKStK9XjtktWfPnuLUqVPiq6++EmFhYW43ZLWm6qO6hJDPfZg6dar49ttvRXZ2tvjhhx9Ejx49RGhoqLhx44YQQh734ccffxReXl5i4cKF4vz58+LDDz8Ufn5+4oMPPtAds2TJEhEUFCT+/e9/i9OnT4u///3vBoexduzYURw9elQcPnxYtGzZ0qWH7hpSWVkpmjVrJmbMmFFrnxw+C0IIMXz4cNGkSRPdcPb09HQRGhoqpk+frjtGDp+Hr776SuzZs0f8/vvvYu/evaJDhw6ic+fOoqysTAhRP+9BcXGxOHnypDh58qQAIJYvXy5Onjwp/vjjDyGENNdcWFgowsPDxbBhw8TZs2fFtm3bhJ+fH4ez29OoUaNETEyMUCqVIiwsTDzxxBO60COEEH/++ad4/vnnRaNGjYSfn5/4v//7P5GTk6N3josXL4revXsLX19fERoaKqZOnSrKy8sdfSmSqhl85HIfUlJSRGRkpFAqlaJJkyYiJSVFb/4audyHnTt3ivvuu0/4+PiI1q1bi3feeUdvv0ajEa+88ooIDw8XPj4+4oknnhCZmZl6x+Tn54vBgweLhg0bisDAQDFy5EhRXFzsyMuw2ddffy0A1Lo2IeTzWSgqKhKTJ08WzZo1Ew0aNBDNmzcXs2fP1ht+LIfPw/bt20Xz5s2FUqkUERERYuLEiaKwsFC3vz7eg4MHDwoAtR7Dhw8XQkh3zT///LN45JFHhI+Pj2jSpIlYsmSJJOVXCFFtmk0iIiKieox9fIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iMhmjz32GNLS0pxdDLubN28e7r//fmcXg4hswOBDRLJXVlbm0PcTQqCiosKh70lEVRh8iMgmI0aMwKFDh7Bq1SooFAooFApcvHgRZ8+eRe/evdGwYUOEh4dj2LBhyMvL073usccew6RJk5CWloZGjRohPDwc7777Lu7cuYORI0ciICAALVq0wJ49e3Sv+fbbb6FQKLB79260b98eDRo0wEMPPYSzZ8/qlenw4cN49NFH4evri6ZNm+KFF17AnTt3dPtjY2OxYMECPPvsswgMDMTYsWMBADNmzMA999wDPz8/NG/eHK+88opudehNmzZh/vz5+Pnnn3XXuWnTJly8eBEKhQKnTp3Snb+wsBAKhQLffvutXrn37NmDxMRE+Pj44PDhw9BoNFi8eDHi4uLg6+uLDh064NNPP5X6V0RE1TD4EJFNVq1ahaSkJDz33HPIyclBTk4OAgIC0L17d3Ts2BE//fQTvvrqK1y/fh2DBg3Se+3mzZsRGhqKH3/8EZMmTcKECRPw1FNPoUuXLjhx4gR69uyJYcOGoaSkRO9106ZNw7Jly3Ds2DGEhYWhX79+uoCSlZWFXr16YeDAgTh9+jS2b9+Ow4cPIzU1Ve8cb775Jjp06ICTJ0/ilVdeAQAEBARg06ZN+OWXX7Bq1Sq8++67WLFiBQAgJSUFU6dORdu2bXXXmZKSYtG9mjlzJpYsWYJz586hffv2WLx4Md5//32sX78e//3vf/Hiiy9i6NChOHTokEXnJSILSLLUKRHJWrdu3cTkyZN1zxcsWCB69uypd8zly5f1VjTv1q2beOSRR3T7KyoqhL+/vxg2bJhuW05OjgAgMjIyhBB/rQq9bds23TH5+fnC19dXbN++XQghxOjRo8XYsWP13vv7778XHh4e4s8//xRCCBETEyOefPLJOq/rjTfeEImJibrnc+fOFR06dNA7Jjs7WwAQJ0+e1G27deuWACAOHjyoV+7PP/9cd8zdu3eFn5+fOHLkiN75Ro8eLQYPHlxn2YjIOl7ODF1EVD/9/PPPOHjwIBo2bFhrX1ZWFu655x4AQPv27XXbPT09ERISgnbt2um2hYeHAwBu3Lihd46kpCTdz8HBwWjVqhXOnTune+/Tp0/jww8/1B0jhIBGo0F2djbuvfdeAECnTp1qlW379u1YvXo1srKycPv2bVRUVCAwMNDi6zem+nteuHABJSUl+Nvf/qZ3TFlZGTp27CjZexKRPgYfIpLc7du30a9fPyxdurTWvsjISN3P3t7eevsUCoXeNoVCAQDQaDQWvfe4cePwwgsv1NrXrFkz3c/+/v56+zIyMjBkyBDMnz8fycnJUKlU2LZtG5YtW2by/Tw8qnoMCCF027TNbjVVf8/bt28DAHbv3o0mTZroHefj42PyPYnIegw+RGQzpVKJyspK3fOEhATs2LEDsbGx8PKS/n8z//nPf3Qh5tatW/jtt990NTkJCQn45Zdf0KJFC4vOeeTIEcTExGD27Nm6bX/88YfeMTWvEwDCwsIAADk5ObqamuodnY1p06YNfHx8cOnSJXTr1s2ishKR9di5mYhsFhsbi6NHj+LixYvIy8vDxIkTUVBQgMGDB+PYsWPIysrC119/jZEjR9YKDtZ49dVXceDAAZw9exYjRoxAaGgonnzySQBVI7OOHDmC1NRUnDp1CufPn8e///3vWp2ba2rZsiUuXbqEbdu2ISsrC6tXr8Znn31W6zqzs7Nx6tQp5OXlobS0FL6+vnjooYd0nZYPHTqEf/7zn3VeQ0BAAF566SW8+OKL2Lx5M7KysnDixAmsWbMGmzdvtvreEJFpDD5EZLOXXnoJnp6eaNOmDcLCwlBWVoYffvgBlZWV6NmzJ9q1a4e0tDQEBQXpmoZssWTJEkyePBmJiYnIzc3Fzp07oVQqAVT1Gzp06BB+++03PProo+jYsSPmzJmDqKgok+fs378/XnzxRaSmpuL+++/HkSNHdKO9tAYOHIhevXrh8ccfR1hYGD766CMAwHvvvYeKigokJiYiLS0Nr732mlnXsWDBArzyyitYvHgx7r33XvTq1Qu7d+9GXFycFXeFiMyhENUbpomIXNi3336Lxx9/HLdu3UJQUJCzi0NEbog1PkRERCQbDD5EREQkG2zqIiIiItlgjQ8RERHJBoMPERERyQaDDxEREckGgw8RERHJBoMPERERyQaDDxEREckGgw8RERHJBoMPERERyQaDDxEREcnG/wdmed262Vc8gQAAAABJRU5ErkJggg==", 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", 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", 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rTnnCwsJUVFTki+JVScuWLRUWFqaNGzeqWbNmkn697MOWLVs0fvx4cwsHoMZIiI4g5AQowo4flDSBPrTsaxUZhl+aQOvWrauJEydqwoQJKi4uVvfu3eV0OrVx40ZFRUW5QkNFmjdvrqysLO3YsUMXXnih6tatq/DwcJ+VuTy1a9fW3XffrQceeEANGjRQ06ZNNXv2bBUUFGj06NF+Lw8AILAQdvzEjCbQxx57TDExMcrIyNB//vMf1atXT5deeqkeeught7qIBg0apGXLlumqq65SXl6eFi5cqJEjR/q83GWZNWuWiouLddttt+mXX35R586d9fHHH6t+/fqmlAcAEDgchmEYlR9mb/n5+YqOjpbT6VRUVFSpfSdOnFBWVpYSExNVq1Ytk0qI6uLvCAD2U9H399mYjQUAAGyNsAOvuuuuu0pNdT/7dtddd5ldPABADcSYHXjVo48+qokTJ5a5r6ImRgAAfIWwA6+KjY1VbGys2cUAAMCFbiwAAGBrhB03sZpvYOPvBwA1F91YlQgLC1NQUJAOHDigmJgYhYWFuS6rAOszDEOnTp3S4cOHFRQUpLCwMLOLBADwM8JOJYKCgpSYmKicnBwdOHDA7OLAQ5GRkWratKmCgmjMBICahrDjhrCwMDVt2lRnzpyxxLWiUDXBwcEKCQmhRQ4AaijCjptKrsx97tW5AQCAtdGmDwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbM3UsFNUVKSpU6cqMTFRERERatmypR577DEZhuE6xjAMPfzww0pISFBERIT69OmjPXv2lHqco0ePatiwYYqKilK9evU0evRoHTt2zN8vBwAAWJCpYeeJJ57QvHnz9Nxzz2nXrl164oknNHv2bD377LOuY2bPnq25c+dq/vz52rx5s2rXrq20tDSdOHHCdcywYcO0c+dOrVq1SitWrND69et15513mvGSAACAxTiMs5tR/Oy6665TXFycXn75Zde2QYMGKSIiQq+//roMw1Djxo31hz/8QRMnTpQkOZ1OxcXFadGiRRoyZIh27dqlpKQkbdmyRZ07d5YkffTRR7r22mv1ww8/qHHjxpWWIz8/X9HR0XI6nYqKivLNiwUAAF7l7ve3qS07V1xxhVavXq1///vfkqQvvvhCGzZsUL9+/SRJWVlZys3NVZ8+fVz3iY6OVteuXZWZmSlJyszMVL169VxBR5L69OmjoKAgbd68ucznPXnypPLz80vdAACAPYWY+eSTJ09Wfn6+2rZtq+DgYBUVFenxxx/XsGHDJEm5ubmSpLi4uFL3i4uLc+3Lzc1VbGxsqf0hISFq0KCB65hzZWRkaPr06d5+OQAAwIJMbdn5+9//rjfeeEOLFy/W9u3b9eqrr+rJJ5/Uq6++6tPnnTJlipxOp+u2f/9+nz4fAAAwj6ktOw888IAmT56sIUOGSJKSk5P1/fffKyMjQyNGjFB8fLwk6eDBg0pISHDd7+DBg7rkkkskSfHx8Tp06FCpxz1z5oyOHj3quv+5wsPDFR4e7oNXBAAArMbUlp2CggIFBZUuQnBwsIqLiyVJiYmJio+P1+rVq1378/PztXnzZqWkpEiSUlJSlJeXp23btrmOWbNmjYqLi9W1a1c/vAoAAGBlprbsXH/99Xr88cfVtGlTXXzxxfr888/19NNPa9SoUZIkh8Oh8ePHa8aMGWrdurUSExM1depUNW7cWAMGDJAktWvXTn379tWYMWM0f/58nT59Wunp6RoyZIhbM7EAAIC9mRp2nn32WU2dOlX33HOPDh06pMaNG+v3v/+9Hn74YdcxDz74oI4fP64777xTeXl56t69uz766CPVqlXLdcwbb7yh9PR09e7dW0FBQRo0aJDmzp1rxksCAAAWY+o6O1bBOjsAAASegFhnBwAAwNcIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNZMDzs//vijbr31VjVs2FARERFKTk7W1q1bXfsNw9DDDz+shIQERUREqE+fPtqzZ0+pxzh69KiGDRumqKgo1atXT6NHj9axY8f8/VIAAIAFmRp2fv75Z6Wmpio0NFQffvihvvnmGz311FOqX7++65jZs2dr7ty5mj9/vjZv3qzatWsrLS1NJ06ccB0zbNgw7dy5U6tWrdKKFSu0fv163XnnnWa8JAAAYDEOwzAMs5588uTJ2rhxo/71r3+Vud8wDDVu3Fh/+MMfNHHiREmS0+lUXFycFi1apCFDhmjXrl1KSkrSli1b1LlzZ0nSRx99pGuvvVY//PCDGjduXGk58vPzFR0dLafTqaioKO+9QAAA4DPufn+b2rKzfPlyde7cWb/73e8UGxurTp06acGCBa79WVlZys3NVZ8+fVzboqOj1bVrV2VmZkqSMjMzVa9ePVfQkaQ+ffooKChImzdvLvN5T548qfz8/FI3AABgT6aGnf/85z+aN2+eWrdurY8//lh33323xo0bp1dffVWSlJubK0mKi4srdb+4uDjXvtzcXMXGxpbaHxISogYNGriOOVdGRoaio6NdtyZNmnj7pQEAAIswNewUFxfr0ksv1cyZM9WpUyfdeeedGjNmjObPn+/T550yZYqcTqfrtn//fp8+HwAAMI+pYSchIUFJSUmltrVr107Z2dmSpPj4eEnSwYMHSx1z8OBB1774+HgdOnSo1P4zZ87o6NGjrmPOFR4erqioqFI3AABgT6aGndTUVO3evbvUtn//+99q1qyZJCkxMVHx8fFavXq1a39+fr42b96slJQUSVJKSory8vK0bds21zFr1qxRcXGxunbt6odXAQAArCzEzCefMGGCrrjiCs2cOVM333yzPvvsM/3lL3/RX/7yF0mSw+HQ+PHjNWPGDLVu3VqJiYmaOnWqGjdurAEDBkj6tSWob9++ru6v06dPKz09XUOGDHFrJhYAALA3U6eeS9KKFSs0ZcoU7dmzR4mJibr//vs1ZswY137DMPTII4/oL3/5i/Ly8tS9e3e98MILatOmjeuYo0ePKj09Xe+9956CgoI0aNAgzZ07V3Xq1HGrDEw9BwAg8Lj7/W162LECwg4AAIEnINbZAQAA8DXCjk3lOAu1ae8R5TgLzS4KAACmMnWAMnxjyZZsTVn2lYoNKcghZQxM1uDLm5pdLAAATEHLjs3kOAtdQUeSig3poWVf08IDAKixCDs2k3XkuCvolCgyDO07UmBOgQAAMBlhx2YSG9VWkKP0tmCHQ80bRZpTIAAATEbYsZmE6AhlDExWsOPXxBPscGjmwPZKiI4wuWQAAJiDAco2NPjypurRJkb7jhSoeaNIgg4AoEZzO+zk5+e7/aAszGe+hOgIQg4AAKpC2KlXr54cDkeFxxiGIYfDoaKiomoXDAAAwBvcDjtr1671ZTkA/FeOs1BZR44rsVFtWucAwAvcDjs9e/b0ZTkAiAUhAcAXPB6gnJeXp5dfflm7du2SJF188cUaNWqUoqOjvVY4oCYpb0HIHm1iaOEBgGrwaOr51q1b1bJlSz3zzDM6evSojh49qqefflotW7bU9u3bvV1GoEZgQUgA8A2PWnYmTJigG264QQsWLFBIyK8PcebMGd1xxx0aP3681q9f79VCAjVByYKQZwceFoQEgOrzuGVn0qRJrqAjSSEhIXrwwQe1detWrxUOqElYEBIAfMOjlp2oqChlZ2erbdu2pbbv379fdevW9UrBgJqIBSEBwPs8CjuDBw/W6NGj9eSTT+qKK66QJG3cuFEPPPCAbrnlFq8WEKhpWBASALzLo7Dz5JNPyuFwaPjw4Tpz5owkKTQ0VHfffbdmzZrl1QICAABUh8MwDKPyw8pWUFCgvXv3SpJatmypyMjAHEiZn5+v6OhoOZ1OLnUBAECAcPf7u1oXAo2MjFRycnJ1HgKwLFYyBgB78CjsnDhxQs8++6zWrl2rQ4cOqbi4uNR+1tpBoGMlYwCwD4/CzujRo7Vy5UrddNNN6tKlS6UXCAUCCSsZA4C9eBR2VqxYoQ8++ECpqaneLg9guopWMibsAEDg8WhRwQsuuID1dCwgx1moTXuPKMdZaHZRbKVkJeOzsZIxAAQuj8LOU089pUmTJun777/3dnngpiVbspU6a42GLtis1FlrtGRLttlFsg1WMgYAe/GoG6tz5846ceKEWrRoocjISIWGhpbaf/ToUa8UDmVjTInvsZIxANiHR2Hnlltu0Y8//qiZM2cqLi6OAcp+xpgS/2AlYwCwB4/CzqZNm5SZmamOHTt6uzxwA1fHBgDAfR6N2Wnbtq0KCxkUaxbGlAAA4D6PLhexcuVKTZ8+XY8//riSk5PPG7MTaJdcCNTLReQ4CxlTAgCosdz9/vYo7AQF/dogdO5YHcMw5HA4VFRUVNWHNFWghh0AAGoyn14ba+3atR4XDAAAwJ88Cjs9e/Z067h77rlHjz76qBo1auTJ0wAAAFSbRwOU3fX6668rPz/fl08BAABQIZ+GHQ+GAwEAAHiVT8MOAACA2Qg7AADA1gg7AADA1gg7AADA1nwadm699VYW6QMAAKbyaJ0dScrLy9Nnn32mQ4cOqbi4uNS+4cOHS5LmzZtXvdIBAABUk0dh57333tOwYcN07NgxRUVFlbpshMPhcIUdAAAAs3nUjfWHP/xBo0aN0rFjx5SXl6eff/7ZdTt69Ki3ywgAAOAxj8LOjz/+qHHjxikyMtLb5QEAAPAqj8JOWlqatm7d6u2yAAAAeJ3bY3aWL1/u+nf//v31wAMP6JtvvlFycrJCQ0NLHXvDDTd4r4QAAADV4DDcvIBVUJB7jUAOh0NFRUXVKpS/5efnKzo6Wk6nk6nyAAAECHe/v91u2Tl3ejkAAEAg8GjMzmuvvaaTJ0+et/3UqVN67bXXql0oAAAAb3G7G+tswcHBysnJUWxsbKntP/30k2JjY+nGAgAAPufu97dHLTuGYZRaSLDEDz/8oOjoaE8eEgAAwCeqtIJyp06d5HA45HA41Lt3b4WE/O/uRUVFysrKUt++fb1eSAAAAE9VKewMGDBAkrRjxw6lpaWpTp06rn1hYWFq3ry5Bg0a5NUCAgCAwJXjLFTWkeNKbFRbCdERppShSmHnkUcekSQ1b95cgwcPVq1atXxSKAAAEPiWbMnWlGVfqdiQghxSxsBkDb68qd/L4dEA5RKnTp0q86rnTZv6/4VUBwOUAQDwrhxnoVJnrVHxWSkj2OHQhslXea2Fx+vr7Jxtz549GjVqlDZt2lRqe8nA5UCbjQUAALwr68jxUkFHkooMQ/uOFPi9O8ujsDNy5EiFhIRoxYoVSkhIKHNmFgAAqLkSG9VWkEPntew0b+T/i4h7FHZ27Nihbdu2qW3btt4uDwAAsIGE6AhlDEzWQ8u+VpFhKNjh0MyB7U0ZpOxR2ElKStKRI0e8XRYAAGAjgy9vqh5tYrTvSIGaN4o0bTaWR4sKPvHEE3rwwQf1ySef6KefflJ+fn6pm6dmzZolh8Oh8ePHu7adOHFCY8eOVcOGDVWnTh0NGjRIBw8eLHW/7Oxs9e/fX5GRkYqNjdUDDzygM2fOeFwOAADgHQnREUpp2dC0oCN52LLTp08fSdLVV19darxOdQYob9myRS+++KI6dOhQavuECRP0/vvv66233lJ0dLTS09M1cOBAbdy4UdKvixn2799f8fHx2rRpk3JycjR8+HCFhoZq5syZnrw8r7HC2gIAANR0HoWdtWvXerUQx44d07Bhw7RgwQLNmDHDtd3pdOrll1/W4sWLdfXVV0uSFi5cqHbt2unTTz9Vt27dtHLlSn3zzTf65z//qbi4OF1yySV67LHHNGnSJE2bNk1hYWFeLau7rLK2AAAANZ1H3Vg9e/ZUUFCQFixYoMmTJ6tVq1bq2bOnsrOzFRwcXOXHGzt2rPr37+9qMSqxbds2nT59utT2tm3bqmnTpsrMzJQkZWZmKjk5WXFxca5j0tLSlJ+fr507d5b5fCdPnvRa11tZcpyFrqAj/ToS/aFlXyvHWejV5wEAAJXzKOy8/fbbSktLU0REhD7//HOdPHlS0q8tMVXtOvrb3/6m7du3KyMj47x9ubm5CgsLU7169Uptj4uLU25uruuYs4NOyf6SfWXJyMhQdHS069akSZMqlbkyFa0tYAU5zkJt2nuE8AUAqBE8CjszZszQ/PnztWDBAoWGhrq2p6amavv27W4/zv79+3XffffpjTfe8OulJ6ZMmSKn0+m67d+/36uPX7K2wNnMWlvgXEu2ZCt11hoNXbBZqbPWaMmWbLOLBACAT3kUdnbv3q0ePXqctz06Olp5eXluP862bdt06NAhXXrppQoJCVFISIjWrVunuXPnKiQkRHFxcTp16tR5j3nw4EHFx8dLkuLj48+bnVXy/5JjzhUeHq6oqKhSN28qWVsg+L+Dt81cW+BsdK8BAGoijwYox8fH67vvvlPz5s1Lbd+wYYNatGjh9uP07t1bX331Valtt99+u9q2batJkyapSZMmCg0N1erVq11XU9+9e7eys7OVkpIiSUpJSdHjjz+uQ4cOKTY2VpK0atUqRUVFKSkpyZOX5xVWWVvgbFZauhsAAH/xKOyMGTNG9913n1555RU5HA4dOHBAmZmZmjhxoqZOner249StW1ft27cvta127dpq2LCha/vo0aN1//33q0GDBoqKitK9996rlJQUdevWTZJ0zTXXKCkpSbfddptmz56t3Nxc/fGPf9TYsWMVHh7uycvzmoToCEuFCCst3Q0AgL94FHYmT56s4uJi9e7dWwUFBerRo4fCw8M1ceJE3XvvvV4t4DPPPKOgoCANGjRIJ0+eVFpaml544QXX/uDgYK1YsUJ33323UlJSVLt2bY0YMUKPPvqoV8thB1ZauhsAAH9xGIZhVH5Y2U6dOqXvvvtOx44dU1JSkurUqePNsvmNu5eIt4scZ6GlutcAAPCEu9/fHrXslAgLCzN1XAw8Y7XuNQAAfMmj2VgAAACBgrADAABsjbADAABsjbDjR1ymAQAA/6vWAGW4j6ugAwBgDlp2/IDLNAAAYB7Cjh9Y/SroAADYGWHHD6x8FXQAAOyOsOMHVr0KOgAANQEDlP3EildBBwCgJiDs+BGXaQAAwP/oxgIAALZG2EG1sVgiAMDK6MZCtbBYIgDA6mjZgcdYLBEAEAgIO/AYiyUCAAIBYQceY7FEAEAgIOzAYyyWCAAIBAxQRrWwWCIAwOoIO6g2FksEAFgZ3VgAAMDWCDsAAMDWCDsAAMDWCDsAAMDWCDsAAMDWCDsAcA4ubgvYC1PPAeAsXNwWsB9adgDgv7i4LWBPhB0A+C8ubguz0YXqG3RjAcB/lVzc9uzAw8Vt4S90ofoOLTsA8F9c3BZmoQvVt2jZAYCzcHFbmKGiLlTOweoj7KBacpyFyjpyXImNavOGhG1wcVv4G12ovkU3Fjy2ZEu2Umet0dAFm5U6a42WbMk2u0gAEJDoQvUth2EYRuWH2Vt+fr6io6PldDoVFRVldnECQo6zUKmz1pz3K2TD5Kt4cwKAh3KchXShVoG73990Y8Ej9C8DgPfRheobdGPBIyX9y2ejfxkAYEWEHXiE/mUAQKCgGwseY4ouACAQEHZQLfQvAwCsjm4sAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAPChHGehNu09ohxnodlFAWos1tkBAB9ZsiVbU5Z9pWJDCnJIGQOTNfjypmYXC6hxaNkBAB/IcRa6go4kFRvSQ8u+poUHMAFhBwB8IOvIcVfQKVFkGNp3pMCcAgE1GGEHAHwgsVFtBTlKbwt2ONS8UaQ5BQJqMMIOAPhAQnSEMgYmK9jxa+IJdjg0c2B7riUHmIABygDgI4Mvb6oebWK070iBmjeKJOgAJiHsAIAPJURHEHIAk9GNBQAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbM3UsJORkaHLL79cdevWVWxsrAYMGKDdu3eXOubEiRMaO3asGjZsqDp16mjQoEE6ePBgqWOys7PVv39/RUZGKjY2Vg888IDOnDnjz5eCKuDCiAAAfzI17Kxbt05jx47Vp59+qlWrVun06dO65pprdPz4cdcxEyZM0Hvvvae33npL69at04EDBzRw4EDX/qKiIvXv31+nTp3Spk2b9Oqrr2rRokV6+OGHzXhJqMSSLdlKnbVGQxdsVuqsNVqyJdvsIgEAbM5hGIZR+WH+cfjwYcXGxmrdunXq0aOHnE6nYmJitHjxYt10002SpG+//Vbt2rVTZmamunXrpg8//FDXXXedDhw4oLi4OEnS/PnzNWnSJB0+fFhhYWGVPm9+fr6io6PldDoVFRXl09dYk+U4C5U6a02p6wUFOxzaMPkq1iEBAFSZu9/flhqz43Q6JUkNGjSQJG3btk2nT59Wnz59XMe0bdtWTZs2VWZmpiQpMzNTycnJrqAjSWlpacrPz9fOnTvLfJ6TJ08qPz+/1A2+x4URAQBmsEzYKS4u1vjx45Wamqr27dtLknJzcxUWFqZ69eqVOjYuLk65ubmuY84OOiX7S/aVJSMjQ9HR0a5bkyZNvPxqUBYujAgAMINlws7YsWP19ddf629/+5vPn2vKlClyOp2u2/79+33+nODCiAA8x8QGVIclro2Vnp6uFStWaP369brwwgtd2+Pj43Xq1Cnl5eWVat05ePCg4uPjXcd89tlnpR6vZLZWyTHnCg8PV3h4uJdfBdzBhREBVNWSLdmasuwrFRtSkEPKGJiswZc3NbtYCCCmtuwYhqH09HS98847WrNmjRITE0vtv+yyyxQaGqrVq1e7tu3evVvZ2dlKSUmRJKWkpOirr77SoUOHXMesWrVKUVFRSkpK8s8LQZUkREcopWVDgg6ASuU4C11BR5KKDemhZV/TwmNhVmyFM7VlZ+zYsVq8eLHeffdd1a1b1zXGJjo6WhEREYqOjtbo0aN1//33q0GDBoqKitK9996rlJQUdevWTZJ0zTXXKCkpSbfddptmz56t3Nxc/fGPf9TYsWNpvQGAAFfRxAZ+MFmPVVvhTG3ZmTdvnpxOp3r16qWEhATXbcmSJa5jnnnmGV133XUaNGiQevToofj4eC1btsy1Pzg4WCtWrFBwcLBSUlJ06623avjw4Xr00UfNeEkAAoAVf3mibExsCBxWboWz1Do7ZmGdHaDmsOovT5RvyZZsPbTsaxUZhmtiA38z69m094iGLth83vY3x3RTSsuGPnlOd7+/LTFAGQD8obxfnj3axNAlYmFMbAgMJa1w5y4ca4VWOMtMPQcAX2Nhy8DFxAbrs/LyIrTsAKgxrPzLE7CCHGehso4cV2Kj2h6FFKu2whF2ANQYJb88zx3/YZUPZMBM3hrPlhAdYbn3FAOUxQBloKbJcRZa7pcnYKZAvVAzA5QBoBxW/OUJe6huN5BZ7L6eEWEHAIAqKivUBPKyBu6OZwvUMEfYAQCgCsoKNT3axAT0sgbujGcL5DBH2AEAwE3lrdU0Z0jHgO8GqmgmVaCvUUXYAQBYipW7Ssob2xLkcNhiWYPyxrMF+pgeFhUEAFjGki3ZSp21RkMXbFbqrDVasiXb7CKVUt61ui5tVt+yC+p5Q6Bfo4ywAwCwBCtfSLJERasED768qTZMvkrP3dJJf77lEvVoE2Nyab3Hyqsju4NuLACAJQRKV0lFY1vW//twwA7irYxVV0d2B2EHtmDlPn4A7gmky3mUNbYl0AfxuiNQ16iiGwsBz+p9/ADcE+hdJVxo1rpo2UFAqwm/pICaJJC7SgKpZaqmoWUHAc3Ov6RynIXatPeIpQZnApLvz82E6AiltGwYUEFHCvyWKTujZQcBza6/pAJ5pVLYmx3PTW+O+Qvklik7o2XHBmpyC4Adf0kFwvRb1Ex2PDd9MeYvUFum7IyWnQBnx19ZVWW3X1KBMv0WNY/dzk3G/NUctOwEMDv+yvKUnX5JBfpKpbAvu52bdh7zh9IIOwGMN6o92bFrDvZgt3PTbuEN5aMbK4DZdXAu7Nc1ZwcsXPkrf5+bvqz3kvD20LKvVWQYAR/eUD6HYRhG5YfZW35+vqKjo+V0OhUVFWVKGTx9Qy/Zkn3eG7WmjdlxF19WKFHVc4GxcebwV73nOAu9Ht74vPEPd7+/CTsyP+xU9w3tizeq3fBlhRJVPRdynIVKnbXmvBbUDZOvqtHvN19/mQdyvfN54z/ufn8zZsdk3hhkbKfBub7AQG6UKOtcmPL2V/pi/8/l3oexcec7e7r2FRlrNPP9b7z+fgrUevf086YmLyHiD4QdkwXqGzqQUMcoUda5UCxpwAubyl1fJdAHsXr7S/TcL3ND0l/+leX169IFar178nnD9f18j7BjskB9QwcS6hglyjoXJMmo4Nd3IM9A8sWXaFlf5pL3W0wDtd6r+nlDy7N/EHZMFqhv6EBCHaNEyblQ1gdfRb++B1/eVBsmX6U3x3TThslXBcT4C199iZYXGCXvt5hWpd7dacHyR1dRVT9vaHn2D6aeWwDTjH2vptaxVWaEWKUc0q/nQtv4uhrwwiYZ5wx+rai1LyE6wi9l91Zd+Wq145Iv8ylvf6Xic/b5osXUnXo/d0Dw6O6Juq5Dgo6fKnLVoz8HDVfl86Y6S4hY6X1ldczGkvmzsQB3VeXDzSozQqxSjrLKZbVlG7xZV76ezZTjLNTCDfv00ob/qNiQaXVY1us8W5BDmtS3rZ746FvLzuzy5Fy06vvK35h6XgWEHQSCqny4+XvabnkhzOrTh620bIMv6sofgc7sOty094iGLthc4TFB0nmtUJL05phuSmnZ0Cflqqqq1KPV31f+5O73N91YQACo6gULvdGF4W4rUkUhzOoXjvRX15Q7fFFX/ui+NbsOy+oGOlexJIdDVeq29Leq1GN558q2fT/ruo7WOJ+thgHKQACo6iDG6s5Aq2gWz9mDPCsbBMtMOPf5qq6stA6XLwYIuwadlzNoWvq1Hif3a2ubSQrlDRIf97fPy32v1nS07ABuMHsgYFUHMVbnmj8VtSKt//fhUq04d3RPrLA1wtNyuFvfFXWfBdrATX9fp8nfdeTLMSYlLVgLN2bppfVZpbqszu6+u6FjY8t0W1ZHeYPEK3qv1tQxPSUYsyPG7KBiVhkI6Mn4C0/GU5Q3BuL5oZ1075uflwo3QZJURgg7d+xAVcrhbn2Xd5xV/l6e8scYGH/XUVljTIIc0sbJV3v9NZbUX2RYkApOFQd8sKnIii8PKH3x5+dtf+6WThr3t89rxJgexuwAXlDVsTK+5Mn4C0/GU9QOC5ZDv66MWyLY4VCxYZS5+vCd3Vvo5Q1ZFbZGuFsOd+u7vOPaxte1zN/LU74eA2PGOV3mytWGtHBjlh66Nsmrz2X2GCJ/uqxZ/TJbfM/9ASJZa6ycGQg7QAWsNsDW1x/kJb/4zw06Mwe2V+fmDcr8YL29e3Pd3r25V1oj3K3v8o7bsu9nS/29rMiMc7q8QcQvrc/S7amJ/G08VF7XZ3khqCaPlSPsABWozoJfgebcX/zSr10Ny+5JUccm9SWpwjEl3vjCcre+yzvu8uZ8yFfGjHM6ITpCo7snasG/skptL5YIotVUXouvP8d/BQJmY6HKatII/5p0qYnyuhoKTv1vCKSvL5vgbn2Xd1zHJvVrzN/LU2ad06O6J+rcCUQEUe8oa8ZdIF7ixJcYoCwGKFeFlQZ/+nM2idkLp/mDlRYqc7e+yzvOKn8vK88KM6OOrLhqNQIbKyhXAWHHPVb5MsxxFuqVDVl6eUOWJUKXnfBl5D1W+mFgJVYJotVh5RBb0zAbC15nhcG6S7Zka/LbpQfQBuKMG6vy1oq7Nf3LwEqz+Kwm0GdLEWIDE2EHbjN7sG7JF0hZTZHMuPGe6n4Z8WVgjR8G8D5CbOBigDLcZvZg3bK+QEow0NEayvoymLLsK32x/2dzC+ZnXCbDnqp62RZYB2EHbimZgdWjTYxpI/zLux5MkEPMuKkGb86uK29G14DnN5W6Zk8gqE69mPHDoCbNkjQLITZw0Y2FSlmlW+LcBbSCJN3RI5FFyarB23/b8haPMxRYzf3eqBdfXXG8rPFQVnmP2p2/r1/mKzVxTB2zscRsrIpYZQbWuWUK9NkcVuCrv+3ZX7znenNMN6W0bOjxY/uDFc/5EmWFmh5tYixbXrsK5M8guwVjd7+/6cZChazYR13WAlqoOl/9bQdf3lTv3HNFwC4gZ8VzXip/cOy278u/RAZ8I1A/g8o7h2pC1ydhBxWij9qavDE+w5d/245N6mvWIHNXMva0jqx6zpcXwvTfX+hns0J54X3Vfd9bNcj7A2N2UKGK+qhrYr+vFXirGdrX4w98NWbFHdWpI6uOyyhv6YfLmte3XHn5bPA+b7zvzV4+xEyM2RFjdtxxbh+13fp9q8qsD3NfjCcJ5PEHZfFWHVmxXipa4bqy8vrrnK3pnw2+4M33vd1WSWcFZXjV2QvN1fSFtcz8MPfFYnWBvqLtubxVR1asl4payyoqb2XnrLeCUE3/bPAVb77vzWxxNRNhB1UWKKvD+uKXrNkf5jW5Gdpddq+jqoawys5Zb4b3QPlsCDTePqetGOR9jQHKqDKrDuA825It2UqdtUZDF2xW6qw1XlvQzuwBfmavYh0IqKPSKjpnvT07JxA+GwIR53T10bKDKqtsAKfZgxMr+iUrqVpls0KrQU1thq4K6uh/Kjpnvd0S443B3WZ/fliVJ+c0dfk/hB14pLw3XnmLnvnzDVfeB/jCDfv00ob/VKu53iozdazWDG3FD1Wr1ZFZKjtnvRnec5yFatIgUsvuSVHBqeIqB00GN1esKuc0dVkas7HEbCxvKWvGgMMh6delQCp8w3nzy7KscgRJUhkf6p7OYrLiTB2z8KEaGMo7Z701O6e654GVV64ONN6ckWi1HzHnYjYW/K6sFpWzo3R5g3m9/WVZ1i/Z0d2b6y//yip13LnN9VV5Y9Nq8CuzB2zDfeWds97o8vPGeRBog5utHAS8UZd2+xFD2LEIK79x3FXeRSDPVlbA8MWX5bkf4JL00oascpvr7fbG9pdA+4JC2aob3r1xHlhhPJy7rP55Ud26tOOPGNvMxnr++efVvHlz1apVS127dtVnn31mdpHc5quZQ/527oyBIIcqvT6SL2c3nX39mopmM/jzejHeuMyDlVhl9o3d6jXQeOM8CJQZR4FwfanKPu8qe6+YPevUF2zRsrNkyRLdf//9mj9/vrp27ao5c+YoLS1Nu3fvVmxsrNnFq5DdEvS5LSrr/324wsG8/vw1V15zvb9aJ6z+a9ATVhiwbcd6DTTeOg/Ke49aqeU7UFozy6pLd98rgdTK5i5bDFDu2rWrLr/8cj333HOSpOLiYjVp0kT33nuvJk+eXOn9zRygvGnvEQ1dsPm87W+O6aaUlg39WhZfqWwwr9nLl/tjYKTdB1+aNWDb7vUaaHxxHlgtzAbqOVfVcpv9ueyuGjNA+dSpU9q2bZumTJni2hYUFKQ+ffooMzPTxJK5x44J+lyVjQcwe00Uf7ROBMqvQU+ZNWDb7vUaaLx9Hlix5dsKrZmeqOp7xezPZW8L+LBz5MgRFRUVKS4urtT2uLg4ffvtt2Xe5+TJkzp58qTr//n5+T4tY0UC9Y3jbWbPbvL1G7smhFozUK/2ZtUwG4hBwJP3itmfy95kmwHKVZGRkaHo6GjXrUmTJqaWZ/DlTbVh8lV6c0w3bZh8lSWbCmuCswc0++KxA2HwZaChXu3NKgPgy+LLzwtfqOnvlYAfs3Pq1ClFRkZq6dKlGjBggGv7iBEjlJeXp3ffffe8+5TVstOkSRMWFYTPsRihb1Cv9hUoY0cChd3eKzVmzE5YWJguu+wyrV692hV2iouLtXr1aqWnp5d5n/DwcIWHh/uxlMCv7NQsbCXUq30FYpeRldXU90rAhx1Juv/++zVixAh17txZXbp00Zw5c3T8+HHdfvvtZhcNAFBNNfULGt5ji7AzePBgHT58WA8//LByc3N1ySWX6KOPPjpv0DIAAKh5An7MjjdwIVAAAAKPu9/fNXI2FgAAqDkIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNYIOwAAwNZscbmI6ipZRDo/P9/kkgAAAHeVfG9XdjEIwo6kX375RZLUpEkTk0sCAACq6pdfflF0dHS5+7k2lqTi4mIdOHBAdevWlcPhMLs4fpWfn68mTZpo//79XBesGqhH76EuvYN69B7q0jt8UY+GYeiXX35R48aNFRRU/sgcWnYkBQUF6cILLzS7GKaKioriTewF1KP3UJfeQT16D3XpHd6ux4padEowQBkAANgaYQcAANgaYaeGCw8P1yOPPKLw8HCzixLQqEfvoS69g3r0HurSO8ysRwYoAwAAW6NlBwAA2BphBwAA2BphBwAA2BphBwAA2BphpwZYv369rr/+ejVu3FgOh0P/+Mc/Su03DEMPP/ywEhISFBERoT59+mjPnj3mFNbiKqvLkSNHyuFwlLr17dvXnMJaWEZGhi6//HLVrVtXsbGxGjBggHbv3l3qmBMnTmjs2LFq2LCh6tSpo0GDBungwYMmldi63KnLXr16nXde3nXXXSaV2JrmzZunDh06uBa8S0lJ0Ycffujaz/novsrq0ozzkbBTAxw/flwdO3bU888/X+b+2bNna+7cuZo/f742b96s2rVrKy0tTSdOnPBzSa2vsrqUpL59+yonJ8d1e/PNN/1YwsCwbt06jR07Vp9++qlWrVql06dP65prrtHx48ddx0yYMEHvvfee3nrrLa1bt04HDhzQwIEDTSy1NblTl5I0ZsyYUufl7NmzTSqxNV144YWaNWuWtm3bpq1bt+rqq6/Wb3/7W+3cuVMS52NVVFaXkgnno4EaRZLxzjvvuP5fXFxsxMfHG3/6059c2/Ly8ozw8HDjzTffNKGEgePcujQMwxgxYoTx29/+1pTyBLJDhw4Zkox169YZhvHrORgaGmq89dZbrmN27dplSDIyMzPNKmZAOLcuDcMwevbsadx3333mFSpA1a9f33jppZc4H72gpC4Nw5zzkZadGi4rK0u5ubnq06ePa1t0dLS6du2qzMxME0sWuD755BPFxsbqoosu0t13362ffvrJ7CJZntPplCQ1aNBAkrRt2zadPn261HnZtm1bNW3alPOyEufWZYk33nhDjRo1Uvv27TVlyhQVFBSYUbyAUFRUpL/97W86fvy4UlJSOB+r4dy6LOHv85ELgdZwubm5kqS4uLhS2+Pi4lz74L6+fftq4MCBSkxM1N69e/XQQw+pX79+yszMVHBwsNnFs6Ti4mKNHz9eqampat++vaRfz8uwsDDVq1ev1LGclxUrqy4laejQoWrWrJkaN26sL7/8UpMmTdLu3bu1bNkyE0trPV999ZVSUlJ04sQJ1alTR++8846SkpK0Y8cOzscqKq8uJXPOR8IO4EVDhgxx/Ts5OVkdOnRQy5Yt9cknn6h3794mlsy6xo4dq6+//lobNmwwuygBr7y6vPPOO13/Tk5OVkJCgnr37q29e/eqZcuW/i6mZV100UXasWOHnE6nli5dqhEjRmjdunVmFysglVeXSUlJppyPdGPVcPHx8ZJ03qyCgwcPuvbBcy1atFCjRo303XffmV0US0pPT9eKFSu0du1aXXjhha7t8fHxOnXqlPLy8kodz3lZvvLqsixdu3aVJM7Lc4SFhalVq1a67LLLlJGRoY4dO+rPf/4z56MHyqvLsvjjfCTs1HCJiYmKj4/X6tWrXdvy8/O1efPmUv2r8MwPP/ygn376SQkJCWYXxVIMw1B6erreeecdrVmzRomJiaX2X3bZZQoNDS11Xu7evVvZ2dmcl+eorC7LsmPHDknivKxEcXGxTp48yfnoBSV1WRZ/nI90Y9UAx44dK5WYs7KytGPHDjVo0EBNmzbV+PHjNWPGDLVu3VqJiYmaOnWqGjdurAEDBphXaIuqqC4bNGig6dOna9CgQYqPj9fevXv14IMPqlWrVkpLSzOx1NYzduxYLV68WO+++67q1q3rGvcQHR2tiIgIRUdHa/To0br//vvVoEEDRUVF6d5771VKSoq6detmcumtpbK63Lt3rxYvXqxrr71WDRs21JdffqkJEyaoR48e6tChg8mlt44pU6aoX79+atq0qX755RctXrxYn3zyiT7++GPOxyqqqC5NOx/9OvcLpli7dq0h6bzbiBEjDMP4dfr51KlTjbi4OCM8PNzo3bu3sXv3bnMLbVEV1WVBQYFxzTXXGDExMUZoaKjRrFkzY8yYMUZubq7ZxbacsupQkrFw4ULXMYWFhcY999xj1K9f34iMjDRuvPFGIycnx7xCW1RldZmdnW306NHDaNCggREeHm60atXKeOCBBwyn02luwS1m1KhRRrNmzYywsDAjJibG6N27t7Fy5UrXfs5H91VUl2adjw7DMAzfRSkAAABzMWYHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHgKWdOnXK7CKcx4plAlA+wg4Av+rVq5fS09OVnp6u6OhoNWrUSFOnTlXJlWuaN2+uxx57TMOHD1dUVJTuvPNOSdKGDRt05ZVXKiIiQk2aNNG4ceN0/Phx1+O+8MILat26tWrVqqW4uDjddNNNrn1Lly5VcnKyIiIi1LBhQ/Xp08d13169emn8+PGlyjhgwACNHDnS9X9PywTAGgg7APzu1VdfVUhIiD777DP9+c9/1tNPP62XXnrJtf/JJ59Ux44d9fnnn2vq1Knau3ev+vbtq0GDBunLL7/UkiVLtGHDBqWnp0uStm7dqnHjxunRRx/V7t279dFHH6lHjx6SpJycHN1yyy0aNWqUdu3apU8++UQDBw5UVS8LWNUyAbAOLgQKwK969eqlQ4cOaefOnXI4HJKkyZMna/ny5frmm2/UvHlzderUSe+8847rPnfccYeCg4P14osvurZt2LBBPXv21PHjx/XBBx/o9ttv1w8//KC6deuWer7t27frsssu0759+9SsWbMyy3PJJZdozpw5rm0DBgxQvXr1tGjRIknyqEy1atWqVj0B8B5adgD4Xbdu3VxBR5JSUlK0Z88eFRUVSZI6d+5c6vgvvvhCixYtUp06dVy3tLQ0FRcXKysrS7/5zW/UrFkztWjRQrfddpveeOMNFRQUSJI6duyo3r17Kzk5Wb/73e+0YMEC/fzzz1Uuc1XLBMA6CDsALKd27dql/n/s2DH9/ve/144dO1y3L774Qnv27FHLli1Vt25dbd++XW+++aYSEhL08MMPq2PHjsrLy1NwcLBWrVqlDz/8UElJSXr22Wd10UUXuQJJUFDQeV1ap0+frnaZAFgHYQeA323evLnU/z/99FO1bt1awcHBZR5/6aWX6ptvvlGrVq3Ou4WFhUmSQkJC1KdPH82ePVtffvml9u3bpzVr1kiSHA6HUlNTNX36dH3++ecKCwtzdUnFxMQoJyfH9VxFRUX6+uuvK30N7pQJgDUQdgD4XXZ2tu6//37t3r1bb775pp599lndd9995R4/adIkbdq0Senp6dqxY4f27Nmjd9991zUYeMWKFZo7d6527Nih77//Xq+99pqKi4t10UUXafPmzZo5c6a2bt2q7OxsLVu2TIcPH1a7du0kSVdffbXef/99vf/++/r222919913Ky8vr9LXUFmZAFhHiNkFAFDzDB8+XIWFherSpYuCg4N13333uaZzl6VDhw5at26d/u///k9XXnmlDMNQy5YtNXjwYElSvXr1tGzZMk2bNk0nTpxQ69at9eabb+riiy/Wrl27tH79es2ZM0f5+flq1qyZnnrqKfXr10+SNGrUKH3xxRcaPny4QkJCNGHCBF111VWVvobKygTAOpiNBcCvypr9BAC+RDcWAACwNcIOAACwNbqxAACArdGyAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbI2wAwAAbO3/AW8G965BJW03AAAAAElFTkSuQmCC", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_validation, filename=\"pysmo_poly_val_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_validation, filename=\"pysmo_poly_val_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_validation, filename=\"pysmo_poly_val_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_pysmo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb) file." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_usr.ipynb new file mode 100644 index 00000000..5e7fd661 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_usr.ipynb @@ -0,0 +1,632 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part -1)\n", + "\n", + "## 1. Introduction\n", + "This notebook illustrates the use of the PySMO Polynomial surrogate trainer to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package. PySMO also has other training methods like Radial Basis Function and Kriging surrogate models, but we focus on Polynomial surrogate model. \n", + "\n", + "There are several reasons to build surrogate models for complex processes, even when higher fidelity models already exist (e.g., reduce model size, improve convergence reliability, replace models with externally compiled code and make them fully-equation oriented).\n", + "\n", + "In this example, we intend to make a surrogate for the physical properties of S-CO2 to be embedded in the property package. This property package will be used to get the physical properties of S-CO2 in the flowsheet simulation. To learn more about property package, see the [IDAES-PSE](https://github.com/IDAES/idaes-pse) Github Page or IDAES [Read-the-docs](https://idaes-pse.readthedocs.io/en/latest/). \n", + "\n", + "\n", + "### 1.1 Need for ML Surrogates\n", + "\n", + "The properties predicted by the surrogate are enthalpy and entropy of the S-CO2 based on the \n", + "pressure and temperature of the system. The analytical equation of getting the enthalpy and entropy from pressure and temperature are in the differential form and would make the problem a DAE system. To counter this problem and keep the problem algebric, we will use the ML surrogates and relate enthalpy and entropy with the pressure and temperature as an algebric equation.\n", + "\n", + "### 1.2 Supercritical CO2 cycle process\n", + "\n", + "The following flowsheet will be used to optimize the design for the cooling of the fusion reactor using supercritical CO2 cycle. We shall focus on training the surrogate for this notebook and move to constructing the flowsheet and the properties package in the subsequent notebooks. The take away from this flowsheet is that, 3 variables can be measured in any given unit which are flow, pressure and temperature and other properties can be calculated using them. Thus, surrogate should have pressure and temperature as the inputs.\n", + "\n", + "In this example, we will train the model using polynomial regression for our data and then demonstrate that we can solve an optimization problem with that surrogate model. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "from pathlib import Path\n", + "\n", + "\n", + "def datafile_path(name):\n", + " return Path(\"..\") / name\n", + "\n", + "\n", + "Image(datafile_path(\"CO2_Flowsheet.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training and Validating Surrogate\n", + "\n", + "First, let's import the required Python and IDAES modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import statements\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Import IDAES libraries\n", + "from idaes.core.surrogate.sampling.data_utils import split_training_validation\n", + "from idaes.core.surrogate.pysmo_surrogate import PysmoPolyTrainer, PysmoSurrogate\n", + "from idaes.core.surrogate.plotting.sm_plotter import (\n", + " surrogate_scatter2D,\n", + " surrogate_parity,\n", + " surrogate_residual,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Importing Training and Validation Datasets\n", + "\n", + "In this section, we read the dataset from the CSV file located in this directory. 500 data points were simulated for S-CO2 physical properties using REFPROP package. This example is trained on the entire dataset because neural network can overfit on smaller dataset. The data is separated using an 80/20 split into training and validation data using the IDAES split_training_validation() method.\n", + "\n", + "We rename the column headers because they contained \".\", which may cause errors while reading the column names in subsquent code, thus as a good practice we change them to the variable names to be used in the property package. Further, the input variables are **pressure**, **temperature** , while the output variables are **enth_mol**, **entr_mol**, hence we create two new dataframes for the input and output variables. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import training data\n", + "np.set_printoptions(precision=6, suppress=True)\n", + "\n", + "csv_data = pd.read_csv(datafile_path(\"500_Points_DataSet.csv\"))\n", + "csv_data.columns.values[0:6] =[\"pressure\", \"temperature\",\"enth_mol\",\"entr_mol\",\"CO2_enthalpy\",\"CO2_entropy\"]\n", + "data = csv_data.sample(n=500)\n", + "\n", + "input_data = data.iloc[:, :2]\n", + "output_data = data.iloc[:, 2:4]\n", + "\n", + "# # Define labels, and split training and validation data\n", + "input_labels = list(input_data.columns)\n", + "output_labels = list(output_data.columns) \n", + "\n", + "n_data = data[input_labels[0]].size\n", + "data_training, data_validation = split_training_validation(\n", + " data, 0.8, seed=n_data\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Training Surrogates with PySMO\n", + "\n", + "IDAES builds a model class for each type of PySMO surrogate model. In this case, we will call and build the Polynomial Regression class. Regression settings can be directly passed as class arguments, as shown below. In this example, allowed basis terms span a 5th order polynomial, a variable product as well as a extra features are defined, and data is internally cross-validated using 10 iterations of 80/20 splits to ensure a robust surrogate fit. Note that PySMO uses cross-validation of training data to adjust model coefficients and ensure a more accurate fit, while we separate the validation dataset pre-training in order to visualize the surrogate fits.\n", + "\n", + "Finally, after training the model we save the results and model expressions to a folder which contains a serialized JSON file. Serializing the model in this fashion enables importing a previously trained set of surrogate models into external flowsheets. This feature will be used later." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "No iterations will be run.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 20466.657669\n", + "\n", + "------------------------------------------------------------\n", + "The final coefficients of the regression terms are: \n", + "\n", + "k | -534397.59515\n", + "(x_ 1 )^ 1 | -2733.579691\n", + "(x_ 2 )^ 1 | 1036.106357\n", + "(x_ 1 )^ 2 | 32.409203\n", + "(x_ 2 )^ 2 | -2.852387\n", + "(x_ 1 )^ 3 | 0.893563\n", + "(x_ 2 )^ 3 | 0.004018\n", + "(x_ 1 )^ 4 | -0.045284\n", + "(x_ 2 )^ 4 | -3e-06\n", + "(x_ 1 )^ 5 | 0.000564\n", + "(x_ 2 )^ 5 | 0.0\n", + "x_ 1 .x_ 2 | 4.372684\n", + "\n", + "The coefficients of the extra terms in additional_regression_features are:\n", + "\n", + "Coeff. additional_regression_features[ 1 ]: -0.002723\n", + "Coeff. additional_regression_features[ 2 ]: 3.6e-05\n", + "Coeff. additional_regression_features[ 3 ]: -0.050607\n", + "Coeff. additional_regression_features[ 4 ]: 169668.814595\n", + "Coeff. additional_regression_features[ 5 ]: -44.726026\n", + "\n", + "Regression model performance on training data:\n", + "Order: 5 / MAE: 134.972465 / MSE: 54613.278159 / R^2: 0.999601\n", + "\n", + "Results saved in solution.pickle\n", + "2023-08-19 23:48:46 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output enth_mol trained successfully\n", + "\n", + "===========================Polynomial Regression===============================================\n", + "\n", + "Warning: solution.pickle already exists; previous file will be overwritten.\n", + "\n", + "No iterations will be run.\n", + "Default parameter estimation method is used.\n", + "Parameter estimation method: pyomo \n", + "\n", + "No iterations will be run.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "WARNING: Loading a SolverResults object with a warning status into\n", + "model.name=\"unknown\";\n", + " - termination condition: maxIterations\n", + " - message from solver: Ipopt 3.13.2\\x3a Maximum Number of Iterations\n", + " Exceeded.\n", + "\n", + "Best surrogate model is of order 5 with a cross-val S.S. Error of 0.156437\n", + "\n", + "------------------------------------------------------------\n", + "The final coefficients of the regression terms are: \n", + "\n", + "k | -519.862457\n", + "(x_ 1 )^ 1 | -8.820865\n", + "(x_ 2 )^ 1 | 3.676641\n", + "(x_ 1 )^ 2 | 0.18002\n", + "(x_ 2 )^ 2 | -0.010217\n", + "(x_ 1 )^ 3 | -0.000783\n", + "(x_ 2 )^ 3 | 1.4e-05\n", + "(x_ 1 )^ 4 | -6.9e-05\n", + "(x_ 2 )^ 4 | -0.0\n", + "(x_ 1 )^ 5 | 1e-06\n", + "(x_ 2 )^ 5 | 0.0\n", + "x_ 1 .x_ 2 | 0.010367\n", + "\n", + "The coefficients of the extra terms in additional_regression_features are:\n", + "\n", + "Coeff. additional_regression_features[ 1 ]: -7e-06\n", + "Coeff. additional_regression_features[ 2 ]: 0.0\n", + "Coeff. additional_regression_features[ 3 ]: -0.000112\n", + "Coeff. additional_regression_features[ 4 ]: 484.312223\n", + "Coeff. additional_regression_features[ 5 ]: -0.1166\n", + "\n", + "Regression model performance on training data:\n", + "Order: 5 / MAE: 0.398072 / MSE: 0.495330 / R^2: 0.998873\n", + "\n", + "Results saved in solution.pickle\n", + "2023-08-19 23:49:20 [INFO] idaes.core.surrogate.pysmo_surrogate: Model for output entr_mol trained successfully\n" + ] + } + ], + "source": [ + "# Create PySMO trainer object\n", + "trainer = PysmoPolyTrainer(\n", + " input_labels=input_labels,\n", + " output_labels=output_labels,\n", + " training_dataframe=data_training,\n", + ")\n", + "\n", + "var = output_labels\n", + "trainer.config.extra_features=['pressure*temperature*temperature','pressure*pressure*temperature*temperature','pressure*pressure*temperature','pressure/temperature','temperature/pressure']\n", + "# Set PySMO options\n", + "trainer.config.maximum_polynomial_order = 5\n", + "trainer.config.multinomials = True\n", + "trainer.config.training_split = 0.8\n", + "trainer.config.number_of_crossvalidations = 10\n", + "\n", + "# Train surrogate (calls PySMO through IDAES Python wrapper)\n", + "poly_train = trainer.train_surrogate()\n", + "\n", + "# create callable surrogate object\n", + "xmin, xmax = [7,306], [40,1000]\n", + "input_bounds = {input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))}\n", + "poly_surr = PysmoSurrogate(poly_train, input_labels, output_labels, input_bounds)\n", + "# save model to JSON\n", + "model = poly_surr.save_to_file(\"pysmo_poly_surrogate.json\", overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Visualizing surrogates\n", + "Now that the surrogate models have been trained, the models can be visualized through scatter, parity and residual plots to confirm their validity in the chosen domain. The training data will be visualized first to confirm the surrogates are fit the data, and then the validation data will be visualized to confirm the surrogates accurately predict new output values." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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FhYXcuXPn9C4iQRAEYTCkjt9BOmtAWZw/fx4bNmxA9+7dERwcDAD4+OOP0bJlS2zfvh39+vUDx3FIS0vDwoULERkZCQDIzc1FWlqaw7nS09MxefJkAEBlZSUOHDiA2bNnC98HBAQgLS0Nubm5AIADBw6gqqrK4Txt27ZF8+bNkZubi27duiE3NxcdOnRAkyZNHK4zYcIE/Prrr7jppptUqRctKCkpQWVlJQCgsDAABQVBSEq6goSEGgBASEgIoqKi9CyiZEpKSrBixQrYbI2wZMlkcFztbvc1NRZMnx6OP/9cC6v1IrKysgxzT4T32LdpVxipTRMEYSwMJcJmzpyJFStWoLy8HN26dcP27duF7/7v//4Px48fxwcffIC3334b1dXVmDJlCgYPHoy9e/cCAIqLix2EEQA0adIEZWVl+Pvvv3HhwgVUV1e7POb3338XzhESEoKIiIg6xxQXF3u8Dv+dOyoqKlBRUSH8XVZWJqVaNIMXLQDwww834eOP7wbHBcBiqcE992xHp04/AoBhRAs/8J4/HwWOc/TMc1wAzp+PhNV60eMAzSIkKqRj36YBwGZrhPPnoxAZWQKr9aLwuVHaNEEQxkJXETZr1iwsWLDA4zGHDx9G27ZtAQDTp0/H6NGjcfz4ccybNw/Dhg3D9u3bYbFYUFNTg4qKCrz99tu49tprAQBr1qxB586dceTIEbRp00b1+/GV+fPnY968eXoXwy38wG6zNRIEGFArWD7++G4kJ/9hSNESGVkCi6XGQYhZLDWIjDyvY6nkQaLCO+zbqqeJhdHaNME2LE6UWCyTP6CrCJs2bRpGjBjh8ZiWLVsK/46OjkZ0dDSuvfZatGvXDs2aNcM333yD1NRUxMfHIygoSBBgANCuXTsAtSsV27Rpg7i4uDqrGE+fPo3w8HDUr18fgYGBCAwMdHlMXFwcACAuLg6VlZUoLS11sIY5H+O8opI/J3+MK2bPno2pU6cKf5eVlaFZs2Ye60cPxCxHRsNqvYh77tleZwA24r2QqJCH2MSCIJTCeaLkDi0nSiyWyV/QVYTFxMQgJiZG1m9rampjkHj3XY8ePXDlyhXk5+cjOTkZAPDf//4XANCiRQsAQGpqKj755BOH8+Tk5CA1NRVArdLv3Lkz9uzZg4EDBwrX2bNnD7KysgAAnTt3RnBwMPbs2YNBgwYBAI4cOYITJ04I50lNTcULL7yAM2fOIDY2VrhOeHg42rdv7/aeQkNDERoaKqs+tERLy5FWs7NOnX5EcvIfOH8+EpGR53UZeJW8VxIV3mG2iQXBLlInQFpOlJyv5c6CTpM35TFETFheXh6+++473HLLLWjcuDHy8/MxZ84cJCcnC8InLS0NnTp1wqhRo7BkyRLU1NRg4sSJuOOOOwTr2Pjx47FixQrMmDEDo0aNwt69e7F582bs2LFDuNbUqVMxfPhwdOnSBV27dsWSJUtw6dIljBw5EgBgtVoxevRoTJ06FZGRkQgPD8ekSZOQmpqKbt26AQD69u2L9u3b4+GHH8bChQtRXFyMp556ChMnTjSEyBJDK8uRVNfaQw89JAhvX7BaL+o24CrtRiRR4R1mckkbFX91h7l71/XCkwWdUB5DiLCwsDBkZ2fj6aefxqVLlxAfH49+/frhqaeeEkRNQEAAPv74Y0yaNAk9e/ZEgwYNcOedd2LRokXCeZKSkrBjxw5MmTIFS5cuRdOmTfHmm28iPT1dOCYzMxNnz57F3LlzUVxcjBtvvBE7d+50CLRfvHgxAgICMGjQIFRUVCA9PR2vvfaa8H1gYCC2b9+OCRMmIDU1FQ0aNMDw4cPx7LPPalBb2qCF5cidaw2owR137EaPHrUrVt99913Dm8mVdiOSqPAOM7mkjYi/usNYEzxkQdceQ4iwDh06CCscPZGQkICtW7d6PKZ379748UfPjTwrK0twP7qiXr16WLlyJVauXOn2mBYtWtRxfZoNrSxHzh0DEICcnDsAQBBiZjGTK9UJkqjwHhZc0v6KP7rDWBQ8ZEHXHkOIMMK/cdUxABbs3p2G66//RXbnEBISouhxSuBLJ1hSUoJz584Jf5Oo8B49XdJELaxZh9SCRcFDFnTtIRFGSEYv0RIZWQKgBs67bPnaYUVFRSErK4upOBS5naA7dw6JCveUlJSgtLRU0rFaCnF/hkXrkFqwKHjIgq49JMIIyeglWqzWi7jjjt3/c0FahM+V6LBYiy+R2wlKdefw+LuokBqDlJmZiZiYGObaiVlh0TqkFqwKHrKgawuJMMIr9BqM+Niv3bvTmOqw1MBTJ3ju3DlRoevJnZORkYGEhAS/FxVSRavVatW0rvx1hSAPi9YhpbGfAHl617WcKDlfy50FXevJmz+8DyTCCMPQo0curr/+F7+YobnrBLOzswG4XyUm5s6Jjo42fKelNKzEILG8QlDNwdA+lpFV65CSsBgGwWKZWH4flIREGMEsrmZdesQ4aTEbczfD9HaVmD+5c6Ti6vnxgz5LMUisrhBUczB0dW5/cIexKBpYKxOr74PSkAgjmCUqKgoPPfQQ3n33XdFj1TKTazUb42eihYWFgrVLjoXGH9w5rnAnlEtLS7F582a3v2NVtLJknSssLHT4TMnB0N1vWHGHEWzAyvugBiTCCKZJTk7W1Uyu5WwsKipK8ibp7vAHd44zUoUyUPf5sShaWbHOuapXtQdDd+8XxTL6L6y8D2pBIoxQDaXceKx0vFrOxqRaaPg69uf8YFKFsrvnx5poZcU651yvp07FqzoYenq/KJbRf2HlfVALEmGEKpgtqFLr2ZgUC42nOvZXd467gdzT82NNtLJonfvhh5uwbdvdUDpXH4+S75c/rKjzJ1h8H5SERBihCmYLqtR6NibFrSi1jjMyMhAdHW36wcfTQC72/FgSray5lPl6dRZggHKDoVLvl9kmfwR774PSkAgjVMcMQZV6zMa8sdCIuXLi4+NVKycreBrIxZ4fL1Tt0VO0smSdc71tGBQdDJV6v8w2+SNqYel9UBoSYYSqmCWoUqvZmNSkieXl5SgvLwdgnjr2FU8DudjzY0Gospow01W9AjUYPfpNNG1apMg11Hi/1Jz86e3y1Pv6WsDq+6A0JMIIVTFTUKUWszEpSRPLy8sd0naYqY59QWwgZ302zWLCTMB9vToLMDmDoVrZ49WcmOjt8tT7+q5QQxSy+j4oDYkwQlWMHlSpx2xMrFMpKnIc/Ixex0oiJrRYn02zOqB4qldf0keoNdCqOTGR6spUy+Wp9/WdUVMUsvo+KAmJMEJVjB5UqcVsTIlZZGpqLvbvTwVgvDr2FalCOTMzE1ar1eXv/aGz9xap9epr/i416l5sYmKf0sUeOW3BXdwZoI3b0NP1tYA1UWg0SIQRiuDc2Zgpb5XasR2+zCKd415SU79GSkqe4erYF/zFbaE1Rq5XsckfvyuFK7yx2HiKOzt58iQ+/fRT0XP44jZkcdGT3qLQaJAII3xGipBg3Q2kF76s5nIV95Kbm4qUlDyH4/yhjlkUAmbAyPUqd/In1WIjFncmRYB5cz1vr68HLIpC1iERRviM1E7EOQ0Aq7NovfC2AxOLe6GtXtjFH1a36YFUFyrgu8XG27gzpS1ErC3IYVEUGgESYYTiuOtsWEgDwCpyOjCxuBcjbPVSUlKCs2fPoqqqyuX3QUFBiI2NZf4+vIHF1W1mwdmFeu7cOZeuR08THvtQCk9i2JsFMV9/nYrdu9MUtRCxtiCHNVFoFEiEEYpC5mh5yOnAWF70IMXSA0DyxttmEiQUyKwuntqJzdYIJ082c9iCyXnC4yza3LU9qe/f11+nIifnDgAWl9eTC2vvP2ui0CiQCCMUwxdztL+7Z7zpwNTKraQUUi09Q4YMqfOZP2Y4p0BmbbCfIDrjacLj3Pakvn9A7bPNyUkDL8CkXE8MVt9/1kShUSARRiiGXHM0uWe868BYX7UmVTBduXLF4W81XDasQ5ZjbXCeIDpjsdQgOLgSBQWJomJYyvtns9mwadMmnD8fBaX33GTt/ddbFBp9Ak8ijFAMueZoOSsEjf7iucKb1VxGujcplh61XDYsQ4HM2uFu/0ugto+64YZDWLNmjGQxLPX9c73lE4e0tN0+PWOW3n89RaEZJvAkwgjFUMIcLcUyYIYXj8fs+6NJeZ5quGyMgN6BzGacyLjD3QRx0KAtiIgoFQQYoKwYdu4TgRrcccdu9OiRKxxj1HfbHlas7u4mfIWFhS7bOgttnEQY4TNKmaOlWgZ8ya3FGqy5FpRE6vNUw2VjBPQMZDbTREYK7iaI119/GAUFiYqLYal94kMPPWSK+mUBTxO+7Oxst+OE3m2cRBjhM0oJCTmWATPE1Ji1E5b6PNVy2YihtyVIz0Bmf1mhKUUMqSGGjTy50vu9kIPYhM/TOKF3GycRRiiCEi+lt50hxdSwjdTnKcVlozR6WoL0DmR2hVlXaHoSQ3wOMbXEMGtCRQpGtZB6mvABYHqcIBFGMIO3naHeMTWEZ8SeZ3BwsHCs2KIEpQWJni5tNa0kcqwYZrAmy7lvFsWw3hjVQuppwsf6OEEijGAKb1YIUnJANpE6uMXExCArK0v3jPl6iBC9V4rxmMGaLNd6Y2SXoVZoZSH11QUqNuFjeZwgEUZIRq1YAbkrBP0hOaDW8RlKXM/bwU3PQc5XEcJS/Iwc6x7rVgIp+GLV9GeBJYbUrZ0A39q5Ui5QdxM+1scJEmGEJNSMFfBlRuqN5cxoaB2foeT1jDK4+SJCWI6fkWrdM5s12QyuVRYQm5y42o9Tbjv3RURLncCzPE6QCCMkoXasgDcvr9lza/FoHZ9h1HgQX/BFhLBaX95Y91i3EniDGVyrrCAW6K5W/KS3IlrKwgsed+OE3pAII2Sh52oqo8Zy+Oq6UqLOPZXB2cVg1hVz9igpQlipLynWPTMGpZvBtcoK7iYnhYUJePvtYapYGuWKaHd9ptS2q3cbJxFGeA0LJn/WBJYYvrqulKhzqWVQ6npqoXQclhKuCpbqS4p1T4mJDEvxcID5XKt64mpykpa2W9jbFVDe0qi0iDbKZJ1EGOEVYrOVc+fOMdGwWcMX15VSbhapsRdquHWUGrCVisNS0qXNmhtMqnXPl3eUxXg4M7lW9cKThVRtS6NaSXNZh0QY4RViLyLvg2ctmR9reOO6UqPz82S5Ufp6Sg7YSsVhKTlLFquv0tJSxMfHSyq3UqgdiMzq1mEsB2AbAVfvhX1slZqWRn8V0STCCK+QOlsxU/C20njrulJ6hihmuVH6emoGsPsSh6XUJEGsvjZv3qzJpESvBSt6u2L1XqjDmlvWV9yVVQuR5I8imkQY4RWuXsTUVPW2lzEqzh0zH/Qux3WldOcnZrlRu7NVKoBdi8FfykIGKfWlxaREjxgYFlyxesb+sOiWVRM1RJLeIlpvSIQRknAVK5CXl4L9+1Oxf38P5OamMhW8rSeeOmZvXH1qrWCTYulSa8WcUsJJi8Hfm4UMfH2dPNkUgAXNmp1UpAzeovVAz8qKRL0EDqtuWSVRWyQZJYBeLUiEEZLgX5TCwkIhPiA3NxUAG8HILOGpM/HG1adW5yRmucnIyEB0dLRi1+NRUjhpMfhLHWB58vNbMbNCUitoReJV9HbLqoUWIsmsAksKJMIIyURFRQkvIiszYC2RG/vhPHh74+pTq3PyZOmKjo5WJZBcyTaj9eAvNsCy4JbTAzMEUysR02X25+/PIkltSIQRshAbBPl4GXcdmNGCWeVsjgy4H7z1SI6pd+yFksJJy8FfygDrj5MSHiMHUysV0+Xr8zdaf0goB4kwQhZig6D9dhHOHZgRg1nlxH6IDd4jR97h4PZTu6PVwq3gajDxJoBdDD0yvUsZYP3NLae3oFcKpWK6fHn+RuwPCeUgEUZ4hdRB0J6zZ886dB6s7rknFamxH2KDt1puP0+o2YlLGUx8FU56BPFKGWDN4JbzBjMGU/sS0+XL8zdjcD9Z9qRDIozwCvvO9+jRo9i3b5/QUbjrPKqqqjyeU6s997SO/TCjdUSJvSedLYCAd52y1p231AHWLHswSsVMg6gSMV1m2/5KLs6TMXf9AFn2aiERRngN/+LYD7pyOw+tOh09Yj/MZh1Rau9JPSyAvuJpgM3MzITVanX7W7PM+s1s3ZAb02Xm7a/kYt9GPPUDRrLsqQmJMMJn5HYeWnY6SrlApVi39Ihb0gKpdWiWwUTqABsTE2NY8SEVs8ctybVaa7n9ldEwSz+gNiTCCJ+R23no2enIdYFKsW6ZMV7GFe7q0CyDiavnaLPZHNzrQUFBqKysRFFREQB9nqsWFiozxi3Z44vVWqvtr4yGWfoBtSERRvhMcHAFAA6Axe5TDsHB6q0o8gVfXaBSYj+MLrDE8FSHZhpMnFf1btq0SfQ3WlqD9Ii/MUPckiv0TrVhtvAFM/UDakIijPCZqqpQOAowALCgqsqzu02PTkeuiVyp2A8zxNWI1aHZBhMeFq1BWsffmM3FxFqqDb2FoJKYtR9QGhJhhGyCgmqbj9iMhz+OR2rMlM1mc3ttuWJFrolcCRejWeJqpNShmQYTV7BmDdJKHJnNxcRC6ABrQlBJzN4PKAGJMEI2sbGxAMRnPPxxPGIdX2lpKTZv3izq+pEjVnwxkVNcTS1S61CJwYRFyyGL1iCtxJEZXUx6T3i8EYIsvg9iuOsHvMGI9y0VEmGEbJw7j7lzz+LYsSAkJl5BQsLNAG52+3Io8cLIESusmMhZs6R4g1YbgLNqOWTRGqSVOGLl/WERX4SClPbL6vvgjNRJltTjzJ53jEQY4RP2jT4+HujcWflrKJHMlaW0EWKWFOekp/ZlY6Vz9VSHCQkJqohsViyHLFqDtBRH5GKqixZCQer7cPbsWV2tRkq7eM2ed4xEGKEb7maOSiSBdYaF2A8eMUuK/b6bzug529OzDlmyHLJqDVJTHJk5bkkJtBYKnq5hH8ahl9VIjXOzGAagBCTCCF2QYlpX+qVjxVQtxZLCitXHGT3qkMXOl1VrkBLxN65gaRLDMlq0VanXMJvViMUwACUgEUboghTTullfOjFLCktWHxZgpR2waA1SOv7GE/4usKSgRVuVcg25IQ8Au2KaxTAAJSARRuiOO9Eh5aUz6qoZd5YUFq0+esNK56u0NUiJtksWKvWR8px4tGirUq4hNeTBSEHurIYB+AqJMEJXxESHp5fOZrMxl8HcE1IsKaxYfVhCrB1ouZBBqfMpGcjNQts2K1KfU2ZmJgBthIKUa0gRamq5K9WcGLMaBuALJMIIXRETHZ5eOo7jJF2DlfgHT1aLc+fOITs7W7TztBcc/mTh8NQOWF3I4Ak5gdxGtfoaGanPyX4/US2Egtg1xCew6ljctVglqlbco16QCCN0RUx0uMs5FRISInnJNkuIdTxinaez4GBVZCiB1BgsgN2FDGJIHQzNniuJdbwVLWoIBW/eB8CzUFPK4u48MZC6st2b91LLuEc9IBFG6Iq35nv72X5RUZHwuZmC2V11nr6IDL0tKHKvL8VyCBj72UsdDNVOgaB3G2EdsefkvDWbO3wRClLi//jdRnjcCTUlYtfcTwwaAYBiljazxz2SCCN0x1tXk/Ns3wzB7J5mub6IDL2zbPt6fbEyGf3ZezsYit1vaWkp4uPjvSoDWdnEEXtOERERmggFsd9LFXlKxK55mhikpuYqGttq5nZnGBE2YMAAHDx4EGfOnEHjxo2RlpaGBQsWICEhAQDwzDPPYN68eXV+FxYWhkuXLgl/f/DBB5gzZw6OHTuG1q1bY8GCBbjrrruE7zmOw9NPP4033ngDpaWl6NGjB1atWoXWrVsLx5w/fx6TJk3Cxx9/jICAAAwaNAhLly5Fw4YNhWMOHTqEiRMn4rvvvkNMTAwmTZqEGTNmqFE1hkSKaV2q9ccMwezOsz3e0uOryJBqGVHLbad21nujP3tvB0Ox+928ebPXYsnsGcmVQMpzYkEoiFmN7C3ISsWuueqj9u9PZWJFsxEwjAi77bbb8MQTTyA+Ph5//vknHn/8cQwePBj79+8HADz++OMYP368w2/69OmDm2++Wfh7//79uP/++zF//nzcfffd2LhxIwYOHIgffvgB119/PQBg4cKFWLZsGd566y0kJSVhzpw5SE9Px2+//YZ69eoBAB588EEUFRUhJycHVVVVGDlyJMaNG4eNGzcCAMrKytC3b1+kpaVh9erV+PnnnzFq1ChERERg3LhxWlQX87jqLOS6mFhJYeArrjpxpUWGnnFzargNzfDsvRkMpdyvXLFkdKui2hhlZZ43YlCJ2DVXfRQQgNTUr5Gbm2qqdBJqYBgRNmXKFOHfLVq0wKxZszBw4EBUVVUhODgYDRs2dLBE/fTTT/jtt9+wevVq4bOlS5eiX79+mD59OgDgueeeQ05ODlasWIHVq1eD4zgsWbIETz31FO69914AwNtvv40mTZrgo48+wtChQ3H48GHs3LkT3333Hbp06QIAWL58Oe666y688sorSEhIwIYNG1BZWYm1a9ciJCQE1113HQ4ePIhXX32VRBjE40/kBMFKtSYYLfZFSZGhZ+yUWgO8WXIHSR0Mne8XADgOyM9v5fOzNLpVUQuMvjJPjSB3d31USkoeUlLymBetemMYEWbP+fPnsWHDBnTv3h3BwcEuj3nzzTdx7bXX4tZbbxU+y83NxdSpUx2OS09Px0cffQQAKCgoQHFxMdLS0oTvrVYrUlJSkJubi6FDhyI3NxcRERGCAAOAtLQ0BAQEIC8vD//4xz+Qm5uLnj17OjTk9PR0LFiwABcuXEDjxo2VqAbDYC98nANHXSF1MPB2U26946PkoJTI0NvKoeYAbxQLhT2+DIbJyX/AMTuLMs/SDFZFpTHbyjw1gtzF+igjvI96YigRNnPmTKxYsQLl5eXo1q0btm/f7vK4y5cvY8OGDZg1a5bD58XFxWjSpInDZ02aNEFxcbHwPf+Zp2NiY2Mdvg8KCkJkZKTDMUlJSXXOwX/nToRVVFSgoqJC+LusrMzlcUZCqvCxR+pg4G2HonZ8klooITL0tnJ4M8BLsVayuIWQN/gyGJ4/HwVA+WdpFquikphxZZ4aZfW2j2L1vdQDXUXYrFmzsGDBAo/HHD58GG3btgUATJ8+HaNHj8bx48cxb948DBs2DNu3b4fFYnH4zYcffoiLFy9i+PDhqpVdDebPn+9ycYGRERM0roSQN4OB3A6F9bQGSosMva0cUp+pN9ZKow+O3pRNq61xjGhVVBuW2xBLOPdR7ia5Dz30ENWpHbqKsGnTpmHEiBEej2nZsqXw7+joaERHR+Paa69Fu3bt0KxZM3zzzTdITU11+M2bb76Ju+++u45FKy4uDqdPn3b47PTp04iLixO+5z+zX+Z9+vRp3HjjjcIxZ86ccTjHlStXcP78eYfzuLqO/TVcMXv2bAd3aVlZGZo1a+b2eCNi/2Lm57dyK4TUHAz0ds1JQekZOAtWDinP1BtrpbepGIyIvVWwb9++2LVrl+rP0uhxT4Q2eJoAeprkhoWFaVVEQ6CrCIuJiUFMTIys39bU1ACAg/sOqI3r2rdvH7Zt21bnN6mpqdizZw8mT54sfJaTkyOIuKSkJMTFxWHPnj2C6CorK0NeXh4mTJggnKO0tBQHDhxA586dAQB79+5FTU0NUlJShGOefPJJYdEAf502bdp4jAcLDQ1FaGiojNowBs4vZm1ci3shpJaLSW/XnFSUmC16GzenNL5Y9Fi3VqqN6/xdiYiMLFF0kmK2uCdCG9RKq+NvGCImLC8vD9999x1uueUWNG7cGPn5+ZgzZw6Sk5PrWMHWrl2L+Ph43HnnnXXO89hjj6FXr15YtGgR+vfvj/fffx/ff/89Xn/9dQCAxWLB5MmT8fzzz6N169ZCioqEhAQMHDgQANCuXTv069cPY8eOxerVq1FVVYWsrCwMHTpUyFn2wAMPYN68eRg9ejRmzpyJX375BUuXLsXixYvVrSiGcfViOmMvhDxtV+SrONHbNaclese0yL0+deTS8ncpMUnxtY0YbcUxoRxapNUxO4YQYWFhYcjOzsbTTz+NS5cuIT4+Hv369cNTTz3lYDmqqanB+vXrMWLECAQGBtY5T/fu3bFx40Y89dRTeOKJJ9C6dWt89NFHQo4wAJgxYwYuXbqEcePGobS0FLfccgt27twp5AgDgA0bNiArKwt9+vQRkrUuW7ZM+N5qtWLXrl2YOHEiOnfujOjoaMydO9cQ6SnU6lBd55JxxF4IRUdHq+ZuYsE1pyV6D4BKtRd/7cjFBKn9hEXu+ym3jRg52z6JR3Xwp0muEhhChHXo0AF79+4VPS4gIAAnT570eMx9992H++67z+33FosFzz77LJ599lm3x0RGRgqJWd1xww034Msvv/RcYMZQM4WDuxeTd0lqLYQoAJltqCO/ipggFZuwqCk2pGbbLywsdDhWb4FjZPHIOv42yfUVQ4gwQhvU3OLG3YupZYyS0dMa+BPUkV/FF0GqldjwZK0DgEWLfmBK4NBWTerC2iTXfiJSWBiAgoIgJCVdQUJCbWy5npMCEmGEqkgJDLdaLyIzMxNWq1X4jRovhN7xUYR3sNaR64UvglQrseHOWpeXl1Jn6xr+mmfOnNF9YKTYQ/VgZZWt/UTE0zug16SARBjhFiX2GWRN+JDAYhup1srS0lKXvzXr8/VVkKotNlxZ64AaQYC5uia/c4aeAyPFHioHq6ts+bFH7B3Qy+pJIoxwiZLpAcw6MBLK406022w2bNq0Sfjb3dZXZo7h8cWyoLTY4N07586dE8rmbK1LTc3F/v09PF5T74HRaLGHLC8mYG3C7QyrgptEGFEHMtETeiKlkzbKllO+oKRlQUmx4S7OLDn5D0yevESw1gFwsIS5uqbeA6ORYg+NsJiA5QkQq4KbRBhRB707RoLwhFQrLctWAykoaVlQUmxIjTMDIHpNFgZGo8Qe0mIC32BVcJMII+rAQsdIEK6QaqU1gtVACkqWTWmxIfYsbLZGaNz4AkaPfhNVVSEur8nKwMhKELkUjOyp0HtixKLgJhFGCPBuDbGOUaqbxPmFs9lsqKqqEv4OCgpCRESEw/VZHhAJ/ZFqpSWrgWvciQ0+tssZT++kp2fhal/YpKTjLs+jx8DIahC5FLTwVKghlliZGLEmuEmEEQLO7o+5c8/i2LEgJCZeQULCzQBulvzySU386oyWlgm9Z2WE93hrpTWy1UAJpIoIfs8/bwZFd88iOLjS6zrXemBkPYjcE2p7KtQSSzQxcg2JMMIB+5cqPh743x7lXiP2IukdWK3m7gCEenjrvvL3+EZPYoPfcBmQNyi6exZVVaEe6zwjIwPBwcEOq131gNX32t3k0NNKVCVduGqLJa0nRqxbPUmEEZpgL7pcuSrkpr+Qi5q7AxDq4o37iuIbxcWGL4Oiq2dhszXyWOfR0dHMD4x6IdUKpYULVy2xpPXEiHWrJ4kwwgE1XHT2symgBoDlf/+x4x5SIjEtoR5yt5xiJfCbZXwdFJ2fhZQ6Z31g1AsxK1St8LraT6nZjtUSS3pMjFhuRyTCCAE1XHTOsykgoM4x9i92aWmpx82I1UDJxLSEOvgyaLO4IoolvB0UpVinpNQ5ywOj3rizQnEcAHjup+QunOLh3Z5qiSWaGDlCIowQUMNF52o25Yz9i71582ZN47D8PXDbSPjSJlhbEcUS3g6K7gSxfYwZf15v65wWy9Tizgpl/2/7fiojI0Nw8yq1cEpNsUQTo6uQCCPcooSLzt2echYLhBc7LW23blnP/T1w26xQzJF3eDsouhrofa1zqfFQQ4YMcUht43xuM4g01/2mI/b9VHR0tFcehLrbgmkfe0YTo1pIhBEuUcpF52429fff9ZCTkwaOC8Du3WmoX/+yLi5ACtw2J/4ccyTXmuTroOhrnUtdlcfvG2rUBLxScNVv8q5IHqX6KbG+XimxRBMj15AII+qghIvO/kVynk0BwJIlk8F3KHq6ACk+wbwYfSCWgzc5ntQYFJWocyn9jxnyTLkSy/ZJc537TVerytXe9cAT3oolf54YeYJEGFEHJVx0rl64Y8eOYdeuXSgoSJR9fqViRjyJRPsy+NusjDA23uR4io+PZ3JQFOt/zBDH6VksXz3O3gqlhmtQrK75WDNn5LYLfxNYUiARRtRBKReduxdO7vmVXL1JszJCDCMHiUsVKiyW310c6aVLDQSxYvQ4Tm82QbdH6Tgqsb7Y21gzwntIhBF1UNtFJ/f8Sq/eZHEAItjA6DsqGFmoOPcPfG7BLVvuExbymCWOU0ws33HHHcjJyRE9j1yLPYVj6A+JMEJASxedEqZ1d/EucjYjJgiekpISFBYWOnym9zZbrvAUU2T0BSd8/3DyZFNs3ToYHHc1ufPu3WlIS9uN3bvTDC8cxMRyUlKS6hZ7qX2xkS3DLEMijBBQ20UnN+u5KzyZ8O1zFTnDquWCYANXsTp5eSnIzU1lKpmvWEyRGSwcVutFnD//t0uRkpBQiMmTlxg+z5QUsSy1v/JGJHnbF6u1qTdBIoxwQs0XSCmRJyXehUXLBcE+zrE627bdDfu0AKwEgUuJKTJaQkxXky9PIsUMeaaUEsveiiRv+2K1N/X2Z0iEEZqihMgTM+HTNkSEr/BCX2ybLd79V1paiitXrjgcFxwcDOv/lrqp5aoRm5AYSajYCwM++74ckWK0Fc1KiGU5IklOezTDylTWIBFGGA5Ps2PqJAgl8LTdlr27yJXrW0tXjdwAfFaFird7fzqnUGA1LsnZVegct6qUWFa7/zPygg9WIRFGGA5Ps2MWcpARxsfdtjHuLDG88CosjK8TMK6mq0YspshVnidX7Zj1tu9OpBghhYJ7V2EjxROiqi2SjL7gg0VIhBGGQcrqTRZykPkTag3eeosCV2kSunfPRUpKXp3BzN79A3AArq7kU9sKK+aukyJSWAy6NtMWN1JdhUpY9dQWSWZY8MEaJMIIw+ApmNSXGBJA+Rxk/oBawlXN84oJO3ukxOo4u394AcajhavG15giFoOuzZhMWcxVqIRVTwuRZLQFH6xDIowwFFI6XTVzkBFXcR4glVqRqsZ5pVp7MjMzHX7nzg122223Yd++fR5jxwDtXDVKxBSxFk9pJIElBa3iqbQQSUZa8ME6JMIIU6BVDjLCNWrVmVLnlWrt4ThO0vliYmIAuIsdq3VJqumqUcNdR0HX6qJlPJXSIslM7mHWIBFGmAItc5ARjojVmbsdDADPz8Sb80p1TYmdMyIiQlI74r935f5JS9uNhIRCVV01arjrxETCuXPnDOcCZAk1XYVqiyQzuodZgUQYYRq0yEFG1EWszjztYAC4j+3y9rxSYsSkPF8p7aioqEj4t14xMkoPeK4WI6Sm5grf8/VNi1Pko1Zb0UIk0TNXBxJhBGEHLcH2Hql15m1sl7vzBgdXoqAgUVaMmFLPV6r729NvWIQXCfxWTfv390BubiplRVcQteKpSCQZExJhBGEHLcGuxZuVhFLqTE5sl6vz3nDDIaxZM0Z2jJhYWaW63JwtD3plzFcLfq9MoK7LtrS0lPncXCxB8VTeoXd6Gq0hEUYQkJaDDKgdbJ1/Z6YOAZCeIsJ+JaGnOvMlzs7+vMHBlYIA8/Y87s7pXFZvXG723xtZlDgP/mIu282bN5NL0gsonko6/pivkUQYQcB1R2mz2bBp0yaH4zZv3lznt0bvEMS2VHGH80pCd24Wb+Ps3Ln65OyGwN+bu21ibLZGsl2bZoFv+4WFhcjOzna74rOwMAFJSccB+Ff9KIGR+wctUSvtDcuQCCMMg9pmaikrJ83WIUiZebq7b7GVhHwCXW/jsJwFsdzziN0bpSK5SlRUlMOKz7S03cjJuQNXk89asHt3Gq6//he/c81rhb+54cTw5f00Ul2SCCMMgdpmalcvrb31xKwDttjMU+y+pdS1nDg7V+f19jye7g0ApSLxQEJCEfTI/u+vsLh1lJ74EsJgNJcmiTDCEKi5rZDYS+svucOcBVda2m5hM2rAt/tWamm+3PM431tqai6lIvEArRLWFqnJhAsLC132cSxZdpTAl1RBRnNpkggjDImS2wqJvbRiHYJzvJERO0RXQjMnJw2AfKGi1C4Gvp7H1b3l5qYCqIH9/fm7yPB2xSuhPGITvuzsbL+wkik1CTCCB4NEmJ9gJB+5GGq+WK7OnZz8h8cOwVUyUqN1iK73QAzwqSNUalWYr+dxJ6K7d/9aSMWghsgw2jsXFRWFzMxMYTEKbdSsPWITPlY2WFcbJSYBRvFgkAjzA4zmI/eEmi+Wu3NPnrzEbYfAuqlbKu5mnvYuSTkdoVLtyZfzuLu3lJQ8pKTkqSIyjPrO8XnNrv5NGzVriScLkFFEhVL4Ogkwyu4nJML8ADXjqbRGzRfL07lddQhGMHVLxd3Ms1OnH3H99b+47AiNklxSbFYtd6N3TxgtLoWHEovqi6e2KpaiRe4erWJoadFVKoQBME5cI4kwP8TdgCBnQ2StUfPFEju3fYdgxlmpu5mn1XoRI0fegejoaOFYVtuHOzzNqjMyMlS9Nz3EutyBkxKL6o+7tirWP8ndo9UTWlt0lWx/RolrJBHmZ3gaEJxf4iFDhiAiIgIAOx2vmi+W2LkzMjIA1NaTUUzdYkideSYkJDDx/L2BhXvTQ6z7OnAa7TmbEVdtVWrfp6TVVQ+LrpLtzwhxjSTC/AhvBwTn7PCZmZl1YkYAbQSa1G2FfHWTeDq3vbXEG4scywHaZrZ8sHBveoh1o7pC/Rmp/ZaYqNB60RKL4RdKujS1gESYH+FuQDh5sinOn/9b6KTdddrOW/jYo3aAsZoDqjcvrX1WcSmzUiMEaBtRYElF73vTOy7FKAOnv+Opf+N3jOBx1z/psWiJxfALFiZf3kAizI9wvSdcDbZuHSx00jfccAiHDt3gttPWc1at1kvjzUtbVFQkfCbF1E1WCf9GSfe5txZVIw2c/oqUZ5qQkCDpXHotWmKxLbEisKRAIsyPcB4QapNVWsBxtduTcFwAfvqpI/jtSpw7bTPPqqW+tL6Yus1cf4R7lIhL8caiymO0gdNMSBFXACQ/UzX2aPUGPSy6LIdxKAmJMD/AXTzVpUsNsGXLfU5Hu94vDqC99gD5pm6ySjhi9g5W6bgUORZVvV2h/opUwZyZmenwt6dnGh8fL3o+PRctKY0RwjiUgkSYH+AsHPiZk83WyIV7koO9EOM7bZpVX0XOS+8v9aekBYDF1blSUTMuRapF1ShL9M2GVMFcVVUl/FspK7maqwG1XGnoT2EcJML8BFedvatO2lVMGN/oaVYtH3+wSkidvQ4ZMkTS+ZxX5xpt1qtGWb21qBphib6ZkSKupD5TdxMcm83m8LeSqwFZWGlo9jAOEmF+iFi6h9tv3+syYaces2qzuK38wSohdVZ69uxZh7+dZ7lmnvX6ihSLKgsDJyFdXEl5pt5McHjrsT1y+0m9Vxr6KlC1KKOvkAjzQ5xfLJvN5pB+wl2nrfWs2mxxAZ7qzwi7FXiLOzG1b98+4d/Os1yx1bn+jphF9dy5c4iOjsaQIUNw5coV4Zjg4GCHHH9maWMsIzUEQYqVXKp7LiIiQlL8mFFQUqCyOk6QCPNT7BtjfHy829lOaWmpg1tIy1m1GeICpFolnHcrYLXDkIqzuEpL242EhCKHZ+dqlutpdS4hblH1tHWNfbLlyspKlJSUGLqNsY7UEARvreRauuf0FjhyBKo7WB0nSIQRANzHr3gSaDxazKqNGhfgblEEjxGFpRiuxFVOzh0ALA7PztUs193qXBJhV5FrkXaVbNnoYp9lvBFXUp+p1qus9RY4csI43PWprCJZhJWVlUk+aXh4uKzCEGyidydt9PQO7urPqMJSDE/iyv7ZuU4e7Hp1rr8j1aIKeDcIqTV4GjlGR0k8iaugIMfhV8pOHXqvstZD4Hgz6TBinypZhEVERMBisXg8huM4WCwWVFdX+1wwguDRu+NxRokBxujC0hOuxdVV+GeXlHTcq9W5/ozUbW3cDUJaDp56u7D0Rqpgjo2N9XqnDj1XWWspcORsJWfUPlWyCLMPpiUILWEpvYOcVUquRBlrwlJJ6u7M4N66JXV1LiFukXY3CP39dz3s3p2mmXXADLGcvqDmikK9VllrLXDkbCVn1D5Vsgjr1auXmuUgCLewlN5B6gAjluOKJWGpFO5SnxQWJtQRAXxdZWRkIDg4WNLqXEqp4Bl3g1BOThoAfawDRnQPKYGaFj49cr/JETi+egy8rUOj9qmyA/NLS0uxZs0aHD58GABw3XXXYdSoUQ7LoAnjwHoMB4tJJ10NMLVlFJ/1syQslcJ+9sq7yKzWi0hKOo7rr//F5bOLjo5mZvGH0XE3CClpHZC6IwJgXPcQi+id+81bgaOHS9qofaosEfb9998jPT0d9evXR9euXQEAr776Kl544QXs2rULnTp1UrSQhLo4vzDurDtax3Do3fHw2A88fD4vVwPMtm13w2KB5Fk/i8LSV/j24e2zI4HliJxJkatBKC1tt2CF5JFrHfB2T0SjuodYRO+kqd4KHC1XVYolH3d1HEvIEmFTpkzBgAED8MYbbwgrPK5cuYIxY8Zg8uTJ+M9//qNoIQl1sX8RPLkPtI7h0LvjAdwPPK5XAAaA42r/5W7Wz4qwlIpcCykLz86oeGtFEBuE6te/rIh1QOr7z++JaFT3EKvo8a4oJXDUXBhi9L5GtiXMXoABtcttZ8yYgS5duihWOEJbWHQfuHpx7IVBZWWlEJjJo+QL5y4GLDi4wuMKQMD1rN9IHYavLgUW7sEX9HLRe2tFcNWm7JMsq2UdEBtYjeoeIq6iRH+lZFwg62EzcpAlwsLDw3HixAm0bdvW4fOTJ0+iUaNGihSM0B4juA/0dJ162mIHqEHtCkDxHFdG6ST0TtSoJyylWZBiRXAug9pxdlIHVjO63FlFLYHiS/tWcmLvjTvcVWw6qwJNlgjLzMzE6NGj8corr6B79+4AgK+//hrTp0/H/fffr2gBeQYMGICDBw/izJkzaNy4MdLS0rBgwQIkJCQIx3z22Wd4+umn8euvv6JevXro2bMnFi1ahMTEROGYzz//HFOnTsWvv/6KZs2a4amnnsKIESMcrrVy5Uq8/PLLKC4uRseOHbF8+XIh9g0ALl++jGnTpuH9999HRUUF0tPT8dprr6FJkybCMSdOnMCECROwb98+NGzYEMOHD8f8+fPrJOhjCSO4D/RynbrqTA4dugGjR7+JqqoQREaeR35+K1PP+o2WidoXWEmz4IsVQa9Nlb1JQqolZrSi8LA0abBHyYm91HfN1c4QPCzmppOlCF555RVYLBYMGzZM2CQ2ODgYEyZMwEsvvaRoAXluu+02PPHEE4iPj8eff/6Jxx9/HIMHD8b+/fsBAAUFBbj33nsxdepUbNiwATabDVOmTEFGRgZ++OEH4Zj+/ftj/Pjx2LBhA/bs2YMxY8YgPj4e6enpAGof4NSpU7F69WqkpKRgyZIlSE9Px5EjRxAbGwugNiZux44d+OCDD2C1WpGVlYWMjAx8/fXXAIDq6mr0798fcXFx2L9/P4qKijBs2DAEBwfjxRdfVKV+lMBI7gOtXafuOpOqqhAkJR0HYO5Zv1apBlgcKPVKs8BieAAgPrBGREQw53JXQqSw2DZ5WJk0OKPmxN7TpJCV+5eCLBEWEhKCpUuXYv78+cjPzwcAJCcnIywsTNHC2TNlyhTh3y1atMCsWbMwcOBAVFVVITg4GAcOHEB1dTWef/55BATUPvDHH38c9957r3DM6tWrkZSUhEWLFgEA2rVrh6+++gqLFy8WRNirr76KsWPHYuTIkQCA1atXY8eOHVi7di1mzZoFm82GNWvWYOPGjbj99tsBAOvWrUO7du3wzTffoFu3bti1axd+++037N69G02aNMGNN96I5557DjNnzsQzzzzDTNC1K4wiJLR2nXqzGS8Ls34l0UoMsDib11oIlZSUCCtwWQ0PkPIusGZt8NW1zmLbdAdLudnUmth7ukeW7l8KPvnGwsLC0KFDB6XKIpnz589jw4YN6N69O4KDgwEAnTt3RkBAANatW4cRI0bgr7/+wjvvvIO0tDThmNzcXKSlpTmcKz09HZMnTwZQ+wIeOHAAs2fPFr4PCAhAWloacnNzAQAHDhxAVVWVw3natm2L5s2bIzc3F926dUNubi46dOjg4J5MT0/HhAkT8Ouvv+Kmm25yeV8VFRWoqKgQ/vZmv04l8bQvHSto7ToV60wyMjIQHR3t8rdGdnMA2okBvWLQXFk49BBCzgM9q+EBRrKYu8Nb17pR4iNZsZ6qmTbC0z0CYOL+vUGWCLt8+TKWL1+Offv24cyZM6ipqXH4nnf/Kc3MmTOxYsUKlJeXo1u3bti+fbvwXVJSEnbt2oUhQ4bgkUceQXV1NVJTU/HJJ58IxxQXFzsIIwBo0qQJysrK8Pfff+PChQuorq52eczvv/8unCMkJETYksb+mOLiYo/X4b9zx/z58zFv3jyJtaEcUl8Ed8cpbab3dD5+cNRjIPDUmfBC3xWVlZUoKSnRRYgp8WzExAD/TLw5pxS0iEETs3BoKYRcPafU1Fzs358KQFux46rd2Gw24d9GzMfEo4SlhNX4SLmTBqX7cDVXgXu6R8DCpPXYE7JE2OjRo7Fr1y4MHjwYXbt2Fd3Y2x2zZs3CggULPB5z+PBhYRXm9OnTMXr0aBw/fhzz5s3DsGHDsH37dlgsFhQXF2Ps2LEYPnw47r//fly8eBFz587F4MGDkZOTI7uMWjJ79mxMnTpV+LusrAzNmjVT/bq+vDBKr1aUej5AG9ep1LxenoJBebR2VSjlQhETvPzm0d6cUwytXApilgu9rD7O95+a+jVSUvI0ETtS283YsXfWmYyybvVVwlLEsrtLzqRBLVerWu1A7B5ZtB57QpYI2759Oz755BP06NHDp4tPmzatzspEZ1q2bCn8Ozo6GtHR0bj22mvRrl07NGvWDN988w1SU1OxcuVKWK1WLFy4UDj+3XffRbNmzZCXl4du3bohLi4Op0+fdjj/6dOnER4ejvr16yMwMBCBgYEuj4mLiwMAxMXFobKyEqWlpQ4dkPMx3377bZ1z8N+5IzQ0FKGhoR7rQy3kvjBKr1aUej4etV2nUgSqfU4mgJ2gUF9dKFJcCmrcq54uFVf3o3WcpKv7z81NRUpKHoBa13dCQoJqg5zUZxcREYH4+HhVyqAWvrqXWXH3uUPOpMEorlYesXs0mqtclgi75pprFMkHFhMTg5iYGFm/5V2gfAxVeXm5EJDPExgY6HCss3sSAHJycpCamgqgdtDp3Lkz9uzZg4EDBwq/3bNnD7KysgDUxp4FBwdjz549GDRoEADgyJEjOHHihHCe1NRUvPDCCzhz5oywojInJwfh4eFo3769rPtlHaU7J1/Pp6SVwJvBjuVZsrcuFFcClN8TElDvXvUKSPd0P1ouuBC7/+joaE2tTay63uTgq3uZ1cUS9vg6aWD1eUuNMzPK4jIeWSJs0aJFmDlzJlavXo0WLVooXaY65OXl4bvvvsMtt9yCxo0bIz8/H3PmzEFycrIgfPr374/Fixfj2WefFdyRTzzxBFq0aCEEwo8fPx4rVqzAjBkzMGrUKOzduxebN2/Gjh07hGtNnToVw4cPR5cuXdC1a1csWbIEly5dElZLWq1WjB49GlOnTkVkZCTCw8MxadIkpKamolu3bgCAvn37on379nj44YexcOFCFBcX46mnnsLEiRN1s3SpjdKdk9j5WAyEZ3mWLFcwuatHNe9VqTgsNTebVtMaxVJAPsuTCjn46l72Nj6SR+0+Sant0Fh+3lFRURgyZIjgdfDkBTHSKnVZIqxLly64fPkyWrZsibCwsDpByefPK9tZhIWFITs7G08//TQuXbqE+Ph49OvXD0899ZQgam6//XZs3LgRCxcuxMKFCxEWFobU1FTs3LkT9evXB1AbvL9jxw5MmTIFS5cuRdOmTfHmm28K6SmA2kS0Z8+exdy5c1FcXIwbb7wRO3fudAi0X7x4MQICAjBo0CCHZK08gYGB2L59OyZMmIDU1FQ0aNAAw4cPx7PPPqtovbCE0gOH2Pmio6Mlu0K0yu/D6ixZDcGk5r0qEYel1GbTJ082xfnzf9exCqhpjWJl9SHLkwpvUWq1npz4SB41Y0KVCIQ3wvN2jkF0Z7VzNUlnNV5Rlgi7//778eeff+LFF19EkyZNVA9679ChA/bu3St63NChQzF06FCPx/Tu3Rs//uhZ2WdlZQnuR1fUq1cPK1euxMqVK90e06JFizquTzOj9MCh1Pm0zO/DkgXDHm8Fk5TVqWrcq5LL2qUmrxTbbHrLlsGwX5molVWABZcKq5MKOfgqUqS2TU+oHVPla/9ltOftyWrnzSRdb2SJsP379yM3NxcdO3ZUujyEgVF64FDifFpmktbCgiHHqueNYJIqWtW4V7WWtUtd4OF8PxwHANpZBZRyKUlBCaGtl+tNLr6UyVPbtI+TBNiNqRJDjYmVWl4II1jtpCJLhLVt2xZ///230mUhTIDSqxXlno9/+e0HCi3iHdS0YMi16nkjmLwRo2rcq9KDtzf7HNrfz6VLDbBly30O51LbKqBmbiV7lBLaerne9ELK/bAcUyWG0hMrNb0QRrPaeUKWCHvppZcwbdo0vPDCC+jQoUOdmLDw8HBFCkewj6+JXtU4zlWusZMnm6k2c9LKguGtVU8J957YrJ71AFhv9znkrRo2WyNdXMtaCBclhTYr6VhYwKjWGbWy26uZ+oLV0A85yBJh/fr1AwD06dPH4XOO42CxWFBdXe17yQhDoPTsXYnzucs15oxSMyetLBj2SJlx+1ouT9dgcXWqK+Tuc8hKcLwWeBPcrEWKEqNiVOuMVv2Xkm5aM72fskTYvn37lC4HYWCUHnCVOp/zzNQZJWdOWudtkjrjllsusWsYJfDV285azT3vWERucLNRrT5qYmTrjNr9l1KC3YzvpywR1qtXL0nH/fOf/8Szzz7rdsZMEGriambKY+SZkxYzbqPO6l3hTeyaHlZNvfBFSJmpffhKaWkpAHNZZ5REScFuxvdTlgiTyrvvvovHH3+cRBihC65mpkANBg/egmbNThm2c9Rixm3kWT3gW5yekTpwX/BFSLHcPrTKC8hfy37LMtasM1rWhTuUFuyeysvfb1FRkcvvWRRoqoowrnaNN0HogruZ6fXXH65zrJHM11rMuI0+qzfjjFlpfBFSrLYPLfMCAq6Dyl0J/szMTM3bmtZ14Q6tBDsr9+stqoowgtAbTzNTPvDYiIOx0ukh+BmkzWaTdA0jiFajPVOt8VVIsZBQ1hkt8wK6wt31rFarKtfzBCsbc2sl2Fm5X28hEUaYHneuKKMEl/OolQrD0wzS/hpDhgxBRESEIUUrcRVfgpu1TCjrK1qv3mR9tajWSWT1DqI3StJcEmGE6VA6JxkrqOVikzozjIiIMJRo1QoW4m68wZd2pJeb19s61nr1JuurRfUQiN60FaXfIdYFsT0kwgjTYeZ4IC3KbJQZJAtIjUPhrYiu0KMt+rqFj5Z4E+vDo/XqTZZXi4oJRPtdRZRui1LOpXQsF+uC2BlVRdhDDz1E2fMJXTCiwGIBI80gWUBqDJL9CjpXsBYszBJyYn20Xr3J8mpRMYHovP2U1m1R6vMtLCwEIN63syyIXSFbhJWWluLbb7/FmTNnUFNT4/DdsGHDAACrVq3yrXQEQWiG0WaQrOGLgGUtWJhlpFhq5QaDy3WLsbpaFPBeILLaFnmxKCYSWRbErpAlwj7++GM8+OCD+OuvvxAeHg6LxSJ8Z7FYBBFGEIRxEJtBlpaWMhcTxko8llQBq7Wrl5X6UQpvhK63qzfluMX0Dj6XgjuBCAAFBYnMhR2IvSNiIpFlQewKWSJs2rRpGDVqFF588UWEhYUpXSaC0BX7gauwMAAFBUFISrqChIRai6/RBi6piM0gN2/ezJTbjKW8QFJcIFq7elmqHyWQInR9Wb0pJ70Fy/GnngRifn4rLFky2WVbtNlsuk22fHlHpApifocD+9/p2f5libA///wTjz76KAkwwnTYD1yeOgSjDFze4GoGmZa2W5P8SnLQOyeUPWICVg9XL0v1owRShK5SosgbMcBqP+BcF/zm62JtcdOmTbr0b67KtW3b3YiNLUbTpq4z4Nvj6tnbbDZs2rTJ4ThX8Zl69ueyRFh6ejq+//57tGzZUunyEISu8C+wWEfF4sAl1/XkPIP8++96yMlJA8cFYPfuNNSvf1m2xUYrd5jeCwrEXCB6BwvrXT9KIDXWx9f2ZKbYSFd1IaUt6tG/ud7rNwBr1oyR3F7lPns9+3PJImzbtm3Cv/v374/p06fjt99+Q4cOHRAcHOxw7IABA5QrIUHogFhHZZ9Z3hVam7idXU/urB6uZnxRUVHIzMzEpk2bYLM1wu7daQB8H4C0coexMmh6coHoGSysVf2oLbi1ivXRWzCrjbs9dfUOXHddLmXbK4vpdySLsIEDB9b57Nlnn63zmcViQXV1tU+FIohTp4CjR4HWrYGmTbW/vtigaW/i9kbwqIX94OfJ6uFukOS3VVFyANLKHabnoCk1BknPYGEt6kdNwa118LvRVtd5C98Wt227G/xkC7AgP7+VLtZR/rm5LlctSrRXVq3BkkWYcxoKglAafia9cWN9zJhhRU2NBQEBHBYutOGBB/7W1LokddCUI3jUxFerh1oDkJodoJ6DplgMkn1Mil6r57SoHzUFt9bB70ZbXScV+zaWnPwHLBaA4/hPLLq5XPnnW5sHLBuxscVYs2aMou2VFWu5K2TFhL399tvIzMxEaGiow+eVlZV4//33KUUF4TX8TNpma/S/VTu1aU9qaiyYPj0cf/65FlbrRU2tS2JL3Fl8sX21eqgxAKldT3oPmp7aY3x8vO6r57SuHzUEt9ZB0yxuTu4rUVFRGDJkCDZv3sycy9X++TZtWuSxvcqZsLB2v/bIEmEjR45Ev379EBsb6/D5xYsXMXLkSBJhhNfwg5TYy6K1dcmdewlg88VWwuqh9ACkRT2xPGiysHpOq/phcWIiFSNtTi4XfussFl2uzhbPuXPP4tixICQmXkFCws0AbpY9YWHxfnlkiTCO4xwStPKcOnVKiC0hCDmw/LI4w2JZ5Vo91ByA1Konfxg0fUGP+hET3GruU+gr3ro9jZwI1xfrqJr3bf+7+Higc2dZp6mD3tZyT3glwm666SZYLBZYLBb06dMHQUFXf15dXY2CggL069dP8UIS/oPeL4s3A5LeZXWHHKuHmnE3atWTc5lLS0tx5coVh2OCg4NRWVmJoqIipgdFNdAjkaiY4NZ7n0IxpJbFqIlwfV3kYLT7NsKOBl6JMH6F5MGDB5Geno6GDRsK34WEhCAxMRGDBg1StICE/6Gna0nKwFVaWiok/PNUVvtZP49WQsCTG9UdapZLrWdqb5UQ2yQb0HZwYMFSovVA6O0WOSzm25OCnE3FWcBXYa7GfattWdM7JlMMr0TY008/DQBITExEZmYm6tWrp0qhCEKqiFBjiyFX7gb7c4eEOFpb3JV13boczVJXSJ3JaTnj09Idxlp2eKNZDJTEmy1yfIEFkcvDYv4pdyhZJ77etxbvCevvl6yYsOHDhwOo7dDOnDlTJ31F8+bNfS8ZQYig5hZDns+dh06dPP9e69QVLM749CoTC/mAjGopkYs7wa1WoD5LIpeF9qYHSty3v70nrpAlwo4ePYpRo0Zh//79Dp/zAfuUrJXQAjW3GJJ67oyMDERHRwOQvjebUrBkCXAHC/vPsbA6z0iWEjm426dQrZWxrAzerLY3tfHX+1YDWSJsxIgRCAoKwvbt2xEfH+9ypSRBeIMv7jQ1UyCInTs6Ohrx8fGalYdHqiVgyJAhwrJ0Z1gQaUrDYtoQby0GRhDXrnBVJq1WEOslcllsb1og976d27Zz3KzZJyuukCXCDh48iAMHDqBt27ZKl4fwU3xxXanZ0cs5N0sZysWC1c0Wk8Ra2hBvLQYsudmUQIsVxHq6A1lrb1oh577F2raSz9FIExlZIqx9+/YuV34RhC/IfSnU7OjlnJvFDOV6B6prBWtpQ7y1GLDiZlMSNVc76+0WY629aYWc+/bUZpV8jkabyMgSYQsWLMCMGTPw4osvokOHDggODnb4Pjw8XJHCEYRU1Ozo5ZybpQzl/hY4zFL2fF8tJe7E87lz55iazTuj1cpYvdyBRsg/pQZK3rd921byORptIiNLhKWlpQEAbr/9dod4MArMJ/RETm4sNc+tZnl4xDovvS0FWsFq9nxfLCWexDOf9JSV2bwzWq2M1csdyOJqZC1Q6r6d23Za2m7VniPrcWayRNi+ffuULgdBuOXUKeDoUaB1a6BpU71L4x49cnWJDUL+EjjM2qDoq8XAlXjetu1uxMYWo2nTIuE4VmbzrtAqKbFe7kCzCSyp+Hrfrtr27t1pSEvbjd270xTbtBswhhdAlgjr1asXvvzyS/zrX/9Cfn4+tmzZgmuuuQbvvPMOkpKSlC4j4YfwgZUbN9bHjBlW1NRYEBDAYeFCGx544G+EhISoKnrknFsPIeA8CAG1s0q+8/KnwGGWBkVf24Ir8QwEYM2aMUwOJFqjhjvQSMHcRsbdxDAhoRCTJy9BcnI6unWL9nnTbqN4AWSJsK1bt+Lhhx/Ggw8+iB9//BEVFRUAAJvNhhdffBGffPKJooUk1Ecta5Oc8/KBlTZbo/9l2q51edfUWDB9ejj+/HMtrNaLyMrKUlz02HfEQ4YMcbkXIb9Jvatz69FJd+r0I/7+u54wi9y9Ow31619Gp04/iloK9Nxayez4UoeuxDPA7kCiNUpPeIwWzG1kPE0Mazd534LERN/r2ZvN5O3Ruv+TJcKef/55rF69GsOGDcP7778vfN6jRw88//zzihWO0IY1a4Bx44CaGiAgAHj9dWD0aP3Oy3esYi9RZWVlnRxdvuDcEbuLJRDriNXYSskZ+xm+zdZIEGBA3YHak6XAeUNlqffoj2hpKeHF87ZtdwMwvztZDkq2T9a2vWIFNdq8FBeyEvXs7Wby9mjZ/8kSYUeOHEHPnj3rfG61WlFaWuprmQiNKCkpwbFjVzBuXCxqanhrE/DIIxxuvPEMEhODZDVEpc6rtSvN/sWXu+2Qmlsp2RMVFYUhQ4Zg8+bNkuK+3AWq02AjDa0sJc5uttjYYqxZM8Yv3MmsoHUcEatuUKXbvCsX8smTTQFY0KzZSV+K6hIpYo+F/k+WCIuLi8Mff/yBxMREh8+/+uortGzZUolyESpTUlKCF154C7/+eh1qatIdvquutmD58k+RlHTc60GFf3ELChJRUzPcp/PqFXTrSyyBmlspOcNnwhcTq662VgKMEbTKClpZSng3W2FhIbKzs9G0aZFf5qHSC63jiFh2gyqd6sF+4ggA+fmtVOl/pMYLstL/yRJhY8eOxWOPPYa1a9fCYrGgsLAQubm5ePzxxzFnzhyly0iowPr1gf+LtwoAwAG4mmrEfgD3dlDhjxcTBlLP6+ol4gfAwsIAKOiNFFBiRaHUcygxCxYTq662VjJK0CqLqN15R0VFObQJlvKemR2tVxMbKaeVEqke+Imjmv2Pp3hBrff3lYIsETZr1izU1NSgT58+KC8vR8+ePREaGorHH38ckyZNUrqMhILwrsIZM2KFgPdaAVYrxJSaaStpxbJ3pdkPgO+8w2HhwlJhtaRSM0Ul3KBSzqFUDBrg/UDtL6krlEas8y4tLVUkTlHpvGesurxYQ+/VxKzmtFJ64qF2/yPWllnq/2SJMIvFgieffBLTp0/HH3/8gb/++gvt27dHw4YNlS4foSCeXIWABenpO9G+/W+KNUKlZ/DOA6Cr1ZJKDCRKCEhvg0/lxqA5X1NqGfUebIyKWOe9efNmRdqhkqv/WHZ5sYaeecdYcY85o4bVSO/+R+/r2yNLhPGEhISgffv2SpWFUBkxV6GSAoxHyazxUlZLKoUSAlLqOeR2cr7kSfPXPe98RUrnrVQ7VEoQGcnlxQJ6uH9Zco85IzfVA+B+oqB3/6P39e3xSYQRxoSlBugKd+JC69mLEgJSyjnkmsZ9tZZQrJH31E2OC3BcbZAxC1YLKbDq8tITvbe9Ysk95owvqR4ARwsrS3tustL/kQjzU1hpgK5wFhd8MKXa4lGPbYcA38Slt9YSvQcbM5Cc/Ac4zv4T6VYLvWOzWHV56Y3e216x5B5zxtd+175O9a5nFvs/EmF+jBYbTMtFD+uNEh2E1Je3vLwc5eXlALS1TOrdCbKMJ4Fks9mEf58/HwU5CVT1js1iyeWltxh1hZ5tnkXvhFSrFY9UC6ue9cxi/0cijPCItzMCLaxJcsWjFpnspbzk5eXlePfddx0+09Iy6Y8CSwypAgmQb7XQOys7Ky4vvcUoS7DknnNGrC/TOuegUsKdtTZFIoyoA5/cU44oYXGmAWiXyR4Qf8mLiooc/rYfjJOSjvt0bUIe3ggfJawWergFWXF56S1GWYLV/pJHynW1sLCaWbiTCCPq4Cq5pzew+BJomcneG6QMxhSfpT1irhVfLJd6uQXVdHnJtVJQjBqb/aU3+GJhldpuzCzcSYT5EXoFnvuKkuVmxSUDiA/GGRkZSEhIMHwnbTSkCgO5Qb1at0G1XV5yrRQsxagR8pFrYfWm3dhjNuFOIsyPYN307Q4ly82KSwYQH4yjo6OZexZmR0wYZGZmwmq1uv29lHaodRtU+72Xa6VgaUJkFvRY8CDXwiq13RQWFjocYzbhTiLMzzDqoK5UuVlahcSSICRqERMGVqvV522J9GiD9u+Pq4G6srJSiFX0ZaD2xkpB7V9ZtI6bUtLC6qnd2OchM6NwJxFG+B2s5EhjSRAStWglDPRqg2oO1N5aKaj9K4vWcVNKWVjdtZvY2GJUVYU6lN+Mwp1EGMEUp04BR48CrVsDTZuqdx1WcqSxIgiJWtQUBiwkilRzoJZjpaD2rw5axU0pYVFz127efHMMAMfym1G4kwgjdId3j2zcWB8zZlhRU2NBQACHhQtteOCBv5mMU/MFFgZjwhEt8jWxFpOp9EAt1UrhbftnMbEryxgtbspVuwE48AmRnctvNuFOIozQFd49YrM1wpIlk8FxFgBATY0F06eH488/18JqvehzHANLK0NZG4yVQsvBUulrafVMWHmmagzUUq0U3tS1mfNDqYXR4qZctRux8ptp4koijNAVviMW6zh8jWNgTfiYbcDQcrBU61pmeyaeUGuglmqlkFrXZs4PpRZGjJuybzfBwZVYs2aM2/LzycSdMeLEFSARRjCCFh2HEV9QoyB1EFRisNTyWmZFyfdNK/e62fJDqYVR4qY8tRtP5fc1mThrkAgjmMAoHYdR0TquRupmvka7lllQ8n3TwspstDgnpZD73hohbsq53djvRSm1/GaIFyQRRnhEq9WKgDE6DiOidVyNlhYLso7IR8n3Te2BzmhxTkrg7XtrxAU/9u1GzoIN+/pxNxljPV6QRBhRBz1XK7KSOsJMaBlXo6TFwt0s99y5c4pfy18w4kANGDPOyVe8fW9Zi3v1Fm/Lb3+cp8kY62EJJMIIB8RWK37zTQ6aNTuJcePugtVqZfqlJuqituVIKYuFFCuAP1pHfMWoA7W/hytIfW89PTd+UsPvjOAMC89dzvWNPhkjEUY4ILZaccuW+2Cx1CA//2onwLq5V09YilnQorNSymIhZfbqj9YRJTDqu+qv4QpKvLdmTvVh9MlYgPghbDBgwAA0b94c9erVQ3x8PB5++GGHjT0BYPPmzbjxxhsRFhaGFi1a4OWXX65zns8//xydOnVCaGgoWrVqhfXr19c5ZuXKlUhMTES9evWQkpKCb7/91uH7y5cvY+LEiYiKikLDhg0xaNAgnD592uGYEydOoH///ggLC0NsbCymT5+OK1eu+F4RGsEPcK7gOwGbrREAzwNmSUkJioqK3P5XXl4uqTysuUekwHd8r7/+utv/VqxYgZKSEk3K46mzUgreYsG3HaUsFjZbIxQUJAptTs1rEezgyn2alHS8zjM2Yv8gFSXe2zNnzjj87ep9Ath33bnC1VhlpMmYYSxht912G5544gnEx8fjzz//xOOPP47Bgwdj//79AIBPP/0UDz74IJYvX46+ffvi8OHDGDt2LOrXr4+srCwAQEFBAfr374/x48djw4YN2LNnD8aMGYP4+Hikp6cDADZt2oSpU6di9erVSElJwZIlS5Ceno4jR44gNjYWADBlyhTs2LEDH3zwAaxWK7KyspCRkYGvv/4aAFBdXY3+/fsjLi4O+/fvR1FREYYNG4bg4GC8+OKLOtSe9zib/52RMtOQGjj50EMPISwszO15WDCTy4G1VApqWo7UzDjvyRWjVnZ7gg2M6j5VEl/f25KSEmzevFn4W4mQBJYs/EZ3VRtGhE2ZMkX4d4sWLTBr1iwMHDgQVVVVCA4OxjvvvIOBAwdi/PjxAICWLVti9uzZWLBgASZOnAiLxYLVq1cjKSkJixYtAgC0a9cOX331FRYvXiyIsFdffRVjx47FyJEjAQCrV6/Gjh07sHbtWsyaNQs2mw1r1qzBxo0bcfvttwMA1q1bh3bt2uGbb75Bt27dsGvXLvz222/YvXs3mjRpghtvvBHPPfccZs6ciWeeecYwgwM/wJ082RRbtgyGveFUSicgNXAyLCzMVHlf3KF3KgU1Oyu1BksxV4wREzeyNIAZASXrwoh17+t7a3+/ZnVtGtlVbRgRZs/58+exYcMGdO/eHcHBwQCAioqKOtaU+vXr49SpUzh+/DgSExORm5uLtLQ0h2PS09MxefJkALWN9cCBA5g9e7bwfUBAANLS0pCbmwsAOHDgAKqqqhzO07ZtWzRv3hy5ubno1q0bcnNz0aFDBzRp0sThOhMmTMCvv/6Km266yeV9VVRUoKKiQvi7rKxMRu0oS+3qqcOorJTfCRg9cFIJWEmloGZnpUaHKxbvoVXiRqUGbxYHMH/ByHWv1Hubl5fic/wUq7sYGHVlvaFE2MyZM7FixQqUl5ejW7du2L59u/Bdeno6pkyZghEjRuC2227DH3/8IVi8ioqKkJiYiOLiYgdhBABNmjRBWVkZ/v77b1y4cAHV1dUuj/n9998BAMXFxQgJCUFERESdY4qLi4VjXJ2D/84d8+fPx7x587yoEe3wpRMweuCkr+gtQo2algBgI/heycGbNRe1P6GkeNDCoqb0e2uzNcL+/al1PvflfdJzcsnSfsC+oKsImzVrFhYsWODxmMOHD6Nt27YAgOnTp2P06NE4fvw45s2bh2HDhmH79u2wWCwYO3Ys8vPzcffdd6Oqqgrh4eF47LHH8MwzzyAgwBjrD2bPno2pU6cKf5eVlaFZs2Y6lsgRd53AuXPnPHY6LAykeqK3CDVyXA0L8R5qCie9XdT+ii/iQSuLmtLv7fnzUXC1Fi81NVdW29N7cmnkfs0eXUXYtGnTMGLECI/HtGzZUvh3dHQ0oqOjce2116Jdu3Zo1qwZvvnmG6SmpsJisWDBggV48cUXUVxcjJiYGOzZs8fhHHFxcXVWMZ4+fRrh4eGoX78+AgMDERgY6PKYuLg44RyVlZUoLS11sIY5H+O8opI/J3+MK0JDQxEaGuqxPtRGyqzBeeDgt5pw1+mwMJDqCQsilPWOyBk1A/19RSnhpJcVwYhxUUriq3jQ0h2n5HNw1Q8BNUhJyZN1Pr0nl4Dx+jVX6CrCYmJiEBMTI+u3NTW1S1LtY6gAIDAwENdccw0A4L333kNqaqpwjdTUVHzyyScOx+fk5CA1tdZEGxISgs6dO2PPnj0YOHCgcJ09e/YIKyw7d+6M4OBg7NmzB4MGDQIAHDlyBCdOnBDOk5qaihdeeAFnzpwRVlTm5OQgPDwc7du3l3W/WmE/uygtLXVYVQPIz0xs5MBJX/F3ESoHVme5SgknvawIesRFsSb6lBQPSrUH5zqy2WyoqqoS/g4KCnKY9MupMyn9kDeTGhYml2bAEDFheXl5+O6773DLLbegcePGyM/Px5w5c5CcnCwIn3PnzmHLli3o3bs3Ll++jHXr1uGDDz7AF198IZxn/PjxWLFiBWbMmIFRo0Zh79692Lx5M3bs2CEcM3XqVAwfPhxdunRB165dsWTJEly6dElYLWm1WjF69GhMnToVkZGRCA8Px6RJk5Camopu3boBAPr27Yv27dvj4YcfxsKFC1FcXIynnnoKEydO1N3S5Qp3nWRERASGDBmC0tJS7Nq1y+eBw6iBk3Jh2ZpjBFib5Yq1f347JUB8kNTLiqB1UDWLwfBKiQelhLTUOnJGTp156ocyMzO9Oh9NLpXBECIsLCwM2dnZePrpp3Hp0iXEx8ejX79+eOqppxxEzVtvvYXHH38cHMchNTUVn3/+Obp27Sp8n5SUhB07dmDKlClYunQpmjZtijfffFNITwHUNsSzZ89i7ty5KC4uxo033oidO3c6BNovXrwYAQEBGDRoECoqKpCeno7XXntN+D4wMBDbt2/HhAkTkJqaigYNGmD48OF49tlnVa4p7/GmA/B24NA7cFLvGTir1hxCHmLtn3fL83gaJJUQAr62by3coSyupFNKPCglpMXqyNc6kxrgL8cr5c8eDqUwhAjr0KED9u7d6/GY6OhoIY2EJ3r37o0ff/Tc0WRlZQnuR1fUq1cPK1euxMqVK90e06JFizquTxaR2kkC3g8ceooQVmbgLOc40lukGg137T84uBIFBYleDZK+CgFf27ce7lBW0rQAyogHNdxxznV0ww2HcOjQDT7VmdL9sJFXW7OIIUQYoQ1inaScgUOvQdxsqQCUFpWsiFQj4ar933DDIaxZM0byIKmUi9pXC5PW7lC9V9KVlJTAZrM5fOareFDaHeeqjn76qSMAi/C33DpT8h1mxcJvlkkkiTACgPRO0qjmZ6OlAnDuYOzjjTyhtPg0ikhVE3fCKTi4UhBggLRBUo0BTI6FSeugaj1X0kmdcAwZMgSxsbFe1b2S/aGrOuIFGA8r+RX1FjdSt8QzwiSSRJgfYj/A84O7N52k0QLsWXKDSEFuoK4vGE2kaomzcDp37hyys7NRUJAoS1goOSjItTBpHVSt50o6qVbDiIgIl8/GeUJUWlrq8L1S7jjXKSQ42AsxWn1Yi9Qt8YwwiSQR5me4G+CV6CRZjAHQ2w0iBykdh5KiyWgiVQ9cDc5i74wr66XSLhJfLExaWrVZWUnnbVuXOiHKzMyE1WoV/lYqhYSrmDBW+y09MGL/7gyJMD/D3QAv1kkOGTKkzlZN9rDqf2choaCvOAsuJUWTGToxvRB7Z5xXS/Io6SLxdvKkZ1C13qEMctq6VCua1WpVZA9TV3V0++17DRf+oRVm6N9JhBECnjrJiIgITTZKVhqjJxR0Flxpabuxe3eaYqLJDJ2YnsgRFkq6SLy1MGkdVM3SSjpf27paFmOxOqLVh+4xev8OkAgjnDDbC8+KG0QOrmbuOTlpcN7/zRfRZIZOTGukCgut4uy8FYJaWqxZWUkH+NbW1bQYu6ojNTLmmxEj9+88JML8HHcDRUZGBqKjowEY84U3Q7Z616ulAhQVTWboxLTGnbDgA/YB9ePsWLIwiaFl3+EqbQEfm+dLW1fbYuxcR0b0OuiF3m5uXyER5sd4Giiio6MN3RGwNAOXi7uZu71L0nkgkbNLgVFFqjNa5g3ydB4t4uzM0L6VxnPagtrP5A7YZDFmG6Ot2LeHRJif4g8B2UYfgNzN3Dt1+hHXX/8Lrr9+IDp2bICEhJsB3OzVoGu2QZyl5LNaxdkZ5dlohdS0BXKshma2GBsx6aneW+IpCYkwP4UCstlFipXKar2Ie++1+tQ5stax+gJLyWfJaqIvYhNM+1ALHnuh4SqPIqCM24s1wWPUpKdmmkSSCPMz+AFebKAwwgzCF1jrDO0xUwejF3omnzWz1cQIiE0wPYVauBYlV/cF9SX2jkXBY+Skp2bp/0iE+Rn2A/w115Rh5kwrqqstCAzksGBBGR544H7TD/Asua7cYeb6Vxu9ks+aMc7OiPhiiZQqSuQsXGJZ8PhDeAqrkAjzQ/gOY9o0IDMT+OMPoFUrC5o2jQAQoWfRNIEl15URYNlq6IyegwlZMNlACUukWDvyZeESi4KHwlP0g0SYn9O0ae1/BOEKI1gN7dF7MGGhDgjf47fUbEd6t1FXUByjfjgnISIIghAwmtWQH0zsocHEP7FaLyIp6bhPSYztUaodsdhGeeshXy6KY9QOsoQRhAEwkktQTygo3n9RMm2Bmu2I1TZq9KSnRoVEGOH36LmSTgosuQRZrSt/DIonYe6I0jF5aooSVgWPWNJTanPKQyKM8Gv0WknnDay4BFmuK38LimdJmLOE0veqZiZ2FrK8e2M9ZDHFhhkgEUb4LSyuUmIVI9SVP3X8zmLT3YDISqyeUVAzEzuLWd69mbwUFRUJn7GWYsPIkAgj/A6+kxNbpWQm15WvsLiii6iFZQul0qjtDlPTomoWa60RJmRGgkQYIRujxgfwneGxY1fwzjscamoswneBgRwmTboTiYlBTJZdL2gJO5v404ColTtMzfeetT7FmzrloQmZspAII2Rh9JiUqKgoREUBr78OPPIIUF0NBAYC//qXBZ07N9G7eB7RMjietwaKregiq6E+iA2I9nsfsjopkgrLGeeNipw6pQmZspAII2TBSrC4r4weDaSn87sGsJ+4VmvXk7MLZe7cszh2LAiJiVeQkHAzgJsNP7gbGXcDYnBwJQoKErFuXY4hg6ZdWdl5QelP1j+t8KZOWU2xYVRIhBF+j1F2DdBr8LEftOPjgc6dVbsU4SWuBsQbbjiENWvGGNZK5NlFRu4wNfC2TllNsWFESIQRqmLvDgGM7xLRA1pIQHjCfkAMDq4UBBhgTCuRmIssOfkPcocpjBwXIwspNswAiTBCVbKzs+t8ZhSXCCvQQgLCGWfBzQ+IBQWJprESubP8Tp68hNxhCiPFxchiig0zQCKM0BwjuERYw8gLCQjlcY7VO3fuHLKzsxULmmZh5bMnyy+5w5RHrE7NkmKDNUiEEYSBMNpCAkI9XA12SgRNs5IZXUxQkjtMecTqlASW8pAIIwgvYMFCoNRCAhbuhVAeX61ErKSCkCso5bjD7N+FwsIAFBQEISnpChISaoRzmvFdIBej/pAII2Thjy+l0XOj2ePrvZCAYxslrEQspILwJCgzMjIQHR3tcLycdmf/LngSnUZ4r72Bf4czMzNx8iSHEydC0bx5BeLirgAAgoKCEBERQe+yypAII2ThKj6Aj0uRitEGcrPkRgN8uxcziVGzoIZFQ69UEKWlpQ5/uxOU0dHRiI+P9/l6fBsXE51GeK+l4q/Ck0VIhBGy8eXlpIHcePCi2TntiDvMNGixjhpB03pkRi8pKcHmzZuFvz3tDqG0Nd6f8o/5o/BkFRJhhCKcOgV8800IbLZGkjosM1mVnOFFCmuWPF+QKpq1wGgWVK1Q+p71yIwuNR4tMzMTAFBUVARAmTguf9yOx5+EJ6uQCCN8Zs0aYNw4oKYmChbLZNGtdEJCQgwprqRi75I1iiVPbD9KT89Ly70syYKqLXqlgpASj6a0O80ft+PxR+HJGiTCCJ84dYoXYLV/c1wAduy4B3PnpggzUnv42Sk/gzU7RhCbvuxHqfVelma2oLKKHqkgxCw0Fy5cAFAr1rZtuxuAMu40f8s/5o/CkzVIhBE+cfToVQHGU11twcWLTaBAzCzTaGkBUgtfVsCxsHrODM+ANVhIWyBmodm1axcAIC8vBbwA4/HVneZv+cf8TXiyBokwwidatwYCAhyFWGBgbSLRU6dqRVrr1uZLKqq1BUgt5MaE2GyN8Ouv1+kaT2KWZ8AaLGRGl2KhsdkaITc31cWvyZ3mLf4mPFmCRBjhE02butpKB/jss6tuyoCA2mNGj9a7tL7Bz/xZsAD5Cn8vYhYHV9YOe/EDcAAsLn/rDiUC683wDFiGhXg6MQuNqwkEAHTvnutVG2DB8kf4LyTCCJ9x3koHAFq0uGodq6mpFWnp6ca2iPEWgn37gMWLjb2iyN7acc01ZZg504rqagsCAzksWFCGBx6436UYchY/tQKsVog5WytcDVpSA+uHDBmC2NhYt2KAVnX5B54sNO4mECkpeV5dgwXLn9aQ8GQHEmGEIthvpbNvn6s4sVqRxh9j1E4gKioK3boBAQEcamq8swCxBj+oTJsGZGbyItqCpk0jAES4/I1r64MF6ek70b79bxg58g5ER9/sdtCSGijN54pyt7qNVnWZE2/edyWDys0ksKTgj8KTVUiEEYrjKU6Mx8idQNOmwMKFNkyfHi50/mlpu3H+fG1ZjWiJEduPUsx92b79b7BaLyIhIcGrZyY3NQat6jIn9v2CzWbDpk2bhO9ctRUKKpcPi32rVMwUb0wijFAcd3Fizi+LkTuBESOq8eefS3D+fCQKCxOwe3eaywBx1ix5cpHrvvSEY2xZDbp3z0VKSp7HgdS+Pj0NwGapd3/EVRvytAjDk8uS2oF54GNJN26sjxkzrKipsSAggMPChTY88MDfzE7axbBwHMfpXQjCNWVlZbBarbDZbAgPD9e7OF5z6tTVODGx2YoRZzYlJSU4duwKunaNdXBNBgZyyMs7g8TEIEN2ClLw5tnaU1RUhNdffx02WyMsWTK5jmvTeYAdN25cnf0BKWO+f+CprVgsNZg8eYlHwT5kyBC0a9dOi6ISKsPHkoq1BZaSNEsdv8kSRqiGmIuL52rGfWOtpIyKisKhQ+7zpDHSF6iC1GfrDncr26SscmSlkyW0wd0ijLy8FPTtu9vt72JjY9UuGqER/KRLbEGOEZM0kwgjdMU5477SKynVtppIiX8j6uIqtoyHVjkS9rhrK/v3pwru64yMDERHRwvfkTXUnJhxQQ6JMEJXXGfcd1xJKRct9hmUGv9GOMIH1ttvOcNj9E6VUBar9SJSU3Oxf38Pp2+uivXo6Og6bmvCfJhxQQ6JMEJX1LQkabXPoHOeNBJg7nEVWJ+Xl4Lc3FTTdKqE8qSk5GH//lTYC3YS6/6J2VbEkggjdMUsliRfY6T8BX6V5dmzZ7Fp0yZYrRfRt+9upKTk0SpHwi1W60UMGGAuCwghHzNts0QijNAdsiT5F1FRUYbOE0dog7MAN5sFhCAAEmEEI5AlyVgoseDB3wWWfR0WFgagoCAISUlXkJBQ65v3dxEaFRWFzMxMh4StZrKAEARAIowgCC9xXvDgLus9Szl7WMO+Dj0lIvX3OrRarXoXgWAAo25zJwUSYYTfILZFDnEVT5auc+fOCf/2JCCMmLNHK/i6cd4Q3TlPmr/XoZkHX3/G2+TcZg5fIBFG+AWexALhiFRLl5iAIMQRSz7p75h58PU3fN12yKzPmEQYYVr42bGYWDDTLFqJWC3733sSryQgfMeMySeVxqyDrz9Rd9uh2m3eamosmD49HH/+uZa5bYe0gkQYYVr4WfS+fcDixXXFQo8ew9G7t3k6eaVjtcTEq5iAsHdbkrXCNWZMPmkPLT4gAHNvO+QrJMIIUxMVFYVu3VwnhE1JiTLV/o5SLVhSOzqxDtOdgACAgoJErFuXQ4H6EjBr6gVafEA4Q5bfupAII0yPWRLCSkWpWC0pHaazgMjPb/U/dwMF6nuDGVMv0OIDwhmzW37lQCKM8Av8KSGsUrFaUjtMXkBQoD7hCoodJOxR2/JrNBc4iTDCb/CXhLBKmvy96TBpsJWOP6VekBo7yMqAqcTiFsIzall+jegCJxFGECZDaZO/uw4zIyMD0dHROHfuHLKzsynewwv8KfWCWHvMzs5mZsCkRMTGxogucBJhBOEGbxMKsoQvJn+p1peEhASHgYjiPbzDKIO4Eu4dT+2RpQFT6cUtzpCVTRuMZJUnEUYQdviaUJAl5Jr8fbHSmHWln7+ipHvHXXtkccBUI77Rn61sWrvfjWSVJxFGEP/D6AkFve3oPFn6fLk/M67081e0cO+wOGCqIQzVtrKxjNbudyNZ5Q0nwioqKpCSkoKffvoJP/74I2688Ubhu0OHDmHixIn47rvvEBMTg0mTJmHGjBkOv//ggw8wZ84cHDt2DK1bt8aCBQtw1113Cd9zHIenn34ab7zxBkpLS9GjRw+sWrUKrVu3Fo45f/48Jk2ahI8//hgBAQEYNGgQli5dioYNG3pVFoItjJ5QUEpHV15ejsrKSixaVKqYpc+fgsz9FTmiROrzZnHAVFMY+usqYq0nrkaxyhtOhM2YMQMJCQn46aefHD4vKytD3759kZaWhtWrV+Pnn3/GqFGjEBERgXHjxgEA9u/fj/vvvx/z58/H3XffjY0bN2LgwIH44YcfcP311wMAFi5ciGXLluGtt95CUlIS5syZg/T0dPz222+oV68eAODBBx9EUVERcnJyUFVVhZEjR2LcuHHYuHGj5LIQ7MLizFwqnjq6kpISvP7664pb+vwpyNxfkfNOiLULfkEHwN6AqaYwZNH9alaMYJU3lAj79NNPsWvXLmzduhWffvqpw3cbNmxAZWUl1q5di5CQEFx33XU4ePAgXn31VUH4LF26FP369cP06dMBAM899xxycnKwYsUKrF69GhzHYcmSJXjqqadw7733AgDefvttNGnSBB999BGGDh2Kw4cPY+fOnfjuu+/QpUsXAMDy5ctx11134ZVXXkFCQoKksihFTU0Ns5YZo1FVVYUGDRqgQYMajBixE/v29UZ1dQCKi4Nw552fMP8yi6GmpY8ElrmRK0q8aResDZhqCUMjT/II5TGMCDt9+jTGjh2Ljz76CGFhYXW+z83NRc+ePR1M4Onp6ViwYAEuXLiAxo0bIzc3F1OnTnX4XXp6Oj766CMAQEFBAYqLi5GWliZ8b7VakZKSgtzcXAwdOhS5ubmIiIgQBBgApKWlISAgAHl5efjHP/4hqSyuqKioQEVFhfB3WVmZxzqprKxEQUEBauz34yFkU11djR49egAAevQARo/+GTU1Abh8uRw//HAYly/rXECFoEGAkANr1iotUEMYsuh+NQtGDI0whAjjOA4jRozA+PHj0aVLFxw7dqzOMcXFxUhKSnL4rEmTJsJ3jRs3RnFxsfCZ/THFxcXCcfa/c3dMbGysw/dBQUGIjIx0OEasLK6YP38+5s2b57oSnOA4DkVFRQgMDESzZs0QEBAg/iPCI5WVlSgtLXX4jOM4XLhwAW3atKnjAjcKfAB+eHhtG6FBgJCLkqKExQFTqzL5o6DVAiOGRugqwmbNmoUFCxZ4PObw4cPYtWsXLl68iNmzZ2tUMn2YPXu2g6WurKwMzZo1c3nslStXUF5ejoSEBJeWQcJ7AgICEBRU95WwWq2IiYlBSEiI4Vy/a9YA48bVbl4eEBCLu+++CZ06/UiDgA4YbTsVtWFxwNSyTKy5X82C0d4hXUXYtGnTMGLECI/HtGzZEnv37kVubi5CQ0MdvuvSpQsefPBBvPXWW4iLi8Pp06cdvuf/jouLE/7v6hj77/nP4uPjHY7hV2HGxcXhzJkzDue4cuUKzp8/L3od+2u4IjQ0tM49uqO6uhoAW2ZVsxIQEICAgAAEBwf7JMK0TP5aUlKCY8euYNy4WNTUXA3At1+FRYOAdhhxOxVAfcsQS/fKo1aZWLT8EfqjqwiLiYlBTEyM6HHLli3D888/L/xdWFiI9PR0bNq0CSkpKQCA1NRUPPnkk6iqqkJwcDAAICcnB23atBHcf6mpqdizZw8mT54snCsnJwepqakAgKSkJMTFxWHPnj2C6CorK0NeXh4mTJggnKO0tBQHDhxA586dAQB79+5FTU2NV2VRCovFouj55FBZCVy+DNSrBxi5/3Dn0nWuYzmdpKNFCnj99dpNxdWAH/ALChJRUzPc4TtahaUPRtxOBWDTWmVUqC4JVxgiJqx58+YOf/P5uJKTk9H0fyaFBx54APPmzcPo0aMxc+ZM/PLLL1i6dCkWL14s/O6xxx5Dr169sGjRIvTv3x/vv/8+vv/+e7z++usAagfbyZMn4/nnn0fr1q2FFBUJCQkYOHAgAKBdu3bo168fxo4di9WrV6OqqgpZWVkYOnQoEhISJJfFLJw9Cxw/fvXvFi0ACbqaSYKCghAbG1tnocPly5dRVlaGIUOGoEGDBl53kqdOXRVgQO3/H3kESE9XxyLGd/IUgM8eRkxPQKJAOaguCWdME81ttVqxa9cuFBQUoHPnzpg2bRrmzp3rkBKie/fu2LhxI15//XV07NgRW7ZswUcffSTkCANq85BNmjQJ48aNw80334y//voLO3fuFHKEAbXpMNq2bYs+ffrgrrvuwi233CIIOallMQOVlY4CDKj929NkfsSIEbBYLLBYLAgODkaTJk1wxx13YO3atV6t8ly/fj0iIiLkFdwDQUFBCAkJqfNfYGAgYmNjZXWiR49eFWA81dXAH38oVGg38AH4FkvtxaUG4JM7RD14YWwPCWOC8F8MYQlzJjExERzH1fn8hhtuwJdffunxt/fddx/uu+8+t99bLBY8++yzePbZZ90eExkZKSRmdYeUshgddykbKio8uyX79euHdevWobq6GqdPn8bOnTvx2GOPYcuWLdi2bZvL4Hgj07p1rQvSXogFBgKtWql/bU8B+BkZGYiOjnY4Xkt3iJE3SJcLrUwlCMIec412hGTsV2q5QspgbGccdEBsbUFoaKiwQOGaa65Bp06d0K1bN/Tp0wfr16/HmDFj8Oqrr2LdunX4v//7P0RGRuKee+7BwoUL0bBhQ3z++ecYOXIkgKvxWk8//TSeeeYZvPPOO1i6dCmOHDmCBg0a4Pbbb8eSJUvqpBXRipKSEgQGVmLhwvqYOdOK6moLAgM5LFhgQ2Dg3ygpUV/0uAvAj46OdliAoiVaxsixBq1MJQiCh0SYH2K/UssTYiu1QkJqY8CcY8LkeLNuv/12dOzYEdnZ2RgzZgwCAgKwbNkyJCUl4f/+7//wz3/+EzNmzMBrr72G7t27Y8mSJZg7dy6OHDkC4GqcYFVVFZ577jm0adMGZ86cwdSpUzFixAh88skn3hfKR5zr+dFHGwkD719/XQTvwWZtRZyauF61CTzyCIcbbzyDxMQgv6gLWplKENqihOFBDUiE+SFSV2BJOS4mBrBaa12QoaG+rY5s27YtDh06BAAOK1gTExPx/PPPY/z48XjttdcQEhICq9UKi8VSJ+XHqFGjhH+3bNkSy5YtE2L77DdY1wLn+nM38LK2Ik4tPK3arK62YPnyT5GUdNyvRKk/weogqDT+cp9GQinDgxqQCCN8JiREmdQUHMcJ7sXdu3dj/vz5+P3331FWVoYrV67g8uXLKC8v95ic9sCBA3jmmWfw008/4cKFC0Kw/4kTJ9C+fXvfC0nIRuqqTTOKUn/PEcXyIKgk/nKfRkNJw4PSkAgjmOHw4cNISkrCsWPHcPfdd2PChAl44YUXEBkZia+++gqjR49GZWWlWxF26dIlpKenIz09HRs2bEBMTAxOnDiB9PR0Uw7s7mB9wPfH4HR/zxHlfN82WyOcPx+FyMgSh+du9PeU5cGeuIq79qcHJMIIJti7dy9+/vlnTJkyBQcOHEBNTQ0WLVokJFDdvHmzw/EhISHCrgE8v//+O0pKSvDSSy8J2z19//332twAQxhhwGctOF2LLYXMKrC8xdOOAQShNqy1PxJhhOZUVFSguLjYIUXF/Pnzcffdd2PYsGH45ZdfUFVVheXLl+Oee+7B119/jdWrVzucIzExEX/99Rf27NmDjh07IiwsDM2bN0dISAiWL1+O8ePH45dffsFzzz2n013qixEGfFaC0426pZAREdsxwGywZHEh2Gx/JMIIWVy5csVjclV3m2EDwM6dOxEfH4+goCA0btwYHTt2xLJlyzB8+HAEBASgY8eOePXVV7FgwQLMnj0bPXv2xPz58zFs2DDhHN27d8f48eORmZmJkpISIUXF+vXr8cQTT2DZsmXo1KkTXnnlFQwYMEDx+yfMg1G3FHIF60HhRtwxQC6sWVwINtsfiTDCa65cuVJnE3NXxMbG1hFi69evx/r160V/O2XKFEyZMsXhs4cfftjh71WrVmHVqlUOn91///24//77HT5zldiXIJxhsYP2BiMEhYstyrDZbLrlrlMSFi0uBJtbuZlm2yJCOr4GbkvdXsibbYjMiK/1fOoUsG9f7f8J9TH6lkKugt8LChJhszXyeJyWiG2ltWnTJpSUlOhWPqXwJOgJ/ZC7lZuakCXMDzFC4LYZ8KWezZhRnlZtagfLrjCxRRlGcPuKwaLFxZ+x71M8tT89+h4SYX4KCSxtkFPPp05dFWAAn1EeSE839h6LRhD/rK3alIPWrjApcWjOgxsrizKUhr9PMUFv1nxwrMJy30MijCAY4+hRx82+AaC6GvjjD+VEmF6bZxtB/LsTCOfOnZPcUesZIK9lbJs3cWhDhgxxSDVjxpWDzoP93LlncexYEBITryAh4WYAN+s+0fBXWK1zEmEEwRitW9e6IO2FWGAg0KqVMuc3o6tTC7KzswGIB7brHSCvpSvMm+SkERERwt8su0t9xf6ZxscDnTvLOw/rK10JZSARRhAMUVJSgsDASixcWB8zZ1pRXW1BYCCHBQtsCAz8GyUl8jte2jzbNe5cQ3KzuuudHZ712DZaOSiO3kKe0A4SYQTBCM4d76OPNhJik/766yJef732czkdL22e7R7ehVRYWChYu5Sy1Ohl8ZET26aE5UWKi9HoqUC0gLY/8h9IhBFew28lpNRxRC3OHaq72CQ5Ha8/b54thaioKMlJW6WitcVHavC7K8ufEpYXqYKTVg4SxFVIhBFeExQUhNjYWNkZ8wn9YN1VxQJKWWq0tvj4sgLMV8uLFMGp9cpBiqkijACNkoQsWBVYn3/+OW677TZcuHDBIRDYE4mJiZg8eTImT56satlYgbU0DKwNlkpZavSw+OglKqQITi1XDlJMFWEU2BxJCdMyYsQIvPXWW3jkkUfqbMo9ceJEvPbaaxg+fLikrY0I+bCSp4nFwVIpa6E/WR2lCk6lVg6KQTFVhFEgEUZoTrNmzfD+++9j8eLFqF+/PgDg8uXL2LhxI5o3b65z6Qgt0XsloTuUshayZnVUGkpOqg1mzKlG1EIijNCcTp06IT8/H9nZ2XjwwQcB1OZgat68OZKSkoTjKioqMH36dLz//vsoKytDly5dsHjxYtx8883CMZ988gkmT56MkydPolu3bhg+fHid63311VeYPXs2vv/+e0RHR+Mf//gH5s+fjwYNGqh/s4Rk9M4d5Utguxrn0ROpgz4lJ1Ufvd8LQl1IhBG6MGrUKKxbt04QYWvXrsXIkSPx+eefC8fMmDEDW7duxVtvvYUWLVpg4cKFSE9Pxx9//IHIyEicPHkSGRkZmDhxIsaNG4fvv/8e06ZNc7hOfn4++vXrh+effx5r167F2bNnkZWVhaysLKxbt07LWyY8wELuKKW2NmF5ixQpeDvoa+Vi9Cd4gS72XrAs5AlpkAgjdNnC5qGHHsLs2bNx/PhxAMDXX3+N999/XxBhly5dwqpVq7B+/XrceeedAIA33ngDOTk5WLNmDaZPn45Vq1YhOTkZixYtAgC0adMGP//8MxYsWCBcZ/78+XjwwQeFoPvWrVtj2bJl6NWrF1atWoV69eppc8MSUHODa9Y3z2Yld5RSwohVgSUGC2KYuCrk9+0DFi+u+1706DEcvXsbt50RVyER5ufotYVNTEwM+vfvj/Xr14PjOPTv3x/R0dHC9/n5+aiqqkKPHj2Ez4KDg9G1a1ccPnwYAHD48GGkpKQ4nDc1NdXh759++gmHDh3Chg0bhM84jkNNTQ0KCgrQrl07NW5PFmpaUFi3zlDuKH3hxbeYGCbLi3ZERUWhWzfXW5ilpESB9Jc5IBHmx5w6dVWAAfwWNkB6ujYWsVGjRiErKwsAsHLlSlWu8ddff+GRRx7Bo48+Wuc7FhcBqCmCWJ41+9NKQhbhRfqxY1fwzjucsK0VAAQGcpg06U5Db2tl1MD2pk1rJ8aPPAJUV9cKsH/9SzuPBaE+JML8mKNHHWdYQO2L/scf2rzk/fr1Q2VlJSwWC9LT0x2+S05ORkhICL7++mu0aNECAFBVVYXvvvtOcC22a9cO27Ztc/jdN9984/B3p06d8Ntvv6GVUrtfE6ph9pWErBMVVWtdqTvoW9C5cxO9i+cV9hY7TzFuRrDsjR5dOzH+4w+gVSsSYGaDRJgf07q1a1O3VnolMDBQcC0GBgY6fNegQQNMmDAB06dPR2RkJJo3b46FCxeivLwco//nLx0/fjwWLVqE6dOnY8yYMThw4ECd/GIzZ85Et27dkJWVhTFjxqBBgwb47bffkJOTIyk/FaEuZlhJaDbMMOjbW/aefTYWHFdr2eO4AOzYcQ/mzk0xlGWvaVNjPgdCHBJhfgwLpu7w8HC337300kuoqanBww8/jIsXL6JLly747LPP0LhxYwC17sStW7diypQpWL58Obp27YoXX3wRo0aNEs5xww034IsvvsCTTz6JW2+9FRzHITk5GZmZmarfGyEO67Fq/ooZBv2oqCgcOuTK2m/BxYtNKKaKYAILx3Gc3oUgXFNWVgar1QqbzVZHrFy+fBkFBQVISkryeYXfqVPGnvWqjZJ1TRCEdpw6BbRoUdfaf+wY9XWEungav+0JcPsN4Tc0bQr07k2dEkEQ5oK39vPRDhTYTrAGuSMJgiAI02KGGDfCvJAIIwiCIEyNGWLcCHNC7kiCIAiCIAgdIBFGEARBEAShAyTCDA4tblUfqmOCIAhCDUiEGRQ+uamn/EqEMvB17JxQliAIgiB8gQLzDUpQUBDCwsJw9uxZBAcHIyCA9LQa1NTU4OzZswgLC0NQEL0uBEEQhHLQqGJQLBYL4uPjUVBQgOPHj+tdHFMTEBCA5s2bw2KxiB9MEARBEBIhEWZgQkJC0Lp1a3JJqkxISAhZGgmCIAjFIRFmcAICAmgrHYIgCIIwIDS9JwiCIAiC0AESYQRBEARBEDpAIowgCIIgCEIHKCaMYfgkoWVlZTqXhCAIgiAIqfDjtliybxJhDHPx4kUAQLNmzXQuCUEQBEEQ3nLx4kVYrVa331s42pOFWWpqalBYWIhGjRr5ZY6qsrIyNGvWDCdPnkR4eLjexTEsVI/KQPXoO1SHykD1qAxq1iPHcbh48SISEhI8pjgiSxjDBAQEoGnTpnoXQ3fCw8Opo1EAqkdloHr0HapDZaB6VAa16tGTBYyHAvMJgiAIgiB0gEQYQRAEQRCEDpAII5glNDQUTz/9NEJDQ/UuiqGhelQGqkffoTpUBqpHZWChHikwnyAIgiAIQgfIEkYQBEEQBKEDJMIIgiAIgiB0gEQYQRAEQRCEDpAIIwiCIAiC0AESYYTu/Oc//8E999yDhIQEWCwWfPTRRw7fcxyHuXPnIj4+HvXr10daWhqOHj2qT2EZRawOR4wYAYvF4vBfv3799Cksw8yfPx8333wzGjVqhNjYWAwcOBBHjhxxOOby5cuYOHEioqKi0LBhQwwaNAinT5/WqcRsIqUee/fuXadNjh8/XqcSs8mqVatwww03CMlEU1NT8emnnwrfU1uUhlg96tkWSYQRunPp0iV07NgRK1eudPn9woULsWzZMqxevRp5eXlo0KAB0tPTcfnyZY1Lyi5idQgA/fr1Q1FRkfDfe++9p2EJjcEXX3yBiRMn4ptvvkFOTg6qqqrQt29fXLp0SThmypQp+Pjjj/HBBx/giy++QGFhITIyMnQsNXtIqUcAGDt2rEObXLhwoU4lZpOmTZvipZdewoEDB/D999/j9ttvx7333otff/0VALVFqYjVI6BjW+QIgiEAcB9++KHwd01NDRcXF8e9/PLLwmelpaVcaGgo99577+lQQvZxrkOO47jhw4dz9957ry7lMTJnzpzhAHBffPEFx3G1bS84OJj74IMPhGMOHz7MAeByc3P1KibzONcjx3Fcr169uMcee0y/QhmUxo0bc2+++Sa1RR/h65Hj9G2LZAkjmKagoADFxcVIS0sTPrNarUhJSUFubq6OJTMen3/+OWJjY9GmTRtMmDABJSUleheJeWw2GwAgMjISAHDgwAFUVVU5tMe2bduiefPm1B494FyPPBs2bEB0dDSuv/56zJ49G+Xl5XoUzxBUV1fj/fffx6VLl5CamkptUSbO9cijV1ukDbwJpikuLgYANGnSxOHzJk2aCN8R4vTr1w8ZGRlISkpCfn4+nnjiCdx5553Izc1FYGCg3sVjkpqaGkyePBk9evTA9ddfD6C2PYaEhCAiIsLhWGqP7nFVjwDwwAMPoEWLFkhISMChQ4cwc+ZMHDlyBNnZ2TqWlj1+/vlnpKam4vLly2jYsCE+/PBDtG/fHgcPHqS26AXu6hHQty2SCCMIP2Do0KHCvzt06IAbbrgBycnJ+Pzzz9GnTx8dS8YuEydOxC+//IKvvvpK76IYGnf1OG7cOOHfHTp0QHx8PPr06YP8/HwkJydrXUxmadOmDQ4ePAibzYYtW7Zg+PDh+OKLL/QuluFwV4/t27fXtS2SO5Jgmri4OACos+Ln9OnTwneE97Rs2RLR0dH4448/9C4Kk2RlZWH79u3Yt28fmjZtKnweFxeHyspKlJaWOhxP7dE17urRFSkpKQBAbdKJkJAQtGrVCp07d8b8+fPRsWNHLF26lNqil7irR1do2RZJhBFMk5SUhLi4OOzZs0f4rKysDHl5eQ7+fMI7Tp06hZKSEsTHx+tdFKbgOA5ZWVn48MMPsXfvXiQlJTl837lzZwQHBzu0xyNHjuDEiRPUHu0Qq0dXHDx4EACoTYpQU1ODiooKaos+wtejK7Rsi+SOJHTnr7/+cphxFBQU4ODBg4iMjETz5s0xefJkPP/882jdujWSkpIwZ84cJCQkYODAgfoVmjE81WFkZCTmzZuHQYMGIS4uDvn5+ZgxYwZatWqF9PR0HUvNHhMnTsTGjRvx73//G40aNRJia6xWK+rXrw+r1YrRo0dj6tSpiIyMRHh4OCZNmoTU1FR069ZN59Kzg1g95ufnY+PGjbjrrrsQFRWFQ4cOYcqUKejZsyduuOEGnUvPDrNnz8add96J5s2b4+LFi9i4cSM+//xzfPbZZ9QWvcBTPereFnVZk0kQduzbt48DUOe/4cOHcxxXm6Zizpw5XJMmTbjQ0FCuT58+3JEjR/QtNGN4qsPy8nKub9++XExMDBccHMy1aNGCGzt2LFdcXKx3sZnDVR0C4NatWycc8/fff3P//Oc/ucaNG3NhYWHcP/7xD66oqEi/QjOIWD2eOHGC69mzJxcZGcmFhoZyrVq14qZPn87ZbDZ9C84Yo0aN4lq0aMGFhIRwMTExXJ8+fbhdu3YJ31NblIanetS7LVo4juPUl3oEQRAEQRCEPRQTRhAEQRAEoQMkwgiCIAiCIHSARBhBEARBEIQOkAgjCIIgCILQARJhBEEQBEEQOkAijCAIgiAIQgdIhBEEQRAEQegAiTCCIAiCIAgdIBFGEARBEAShAyTCCIIgZFBZWal3EerAYpkIgnAPiTCCIAgAvXv3RlZWFrKysmC1WhEdHY05c+aA39ktMTERzz33HIYNG4bw8HCMGzcOAPDVV1/h1ltvRf369dGsWTM8+uijuHTpknDe1157Da1bt0a9evXQpEkTDB48WPhuy5Yt6NChA+rXr4+oqCikpaUJv+3duzcmT57sUMaBAwdixIgRwt9yy0QQBBuQCCMIgvgfb731FoKCgvDtt99i6dKlePXVV/Hmm28K37/yyivo2LEjfvzxR8yZMwf5+fno168fBg0ahEOHDmHTpk346quvkJWVBQD4/vvv8eijj+LZZ5/FkSNHsHPnTvTs2RMAUFRUhPvvvx+jRo3C4cOH8fnnnyMjIwPebufrbZkIgmAH2sCbIAgCtZanM2fO4Ndff4XFYgEAzJo1C9u2bcNvv/2GxMRE3HTTTfjwww+F34wZMwaBgYH417/+JXz21VdfoVevXrh06RI++eQTjBw5EqdOnUKjRo0crvfDDz+gc+fOOHbsGFq0aOGyPDfeeCOWLFkifDZw4EBERERg/fr1ACCrTPXq1fOpngiCUA6yhBEEQfyPbt26CQIMAFJTU3H06FFUV1cDALp06eJw/E8//YT169ejYcOGwn/p6emoqalBQUEB7rjjDrRo0QItW7bEww8/jA0bNqC8vBwA0LFjR/Tp0wcdOnTAfffdhzfeeAMXLlzwuszelokgCHYgEUYQBCGRBg0aOPz9119/4ZFHHsHBgweF/3766SccPXoUycnJaNSoEX744Qe89957iI+Px9y5c9GxY0eUlpYiMDAQOTk5+PTTT9G+fXssX74cbdq0EYRSQEBAHddkVVWVz2UiCIIdSIQRBEH8j7y8PIe/v/nmG7Ru3RqBgYEuj+/UqRN+++03tGrVqs5/ISEhAICgoCCkpaVh4cKFOHToEI4dO4a9e/cCACwWC3r06IF58+bhxx9/REhIiOBajImJQVFRkXCt6upq/PLLL6L3IKVMBEGwAYkwgiCI/3HixAlMnToVR44cwXvvvYfly5fjsccec3v8zJkzsX//fmRlZeHgwYM4evQo/v3vfwtB8Nu3b8eyZctw8OBBHD9+HG+//TZqamrQpk0b5OXl4cUXX8T333+PEydOIDs7G2fPnkW7du0AALfffjt27NiBHTt24Pfff8eECRNQWloqeg9iZSIIgh2C9C4AQRAEKwwbNgx///03unbtisDAQDz22GNC2gdX3HDDDfjiiy/w5JNP4tZbbwXHcUhOTkZmZiYAICIiAtnZ2XjmmWdw+fJltG7dGu+99x6uu+46HD58GP/5z3+wZMkSlJWVoUWLFli0aBHuvPNOAMCoUaPw008/YdiwYQgKCsKUKVNw2223id6DWJkIgmAHWh1JEAQB16sRCYIg1ITckQRBEARBEDpAIowgCIIgCEIHyB1JEARBEAShA2QJIwiCIAiC0AESYQRBEARBEDpAIowgCIIgCEIHSIQRBEEQBEHoAIkwgiAIgiAIHSARRhAEQRAEoQMkwgiCIAiCIHSARBhBEARBEIQOkAgjCIIgCILQgf8HFkwbEKpg6twAAAAASUVORK5CYII=", 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", 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AcXFxWLJkCX744Qe8/PLL+PjjjzFr1iwAQK9evbB48WKkpqaGwi/++Mc/AgimnD/88MP47rvv8Pbbb2P//v0YO3aspd9FCMcIn3nz5qFHjx5ISUlBZmYmhg8fjt27d0dcc+bMGUydOhXp6elo0aIFRo4ciSNHjtjUYmNp1coHjyfS5+rxBNCq1XGbWkQQBEG4lYsuugj79+8HAEyfPh1XXXUV8vPz0b9/fzzyyCNYs2YNgKArzuv1wuPxICsrC1lZWWjRogUAYPz48RgyZAjat2+PK664AkuWLMEHH3yAX375xa6vBcBBwufTTz/F1KlT8eWXX6KsrAz19fW4+uqrcfr06dA1d999N9577z28+eab+PTTT1FZWYkRI0bY2Grj8HpPYdiwDSHxw8f4eL2nbG4ZQRAE4TY4jgu5rjZt2oQBAwbgggsuQEpKCm655Rb4fD7U1tZKvse2bdswbNgwtG3bFikpKejbty8A4MCBA6a3XwrHxPh8+OGHEX+vXLkSmZmZ2LZtG/r06QO/34/ly5dj1apV6N+/PwBgxYoV+O1vf4svv/wSV1xxhR3NNpTu3b9FYeFeHD/eCq1aHSfRQxAEQZjCrl27UFBQgP3792Po0KG4/fbb8eijj6JVq1b4/PPPMWHCBNTV1YlWrD59+jQGDRqEQYMG4bXXXkPr1q1x4MABDBo0yPaYVMcIn2j8fj8AoFWrYIzLtm3bUF9fj5KSktA1F110Edq2bYvy8nJR4XP27FmcPXs29PfJkydNbLV+vN5TJHgIgiAI0/j444/x/fff4+6778a2bdsQCATw5JNPIi4u6CTi3Vw8SUlJaGhoiHjsxx9/hM/nw+OPP468vDwAwNdff23NF5DBkcInEAhg+vTp6N27Nzp37gwgeDpuUlJSo1oCbdq0weHDh0Xfa968eZg7d66ZzXUc4emVlZVxqKhIQEHBOeTkBN1sRqZXHjoE7NkDdOwI5OYa8pYEQRCEQs6ePYvDhw9HpLPPmzcPQ4cOxa233oqdO3eivr4eS5cuxbBhw/DFF1/g+eefj3iP/Px8/PLLL/joo4/QrVs3NGvWDG3btkVSUhKWLl2K2267DTt37sTDDz9s07eMxDExPuFMnToVO3fuxBtvvKH7vWbPng2/3x/6d/DgQQNaaBxJSUmGXicHn165bNky3HbbV+jRozWuvz4dl13WGmPGfI9ly5bhmWeewb59+3R/1vLlQLt2QP/+wf8uX27AFyAIgiAU8+GHHyI7Oxv5+fkYPHgwNm/ejCVLluCdd95BfHw8unXrhkWLFmH+/Pno3LkzXnvtNcybNy/iPXr16oXbbrsNpaWlaN26NRYsWIDWrVtj5cqVePPNN9GpUyc8/vjjeOKJJ2z6lpF4OI7j7G6EGqZNm4Z33nkH//znP1FQUBB6/OOPP8aAAQNw4sSJCKtPu3btMH36dNx9992K3v/kyZPwer3w+/1ITU01uvmasLLAVVVVFZYtWwa/PwWLF0+PSqHnMHBgGXr3LgegvbaEz+fD/v3ncPnlmQgEzh84Fx/PYevWo8jPT6BijARBOIIzZ86goqICBQUFaNKkiarXslDHx2lI9bfS9dsxri6O43DHHXfgrbfewieffBIhegCgqKgIiYmJ+OijjzBy5EgAwO7du3HgwAEUFxfb0WTDUDPgjRJJQnWDAA82bSpB5847NRdN5G/0iop8BAJjIp5raPBg6dIPUFDwE93oBEG4nvT0dEybNs3Wys2xiGOEz9SpU7Fq1Sq88847SElJCcXteL1eNG3aFF6vFxMmTMCMGTPQqlUrpKam4o477kBxcbErMrqUYOTuoVUrH4AAor2hfNFErQHW/A3O1yUKF1fhdYnsjvonCIKwAhI11uOYGJ/nnnsOfr8f/fr1Q3Z2dujf6tWrQ9c89dRTGDp0KEaOHIk+ffogKysrVHkyFjCyCqjXewoDB24CEOkJNapoItUlIgiCIOzAMRYfJaFITZo0wV/+8hf85S9/saBF9hPt1qquro543u9PwfHj6WjVyqdJUPCxPJs2lUQcjGqUOKG6RARBEITVOEb4EJHIubWkTnJXQ+/e5ejceadp4oTqEhEEQRBW4hhXFxGJlLtK7CR3vz9F02d5vadQUPATCRSCIAjC8ZDwcSF6TnI3qh4QQRAEQbAIubpcQng8j1zGlBTp6ekYPXo0Xn31Vdlr3SCSrKxSTRAEQdgPCR8XIBTPM2zYhkaPKXVVFRYWmlZbQqlYqq2tRVVVleGfH054nJRUTJSS9H8SUARBEM6AhA/jiBUk5DO4xOJ5pk9fjOnTFwsGJfPiw8qK0DxKCnbV1tZGWJzEstP0Fjnk2yDWh4WFexUVajRSQBEEQbiFTz75BFdddVWjExWkyM/Px/Tp0zF9+nTT2kXCh2GUFCSUiucpKPgJ48YNREZGRug5XszYWSpd7v3CLT1SQsKoIodSfajESmaUgCIIgrCSsWPH4uWXX8aUKVMaHTw6depUPPvssxgzZgxWrlxpTwNNgoKbGUbJQsnH84QTHs+TkZERUfCRFx1GFjs0C6Oz08SQ68Pq6mr4fD7B1/p8vpD1TU9QOUEQhB3k5eXhjTfewK+//hp67MyZM1i1ahXatm1rY8vMg4SPw5GrgKw0psbvT0FFRb6kqPD5fKiqqhL9JyYOtGKVkBDqw+Li8tDz69evxzPPPNPo+/FWM746uJyAIgiCYI3u3bsjLy8v4pSD9evXo23btrj00ktDj509exZ33nknMjMz0aRJE/zud7/DV199FfFef//73/Gb3/wGTZs2xVVXXYX9+/c3+rzPP/8cV155JZo2bYq8vDzceeedOH36tGnfTwhydTkIsVgXvgLyJZdch4svTkZOTg8APRTH6CgpdmiHa0xPdppa+D7curUntmwpxpYtvVFeXizpWhOyhhUXl6O8vNiUStcEQbibQ4eAPXuAjh2B3FzrPnf8+PFYsWIFbr75ZgDASy+9hHHjxuGTTz4JXTNr1iysW7cOL7/8Mtq1a4cFCxZg0KBB2Lt3L1q1aoWDBw9ixIgRmDp1KiZPnoyvv/4a99xzT8Tn7Nu3D4MHD8YjjzyCl156CceOHcO0adMwbdo0rFixwrLvS8LHIciJk+DZWonIzm6j6n3l4lJ47HCN8ZYYrdlpWigvLwZvCBXrCz4oPPyIkPDfBwigV68v0LPnVhI9BEEoYvlyYPJkIBAA4uKAZcuACROs+ezRo0dj9uzZ+OmnnwAAX3zxBd54442Q8Dl9+jSee+45rFy5EkOGDAEA/PWvf0VZWRmWL1+OmTNn4rnnnkNhYSGefPJJAMCFF16I77//HvPnzw99zrx583DzzTeHApc7duyIJUuWoG/fvnjuuefQpEkTS74vCR8HoFScaEFrYK/ec8CUYuV5Xkr6QsjyFf37AHEoLy9Gz55bTWsrQRDu4dCh86IHCP53yhRg0CBrLD+tW7fGNddcg5UrV4LjOFxzzTURSTH79u1DfX09evfuHXosMTERl19+OXbt2gUA2LVrF3r27BnxvsXFxRF/f/fdd9ixYwdee+210GMcxyEQCKCiogK//e1vzfh6jSDh4wDEFuSDB3Ph9e7S9d5a3ElGnQOmFDPO8/L5fPD7/RGPyfWF3+9HfX19o/fSmxUWi1DdI4I4z54950UPT0MDsHevdS6v8ePHY9q0aQBg2kHfv/zyC6ZMmYI777yz0XNWBlKT8HEAQgsyAKxdex3q6s6Ljj179qC6uhrNmjVDYWGhovdW604y0/rEozQgW2vlaLF4Jbm+WL16daPX+P0pOH26GYAAwnMFhMSjGypdGwHVPSKISDp2DLq3wsVPfDzQoYN1bRg8eDDq6urg8XgwaNCgiOcKCwuRlJSEL774Au3atQMA1NfX46uvvgq5rX7729/i3XffjXjdl19+GfF39+7d8e9//xsdrPxiApDwYRh+oYxekM8TKTo2b94cemb06NGS4id8EZZyJ0Uv1lZYN5QUOdRjEZB6XzWutci4Hg68+IkWTCNGjEBOTg4t4v+F6h4RRCS5ucGYnilTgpae+HjghResDXCOj48Pua3i4+MjnmvevDluv/12zJw5E61atULbtm2xYMEC1NbWYsJ/A5Fuu+02PPnkk5g5cyYmTpyIbdu2Nar/c++99+KKK67AtGnTMHHiRDRv3hz//ve/UVZWpih5xihI+DAMLwAqKysBrEdS0lmsXXt9xDVioqO2tlbRe6sVF1ZlWlkpEqLjlZS41g4dysa77w7FeSuPBx4Ph5Ej1yAv71DE6zMyMkj0CEAuQoI4z4QJwZievXuDlh4rRQ9Pamqq6HOPP/44AoEAbrnlFpw6dQqXXXYZ/vGPf6Bly5YAgq6qdevW4e6778bSpUtx+eWX47HHHsP48eND79G1a1d8+umnmDNnDq688kpwHIfCwkKUlpaa/t3CIeHDOOnp6SFxkpd3UJfoMOKICjsyrcxES7wS/5roMlgcF4fmzWsb9QW5uM4TXvDRynIFBOEEcnOtFTxyFZnffvvt0P83adIES5YswZIlS0SvHzp0KIYOHRrx2Lhx4yL+7tGjBzZu3Cj6HkK1f4yGhI8DEHN5eTwB/O53n+GHHy5G27Y/ITdX/FBPvXV4tLrGWEbO1TJixIhQZkNNTQ3WrFkjkMF1nvBFm3+tm4N0pYR0TU0NPB4PvF5v6DG/3x8RJ+U2EU0QhDMg4eMAwt1Se/bsQWFh8PDRf/2rBz77rA8ADwAO3bp9h//5n3cE30NvHR6z427sQM7Vwh/3IfeaIJGLttBr3YRSIS2F35+Cli1PYMKEF1Ffn2R6uQKCIAiAhI9j4AVFdXU1vN5TOHWqBXbt6oSg6AEAD777rht69PiXpOWHR0sdHieJGiVocbWIvWbChBcj+t1Jli8t6A08FnIxFhT8ZFDrCIIgxCHh41AOHGiH86KHx4ODB9vKCh+r6/CwihZXi9hr+D6P1QwuKSEd/ZwVJRHUYETsG0EQzoGEj0Np2/YnBFOow8UPh7y8A5KvY23RMRuhRS28cKFUvFL4dUpfE4uiR0pICz3XsuUJRdlcVljN7DiDjnAnHMfZ3YSYwIh+JuHjMJo1awYAyM2tQrdu3+G777ohPMaHtzzw10UTSynEShe1SZOGIC0tDcD5IGZAuGCh0Gt47LYMKLVc+Hw+HDt2TLAKNQAkJCQgMzNT0XeREtIABJ+bMOFFSRejlVYzO86gI9xFYmIigGAJkaZNm9rcGvfDl2rh+10LJHwcRmFhIUaPHo3a2lqMGAF8//1ebN/eHJdcchpdusQDGCFZuVkurqWiokL0s7Us7HrdCGpeH31t+CGiUqSlpakORNbyGjNRKvJGjx6NV199VdF7KrFySAlpwCP4XH19kqSL0c66R1adQccK5ObTT3x8PNLS0nD06FEAwU2nxxMdhkDoheM41NbW4ujRo0hLS2tUZFENJHwcSLio6dIFuOkm5a+Vi2spKyuTfL0ak79eN4Ka1wOQvVbLouaUhVCpRUKusKXa95QT0mLPFRT8ZNnhs0phLfbNbFFCbj7jyMrKAoCQ+CHMIy0tLdTfWiHhEyMorcPDI7bgqzH5N46tUfeeel8fjp5ChXoXQrkFrLa2VtQ1CWhb4JQKNr8/BQcP5gEIFshUK0DkhLTUc2IVsu3IiGMp9o13RUq5Wnn0iBIj769Yx+PxIDs7G5mZmaIuZEI/iYmJuiw9PCR8YoT09HSUlpaGJlN+Yjt+PD3ib8Ccna/e91T7+vBJHBCONZFa1IxaCI2odwOoW+CU9tU331wadewGh2uvfU/R76JUSEs9N2rUKGbipFiJfVM7XrSKkvAK2gB71i4zsMKtFx8fb8jCTJgLCZ8YIryKrthEZ8bOV+97qn199HcrLi5XvagZtRAatVtW+j5K+8rvT4kSPQDgUfy7yBW0FKrcHA5rsSOsHJ8h1p9GulyjxRVL1i6zILceEQ4JnxhEaqJTsuCr3TnpFRFqXi/03crLi8GfnM6jtVCh3oVQbgHTu8Ap7augpa9xBWo1v4vUAsFS4DeP0LjlrR6sHp/h96dg69aeKC8vNswaE90HrFi7zIRVtx4Fl9sDCR8GsHrwS010cgt+9HlLYvABx4B+EaHm9WLfrVevLxotHloKFepZCOTcCUa4G5T2VdAFGCkGxa41ErsmeiU7ftbOoGvsijTHGpOYeJYJa5dVsOLWIyuUfZDwsRmzBn/0AlNTU4Njx44BkF4c5RZ8pYF74Z+tV0Soeb3Yd+vZcyt69twqu6iZdRirnDvBKHeDXF+dOHEidN21126IWFjNtnLYOdEr3cmPGzcwdDAtjx27bn486LXKyREuAviCqFrGgVMsFyy59Vi1QsUCJHxsxowCanILjNTiqPbgSKWuGSWZZFIofb3cwj98+GVo2bIodH10ESyzDmOVcyfodTcoFWybN29udN3Bg7kAgLy8Q6ZaOVgqFig2blk5XFb8MFzjrDHRIiBYCLXxuXNyOMlywapbjxUrVKxAwseFyO0kxMSNkoMjExLODxm5mzV64VSbvqz19UoX/nDCJ2UzJmc5F5Red2C0YKuursb69etlXxcUgi3/+9dvAQhXbjZjR29XjSQnLDJC4wEw1ionLK6CBSbDkRPALAlaOVgJYg+HJStUrEDChzGMXgyiJ/muXXdgx46ujcSN3M3HHyPAT15Kbla91hO1rxe6NloA2GVOlrNEGRFTJCU8xL53x44dZS0cZuzo9YgPPSLMKYtM9HgAAujVqxw9e241zConJwK0Hh3CctFPFoPYWbVCuRkSPgxh9E5UaJI/f7aXumwu/hiBqqqgCVzpzarXeqLm9XLXSvWv2PEWemMT1BSOlHq+pqZG9DOECiAaWaPF6FgEPeJDrwize5FRUsySR64GktLz1MSQEwFajg4x8x4zyuqo1+1uNCxaodwOCR9GMGMnKmzKjjxDRmk2VzRqrmch8FGuf6XcQnpiE5RYraQqN/NZdPzBqWoxelwZIc71iA+9bhUzFhk1h8MqPU/N6Ere0a/nsTKAX889plfw6nW7mwlrVigW5muzIeHDCGbsRIXjBIKZGzxKs7miUXo9K4GPSvrXLDeYlZNE9HcwclwZJaLkxIeUdSvaaiD2m9XU1Ai68IxeZNSMb6XjqFmzZqYGWNsVwC9FZWVlqD1Cn620744ePYpjx44JZp9effXV4DgOLVu2bFQxXOxzwzFTELBihWJlvjYbEj42w0/yShYDtZOh0CQvFOPD32RKdn9qd4uspGzK9a8TAl4B6fgJoe9QWLjXMAuHUSJKTnwotW5J/WZr1qyJmJzNsnLosUDZGQtjRwA/IP6do61Bcgur2PsoHTtayoMYLQhYtEKxMl+bDQkfm/F4gtYXucWAv06O6DN4hCb5/v0/Fpz0R4wY0aiGCRC5k9GzW7RTXMil8EtZMsL7004zr1T/iX2H6dMXG2bh0OsmUntQrhRKrE/hY9QsK4dQu5SIGacIbTWI3WMAUFGRj8rKbGzaVKLoO0v9Tkb0ndqF24zMNavGpFbcOEZ5SPjYTPhZRlKLgdiZR+GI7UqidxJiOwulGRxabkQWsmnE+lfOkqF2N2oGcv0n9R2MsnDodRMpmeijK4OLCQkt1iezfzOlCwUL94KRSAnaffs6YPHi6f/9rufd7Fq/s9q+M8uqZtT76hmTZrre3DZGoyHhwyTKrDvRaDU/lpaWonXr1rbH2JiBEnOymCUjMbEOFRX5TJh55fpPzhpjVEVivZYaNZ8nJSTE4tcqK3Ma1Z6yAjULhZ57gaXA0/C2lJaWhuJqTpw4gc2bN4sUSDwPx8Xh4MFcHD/+q2IBoabvvviiWLGFSQ0sWELMjsWxO/vRbEj4MIQZ6ezRhQuFdiler9f0ydKulE0pKwNf40csFmr58onMmHnl+k9JarLSGDGh407CsSIWQU5IeL2nUFKyCWVlA3F+QfVg06YSdO680/LJWc1CofVeYCnwVElbpKpPA8HvvHbtdQCU32NK++6LL4ojxoZRFgtWLCFmF410e4o9CR9GMDvtWCiomZ9ktAROq8XOlE21tT0SE+tCogfQ9lsYvTNX0n9GZIYoXVxLS0sj3K9WHqTLf6+cnCqIlWewWvioWSi03gssVUhW8hlSWaXBxwH+LDKl95iSvvP7U1BWVgIzxgarlhCjXXqspdgbDQkfRjA77ViscKHXe6pRBoxZsJKyySPmBquoyNf1Wxi5M1cbEKzXGqM0q8Pr9ZoqlpUICZZ2pUoWCqMzy1iqkCzUFqE+KSnZhJycSpw+3Rxr114f8R5S95ia++D48XQIHe5qxNhgaczxmOV6Y22+NhISPoxg5A2lpnChUAaMkbCYsskjdr6V3t/CyJRQvQUQAe3WGBaz8ABExF3ZvStVI2aMzOJhIc5ESVvE+sTvT1F1j6kJjBezNJWUbNI9NlgYc+EY7Slgeb42EhI+NsMPILkbSs1AU1O40GxYT9kU+lwjJzcjFigtfRPuaqurqwsdNcIj1+d2xTIozRAK70ujavJoQe34NmKcsxJnoqQt0SUyampqsGbNGk3znVLraPR7AwEMHLgJvXuXN7pWKWbVgeLR6ho32vWmdDwDaDSnKGkvK5DwsZnogfbAA8ewf38C8vPPISenB4AehsSDhMf4AADHAfv2dbBklyh3ynf4wszKDWOEmdeuBcoIV5vchGrW2WZiVji5vozOWjN7HNmZXcVSnImSM/7CXaLZ2dmGz3c8/Ng5duwYRoyox7Rpu3HwYDLy8s4iKysbwAgkJCRoOuPMzA2cnvvVDNeb3HdQ2t5Ro0ZpqpBtBSR8GCB8EGRnA0VF2t5HalcCAN991zXsaut3iSxlpQhhtJnXrgXKCFeb3ITKixGh99X7+2nZ2arJWtNL9Dg2qx/4zwoXgQBbcSZa2mLEfCclPL1eL5KSktCli7FziFlzktIwg/BjPfixYIfrTWl7papo233kBQkfFxG9KwkvBldRkY/ogD+rd4msl0M3elfHwgKl1dUmN6FKva8Zvx8LfckT/v3M7AdhgRWMb2IlzkTrwqvHYsb6BkovSo/14NHqejPKaikVZM/aHM9DwsdlRO6mslFaWioa8Kd04TDDrM9ScGY4Rk6UdgdC6nW1SQWmWu3Cs7svhTC7H+QE1vTpi22LbdIT86JXuLC+gdKD2nlR7JghwBrxKNVeVud4gISP6+FrrejZmRm9u2IpONNs7EwJVeNq48Wt3++PeFzI3WeXC8+oOkVGiXir+kHqHLaCgp8iFj+r4if0WEeNFC4sL65qkZoXAQj2kVYXrxG/gVx7WZ7jSfi4HL3ZCGbsrlgKzjQDVlJClVr5lIrbgQMHoqyszFK3k5F9aXRsjlX9wFJ8UzhGCCw9wkXvBoql4z8A8d9569aeKC8vNk3caf0NpMYl4GF6jifh43Ksqh2iJsuHpXgNM2AlhV+plU+puE1JSVH1vkZgZF9qic0RWhytDix16/2iV7joPfOMtTghsd95y5ZiqK1wrRQ9v4HcuGR5zJLwiQGsqB0iFngHNJ48WIzXMBpWgirVuoeU7v6sdOEZ3ZdKJ3tpC1HwMSv6wa33i17Lrx5ByGKckNDvXFxcji1bekdcZ6TlRM9vIDcuWR6zJHxMhDVTqh6U3CBqJg83l0O3G63uIbW7P7H3ZR2lk71SC5EV/eDG+0WvJcsoQWh3nJBcGRLezcVjpOVEy2+gNHyC5TFLwsckWDSl6kHuBlEyebAS++J2tLqH5ARBYmKios9n/fdTO9lrdQfI9YPcxkhJoLmTMUK46F1cWUi0ELpf+cKdgHLLiZaNtpbfQGp+CS+hwr8/i3M8CR+TYOkkZSOQukGUTh6sxL7EAlr6UE4QeL1eV/x+aid7qaDTq6/eBKBxWrFcPygNtFaC3YuIHrQIF6UbqJqaGtnfgZVEi+g2qk1K0bPR1vIbiPVpdGVuIViYI0j42Ex4UDALA0IKsRtEzeTB8veLdeQEQXV1daOaIayPWTHUTPbCZ98BW7YUo2fPrZoyq5S60cTK/vM4sf/1Wn7Dj6YIty5Ei0e+crCUVZ3VwHG1m0S1MUtmWt+dMB5J+FiE0mqcrLm+lNwgrBVHJLQjJQhWrCgz7WgGO5CyFACR2VtCQaaAfsuAnLU0LS3NllR1M1GyqNfW1goersuTlJQUqlEGaK+grcT6Z9ccpfU9lYQdxLr1nYSPBagJngs/j4WFgSd1g/B+aJaKIxLqUSJu9aR/R3+WXb+l0t1r+BlD/IalU6cfItKKAWMsA6y4WqxGzg24bNmy0N9im8bS0tLQ82ZUKA++t1/SqsQzevRoNGvWTPQzrBr3avoiludUEj4mo/amZNECpOTztfiJWUwpjUXExK3Sk9F5rDy8UwtKdrnhC1202OvWbQd27OhqaHouq64WO1HqBqyvrwegTTwqdfVwHKeoLa+++mroOjvHfawKabWQ8DEZsYF48GAujh//VTaYMdwCFA4L1iAj/cR2p5TGOkYEgFp1eKce9ByrsmNHV0yY8CLq65MMS891a40eI1AquLWeEK/E1XP+wGdlbZEa92JzOf9ZRsznRgpplq23eiHhYzLCgZEBrFt3XcTNEbSWKD+RF7BmByE3+I0w8bKQUkqIY1X6N0uIib36+iQUFPwk+3o1qeos1zuxE6WCW6t4VDJ38jFGSuuYKSnyaqZFyCghzbr1Vi+uFD5/+ctfsHDhQhw+fBjdunXD0qVLcfnll1vaBt7KET0QgQCC55h4AARvjnffHQqPB6LWDrtcQFbF4JB5lm2MSv920u+pVuyFWzW1pKqrrdETLqwqK+NQUZGAgoJzyMkJhNrjxAUpHDW/gdniUUlblIx7KyyhRvSFE6y3enCd8Fm9ejVmzJiB559/Hj179sTixYsxaNAg7N69G5mZmZa1I9qUOm3abrz22lacPt0ca9deH3V1HHhXcvQuwU4XkFUxOBTnwD5607+d9nvKiT2pE9GVLhpKEHIThwsrNanwThNDagW3mQUelbRFbtybaQmtra1t1F4j0tPdYL0VwnXCZ9GiRZg0aRLGjRsHAHj++efx/vvv46WXXsJ9991naVuiJ5mCgp/g96cI1gQJh98lAGBm0JkpwJTUjwnHaRO4W1C6sLglbkVK7Cmp2yO3aGit0aMk7gQA5s//l+NdE1K/QUKCsuXLqAKPcuJfbtwbZQmNdqOqyTxT+9u7wXorhKuET11dHbZt24bZs2eHHouLi0NJSQnKy8sFX3P27FmcPXs29PfJkydNaZuc6yv4Lwi/S2Bl0Jml+pXGOQjFOTltAnciShcMoevcEreix4ogd/+qrdHDL3jV1dXw+1Pwww8Xi1aU5s93it6kHD161HEuMrHfIC0tzfJaNHLjQWrcG2EJFXejpsh6CKRiMcVwg/VWCFcJn+rqajQ0NKBNmzYRj7dp0wY//vij4GvmzZuHuXPnmt42MddXq1bHsW9fB9FdAguDzgwB5vP5InYpgPCkQinu9qG3yJmZrgez0CP2opFbNKItmeHvHd2njd1b0//7vhzCN01AIOJQy+hNCl+jSGqBNHNToSQ2Sc1vYLZI02ItEhv3RlhCpdyoJSWbsGlTiaEbVLdYb6NxlfDRwuzZszFjxozQ3ydPnkReXp4pnxV+kyYlJWHLlmB2iNgugZVBZ4bqVxI/RCnu9qNmYTFSNNiFkRVt5e5fvkaSkmwZMfdWUPQExY/HE0D37tuwbVuPiHaozT4ya1OhNDZp2rRpzFQVVlplOryOjxRGWUKFfsOyshKEF9jkH9frIXCL9TYcVwmfjIwMxMfH48iRIxGPHzlyBFlZWYKvSU5ORnJyshXNi0BJRWSAjUFntgATmgQLC/dKTs6xHPfDan0Nt5TBN7J9UvevlmwZIesr4MGgQR8iEOAXv6hnNWQfmYHSmjh1dXVMHdOhZDxIjfvw+RwwxhIqPA7iTPMQONF6K4WrhE9SUhKKiorw0UcfYfjw4QCAQCCAjz76CNOmTbO3cQKI3VBmHiCnFbMEmNgkOHLkOsnJOVbjflg/5kPrZ/Jizu/3hyryhpOQkIC0tDRHCKdoxFy4WuLmxKyveXkHsHz5RETv+LVkH5kNK7GLRiI1Js2whIr9hr/73Wf47LMrwYsgrRtUN1hvpXCV8AGAGTNmYMyYMbjssstw+eWXY/HixTh9+nQoy4slpHbupaWl4DhONOvDDv+2GQJMbBIEONWTcyzE/ZhZYsAuS5KYmHNS0bTwvuMPOZVC6+IvZn2tr08WzBQdOXItOnfepeg9rBIddgsvqzHDEir0G3btugOff35e9JSUbNIcGuAW660YrhM+paWlOHbsGB544AEcPnwYl1xyCT788MNGAc9244TKmFYMfvEd7CHRyVlJQbhYwMgYKDstSUJiTioziTWBK3wv5wseqllfX4/169frWvyFrK9CZTL4+0jpe1iF3cLLDsyYw8N/w8TEOixfPjHCgrhpUwk6d96puV+dKmqU4DrhA5wPjmMZlitjWrnzl5oEhSZntwQ8C/VxuJuHd+2EE25Zk3OViGUM8e8T/fuxcmDsN99cinffHYpwl42YG4iVWCel97LX6w1dp3fxj7a+ank/O+M2WIhdVAor40wI/jesqMiXtSA61S1lBq4UPk6CtcqYduz8pSbB8MmZtb7SitI+FqK0tBSAvKtE6ow3QPr3k1q8wwWVkglfyaLBw/++0XEqQOR3q66uRk1NTSg1WwqjLVRC34fvE7Xj0+jFX+r9RowYgcTExEYlJISwKnHACQGzrFnmxcSLnAVx1KhRrrbgqIWEj82wFuhn1c5fafxQuHtATV+xvEtT0ndi/c5bhJS6StT+fkoPWuSRmvCVCjwpMSf03YQOe+Rfb6aFSnoR1HYvK138le7Wxd4vIyND0Xv4/Sl48slvmHK52wlrlvno8AM+Y0zO4idVIdwKDh0C9uwBOnYEcnNtbQoAEj62w3Kgn5luJTXxQ/wJyUr7ivXMp2iixYmSflfi2tDy+6ldvKV+P6UiWkrMAcKZSeHfLVjHhkN4JosZ7k+5RbCwcK/s+NSaLSN2v0SnSksh9B7hr5caL5WVlairq9NV7ZnlTCGxjZJWa56ZiPU1a+5Dvk9XrWqKWbO8CAQ8iIvjsGCBHzfd9Kutm08SPjbDaqCfFTe62kGvtK+U7r5YCJLVU31VaqIzOl06MbEOFRWNA3a1fk8lYg4IoFevcvTsuVXyu4Uf+WLFgiTWt9OnL5Ydn3oSBoQeUysmxO45oe/07rtDkZR0Fnl5B7F+/Xrd1Z5ZzRRS4s6ywjJvhJWaFfch36d+fwoWL54Ojgven4GABzNnpuLnn1+C13vKts0nCR8GYE2pA2y54MInd6m+ElsEWM0C01J9NfpgRrGJzsh06a5dd4QyRrRYVNSIMCX3gpRLTOn31INU3yppv5ETvVFiQqwg3tq11ysS5Eo3ESxYWKNRYsk7fbqZqZZ5rbFESoUvfy6iVeez8X0qNw/Ztfkk4cMIrCh1HpZccHomd5azwLRUX5U7mJF3XRiVLi2UJitnUQk/TFPse4ZPfqdORb6P3L0g5hJT+z21Ite3Su9lo+LQjFi0pPqUT41mZSNkFmICneOA4GYkEOojoy3zWmOJ5OZGPglg9erVtpzPxtI6Eg4JH5tg2d8NsOeC03JTsuSXF0JsUgjfXYu5S+RQ+/uJBZsrSZMNRyi+Sm7yKysrAxDMPPF4PKKVm8+dOycayMkvTlaMU633Rngfs5Yt1NjNGAnvegy3RrKwgKlFKitPvJgqTxw4LoDrrluDvLxDmsaYGbFESsaHXeezsbaO8JDwsQm9JmorspZYdMGpgSV3nRBik0L37t+ic+eduvtdze8nli2idscmNCaFvmdxcXmj69LS0iTPaOKD3IW+GwBLx6lc6nhGRkbE9dH3I2vZQsD573TwYC7WrbtOtSBnHbmsPDlLYpA4NG9eq1n02BVLZOdcyOI6QsLHRvSca2TWblFpmnl0WX6zgxK1CD25RVttTRqjUBKzJNbvchZAPceMCH1/vTs2fmwWFu7F9OmLsXVrT2zZUowtW3qjvLxYl+sx+rtp6S89SKWOKz1kkzWrZPA77UJdnXmC3C6UiE0xSyKPmOhXMs6UfL5ZriG7XU6shXKQ8HEgZu4WhSxRfr8/ovCZ35+C+fP/ZZlZXmt6utyiraYmjZGIWfuUVG62KnNGb0A5IJyxVl5eDH4hUbvIKxUxpaWl8Hq9hotZM9zTcjvx6upqU0W5WFvVCnInISU2o7/3vn0dROcP3rKn9veRE7tmuIZYdTnZBQkfB2PWblHqJrbDLK+0HgxfayTcGiV2rpEdxzGEI9THSq0EWt5by3voEVBCY1NvgKzd6dBmfL6weyVYPgA4L87NEuXh3ym6JpAbRI4QcmIz/HtLiX5e9NTV1UW4YcMRGg9yn2+Wa4hFl5NdkPBxMEb6baVcSXYU8QpvT7hLSkp4RVtw+J2/0iJtRCR6FlrxQFF9AbJ2p0Mb/fmNg4qDhRiXL58YMTaPHj1qqqDTC4vnQEXPafw8otbtIyYAhSzhSkIOlHy+2GfqPU7EKjHLevIOCR8HY5TfVmnMkFUBcmKuLTHhlZl5GPX1yY3a6/V6I6worMVTuBmtGWt6YfmoEjG6d/8WmZmHJcsGrFmzxnRXrFpXYvjrWOtT8TktRdbtM3DgwFCmoRRcMAAIgDpLuFa3k5OOE7HbOisHCR8HY5TfVmnMkFUBcmKurdOnmwkKL6XF9VjP8tKK0sXeSlFgdsaaEE47qiSc+vpkwbG5dWtPXH31JgDmu2JZX6zUoGROE3P7FBQUKOoH/nktGyolWYFKLdVKDwGWwgzLC8vjhISPwzHSbyt3A9sRIBd5JlMAQVeAJ+wKTvGEY3dmgxkoXeyHDBmCDz74QPa6UaNGhYKqtSxyZmasyWHVAbtGEP1dxVKpt2wpbnRkh5mwvFhpQcmcJoSSfuDjevRUSVeSFajHUu0mMWskJHxcgFF+WyU3sJUBco3PZOKrp3JhQkj5hOPGzAali70S0QMAa9asifhbrXVEbKKtqanBuXPnAACJiYkRrhLA+MmX9Vguvp8qKytDRRmLi8uxZUvvqCudb5G0Ey2iRK0AV7qh0mp90WupjjVRowQSPg7ELPOl0hvYqgA5sSMdRo5cg+bNaxsdpyDW3nDcnNmgZrFXen6Z1pII0RiVsaYEu2K51LoS09PTI67v2XMrtmw5n+4PON8iaTdyc1p0sUktAlzphkqr9cWNlmq7IeHjQMwyX+q1iBjtJxa74cPLxStpr56ifk5B7nTt8O8rdhAjiwe5asGOWC4j4ou83lO49lp3WSTtRm5OU1NsUgqlGyot1hc3WqrthoSPQzHLfKm2FD+PGX5iJTe8VHtramqQnZ0dE35uudO1eeuPmEDyeMCsW0gtduyQtcYXRYttN1sk7cKqPjXTEk7jwlhI+BCNMKIUv1EZREpueLH2hqcAO1nUKEHudG3e1SMmkPjMXDek+Bu5Q9YyjtW4HNPT01FaWhpRD8athQPtRKxPo4/eiUZqnrI6Y4rGhXGQ8CEMv4H1niWm1DUVDssZPGrRstg2LoQXCe/qUXIQoxtS/I3YIatxX/FoiS9q3bq1ovY42RVrNUr7KjqYXwixecpsSzLrRQCdDAkfwrAbmF+wlVZaFvs8Je2pqakJTVqsZ/CoQU+siNzp2rwAiBRIAQTLA3gaXes0jI7l0uK+0hJfFAuuWKtR0qdKKy/zR+FEY/ZvQuPCPEj4EAD0xwwJWXkOHszTVX9CCW6rxqzUQsVfJ7zYC5+uLVaSQOogRidh5kKhVFxrjS+ixct41PSpmqNwwjG7GKZV48KJFc/1QMKHMASxSqnRGO1GcWs1Zh65tPPwxT58Byvl6iktLQUARdc6zYxuxuSsRlxTBo7zUPL7usmVHo3e0AQnQsKHMJTGRQcjMdqNwmKNC6N2T0qtDPx7ZWdnq7J4kBldGXLiOvrgSMrAcRZyv6+bXOlCKD2yyA0ij4eED2EowllDQczY/bK2wzbqvCitLjy1VZYJeeTEtZArRCy+qLa21ryGEpqQ+n3l7sNo0RuO0zYOesMGnOQuI+FDGIpw1lAA1123NqLwoJGwtMNWG6Mjhlorg55JxUkTlh0oEddKK2G/+uqrrnIZuAGp37eiIl/yPpSK/wG0u4fsuCf1hA04zV1GwocwFLFJpHPnXYZ+jpXVmPVMQkoXxGi0WBm0TCqsnWjOqgiTEtdi7gE3x4U4GaHsU7HfV6kr3cjf2q57Uk/YgNPcZSR8CMNRaoHRI0qsSvXUMwnpiQ3Q4sLTMqkYZaEyAtZEmBJxLeYe+PXXJti0qcS1cSEsokQ0AxAdY0K/r5L70OgYIK1VwPViRNiAU7JsSfgQpiBmgeGPvTBClFix+GkVBlongPDFVkxAarUiKcHM95bDbhEmtHCWlpaivr4eAJCQkIC0tDQAQHV1NdavXy/qHigrKwF/2Cirh6S6CaWimc9olOOqq67C5s2bAUhv5Mxe6K0OrNYbNuCULFsSPoQhKLXe5OTkOHryVSoM1E4A4YvWqFGjcO7cudBzJ06cwObNPwEwdyJkLXvFShGm1dok5h5w4iGpTkaplYQXsXLXRVfTFtvImbnQ22U90XM0BotZtkKQ8CEMIRaqjKoRBmomAKWLlpkTodWTrJB1QmnFbzPQ6l4Qcg+UlGwKubl49Ez+Siw5drlHWETp2JG6Li0tDdOmTcOxY8caiSWA34xsNnWht8p6YuTRGKxl2YpBwocIcegQsGcP0LEjkJur/vVOFjVyqBUGaiYApYuRmROhlSZqOaFnd5yAWtEl5B5o2vSMYYekqnXhsGa5sxKlY0fpdeFHWghh5kJvlfUkugiqkNBLSEhAXV0dfD6f7DzPUpatGCR8CADA8uXA5MlAIADExQHLlgETJtjdKnZQKgyUxOhEXxeN2G7dzInQqPfWYp2Ixuo4AZ/PF7I2KV0Q5QKfjZr8lYpifrGyWzTajdKxo+Q6pVY0sxZ6K60n6enp8Pl8skIPUOYu1eMuswISPjGOz+fD/v3nMHlyJgKB4EGVgQAwZQqHSy45ivz8BFdbcpSiVBjodflJ7dbNnAiNeG+tAabRC4qVcQLRbVa6cAr9zuEH5wLmlFiQi3tySnCpWSgdO2rHmJwVzaxyGlZaT/QkFzjtJHkSPjEMP+lXVOQjEBgT8VxDgwdLl36AgoKfLAmIDLcUVFbGoaIiAQUF55CTEwBgf3yQGmGgtZ1KdutGn6tlhIWKR611AhBfUKza6Ua3Wc2CGP07qz0yRC1KXFhOCS41C6X3qZr7We6+5DNVo9H6W0dX97arCria5AKnxXiS8Ilh+EEqN1maHRAZvuuWmtztyEgxUhjIIbdbF5tg+c/X0jdmTlhyE6fYgpKZeRgtW57AhAkvor4+yfKDU4uLy7FlSzEA9aLLrPGp1IXllOBSM5G6TxMSEhRdF+6SlbsvMzIykJ2dbVj7mzVrZuh1WlAisp1cPoGED2H7ZMnfPHKTux0ZKWYKg+gKsnIC1OgJlseMyUnJxCm2oLz44kSEi46CgmAq/4gRI0wthxDd5uLiL9Cz51bL7gOpTDc1LiwnBJcajdJK7pmZmYrv56qqKgD2W9Gsrq2lRGQ7vXwCCR8CABuTJavxCWbcuEITh90C1CiUWieEz3XjIFb4LyMjw1KLSnl5MXr23BpxnVnWJrmFRG7xDbdkAOYe4aIGq6wCZm5Q7Lwv7cjQ0xL4LQar5RNI+BAh7I7El5vc/X5/6HEW44DUIDYhmO1OswKlAlZoQbFL+CpxM5ppbZJbIOQWX77uDEuuB6utAmZ+Nzs2hnZl6GmxcNlZ8V0LJHxsRG/dHBYw8jvITe58qiVrcUBGED1x8N/ZyCM+rEKNdSJ8QUlMrMPy5RNtcSkocTNa2f9CC4mcKGZtfBhhFbAzjsTKg5CF0GIBN6K/1Fq4nFg3ioSPTbihbo4Z30FuZ8ViHJBepCYOs+J6zEStdYI/9wqAbS4FltyMUuNh3LiBjQLcWRQ9Qqi1CtgdR6LEfVZbW4u6urpQPFA0en4btZYXvf2lJpFDaVwmq5DwsZhDh4AtW4BJkwCOCz4WrJsDDBrkDMuP2bV/pFxurMUB6d1hyU0cNTU1jhE+aibO8D6xMnNOT5v1IjVWlBZQdKIQBrRZBViII5G6d30+H5YtWyb7HlqFmVoxrre/1MRJ8UKPtflYKSR8LCTcQhJNQwOwd6+1wkdL0Sm7a//YnWERjhE7UrmJY82aNY5x32kNMLWzBohVn610rMiNh/DzzIxuo1kYZRVgLY7ELGFmlBjX0l9qxxBL87EaSPhYxKFD4qIHAOLjOaSkHIXPZ12lZC2Tvhm1f9TspllySRhxMKSSicNJ7jutY9fORduKz1b6G8qNB94lKASrAtkIq4AT4kiMEmZGiHEj+0uqzAJL87EaSPhYxJ494qLH4wngmms2YMMGYwJ01QQca/0cIwe82CF5hw8n4MCBZLRtexbNm59AWVkZADZS76PROtEI9WNJySYmvhNhHmKLpJ77ilWBrNcq4IQ4EqOFmZ7538j+UmKplJqPza4urRUSPhbg8/mQmnoOcXHnY2KA4M0/cuRa5OUdUmwhkMPKoGkjBUj0IXnCE8n568XigIRcAWa7AbRMNNHm7F9/bYKyshJwXBw2bSpB06ZnmNvRsoSTq8bKLZJKAvxZcvnIoXeTxLr7T6vQMGsMGxl3o3QtEpuPX331VSYtkSR8TCZcMQ8d2njC69x5l2GfFe1O0xM0rdRqZGTtHyMyBVasKBNcFMy8+bRMNOnp6SgtLcXq1avh96dg06YSiBXuU4vZosBu0WF3to8etJz7FJ715gSXD49RsSqsu/+0pp2bNYbNjLvRIrpZtESS8DGZ8B/dTBeNz+fDl18CgUDkTdLQAGzd6kPTpsrNp3an2mvdsUgtCmbefFonGq/XC8DYHZrZooAF0aH0t6ysrBS91i6LkJ5zn5zg8gnHqMBx1t1/Wu5/MzPWzIq7kRPdTrJEkvCxGDOqI/OLkd+fAo9neqMb8IsvXsbOnadkFyMlaepWIDeRhO+K+d2wnYuC3onGyB2aEQHXdr6/FsTaIGUJAOyxCOn5rZ2YOqynf5VajAB7F10jhIYR7TezNIPc/OokSyRAwscV8IuM3A0oVyFVSZr6qFGjzPsi/0Xuewjtiu1eFPRY8+zaoemFhclOTxvsMMHr+a2dmjqsFSmLEQvuP6OEhlHtN7M0g9T8CsBRlkiAhI/r0LoAK01T93g8gq+PRm/BN7Xfw45FQW9Jezt3aHphwe2itA0smOCN+K1ZTh02K+5L7jV2jkMjhIbR7TfLgik1v9q96dQCCR8XosedJje5er1ey4rNqfkediwKeic+u3ZoRvQJC5OdkjawYJUC9P3Wdla2VoIdcV81NTUA7B+Her+PGe03Q4TKza9Os0SS8GEQu0/hFppc+V1zZWUciorYypThsaK+j9GTih07NCe8vxFtsMsaYMYYYe3k9XCsPlrC5/NhzZo1ANgYh3owuv1Gi1Ah0X3wYC4AD/LyDgJg2xIphmLhc/LkScVvmpqaqqkxsQxrp3CHW1vCd82vvMKZluWVlJSkyC0h5w4w64aLnlTE2jpq1CikpaUJvodVv6/ZkxELk51cG+ywBphl/bBjTtAq4Mx2LYa3iYVxqAej22+0CE1PT8eoUaNCQnPfvg6CFlQWLZFSKBY+aWlpsvEdHMfB4/GgoaFBd8PcgtIfPScnhwnBE030rjkQ8Bh2GCkPP8GuWtUUTz99NwIBD+LiODzwwM8YMeIEEhMTQ6nfUgdcSmHEzRc+WUi5UPhJQgyrsonMtoDZVUFbqevHDmsACwdrGoEaAReOHa5Fpy26APvuy3D4TZycBXXcuIGNalCxspGPRrHw2bx5s5ntcC2sm6nlENo1G3kYaXgq/uLF08FxfBq9B3Pn5sDvXwOvVzwV347+1RtUa+aipzfg2qj39/v9oeuNHttyvzmf8cOCNYCFwGotaBFwdrkWxfq4tLSUaUuZFfOWkeNPTw0q1lAsfPr27WtmO1yN2TefmVYPIw8jFYJ/vdxNJfU5/HEX/DWVlXGoqEhAQcE55OQEUFdXB5/PZ9jvwHJQrdkTavj719TUiFq3+KNHAHMsXFLvx8pumpXAaiNQsoBqdS3qiYmS6mPeSmwlLBT45DF6/Dk9nioczcHNNTU1WL58OXbtCh65cPHFF2P8+PG2DLZYx4jFTmwRsGrXrOemCp9spG52oyYbVoNqecyeUNW+v9VuHRasrHaPASNRuoBquYf1CAUW+5gVV6cZfcOCBdUoNAmfr7/+GoMGDULTpk1x+eWXAwAWLVqERx99FBs3bkT38NMkCUvQO4lHLxbhBcKsiOUwoiy93M1u1GQT3VYg8kR1u1Ns7YIlt47drmOrxoDZ56apWUC13MN6hIIT7jO77gkj+0apBZV3bwu93u77MRpNwufuu+/Gtddei7/+9a9ISAi+xblz5zBx4kRMnz4d//znPw1tJGENUoPTzGwpHr0Cy8qJkD9RfdOmxiequ8kkrBRW3Dp2H6DKY8UYsMKtouSessu1yPp9JnVPhJ8ob8aYNLJvpCyo4e7ucPd2NKwdGKzZ4hMuegAgISEBs2bNwmWXXWZY44jYQ4/AsnIi5E9UF9sJS+18wyc9HhZ3RUphxeXAUnyFFW4BK85NU3JPGelaVFPOQq6P7cyIkrsnos+RM2pMqukbNZsEvW1jLYtRk/BJTU3FgQMHcNFFF0U8fvDgQaSkpBjSMIJQi9LFRioQGlBWQVduJyy18xU7PJO1XZFSWHE5sBBfYZf1wyyLm9J7yohxq/Q7RAutBx44hv37E5Cffw45OT0A9LB9I6H2njBqTCrtGwDMbBLsQJPwKS0txYQJE/DEE0+gV69eAIAvvvgCM2fOxI033mhoAwFg//79ePjhh/Hxxx/j8OHDyMnJwejRozFnzpyICWTHjh2YOnUqvvrqK7Ru3Rp33HEHZs2aZXh7CG0cOgTs2QN07Ajk5przGXLuMj2B0OHFvJTshMVTve0/0dxIWHU52BFfwWJ5BS1uFasFnFqrYfh3yM4GiooMaYZhqL0n/H6/YangSvqmqqpK0XtVVlY2ek8pWIrzk0KT8HniiSfg8Xhw66234ty5cwCAxMRE3H777Xj88ccNbSAA/PjjjwgEAnjhhRfQoUMH7Ny5E5MmTcLp06fxxBNPAAhWlr766qtRUlKC559/Ht9//z3Gjx+PtLQ0TJ482fA2uR2jUuTDixPOmuUNFSdcsMCPm276FbW1tYa2B5B2l+kNhOaLecnthPlK3AAbJ0mbCYvZHnb2s9U7ZDnrgha3itUCjhWroVGI3RMAUFGR30gYrF69mknrCj92lLTNSXObJuGTlJSEp59+GvPmzcO+ffsAAIWFhWjWrJmhjeMZPHgwBg8eHPq7ffv22L17N5577rmQ8HnttddQV1eHl156CUlJSbj44ouxfft2LFq0iISPBoyY+KSKE86cmYqff34JXu8pjB49WnLsmGG2NmKildoJCxXzYiUWxihYqZcTjZZ+ZiUoWgtqrQtqjiuwClathmqRuif27evw33lQWBjYafGVs9TItc1pc5uuQ0qbNWuGLl26GNUWVfj9frRq1Sr0d3l5Ofr06RMx8AYNGoT58+fjxIkTaNmypeD7nD17FmfPng39reZMMrcg5oIyKqBNTmQ0a9ZMl5lXjXWKb5NRE62aYGy37WpZqJcjhNp+ZikoWgtqrQss4YRAZTWIlQVhWRgYYalx2tymSficOXMGS5cuxebNm3H06FEEAoGI57/55htDGifG3r17sXTp0pC1BwAOHz6MgoKCiOvatGkTek5M+MybNw9z5841r7GMs3w5MHkyEAgAcXEw5QBSs3dzahZg3ret1T2jxwXoll1tOCwKAb0WECfGYKm1LrCC2kBlJ1jmhD6fVWEgJMjefXcoMjMPIzdXWRwQIH/PRWey2v07aRI+EyZMwMaNG3Hdddfh8ssvlz28VIz77rsP8+fPl7xm165dEdljP//8MwYPHozrr78ekyZN0vS54cyePRszZswI/X3y5Enk5eXpfl89WBEE7PP5sH//OUyenIlAgHdBwfADSAFrYkCUtNXn84VuQL8/BS1bnsCECS+ivj5Jcd0gPVYOFmNh3IiefparvWL3hB2O2LlpZloXzBAeSgOVnWyZExIGAIfKyhwUFPxkW7uEBBkQh+XLJyoSy0otdkKZrHb+TpqEz4YNG/D3v/8dvXv31vXh99xzD8aOHSt5Tfv27UP/X1lZiauuugq9evXCsmXLIq7LysrCkSNHIh7j/87KyhJ9/+TkZCQnJ6tsufHIBQEbOeHyE0hFRT4CgTERzxl5AGk4dp3kzSOXzaVm8tHTJ3b3g5vRG3OktPYKKwurmFvFLOuC3cKDhXIFagkXBiUlm1BWNhAAbyjwYNOmEnTuvNO2eUBYkCkXy0IWu+++O42dO9+W/U52/k6ahM8FF1xgSL2e1q1bo3Xr1oqu/fnnn3HVVVehqKgIK1asQFxc5A9VXFyMOXPmoL6+HomJiQCAsrIyXHjhhaJuLlaQCwL+8ssy5OUdxJw5YwyZUJTGuRg9MK2o/iyG0mwuMzD7xHQ3o7bImp6YIzHBcPBgLrzeXaHHWFpYhb6LWS5VJwoPuwkvgZGTU4XzoieIXe6uaEvNu+8OBaBNLEdb7HJyqnDw4PnXsJjirkn4PPnkk7j33nvx/PPPo127dka3qRE///wz+vXrh3bt2uGJJ57AsWPHQs/x1pybbroJc+fOxYQJE3Dvvfdi586dePrpp/HUU0+Z3j69yAUBr117PTyeAC644CTuuce4z41F14vS3bCRIoTVIGDW0WJh0NOHYrvfdeuuQ10dmzEyQlh1X9u9oNn9+UrhS2DoPYjZyPmDn5OCdXrWIzPzMJYvn2i4WGY1xV2T8Lnssstw5swZtG/fHs2aNQtZWHiOHzc2WLOsrAx79+7F3r17kRsV9MJxHADA6/Vi48aNmDp1KoqKipCRkYEHHnjAUansYhMvEFycZ83yol+/I4bG38Sa60Vu8hkxYgRycnIMFyEkatRjddCx2O6XpQwcKawsL2D3gmb356tBb+aaWS7G8Gtzc6sMz6pjOZNNk/C58cYb8fPPP+Oxxx5DmzZtNAc3K2Xs2LGysUAA0LVrV3z22WemtsVMom+MaAIBc+JvzHJBGVUE0UjkJp+MjAwSKQxi1ULXvfu3SEo6i7Vrr494nIUMHDmssizavaDZ/flq0XvEhpkuRjOP/2A1kw3QKHy2bNmC8vJydOvWzej2xDz8Tu3gwVysXXsdwneeZsbfmAGrLp5Ys3I5HbmFrqamxtA6UHl5Bx1bdsCKe8nuBc3uz9cCa0dsiLnOcnICyMmpM2ReZrl8hybhc9FFF+HXX381ui3EfwlaYHahrs758TesWk/sDLQm1CG30K1Zs0aXBTQ83mH9+vUxGfumBrsXNLs/3270xjZZlZ3H8n2kSfg8/vjjuOeee/Doo4+iS5cujWJ8UlNTDWlcrEOWCYJQttDptYCmp6dHvAfde+LYtaC5rcqzFoxw+ZqdncfqUTbhaBI+/LlZAwYMiHic4zh4PB40NDTobxkBQJtlItyMWVkZh4qKBBQUnENOTrDCthkHg7IOi/FGhDKEYt84Dti3r4OhcT5UdkAaMxY0PeUKjIxHcQJaY5ui+zi6irLRsBriEI4m4bN582aj2xHTGDmRyhXq4xcKOw4GtRMn3IyxiNTCFz5BFxbuxX8TOP+L8QGtRo8RJxyvoAYz+kdPuQIWYmWsREtsk5I+NqMsAOvjWpPw6du3r6Lr/vCHP+DPf/4zMjIytHxMzBA+ofDVV7WitFCf3oNBnQjrN2OsoXThA4KTvtYCa2qtCkZgd5VjszCyrVQQUR1aYpvk+s7IbEknCX1dp7PL8eqrr+KPf/wjCR8FqB0QcucGOTHzgYgtlNbpAbQHtNolQNxw8KnVOKUgodUYGdsU3scADCsL4DShb6rw4SJt04QCpM4RCp8U5M4NivXMB8JZyO08tQbUsiBAnFRszy6s7iMnWSeMim2K7uPi4nLDNsdOs96ZKnwIcaRuvFGjRqGmpgYbN24EID0piL0Hy6mEBBGO0qBNvZlWdggQpxXbswOr+8hp1glAf2yTUB+XlxcDCECsVpybIeFjA2piG/RMCpSSSzgBNW5ZrZlWdgkQcjnLY3UfsW6dMMMaJdbHvXp9gfLyYsPLArDutiThYwNqYhv0TgpUqM8+rDCnO8lkL4acW7a0tBRer1f09Uq+o10ChFzO8lAfnccsa5RYH/fsuRU9e27F8eOtcPPNPdG5s/6yAE5w7ZLwsRm5QUKTgjOxwpzuRJO9EHJuWa/XqzsD0a77iCWXM6si2e4+Ysk6YZY1Sv4eO4XOnQfpvs/kLKtiNYSsHnuqhc+5c+fw2GOPYfz48Y1OSo9m9OjRVMVZAiXmd7WTAhXqY2OCtyKoloXAXaMw2y1r5+LKgsuZRZHMQoVf1q0TekWZUB8fPJgLwIO8vIMGtjSInGVVqlSLlWNPtfBJSEjAwoULceutt8pe+9xzz2lqVKyg1PyuZuKM9UJ90RO82MRh5U1mxeRqxWcYLSitrpRspQCx4rup+T1YFMl2z1WsB54bcU+np6dj1KhRWLNmDYBgtXMz5wklllUWxp4mV1f//v3x6aefIj8/3+DmuJfwSYo396kxv6uJ1XGrqFFC+M2jJRvOaKyYXK34DDMsBlYsfHYdQxH93fx+P+rr6yOuSUhIQF1dHaqqqlR/Tz2/B0tWDjvnKpYDz428p9PS0gx/z2iU1hpiZexpEj5DhgzBfffdh++//x5FRUVo3rx5xPPXXnutIY1zC2KTlBHmdze7rPTAym7OisnVis8wy2Jg9sJnp1WBf0+fz4fVq1fLXq9GNGr9PVi5L1iA5fhJuXta6rwtsfFs5jwhVWsoKakA69d/y9TY0yR8/vCHPwAAFi1a1Og5OqS0MVKTrpT5fdSoUSG1LoSbXVZ6YWU3Z8XkavUEzsquTSl23yNmu5nU/B6s3Bd24oRT3uXuabljjYREtNnzhFitoaqq4OHYLI09TcInEAgY3Y6YRsz8npmZafuk7VRY2c1ZEVRrZeAuS7s2J2K0aFT7e7ByX9iJE05513tPh4toVoQeS2NPk/D529/+htLSUiQnJ0c8XldXhzfeeENR4DPRmBEjRoTONbP7xnM6dqfIhmNFUK1RnyEWMMub1lnatTkNM0Sj2t+DpfvCTlg95V1pphuPkqwvVoQeS2NPk/AZN24cBg8ejMzMzIjHT506hXHjxpHw0UhGRobjT0xnIZWcx840YiuCao3+DCUBsyzt2pyGGaJRy+/BQno9IYxcTFp1dXXIzaXGesiK0GNl7GkSPhzHwePxNHr80KFDkhVWiSAsFcwyEhZrhdhVudqKoFqjP0NJLApLuzanYYZoVPp7qBXJLG1gYg0l/WqVy9mIcWBXZqUUqoTPpZdeCo/HA4/HgwEDBiAh4fzLGxoaUFFRgcGDBxveSDfhtMBQNbBwBg5LBRytWBjM+gypccrKrs1pmCUalfweakQyixsYlrFDJOqxHiptr1HjwO56TUKoEj7Dhw8HAGzfvh2DBg1CixYtQs8lJSUhPz8fI0eONLSBbiLWAkPtsGyxeJM5DaUVxe3etTkRo0Sjll202anysYhdIlGr9VBNe40cB6zNt6qEz4MPPggAyM/PR2lpKZo0aWJKo9wGP/nIqXQ3LRp2WrZYu8mchtw4DQ/CD4cEpTBmmPqtEvhutlAbgV0iUav1UGk7KisrI/522zjQFOMzZswYAMFOPHr0aKP09rZt2+pvmYvgJ6n9+8/hlVc4BALn46Pi4znccccQ5OcnuGbRiDXLltuQ2026IQjfSswSKWbPF3Qfq8MKcWD0+WZiQi28TpAbx4Em4bNnzx6MHz8eW7ZsiXicD3qmAoaNSU9PR3o6sGwZMGUK0NAAxMcDL7zgQVFRG7ubZyiU8uxsKIDZeJy4qaH7WDlWiQMjRbRSoebGcaBJ+IwdOxYJCQnYsGEDsrOzBTO8CGEmTAAGDQL27gU6dABkDrh3JG5KeY7V7BYKYCbcdB+bjZXiwIj5RkyoZWYeRn19coQFyI3jQJPw2b59O7Zt24aLLrrI6PbEBLm57hQ8PE61GESLHL/fb/gZSyzDWtqpkaIzVgWsHpx6H9uB08SBmFB78cWJACItQG4cB5qET6dOnSQPSSNiE6P9z1aiNNtBCDWBiywvwCxlxBmZLUPp2dqJRcuflnvUaeJASKgBHABhV53bxoEm4TN//nzMmjULjz32GLp06YLExMSI51NTUw1pHOEsWFo41aJEvOhNz3fCAszKb2NktgylZ6uDNcuflei5R50kDoSEmpyrzk3jQJPwKSkpAQD0798/Ir6HgpsJVhZOozEiY4OFAo9OxMhsGbel5ZqBkzcwelErkp0mEsWs8omJdVi+fKKoq85tJSw0CZ/Nmzcb3Q6CYIrwCQ+AKRkbbj26xEiMzJZxY1quWThxMTMaJSLZaSIxur3hZ39JuercVsJCk/Dp27cvPvvsM7zwwgvYt28f1q5diwsuuACvvPIKCgoKjG4jQVhK9IRXXFxueMYGWR6UIZctEx5rKLfAuDEtlzAHNSKZFVGjlPD2aonLZDlOUSmahM+6detwyy234Oabb8a3336Ls2fPAghmwTz22GP4+9//bmgjCcIqhCa8LVuKDc3YIMuDcuSyZcILrQHS8VF2Zt64YbHQihO/u1EimfXvrtZiFR0DJWa1Zj1RQJPweeSRR/D888/j1ltvxRtvvBF6vHfv3njkkUcMaxxBWI3QhAfEobj4C5SXFxuSsUGWB+WIZcsAQEVFvqoAZbsyb9yyWGjBCQH9Qhghkvft24dXX31V9jq7v7uazw6/v6Ss1qzHKWoSPrt370afPn0aPe71elFTU6O3TYRLYX33A4hPeD17bkXPnlsNSc93Ws0Pu4k2we/b1wGLF0/X5CbUm3mjZQy7ZbHQglMz6vSKZJ/P10j0OOW7K8HpVmtNwicrKwt79+5Ffn5+xOOff/452rdvb0S7CJfB+s6PFy9yE96kSUOQlpYW8Tq17XVazQ87EMuW0TLhGpV5o3cMW7lYsLjJ0BvXZvV30iOSo9v5xRfF2LSpxDUxfU63WmsSPpMmTcJdd92Fl156CR6PB5WVlSgvL8cf//hH3H///Ua3kXABrKdyR/u6H3jgGPbvT0B+/jnk5PQA0EP3xOrkAo9WI5Z9omXCNSrzRq/1wqrFgkXXml7RZ9XGyYz09C++KEZZ2UAAwdIvWgUvS2LW6VZrTcLnvvvuQyAQwIABA1BbW4s+ffogOTkZf/zjH3HHHXcY3UaCsITwSSM7GygqMv79nZT6ajdC/aB1wjW6T7VYL6xaLFh0rekVfVa5zIy+R/3+FJSVlYAXPTxqBS9rYtbpVmtNwsfj8WDOnDmYOXMm9u7di19++QWdOnVCixYtjG4fQbgKEjX6YGHC1Wq9sLrtLMVhGCn6zC4FYeQ9evx4OvhjIMJR+91ZFLNOqlQdjSbhw5OUlIROnToZ1RaCIAhZpCZcoTMEjbai6bFeWLlYsBSHYZToM1LMWeE6EjsTq6Rkk6bfgCUxC4i7AllHl/AhCIKwAqWxF9F1fXiMdAHotV5YtViwFodhhOgzsr6OkpihUaNGhZIZjEhkAAIYOHATevcuV/U+PHaLWaWxTazHKZLwcRgsBbjpgY5rINQgFnsRXnIfsCZlWK31wq7Fwm63oM/ng9/vb9QmPcHCRok5uZgh/u+//vUD3TE0coJPze9ut5h1S5wiCR8HwXpKuFLouAb3YKUQl3sfK8eVGuuFnYuFXXEYaiwqmZmZir+7GWIuetx07boDO3Z01RVDo9RCOXr0aFW/u91iFnBHnCIJHwfBekq4FPxEIOejZt1ESpyHJSFuReyDnlRnOxcLO+IwlGZhpaWlGW5BUYPQuPnuu27Qm3pupth1clAxK5DwISyBnwg2bwaeeqqxj7p37zHo188duwkjcIJLkyUhbkXsg1PM/KzFYRhhiTOjvg4gdkSNvtRzHjPHgVODilmBhA9hGenp6bjiCiAuDggEzj8eHw/07JkO0jxBWLKkqMHOuC2rYh9Y6m8xWBJoWi1xQsJ/1KhROHfuHAAgMTERXq834nkt30ks6ypc/LBQmI81Met0SPgQlpKbCyxbBkyZAjQ0BEXPCy8EH2cNu6wuLFlSlGJ33BYLsQ8swYpA02KJs7JYn9C4EYrxsXscsSRm3QAJH8JyJkwABg0C9u4FOnRgV/Q40epiB6zUFqHYB/bQYomzulif0Ljp3/9j5sZRrM8zRkLCx4VEF3FjcSeQm8um4OFxotXFLuysLWJW7AdhDHoscWYKarlxQzE0woRbwSsr41BRkYCCgnPIyQnGLrC41ghBwseFCBVxI8uEc2G95pGdtUXIBWA/Qouh11sTel6rJc5MQR09bvx+P1avXi37ulgW0OFWcClLnBPWGhI+DkLPTUeWCWdid+yMEuyOr2F9knUz4othSwwbdmlorGqxxJktqCMPJc4mAS3DeZEobYlzwlpDwsdBCO1uoyvXEu6BldgZMcIXLaldfSzvkt2O0sVwxIgRyMjIiHitnJCwWlDHsqhRg93HZhgBCR+HQTenPdjhbmJ9giE3E8EjN1YzMjKQnZ2t+n0pYJ097D42wwhI+BCEDFa7m3gLidwEw4IlhUQNAehfDMPjhKKTMyjQmC3sdm0bAQkfgpDADndTuCXlggtO4t57vWho8CA+nsP8+Sdx0003kiWFYAo9i6Fw3Z58WeuqUuHvhCroTsPpljgSPgQhAD+pypnwzbK68BPxPfcApaV8zSMPcnPTAKSZ8pkEoQeti6HSuj3hcUJKxYqVxRDV4Ia0cCdb4kj4EIQAvNVl//5zeOUVDoHA+RL28fEc7rhjCPLzEyyZnFiveRRLkPVAGj2LoZx1VUuckNXFEJXgprRwp0LCx+HQGS7mkZ4ePD+s8REbHhQVtbG7eYTFUDVvczEzmJ+lDEmnpoW7aa0h4eNgDh0C9uxJx4gRdyIj44zodeG7UNqxqscJR2wQ5hN934i5TVhbsMzEyMXQzGwhFjMkWWyTFG7K4iTh41CWLwcmTw6ech4X1xLLlgUXaCmcumNlQayRu4kIxwmFJa3AyMXQzGwhFlOwWWyTHCytC3og4eNADh06L3qA4H+nTAlaJaQWZyeeP+VUsUa4F5bcJixg5H1nVrYQiynYLLYpViDh40D27DkvengaGoKuGLdZJZwo1gh34zQXhdMwK1uIxRRsFtsUC8TJX8IWZ8+exSWXXAKPx4Pt27dHPLdjxw5ceeWVaNKkCfLy8rBgwQJ7GmkyHTsCcVG/XHx8MP6EIAhz4V0U4YS7KKqrq1FVVYWqqir4fD47mugorAya9XpPoaDgJ6YEBottcjuOs/jMmjULOTk5+O677yIeP3nyJK6++mqUlJTg+eefx/fff4/x48cjLS0NkydPtqm15pCbK5Rp5D5rjxthIV6J0IeciyL67Dynu2HNHrNmBs26KROJMA5HCZ8PPvgAGzduxLp16/DBBx9EPPfaa6+hrq4OL730EpKSknDxxRdj+/btWLRokeuED0CZRk6E4pXcg5CLwo1ZXlYVADRrvLOYiURizH4cI3yOHDmCSZMm4e2330azZs0aPV9eXo4+ffpEDJZBgwZh/vz5OHHiBFq2bCn4vmfPnsXZs2dDf588edL4xpuEUKZRMMU96A4jMcQWFK/kbKIXovBYFLdmebFYAFAtrG0i1IoxN1R5Zg1HCB+O4zB27FjcdtttuOyyy7B///5G1xw+fBgFBQURj7Vp0yb0nJjwmTdvHubOnWt4m+0gMsUdilLc3QJ/sKGbJwFyk9lL9IJVXV2N9evXx0SWVyx8RytRep9SlWdzsFX43HfffZg/f77kNbt27cLGjRtx6tQpzJ492/A2zJ49GzNmzAj9ffLkSeTl5Rn+OWajJMXdzSbW8LgKN04C5CZjA6G+dUuWl5BlweutAeCe7+g0nFrlmXVsFT733HMPxo4dK3lN+/bt8fHHH6O8vBzJyckRz1122WW4+eab8fLLLyMrKwtHjhyJeJ7/OysrS/T9k5OTG72vE1GS4s6iv1sOLSLMjZMAucnYxYmF6KIRtyy0xLBhl6KwcK/jvyNLqHVfkfA0FluFT+vWrdG6dWvZ65YsWYJHHnkk9HdlZSUGDRqE1atXo2fPngCA4uJizJkzB/X19UhMTAQAlJWV4cILLxR1c7kJPsU9XPwIpbizJGqUEC7WqqursWJFmWBwpVvhJ0jelUewhxsK0clZFqZPX+z478gKatxXPG4Q1yzhiBiftm3bRvzdokULAEBhYSFy/2vOuOmmmzB37lxMmDAB9957L3bu3Imnn34aTz31lOXttQM3p7jzYm3VqqZYvHi6awJIxTJkeJS6twj7MaIQHQsxXFKWBSq2Zwxa3FduENcs4QjhowSv14uNGzdi6tSpKCoqQkZGBh544AFXprKL4eYU90OHgFmzvOA4DwDnB1cqyQKSWgTlRBNhPlJZXlLXCWFV2rgccpYFs6oqxyJq3VckPI3DkcInPz8fHMc1erxr16747LPPbGgRO7j1MM1gDJMn4jGn+bj5BVBupye3UNqROs2CNYI1jIyZYyVtXKtlwYkJEXajxX1FwtMYHCl8iNgjGMPERYgfp/m4+YVy82bgqaca7/R69x6Dfv2k47DsSCumjDJxjP6+LKSNS1kWRowYgYyMjIjrY1H0GgG5r+yDhA/hCHJzgQUL/Jg5M9XRk0R6ejquuEI4EL1nz3TIrR92ZHdQRpl1sJK9I2ZZyMjIQHZ2tmXtcDty7is3lyCxExI+MYzTqjyPHduAn39eLOvjZn0S0BqI7ven4PTpZpLmcda/OyFNLGXvUEXiIFLuKyeWIHECJHxiFCdWeU5PT8ecOWNcMVmqDUQPj/sAAqHF0eMJ4MEHKzF58o2WfXcKrDYPu9wftbW1iq4zSlhTRWLlxPr3NwMSPjGIkirPrMJPAk4UbtEoDUSPjvsA4sBxAVx33Rrk5R3C5Mk3yrofjApOduuZVCxhdfaOz+fDq6++GvpbTNiOHj3asEU4lisSk/vKfkj4xCBKqjyzjJOFmxr4iU8o7gOIQ/PmtYqywIwKTmYh8DZWsDJ7R2lGmdDh0HphJabJSnj31f795xpZrHmcYrl2KiR8YhClVZ5ZxenCTSnhE+Qrr0RmtMXHc7jjjiHIz0+QnSCNCk6OxUXKKliwAigRtkbH5cRSTBNwvv9WrWqKWbNaIRDwIC6Ow4IFftx0068keCyChE8MYnaVZ7NrvjhduKkhPT2Y6dX49/KgqKiNoZ9VXV0t+dvE2iJlJSwEscoJW7/fj9WrVwMwLi4nllK6ecur35/y3wr0wY1MIODBzJmp+Pnnl+D1nqK4Jgsg4ROjmFXl2YqaL24+nkMMI38vsRgO/oT76N+GtzLILVIUk6APuxc7OWFbX18PIDh+3n13KABj4nJipSIx3y9yAtONcU2sQcInhjGjyrNVNV/cfDyHGEb8XlqOyoi2RjzwwDHs35+A/PxzyMnpAaAHmehdgJyw/eWXXwAAW7f2BC96ePS6PGOpIjFZTu2HhA/hWNx6PIdZCMVwvPvuUGRmHkZubpXka8NFTXY2UFRkalMJm5CyvmzcuBF+fwrKy4sFXkkLt1Jiyb3HKiR8CCJGEMsOW758IqWlEyGkrC/CYwjo1atc1cLNQjC3ncSKe49VSPgQRIwgZGIHKC091lEjLsTcND17blX1mSwEc9tNLLn3WIOED0G4nOjg5PDAVB5KS49dlIiQmpoarFmzxlA3jZtFDcE2JHwIwuXwC1tlZSWA9cjMPIzlyydScCURQo0IITcN4XRI+BBEDJCenh7a0efmVlFwJSGKUJFCr7cm4hopN41b43L0EutxTSxBwocwFLq52SW8z6V27fTbxC7ih4e2xLBhl8oGwI8aNYpcWCJQXBM7kPAhDIVubnah34aQQ+nhoWKkpaVZ0UzHQvcWG5DwIQwn/OY+dCh4tlbHjlRzhwVo4iWUoPVcNrIWEk6AhA9hGsuXnz9FPS4ueMzEhAl2t4ogCDnkqgsPHDgQBQUFEa8hayHhFBpXoiIIAzh06LzoAYL/nTIl+DihDZ/Ph6qqKtF/Pp/P7iYSLoFPW/d4zp8EzHHAvn3Bk4DLysqQlJSE7Ozs0D8SPYRTIIsPYQp79kSeng4EDxTdu5dcXlqw4vBXgginsHAvOC78EX2HkRLOxk1hC2TxIUyhY8egeyuc+PjggaKEeqIXGb8/BRUV+fD7UySvIwitHD+eDrFCl0RswFuZn3yyBu3acejfH2jXjsOTT9Y42spMFh/CFHJzgzE9U6YELT3x8cALLzh/p2Am4fVToqmurg79v5IT1glCL3SKeGzDW5n9/hQsXjwdHOcBAAQCHsycmYqff34JXu8pR1qZSfgQpjFhAjBoUNC91aEDiR4plLqytKYZE4QSwrOy6BTx2IbfhMll+DnRykzChzCV3Fz3Cx4pSw2gLNtFyJV1/Hg6WrXyRSw0WtOMiSBCVYkLCs4hJycYkBbrmUnp6ekoLS3F6tWrAdDxFIQ7LX8kfAhCB9GWGjHBosYcLOXKcuMkZBXiVYkj+9iJpnsj8Xq9UX/TKeKxjBstfyR8CEIH4ZYaqcVUqTlYzpXlxknIKpRWJXai6Z4gzMRtlj8SPgRhAEbF3ihxZUlNQuFB0LHuthGD3IXS0Hl7hBBusvyR8CEIAzBqMVXqyoqehHgX24oVZZpdbLECuQuloTPdCLdDwocgDMCoxVTOlTVixAhkZGSguroa69evB2CMiy2WIHehPCRqCDdDwocgDMDIxVTKlZWRkYHs7OzQ35Terg23xSwQhNG42eVJwodgCieXRdezmEZPHmL+9OjrKF5FO26KWSAIo3Gzy5OED2E7fG2VVauaYtYsLwIBD+LiOCxY4MdNN/3qqJtL62KqdZKheBWCIMzCKfOuWkj4ELbi9LLoRpqDtXw/ildRjptN9+FQkUaCkIaED2ErTi+LzoI5mOJVlMHCb2U2VKSREMPJYQRGQ8KHYAInu2xYWEAoXkUZLPxWZkJFGolw3BRGYCQkfAgmiBWXjVG7rlhx2xDaoKB3wulhBGZCwodgBre7bJYvByZPBgIBIC4OWLYseIK9FmLBbUNox8kWVMIYnB5GYCYkfAimcKPLxufzYf/+c5g8OROBAL/rAqZM4XDJJUeRn5+gSaCQqCHEiBULKiEPieDGkPAhCBPhzc0VFfkIBMZEPNfQ4MHSpR+goOCnmDQ3E+ai5Ew3yvpyPySCG0PChyBMhDcjy+26YtHcTIhjVEq6mAV1/fr1lPUVQ7g9jEAtJHwciltSE2MlSJd2XYRSrEhJZy3ri2oPmY8bwwi0QsLHgRgZJGs3sRSkS7suQgl6UtKVbhBYyvqi2kPuwgmbchI+DuPQofOiB+CDZIFBg9gdZHK4cTLjb/7U1MjFhXZdhFK0iBO5jUR1dTXWr1/PVMAr1R5yPk6rF0TCx2Hs2XNe9PA0NAB79zpX+LiNSItcJoYOvTS0ayUIpWgVJ0pjf1hzvZplhYpVN5pVYQROrBdEwsdhdOwYdG+Fi5/4eKBDB/vaxDJWm10bW+Q8EbtWglCK2eKENderGVaoWHajWRVG4MR6QSR8HEZubjCmZ8qUoKUnPh544QWy9ghhRyyUkEWOKuYSWjFbnLDkejVD6MW6G81KMceS+1QOEj4OZMKEYEzP3r1BSw+JnsbYFQslZJFTcvM7PWuNMA+WxInZmCX0WArmdissuk/FIOHjUHJzSfBIYUcslM/nQ3x8HRYsaIp77/WiocGD+HgODz10GL///UAkJibC6/U2ep1bYwwI9nBC+QgzhJ6TrBFOhjX3qRgkfAhXYnUsVHgsAQDceWdK6OZvaDiF9euDj7sxloAwFjPFSSyVjwjHSdYIp+MECyUJH8KVWB0LFb2QiN38bo0lcAJOqC8CmC9OWBM1VlmhnGKNIMyHhA/hWigWinBafREeFttkFlZaoZxgjSDMh4QP4WooFip2cWJ9kViF+t+5OCFuLBoSPoTrCC9YJgSru3zCWJxYX4QwFicuyk7DiXFjJHwIVxEdZCwG7fJjB8roiV2cuCg7Eaf1HwkfwlUo3b3H6i7fKQG+RkIZPbGN0xZlwnxI+BBEjGBHJWtWoIwegiB44uQvIQhCDtZjCcQqWR86ZEtzbMHrPYWCgp9I9BBEjEMWH4IwAJZjCXw+H778EggEIj+7oQHYutWHpk3JHUAQROxAwocgDIJF8RCe0u3xTG8U4PvFFy9j505K6SYIInYgVxdBuBjeAsUH+Ho8QV9XdICvG4O9WXc/EgRhD2TxcRBUn4bQQ6wF+LLsfiQIwj4cJXzef/99/PnPf8aOHTvQpEkT9O3bF2+//Xbo+QMHDuD222/H5s2b0aJFC4wZMwbz5s1DQoKjvqYgVJ9GGbTLlybWSvbH8r1AEIQwjlEE69atw6RJk/DYY4+hf//+OHfuHHbu3Bl6vqGhAddccw2ysrKwZcsWVFVV4dZbb0ViYiIee+wxG1tuDFSfRhm0yycIgiCkcITwOXfuHO666y4sXLgQE8IKj3Tq1Cn0/xs3bsS///1vbNq0CW3atMEll1yChx9+GPfeey8eeuihmN3hxyIkapxHLBZWtAJyjxNEYxwhfL755hv8/PPPiIuLw6WXXorDhw/jkksuwcKFC9G5c2cAQHl5Obp06YI2bdqEXjdo0CDcfvvt+OGHH3DppZcKvvfZs2dx9uzZ0N8nT54098sQBAHAuSenOwVyjxOEMI7I6vq///s/AMBDDz2EP/3pT9iwYQNatmyJfv364fjx4Hk7hw8fjhA9AEJ/Hz58WPS9582bB6/XG/qXl5dn0rcgCIKHX5QXLnwdM2emIhCIPDl94cLX8cwzz8Dn89ncUudC7nGCEMZW4XPffffB4/FI/vvxxx8R+G+52Tlz5mDkyJEoKirCihUr4PF48Oabb+pqw+zZs+H3+0P/Dh48aMRXIxzKoUPA5s3uqWjMarC3kpPTw68j9OP3p6CiIh9+f4rdTTEcn8+Hqqoq0X8koIlwbHV13XPPPRg7dqzkNe3bt0dVVRWAyJie5ORktG/fHgcOHAAAZGVl4V//+lfEa48cORJ6Tozk5GQkJydraT7hMtx4lhXrwd50cro1fPPNpY0Oae3e/Vu7m2UI5NJjF1ZjzGwVPq1bt0br1q1lrysqKkJycjJ2796N3/3udwCA+vp67N+/H+3atQMAFBcX49FHH8XRo0eRmZkJACgrK0NqamqEYCIIIcTOsho0yPnBtixP9nRyuvn4/Smh/gWCFrX33huKwsK9ruhncumxCcuC1BHBzampqbjtttvw4IMPIi8vD+3atcPChQsBANdffz0A4Oqrr0anTp1wyy23YMGCBTh8+DD+9Kc/YerUqa6w6LDqsnALe/acFz08DQ3A3r3OFz6s4/TCiqzuanmk3IlO62vCObAsSB0hfABg4cKFSEhIwC233IJff/0VPXv2xMcff4yWLVsCAOLj47FhwwbcfvvtKC4uRvPmzTFmzBj8+c9/trnlxsC6y8LpdOwYdG+Fi5/4eKBDB/vaFEs4tbAiy7tanlhzJ/r9KTh+PB2tWvkcOaYI83GM8ElMTMQTTzyBJ554QvSadu3a4e9//7uFrbIWEjXmkZsbjOmZMiVo6YmPB154gaw9hDQs72p5Ysmd6OZYJqfDkiB1jPAhCLOZMCEY07N3b9DSY7TooSJ97oelyT3c7S3lTnSLe9ztsUxOhjVBSsKHIMLIzTVHlLgxY0wPboxZY21y593jx44dQ319veA1bjjHkIdimdiERUHqnlFPEBoxOzjVzRljWnFbzBqLkzvP6tWrZa9xQ6p3rMUyOQUWBSkJHyKmsSI4lTLGhOH7U0x41tXVoaqqSrEAsjO7isXJHXBGDJJeeKugXCyTk6yHboJFQUrCh4hpzF4YfD4fUlPPIS4uM3QsAwDEx3NISTkKny/B8TttPRglPO3OrrJ6cmc9hd5Koq2HDzxwDPv3JyA//xxycnoA6BFT/cEKLAtSEj4EYRLhi/HQoZHxH9dcswEbNgTjP9zgZtBK9OItFhwsJzzttmxYmTmlRuRFw1LwtZGE3z/Z2UBRkY2NIQCwLUhJ+BCESYQvslJZNU52MxiJkcHBVi3wdmROaRV5rAVfE+6HVUFKwocgLMKpRfqswMjgYCsXeKcEabMcfE0QVkPChyAI2zEqONiOBd5uUaMEVoOvCcIO4uQvIQiCMBc+ODgcLcHBUgu8W/H7U1BRkQ+/P0X0GqP6lyDcAFl8CIKwHaOCg1lMnZVCb3aWnFuP5cwaFqFsudiAhA8R07ixgrASWJzgjTil3UnnUulNwVfi1mM5s4Y17C6JQFgHCR8ipnFKcKqRRE/wYhlQVkzw0YJSLABcTng68VwqvSn4SuN2WM2sYQ27SyIQ1kHCh4h5zFrcWbUmhU/cUq4SKyZ4o4RnLAlYfrzIufVYEnkEwRIkfAjCJFhfjFlJcTbq+7tB1CghfFxdcMFJ3HuvFw0NHsTHc5g//yRuuulG14g8gjADEj4EYSIsLz6U4uxc+HF1zz1AaWnw3LcOHTzIzU0DkGZbu1iMHSOIaEj4EESM4rQMKEKY3Fw2Drul4GDCKVAdH4KIUfgMKL6+C8sZULGAkno8LEPBwYRTIIsPQcQwRqSQE/qhc7QIwjrI4kMQMY7XewoFBT+R6LEYPutKLMict/xQdpY1RPezmAWOfg/nQxYfgogxWE2zjzX47KzNm4GnnmocZN679xj068d2gLybCM+WW7WqKf78Zy8CAQ/i4jgsWODHTTf9SsHZLoGED0HEGKyn2ccS6enpuOIKIC4OCIQdpRUfD/TsmQ76CawlPT0dhw4Bs2ad/z0CAQ/uvTcNpaVp9Hu4BBI+BBGDkKhhh9xcYNkyYMoUoKEhKHpeeIGNTK1YZM+eSBEKBH+XvXvpN3ELJHwIgiBsZsIEYNAgvh4PLbB20rGjsAWuQwf72kQYCwU3EwRBMEBuLtCvn3NFj1tix3gLXHx88G+ywLkPD8dxnN2NYImTJ0/C6/XC7/cjNTXV7uYQBEE4hvDKzZWVcaioSEBBwTnk5ATNJ06KHTt0iCxwTkPp+k2uLoIgCMIQeFGzfDkweXLQXRQXF7SgTJhgc+NUwkpFbMJ4yNVFEARBGMahQ+dFDxD875QpwccJggVI+BAEQRCGIZUVRRAsQMKHIAiCMAw+KyocyooiWIKED0EQBGEYlBVFsA4FNxMEQRCGQnWJCJYh4UMQBEEYDmVFEaxCri6CIAiCIGIGEj4EQRAEQcQMJHwIgiAIgogZSPgQBEEQBBEzkPAhCIIgCCJmIOFDEARBEETMQMKHIAiCIIiYgYQPQRAEQRAxAwkfgiAIgiBiBhI+BEEQBEHEDCR8CIIgCIKIGeisrig4jgMAnDx50uaWEARBEAShFH7d5tdxMUj4RHHq1CkAQF5ens0tIQiCIAhCLadOnYLX6xV93sPJSaMYIxAIoLKyEikpKfB4PHY3x3JOnjyJvLw8HDx4EKmpqXY3x7FQPxoD9aN+qA+NgfrRGMzsR47jcOrUKeTk5CAuTjyShyw+UcTFxSE3N9fuZthOamoq3dwGQP1oDNSP+qE+NAbqR2Mwqx+lLD08FNxMEARBEETMQMKHIAiCIIiYgYQPEUFycjIefPBBJCcn290UR0P9aAzUj/qhPjQG6kdjYKEfKbiZIAiCIIiYgSw+BEEQBEHEDCR8CIIgCIKIGUj4EARBEAQRM5DwIQiCIAgiZiDhE6P885//xLBhw5CTkwOPx4O333474nmO4/DAAw8gOzsbTZs2RUlJCfbs2WNPYxlFrg/Hjh0Lj8cT8W/w4MH2NJZh5s2bhx49eiAlJQWZmZkYPnw4du/eHXHNmTNnMHXqVKSnp6NFixYYOXIkjhw5YlOL2URJP/br16/RmLzttttsajGbPPfcc+jatWuowF5xcTE++OCD0PM0FpUh1492jkUSPjHK6dOn0a1bN/zlL38RfH7BggVYsmQJnn/+eWzduhXNmzfHoEGDcObMGYtbyi5yfQgAgwcPRlVVVejf66+/bmELncGnn36KqVOn4ssvv0RZWRnq6+tx9dVX4/Tp06Fr7r77brz33nt488038emnn6KyshIjRoywsdXsoaQfAWDSpEkRY3LBggU2tZhNcnNz8fjjj2Pbtm34+uuv0b9/f/z+97/HDz/8AIDGolLk+hGwcSxyRMwDgHvrrbdCfwcCAS4rK4tbuHBh6LGamhouOTmZe/31121oIftE9yHHcdyYMWO43//+97a0x8kcPXqUA8B9+umnHMcFx15iYiL35ptvhq7ZtWsXB4ArLy+3q5nME92PHMdxffv25e666y77GuVQWrZsyb344os0FnXC9yPH2TsWyeJDNKKiogKHDx9GSUlJ6DGv14uePXuivLzcxpY5j08++QSZmZm48MILcfvtt8Pn89ndJObx+/0AgFatWgEAtm3bhvr6+ojxeNFFF6Ft27Y0HiWI7kee1157DRkZGejcuTNmz56N2tpaO5rnCBoaGvDGG2/g9OnTKC4uprGokeh+5LFrLNIhpUQjDh8+DABo06ZNxONt2rQJPUfIM3jwYIwYMQIFBQXYt28f/vd//xdDhgxBeXk54uPj7W4ekwQCAUyfPh29e/dG586dAQTHY1JSEtLS0iKupfEojlA/AsBNN92Edu3aIScnBzt27MC9996L3bt3Y/369Ta2lj2+//57FBcX48yZM2jRogXeeustdOrUCdu3b6exqAKxfgTsHYskfAjCJG644YbQ/3fp0gVdu3ZFYWEhPvnkEwwYMMDGlrHL1KlTsXPnTnz++ed2N8XRiPXj5MmTQ//fpUsXZGdnY8CAAdi3bx8KCwutbiazXHjhhdi+fTv8fj/Wrl2LMWPG4NNPP7W7WY5DrB87depk61gkVxfRiKysLABolKlw5MiR0HOEetq3b4+MjAzs3bvX7qYwybRp07BhwwZs3rwZubm5ocezsrJQV1eHmpqaiOtpPAoj1o9C9OzZEwBoTEaRlJSEDh06oKioCPPmzUO3bt3w9NNP01hUiVg/CmHlWCThQzSioKAAWVlZ+Oijj0KPnTx5Elu3bo3wzxLqOHToEHw+H7Kzs+1uClNwHIdp06bhrbfewscff4yCgoKI54uKipCYmBgxHnfv3o0DBw7QeAxDrh+F2L59OwDQmJQhEAjg7NmzNBZ1wvejEFaORXJ1xSi//PJLhLKuqKjA9u3b0apVK7Rt2xbTp0/HI488go4dO6KgoAD3338/cnJyMHz4cPsazRhSfdiqVSvMnTsXI0eORFZWFvbt24dZs2ahQ4cOGDRokI2tZo+pU6di1apVeOedd5CSkhKKlfB6vWjatCm8Xi8mTJiAGTNmoFWrVkhNTcUdd9yB4uJiXHHFFTa3nh3k+nHfvn1YtWoV/t//+39IT0/Hjh07cPfdd6NPnz7o2rWrza1nh9mzZ2PIkCFo27YtTp06hVWrVuGTTz7BP/7xDxqLKpDqR9vHoi25ZITtbN68mQPQ6N+YMWM4jgumtN9///1cmzZtuOTkZG7AgAHc7t277W00Y0j1YW1tLXf11VdzrVu35hITE7l27dpxkyZN4g4fPmx3s5lDqA8BcCtWrAhd8+uvv3J/+MMfuJYtW3LNmjXj/ud//oerqqqyr9EMItePBw4c4Pr06cO1atWKS05O5jp06MDNnDmT8/v99jacMcaPH8+1a9eOS0pK4lq3bs0NGDCA27hxY+h5GovKkOpHu8eih+M4znx5RRAEQRAEYT8U40MQBEEQRMxAwocgCIIgiJiBhA9BEARBEDEDCR+CIAiCIGIGEj4EQRAEQcQMJHwIgiAIgogZSPgQBEEQBBEzkPAhCIIgCCJmIOFDEARBEETMQMKHIAjHUFdXZ3cTGsFimwiCEIeED0EQttGvXz9MmzYN06ZNg9frRUZGBu6//37wJ+nk5+fj4Ycfxq233orU1FRMnjwZAPD555/jyiuvRNOmTZGXl4c777wTp0+fDr3vs88+i44dO6JJkyZo06YNrrvuutBza9euRZcuXdC0aVOkp6ejpKQk9Np+/fph+vTpEW0cPnw4xo4dG/pba5sIgmADEj4EQdjKyy+/jISEBPzrX//C008/jUWLFuHFF18MPf/EE0+gW7du+Pbbb3H//fdj3759GDx4MEaOHIkdO3Zg9erV+PzzzzFt2jQAwNdff40777wTf/7zn7F79258+OGH6NOnDwCgqqoKN954I8aPH49du3bhk08+wYgRI6D2yEK1bSIIgh3okFKCIGyjX79+OHr0KH744Qd4PB4AwH333Yd3330X//73v5Gfn49LL70Ub731Vug1EydORHx8PF544YXQY59//jn69u2L06dP4+9//zvGjRuHQ4cOISUlJeLzvvnmGxQVFWH//v1o166dYHsuueQSLF68OPTY8OHDkZaWhpUrVwKApjY1adJEVz8RBGEcZPEhCMJWrrjiipDoAYDi4mLs2bMHDQ0NAIDLLrss4vrvvvsOK1euRIsWLUL/Bg0ahEAggIqKCgwcOBDt2rVD+/btccstt+C1115DbW0tAKBbt24YMGAAunTpguuvvx5//etfceLECdVtVtsmgiDYgYQPQRBM07x584i/f/nlF0yZMgXbt28P/fvuu++wZ88eFBYWIiUlBd988w1ef/11ZGdn44EHHkC3bt1QU1OD+Ph4lJWV4YMPPkCnTp2wdOlSXHjhhSFxEhcX18jtVV9fr7tNBEGwAwkfgiBsZevWrRF/f/nll+jYsSPi4+MFr+/evTv+/e9/o0OHDo3+JSUlAQASEhJQUlKCBQsWYMeOHdi/fz8+/vhjAIDH40Hv3r0xd+5cfPvtt0hKSgq5rVq3bo2qqqrQZzU0NGDnzp2y30FJmwiCYAMSPgRB2MqBAwcwY8YM7N69G6+//jqWLl2Ku+66S/T6e++9F1u2bMG0adOwfft27NmzB++8804okHjDhg1YsmQJtm/fjp9++gl/+9vfEAgEcOGFF2Lr1q147LHH8PXXX+PAgQNYv349jh07ht/+9rcAgP79++P999/H+++/jx9//BG33347ampqZL+DXJsIgmCHBLsbQBBEbHPrrbfi119/xeWXX474+HjcddddoRRxIbp27YpPP/0Uc+bMwZVXXgmO41BYWIjS0lIAQFpaGtavX4+HHnoIZ86cQceOHfH666/j4osvxq5du/DPf/4TixcvxsmTJ9GuXTs8+eSTGDJkCABg/Pjx+O6773DrrbciISEBd999N6666irZ7yDXJoIg2IGyugiCsA2hLCqCIAgzIVcXQRAEQRAxAwkfgiAIgiBiBnJ1EQRBEAQRM5DFhyAIgiCImIGED0EQBEEQMQMJH4IgCIIgYgYSPgRBEARBxAwkfAiCIAiCiBlI+BAEQRAEETOQ8CEIgiAIImYg4UMQBEEQRMxAwocgCIIgiJjh/wN9s77nJ4/qUAAAAABJRU5ErkJggg==", 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27YrLL78cu3fvVhzz+++/Y968eYiJiUHHjh0xdepUHD58uIVG3Db0798f+/btAwDk5ORgzJgxSEpKwsUXX4xHH30Uubm5AFzLcHa7HTabDd27d0f37t3RsWNHAMD111+PiRMnonfv3jj//PPx4osvYt26dTh58mRLXRYREbWwzp0rYLMp80htNic6dz7WQiNyaTOBz9dff4158+bh22+/RV5eHmprazFu3DicOnVKOub222/Hxx9/jH/961/4+uuvUVpaiilTprTgqFs/QRCkpav8/Hxccskl6NGjByIjI3HttdeioqIC1dXVhufYsmULJk2ahJ49eyIyMhKjR48GABw4cKDJx09ERK2T3V6FSZPWSsGPmONjt1e16LjaTI7PZ599pvh52bJl6Nq1K7Zs2YJRo0bB4XBgyZIleO+993DxxRcDAJYuXYqzzjoL3377Lc4///yWGHart2vXLiQnJ2Pfvn247LLLcPPNN+Oxxx5D586d8c033yArKws1NTW6LTpOnTqF8ePHY/z48Xj33XfRpUsXHDhwAOPHj2cHeyIiPzds2A9ISdmLY8c6o3PnYy0e9ABtKPBRczgcAIDOnV1rhVu2bEFtbS0yMjKkY/r374+ePXuioKBAN/A5c+YMzpw5I/184sSJJhx16/Lll1/ip59+wu23344tW7bA6XTi2WeflZqvistcopCQELcWEr/88gsqKirwxBNPIDExEQDw/fffN88FEBFRq2e3V7WKgEfUJgMfp9OJnJwcjBw5EoMGDQIAHDp0CCEhIW51Zrp164ZDhw7pnmvhwoVYsGBBUw63VThz5gwOHTqk2M6+cOFCXHbZZbjuuuuwY8cO1NbWYvHixZg0aRI2bdqEV155RXGOpKQknDx5El988QWGDBmC8PBw9OzZEyEhIVi8eDFuuukm7NixA4888kgLXSURETUXeYmW0tIAFBcHITm5DiEh5bqfcTgicexYDDp3Zh0fj8ybNw87duzAN9980+hz3X///bjjjjukn0+cOCHNXLQnn332GeLi4hAUFIROnTphyJAhePHFFzFz5kwEBARgyJAheO655/Dkk0/i/vvvx6hRo7Bw4UJcd9110jlGjBiBm266CZmZmaioqJC2sy9btgx//etf8eKLL2LYsGF45plnMHny5Ba8WiIiakpFRUVYvnw5AGDTpnTk5WUAEGv1FGLYMPfPqGv69OpVjdtua95xA4BNEASh+b/We9nZ2fj3v/+N//znP0hOTpZe//LLL3HJJZfg+PHjilmfXr16IScnB7fffrul8584cQJ2ux0OhwNRUVGK937//XcUFxcjOTkZHTp08GjcLV3Hp61pzL0mIqKmI3+euYKesQAaGkvbbE7k5CyC3V6FKVOmICgoCAcPAhMm9IfT2XBcYKCAwsIjSEoK8slzz+j5LddmZnwEQcAtt9yCDz/8EF999ZUi6AGA1NRUBAcH44svvsDUqVMBALt378aBAweQnp7eEkNWiImJQXZ2dotVbiYiIgIa30VAXqAwPz8D8qAHaKjVY7dXISgoCLm5uSguToLTeZbiuPp6GxYvXofk5P3N+pf+NhP4zJs3D++99x7+/e9/IzIyUsrbsdvtCAsLg91uR1ZWFu644w507twZUVFRuOWWW5Cent5qdnQxqCEiopbky9UHrQKFLg21eurq6gA01PSRHy+v6dOcu4DbTB2fl19+GQ6HAxdddBHi4uKkXytWrJCOef7553HZZZdh6tSpGDVqFLp37y5VJSYiIvJ3vuwioFWgEBAwdmy+2y6u1lTTp83M+FhJRerQoQP+8Y9/4B//+EczjIiIiKhlNXbZSr7LytMgRAxm5AnLGRn5GDmyQPP41lLTp80EPkRERNSgsctWRp3TrfI0mGkNNX3azFIXERERNWjMspVe53SHI9LjcdjtVUhO3t/iAY1VDHyIiIj8THN1Tg8ODvbp+XyBgQ8REVE74HBEorg4ydKsTWM6p4eEhFgaw8SJE1FbW2s+8GbGHB8iIqI2ztN8Ha3EZPkuq/JyZdsJeZK0vC6dw+GQdlerx1BUtPaP/J8k0+Rpo2DK1xj4UKN99dVXGDNmjFvVbCNJSUnIyclBTk5Ok46NiKg1aMzuK73PisGJXr5OSspet2BDHmDoJSY7HJF49tmtbsGKPElaPVa9Mbg2ZLsHY5mZmbDb7abX3hQY+PiBWbNm4a233sLcuXPdGo/OmzcP//znPzFz5kwsW7asZQZIRNSONWb3lZXPGuXrqAMfrS4C5eXlUs07o5kjo8BNbwzy38uDMbvdjri4OMPraioMfPxEYmIiPvjgAzz//PMICwsD4OqH9d5776Fnz54tPDoiovarMbuvPCkkqFcVWU1vdsVs5mjPnj0oLy9HUFCQNLsvzjppjUFNLxhrbkxu9hPDhg1DYmKiopL16tWr0bNnTwwdOlR67cyZM7j11lvRtWtXdOjQARdccAG+++47xbk+/fRT/OlPf0JYWBjGjBmDffv2uX3fN998gwsvvBBhYWFITEzErbfeilOnTjXZ9RERtaSKigqUlZVp/lLny3hyPvVntRKYzaoiW82f0Zu12blzAByOSGzYsAGrV69Gbm4uXnvtNbz22mvSM0VrDIB3ydNNjTM+LeTgQWDPHqBvXyAhoXm+8/rrr8fSpUtxzTXXAADefPNNzJ49G1999ZV0zD333INVq1bhrbfeQq9evfDUU09h/Pjx2Lt3Lzp37oySkhJMmTIF8+bNw5w5c/D999/jzjvvVHxPUVERJkyYgEcffRRvvvkmjh49iuzsbGRnZ2Pp0qXNc7FERM3E6lKWyKxast75jJahHn00GQ8+eBT79gUhKakO8fHnAjjXo/wZ7VkbAZ9/PgHr14+Tvk9v/OqcoaKiPrrJ0y2JgU8LWLIEmDMHcDqBgADgtdeArKym/94ZM2bg/vvvx/79+wEAmzZtwgcffCAFPqdOncLLL7+MZcuWYeLEiQCA119/HXl5eViyZAnuvvtuvPzyy0hJScGzzz4LAOjXrx9++uknPPnkk9L3LFy4ENdcc42UuNy3b1+8+OKLGD16NF5++WV06NCh6S+WiKiZeNJg08ruK08KDorLULGxsYiL64bUVOvjFpOmxVkl9U4vQIDYeV38vtOnOyA/P0N3/PLKzK2lRYUaA59mdvBgQ9ADuP45dy4wfnzTz/x06dIFl156KZYtWwZBEHDppZciNjZWer+oqAi1tbUYOXKk9FpwcDDOO+887Nq1CwCwa9cupKWlKc6bnp6u+PnHH3/E9u3b8e6770qvCYIAp9OJ4uJinHXWWU1xeURErZonu6/Un/v554GWE5it0JtVEoOVnTsH4PPPJ7h9nxj0WB1/a2hRocbAp5nt2dMQ9Ijq64G9e5tnyev6669HdnY2ADRZM9eTJ09i7ty5uPXWW93eYyI1EbV3ektBnuy+EslniOQzMIB7zox627vD4VAUENRKStZit1dhwICdWL9+nGq87snLngZf4r3ZsaNSeo3b2du5vn1dy1vy4CcwEOjTp3m+f8KECaipqYHNZsP48eMV76WkpCAkJASbNm1Cr169AAC1tbX47rvvpGWrs846C2vWrFF87ttvv1X8PGzYMOzcuRN9muuiiIh8RAweKisrUVdX5/Z+cHAw7Ha77sPaaCnL091X6hkiV9DjCn7UOTPyQoLeUAdrep3X5TM+6vFPmTIFQUFByM3N1fwO+b15+23lvdFrpNoUGPg0s4QEV07P3LmumZ7AQODVV5svwTkwMFBatgoMDFS8FxERgZtvvhl33303OnfujJ49e+Kpp55CdXU1sv5IQrrpppvw7LPP4u6778YNN9yALVu2uNX/uffee3H++ecjOzsbN9xwAyIiIrBz507k5eV5lABIRNScPE1SVj+szZayzKolq3dfac0QATaMH/8ZBgzYqZhlEVyVAr2iF6xp5eiEhf2uO35XnlGcok6QWCPI7N54kifVWAx8WkBWliunZ+9e10xPcwU9oqioKN33nnjiCTidTlx77bWoqqrC8OHD8fnnn6NTp04AXEtVq1atwu23347FixfjvPPOw+OPP47rr79eOsfgwYPx9ddfY/78+bjwwgshCAJSUlKQmZnZ5NdGROQtTx++6uPNlrKmTJmCOXNiLe++0pshEoOeKVOmIDY2FiEhIW5jUc/g6C2/WQnWjHZuaS1xac3ceLPM11QY+LSQhITmC3jMKjJ/9NFH0u87dOiAF198ES+++KLu8Zdddhkuu+wyxWuzZ89W/Hzuuedi/fr1uufQqv1DRNSWOBwORfVhs6UscUYkLg6Wdl+ZzRCJ5wOAsrIy6XPqGZzBg7dj+/bBmstv3gQkegnL1dXVutfi6TJfU2IBQyIiIg1iscCDB+M0u56vWLECFRUV0hKVrwoJqvtp5eQswsyZy5CTs0ixdVzrfFozOD/+OMRtRke8FrMu7cOHD8eYMWMwZMgQ03EvX74cFRUVmu+Z3ZvmxBkfIiIiFa3dVFp1a2pqatzyWhpbSFCrn5aa3vn08oLk5DM6ZrNK33//veb36y2dGY25tdT1YeBDREQko72byhUwrFmjXbdGHoRYXcoy4u0OJ73qy0bb4D0NSKwUYdTTGur6cKmLiIhIRnvWRBSAwsI0nfdantaS0pAhP+ouMbkSrudg9uyxSE7eL9ser73Mp5cMrV4GbM044+OFxmwbJGt4j4mopZh1Gi8oSEdaWqHhzIW6mCAARW0gsR6QXGMK+anzgtQzOBdf/KXmjE58fLzbdxot83XqdNyjZGhv8pqaGgMfD4h1b2pqahAWFtbCo2nfxP9hqGsNERE1FXWSsnK5q4HZridP6wHJmRXyq6iowNGjR3H8+HFFVWYAOP/88yEIAqKiotC5c2cA0K3cDGgHWkbLfB9/fBmyst7waHdWY/KVmgoDHw8EBQUhPDwcR48eRXBwMAICuFLYFJxOJ44ePYrw8HAEBfGPKBGZ05phARraNqgf+iLxoSt+PjMzE7W1tUhL+w0pKYtQUpKAVaum6T7o9+zZg/LycsvtIMwYBQiNLbBohdEynyAE4MCBnooKzuqlM71rb+7gxgifKh6w2WyIi4tDcXGx1OGcmkZAQAB69uwJm81mfjAR+bXGzLAAwIwZM7B8+XK31+12wG7fhZoa/V1PGzZsMD2/1g4ovV1RRhpbYNEK42U+AevXT5DaV8THl7otna1evVr33M3ZlsIIAx8PhYSEoG/fvs1aXtsfhYSEcEaNiCyx+v9jvWBDXXhPfZw6ZwYAiouTLAUtWjugAOjuiqqsrFQURTS7npKSRFRXhyE8/DQSE0savWPKfZlP3BHWsDNM7NKek7NI8/u82erenBj4eCEgIAAdOnRo6WEQEZEGrQev1S3YeseJ27C13ncFRdbbQbj2bmi3iMjNzbU0M7J161CsWXMZlJuzBUye/LHlreVyesnRwcE1OHCgJ9avn6A4Xp7nNGbMGGnmy+g+y5fBWnLpi4EPERG1G3qBiVE/KpFZ3yqt99esuQw2GzxqB6GmTpY2mxlxOCI1gh4AsGlelxUxMTHIzMyUOrzL6+1ERp5EXt443TwnsZej2f1TL4O11NIXAx8iImqV9BKWxW3h4pZwcSZB78E7deoqS1uwzfpWaSf+BkCsvqF+0Ov1p5LP+IivedKz6tixGOiV4ZOPt7y83KOZFfX2enHmrLQ0DvIKI3rtJvTuX0lJAo4dO+02I3bkyBEGPkRE1HbpBSoiTx7C3iQs68+wCJa2YJs10jSr7yN+n147CNf7wJAhDU1DxdeKivpoLgnJORwOaRyAE1rBj3y84gyLNzMrWrV8Gq4RSEnZK/0cHBwsjcv9/jilXXHqGTGry3q+xsCHiIgazWqgkp2dDcC1nCMv6CcXHBzsVqPGCr3AJTHxoGE/KpFZ3yr3QMYJV0Cg3w4iJWUvlPVYA/4IepSvGS0JqdntVZg8ea3bcpfedZltkRff15s5U/f6AhqCu8zMTGmmSO/+CIKyFpB8Ka4lEp4Z+BARUaNZfYAdOXIEubm5Hp9fb6eQ+nW9wMWoH9Xx48el35v1rVK/X1TUxzCg0lqW8qYoopo4jpKSBGlXV3R0JWprQ+FwRFo6j16watyyQxncqZfH5Pfn1KkIrFx5heJ9QXC1/Bg3Lt/KZTYJBj5ERNRs1DM8YuASHHwGtbWhmlvE9XYK6b2uF7joNchU1+Ixa6Qpf98sUPI2z0cv0Js+fTpsNhuOHz+OY8eOSd3Tt24diiVLbvCocahesGq2pJeeXqAYk7rdhHh/HI5IzfNs3mze8qMpMfAhIiKfs1Kgz6gnlPjQ1ktY7tr1kOEOotmzxyI2Nlaq3FxVVYW8vDzT8U2ZMgWxsbFuY62urkZ4eLjs8w63dhB1dXWKZSrxO7QqHQPutXysbL2Pjo6W6vyUlZXh+++/N91NZZX8nui37HAiLa1Q+knM25K3pSgvL8fq1atht1chPb0AmzePVJ3Ds9ktX2PgQ0REPmWlZo5ZTyjxoa2/U6inpZ1a6qUYs/GJiboN42wIcKqrq6XWFOJ55QnbZWVlut8xbNgWJCcXIzHxoDQ+rZkisyBGnvhcWVkJwHw3Wnl5uVs+lTqPSuue5OQsQmFhGjZvTgfgHqBlZmZK166XoJyWVih9XuTpLjZfY+BDREQ+Y3X2wawnlPjQ1k9YPmC4A0svQdhsfGIdG0+odyZpfceWLediy5ZUTJ7cEGRpLamZBTFa12W2G80sWVrvnuTkLMK4cflISyvUXcorKytz260nX/oSE7HNEsubEwMfIiI/5Mut53JmD26RUR6JMnlWO2E5IaHM0k4t9ZKW1fFp0VseO3LkCGpqalBcXKx7D1zcCyKqz2cWxGgx241mxuye6OU8yYNEefCnLoZolgfV3Bj4EBH5GU+2nnsa/Fh9cOv1hLLZnEhPL1Acq/fgNHug6lVxthpYyAMTrd1b8no0ZvdAJAYUeufTC2IA4/5gRvdCHWCpf/Yk2LLah6tLly6Kn/WCJ3VidHNg4ENE5Gesbj0vLS3VPFZrNkh8gJnNPshzaNQ9oXbuHIjNm9OxefNIFBSkK4KL2bPHSnkpVVVVqKurQ1BQECIjI6Xzie+vXr3acPnGyuyIMvFaWa/HLHlYq3ChyGZzIji4Rne5DQA6dTqOrKw3UFsbIm2ZX7Qox3THlnw3lRgkqQOswYMbiifKz+XpPTHbOaZOeNbSUv26GPgQEZEmo9yQGTNmICUlRfpZ/aB78MGj2LcvCElJdYiPPxfAuZp/u5c/rAsKGpJg1cFFcHCwpfybzMxMAMbLN2YzRe6J19br7ogzIl27HsLUqatQXJyELVtSIU8Orq0N1RxbYWEaCgrSFYFF587HPNqxpQ5O1A1Rf/xxCOQB3Jo1rh1ynt4TKzvHWqoJqRkGPkREfs7K1nO15cuXuy2FyX8fFwekpmp/VgyQKisrcfToUWzYsME0z0RdyVlvzOJxZss3RrV6zAr4qc8l0tueP3ZsPuLjS6WAQqu+jc3mVOx+EgMLq33GHI5IlJQkugUnGiNX/RyAJUtuUCy1ybf0OxwOrFixolG5Ua0NAx8iIj9mtnwhPlABIDGxxDCvwyoxQIqLi0N0dDQ2bNjgUZ6JlSUXsyW3MWPGSF3FxWanlZWVyM3N1e05pe7CbtTZXT6rkp+fgZycRbqtL8S8JnW9GzGAMrsvyoDLjLLvlvg98tmb2NhYqVaQyJuk69aKgQ8RkZ8Rm12aLV9s3TpU1Q9KwOTJH5tWBPaGWaAitpXwZMnFaPlGXa05MzNTqnOjNxajpSCr2/P1xgZAWuYSWekz5h5wmbFBq8Gp2exNY3eOtSYMfIiI/EhFRYWUK2O0fAHArQkmYPOqIrBVVgIVsyWXoCDlY029pKW3RKbOH9Ibi951W9mer/5u9djUDT4zMvJN+4xZWZZTEuD6d6qc+bEye9PatqV7i4EPEZEfkS9PGS1faDXXBHyf16HX50mLwxGJU6fCoZ6xkD+0o6OjNdsnANYrSquDE6BhaUys3NxwvCsHxmh7/qRJaw23w4uGDfsBp093kNpb5OdnICzsdyn3RmzDIb8uvWW5//f/PsGnn14K5b9DebBjcxujle3mrWlburcY+BAR+Sm7vQoZGfnIy8uAVksCrSURX+d1GG171gtaXA9sp86YtXcTWVkiMwqM+vbt65b3Aigf+Ort+eJ2dADSdnSt7x47dizy8vLgcERKQY/eGB0OBwRBkGa29Jaghg37AUFBgmpLvjqQtWH8+M8wYMBOxf2TX1Nr3pbuLQY+RER+auvWocjPbwh6MjLyFe0UJk9eq1juaqq8DrOHplbisM0mYOrUXEXvKwCKXlbyB7jZEplZYORwODQDH3lgoG5cevz4cWzYsB/FxUmG3y3WIvKmXQXgvgR15ZUXoK5uLMaNs+Haa/+LtWt/QXBwjdS9XWSzOaWgR9zJpRXEtKWgxgoGPkREfkjrQZ+fn4FBg3a4VUYuKUkAALcgo7noBQQAZDMhriWqpUvzFGOcPn06APNdSWZBx4oVK5CZmQm73e4WHMh3qQHu7UDMvltM3G7Mzin5ElRiYqKig/vPP38OAIbJyVo7udorBj5ERH7ISl0WMZjQC3iaK69DL3F45cppqKlxtXPQW6Iy26ll1CMLEPC//yUDsKFz5wpFAvT06dMVuT4icUu8nNl3i4nbWsdlZOT/kW/lHuQFB59BbW2oYf0lvaU4dXJyW8rRaSwGPkRE7YxRA1JxKchsdsEo32XKlCmIj49vtiUQ/RYQAX+85vo94P329oZ8p7GQJwBv3DgKGzeONu3PpUcMUlJS9iInZ5Hpjij5GEtL46WcH3nPLr0Eaq0yA+0xR6exGPgQEbUjVhuQGs1CmOW7xMbG+uxBaSVIA1wBQUjIGaxceYXiGK2t3PKZq5MnTyreM9o1Fh9fBvfKxsb9uYyqXpsFj4CyLYh8JufUqQgp6Vz8/jVrLpOKKFoZm8ifghorGPgQEbUj6iDC6MGsNwPSXO0JrAZposTEEs1ZKvmMj4urESgArF+/HoC1pSmjWjyA+z0wCmysBI9yWu0u3AX8ca3mYyN9DHyIiNow9YyJfJbEbMZB/fA1qg3TFO0JrAZpY8aMwYYNG3RnqQD18o+y/xTgqu+jlbwbFxeH6dOnIzc3V7cWj9Y9MAtsPAke9dpduHOqZnwatNX2ES2BgQ8RURtlNGNiZcZBbxdPS7QnMArSxJ5agPYslcMRiYkTlQX7zJZ/5OQzQWY5NlZnxTwJHq02RTXL8eFsjzUMfIiIPGCUkwI0b6Ko0YxJY5ermrM9gVmQdvr0acXx8jwdowad3i7/iOdPTt6PQYN2aN6D0tI4GM0IeRI8mrW7mDp1pWJnnVaRRH/doeUNBj5ERBZZzUnJzs5u9oRS9YxJRka+x8tVLdWewCxIW7dunfS6PLgDoBv0ANaXf4yuR+seiBWWlUtSgtRbS2Q1eDRrdzFo0C7TMQGuRqtdunRhMrMJBj5ERBYZzfR4c5yv6BUjzMjI112q0dJSW5+tLgspZ3ecGDhwp2HQo3W9ejN2mZmZqK2txenTpxWBlhbtpSkb4uNL3Y61GjzqtbuQf1YrL0t+PgY81jDwISLSYZQ43JrozZjEx5daqh0j583Ds7HLf1aWhdwTgAPw88+DoLXcpF4ako9TPmOnl0w9Y8YMhIeHKz4r7xvmmm3Sb5QKQKrybHY/srOzcfToUanRqdG/o+asndSeMfAhItJgZVnLaKu4L8fRmGKETb1cZTWY0Fr+czgc0u/NloX0E4Ddu4yrl4YA96DVKJm6pqYGKSkputdcVNQHymUuZaA2ffp09O/fX/fzcjExMSwy2MwY+BARaTh69Kjh+0YPTl/xRTFCwH2JxBcPUTEgs7p9Xv1Qr6hQtoAwCyKNa+xodxmXn1NdKNAomTo3N1c3T0v8rHKWyZVwLNKqF2SGQU3zYeBDRKSifiirmT04fcUXxQgB3zeg1JrlKSlJ9OieyK/NKGAaO3Ys8vLypOBO3i1eJO8ybuWcJSWJmkuDhYVpGDcu3218clZ2y3FXVevGwIeISOXIkSNur/lyq7g3rMwwNdcuLL2gRc3KPTELIiMjI6VjxeCusDANBQXplvOB5OcsKurzR/DkbvPmdKSlFf5RG8ihGSyaJWJPnz6dszetHAMfIiKZiooKtwaUmzalS32TrGwVl+euaPF0qcksOGip3T7uCcdK8nuizrERf/Y0iLTbqzBuXD7S0go9ygcShACUlCT8sUylVyyw4XtXrFihudxltqzozTIXNa92Gfj84x//wNNPP41Dhw5hyJAhWLx4Mc4777yWHhYRtQHqJY4vvrgIGzeOgrwhpNlWcaNlMpEntX7MggNfL2VZZVRxWH1PVq9erblUFxx8xjCIDArSfkwZ7YDSzgcSUFycbFIhWUBpaTySk/cD0F/uas7ijuR77S7wWbFiBe644w688sorSEtLw6JFizB+/Hjs3r0bXbt2benhEVEbsmlTuiLoEcm3ig8adDmGDIlAfPy5AM5VNL0E9PNyPKn101y9szylHWA4MW2a+3ZyraU6wLz9QnR0tGLLtxV2exUyMvKRlzcWDf/ubNiyJVUzIJIfk5+fgUGDdrgFMy1V3JF8r90FPs899xxuvPFGzJ49GwDwyiuv4JNPPsGbb76J++67r4VHR0RthcMR+cfyllbDyIat4hMnhiEurpvmOYzyctRLP0ZLUi3RO8sKvXGpt5NrLdU15NnIG3M6kZX1BhISyhSfN9ry7XA4UFtbi5MnT0qd2F1jc8D9310A0tM3KfKDrC6zcct5+9GuAp+amhps2bIF999/v/RaQEAAMjIyUFBQoPmZM2fO4MyZM9LPJ06caPJxElHrd+xYDLRzQQSMHdvQmkDvb/hmeTny7dUio+Wv1rq8otc0VD7Lpb0kpnVvA1Bbqz9jor43elviS0vj/mgpoWSzOZGWVijlBwUH12DJkhssz6QxqGkf2lXgU15ejvr6enTrpvzbV7du3fDLL79ofmbhwoVYsGBBcwyPiNoQvTyRCy/8D0aOdP1FSmsHT2VlJQDjvBzxfbPlr9a4vFJRUeE2W6XXNFScATp9ugPUFZZd9Jt8ioyuTX93mdZ3KWfJxH+2xpk0alrtKvDxxv3334877rhD+vnEiRNITExswRERUWvg3jjSibFj86WgB4Bm3mBdXR0A/byc0tJ4vP32dZYKH7a25RWzKs0HD8ZpznIJAqC9ZOhedVledNFqiwb33WXu3zVt2krNis6tdSaNmk67CnxiY2MRGBiIw4cPK14/fPgwunfvrvmZ0NBQhIaGNsfwiKiNMXooZmZmKh7KYiXj48ePA3AFToMHb8ePPw6B+IA/66xd0k4wwFrhQ/E79FpX1NTUoKysrFkCIKOig65rHQz1EpbxLipADHq0cnvEa1NTX6vR7jLAFXAmJh7Ufd+sRxa1L+0q8AkJCUFqaiq++OILXH755QAAp9OJL774AtnZ2S07OCJqE6wuL3Xp0kX6vVZrCYcjEtu3D4Z8x9CuXWd5VfjQausKeY6QGCiJyb9qQUFBiI6O9ipg0spfagjwlFyzXoB+7RzX59W5PWIOlFHvL5HesqTWTJInuEOrfWpXgQ8A3HHHHZg5cyaGDx+O8847D4sWLcKpU6ekXV5E1HY1tgu4Fd4sL2kdq5fjY9bVW4vV1hVHjx5FTEyMbqCk9zmrncSNrk0v6HHftu7841jj3B7Aeu8vrd1lGRn5iI8vdZupE5fQ1Oewct3UPrS7wCczMxNHjx7Fgw8+iEOHDuGcc87BZ5995pbwTERtS2O6gHuqsZ93OCJx6lS4Zo6PUeFDK4yCAbHasFagpNXmQf458Tgr99RohqWBcvlKvmRYVNTHNKHY035oVnN1YmNjGdD4uXYX+ACu/0i5tEXUvlhtamn0t/jmmDFS7i5ySgGCfJyDBu3QfEDv2bMHJSUlCAsLk5aigIaaP3r1cOTBQGlpqdt41I09tYIIT+6p1gzL4MHbsX37YMXn5Tk78iVDoyBlzJgx2LBhg1f90Kzk6nD5itpl4ENE7Ze3ndG9yZNp7NiAAAiCE9Om5SoqGes9oDds2GB4fr16OPKu4vL6QOJ4tPJr5EGE2T0VAy9xqz6gHbxcfPGXhvV85PTuQadOnQBYq1ZtNYiZPn261/lM1P4w8CGiNsXbzuhW82TUx1mZJTIaGxCAiIhq0w7lWmNRv965cwXUOUIAUFDg6ioujiE4+Axqa0Nx6lS4pQaievd0584BGDBgp2axRcA9eDGr5yPftm8UFInnMqux09q2+1PbwMCHiNoUX/StMnsoi6zOEmVmZloam7iMIzLKvdEb44gRBdi8eaTi+wUhQHEe+Y4mrUAJEBRBhF7OzuefT8D69eMMaw1pMZtBMrr/8qakVvJ2GNSQp8wKLBARtSriTIDroe7eBdyM3kPZ4Yh0O9ZqI1Fxu7jZ2MRlHMAVfD3/fA42bx7pNha9QoAOR+QfMztO1Qic2Lw5XRa4NHSSbygSKCcgJWWvdD+OHYtBRka+NG55orLe/XE4IlFcnKR534xm5czuv82m3B1mt1chOXk/6+yQz3DGh4janMZU2/V2qQzQX56xOksRHBwsncco9+bAgZ6aYywsTPsj8FFvHbdpvCZ/T811verdVRdcsBEbN17oNi71/dm0Kd1tZ5rrml3LbHo72jp3PmZ6/wVBHaRpY5IyeYuBDxG1Sd5W2/V2qcxoeSY6OtpSron4vlGlYZvNiZ49D2h2Dt+8OR3x8aXQDny0lrS0CwjabE4EB9e4zbx884170OPScH82bUpHXt5YyGeE1qy5DDabOMMkzha572gT/30Z3X+r95JLXOQtBj5E1CZY/Ru+2XFWkmbVrOwks/IgFtsvaOfUNCyNJSSUIT3dPZfHFZQImoHDBRdsxDffXOiW46MuICi+VlsbaqnAorwbvcMRiby8DLgHXgFomKixyV7T3tFmJWmZqKkw8CGiZuVtLR1f7uDxdKmsMctjWrQaoI4YUYABA35GbW2olMuzeXM61DM1iYkHNWvoNAQ9Tlx44Ub07l2suDb19TockRrBlxOpqVuwZUsqgIYKyIMG7UBxcRJOnQqHZ6mh2jva2BiUWhIDHyJqNt5WX/Y0WNI6Xl6DBtBfKquurnZ7zRc7ycRxisSHf0lJAgAbHA47liy5QTELMnmy9syIPHAIDq6RPufiWrIaPnyL7lZz8Wd18AXYsGXLubDZnEhP34S0tEIUFfXBokU50hi0ZoT+uCNu12t0j9gYlFoKAx8iajbeVF/2NFiyugVdz/Lly90CL2+Wx7TExMRg+vTpyM3NBQBFcrHWTqqcnEXIyVmkOTMiBg7FxUlez0bJg69Vq6ZBEBq+v6AgHQMG/Oy2xOcKAMXgx4mxY/MRFva723Wo75F6Kz9RS2HgQ0TNzpPqy54ESxUVFW4tG4wK5ZkVMdSaodEKQjzZYSS2oXCv8qycMRGDF/lWbq3xms1GjRs3DklJSVKX9uPHjysCELu9CseOndYMnkpKtHeXTZuWi4iIas2ltODgGtTWhrjdI/lWfqKWxMCHiJqdWc5MeXm52/KVWbC0b98+rF+/XnFOo0DJShHDpqwMbLSzC3BfJtIbr9lsVMeOHeFwOKRGpIB2RWit4Ckx0X13mZhnZLUFhUjcym+G29SpqTHwIaJmp72rybXFGmjoNyVvNmwWLKmDnoMH4xTNOeWBEgDLM06N2WGklWsk9r0y6nAu341VXJyE4OAzhuPVmo0Sg5ulS/MU16QOoNLTC5CWVqgZPCUklPlkiQ8A7HY7t6lTq8DAh4ianXtirQAgAEuW3KCb66M3K3HqVAQcjkjNh7teIT7AZhhEFRcXSwGKSN4t3coD2izXSGumJiMjH/Hxpejc+ZgiqVirRo86j8dKnyytWbPNm0di8+Z0TJ68VjOfyGiJb+zYsQCAvLw8w3th9Z4RNQcGPkTUIoYN+wFdux5S7EgymnnRChQEAVi58grDh7ucfPnIKC/GyoPcrIu7laaoekGFVpd3efKzerzq79GbHdJfXmtIpk5O3u/2rt4yVnJyMuLi4tCvXz/O5FCbwcCHiFqMXhE9vR1J8l1IK1dOg9Yylv7DXblMY7SEY9Y5HLDexwswzieaNu18REa6+lSJicfa12DTrYQ8ZcoUAK4lQqMlQb3CifJjPFnGEvNxGNRQW8LAh4iajTpx1Zv6OOIuJL2lH71zZmW9gYSEMuk1vdkWdZDiWn4qMwyCRPKcHnGpzCwpW5w1AVyVnTds2GB4DVo7pmJjYy3dU/clRrgdA7gCqaCgINTV1SE4OBh2u93tWjmLQ20VAx8iajbiLqnS0lKsXr3aUn0crV0+njzc5Ym6auolHIcj0i0hWuxLpbfzS6SX0+NN1WdPrsHqZ9XLa4WFaVJlaPUxsbGxUjBG1N4w8CGiZhUTE6NYJjJKni0vL0dsbCxmzJiB8PBwlJeXWwqYzBJyO3XqJCUqi+cEgMLCNLi3ZFAWFdTKPwL0c3qCg894VfXZk7YO8gaoVj5rt1dh3Lh8pKUVsm0E+R0GPkTUJIzaTKh3TOklz4oBCeBKJpYv6Vh5uGuds1+/fppLNA5HJAoK0g2vSV1rSO+a1Mtlgwdvx/btgw23hFdUVFi+L1OmTJHuhbjkJDZANfusp8cQtTcMfIjI56y2mdBiVE1Zveyl9+AeO3YsIiMjFVvQAeO8FLOCgi4NMzXyoEw9fnVOz/btg3XzcwDzre9q8fHxbtfhy8J/LCJI7RkDHyJqNPXsjtHshzxPRr4byexYQFlJWV2NWE6+Hd1s27nIaMeTi4CxY/PdcoLUQZpeTk9tbYjmVvHKykrU1dUpXtML/qZMmaIZ9ADWq0wDxjvSmLRM7R0DHyJqFP3ZHdcWbaMdTfKlK6v9uzx9KFvddq7Vrdxma2jMmZGRj5EjC6Tj9YI0s51q4jKVGLiJDUvNziu/HnFZSx2kMGAhMsfAh4gaxaiJaHp6geUdTd7sfpLzZDlNzqgRqWtc7jlEZkGaUeK13o4phyMSJSWJhm02nn12q25XeiKyhoEPEfmEdjuEdMs7msxmSiorK3W3WFtpOKpHXCI6evQoamtrUVVV9cfSU2/pmKCgP6Fz586ora01LRKo1zvLiHz8aoIQgMLCNBQUpOt2pSci6xj4EJFPaCcHByA9fZPbQ1srEDCbKcnNzdWc3bC6RKanoqICR48e1c0XksvMzARgrfCiXuJ1ZWUlAP0Ch+6c0v3z5vqISImBDxH5hF4wkJZWaLlejNlMyc8//4xOnTohODgYtbW1ABq3RKa3m0pv2Uz8TrMgTZ3HI6fO6THeTebEiBEF2Lx5pFfXR0TuGPgQkU+YBQNaD2mHw4EuXbq4nQdwBQTqz23YsMHtHGazL+Xl5bo7lbSKDhotK8lZWc4SBMHt/OqASns3mashqc0GRESc8qoAIhFpY+BDRD7jaW7LihUrkJ2djczMTGlmxNN8HbOAS9wqb5YEvHXrUEViMeC+rBQUpPxfppXCi/Lzq3uAjRxZoLGbrKELuyAEID8/AxkZ+cjPzzBdLiQicwx8iMinPK0GXFNTIzXB1MvXCQk5g8TEEt3u6VYCrtLSUqkIohgAifk24ve6t6tQLitFR0djxowZWL58uSe3RPO6XD3AgJEjC6Tx//zzAKxfP8Ht++PjS5GTs4jtJYh8gIEPETWK1Sq/enkz5eXlCA4OBqCfr7Ny5RXSTAcAzRkhMeByOCJRXJzk9j3q9hdAQ76NUZ6NelkpPDxc87qCg8+gtjZUczu99vltyMvLwKBBO6SxDxy4E3l543SbrzLgIWo8Bj5E1Ch6FYPlzT+Nlq/kAYlR9WRx9seVNqO9w0lrOSk+vswtGCktLVWcW+971ctK6l5aym3oYl6O+/Kc/nUpk5StdKtXY3sJIs8w8CFqJw4eBPbsAfr2BRISmve7jXJnPNlu7p7voqT32rFjnQG4V4l2LSe5ByPqHJyioj5Q5iG7dlOlpRXqzhq5b0PX7+Jut1chIyNfGo9IK0nZaNlO3pwUYHsJIm8w8CFqB5YsAebMAZxOICAAeO01ICurpUflord8VVKSALt9l9vx4oO/pCQBK1dOgzzvxjVrArfXOnc+prucJH6fXrCll9+jDnrUn/n554G6y2Na283Fdhd5eRkAzGsaab2uV/WZiKxj4EPUhlVUVGDfvjrMmdMVTqfrIe90AnPnCjjnnCNISgpq8RkBvWWeVaumoaZGe8eW68G/CzU1a92Wrk6disDmzenQCh6MmoyKwdaxY6dNm4qql6DS0tJQWFgIQHt5S01vu/nIkQUYNGiH10nKXNYiajwGPkRtlFh8r7g4CU7nTMV79fU2LF68DsnJ+xXbuOVd1EtLA1BcHITk5DrExzsBNM3Sibh8ZbZVXIt82ae0NF6xpTs9fZNiVsZoW7iLE6tWTfO4qSgAKejRXt4SVP909Sgzuh9a1ztjxgy3xGk5LmsR+QYDH6I2SgxgzB7c4nHyKsVGyca+anqpbv4ZEnIGK1deoThGvSQk3/kFQPp9587H8Pbb1ynydwoK0jFgwM+629rlgRLgBGCDIGgvfVlNKNZbThs//jMkJh7Azp0DUVCQjs2bR6KgIF1xX9X5Oep7xaCGqHkw8CFq46w+uMUAyCzZWK/ppXy2SIv64S3u9iotLcXq1auRmFhiGKC5LyEJEJez9Lq8L1lyg+629uTk/dKy0qlTEYZBl9XCi8HBZ+AKopTXMGDATgAw7KnF/Byi1oGBD1E74EnFZG96W+n1tFJTzxbFxMRIwZJRgKa9hNQwO6PV5R0QTHeKyWv7eNtUVCQGZq6gR7l13W6vQnFxktc9w4io+TDwIWon9B7cYu2ZHTsqUVychODgMx73fjKa6TE7Tr3kpRWgGTfqBNRd3tWzLoBxkGG1qahaZWUlcnNzdQIzJ6688n3067cXgLWO7UTU8hj4EDVCc9XOacz3rF69WraMdBZsNicGD96O7dsHe937Sa8Ksxa9AodAQ5FDo8KFgLLLe0lJAqqrw7Fu3f/zKMgwmhVT9+ACXAFbdHQ0AP2dXx98cJViic3T4oNE1PwY+BB5qblq5zT2e7RyerZvH4ysrDdQWxvi8bZqT5uIAsYFDgHtGRmxXo88gFDmATmlYEkdZOgFZpdfPhydOnVCVVUV8vLypNfF1hVqmZmZAPS35KuX2IyCK25FJ2odGPgQWaCecTl4sCEYAcTaOcD48b6b+TGr0RMR4bB0Hr2cntraECQn73c7vri42O01dTNPK1WYzVRUVEjnBdxnZFxjbwgg3JebAiAITkyblovExIPS9xsFZhs2bPBojLW1tQD0t+SL90BcYtNbMuOuLaLWg4EPkQmtGZfevRuCHlF9PbB3r28CH6s1eqzwNPdEPhOi5k1itBa9ZGl1ntLEiWejY8d+WL9+ve5yU0REtWKmx5vATG+GSL4ENmzYD+ja9ZC0k0wkv5fcuUXU+jHwITKgPbMjYM2acgQExEozMQAQGCggMvIIKioaXy3Zao2e6dOnS3koamL+jNluKqu5OlbG4+n1mdm8eTOmT59u+bsLC9N0AzMAmtdqNEMUHR2t2JKfkFDGPB6iNo6BD5GBPXu0ZnZsyM39BJdd1lnxALz44nwsXuzqBD5//kyfLG1oBS0ZGfk4dsx17rq6OgDmSylauSd6D3yjYKipEniNvlO8RrPvdjgi/2hloWSzOVFaGi8VQFRfq9kMkfq+Mo+HqG1j4EOko6KiAlFRdQgI6KqY2RFnGZKT9+u2U+jR4wTuvNM34zBq21BUtBbDhrk6hmdmZqJLly66AZB8GUnvgX/6dAfF+bUSl40e/A6Hw+N8FnUAlpGRLzX0BICTJ09a+m5XMOi+K2zYsC2yCs7K4Mbq0p3RzjQR83iI2gYGPkQa5Dkol13mPjMi7w8FwK2dwr332pGZ2ZAI3dgt73rf8/HHl6Fr10NISCjDihUrAFhrOaH3wNcLEOz2KowdOxaAKwdIr2aQJ2MAtAOwvDzX94jBz/r1693uhdZ3a++8ciI5eR+2bDnX7VpLShKQmHjQ8tIdgxqi9sGoYhiR35L/zX7YsB+Qk7MIM2cuQ07OIrcZEK0gor7ehsLCCrzwwkn06gVcfDHQq5crUdpbesHKG2/cgK1bh7qN3WjZRQwSlLS3a4v5MXl5eYaJz3JWc3j0el/l5WXA4Yi0dA6RuBQmXpfN5sTkyWulVhlqK1dOQ1FRH7fPMGeHqH3jjA+RBUbtDPSSbj/7bNUfO4Bcr8m3oicleZ4ArV/kT3vnktbyjF7CM+BEauoWbNmSCuXfh5w4dSoCDkek5vV7mhztyTXpNS/V+x6HIxKdOh3XrE/k2oo+Ccpu7a77lpOzCDk5i5izQ+QnGPgQNZJe0m1tbajmTJC4Fd3TLujuwUoDvS3lVhKeCwvTUFCQ/sdykICGdhCujuYrV16hme/jTSFDrWvKyMj/Y3nLPY/K6vdoHSPf7p+Sshdify058b4lJ+/H7NljFTV4mLND1D4x8CHyAa2kW7PGmGaJsnrf07XrIbzxxg1Qdwi3sqVc67zyjuKuxpsCJk78GOvWXQpBaGgUKp9VcjgiFcX8jOrlaHV1dzgaii+KuTx5eRlQV2q2suvKyjF6ic+swUPkfxj4EPmIejmsMVu/5ctU4vKUKCGhDJMnN+68Yl0avbyhurpgw1mlwsI0WGkSarWr+8iRBRg0aIel5qXq77FyjPaSmoCMjHzm8xD5GQY+RI1klH9itP3ajLjMIm/r4KvzmhVI7NnzgO5slcMRiYIC93o5gPusk9UkZ8AVKN5440TYbDZpd5iVooWdO1dA3a1dfYxWTtPYscpt88znIfIPlgOfEydOWD5pVFSUV4Mhamus5J+IM0EORySKi5M8SgQuKipSNNBUB1mNna3Qm5UyqlBcXJyk2UV9xIgCaTzl5eWKf+qNX01dhdrKrFlRUR8oc3e0Z8CMgsUZM2Ywn4fIT1gOfKKjo2Gz2QyPEQQBNpsN9fX1jR4YUUuy8rd/T/pCaQVI5eXlhgm0FRUVWL58ueE5tJKJPZ250AsI9F7Xm4VJSyuUfpYvzXk6fqvjAxr+HSgTo8VkZpcZM2YgPDxc9/xMYibyL5YDH0+7GhO1ZUaVeh0OB1asWGG56q9+gLQIdnuV7u4u+XebJROLXcHFoKesrEz32kJCQtyCI73ZI73X09ML/mgPYS3HSO8eiMUXjRjNEpn9O5g+fTpSUlIMz09E/sVy4DN69OimHAdRq2M2C2C1YafZw/nIkSOm32WWTCzuSLKaTJydnW0Y2NXW1iIoKAjR0dGK5Gr1rE16+iakpRW6BXrqQMWo+OLkyQ0zPw6HA3a7XTrGbJbI7N+BXgNXIvJfXic3V1ZWYsmSJdi1axcAYODAgbj++usV/9Mias+s7trS21FUWhqP5OT9yM3NRWZmptt/O2J+TFMkE9fU1Ohu3dZ7XWvWpqAgXbHEpReoWC2+WFtba/h96qXEpmqaSkTtl1eBz/fff4/x48cjLCwM5513HgDgueeew2OPPYb169dj2LBhPh0kUWtllH8yZswYbNiwQadInw35+RkYNGgH7PYqrFixQndJR7utgzKZWI83lZXVdXfEAMxs5sosULFSfPH48eMICgqy9H0idksnIk94FfjcfvvtmDx5Ml5//XXpf1J1dXW44YYbkJOTg//85z8+HSRRa6aXB9OpUyfp9/HxZdCrGmy3Vxku6VhJJtZidM7KykrNmR2jpTKzZSWzQEWv+KJ85kqeS2j2fdOnTzdcymLSMhFp8apJ6ffff497771XCnoAICgoCPfccw++//57nw2OqL3Qagoqr4ujNVMiNunUar7pbTKxeM7c3FxUVFS4fc69wrJrC77Yq0tvHA5HJE6dCte9RlFk5EmoA0C9zaJm1921a1fExcXp/mLQQ0RavJrxiYqKwoEDB9C/f3/F6yUlJYiM9KyjMlF7dfz4ccVSkyd1cdRLOmYFC9VLOlaWiczygfRmjNTjkB8HOKVZGq0AzdU6Qn/mSyTPeXrwwaPYty8ISUl1iI8/F8C5nM0hIq95FfhkZmYiKysLzzzzDEaMGAEA2LRpE+6++25cddVVPh0gAOzbtw+PPPIIvvzySxw6dAjx8fGYMWMG5s+fr/gf/vbt2zFv3jx899136NKlC2655Rbcc889Ph8P+Y+DB4E9e4C+fYGEBM8+++yzlfj44xxFEKDVBTw4+AzMKg8D+ktqWsX3rOw4UxcXlL9mlq8zfvxAfPvtfrfjgAAIghPTpuUiMfGgpURvm82J4GBlEGa326WluLg4IDXVbahERF7xKvB55plnYLPZcN1116Gurg4AEBwcjJtvvhlPPPGETwcIAL/88gucTideffVV9OnTBzt27MCNN96IU6dO4ZlnngHgqiw9btw4ZGRk4JVXXsFPP/2E66+/HtHR0ZgzZ47Px0Ttl5jc+957YbjnHjucThsCAgQ89ZQDV199GtXV1abn0AsccnIWKbqGi7MlrqDH1T1cPVMi1ujRojfzoZVMLAiuKsdino9WkUGR2YzRt99+q3scEICICNc9Uleqdh+XAEEIwJIlN3jV3Z2IyFNeBT4hISF44YUXsHDhQhQVFQEAUlJSDKujNsaECRMwYcIE6efevXtj9+7dePnll6XA591330VNTQ3efPNNhISEYODAgdi2bRuee+45Bj5kmZjc63BEYtGiHKk7udNpw913R+G3396E3V6lWw1YrHljZanJfbbEFfRkZb2hKOpn1jVcvgtL3tcrJWUvBEF+pH5laTWrNYr0jistjcfbb18nLX+NGFEg1fvRSnI2qnpNRORLjWpSGh4ejrPPPttXY/GIw+FA586dpZ8LCgowatQoxdLX+PHj8eSTT+L48eOKHTZyZ86cwZkzZ6SfPelJRq2fp0tVYgBhFriEh4cbBiNWAge976ittb4F22gXliufxlplafWWd6v1cbSOy8jIR35+hmL5a/PmkSgoSJdmdWprQy2NjYjI17wKfH7//XcsXrwYGzZswJEjR+B0KndybN261SeD07N3714sXrxYmu0BgEOHDiE5OVlxXLdu3aT39AKfhQsXYsGCBU03WGoxS5YAc+YATicQEAC89hqQlWXts1ZnPPRYCRysfodRLRqtXVhiEGPl/EZb3q12gE9J2YupU1cBEJCYeFC37pB8Vqex95eIyFteBT5ZWVlYv349pk2bhvPOO8+0eame++67D08++aThMbt27VLsHvvtt98wYcIEXHHFFbjxxhu9+l65+++/H3fccYf084kTJ5CYmNjo85JveTJzU1FRgX376jBnTlc4neJSFTB3roBzzjmCpKQg0x1BRoGLwxGJTz89jXPOOYz4eFfQr5VrYxY4mAVHU6ZMQXx8vNt55Utb8gRlrSDG6PxWKyMbzcBofaerQagyWVskzuokJ+83HBsLDxJRU/Eq8Fm7di0+/fRTjBw5slFffuedd2LWrFmGx/Tu3Vv6fWlpKcaMGYMRI0bgtddeUxzXvXt3HD58WPGa+HP37t11zx8aGorQ0FAPR07NwSzJWCvYEJd+iouT4HTOVLxXX2/D4sXrkJy8X7cxqJzZ1m31DEl2drbbOcwCB6PgKDY2Vvf61IySqbV2kgHWKyOrk6srKyuRm5ur+51ZWW9AvWVdJJ/VefTRZG5VJ6Jm51Xg06NHD5/U6+nSpQu6dOli6djffvsNY8aMQWpqKpYuXYqAAOX/sNPT0zF//nzU1tYiODgYAJCXl4d+/frpLnNR6yU+4A8ejMOSJTfoJhmrAxhxJsRsKcVqTyt54GI2Q2L1nEbfISdPVAZcsyB6S1unToXrBjHJyfs1z291uUkvuVovcPr1177QDnyUszribBa3qhNRc/Iq8Hn22Wdx77334pVXXkGvXr18PSY3v/32Gy666CL06tULzzzzDI4ePSq9J87mXH311ViwYAGysrJw7733YseOHXjhhRfw/PPPN/n4yPdqamqwdetQrFkjbvVuIJ+V0As2mqJ5pZUZEqtLNOpdYQ6HAytWrFAck5ub6/a5zMxM6ffq2ScrtYDUiczKreVOpKcXuH2nuJymnonRazz6n/+Mhrg1Xz4Wcbea3hIeEVFz8CrwGT58OH7//Xf07t0b4eHh0gyL6Ngx3yYo5uXlYe/evdi7dy8SVAkewh/7de12O9avX4958+YhNTUVsbGxePDBB7mVvQ2qqKjAjh2Vsvo2SlaTYK0m51plZYYkJiYG2dnZhrM/VpZy9JqLit3LtWafXGNzBT9agZ5RJebCwjQUFKS77b4ClPV+srOzpeBOv/GoWMFZUHyXuEWfQQ8RtSSvAp+rrroKv/32Gx5//HF069bN6+Rmq2bNmmWaCwQAgwcPxsaNG5t0LNS05Dk6gnCW2/ueztyY5dioGc3YWJ1FauxD3SiPSKQ3+zRtWi4iIqrdAj2jZToAKChIN0xyFtXU1CAuLg7Z2dkoLS0FsBohIWewcuUVqqsIwNSprrFcc00aBg1i/g4RtQ5eBT6bN29GQUEBhgwZ4uvxkJ8zytEB3Iv7+ZrWjI1YlBDwzSySfFeW/DsAazutAP3ZJ602EYDxMh1gs5TkLBcTEyNdQ2JiieFYBg0ab1jziIioOXkV+PTv3x+nT5/29ViIJHqzK00Z9IisbHX3dtnMqOAgYH2nlZWt8LGxsVLQZrZMp/XeqVMRUld2I02RT0VE1FS8CnyeeOIJ3HnnnXjsscdw9tlnu+X4REVF+WRw5N98naPTGhgVHLTbqzwq7Ge2FV4+y2IWnKjfEwRg5cordJfaAOWyoNFYWJOHiFoTrwIfsW/WJZdconhdEATYbDbU19c3fmTklxwOh+z32gm+Rqw+ZD15GDfFOQH9XB6jACUoSPmfrN7sk9hI1WpwIr5XUpKAlSunwUoPLV8lchMRNSevAp8NGzb4ehxEqKiokLZ0W0nwBdyDjaZ4GHtyTq38HTmxNo9RLo9RgBIdHY0ZM2Zg+fLlhmNevny5VOMoOzsbR48exYoVK0yX6U6csMOTHloMaoiorfEq8Bk9erSl4/7yl7/g73//u6LqK5EeMWCwkuBrVAumKR7GVs5plr8jZ5bLoxegeDKrJN7PmJgYRe0rLfJAU6sGD3toEVF70aju7GaWL1+Ou+66i4EPAdDezSQnzoZYSfDVaufQ0szyd+T0dq1pJRSLicrirFJZmTLB22xJUD6TpvUZAKpaPDaIwQ97aBFRe9OkgY9YXJBIPRviaVCgnnVo7Q9gvaU6+XWrqyYDNs2EYjFRuaKiAmVlZaaNSdVLguqATP2Z9PQCjW7qNowf/xkGDNiJ2bPHIjaWNXiIqH1o0sCH/JNRnRrA/GFttgNp+vTprfoBrLdUd/p0B+TnZyiuKSdnEUpKErBq1TSpH5nW0p7WMprZkqB4z+X3XuszBQXp0Gp3MWDATtjtVbq9uoiI2iIGPuRTZnkuVgv0mSX4tmZ6S3Vi0CP+LHZPj4g4bbq0p7VEaLYkKG81YfaZESM2SdWbubxFRO0ZAx/yKbM8F6sF+oDGFQpUM8ovqqyshM1mg91u13zf0yUevfwdvev2pHaP2feYfa60NA5ayctpaYVISyvEoEGXY8iQCMTHs8UEEbVPDHyoyWgtaaWk7PXqId8Ynuy20iNuDbdCa6kuIyNfMeMDNFy3N5WPxYBSfl6zzzkckcjPz4A86AEEZGTkS5+ZODEMcXHdLF0nEVFb5HHgU1dXh8cffxzXX3+9W6d0tRkzZrCKs5/SW9LKyVnU6PYG4tKL2S4xcbbCk91Weu8ZfY8WraW6sLDfda/bkyrV6oDyggs2IizsNHr2PICEhDLda9CabQNsiI8vVdwzIqL2zOPAJygoCE8//TSuu+4602NffvllrwZFbZ/RkpbRQz4zM1N3yQlQFgq0skssOztb8XmjxGqj98QEYaOlH3XQoF6qk193cHANamtDFVvXjWr3GNU42rhxFMSt54MHb8f27YMVM00jRxYAMF8aa+1J40REvuDVUtfFF1+Mr7/+GklJST4eDrVWVmdXRGYPWb2HvN1ut7SDSD4Wo4BFfpxRYjUAw6RreaKwetlLfm+mT5+O48ePIy8vT3PcdnsVior66I5XrNkjUtfu0Zu1Ecf8449DFD/n5Y0FAIwcWWC6pNa1a1fDe05E1B54FfhMnDgR9913H3766SekpqYiIiJC8f7kyZN9MjhqHazmyMhnV7zt2O3pUovVXWKA8SwUYNN87+efB2DgwJ26y15W7o1RsUD1eOU1e2pqalBTU6Oo3aOdOC1nc/s5Ly8DgwbtgN1ehUcfTcaDDx7Fvn1BSEqqYxIzEfkdrwKfv/zlLwCA5557zu09Niltf6zmt6iPM1rSUs9sAN41tPRkl5jZLJR7QCFg/foJyMsbp9srzCx/yEqxQPV4jYIpdUCp3qHl/jMANJzfFVh1Q2qq5umJiNo9rwIfp9Pp63FQO2GW5yLS67PlKU+2dJvNQukFFEazSHLqIEe9k0sQArB5c7rpeM0CTXlAWVoar9jVddZZu7Bz5wCot6uz1xYRkYtXgc/bb7+NzMxMhIaGKl6vqanBBx98YCnxmdqnpuiObsTTJTWjWSjxvZ9/HoD16ycoPmfUoRzQXnJTb193CUB6un6xQL1zqxO3xYAyOXk/Bg3aobieTZvSkZeXAcC7XXNERO2ZV4HP7NmzMWHCBLdkyKqqKsyePZuBj58zCmrE3BV1o02RN0GR2VZwq7NQ4nsDB+5EXt44j2oN6S25abWCEIsFerN1XS8Rury8XErAHjmywC0YIiIiF68CH0EQYLOp8wiAgwcPGm5FpvbBrBu4Hk+SpD0NfoyCmZiYGEyfPh25ubnSa0bX4MkskthRXm/JzajAoNb5HA4HamtrpTFaSYQGrAd3rNNDRP7Oo8Bn6NChsNlssNlsuOSSSxAU1PDx+vp6FBcXY8KECQZnoLbOSjdwPd4mSWux+gAXj5P397JyDVYKClZUVEjBlF6wNGzYD7qzL2lpaQgPD0d1dTUKCwsBACtWrJDe9yRxu7mXGImI2iqPAp/LL78cALBt2zaMHz8eHTt2lN4LCQlBUlISpk6d6tMBUuvhydZxq+fzZuYI8P5B78k1mM2aqL87JWUvpk5dBUBAYuJB08KEYrCjJt6X4OAzHrX3YFBDRGTOo8DnoYceAgAkJSUhMzMTHTp0aJJBUesiPujNZiA8WUZpzMyRyJsHvdk1WK0crebJ9RgFfOrzqCsxM1GZiKhxvMrxmTlzJgDX33iPHDnitr29Z8+ejR8ZtRri7Mq+fXV45x0BTmdDfldgoIBbbpmIpKQgy4GIr2eOPGFeUdpa5Wg5s+sZN24c1q9fD0A/QHI4IlFSkog1ay6DmAwtCAHYvn0wsrLeQG1tiNtSGfN1iIg851Xgs2fPHlx//fXYvHmz4nUx6ZkFDNufmJgYxMQAr70GzJ0L1NcDgYHAq6/akJrqWTdvT3JXfM3bitJGzK5HXBLWC5BOn+6gs/XddUxtbQiSk/crdnExX4eIyDteBT6zZs1CUFAQ1q5di7i4OM0dXtQ+ZWUB48cDe/cCffoACQmen8Ns1kVszyDnywe9J53QrbBaRFEvQBJr7miRn0e+i4uIiLzjVeCzbds2bNmyBf379/f1eKgNSEjwLuARmc26yBuCynm6zV3ePPTgwYNuY/DVdm+rs0j6fbb0gx7m9BAR+ZZXgc+AAQM0/1ZOZEQeVHgz61JaWoqamhpLsz9WawbJzZgxw3JgVVFRofhvwMr1uPfZ0mazOTF16krFzjAiIvINrwKfJ598Evfccw8ef/xxnH322QgODla8HxUV5ZPBUfui3oIurzYsp7frSX6s2eyPUfNQAJrnDw8Pt3QdekGV3iySvN7VsGE/ICTkDFauvELz3OIsz6BBu9zeYzIzEVHjeRX4ZGRkAAAuvvhiRX4Pk5vJjNmMitVt4VaLIarP6WohYQNg83obvVlHdtGUKVMQHx/v9vnExBKNJS8npk1zn+URt9czmZmIyDe8Cnw2bNjg63EQ4eDBOM1dTyEhZ5CYWOJ1kUTl0lJDsOGLbfRGgVpsbKwUrGRnZ6O0tBSrV6/WzQkSZ3nE3VsMdoiIfM+rwGf06NHYuHEjXn31VRQVFWHlypXo0aMH3nnnHSQnJ/t6jOQHtm4dqqhhIxKEAKxceYXh7Iw8iVkk5t9o7aRSn9/bbfRm9XvkOUAhISGK2R+jnKD4+HgGPERETcSrwGfVqlW49tprcc011+CHH37AmTNnALgaLD7++OP49NNPfTpIat/EAEJvdxOgPztjlsSsv5PKxWwbvUjepsIoqBKEAJSUJODYsdNYujRPMdbs7Gz20yIiamFeBT6PPvooXnnlFVx33XX44IMPpNdHjhyJRx991GeDI/9gNisj0pqdMcu3cd9J5Z7jY7aNXo9e/Z6VK6cBcF/6qqmpYR0eIqIW5lXgs3v3bowaNcrtdbvdjsrKysaOiVoxrWUlOW9mLLRnZcQ2KNYadAL6+TbDhv2A06c7yAoFOjFixGakpRVqLnFZaZ4qHpORkS9VXXZdQ8OYm7MVBxERWeNV4NO9e3fs3bsXSUlJite/+eYb9O7d2xfjolbIam0cq4UGxeUjvWRfAJZbSxjl2wBAfr68OnIACgrSkZbm3h3dbFeZwxGJwsI0FBSkS8dkZOQjPr4Up05FuG1Tb65WHEREZI1Xgc+NN96I2267DW+++SZsNhtKS0tRUFCAu+66Cw888ICvx0ithNUt5FaPU9f1efDBo/j55zPYtm2lFCjoJQCra9oY5dtERJy21BvMLFlZKwFbEAKQn5+BnJxF6Nz5mKXWFURE1HK8Cnzuu+8+OJ1OXHLJJaiursaoUaMQGhqKu+66C7fccouvx0jtmHxmKC4OSE0FLr10psfLaXpJzKtWTUNGRr7GewJKS+ORnLxfeqWkJFE3QAKguetMfkxy8n6fN0AlIiLf8irwsdlsmD9/Pu6++27s3bsXJ0+exIABA6Qu1ESN4c2uJnG5TG9G5oILNmLjxlFwJTYDgA35+RkYNGiHajZHSZyxKSxMg5VGor5ugEpERL7lVeAjCgkJwYABA3w1FiJNRgnV6n5ZWu0gBCEAYWGn0RD0NLwuzuZobaeX5xoVFKRrfr/WrI5e6woiImp5jQp8iJqap81GtdpB2GxO9Ox5QDf/Rm87/dSpKzFo0C4UFydpvj9w4E8YNy7PcpDDXltERC2PgU870xTbzfVY2fbdWFb7Yo0ZMwYbNmzQ3SGWkFCmm39z6FA3uLbPK4OixMSDAPTr9WgFPWJvLTUWJiQiah0Y+LQjvt5ubsRqM1EzZoGavC6U0Xd26tRJOk4vz0br9Q8//DN+/HEIXMtgArQKG+oFU/KgZ/r06ejatSuDGyKiVo6BTzvi6+3mauJSjdm2b6tLOp4sY5l9Z1CQ8o+yPM9Gq5oz4GqK2hD0AGLwc+WV76Nfv72K8xklLU+fPh1nnXWWpesgIqKWxcCHLBPr7mzYADz/vPu275EjZ+Kii1w/l5WV6Z5HXPbxJADTq9Mj1uKJjo6WagKVl5dL7SeMZokOHOgFdcIzYMOxYzEA9kJNL2k5Ojra8nUQEVHLYuBDHomJicH55wMBAYDT2fB6YCCQlhYDwPpym5pRzpBeno28OGBMTAwqKioU5zOaJerZcz/E5a0GAhITDwAA0tLSUFjoXt1ZjUnLRERtBwMf8khFRQUCA2vw1FNhuPdeO+rrbQgMFPDkkw4EBp7G0aMOS+dRz/aY5QxZybNRL50ZFSS026uQkFCGIUN+VOT4DBnyIxISXLNVPXr0YDd1IqJ2hoEPWaYOLG69NVLKeTl5sgqvvebdec1mZkRmxQHlAYoYSKmpZ4n+7//+jXPP/S9KSnoiMfGAFPSIGNQQEbUvDHzaAXFnlLyYX1NQz3z4qlCfWf6Ole8MCQmRxqcOpBq4zxI5HJGorQ3FgAE73c6rTpgmIqK2j/9nb+M8LfDX3KzU+jHL39GqjeNwOFBbWwsAKC/vgA0bALu9EoB2IAUA06a5ChKKzJbXunbt6t1FExFRq8XAp43zZmt6cyXjWq31Y5a/Y7fbERcXJx1fUVGBFStWaHxHJ0yaNBQpKXs1AymxICFgvrw2ffp0LnMREbVDDHz8xJQpUxAbG9tsybhW8nbkAZhR/o46UNNb0hK/IydnkWkitJXt8URE1P4w8PETsbGxilkTXzBaxrKStyPWBfJ215TRd5glQlvZHk9ERO0PAx/yitkylllgIc7ieDL7pE7iNvsOo+RrK9vjiYio/WHgQx6zkh8THR2NHj1OqGr9nMDVV1/l1XKbOolbnG3KyMhHfn6GV8GL2awQERG1Pwx8yDJxlsZsGUts1nnnnUBmJrB3L9Cnjw0JCdEAor36bq0aPWKwM2zYFiQnFyM6uhK1taFwOCJht1dhzJgx6Nq1q2JHWGVlJXJzc6WffbUln4iI2gYGPmSZmJOzb18d3nlHgNPZ0OohMFDALbdMRFJSkGI2JyHB9Utk1o3dbDZIa7Zpy5ZzsWVLKlzVlxu6qwMbAPimGz0REbUPDHzaOKtb0321hT0mJgYxMcBrrwFz5wL19a4+Xa++akNqajfDz1qtOWQUqOjV6AEaXlMvvckDrea+X0RE1Low8Gmj5DMnmZmZUjE/UVBQEKqrO6O4OAj9+gUgJqaTT78/KwsYP15cxlLO6uhRz/To7QozmhHSSmjWolf5ubE7yYiIqG1j4NMGWZk52bp1KNaunQSn04aAAOC5505i+nT9XBZvHvbqZSxPWC1uqKbejaXHaGs6gxoiIv/FwKcNMqvW3JAH48rBcTqB228Px/79rxkm8jZXLozVpqR6xN1YhYVp2Lw5Ha5lLidsNnBrOhERGWLg0w550vRTzpv2F97wdnxydnsVxo3LR1paobQd3XVubk0nIiJ9xokSrdCZM2dwzjnnwGazYdu2bYr3tm/fjgsvvBAdOnRAYmIinnrqqZYZZAsT82DkWqoqcUVFBcrKylBWVuZWeNCT8eklG9vtVUhO3i9tSxd/T0REpKXNzfjcc889iI+Px48//qh4/cSJExg3bhwyMjLwyiuv4KeffsL111+P6OhozJkzp4VG2zLEPJhPPpkkFQ+89FLzpZ/KykrD9z3NA9LLRTKrmlxeXu72XWJS8pEjRxR1eIiIiDzRpgKfdevWYf369Vi1ahXWrVuneO/dd99FTU0N3nzzTYSEhGDgwIHYtm0bnnvuOb8LfABXHsyDD6ahqqobIiOPYO1a88RhKwGFJ3lARru45FWTg4NrFIUHV69erfldrq30DbuyHA6H1KXdCLemExGRqM0EPocPH8aNN96Ijz76COHh4W7vFxQUYNSoUYqH3Pjx4/Hkk0/i+PHj6NRJezv3mTNncObMGennEydO+H7wzUgeXMTHOxEXB5SVOc0/aJG3eUB6u7iKivro7u46evSo9H2lpQEoLg5CcnId4uNd19OlSxduTSciIo+0icBHEATMmjULN910E4YPH459+/a5HXPo0CEkJycrXuvWrZv0nl7gs3DhQixYsMDnY24J6uCic+dDyMpqyK3xBYfD4XGXd71dXF27HjLc3SXO5hhtfc/OzvZ513kiImq/WjS5+b777oPNZjP89csvv2Dx4sWoqqrC/fff7/Mx3H///XA4HNKvkpISn3+Hr2kt3WgFFw8+2B1PP/2+tHTkCytWrEBFRYVHn9HbxfXrr311d3eJ9IImhyMSQPPtRCMiovahRWd87rzzTsyaNcvwmN69e+PLL79EQUEBQkNDFe8NHz4c11xzDd566y10794dhw8fVrwv/ty9e3fd84eGhrqdt7XTqj68aVMInn++cVvErfI02NCrtrxx42gATsjjb3F3l7hkd+pUeKO3vhMREYlaNPDp0qULunTpYnrciy++iEcffVT6ubS0FOPHj8eKFSuQlpYGAEhPT8f8+fNRW1uL4OBgAEBeXh769eunu8zVlqnzVs4/HwgIcBUrFOltEZfnAc2ePRZBQUGorKzE+vXrm2Ss4i6uNWsug7qnlisgcgU/4jKWOu9HLzgiIiLyVJvI8enZs6fi544dOwIAUlJSkPBHz4Srr74aCxYsQFZWFu69917s2LEDL7zwAp5//vlmH29LSEhQNw7V3sKulQdUX/+62/n0+mgZUXdel+cWDRv2A0JCzmDlyisUnxGEAEyblouIiGopmFm0KEextKUVHHG2h4iIvNEmAh8r7HY71q9fj3nz5iE1NRWxsbF48MEH/Woru7xxqNYWdq18mYceisNtt0UqAgl5cAQ4MXZsPkaOLDD8bnXNnobAqeHciYklbkteNpsTiYkHpWOKi5M0l7bkwRGDHiIi8labDHySkpIgCILb64MHD8bGjRtbYESth9g4VGsLu1aSsdNpU+TLqIMjIAB5eWMBQAp+Kisr3XZSyWd69HZhmRUuBLTzgdTBERERkbfaZOBD3tEKKgICBEW+jFZwBNiQn5+BQYN2wG6vQm5urm4hQ7MGpPLChVqzN1aCIyIiIm8x8PEjWkHFgw+WAlDOuKiTiQH3nVR6O7usNCAV+2rpMQuO5FiVmYiIPMHApx04eBDYswfo29e1zAXoBwTqoCIz8/9B3fUhNXULtmwZDsAmvWZ1J5XeUtWpUxFSSwor9IKjKVOmIDY2FgCrMhMRkecY+LRxS5YAc+a4trEHBLh2dmVludf60Wr5EBISopub45r1AQAbbDYnMjLyDYMWscGp1qySIAArV17hVnXZG7GxsazUTEREXmPg04YdPNgQ9ACuf86d69rZlZDQUOtHLzgCgF9++QWAdlKzK/gRIAgByM/PQFjY75pBS0VFhaLBaUrKXkydugrV1R3w6aeXQlw2U+f7yGdvqqursXz5ctNr5tIWERE1BgOfNmzPHmXBQsBVw2fv3oYlL6PgKCysQuqHpZ3UrCw2KA9a5Ix2dGnlCv388wAMHLjTbfaGDUeJiKipMfBpw/r2da/WHBgI9OnT8LNRcNSvX0OQoddWQk6vVYTD4fjjn5GK6syucwmQ5woBAtavn4C8vHGw20sxZ06ZFNAwqCEioqbWok1KybqKigqUlZUpfgUGluGppyoRGOiqaRQYCLz6asNsD9AQHMkFBgIxMccVlZXF3BzXLA0gLnPJaSU4V1Q0zBoVFqbB/Y+UDQ35Qg1BkCAEYMGCeDz99Pt46aWXPG58SkRE5A3O+LQB6qrIarfeGoljxzrjzjv/jLPPVvYlc29lATz77EmsXv2i23nE3BxAQGLiQbeeWfJ6OmKujbg0dfBgHDZvTtcYnRM33PAGDhzoifXrJyjekc8gscs6ERE1BwY+bYBZUCBu/Y6N/V3zfXkriz59gMDAKrz2mvIYvWrLKSl7MWjQ5RgyJALx8ecCOFdamqqoqEB5ebn0Wa0JxBEjCpCQUIbIyJPIyxvnts2dzUaJiKg5MfDxE2IrCwAoK1O+d/BgnFtujjyReeLEMMTFdVN8RpyFcu0Gy9HMDbLZnEhLKwTAisxERNQ6MPDxc3qzNXqJzCJxFkp7NxgAuAc2nlRkJiIiagoMfPyYe+2eBo2t1JyV9QYSEsrcjjdrV0FERNSUuKvLj1mdrTEqGqjeDSYuYWkFPURERC2NMz5+zGy2ZsqUKYiPjzetr8MlLCIiaisY+PgxvYRjcbYmNjZW2r2l7vllt1e6nasxAQ9bURARUXNg4NMGWA0KvDnOaLYmJCREUUNIueW9EyZNGmracHTKlCkIDw9HeHi44XhYtZmIiJoDA582QN1pXYsnwYMn5yv7Y++7OhHaqHeXHLupExFRa8LAp42QBzUHD7p6cPXtq2xP4e35rNBKhDbb8g5wCYuIiFoXBj5tzJIlDd3WAwJc7Siysnz/PWJeT3l5ORyOSJw6Fa6ZCC1ueZ8yZQpiY2MV5+ASFhERtTYMfNqQgwcbgh7A9c+5c13tKOQzP/JkZC1mAYl7Xo9YmdkpBT/qystc0iIioraAgU8bsmdPQ9Ajqq939eASAx+zhqai7Oxs3eBHDJrcCxwGQBCcmDYtF4mJB6Wgx+GIxKZNITj/fO+X3oiIiJoDCxi2IX37upa35AIDXY1HReqZHocjEsXFSXA4IhWvW+mGrl3gMAAREdVS0LN161AsWpSDK66IQa9eAp59thJlZWWoqKiwfF1ERETNhTM+bUhCgiunZ+5c10xPYCDw6qvK2Z7y8nLpeL2O61bpFTgU83rUM0JOpw133x2F3357E3Z7leGsEhERUUvgjE8bk5UF7NsHbNjg+qeY2Cwuca1evRqA/vZz9cyPEb12FOJsj9FOL8DarBIREVFz4oxPG5SQ4PpVUVGBsjJXcCGf6QGAkpJEw+3n5eXlUhXm5OQ6xMe7ghv19nOjAodmM0JEREStDQOfNkqdxOxwROLYsSR07lyBoqI+WLPmMrfPyIOSv/2tGB9/PFBzGSwzM1PxOb12FHotL9iri4iIWisGPm2UfBlJncsjCIB6FVMelJhVYa6trbU8DjYoJSKitoSBTxunFcRomTp1JQYN2gXAvApzUJBnfywa26CUiIiouTDwaeO0t5wr2WxOJCYelH42y82Jjo427OVVXl4uJVETERG1JQx82jitIAYQANj++L173o2V3BxuQyciovaIgU8bpw5ilEEPYLMBKSl73T7XmNwcq41H2aCUiIhaGwY+rZyVTuxiELNz5wB8/vkExXtGHdS9zc2JiYkxXAoD2KCUiIhaJwY+rZDYZPS998Jwzz12OJ02BAQIeOopB66++rTmTIrdXoUBA3Zi/fpxurk7Y8eORV5enk/GyKCGiIjaIgY+rYxYn8fhiMSiRTkQBNeylbodxIwZM9w+a5a7I9+t5ar7E4POnSvcZn24REVERO0VA59WRlw+MttyHh4eLi03yXdZGeXurFu3DoBxD68ZM2YgJibG0hIbERFRW8NeXa2UuFtLTt0OIiYmBnFxcYiNjVUcZ7dXITl5v2b+jlkPr+rqajz7bCV69RJw8cVgx3UiImpXGPi0UkYNQh2OSGzaFIKDf5Tm8WRpyqyx6NKlebj77ig4ncoltqeffh8vvfQSgx8iImrTuNTVimktW4nLVM8/H4CAAOC114CsLPNdVpWVlcjNzTUtXmi2xMaO60RE1JYx8Gnl5FvO1ctUTicwd66Ac845gqSkIMTFxVk6n1ECNDuuExFRe8bApw3Rmo2pr7dh8eJ1SE7ej+zsbLdt5uLW+PLycmknV0rKXuTkLNJMgGbHdSIias8Y+LQhZrMx8mWoiooKHDlyBLm5uXA4IlFYmIbNm3MAuO/kUmPHdSIiaq8Y+DQjK1vEjRKVrc7GiLWAAOXWdZEgBGDNmsuQkrJXN6hhx3UiImqPGPg0kyVLgDlzXHk5DUnJ7sdptYOwWqdHJH5WnROkFIDCwjSMG5dvWMyQiIioPWHg0wwOHmwIegAxKRkYP1575kedp1NaGoDi4iQpMLE6G6OVEyRXUJCOiIhTyM/P0CxmSERE1N4w8GkGe/Y0BD2i+npg717zqsiumaKucDpnehyYaOUEyQlCgBT0iD9//LHxEhjbWRARUVvGwKcZ9O3rWt6SBz+BgUCfPu7HyvOAAHGmyFVM0EpgIqfOCQIEADbZEe5Bkbxmz5QpUxRVodlxnYiI2jpWbm4GCQmunJ7AQNfPgYHAq6+6z/YsWQL06oU/WkUAL7zgPlMkr7JsxbBhPyAr6w0ATiiDHgEXXrjRsC1GbGws4uLipF8MeoiIqK3jjE8zycpy5fTs3eua6VEHPVp5QM8/D9hsgCA0HGdUTFBvGaq2NhTuMa4NvXsXo1MnB2v2EBGR32Dg04wSEvRzevTygG666SRefz0C9fU2BAQIePDBUkyZMhbBwcGw2+3SsUbLUEb1f5KT97NmDxER+Q0GPq2EVh6QzeZEWNhruPVWSIEJUIU/drZrVmoG3Gd+zOr/sGYPERH5CwY+rYSYBzR3rmumRys4Udu3rw7bt7sXRBRrAZWWlnpU/4eIiKi9Y+DTioh5QIWFFdi06S3D4GTr1qH4+9+7/lEQUcBTTzlw9dWnpSWvmJgYt07qnNkhIiJ/x8CnlUlIAAIDa7Bjh36A0lCR2bVLy+m04e67o/Dbb2/Cbq/SXQLzFGv2EBFRe8PApxUQO6iLysvLDY/Xqsgsr78jnstq4DJ9+nRER0crXmPNHiIiao8Y+LQweUNRq8y6tIu0+n6pMcAhIiJ/wsCnhRkFJXrNQ612aQfc+34RERH5MwY+rdTWrUPdAht5jy7u0iIiIvIcW1a0Qg3Jy8rmoQ5HpOI4u70Kycn7GfQQERFZxMCnFdJLXu7V65IWGhEREVH7wMCnFRKTl+VsNif27/+ihUZERETUPjDwaYXE5GUx+PG0eSjr7xAREWljcnMTU9foUXM4HJqvGyUvT5kyBbGxsZqf4/Z0IiIifW0q8Pnkk0/w97//Hdu3b0eHDh0wevRofPTRR9L7Bw4cwM0334wNGzagY8eOmDlzJhYuXIigoJa5TG9q9MjptZiIjY1FXFxcY4ZGRETkl9pM4LNq1SrceOONePzxx3HxxRejrq4OO3bskN6vr6/HpZdeiu7du2Pz5s0oKyvDddddh+DgYDz++OMtMmajmR45sXJyeXm51FSUiIiIfK9NBD51dXW47bbb8PTTTyMrK0t6fcCAAdLv169fj507dyI/Px/dunXDOeecg0ceeQT33nsvHn744Vad9xIdHe02g6NXvBBQtrTg0hYREZF1bSLw2bp1K3777TcEBARg6NChOHToEM455xw8/fTTGDRoEACgoKAAZ599Nrp16yZ9bvz48bj55pvx888/Y+jQoZrnPnPmDM6cOSP9fOLEiaa9GANivo9Z8UL1rJCvmpISERG1d21iV9f//vc/AMDDDz+Mv/3tb1i7di06deqEiy66CMeOufpTHTp0SBH0AJB+PnTokO65Fy5cCLvdLv1KTExsoqswVlFRgRUrVlguXihndUmNiIjI37Vo4HPffffBZrMZ/vrll1/gdLq2dc+fPx9Tp05Famoqli5dCpvNhn/961+NGsP9998Ph8Mh/SopKfHFpelyOCJRXJzkFsiIwYtR53W9z1ZWVjbpmImIiNqLFl3quvPOOzFr1izDY3r37o2ysjIAypye0NBQ9O7dGwcOHAAAdO/eHf/9738Vnz18+LD0np7Q0FCEhoZ6M3yPmS1hAfqd10tL4/H229dpfjY3N1dKkGbODxERkb4WDXy6dOmCLl26mB6XmpqK0NBQ7N69GxdccAEAoLa2Fvv27UOvXr0AAOnp6Xjsscdw5MgRdO3aFQCQl5eHqKgoRcDUUvSWsFJS9iqO0+q8npGRj/z8DM3PionPubm50jmY80NERKStTSQ3R0VF4aabbsJDDz2ExMRE9OrVC08//TQA4IorrgAAjBs3DgMGDMC1116Lp556CocOHcLf/vY3zJs3r9lmdNTkO8mMlrBCQkJw5MgR6XV18UKjz2rV+WHODxERkbY2EfgAwNNPP42goCBce+21OH36NNLS0vDll1+iU6dOAIDAwECsXbsWN998M9LT0xEREYGZM2fi73//e4uNOSYmBtnZ2aipqUFpaQDeeUeA02mT3g8MFHDnnX8G4FTM2ADuxQu1lr86dz7W5NdARETUntgEQRBaehCtyYkTJ2C32+FwOBAVFeXTcy9ZAsydC9TXA4GBwKuvAllZQFlZGV577TXDz8rzg1ycGDs2H/HxZW61fubMmcPKzkRE5FesPr/bzIxPe5CVBQweDHzzDXDBBcC552ofp1W8MCVlL5QhagDy8sYCsOkmShMREZESA59mtGQJMGcO4HQCAQHAa6+5giE5vZ1fx47FwL36gGvZTCvZmYiIiNy1iQKGbV1FRQW2bDmMOXME/FGSCE4nMHeugC1bDuPgwYMA9Hd+ORyR0jZ3PWKyMxEREelj4NPExA7tixd/pkhsBoD6ehsWL16HTz/9FIDxzi9xm3tD8KNMzWKyMxERkTkudTUxcWu5XmFCebBidsxf/hKKlJRFOHasM0pL46XaPuKSmLjM1ZobshIREbUkBj5NTGw8qlWYcNKktQCA4uIkKZF50qS1WLPmMrgm45QBTXx8PObPHy4FU6WlR7FvXxCSkuoQH38ugHNZuZmIiMgAA58mVltbK/1eXZiwqKgPFi3KUVRoDgv7HTYbIAiATbkyhqCgIEVQExcHpKY215UQERG1fQx8mplYmFArkdm1PR3Q260VHR3dMoMmIiJqJ5jc3EK0EpldAY9ymoe7tYiIiHyHgU8LMdueLuJuLSIiIt9h4NNC7PYqZGTkQ70t3cX1GndrERER+RZzfJpQRUUFqqr0KymPHFkAAMjLy4AyBrUBcCIr6w0kJJRhypQpiI+P524tIiKiRmLg00TEwoUirf5bgCv4sdsdWLnyCtUZAlBb65rhiY2NZdBDRETkAwx8mohYawfQ778lSkwsMSxcyCUuIiIi32COTxMz6r8lUrejkOf2ZGZmcraHiIjIRzjj08TM+m+J1MUNxffsdnuzjpeIiKg9Y+DTxKz06BKJxQ2JiIioaXCpq4npLWMBrh5d8iUvIiIialqc8WkGZj261MnORERE1DQY+DQTox5dH398Gbp2PYTa2lC37e7c0UVEROQ7DHyaiF7Aopfs/MYbNwAIQECAgKeecuDqq08jJCSEO7qIiIh8yCYIglbPBL914sQJ2O12OBwOREVFNepcFRUVqKmpQXl5OVavXg3Atb1dXOZqIEDenDQwENi3D0hIaNTXExER+Q2rz28mNzehmJgYxMXFITY2VnpNK9lZ3ZG9vh7Yu7c5R0pEROQfuNTVAuTJzsHBNViy5AbFDFBgINCnTwsOkIiIqJ3ijE8z0Mr3sdurkJy8HwkJZYoZoMBA4NVXucxFRETUFJjjo+LLHB85Md8HAEpLA1BcHITk5DrEx7sCnvLyDqio6IQ+fRj0EBERecrq85tLXc1E3J21ZAkwZw7gdAIBAcBrrwFZWUBcXAsPkIiIyA9wqasZHTzYEPQArn/Onet6nYiIiJoeA59mtGdPQ9Aj4g4uIiKi5sPApxn17eta3pLjDi4iIqLmw8CnGSUkuHJ6AgNdP3MHFxERUfNicnMzy8oCxo93LW9xBxcREVHzYuDTAhISGPAQERG1BC51ERERkd9g4ENERER+g4EPERER+Q0GPkREROQ3GPgQERGR32DgQ0RERH6DgQ8RERH5DQY+RERE5DcY+BAREZHfYOBDREREfoOBDxEREfkN9upSEQQBAHDixIkWHgkRERFZJT63xee4HgY+KlVVVQCAxMTEFh4JEREReaqqqgp2u133fZtgFhr5GafTidLSUkRGRsJms3l9nhMnTiAxMRElJSWIiory4QjbDt4DF94H3gOA9wDgPRDxPjTNPRAEAVVVVYiPj0dAgH4mD2d8VAICApCQkOCz80VFRfntH2wR74EL7wPvAcB7APAeiHgffH8PjGZ6RExuJiIiIr/BwIeIiIj8BgOfJhIaGoqHHnoIoaGhLT2UFsN74ML7wHsA8B4AvAci3oeWvQdMbiYiIiK/wRkfIiIi8hsMfIiIiMhvMPAhIiIiv8HAh4iIiPwGAx8PvPzyyxg8eLBUcCk9PR3r1q2T3v/9998xb948xMTEoGPHjpg6dSoOHz6sOMeBAwdw6aWXIjw8HF27dsXdd9+Nurq65r4Un3niiSdgs9mQk5MjveYP9+Hhhx+GzWZT/Orfv7/0vj/cAwD47bffMGPGDMTExCAsLAxnn302vv/+e+l9QRDw4IMPIi4uDmFhYcjIyMCePXsU5zh27BiuueYaREVFITo6GllZWTh58mRzX4pXkpKS3P4c2Gw2zJs3D4B//Dmor6/HAw88gOTkZISFhSElJQWPPPKIol9Se/9zALjaJOTk5KBXr14ICwvDiBEj8N1330nvt8d78J///AeTJk1CfHw8bDYbPvroI8X7vrrm7du348ILL0SHDh2QmJiIp556qnEDF8iyNWvWCJ988onw66+/Crt37xb++te/CsHBwcKOHTsEQRCEm266SUhMTBS++OIL4fvvvxfOP/98YcSIEdLn6+rqhEGDBgkZGRnCDz/8IHz66adCbGyscP/997fUJTXKf//7XyEpKUkYPHiwcNttt0mv+8N9eOihh4SBAwcKZWVl0q+jR49K7/vDPTh27JjQq1cvYdasWUJhYaHwv//9T/j888+FvXv3Ssc88cQTgt1uFz766CPhxx9/FCZPniwkJycLp0+flo6ZMGGCMGTIEOHbb78VNm7cKPTp00e46qqrWuKSPHbkyBHFn4G8vDwBgLBhwwZBEPzjz8Fjjz0mxMTECGvXrhWKi4uFf/3rX0LHjh2FF154QTqmvf85EARBmD59ujBgwADh66+/Fvbs2SM89NBDQlRUlHDw4EFBENrnPfj000+F+fPnC6tXrxYACB9++KHifV9cs8PhELp16yZcc801wo4dO4T3339fCAsLE1599VWvx83Ap5E6deokvPHGG0JlZaUQHBws/Otf/5Le27VrlwBAKCgoEATB9YckICBAOHTokHTMyy+/LERFRQlnzpxp9rE3RlVVldC3b18hLy9PGD16tBT4+Mt9eOihh4QhQ4Zovucv9+Dee+8VLrjgAt33nU6n0L17d+Hpp5+WXqusrBRCQ0OF999/XxAEQdi5c6cAQPjuu++kY9atWyfYbDbht99+a7rBN5HbbrtNSElJEZxOp9/8Obj00kuF66+/XvHalClThGuuuUYQBP/4c1BdXS0EBgYKa9euVbw+bNgwYf78+X5xD9SBj6+u+Z///KfQqVMnxX8P9957r9CvXz+vx8qlLi/V19fjgw8+wKlTp5Ceno4tW7agtrYWGRkZ0jH9+/dHz549UVBQAAAoKCjA2WefjW7duknHjB8/HidOnMDPP//c7NfQGPPmzcOll16quF4AfnUf9uzZg/j4ePTu3RvXXHMNDhw4AMB/7sGaNWswfPhwXHHFFejatSuGDh2K119/XXq/uLgYhw4dUtwHu92OtLQ0xX2Ijo7G8OHDpWMyMjIQEBCAwsLC5rsYH6ipqcHy5ctx/fXXw2az+c2fgxEjRuCLL77Ar7/+CgD48ccf8c0332DixIkA/OPPQV1dHerr69GhQwfF62FhYfjmm2/84h6o+eqaCwoKMGrUKISEhEjHjB8/Hrt378bx48e9GhublHrop59+Qnp6On7//Xd07NgRH374IQYMGIBt27YhJCQE0dHRiuO7deuGQ4cOAQAOHTqk+B+c+L74XlvxwQcfYOvWrYr1a9GhQ4f84j6kpaVh2bJl6NevH8rKyrBgwQJceOGF2LFjh9/cg//97394+eWXcccdd+Cvf/0rvvvuO9x6660ICQnBzJkzpevQuk75fejatavi/aCgIHTu3LnN3AfRRx99hMrKSsyaNQuA//y3cN999+HEiRPo378/AgMDUV9fj8ceewzXXHMNAPjFn4PIyEikp6fjkUcewVlnnYVu3brh/fffR0FBAfr06eMX90DNV9d86NAhJCcnu51DfK9Tp04ej42Bj4f69euHbdu2weFwYOXKlZg5cya+/vrrlh5WsykpKcFtt92GvLw8t7/d+BPxb7MAMHjwYKSlpaFXr17Izc1FWFhYC46s+TidTgwfPhyPP/44AGDo0KHYsWMHXnnlFcycObOFR9f8lixZgokTJyI+Pr6lh9KscnNz8e677+K9997DwIEDsW3bNuTk5CA+Pt6v/hy88847uP7669GjRw8EBgZi2LBhuOqqq7Bly5aWHhqpcKnLQyEhIejTpw9SU1OxcOFCDBkyBC+88AK6d++OmpoaVFZWKo4/fPgwunfvDgDo3r27244O8WfxmNZuy5YtOHLkCIYNG4agoCAEBQXh66+/xosvvoigoCB069bNL+6DWnR0NP70pz9h7969fvNnIS4uDgMGDFC8dtZZZ0lLfuJ1aF2n/D4cOXJE8X5dXR2OHTvWZu4DAOzfvx/5+fm44YYbpNf85c/B3Xffjfvuuw9XXnklzj77bFx77bW4/fbbsXDhQgD+8+cgJSUFX3/9NU6ePImSkhL897//RW1tLXr37u0390DOV9fcFP+NMPBpJKfTiTNnziA1NRXBwcH44osvpPd2796NAwcOID09HQCQnp6On376SfEvOi8vD1FRUW4PkNbqkksuwU8//YRt27ZJv4YPH45rrrlG+r0/3Ae1kydPoqioCHFxcX7zZ2HkyJHYvXu34rVff/0VvXr1AgAkJyeje/fuivtw4sQJFBYWKu5DZWWl4m/FX375JZxOJ9LS0prhKnxj6dKl6Nq1Ky699FLpNX/5c1BdXY2AAOWjJDAwEE6nE4B//TkAgIiICMTFxeH48eP4/PPP8ec//9nv7gHgu3/v6enp+M9//oPa2lrpmLy8PPTr18+rZS4A3M7uifvuu0/4+uuvheLiYmH79u3CfffdJ9hsNmH9+vWCILi2rvbs2VP48ssvhe+//15IT08X0tPTpc+LW1fHjRsnbNu2Tfjss8+ELl26tKmtq1rku7oEwT/uw5133il89dVXQnFxsbBp0yYhIyNDiI2NFY4cOSIIgn/cg//+979CUFCQ8Nhjjwl79uwR3n33XSE8PFxYvny5dMwTTzwhREdHC//+97+F7du3C3/+8581t7MOHTpUKCwsFL755huhb9++rXoLr1p9fb3Qs2dP4d5773V7zx/+HMycOVPo0aOHtJ199erVQmxsrHDPPfdIx/jDn4PPPvtMWLdunfC///1PWL9+vTBkyBAhLS1NqKmpEQShfd6Dqqoq4YcffhB++OEHAYDw3HPPCT/88IOwf/9+QRB8c82VlZVCt27dhGuvvVbYsWOH8MEHHwjh4eHczt5crr/+eqFXr15CSEiI0KVLF+GSSy6Rgh5BEITTp08Lf/nLX4ROnToJ4eHhwv/93/8JZWVlinPs27dPmDhxohAWFibExsYKd955p1BbW9vcl+JT6sDHH+5DZmamEBcXJ4SEhAg9evQQMjMzFfVr/OEeCIIgfPzxx8KgQYOE0NBQoX///sJrr72meN/pdAoPPPCA0K1bNyE0NFS45JJLhN27dyuOqaioEK666iqhY8eOQlRUlDB79myhqqqqOS+jUT7//HMBgNt1CYJ//Dk4ceKEcNtttwk9e/YUOnToIPTu3VuYP3++YvuxP/w5WLFihdC7d28hJCRE6N69uzBv3jyhsrJSer893oMNGzYIANx+zZw5UxAE313zjz/+KFxwwQVCaGio0KNHD+GJJ55o1LhtgiArr0lERETUjjHHh4iIiPwGAx8iIiLyGwx8iIiIyG8w8CEiIiK/wcCHiIiI/AYDHyIiIvIbDHyIiIjIbzDwISIiIr/BwIeIGu2iiy5CTk5OSw+jyT388MM455xzWnoYRNQIDHyIyO/V1NQ06/cJgoC6urpm/U4icmHgQ0SNMmvWLHz99dd44YUXYLPZYLPZsG/fPuzYsQMTJ05Ex44d0a1bN1x77bUoLy+XPnfRRRfhlltuQU5ODjp16oRu3brh9ddfx6lTpzB79mxERkaiT58+WLdunfSZr776CjabDZ988gkGDx6MDh064Pzzz8eOHTsUY/rmm29w4YUXIiwsDImJibj11ltx6tQp6f2kpCQ88sgjuO666xAVFYU5c+YAAO6991786U9/Qnh4OHr37o0HHnhA6gq9bNkyLFiwAD/++KN0ncuWLcO+fftgs9mwbds26fyVlZWw2Wz46quvFONet24dUlNTERoaim+++QZOpxMLFy5EcnIywsLCMGTIEKxcudLX/4qISIaBDxE1ygsvvID09HTceOONKCsrQ1lZGSIjI3HxxRdj6NCh+P777/HZZ5/h8OHDmD59uuKzb731FmJjY/Hf//4Xt9xyC26++WZcccUVGDFiBLZu3Ypx48bh2muvRXV1teJzd999N5599ll899136NKlCyZNmiQFKEVFRZgwYQKmTp2K7du3Y8WKFfjmm2+QnZ2tOMczzzyDIUOG4IcffsADDzwAAIiMjMSyZcuwc+dOvPDCC3j99dfx/PPPAwAyMzNx5513YuDAgdJ1ZmZmenSv7rvvPjzxxBPYtWsXBg8ejIULF+Ltt9/GK6+8gp9//hm33347ZsyYga+//tqj8xKRBxrV4pSISBCE0aNHC7fddpv08yOPPCKMGzdOcUxJSYmii/no0aOFCy64QHq/rq5OiIiIEK699lrptbKyMgGAUFBQIAhCQzfoDz74QDqmoqJCCAsLE1asWCEIgiBkZWUJc+bMUXz3xo0bhYCAAOH06dOCIAhCr169hMsvv9z0up5++mkhNTVV+vmhhx4ShgwZojimuLhYACD88MMP0mvHjx8XAAgbNmxQjPujjz6Sjvn999+F8PBwYfPmzYrzZWVlCVdddZXp2IjIO0EtGXQRUfv0448/YsOGDejYsaPbe0VFRfjTn/4EABg8eLD0emBgIGJiYnD22WdLr3Xr1g0AcOTIEcU50tPTpd937twZ/fr1w65du6Tv3r59O959913pGEEQ4HQ6UVxcjLPOOgsAMHz4cLexrVixAi+++CKKiopw8uRJ1NXVISoqyuPr1yP/zr1796K6uhpjx45VHFNTU4OhQ4f67DuJSImBDxH53MmTJzFp0iQ8+eSTbu/FxcVJvw8ODla8Z7PZFK/ZbDYAgNPp9Oi7586di1tvvdXtvZ49e0q/j4iIULxXUFCAa665BgsWLMD48eNht9vxwQcf4NlnnzX8voAAV8aAIAjSa+Kym5r8O0+ePAkA+OSTT9CjRw/FcaGhoYbfSUTeY+BDRI0WEhKC+vp66edhw4Zh1apVSEpKQlCQ7/838+2330pBzPHjx/Hrr79KMznDhg3Dzp070adPH4/OuXnzZvTq1Qvz58+XXtu/f7/iGPV1AkCXLl0AAGVlZdJMjTzRWc+AAQMQGhqKAwcOYPTo0R6NlYi8x+RmImq0pKQkFBYWYt++fSgvL8e8efNw7NgxXHXVVfjuu+9QVFSEzz//HLNnz3YLHLzx97//HV988QV27NiBWbNmITY2FpdffjkA186szZs3Izs7G9u2bcOePXvw73//2y25Wa1v3744cOAAPvjgAxQVFeHFF1/Ehx9+6HadxcXF2LZtG8rLy3HmzBmEhYXh/PPPl5KWv/76a/ztb38zvYbIyEjcdddduP322/HWW2+hqKgIW7duxeLFi/HWW295fW+IyBgDHyJqtLvuuguBgYEYMGAAunTpgpqaGmzatAn19fUYN24czj77bOTk5CA6OlpaGmqMJ554ArfddhtSU1Nx6NAhfPzxxwgJCQHgyhv6+uuv8euvv+LCCy/E0KFD8eCDDyI+Pt7wnJMnT8btt9+O7OxsnHPOOdi8ebO020s0depUTJgwAWPGjEGXLl3w/vvvAwDefPNN1NXVITU1FTk5OXj00UctXccjjzyCBx54AAsXLsRZZ52FCRMm4JNPPkFycrIXd4WIrLAJ8oVpIqJW7KuvvsKYMWNw/PhxREdHt/RwiKgN4owPERER+Q0GPkREROQ3uNRFREREfoMzPkREROQ3GPgQERGR32DgQ0RERH6DgQ8RERH5DQY+RERE5DcY+BAREZHfYOBDREREfoOBDxEREfkNBj5ERETkN/4/oCIjCbnlO2gAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_training, filename=\"pysmo_poly_train_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_training, filename=\"pysmo_poly_train_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_training, filename=\"pysmo_poly_train_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Model Validation\n", + "\n", + "We check the fit on the validation set to see if the surrogate is fitting well. This step can be used to check for overfitting on the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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YtmwZ3N3dAQAJCQmIiIhQXCsyMhKxsbEAilYbnjhxArNnz5bP29jYICIiAgkJCQCAEydOoKCgQHGd5s2bIyAgAAkJCejQoQMSEhLQunVreHt7K77npZdewh9//IF27dpVyHMhIiIi4zIzM7FmzZpi28XExMDDw6MSemRF05EAMHPmTNSuXRseHh64cuUK/ve//8nn/v77b1y+fBnbtm3DZ599hri4OJw4cUIxZZmenq4IjADA29sbOTk5uHfvHjIyMlBYWGi0TXp6unwNe3t7uLm5mW1j7Brac6bk5eUhJydH8apuRo0aBUmSIEkS7Ozs4O3tjR49euCTTz4pVamNuLg4g38HREREpujX1FSrXZCSEgi12sVsu4pk0SBs1qxZ8h9kU6/z58/L7adPn45Tp07hu+++g62tLUaMGAEhBICiUgJ5eXn47LPP0LlzZzz99NP4+OOPcfjwYSQlJVnqFktlyZIlUKlU8svf37/CviszMxNpaWkmX5mZmRX23b169UJaWhouXbqEffv2oVu3bpg8eTL69u1brosXiIiIjDl5sh1WrozFhg0jsXJlLE6etMwMlUWnI6dNm4ZRo0aZbdOoUSP5Z09PT3h6eqJp06Zo0aIF/P398csvvyA8PBy+vr6oVasWmjZtKrdv0aIFgKKVis2aNYOPj4/BKsbr16/D1dUVTk5OsLW1ha2trdE2Pj4+AAAfHx/k5+cjOztbMRKj30Z/RaX2mto2xsyePRtTp06V3+fk5FRIIGbpIVkHBwf5OdSvXx8hISHo0KEDunfvjri4OIwdOxbvvPMOPv30U/z9999wd3dHVFQUli1bhjp16uD777/H888/D+Bh0vz8+fOxYMECfP7551i1ahWSkpJQu3ZtPPPMM1i5ciXq1atX7vdBRETWR612wa5dfSFE0TiUEDbYtasvgoMvQqXKrdS+WHQkzMvLC82bNzf7MlWCQTt1lZeXBwB46qmn8ODBAyQnJ8tt/vrrLwBAw4YNAQDh4eE4ePCg4jrx8fEIDw8HUFSKIDQ0VNFGo9Hg4MGDcpvQ0FDY2dkp2iQlJeHKlStym/DwcPz++++KFZXx8fFwdXVFy5YtTT4PBwcHuLq6Kl4VoaRDrZU5JPvMM8+gTZs22LFjB4CiXLzVq1fjjz/+wIYNG3Do0CHMmDEDANCxY0esXLkSrq6u8sjdyy+/DAAoKCjAokWL8Ntvv+Gbb77BpUuXig30iYioesjMzMT58+fx+++/G7wuXLgAAMjK8pADMC0hbJCV5V7p/bWKxPzExEQcP34cnTp1Qt26dZGcnIy5c+ciODhYDnwiIiIQEhKC0aNHY+XKldBoNJgwYQJ69Oghj46NHz8ea9aswYwZMzB69GgcOnQIW7duxZ49e+Tvmjp1KkaOHIn27dvjySefxMqVK3Hnzh155EWlUmHMmDGYOnUq3N3d4erqiokTJyI8PBwdOnQAAPTs2RMtW7bE8OHDsWzZMqSnp+PVV1/FhAkT4ODgUMlPz3o0b94cZ86cAQB5sQQABAYG4vXXX8f48ePx/vvvw97eHiqVCpIkGYwsjh49Wv65UaNGWL16NZ544gncvn0bderUqZT7ICKiypWZmYmbN29iy5YtxbZ1d8+EJGkUgZgkaeDunlWRXTTKKoIwZ2dn7NixA/Pnz8edO3fg6+uLXr164dVXX5WDGhsbG+zatQsTJ05Ely5dULt2bfTu3RvLly+XrxMUFIQ9e/ZgypQpWLVqFRo0aICPPvoIkZGRcpvBgwfj5s2bmDdvHtLT09G2bVvs379fkWi/YsUK2NjYIDo6Gnl5eYiMjMT7778vn7e1tcXu3bvx0ksvITw8HLVr18bIkSPx2muvVcLTsl5CCHl68cCBA1iyZAnOnz+PnJwcPHjwAPfv38fdu3fN7hBw4sQJLFiwAL/99htu3bolj5heuXLF7CgkERFZJ/0UG7XaBVlZHrCzy0N2dl0AgL//VXmqUaXKRVTUbnlKUpI0iIraXelTkYCVBGGtW7fGoUOHim3n5+eH7du3m23z9NNP49SpU2bbxMTEICYmxuR5R0dHvPfee3jvvfdMtmnYsCH27t1rvsOkcO7cOQQFBeHSpUvo27cvXnrpJSxevBju7u746aefMGbMGOTn55sMwu7cuYPIyEhERkZi48aN8PLywpUrVxAZGVmpU6tERFSxMjMzkZ+fD7VarUj9OXmynU6+lwCgLbYt0K/fLoSEFP39Dwk5heDgi8jKcoe7e5ZFAjDASoIwqv4OHTqE33//HVOmTMGJEyeg0WiwfPlyeWPyrVu3Ktrb29sbbCN0/vx5ZGZmYunSpfKChl9//bVyboCIiCqF8ZGvQNjZ5SkS7h8GYEU/6yffq1S5RoOvytwOkEEYVbq8vDykp6ejsLAQ169fx/79+7FkyRL07dsXI0aMwNmzZ1FQUIB3330XUVFR+Pnnn7Fu3TrFNQIDA3H79m0cPHgQbdq0gbOzMwICAmBvb493330X48ePx9mzZ7Fo0SIL3SUREVUEUyNf+nle+rTJ9ypVLgYMGABPT0+DNvb29pVWqBWwsmKtVD3s378fvr6+CAwMRK9evXD48GGsXr0a//vf/2Bra4s2bdrgnXfewZtvvolWrVph48aNWLJkieIaHTt2xPjx4zF48GB4eXlh2bJl8PLyQlxcHLZt24aWLVti6dKlePvtty10l0REVN4yMzPlmRFjpSaKpiCN002+9/T0hK+vr8GrMgMwgCNhNVJJh1orYkg2Li4OcXFxxbabMmUKpkyZojg2fPhwxfu1a9di7dq1imNDhw7F0KFDFce0BX2JiMi66eb3Gis1UTQFKfT+CYsm35vDIKwG8vDwQExMjNlk9coekiUiIioNY6UmikiQJA2GDPkSBQVF+0v7+1+rcgEYwCCsxmKARURE1iw5uTFMTXQIYQN7+wI0a3bR6PnKTL43h0EYERERWRVtPpip1HZjxVd79OiBunXrol69elVmIIJBGBEREVmUtu6XKfopMsbzwYro538NGDAAfn5+VSbw0sUgjIiIiCxGv+6XKbpF1I3ng2kwcOBXBvlfVTUAAxiEERERkYVkZmYiNTVVcUy77ZC7e6YimNIdKTO19VCrVucAFE09BgUFVflFZgzCiIiIqFKZ2nBbv/hqVNRueashQJlQb27roWbNmlXp4EuLQRgRERFVGlPTj9eu+WLnzofJ9kLYGGw1VN1KLDEIIyIiokqjH0Cp1S5ITAzD0aMdodzvUbnVkJa1BFglwW2LqFr5/vvvIUkSsrOzS/yZwMBArFy5ssL6RERUk2VmZiItLU1+ZWRkyOdOnmyHFSticfToU9APwADjpSaqEwZhVKlGjRoFSZIwfvx4g3MTJkyAJEkYNWpU5XeMiIjKnXbqcf369fJrx44dAIqv9QVUza2GyhODMKp0/v7+2Lx5M+7duycfu3//PjZt2oSAgAAL9oyIiMqTsanHlJRAeQWkuVpfY8d+pEjKr44YhFGlCwkJgb+/v/xfQwCwY8cOBAQEoF27dvKxvLw8TJo0CfXq1YOjoyM6deqE48ePK661d+9eNG3aFE5OTujWrRsuXbpk8H0//fQTOnfuDCcnJ/j7+2PSpEm4c+dOhd0fEREZOnmyHVaujMWGDSOxYkUs/v47EJKkMWinXRXZoEGa4nhV2WqoPDEII1y7Bhw+XPTPyjJ69Gh8+umn8vtPPvkEzz//vKLNjBkzsH37dmzYsAEnT55E48aNERkZiaysovyAq1evYsCAAYiKisLp06cxduxYzJo1S3GN5ORk9OrVC9HR0Thz5gy2bNmCn376SVH0j4iIKpZ26vHhyJcNfvyxC1q0OKcTiGnQsePPiI1dqRgBGzBgAGJiYqpVQr4WV0fWcB9/DIwbB2g0gI0NsH49MGZMxX/vc889h9mzZ+Py5csAgJ9//hmbN2/G999/DwC4c+cO1q5di7i4OPTu3RsA8OGHHyI+Ph4ff/wxpk+fjrVr1yI4OBjLly8HUFQX5vfff8ebb74pf8+SJUswbNgwxMbGAgCaNGmC1atXo2vXrli7di0cHR0r/maJiGo441OPEs6da4ExYz5CQYG9Qa0vrapc8f5RMQirwa5dexiAAUX/fPFFIDISaNCgYr/by8sLffr0QVxcHIQQ6NOnDzw9PeXzycnJKCgowFNPPSUfs7Ozw5NPPolz54oqIp87dw5hYWGK64aHhyve//bbbzhz5gw2btwoHxNCQKPRICUlBS1atKiI2yMiqlFM7f2oXQnp7p4JQAP9CTghbFBQYI+goMsGnx00aFCV2my7IjAIq8EuXHgYgGkVFgIXL1Z8EAYUTUlqpwXfe++9CvmO27dv48UXX8SkSZMMznERABHRoyvJ3o8qVS569DiA+Pge0C1FoVuCYsCAAfJ/jFtTwdVHwSCsBmvSpGgKUjcQs7UFGjeunO/v1asX8vPzIUkSIiMjFeeCg4Nhb2+Pn3/+GQ0bNgQAFBQU4Pjx4/LUYosWLbBz507F53755RfF+5CQEPz5559oXFk3RURUjRkb8dKt+wWY3vvxqacSAAAHDkQotiXStvH09ISvr28F30HVwiCsBmvQoCgH7MUXi0bAbG2BDz6onFEwALC1tZWnFm1tbRXnateujZdeegnTp0+Hu7s7AgICsGzZMty9exdj/i9pbfz48Vi+fDmmT5+OsWPH4sSJE4iLi1NcZ+bMmejQoQNiYmIwduxY1K5dG3/++Sfi4+OL/S83IiJ6GHip1WqDvR71Fbf341NPJaBVq7NG93usiRiE1XBjxhTlgF28WDQCVlkBmJarq6vJc0uXLoVGo8Hw4cORm5uL9u3b49tvv0XdunUBFE0nbt++HVOmTMG7776LJ598Em+88QZGjx4tX+Pxxx/HkSNHMGfOHHTu3BlCCAQHB2Pw4MEVfm9ERNbO3FSj/oiX/gpIY3s/AkVTk8aCr+pYgqI4khBCWLoTZFxOTg5UKhXUarVBsHL//n2kpKQgKCiIK/wqGJ81EdVUaWlpWL9+vcFxYyNedevewoYNIw3ajhwZh6Cgy4qcL33VLQfM3N9vXRwJIyIiohJRq11w9aq/0RGvMWM+giRpFKUodBPva2LOV3EYhBEREVGxdEe/9GlLTURF7TYYIavpeV/mMAgjIiIime4KSO3KR8OK90raEa+goMsIDr7IxPsSYhBGREREAEwn4he32bbuiBcT70uOQZiV47qKisdnTETVUXJyMu7evas4duvWLcV77QpIO7s8g3wvQIOBA7+Cv/81RdBlLAG/uiXelxcGYVZKW1crPz8fTk5OFu5N9aYdltevZUZEZK2Sk5PxxRdfmG2jvwLy8cfP4MyZxxX5Xq1anTP4XHXe67G8MQizUrVq1YKzszNu3rwJOzs72NgYHyamR6PRaHDz5k04OzujVi3+z4WIqgf9ETB9xmp+nTnzuMnNtgcNGgQ3NzeOeJUS/6pYKUmS4Ovri5SUFFy+bLjxKZUfGxsbBAQEQJKk4hsTEVmha9d8ceVKQwQEXEaDBmlGc8B0N9uuifs8VgQGYVbM3t4eTZo0MbpzPZUfe3t7jjQSUbX19df/xm+/tUHRxtoCbdr8hmeeOcSaX5WAQZiVs7GxYRV3IiIqk2vXfHUCMACQ8NtvbfDEE8dY86sSMAgjIiKq5nRrfwEPV0FeudIQDwMwLQlXrwYgPDyRNb8qGIMwIiKiaszcJtwBAZcBCCgDMQF//ysAWPOrojEIIyIismL6o1z61Gq13vui2l/u7plo0CANbdr8ZpAT1qBBGgCgW7duaNKkieLzTMQvPwzCiIiIrJS5US5j9Gt/RUXtxv/7f//DE08cw9WrAfD3vyIHYABQv359JuBXIAZhREREVkJ/1Eu7t2NJGKv9tWtXXwQHX0SDBmkYPrw56tYNl9s7OzsjODi4/DpPBhiEERERWYGSjHrpTjXq53KZqv2VleUOlSoXTZo04ahXJWMQRkREZAWKqwlpbKoxJOSUfN7dPdNs7S+qfKxASUREZOVMTTWq1S5yG5UqF1FRuyFJGgBg7a8qgCNhREREVq64qUatkJBTrP1VhTAIIyIiskK6+V+pqb7Qr/dlaqqRtb+qDgZhREREVVxmZqZiJaRu/hegQVHwpSy4GhFxQA62Bg0aBDc3N5PXZ+0vy2AQRkREVIXpr4rUz/8ynt4twc8vVX5Xr149BllVEBPziYiIqjD9VZFXr/ob5H/p052KHDx4MAOwKoojYURERBZQ3HZDxqYItdOQhjSQJCjKU2inIlUqVXl2m8oRgzAiIqJKVtLthmJiYuSfDachtTTo1283Vz1aIQZhRERElUx/BMxUpXvddsbKUADAwIFfoVWrcwDAVY9WhkEYERGRBZmrdJ+dnS2vajRV8d7f/xoAYMCAAfD09FRcm6seqzYm5hMREVlIcZXut27dCrVaDaD4iveenp7w9fVVvBiAVW0cCSMiIqogppLvtTW/TFW6T0wMQ8+eBwAABQUF8jlWvK9eGIQRERFVgJIk3xubYgSAo0fDERaWCJUqF7VqKf9Us+J99cEgjIiIqAKYKz+hpVLlIjw8AUePPqV35uG+j25uboiJiSl1OQuq+hiEERERVQJTKyDDwhJx9Gg4dNO09fd9ZIBVPTEIIyIiqmD6KyAjIg7gqacSABSNhvXrt9tghSTzvao/BmFEREQVyNgKyPj4HgAgB2JMuK+ZWKKCiIioAhkvsiohPj5CLkUBFI2IBQVdZgBWgzAIIyIiqkDaFZCGipLvi8NVj9UXpyOJiIgqkEqVi4iIA/83BSnJx3WT741Vuwe46rG6YxBGRERkgqliq1rmgiTdESxt7ld8fAQAw+R7bbV7qlkYhBERERlRkmKrADB48GCoVCrFMW1wNnjwYGzZsgVAUSDWqtVZJt+TjEEYERGRESUptgoA69fvNVr/KyYmxiA4M1Xtnmomq0nM79evHwICAuDo6AhfX18MHz4cqamp8vkFCxZAkiSDV+3atRXX2bZtG5o3bw5HR0e0bt0ae/fuVZwXQmDevHnw9fWFk5MTIiIicOHCBUWbrKwsDBs2DK6urnBzc8OYMWNw+/ZtRZszZ86gc+fOcHR0hL+/P5YtW1bOT4SIiMpTZmYm0tLS5Jd2f0cttdoFKSmBihWNJ0+2w8qVsdiwYSRWrozFyZPt5HP5+fklTqpn8n3NZDUjYd26dcMrr7wCX19f/PPPP3j55ZcxcOBAHD16FADw8ssvY/z48YrPdO/eHU888YT8/ujRoxg6dCiWLFmCvn37YtOmTejfvz9OnjyJVq1aAQCWLVuG1atXY8OGDQgKCsLcuXMRGRmJP//8E46OjgCAYcOGIS0tDfHx8SgoKMDzzz+PcePGYdOmTQCAnJwc9OzZExEREVi3bh1+//13jB49Gm5ubhg3blxlPC4iIiqF4qYe9YutRkXtRnDwRYP6X7t29UVw8EV5tMvDw4NbDpFJkhBCWLoTZbFz5070798feXl5sLOzMzj/22+/oW3btvjhhx/QuXNnAEXz9nfu3MHu3bvldh06dEDbtm2xbt06CCHg5+eHadOm4eWXXwYAqNVqeHt7Iy4uDkOGDMG5c+fQsmVLHD9+HO3btwcA7N+/H//6179w7do1+Pn5Ye3atZgzZw7S09Pl/7qZNWsWvvnmG5w/f77E95iTkwOVSgW1Wg1XV9cyPysiIjIvLS0N69evN3pOrXbBypWxilpfkqRBdPR2fPXVfwzajxwZh6Cgyxg3bhyT7Wuokv79tprpSF1ZWVnYuHEjOnbsaDQAA4CPPvoITZs2lQMwAEhISEBERISiXWRkJBISilatpKSkID09XdFGpVIhLCxMbpOQkAA3Nzc5AAOAiIgI2NjYIDExUW7TpUsXxfByZGQkkpKScOvWrUe8eyIiqmhqtQvOnm2JY8dCceJEiEGx1aL3wqD+l/6ej0TmWM10JADMnDkTa9aswd27d9GhQwfFiJau+/fvY+PGjZg1a5bieHp6Ory9vRXHvL29kZ6eLp/XHjPXpl69eorztWrVgru7u6JNUFCQwTW05+rWrWu033l5ecjLy5Pf5+TkGG1HRETlS61Wyz+fPNkOO3dGQbemFyCgX+PL3/8aoqK45yOVnUVHwmbNmmU0mV73pTt9N336dJw6dQrfffcdbG1tMWLECBibTf3666+Rm5uLkSNHVubtPLIlS5ZApVLJL39/f0t3iYio2svMzJTLSKjVLti5sy+UARj+733R3xvdYCsk5BRiY1di5Mg4xMauREjIqUrtO1k3i46ETZs2DaNGjTLbplGjRvLPnp6e8PT0RNOmTdGiRQv4+/vjl19+QXh4uOIzH330Efr27WswouXj44Pr168rjl2/fh0+Pj7yee0x3Xn869evo23btnKbGzduKK7x4MEDZGVlKa5j7Ht0v8OY2bNnY+rUqfL7nJwcBmJERBVMN2k+MTEMpscnJERG7kfLln8qRrtYdoLKyqJBmJeXF7y8vMr0WY2maB5ed/oOKMrrOnz4MHbu3GnwmfDwcBw8eBCxsbHysfj4eDmICwoKgo+PDw4ePCgHXTk5OUhMTMRLL70kXyM7OxsnTpxAaGgoAODQoUPQaDQICwuT28yZMwcFBQVyzlp8fDyaNWtmcioSABwcHODg4FCGp0FERI9KrXZBQkK4yfOSpDEIwMxh2QkqjlXkhCUmJuL48ePo1KkT6tati+TkZMydOxfBwcEGo2CffPIJfH190bt3b4PrTJ48GV27dsXy5cvRp08fbN68Gb/++qu8IkaSJMTGxuL1119HkyZN5BIVfn5+6N+/PwCgRYsW6NWrF1544QWsW7cOBQUFiImJwZAhQ+Dn5wcAePbZZ7Fw4UKMGTMGM2fOxNmzZ7Fq1SqsWLGiYh8UERGVWVaWh0ECvpZ+vpexKvm6WHaCSsIqgjBnZ2fs2LED8+fPx507d+Dr64tevXrh1VdfVYwcaTQaxMXFYdSoUbC1tTW4TseOHbFp0ya8+uqreOWVV9CkSRN88803co0wAJgxYwbu3LmDcePGITs7G506dcL+/fvlGmEAsHHjRsTExKB79+6wsbFBdHQ0Vq9eLZ9XqVT47rvvMGHCBISGhsLT0xPz5s1jjTAiIgsobv9HbVK+u3smJEmjF4hp8K9/7UGzZhf0piBVLD9Bj8xq64TVBKwTRkT0aPSLsKrVLka3GNIyVpTVWLI9a4CROSX9+20VI2FERERloTsCVpIAKyTkFIKDLxa7yTbzvag8MAgjIqJqT612KXaLIS1Tqx0HDBgAT09P5ntRuWEQRkRE1YpuDph2E25jSfdC2CAry73Eqx39/PwYfFG5YhBGRETVhvEcsEDk59cySLrX3WJo0KBBcHNzM3ldjn5RRWAQRkRE1YapHLCH2w4V/VO/5ISbmxsT7anSMQgjIiKrUFypCd1kef0csIfbEEkANBgz5iM0aJBWYX0lKgkGYUREVOXpTzOa0qNHDwDmC68CNigo4OpGsjwGYUREVGVpR7+0CfZa2npfdnZ5KChwkOt+xcfHAzBVeLWIbi6YFktOkCUwCCMioirJ1OiXsVwv/bpfKlUuoqJ2m2ynzQUbMGAAVz2SxTAIIyKiKkk//0utdsHVq/5Gc72M1f3SLbxqZ5ePggJ7gwKsnp6eDMDIYhiEERGRRZlKuNedglSOfhlnrO6XqcKrRFUBgzAiIrKYkiTcG650NM5YrhdRVWb+N5qIiKgCGZtyTEkJhFrtIh8zv9JRAIBBrlfPnj1L9P1MyCdL4kgYERFVCaY22HZ3zwSggXLcQIOBA7+Cm1u20VyvwMBAxMTEFFtXjPlgZEkMwoiIyOLMbbBdRFK0lyTA3/+a2XwvBlhU1TEIIyIiizO3wXZRACYZPWcqCOM0I1kDBmFERGRxxoqr6ibamzvXo0cPBAUFyec4zUjWgon5RERkcdriqpKkAaBMtDd3DgCCgoLg6+srvxiAkbXgSBgREZW7kmy2rR8s6RZX1U+0N3eOyFoxCCMionJV0s22Y2JiDHK3zBVXZeFVqm4YhBERUbkyVvsrK8tD3mRb648//kDdunXRu3dvODk5oVatWnBzc5PPZ2dnY+vWrcV+H5PwyVoxCCMiogqjX/srIuIA/PzS4O6eicOHDxu0j4mJkacpfX19WeuLqjUGYUREVCGM1f6Kj++BonITGnTsmICwsETF6Jh+wMUAi6ozro4kIqIKYXy7IW29LxscPfoUVq6MxcmT7Sq7a0RVAoMwIiIqF5mZmUhLS0NGRgaAh7W/zNFWxtfdK5KopuB0JBERPTJjKyK19b0eTkkK6Fe+B4qvfk9UXTEIIyKiR2ZqRWRw8EXExq5EVpY7UlP9EB8fAf1JGN3q90Q1CYMwIiIqE92CrNopSMBwRWRU1G6EhJxCUNBltGp1FomJYUhICFec5ygY1UQMwoiIqNSSk5PxxRdfyO+LRr4CYWeXZ7AicteuvggOvigXW+3Z8wDCwhJZ/Z5qPAZhRERUKpmZmYoATHfkC9BAf7rRWM6Xqer3LLxKNQmDMCIiKpUbN27IP6vVLti5sy8eBl6GCfi6OV/dunVD3bp1FdfTVspn4VWqaRiEERFRiWVmZiq2Evrhh84wrHYkQZI0RnO+mjRpAl9f38rrMFEVxiCMiKiG0E2kN6YkI1G6n//553CcONHeSCsNxoz5CAUF9sz5IjKDQRgRUQ1grI6XMTExMQAMS05oaVdBqtUu/1duwrDuV8eOCWjQIM3o55nzRfQQgzAiohrA3AiYrqSkJMTHx8vvtfW+3N0zFSNaWVkeMLbpiiRpEBaWKL8fMGAAPD09AXCzbSJ9DMKIiGogU8GVbgBmqt4X8HBLIuXekAIREQcU1/P09GQOGJEJDMKIiKqp0hZT1Q3MABRb70u5JZEGPXocwFNPJSj6wOlHItMYhBERVUOmcsDUahejwdW9e444cCBCDszCwxP0RrkM632FhJxCcPBFk0VXn3vuOU4/EpnBIIyIqBoytZfjnTvORoMrbQCmfZ+QEA79wqvG9ng0VXR18ODBCA4OLp+bIaqmGIQREVVzhhXtlcVUAf3crqJArGPHn8u8x6OXl1d5dZ+o2mIQRkRUjelPPxaNbGkgSUIOriIiDihGwoCHqxxN7fGou+pRH1dBEpUMgzAiomosK8vDYJQLsEF09FbUrn1XDq6cnO4bJOtrgy5jo19c9Uj06BiEERFVI9oVkdrVkMZKSUiSBv7+1xTBVXFJ9vq46pHo0TEIIyKyYrplKNRqNbZs2SKf0ybj6043msvtMpdkr1Kp5PecbiQqHwzCiIislLmtiPRrgUVEHICfX6rBKJd+gKWPARdRxWEQRkRkpUyVobCzyzOoBXbgQARiY1cajHR5eXkxyCKyEAZhRETVgGEZCtOFVrUrGznKRWRZDMKIiKxQZmamnHxvvAyFshaYbqFVrmwkqhoYhBERWRn9XDDjZSgkeVVkaQutElHlYBBGRGRl9HPB7OzyjJahGDPmIxQU2Jeo5AQRVT4GYURElUi3pIQxpc3TUuaCFU1Bake+GjRIe/QOE1GFYRBGRFRJzJWU0BUTE1OiQMwwF0wCUDQCZi4AY6FVoqqBQRgRUSUxVVLC3T1TMV1obqRMl6ktiQoKlEHWoEGD4ObmBoB1v4iqEgZhREQWoF9MNSpqN0JCTpXqGqa2JNKughwwYAD8/PwYdBFVUfr/CUVERBVMfxpRCBvs2tUXarVLqa6jUuUiKmo3JEkDAAarID09PRmAEVVhHAkjIqpkxqYRdYuplkZpN94moqqDQRgRUTkztQJSW1y1uGnE4ugn1pvaeJsJ+ERVG4MwIqJyVJIVkNppRP2csJKOYnl4eCAmJqZcS10QUeUrcRCWk5NT4ou6urqWqTNERNaupCsgH3UakQEWkfUrcRDm5uYGSZLMthFCQJIkFBYWPnLHiIisXXErIE1NI969e7cyu0lEFlLiIOzw4cMV2Q8iomrF1ArI4OCLxY56ffHFFyUu2EpE1qvEQVjXrl0rsh9ERNVKSVdAPmrBViKyXmVOzM/OzsbHH3+Mc+fOAQAee+wxjB49GiqVqtw6R0RkrUqyArI8CrYSkfUqU7HWX3/9FcHBwVixYgWysrKQlZWFd955B8HBwTh58mR595GIyOoUV0i1vAq2EpH1KtNI2JQpU9CvXz98+OGHqFWr6BIPHjzA2LFjERsbix9++KFcO0lEZI3MrYAsz4KtRGSdyjwSNnPmTDkAA4BatWphxowZ+PXXX8utc7r69euHgIAAODo6wtfXF8OHD0dqaqqizbfffosOHTrAxcUFXl5eiI6OxqVLlxRtvv/+e4SEhMDBwQGNGzdGXFycwXe99957CAwMhKOjI8LCwnDs2DHF+fv372PChAnw8PBAnTp1EB0djevXryvaXLlyBX369IGzszPq1auH6dOn48GDB+XyLIio6jJWSDUo6LJBYKWdrtRVmoKtRGT9yjQS5urqiitXrqB58+aK41evXoWLS8UMpXfr1g2vvPIKfH198c8//+Dll1/GwIEDcfToUQBASkoK/v3vf2Pq1KnYuHEj1Go1pkyZggEDBshTpCkpKejTpw/Gjx+PjRs34uDBgxg7dix8fX0RGRkJANiyZQumTp2KdevWISwsDCtXrkRkZCSSkpJQr149AEUjgXv27MG2bdugUqkQExODAQMG4OeffwYAFBYWok+fPvDx8cHRo0eRlpaGESNGwM7ODm+88UaFPB8iqhqKK6SakZGBHTt2PHLBViKyfpIQQpT2Q5MmTcLXX3+Nt99+Gx07dgQA/Pzzz5g+fTqio6OxcuXK8u6ngZ07d6J///7Iy8uDnZ0dvvrqKwwdOhR5eXmwsSka4Nu1axf+/e9/y21mzpyJPXv24OzZs/J1hgwZguzsbOzfvx8AEBYWhieeeEKueK3RaODv74+JEydi1qxZUKvV8PLywqZNmzBw4EAAwPnz59GiRQskJCSgQ4cO2LdvH/r27YvU1FR4e3sDANatW4eZM2fi5s2bJd5KJCcnByqVCmq1mgVwiaqJtLQ0rF+/Xn5ftDrScLpy3Lhx8PX1tUQXiegRlfTvd5mmI99++20MGDAAI0aMQGBgIAIDAzFq1CgMHDgQb775Zpk7XVJZWVnYuHEjOnbsCDs7OwBAaGgobGxs8Omnn6KwsBBqtRqff/45IiIi5DYJCQmIiIhQXCsyMhIJCQkAipaEnzhxQtHGxsYGERERcpsTJ06goKBA0aZ58+YICAiQ2yQkJKB169ZyAKb9npycHPzxxx8m7ysvLw85OTmKFxFVLyWdruS+j0TVX5mmI+3t7bFq1SosWbIEycnJAIDg4GA4OzuXa+f0zZw5E2vWrMHdu3fRoUMH7N69Wz4XFBSE7777DoMGDcKLL76IwsJChIeHY+/evXKb9PR0RWAEAN7e3sjJycG9e/dw69YtFBYWGm1z/vx5+Rr29vZwc3MzaJOenm72e7TnTFmyZAkWLlxYwqdBRNaI+z4SkVaZRsK0nJ2d0bp1a7Ru3bpMAdisWbMgSZLZlzb4AYDp06fj1KlT+O6772Bra4sRI0ZAO5uanp6OF154ASNHjsTx48dx5MgR2NvbY+DAgSjDjKtFzJ49G2q1Wn5dvXrV0l0iogrg4eEBX19fky8GYEQ1Q5lGwu7fv493330Xhw8fxo0bN6DRKFf4lLRW2LRp0zBq1CizbRo1aiT/7OnpCU9PTzRt2hQtWrSAv78/fvnlF4SHh+O9996DSqXCsmXL5PZffPEF/P39kZiYiA4dOsDHx8dgFeP169fh6uoKJycn2NrawtbW1mgbHx8fAICPjw/y8/ORnZ2tGA3Tb6O/olJ7TW0bYxwcHODg4GD2eRAREVH1UKYgbMyYMfjuu+8wcOBAPPnkk8Vu7G2Kl5cXvLy8yvRZbeCXl5cHoGjDW21Cvpatra2irf70JADEx8cjPDwcQNEUQGhoKA4ePIj+/fvLnz148CBiYmIAFOWe2dnZ4eDBg4iOjgYAJCUl4cqVK/J1wsPDsXjxYty4cUNeURkfHw9XV1e0bNmyTPdLRBUnMzOT04NEVOnKtDpSpVJh7969eOqppyqiTwYSExNx/PhxdOrUCXXr1kVycjLmzp2L69ev448//oCDgwMOHTqEiIgILFiwAEOHDkVubi5eeeUVnD9/HufOnYOTkxNSUlLQqlUrTJgwAaNHj8ahQ4cwadIk7NmzR1GiYuTIkfjggw/w5JNPYuXKldi6dSvOnz8v53W99NJL2Lt3L+Li4uDq6oqJEycCgFwuo7CwEG3btoWfnx+WLVuG9PR0DB8+HGPHji1ViQqujiSqeJmZmfJqaHO4oTYRlVSFro6sX79+hdUDM8bZ2Rk7duxA9+7d0axZM4wZMwaPP/44jhw5Ik/fPfPMM9i0aRO++eYbtGvXDr169YKDgwP2798PJycnAEXJ+3v27EF8fDzatGmD5cuX46OPPpIDMAAYPHgw3n77bcybNw9t27bF6dOnsX//fkWi/YoVK9C3b19ER0ejS5cu8PHxwY4dO+Tztra22L17N2xtbREeHo7nnnsOI0aMwGuvvVZJT4yISqqkG2VzQ20iKm9lGgnbt28fVq9ejXXr1qFhw4YV0S8CR8KIKoPxul0ecHfPZN0uIiqTkv79LlNOWPv27XH//n00atQIzs7Och0urawsbrtBRNbn5Ml2BhXsQ0JOWbpbRFRNlSkIGzp0KP755x+88cYb8Pb2LnNiPhFRVaFWu8gBGFC0mfauXX0RHHyRWwkRUYUoUxB29OhRJCQkoE2bNuXdHyIii8jK8pADMC0hbJCV5c4gjIgqRJkS85s3b4579+6Vd1+IiCzG3T0TkqSseShJGri7M72CiCpGmUbCli5dimnTpmHx4sVo3bq1QU4Yk8iJqDKUZ30vlSoXUVG7DXLCOApGRBWlTEFYr169AADdu3dXHBdCQJIkFBYWPnrPiIjMKK/6XrobZYeEnEJw8EVkZbnD3T1LEYBxQ20iKm9lCsIOHz5c3v0gIioV/REwU6UlUlNTFW31R8e4oTYRWUqZgrCuXbuWqN1///tfvPbaa/D09CzL1xARlYi50hK6hZS19EfHGGARkSWUKTG/pL744gvk5ORU5FcQUQ1nqrSEWm16Vw9WvyeiqqBMI2ElVYZi/EREpWKutIT2vP4UJRFRVVChQRgRUUXTlpbQDcQkSYPUVD989tkIVr8noiqrQqcjiYgqmra0hLbGlyRpEBFxAAcORJRqipKIqLJxJIyIqixzdcAyMjLkn/VLS7D6PRFZAwZhRFQllbQOmJZKlasIsIxNUbL6PRFVJRUahD333HOsnk9EZVLSFYwDBgyQy+BkZGRgx44drH5PRFahzEFYdnY2jh07hhs3bkCjUe63NmLECADA2rVrH613RFQjZWZmKqYbAdPFWD09PeHr6wuA1e+JyLqUKQjbtWsXhg0bhtu3b8PV1RWSJMnnJEmSgzAiotIyNg1prhirLla/JyJrUqYgbNq0aRg9ejTeeOMNODs7l3efiKgGM7YdkbFirMHBF41OLzLAIiJrUaYSFf/88w8mTZrEAIyIKtzVq/5mi7ESEVmrMgVhkZGR+PXXX8u7L0RECidPtsP27dEGx7nSkYiqgxJPR+7cuVP+uU+fPpg+fTr+/PNPtG7dGnZ2doq2/fr1K78eElGNpD8N+ZBypaNarZYT84mIrEmJg7D+/fsbHHvttdcMjkmShMLCwkfqFBHVLLpFWbWrIo0VXAWAgQO/QqtW5+T3W7ZsQUxMDHPBiMjqlDgI0y9DQURUHkwVZTW1J6S//zWDtiWtKUZEVJWUKSfss88+Q15ensHx/Px8fPbZZ4/cKSKq2jIzM5GWlmbylZmZWeJrGVsNmZISCAAGe0J26vQjsrI8uAckEVULkhBClPZDtra2SEtLQ7169RTHMzMzUa9ePU5HlpOcnByoVCqo1WruPEBVhv7IlakiqoMGDYKbm5v83lR9rrS0NKxfvx6A8Xpg9+45Ij4+AkX/zSgASAa1wsaNG8e8MCKqMkr697tMdcKEEIoCrVrXrl2DSqUqyyWJyErojlyZK6K6detWg8+ay90yVg9s586+KPq/Gu2gvSSfM1crjIjIGpQqCGvXrh0kSYIkSejevTtq1Xr48cLCQqSkpKBXr17l3kkiqnpKW0QVMJ+7ZTwR3wamxuq1tcIYhBGRtSpVEKZdIXn69GlERkaiTp068jl7e3sEBgYiOtqwpg8RVT/GgibdIqrGpijNMZaIXzT9CGhHwHSxVhgRWbtSBWHz588HAAQGBmLw4MFwdHSskE4RUdVnavViaqofPvtsRLH7POpTqXIREXEA8fE98DDokgBoIEni/75HmRPGUTAismZlygkbOXIkgKKphRs3bhiUrwgICHj0nhFRlaZS5SIqarciJywi4gAOHIgo1RSlLj+/NBiOetkgOnorate+Czu7fBQU2MPdPUtxPXt7+3K8MyKiylGmIOzChQsYPXo0jh49qjiuTdjn6kiimiEk5BSCgy8iK8sd7u5ZZqcoVapcuRAr8HC1pG4AZa42mEqVi27duqFu3boAgFq1asHNzc3kqksioqquTEHYqFGjUKtWLezevRu+vr5GV0oSkXXRrVpvjKlgR6XKVYxKGQuitLlbO3bsUHxWu1py0KBB2Lp1q9HRNd1pxyZNmrAUBRFVG2UKwk6fPo0TJ06gefPm5d0fIrKAktb+iomJMTv1V1wQpU8b9OnWHNQfXeO0IxFVV2UKwlq2bKmYViAi61bS2l/5+fnw9fVFTEyM4jNqtRpbtmwBYDyIMhXUaXl4eBhcUx+nHYmouilTEPbmm29ixowZeOONN9C6dWvY2dkpzrO6O5F1KmntL/1gSDcwy8jIwI4dO+T25oI6XQywiKimKVMQFhERAQB45plnFPlgTMwnsm7FJdabYyyIUqtdsHNnX2gr3rPSPRHRQ2UKwg4fPlze/SCiKsDU6sSyFkVNTAzDwy2HirDSPRFREf09Qkqka9eusLGxwYcffohZs2ahcePG6Nq1K65cuQJbW9vy7iMRVRJtYr0kFdX+00+sz8jIQFpaGjIzM4u9llrtgoSEcCNnWOmeiAgo40jY9u3bMXz4cAwbNgynTp1CXl4egKLk3DfeeAN79+4t104SUcXQlqXQXWhjbnWibokJc5txA6b2ggQ6dkzgKBgREcoYhL3++utYt24dRowYgc2bN8vHn3rqKbz++uvl1jkiqjj6ZSl06db+MrWy0dxKRsD01GZYWKL8niUniKgmK1MQlpSUhC5duhgcV6lUyM7OftQ+EVEl0A+ijAVbJV3ZaExxNcMGDRrEFZFEVKOVKQjz8fHBxYsXERgYqDj+008/oVGjRuXRLyKqRMaCreDgiyUqV6FPd3TL3NSmboFWIqKaqExB2AsvvIDJkyfjk08+gSRJSE1NRUJCAl5++WXMnTu3vPtIRBXIVG2w6OjtZSpXwcKrREQlU6YgbNasWdBoNOjevTvu3r2LLl26wMHBAS+//DImTpxY3n0kogpkqjYYIMpcroIBFhFR8cpUokKSJMyZMwdZWVk4e/YsfvnlF9y8eROLFi0q7/4RUQXTJtDrkiQN/P2vmS1XQUREj6ZMI2Fa9vb2aNmyZXn1hYgswFwCvbmcLiIiejSPFIQRUfVgLtjSLVehi+UliIgeDYMwohpKP4gyFWwNGjQIbm5uBp9l3hcR0aNhEEZUQ3EVIxGRZTEII6oCtNsHAUBqqg1SUmohKOgB/PyKkuIrKhhigEVEZDkMwogsTHf7IHMV6ovbq5GIiKxLmUpUEFH50Y6AmSqaqla7KNoREVH1wCCMqIowVTQ1K8vdQj0iIqKKxCCMqIowVTS1JBXqiYjI+jAII6oitEVTWaGeiKhmYGI+URXCCvVERDUHgzCiKsZU0VQiIqpeGIQRWYmMjAwALKBKRFRdMAgjsrCS7sG4Y8cO+WfWDCMisn4MwogsTHf7oIyMDEWwpVa7ICvLA+7umYopSv2aYboV943h6BkRUdXDIIyoBK5dAy5cAJo0ARo0KP/rGwuQzFXP105NAoBarcaWLVuK/Q6OnhERVS0MwohM0I4ubdrkhBkzVNBoJNjYCCxbpsazz96r0NElU9Xzg4MvQqXKVYyWGftsSUbPiIjIshiEERmh3c9RrXbBypWxEEICAGg0EqZPd8U//3wClSq3wkaXzFXPV6lyTQZa5kbPiIioamEQRmSEdtSouGCookaXtNXzdb9bWz3fVKBV3OgZERFVLayYT2SGpbYSMlU9H4DJTb659yQRkXWxmiCsX79+CAgIgKOjI3x9fTF8+HCkpqYq2mzduhVt27aFs7MzGjZsiLfeesvgOt9//z1CQkLg4OCAxo0bIy4uzqDNe++9h8DAQDg6OiIsLAzHjh1TnL9//z4mTJgADw8P1KlTB9HR0bh+/bqizZUrV9CnTx84OzujXr16mD59Oh48ePDoD4IqVWVvJZSdnS3/HBJyCrGxKzFyZBxiY1ciJOSU2UCLe08SEVkXqwnCunXrhq1btyIpKQnbt29HcnIyBg4cKJ/ft28fhg0bhvHjx+Ps2bN4//33sWLFCqxZs0Zuk5KSgj59+qBbt244ffo0YmNjMXbsWHz77bdymy1btmDq1KmYP38+Tp48iTZt2iAyMhI3btyQ20yZMgW7du3Ctm3bcOTIEaSmpmLAgAHy+cLCQvTp0wf5+fk4evQoNmzYgLi4OMybN6+CnxJVBGPBUEWRJMnYUfknc4EW954kIrIukhBCWLoTZbFz5070798feXl5sLOzw7PPPouCggJs27ZNbvPuu+9i2bJluHLlCiRJwsyZM7Fnzx6cPXtWbjNkyBBkZ2dj//79AICwsDA88cQTcvCm0Wjg7++PiRMnYtasWVCr1fDy8sKmTZvkIPD8+fNo0aIFEhIS0KFDB+zbtw99+/ZFamoqvL29AQDr1q3DzJkzcfPmzRIX58zJyYFKpYJarYarq2u5PDcqmbS0NKxfv77YduPGjYOvr2+FfK+p3K/iku+LpiYN954s774SEZFxJf37bTUjYbqysrKwceNGdOzYEXZ2dgCAvLw8ODo6Kto5OTnh2rVruHz5MgAgISEBERERijaRkZFISEgAUJSMfeLECUUbGxsbREREyG1OnDiBgoICRZvmzZsjICBAbpOQkIDWrVvLAZj2e3JycvDHH3+YvK+8vDzk5OQoXlQzmUqyV6tdih2ZU6lyERR02WAErKTBPxERVQ6rWh05c+ZMrFmzBnfv3kWHDh2we/du+VxkZCSmTJmCUaNGoVu3brh48SKWL18OoGh0ITAwEOnp6YrACAC8vb2Rk5ODe/fu4datWygsLDTa5vz58wCA9PR02Nvbw83NzaBNenq63MbYNbTnTFmyZAkWLlxYiidClqZbNFXXo9YQK25VpqlNvgcNGmTwu1ke/SEiovJn0SBs1qxZePPNN822OXfuHJo3bw4AmD59OsaMGYPLly9j4cKFGDFiBHbv3g1JkvDCCy8gOTkZffv2RUFBAVxdXTF58mQsWLAANjbWMeA3e/ZsTJ06VX6fk5MDf39/C/ao5irLfo76HqWGmLkSFQAwYMAAeHp6Kj7DQIuIyLpYNAibNm0aRo0aZbZNo0aN5J89PT3h6emJpk2bokWLFvD398cvv/yC8PBwSJKEN998E2+88QbS09Ph5eWFgwcPKq7h4+NjsIrx+vXrcHV1hZOTE2xtbWFra2u0jY+Pj3yN/Px8ZGdnK0Yc9Nvor6jUXlPbxhgHBwc4ODiYfR5UOXT3c9Snv7+jKY9SQ0ybZK+f+6Ud/fL09GR+FxGRlbNoEObl5QUvL68yfVajKVoBlpeXpzhua2uL+vXrAwC+/PJLhIeHy98RHh6OvXv3KtrHx8cjPDwcQNFIQmhoKA4ePIj+/fvL33Pw4EHExMQAAEJDQ2FnZ4eDBw8iOjoaAJCUlIQrV67I1wkPD8fixYtx48YN1KtXT/4eV1dXtGzZskz3S5WvpKNKpqrXP6qQkFMIDr5oNMmeiIisn1XkhCUmJuL48ePo1KkT6tati+TkZMydOxfBwcFy4JORkYGvvvoKTz/9NO7fv49PP/1ULiGhNX78eKxZswYzZszA6NGjcejQIWzduhV79uyR20ydOhUjR45E+/bt8eSTT2LlypW4c+cOnn/+eQCASqXCmDFjMHXqVLi7u8PV1RUTJ05EeHg4OnToAADo2bMnWrZsieHDh2PZsmVIT0/Hq6++igkTJnCkq5op722C9KdBTeV+McmeiMj6WUUQ5uzsjB07dmD+/Pm4c+cOfH190atXL7z66quKoGbDhg14+eWXIYRAeHg4vv/+ezz55JPy+aCgIOzZswdTpkzBqlWr0KBBA3z00UeIjIyU2wwePBg3b97EvHnzkJ6ejrZt22L//v2KRPsVK1bAxsYG0dHRyMvLQ2RkJN5//335vK2tLXbv3o2XXnoJ4eHhqF27NkaOHInXXnutgp8UVaaK2CbI3DSoFnO/iIiqB6utE1YTsE5Y1aSt5ZWSEogNG0YanB85Mg5BQZfN1uXKzMxkoEVEVE2V9O+3VYyEEVVFxa1gNCUzM1Oxk4OpnLJHWV1JRERVH4MwojIqbgWjKbojYOZyyh5ldSUREVV9DMKISkk3Kd7cCsbikucrIqeMiIisB4MwolIqr+T54qriExFR9cYgjKgMyiNXq6w5ZUREVD0wCCOrZ+mVhmX9/rLmlBERUfXAIIysin7Ao1arsWXLlmI/Vx4rDY0FW/rfX9qVjqyKT0RUczEIoypLP+jJzs7G1q1by3StR11pqF9WwpiyrnQ0VRWfiIiqNwZhVCWVJOipTIYjYMoRr9KsdCzplkPcmoiIqHpjEEZVTmZmJlJTU8v02YraTFuXsRGvunVvlXilI7cmIiIigEEYVTGPMgJW3ptp6/YpIyMDgOnaXmPGfFSqlY4MsIiIyKb4JkSVpzS5W2q1C1JSAqFWu5gMjtRql0fqjzYo3LFjBwDg6lV/oyNeBQX2iIraDUnSAABXOhIRUbE4EkZWSX/UKzw8oUIKnxrbYkifdsQrKOgyVzoSEVGJMQgjq6Cb6wXAYNTr6NHwCi18qj/S9pByxIsrHYmIqKQYhFGVV5JRL8AG4eE/IyEh3Gjh00ddaWhsiyEAGDjwK7Rqda7Yz3OlIxER6WMQRlWasVyvhIRwABropjRKkgZhYYkIC0tEmzbRaNXKEX5+TwB4olxWGpraYsjf/5qi3aBBg+Dm5qY4xpWORERkDIMwqtJMJcJ37Phw1MvWVuDNN3Pw7LNDKyzgKW6LoQEDBsDPz4/BFhERlRiDMKqyTp5sh507jSfCL1jgDh8fNTIz66JxYwkNGrgBcCv1dxS376NarZZ/NrfFkKenJwMwIiIqFQZhVKVoc6e005D6VVQe1v96Ch4edR/pu/RrkpWk0CsT74mIqLwwCKMqRVtN/vBhYMUKw0T4devUiI5+qlxGnYyVnyhroVcm3hMRUWkxCKMqx8PDAx06ADY2gEbz8LitLfCvf9VFec/6Fbfvo7Fke11MvCciorJgEEZVUoMGwPr1wIsvAoWFRQHYBx8UHS9vxspP6BZ6dXNzg6+vb/l/MRER1WgMwqjKGjMGiIwELl4EGjcufQBWXNJ9dnY2ANPlJ8qr0CsREZExDMKoSmvQoGyjX6XZCLy48hNEREQVgUEYVUv6I2DGVj7qHjNXfoKIiKgiMAijas/YykcARldDMvgiIqLKwiCMqjVjKx937uwLSYLJ1ZD6WH6CiIgqAoMwqtaMb7xtAyGUR3RXQw4YMACenp4AWH6CiIgqDoMwqtaMrXwENIqRMEC5GtLT05MlKYiIqMIZliQnqka0Kx8lqajqqyRp0K/fboNjXA1JRESVjSNhVO2ZWvnI1ZBERGRJDMKoWtJPpje28TY34yYiIktiEEbVknYjcN16YWq1Glu2bCn2s1wNSURElUESQn+dGFUVOTk5UKlUUKvVcHV1tXR3Hllx2whVxkrEqtAHIiKq3kr695sjYVQpSrqNUExMTIUGQQywiIioquDqSKoU5kafytKOiIjI2jEIIyIiIrIABmFEREREFsAgjIiIiMgCmJhPFqFWuyArywPu7pllrtWlu9IxNdUGKSm1EBT0AH5+RZXwudKRiIiqMgZhVOlOnmyHXbv6QggbecugkJBTpbqG7mpLc9er6NWWREREZcXpSKpUarWLHDABRZto79rVF2q1S6muox0BK+56XG1JRERVFYMwqhTaKvRZWR5ywKQlhA2ystwV7UqquOsRERFVVZyOpEqh3Ubo0qUH+PxzAY1Gks/Z2gpMnNgbgYG1Sj116O6eCUnSKAIxSdLA3T2r3PpORERUETgSRpXGw8MDoaHeWL9egq1t0TFbW+CDDySEhnqXKXdLpcpFVNRuSFJRMr42J4wbcxMRUVXHkTCqdGPGAJGRwMWLQOPGQIMGj3a9kJBTCA6+iKwsd7i7ZzEAIyIiq8AgjCyiQQPAyamoxERa2qOXmFCpchl8ERGRVWEQRhZR2hIT164BFy4ATZo8+sgZERFRVcCcMLKIkpaYSE1NxfLl2WjYUOCZZ4CGDQWWL8+GWq0u0feUdrUlERFRZeFIGFmUuRITKlUuPv00HitXxkKIotWUGo2E6dNd8c8/n0ClAgYNGgQ3Nzej12bFfCIiqsoYhJFFFVdiorggzc3NDb6+vpXaZyIiovLA6UiyqOJKTGiDNF2sA0ZERNUBR8LI4syVmNAGafqJ+1wJSURE1o5BGFUJ5kpMsA4YERFVRwzCyCqwDhgREVU3zAkji2DpCCIiquk4EkYWod3QW1svTF9GRgZ27NhRyb0iIiKqPAzCyGLM1fAq6UgZR9SIiMhaMQijKqm4kTKAxViJiMi6MQijKosBFhERVWdMzCciIiKyAAZhRERERBbAIIyIiIjIAhiEEREREVkAgzAiIiIiC7C6ICwvLw9t27aFJEk4ffq04tyZM2fQuXNnODo6wt/fH8uWLTP4/LZt29C8eXM4OjqidevW2Lt3r+K8EALz5s2Dr68vnJycEBERgQsXLijaZGVlYdiwYXB1dYWbmxvGjBmD27dvl7ovREREVHNZXRA2Y8YM+Pn5GRzPyclBz5490bBhQ5w4cQJvvfUWFixYgPXr18ttjh49iqFDh2LMmDE4deoU+vfvj/79++Ps2bNym2XLlmH16tVYt24dEhMTUbt2bURGRuL+/ftym2HDhuGPP/5AfHw8du/ejR9++AHjxo0rVV+qk2vXgMOHi/5JREREJSSsyN69e0Xz5s3FH3/8IQCIU6dOyefef/99UbduXZGXlycfmzlzpmjWrJn8ftCgQaJPnz6Ka4aFhYkXX3xRCCGERqMRPj4+4q233pLPZ2dnCwcHB/Hll18KIYT4888/BQBx/Phxuc2+ffuEJEnin3/+KXFfSkKtVgsAQq1Wl+pzlSEjI0OkpqaKt9++JWxsNAIQwsZGI95++5ZITU0VGRkZlu4iERGRRZT077fVjIRdv34dL7zwAj7//HM4OzsbnE9ISECXLl0U29hERkYiKSkJt27dkttEREQoPhcZGYmEhAQAQEpKCtLT0xVtVCoVwsLC5DYJCQlwc3ND+/bt5TYRERGwsbFBYmJiiftiTF5eHnJychSvqigzMxNr1qzBW299ienTXaHRSAAAjUbC9OmueOutL7FmzRpkZmZauKdERERVl1UEYUIIjBo1CuPHj1cEP7rS09Ph7e2tOKZ9n56ebraN7nndz5lqU69ePcX5WrVqwd3dvdjv0f0OY5YsWQKVSiW//P39TbatTPrTjdqthLKyPKAfxwthg6wsd0U7IiIiMmTRIGzWrFmQJMns6/z583j33XeRm5uL2bNnW7K7FW727NlQq9Xy6+rVq5buEj7+GGjYEHjmmaJ/fvwxkJ2dDQBwd8+EJGkU7SVJA3f3LAv0lIiIyLpYdO/IadOmYdSoUWbbNGrUCIcOHUJCQgIcHBwU59q3b49hw4Zhw4YN8PHxwfXr1xXnte99fHzkfxpro3tee8zX11fRpm3btnKbGzduKK7x4MEDZGVlFfs9ut9hjIODg8E9WkpmZiYuXXqAcePq6Uw3AuPGCUyevA8qFZCc3BhCPPyMJGkQFbUbKlWuhXpNRERkPSwahHl5ecHLy6vYdqtXr8brr78uv09NTUVkZCS2bNmCsLAwAEB4eDjmzJmDgoIC2NnZAQDi4+PRrFkz1K1bV25z8OBBxMbGyteKj49HeHg4ACAoKAg+Pj44ePCgHHTl5OQgMTERL730knyN7OxsnDhxAqGhoQCAQ4cOQaPRlKovVZk25yslJRAazUjFOY1Gkqcbd+3qC93BVCGA4OCLldlVIiIiq2UVOWEBAQFo1aqV/GratCkAIDg4GA0aNAAAPPvss7C3t8eYMWPwxx9/YMuWLVi1ahWmTp0qX2fy5MnYv38/li9fjvPnz2PBggX49ddfERMTAwCQJAmxsbF4/fXXsXPnTvz+++8YMWIE/Pz80L9/fwBAixYt0KtXL7zwwgs4duwYfv75Z8TExGDIkCFy6YyS9KUq0+ZymZtuNJYPBjzMByMiIiLzrCIIKwmVSoXvvvsOKSkpCA0NxbRp0zBv3jxF/a6OHTti06ZNWL9+Pdq0aYOvvvoK33zzDVq1aiW3mTFjBiZOnIhx48bhiSeewO3bt7F//344OjrKbTZu3IjmzZuje/fu+Ne//oVOnTopaoCVpC/WQKXKRVTUbjkQ051uZD4YERHRo5GE0M3qoaokJycHKpUKarUarq6ulfa9aWlpiqBSrXZBVpY73N2zFPleJ0+2w65dfSGEjRyghYScks+PGzdOkVtHRERUE5T077dFc8LIOqhUuUaT7UNCTiE4+KLRAA2Aok4aERERKTEIo0diKkAbPHgwPDw8LNAjIiIi61BtcsKoalGpVJbuAhERUZXGIIwqBKciiYiIzON0JBkoaQA1ePBgoyNe9vb2nIokIiIqBoMwMuDh4YGYmBizez8y0CIiIno0DMLIKAZYREREFYs5YUREREQWwCCMiIiIyAIYhBERERFZAIMwIiIiIgtgEEZERERkAQzCiIiIiCyAQRgRERGRBTAIIyIiIrIABmFEREREFsCK+TVEZmYmtyEiIiKqQhiE1QCZmZlYs2ZNse1iYmIYiBEREVUSTkfWAOZGwMrSjoiIiB4dgzAiIiIiC2AQRkRERGQBDMKIiIiILIBBGBEREZEFMAgjIiIisgAGYUREREQWwCCsBrC3t1e8V6tdkJISCLXaxWw7IiIiqjgs1loDeHh4ICYmBvn5+di0yQmvvaaCRiPBxkZg2TI1nn32HivmExERVTJJCCEs3QkyLicnByqVCmq1Gq6uro98vWvXgIYNAY3m4TFbW+DSJaBBg0e+PBEREaHkf785HVmDXLigDMAAoLAQuHjRMv0hIiKqyRiE1SBNmgA2ev/GbW2Bxo0t0x8iIqKajEFYDdKgAbB+fVHgBRT984MPOBVJRERkCUzMr2HGjAEiI4umIBs3ZgBGRERkKQzCaqAGDRh8ERERWRqnI4mIiIgsgEEYERERkQUwCCMiIiKyAAZhRERERBbAIIyIiIjIAhiEEREREVkAgzAiIiIiC2AQRkRERGQBDMKIiIiILIBBGBEREZEFMAgjIiIisgDuHVmFCSEAADk5ORbuCREREZWU9u+29u+4KQzCqrDc3FwAgL+/v4V7QkRERKWVm5sLlUpl8rwkigvTyGI0Gg1SU1Ph4uICSZJK9JmcnBz4+/vj6tWrcHV1reAeVl18DkX4HPgMtPgcivA58BloVeRzEEIgNzcXfn5+sLExnfnFkbAqzMbGBg0aNCjTZ11dXWv0/7i0+ByK8DnwGWjxORThc+Az0Kqo52BuBEyLiflEREREFsAgjIiIiMgCGIRVMw4ODpg/fz4cHBws3RWL4nMowufAZ6DF51CEz4HPQKsqPAcm5hMRERFZAEfCiIiIiCyAQRgRERGRBTAIIyIiIrIABmFEREREFsAgzAqsXbsWjz/+uFxQLjw8HPv27ZPP379/HxMmTICHhwfq1KmD6OhoXL9+XXGNK1euoE+fPnB2dka9evUwffp0PHjwoLJvpdwsXboUkiQhNjZWPlZTnsOCBQsgSZLi1bx5c/l8TXkO//zzD5577jl4eHjAyckJrVu3xq+//iqfF0Jg3rx58PX1hZOTEyIiInDhwgXFNbKysjBs2DC4urrCzc0NY8aMwe3btyv7VsosMDDQ4HdBkiRMmDABQM35XSgsLMTcuXMRFBQEJycnBAcHY9GiRYp9+2rC70Nubi5iY2PRsGFDODk5oWPHjjh+/Lh8vjo+gx9++AFRUVHw8/ODJEn45ptvFOfL657PnDmDzp07w9HREf7+/li2bFn53ICgKm/nzp1iz5494q+//hJJSUnilVdeEXZ2duLs2bNCCCHGjx8v/P39xcGDB8Wvv/4qOnToIDp27Ch//sGDB6JVq1YiIiJCnDp1Suzdu1d4enqK2bNnW+qWHsmxY8dEYGCgePzxx8XkyZPl4zXlOcyfP1889thjIi0tTX7dvHlTPl8TnkNWVpZo2LChGDVqlEhMTBR///23+Pbbb8XFixflNkuXLhUqlUp888034rfffhP9+vUTQUFB4t69e3KbXr16iTZt2ohffvlF/Pjjj6Jx48Zi6NChlrilMrlx44bi9yA+Pl4AEIcPHxZC1IzfBSGEWLx4sfDw8BC7d+8WKSkpYtu2baJOnTpi1apVcpua8PswaNAg0bJlS3HkyBFx4cIFMX/+fOHq6iquXbsmhKiez2Dv3r1izpw5YseOHQKA+PrrrxXny+Oe1Wq18Pb2FsOGDRNnz54VX375pXBychIffPDBI/efQZiVqlu3rvjoo49Edna2sLOzE9u2bZPPnTt3TgAQCQkJQoiiX1IbGxuRnp4ut1m7dq1wdXUVeXl5ld73R5GbmyuaNGki4uPjRdeuXeUgrCY9h/nz54s2bdoYPVdTnsPMmTNFp06dTJ7XaDTCx8dHvPXWW/Kx7Oxs4eDgIL788kshhBB//vmnACCOHz8ut9m3b5+QJEn8888/Fdf5CjR58mQRHBwsNBpNjfldEEKIPn36iNGjRyuODRgwQAwbNkwIUTN+H+7evStsbW3F7t27FcdDQkLEnDlzasQz0A/Cyuue33//fVG3bl3F/yZmzpwpmjVr9sh95nSklSksLMTmzZtx584dhIeH48SJEygoKEBERITcpnnz5ggICEBCQgIAICEhAa1bt4a3t7fcJjIyEjk5Ofjjjz8q/R4exYQJE9CnTx/F/QKocc/hwoUL8PPzQ6NGjTBs2DBcuXIFQM15Djt37kT79u3xn//8B/Xq1UO7du3w4YcfyudTUlKQnp6ueA4qlQphYWGK5+Dm5ob27dvLbSIiImBjY4PExMTKu5lykp+fjy+++AKjR4+GJEk15ncBADp27IiDBw/ir7/+AgD89ttv+Omnn9C7d28ANeP34cGDBygsLISjo6PiuJOTE3766aca8Qz0ldc9JyQkoEuXLrC3t5fbREZGIikpCbdu3XqkPnIDbyvx+++/Izw8HPfv30edOnXw9ddfo2XLljh9+jTs7e3h5uamaO/t7Y309HQAQHp6uuL/ZLXnteesxebNm3Hy5ElFjoNWenp6jXkOYWFhiIuLQ7NmzZCWloaFCxeic+fOOHv2bI15Dn///TfWrl2LqVOn4pVXXsHx48cxadIk2NvbY+TIkfJ9GLtP3edQr149xflatWrB3d3dap6Drm+++QbZ2dkYNWoUgJr1v4lZs2YhJycHzZs3h62tLQoLC7F48WIMGzYMAGrE74OLiwvCw8OxaNEitGjRAt7e3vjyyy+RkJCAxo0b14hnoK+87jk9PR1BQUEG19Ceq1u3bpn7yCDMSjRr1gynT5+GWq3GV199hZEjR+LIkSOW7laluXr1KiZPnoz4+HiD/9KrabT/dQ8Ajz/+OMLCwtCwYUNs3boVTk5OFuxZ5dFoNGjfvj3eeOMNAEC7du1w9uxZrFu3DiNHjrRw7yzj448/Ru/eveHn52fprlS6rVu3YuPGjdi0aRMee+wxnD59GrGxsfDz86tRvw+ff/45Ro8ejfr168PW1hYhISEYOnQoTpw4YemukQmcjrQS9vb2aNy4MUJDQ7FkyRK0adMGq1atgo+PD/Lz85Gdna1of/36dfj4+AAAfHx8DFZEad9r21R1J06cwI0bNxASEoJatWqhVq1aOHLkCFavXo1atWrB29u7RjwHY9zc3NC0aVNcvHixxvw++Pr6omXLlopjLVq0kKdltfdh7D51n8ONGzcU5x88eICsrCyreQ5aly9fxoEDBzB27Fj5WE35XQCA6dOnY9asWRgyZAhat26N4cOHY8qUKViyZAmAmvP7EBwcjCNHjuD27du4evUqjh07hoKCAjRq1KjGPANd5XXPFfm/EwZhVkqj0SAvLw+hoaGws7PDwYMH5XNJSUm4cuUKwsPDAQDh4eH4/fffFb9o8fHxcHV1NfhDVlV1794dv//+O06fPi2/2rdvj2HDhsk/14TnYMzt27eRnJwMX1/fGvP78NRTTyEpKUlx7K+//kLDhg0BAEFBQfDx8VE8h5ycHCQmJiqeQ3Z2tmKU4NChQ9BoNAgLC6uEuyg/n376KerVq4c+ffrIx2rK7wIA3L17FzY2yj9ntra20Gg0AGre70Pt2rXh6+uLW7du4dtvv8W///3vGvcMgPL79x4eHo4ffvgBBQUFcpv4+Hg0a9bskaYiAbBEhTWYNWuWOHLkiEhJSRFnzpwRs2bNEpIkie+++04IUbQMPSAgQBw6dEj8+uuvIjw8XISHh8uf1y5D79mzpzh9+rTYv3+/8PLysrpl6Pp0V0cKUXOew7Rp08T3338vUlJSxM8//ywiIiKEp6enuHHjhhCiZjyHY8eOiVq1aonFixeLCxcuiI0bNwpnZ2fxxRdfyG2WLl0q3NzcxP/+9z9x5swZ8e9//9vo0vR27dqJxMRE8dNPP4kmTZpU6eX4xhQWFoqAgAAxc+ZMg3M14XdBCCFGjhwp6tevL5eo2LFjh/D09BQzZsyQ29SE34f9+/eLffv2ib///lt89913ok2bNiIsLEzk5+cLIarnM8jNzRWnTp0Sp06dEgDEO++8I06dOiUuX74shCife87Ozhbe3t5i+PDh4uzZs2Lz5s3C2dmZJSpqitGjR4uGDRsKe3t74eXlJbp37y4HYEIIce/ePfHf//5X1K1bVzg7O4v/9//+n0hLS1Nc49KlS6J3797CyclJeHp6imnTpomCgoLKvpVypR+E1ZTnMHjwYOHr6yvs7e1F/fr1xeDBgxX1sWrKc9i1a5do1aqVcHBwEM2bNxfr169XnNdoNGLu3LnC29tbODg4iO7du4ukpCRFm8zMTDF06FBRp04d4erqKp5//nmRm5tbmbfxyL799lsBwODehKg5vws5OTli8uTJIiAgQDg6OopGjRqJOXPmKEoK1ITfhy1btohGjRoJe3t74ePjIyZMmCCys7Pl89XxGRw+fFgAMHiNHDlSCFF+9/zbb7+JTp06CQcHB1G/fn2xdOnScum/JIROSWEiIiIiqhTMCSMiIiKyAAZhRERERBbAIIyIiIjIAhiEEREREVkAgzAiIiIiC2AQRkRERGQBDMKIiIiILIBBGBEREZEFMAgjomrl6aefRmxsrKW7UeEWLFiAtm3bWrobRPQIGIQREVUh+fn5lfp9Qgg8ePCgUr+TiIowCCOiamPUqFE4cuQIVq1aBUmSIEkSLl26hLNnz6J3796oU6cOvL29MXz4cGRkZMife/rppzFx4kTExsaibt268Pb2xocffog7d+7g+eefh4uLCxo3box9+/bJn/n+++8hSRL27NmDxx9/HI6OjujQoQPOnj2r6NNPP/2Ezp07w8nJCf7+/pg0aRLu3Lkjnw8MDMSiRYswYsQIuLq6Yty4cQCAmTNnomnTpnB2dkajRo0wd+5cFBQUAADi4uKwcOFC/Pbbb/J9xsXF4dKlS5AkCadPn5avn52dDUmS8P333yv6vW/fPoSGhsLBwQE//fQTNBoNlixZgqCgIDg5OaFNmzb46quvyvtfERHpYBBGRNXGqlWrEB4ejhdeeAFpaWlIS0uDi4sLnnnmGbRr1w6//vor9u/fj+vXr2PQoEGKz27YsAGenp44duwYJk6ciJdeegn/+c9/0LFjR5w8eRI9e/bE8OHDcffuXcXnpk+fjuXLl+P48ePw8vJCVFSUHCwlJyejV69eiI6OxpkzZ7Blyxb89NNPiImJUVzj7bffRps2bXDq1CnMnTsXAODi4oK4uDj8+eefWLVqFT788EOsWLECADB48GBMmzYNjz32mHyfgwcPLtWzmjVrFpYuXYpz587h8ccfx5IlS/DZZ59h3bp1+OOPPzBlyhQ899xzOHLkSKmuS0SlUC7bgBMRVRFdu3YVkydPlt8vWrRI9OzZU9Hm6tWrAoBISkqSP9OpUyf5/IMHD0Tt2rXF8OHD5WNpaWkCgEhISBBCCHH48GEBQGzevFluk5mZKZycnMSWLVuEEEKMGTNGjBs3TvHdP/74o7CxsRH37t0TQgjRsGFD0b9//2Lv66233hKhoaHy+/nz54s2bdoo2qSkpAgA4tSpU/KxW7duCQDi8OHDin5/8803cpv79+8LZ2dncfToUcX1xowZI4YOHVps34iobGpZMgAkIqpov/32Gw4fPow6deoYnEtOTkbTpk0BAI8//rh83NbWFh4eHmjdurV8zNvbGwBw48YNxTXCw8Pln93d3dGsWTOcO3dO/u4zZ85g48aNchshBDQaDVJSUtCiRQsAQPv27Q36tmXLFqxevRrJycm4ffs2Hjx4AFdX11Lfvym633nx4kXcvXsXPXr0ULTJz89Hu3btyu07iUiJQRgRVWu3b99GVFQU3nzzTYNzvr6+8s92dnaKc5IkKY5JkgQA0Gg0pfruF198EZMmTTI4FxAQIP9cu3ZtxbmEhAQMGzYMCxcuRGRkJFQqFTZv3ozly5eb/T4bm6IMEyGEfEw7NapP9ztv374NANizZw/q16+vaOfg4GD2O4mo7BiEEVG1Ym9vj8LCQvl9SEgItm/fjsDAQNSqVf7/l/fLL7/IAdWtW7fw119/ySNcISEh+PPPP9G4ceNSXfPo0aNo2LAh5syZIx+7fPmyoo3+fQKAl5cXACAtLU0ewdJN0jelZcuWcHBwwJUrV9C1a9dS9ZWIyo6J+URUrQQGBiIxMRGXLl1CRkYGJkyYgKysLAwdOhTHjx9HcnIyvv32Wzz//PMGQUxZvPbaazh48CDOnj2LUaNGwdPTE/379wdQtMLx6NGjiImJwenTp3HhwgX873//M0jM19ekSRNcuXIFmzdvRnJyMlavXo2vv/7a4D5TUlJw+vRpZGRkIC8vD05OTujQoYOccH/kyBG8+uqrxd6Di4sLXn75ZUyZMgUbNmxAcnIyTp48iXfffRcbNmwo87MhIvMYhBFRtfLyyy/D1tYWLVu2hJeXF/Lz8/Hzzz+jsLAQPXv2ROvWrREbGws3Nzd5+u5RLF26FJMnT0ZoaCjS09Oxa9cu2NvbAyjKMzty5Aj++usvdO7cGe3atcO8efPg5+dn9pr9+vXDlClTEBMTg7Zt2+Lo0aPyqkmt6Oho9OrVC926dYOXlxe+/PJLAMAnn3yCBw8eIDQ0FLGxsXj99ddLdB+LFi3C3LlzsWTJErRo0QK9evXCnj17EBQUVIanQkQlIQnd5AEiIiqR77//Ht26dcOtW7fg5uZm6e4QkRXiSBgRERGRBTAIIyIiIrIATkcSERERWQBHwoiIiIgsgEEYERERkQUwCCMiIiKyAAZhRERERBbAIIyIiIjIAhiEEREREVkAgzAiIiIiC2AQRkRERGQBDMKIiIiILOD/A1M3HSEaHErLAAAAAElFTkSuQmCC", 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", 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8eTMAwMvLC8HBwWjfvj0GDx6MESNGmD06bdOmTUhLS0NhYaEdS0tERK4mPz8fa9eu1T1XqwNQUBCC4OB8qFTFuu2pqakuH34YfBzImWm5V69e2LhxIyorK3H9+nV89dVXmDx5Mj799FN88cUX8PLiR4GIiAyr/t114kRH7NzZF0J4QKHQoF+/XUhIOFnrOFfFbzsHqZmWjbFXWvbx8dFN5NikSRMkJCTgoYcewhNPPIFNmzZhzJgxWL58OTZu3Ijff/8dwcHB6NevH15//XU0bNgQ3377LUaOHAngr47Hc+fOxbx587BlyxasWrUKmZmZ8Pf3R/fu3bFy5Uo0btxY8usgInK0+tLEIwW1OkAXegBACA/s3NkX8fEX9Gp+XBmDj4OYm4IdmZa7d++ODh06ID09HWPGjIGHhwdWr16NuLg4/P7773j++ecxffp0vPXWW+jSpQtWrlyJOXPmIDMzEwDQsGFDAEB5eTkWLFiAVq1a4caNG5gyZQpGjBiBL7/80mHXQkRkD87+o9XVFBSE6EKPlhAeKCgIZvAh99C6dWucPn0aAJCWlqbbHhsbi9deew3jx4/HW2+9BaVSCZVKBYVCUWsJkFGjRul+bt68OVavXo0HHngAt2/f1oUjIiJ35Ip/tDpTcHA+FAqNXvhRKDQIDi5wYqksw+HsMieE0DVd7d+/H0888QSaNGmCgIAADBs2DPn5+SgpKTF5juPHj6Nfv35o1qwZAgIC0K1bNwDApUuX7F5+IiJyHJWqGP367YJCUbVepbaPj7vU9gCs8ZG9c+fOIS4uDhcvXkTfvn0xYcIELFy4EMHBwTh8+DBGjx6NsrIyo0t03LlzB8nJyUhOTsaHH36IsLAwXLp0CcnJybL5C4iISE4SEk4iPv4CCgqCERxc4FahB2DwkbVvvvkGZ86cwYsvvojjx49Do9Fg2bJluuHtH3/8sd7xSqWy1hISv/76K/Lz87FkyRI0bdoUAPDTTz855gKIiMgpVKpitws8WmzqkonS0lLk5ubi6tWrOHHiBBYtWoS///3v6Nu3L5599lm0aNEC5eXlWLNmDX7//Xds2bIF69ev1ztHbGwsbt++jQMHDiAvLw8lJSVo1qwZlEql7nVffPEFFixY4KSrJCKyL7U6ANnZsVCrA5xdFIcyd41Kd1jLkjU+MvHVV18hMjISXl5eaNSoETp06IDVq1dj+PDh8PDwQIcOHbB8+XIsXboUs2bNQteuXbF48WI8++yzunN06dIF48ePR0pKCvLz83XD2Tdt2oSXX34Zq1evRkJCAt58803079/fiVdLRCQ9U/PXSM3VhtCHhIQgNTVVr0yFhYWoqKjQPff29kZZWRlycnKcUkZzKYQQwtmFcCVFRUVQqVRQq9UIDAzU23f37l1kZ2cjLi4ODRo0sOi8HBJpGVvuNRGRVHJycvDOO+9ArQ7AypVptUYzpaWthEpVjLFjxyIyMlKS93SH7wtXnMnZ1Pd3dazxcRBDabkmV03HRERypW26qWv+GimbeNxhCL07z+TM4ONADDVERO5F+0frxYsV2LJFQKNR6PZ5egpMmtQbsbFedv3/u7HaFFfgjjM5M/gQERGZEBISgpAQ4J13gHHjgMpKwNMTePttBRITw+363qZqU/Ly8gBY31ogRT8id5zJmcGHiIjIDKNHA8nJwIULQIsWQHS0fd+vrtqU9PR03bGW9qWRqh+RO87kzOBDRERkpuho+wceLUtqU2rW3NRVm6NWq80qQ119dLQzOdeslXLV2h6AwYeIiMglGapNATS4c8cfanWA0XBhbm1Odbb0I3K3mZwZfIiIiFxQzdoUQANAgU8/fcrkPEKWjqSSYn4id5rJmcGHiIjIhVQfGq+tTbl8ORo7dvwDQlSNKrNk9JSp2hxrR2WZO3y/sLAQJSUlRtd71J7LkaOeGXyIiEg2pJoR2Z4zK1ef9y0vLw/p6ekoKPjTqtFTddXmWDsqS1vGmzdvYvv27UaPq7nmoytMdMjgQzb79ttv8fjjj+PWrVsICgoy6zWxsbFIS0tDWlqaXctGRKQl1UgmR8ysXPN11oyeMlWbA1SFHm/vUqtHZYWEhNQKf6Zql1xlokMuUioDI0aMgEKhwPjx42vtmzhxIhQKBUaMGOH4ghEROZBUMyIb+rI3tHCplF/m2v4+CoUGAMwaPWWsNufo0c5YuTINmzcPx4YNY9C+/WmLzmvMiRMddedduTINJ0501O0zFsKcsdgra3xkomnTpti2bRtWrFgBX19fAFXrYW3duhXNmjVzcumIiBxPihmRLe0YbGkTmaH+PoZGTxnqc2NsVFhGRpJeADl9uj1Gj/5/KC9XmnVeQ+rqK+RKEx0y+MhEQkICsrKykJ6ejiFDhgAA0tPT0axZM8TFxemOKy0txbRp07Bt2zYUFRWhU6dOWLFiBR544AHdMV9++SXS0tJw+fJlPPTQQxg+fHit9zt8+DBmzZqFn376CaGhofi///s/LF68GP7+/va/WCKiOkgxksnSjsHWNJHZss6joTl2kpIycOTIw3rHCeGB8nIl4uL+wOOPP47GjRtDpVJZ1E+prmDjShMdsqnLSa5cAQ4erPrXUUaNGoWNGzfqnr/33nsYOXKk3jHTp0/Hjh07sHnzZpw4cQItWrRAcnIyCgqqPpyXL1/GgAED0K9fP5w6dQpjxozBzJkz9c6RlZWFXr16YeDAgTh9+jS2b9+Ow4cPIzU11f4XSURuJT8/Hzk5OUYf+fn5kr+nVM0upr7sDbG2qS0kJASRkZFGHzXDSc1aorS0lRg+fBPS0laic+ejumYtreoB5ODBg9i+fbvFnbO1wcbYea1pqrMX1vg4wYYNwNixgEYDeHhUrf8yerT933fo0KGYNWsW/vjjDwDADz/8gG3btuHbb78FANy5cwfr1q3Dpk2b0Lt3bwDAu+++i3379mHDhg2YNm0a1q1bh/j4eCxbtgwA0KpVK5w5cwZLly7Vvc/ixYsxZMgQXcflli1bYvXq1ejWrRvWrVuHBg0a2P9iicjlOaKTsCFSNbu4Ui1GdYZqibSjwwCYNdOyqZBWvblOu16YOTM4u8pEhww+Dnblyl+hB6j6d9y4qvVf7D0NelhYGPr06YNNmzZBCIE+ffogNDRUtz8rKwvl5eV4+OG/qkG9vb3x4IMP4ty5cwCAc+fOoXPnznrnTUpK0nv+888/4/Tp0/jwww9124QQ0Gg0yM7Oxr333muPyyMiNyNVZ2NLSRVYbF2uwZ6rrtdVC6QNIN7eZSgv9zE5E3R1psKqOcHGFSY6ZPBxsPPn/wo9WpWVVYveOWL9l1GjRumanP71r3/Z5T1u376NcePG4YUXXqi1jx2picgYY0FAW6ugZeuEd1KuL2VJLUZhYaHuZ1N9jAoLCxEZGWlxWUzR1gJdu3YN6enpUKmKkZXVwuJ+Tuas3WXNfTS3E7UUGHwcrGXLquat6uHH07NqpV9H6NWrF8rKyqBQKJCcnKy3Lz4+HkqlEj/88ANiYmIAAOXl5Th27Jiu2eree+/FF198ofe6//znP3rPExIS8Msvv6CFoy6KiNyeqSBQfRVyLWuav2wZIWXsPIDxL/uax1VUVACou1O09jipVZ93x9oZm2syFlYHDBiga1HgzM0yFx1d1adn3Liqmh5PT+Dttx232q+np6eu2crT01Nvn7+/PyZMmIBp06YhODgYzZo1w+uvv46SkhKM/l8npPHjx2PZsmWYNm0axowZg+PHj2PTpk1655kxYwYeeughpKamYsyYMfD398cvv/yCffv2WbxwHhHVf9Z8CVvT/GXLCCkpz+MKQ7ulKIO5I+P8/PwcHm5MYfBxgtGjq/r0XLhQVdPjqNCjFRgYaHTfkiVLoNFoMGzYMBQXF6NTp074+uuv0ahRIwBVTVU7duzAiy++iDVr1uDBBx/EokWLMGrUKN052rdvj0OHDmH27Nl49NFHIYRAfHw8UlJS7H5tROR+6hodJWU/GKm+fG05T3BwPgABQKHb5uhO0Zb2c9J2aNY2O9Y1K/SyZSecuiyFKQw+ThId7bjAU7NGpqbPP/9c93ODBg2wevVqrF692ujxffv2Rd++ffW21RwW/8ADD2Dv3r1Gz3Hx4kWTZSIi+TD2JXztWhTef/9Zm+bacSXe3t7VnukHHyGMHWc9Q5MlWjIKq/p5atbWm5oVWjtBojOXpTCFwYeIiJzK0Jdwjx77sX9/D5v7oLgSlUoFoCo01J5G769mJu1xtjBnqgBz+zkZCizGwuqRI0nQXpur/s4YfIiIyClMdTa2pQ+KPVdOl4Ij5v8xt3Zl5Mi/6U1rAtR9f7QdmquHU1OzQjtjWQpTGHyIiMgp6ppoz5pw4KxJES0h5XB6W4WGhlo0dL5mh+YePfYjKuqa7vdSfR0wwDUmdKyJwYeIiJzGWPiwNhwYWjndUOdoZ/Q3MXc4ffX5fqq/1pagJsVkiYY6NO/f3wNpaSt153SVQGcKg48VRPVeaGQXvMdE8iTVXDuANAuRSsnYMHi1Wo3t27frnn/88ccGX29tLZVU98Gc5kdXWZbCFAYfC2jnvSkrK4Ovr6+TS1O/af/HUHOuISKq36Saa0eqCfqkZk5wkbKWSsr7YG7fJFdYlsIUBh8LeHl5wc/PDzdv3oS3tzc8PLi4vT1oNBrcvHkTfn5+8PLiR5RIbqToe+MKkwRaQ+paKinvg619kxy5LIUp/FaxgEKhQGRkJLKzs3UrnJN9eHh4oFmzZlAoFHUfTERUg6uunG6KpbUzpkavaefrkeI+mNv8OGjQIAQFBRk9hytMXggw+FhMqVSiZcuWLjMRU32lVCpZo0ZEVnOlkVPmsqR2xtzRa3XdB3NqYaRqfnQVDD5W8PDwQIMGDZxdDCIiMsEdOtpWZ0ntjLl/fA8YMABjx4ZizpybuHjRC7GxFYiKegDAAxaFFXcJNeZg8CEiIqeScsJBa1dOdwX2qKXSztMTGQkkJkpYWDfG4ENEREbZexZkqSccdPdmGWtrqaSYp0cuGHyIiMggR8yCbI8JB1011Bhjay2Vq81X5OoYfIiIZMicmhxHz4Is1y9wW2qpXHW+IlfG4ENEJDPm1uSkpKTofrZ3KJH7F7i1tVTuOl+RM3G8MBGRzBiqycnOjoVaHaC3vby8XLffUCipebwtTH2Bk3HakWDVufp8Rc7GGh8iIhkzpybHEbUK7jjhoDNp+/tIMU+P3DD4EBHZkb1HRdnC3OYlR4QSd5xwsC72/N3X7Bdk6zw9csLgQ0RkJ44YFWWLumpybt26BcBxocRdJhw0J9AAsPvvvvrrOE+P+Rh8iIjsxNzRTs5aAqeumpyDBw/qttsrlLjbhIPmhtlBgwbpPbf3iDgyH4MPEZFMmVOTU/MLW+pQ4owJB21pgjJ3iH9FRYXuZ7kO03dVDD5ERA7iirPrmqrJMfWFPWDAAISGhkoSShzZzFezxsbY78ScJihzAo3ch+m7IgYfIiIHcKW/+s1pXqrrC1u7BpS7qV5jY+p3UlZWZrBmKC8vD4D5gYbz7LgeBh8iIjtztb/6TTUv5eXlIT093SFf2NWDRWFhoa55KDfXC5cu+SA+XoNWrfwBSN/cVdfvpLCwEB9//LHR15t7fzhM3/Uw+BAR2Zmj/uq3pO9KXSHC1i/suspSUlKCDz74QPdc2+R07Vok9u/vYbAWRsrRb3X9Tqr30alePm2TmLn3pz4O03d3DD5ERHbmiL/6pR46b8sXtrn9aLSqNzkBAoACQO1aGEtGQBkLXtqmKkt+J8aaxMy9P+4yTF8uGHyIiOzEkbPr2mNBUWu/sM3tR6MtZ/UmJ23o0bKmZsyc4GVusDPVJGbq/nh7e+udx9WH6csJgw8RkZ04a3ZdWzpSSzmvjjl9mww1OVVnTc2YucHLnGBXV5OYsfsTFhbm8GH6ZB4GHyIiO3L07Lq2dqSWcl4dc/o2GWpy0jZ32dofxpx7YSy4aNXVJKYd1l8dA41rY/AhIqpHpOhILdWXtjn9aAw1OfXosR9RUdds7g9jy73QNlXV1STmrsP65axeBp9//etfeOONN5Cbm4sOHTpgzZo1ePDBB51dLCIiu3Ol4dPm9qOxV+dfa2prgKoam+o1XuycXL/Uu+Czfft2TJkyBevXr0fnzp2xcuVKJCcnIzMzE40bN3Z28YjICVx5hXSp2Wv4tLX30NzQUFeTkzVsqa3Jz883q3zsnOx+6l3wWb58OZ577jmMHDkSALB+/Xrs3r0b7733HmbOnOnk0hGRozl6hXRXCFlS11DYusyDVKGmpKQEOTk5RvcburfW3gtnrCFGjlGvgk9ZWRmOHz+OWbNm6bZ5eHigR48eyMjIMPia0tJSlJaW6p4XFRXZvZxE5DiOXCHd0SGrOnuucm7JMg+WvMfQoUPh5+cHQH/mZi1vb2+oVCoAxic8NBS+arI2eDHU1E/1Kvjk5eWhsrIS4eHhetvDw8Px66+/GnzN4sWLMX/+fEcUj4hcgD0XCrXHXDrmckQNhbkjxqwpS10dhKvX9NQVvswNXmymkqd6FXysMWvWLEyZMkX3vKioCE2bNnViiYjIXhy5UKgzFiW1dw2FJaOk7FUWc8IXm6nIlHoVfEJDQ+Hp6Ynr16/rbb9+/ToiIiIMvsbHxwc+Pj6OKB4ROZEjFwp1tUVJpeIKI8bMDV8MNWSM8eky3ZBSqURiYiIOHDig26bRaHDgwAEkJSU5sWRE5GymvjDd+b0cSTtKSqHQAIBTFtzUhq/quNo5WaJe1fgAwJQpUzB8+HB06tQJDz74IFauXIk7d+7oRnkRkTw5srbCFWpG7MXUKCntAqDXrnkgO9sLcXEViIqqCilSNS1xtXOyVb0LPikpKbh58ybmzJmD3Nxc3H///fjqq69qdXgmInlx5Bdmff9yNjZKKj093WTfJqlGs3FCQbJFvQs+QNV/XIaGNBKR/FQfuWPqC1PqET716cvZ3HtTV98mKUez2WPCQ5KHehl8iIi0HDnCx55z6ThTXfcwLy8P6enpkqwTZgyHqJNUGHyIqN5z1Aif+jyM2pwy27NvU32+t+RYDD5ERBKS8xevvfs2yfneknQYfIiISDL1qW8T1U8MPkRUL7jC4qBURdu3Sa0OQHZ2rG7Jjry8PP4eyOkYfIjI7TlzcVAyzNCwdiAdAH8P5FwMPkTk9py5OKirsqUGrPprzZmMsOZIKkcOayeyFIMPEdUrzlgc1NXUrAEzFgQN1bxUf625kxFqR1xdu3bN7sPaiWzF4ENE9UZ9XRzUUtVrVEyFF0M1L9ptltbahISE6LbV5yU7yP0x+BBRveEuNQ2O6ohtSxC05V7W9yU7yL0x+BBRveGomgZb+884qiO2LeHF1nvJYe3kqhh8iKjecERNg63BxdaO2OaELi1bwosU95LraZErYvAhonrF3jUN5o5IMuc4Sztim9tpedCgQQBsDy+staH6iMGHiNyeMxcHNRY+zHmdpf1v6uq0XBVSQvDf/6p1x9kaXiypteFCouQOGHyIyO05awFLUzU2eXl5Jt/blv43hkLTF1/0hUJR9fP772vQr98NXVmMhRepZ1LmQqLkDhh8iKhecPSXaV01Nunp6XrH1+zzY0v/G0OhCfCAEDBYluplrl47pS1j9bLZWmvDUEOujsGHiMgKxmpsLl+ORkHBn3V2Vral/42h0FRTzdojc+fzYa0N1XcMPkREVjAcPjTYseMfZndWNtX/pnpTGVAVNtTqqr47NUMToAGg+N+jSvXaI0v7EzHUUH3G4ENEZAVj4UOIqvBhLFyY0xFbrQ7AsmUnTHaYjo+/gIEDdwAQaNr0CrKyWhitPXKXiR2JHIHBh4jIAtWDS/Uamzt3/PHpp0/pHWsoXBhqSsrLy9P1tzFniLuxY4zVHnEJCaK/MPgQEVmgZnDRhha1OsDscGGsKcmcJqm6jjFUg8MlJIj+wuBDRGQhQ8FFinBhTpOUtc1WnIyQqAqDDxGRRGwNF+Y0Sdm6DAUDD8kdgw8RkQ2knDXanFqjrKwWuvl6ANQ6ZsCAAQgNDQUAqNVqbN++3eJrIKrPGHyIiGwg9bw3pmqNtP17gL9qe4SoGuGlFRoaisjISABAZGQk5+QhqoHBh4jITOasjC5FiDBWa2RsxmZT/XsYaoj0MfgQEZmh5sroxtRcmsIc5jY1mdO/h81WRKYx+BARmaFmTY+xVdlN1QgZU1dzmXbIfF19gFJSUljDQ1QHBh8iIguZM8mgpaToA6RSqWwqA5EcGF/hjoiIajE2gaBaHeCwMqhUxYiL+4ND04mswOBDRGQBUxMI2ou5/XbYv4eobmzqIiKLOGpkk6tyxrpXUg+ZJ5IzBh8iMlvNkU3GOvhaM7LJXThr3av6ej+JHI3Bh4jMVr3GwVQHX2tGNrkTrntF5L7Yx4eILOYKHXwdzdDSFIY6GLOfDZFrY40PEVnM2hXCq3N0XyFb34/9bIjqBwYfIrKYrR187TkLsj3fj6GGyP2xqYuILKbt4KtQaADUXiG8Lub2AZKqr5Cj34+IXBdrfIjIKlJ28DU2OsxeHP1+ROQ6GHyIyGrGVhG3hD2Wf3Cl9yMi18KmLiIym9QzCDt6dJgcR6MRkT7W+BCR2aQe2STF6DBLOPr9iMj1MPgQkUWkHNnk6OUfnLHcBBG5FjZ1EZHT2Do6zNXfj4hcD2t8iNxE9Qn4rl3zQHa2F+LiKhAVVfUl7k6T51XvA2RqdJhUsyA7+v2IyHUphBDC2YVwJUVFRVCpVFCr1QgMDHR2cYgA6E/AZ2pUkjstDupuMzcTkWsz9/ubNT5EbkD7hW1sVFJ8/AWoVMVuNQGfo0MGQw0RAQw+RG7FVUclsTaFiNwFgw+RG3HEqCRLQ4yj190iIrIFgw+RG9GOSqrZx0eq2h5rQgzXwSIid8LgQ+RmpFwjqyYpQgzXwSIiV8bgQ+SGpFgjyxyWhhiug0VEro7Bh4gMsjTE1DXijIjIFXDmZiI3IPXioHWxZjFPUyPOiIhcBWt8iJzI3BFUUi8OaqwseXl5AKwbNs91sIjIHTD4EDlJzRFUxvrTaEdQ2XMoeM2yWBNi7D3ijIhICgw+RE5SvfbGVH8aU7U8Uk0cWPMcloQYroNFRO6EwYfIyaztFGzPiQPV6gA0anQLo0f/P5SXK02GGEc0wxERSYXBh8jJrF2GombQMNZUZunEgYZqn+Li/gAADBgwAFFRUbVCDEMNEbkLBh8iJ5OiU7BU8+fUVfsUGhrKkENEbo3D2YmcTNufRqHQAIDFnYKtGXpuDIekE1F9xxofIhdgyzIUUq7YziHpRFTfscaHyEWoVMWIi/vD6rBSnbVhxdbaJyIiV8caHyInkWo2Zinmz+GQdCKSCwYfIieRchi4rSu2c0g6EckFgw+RE9kSJGrWvhhbsd3cWhqGGiKSAwYfIjdlay2NVLM+ExG5E7cIPhcvXsSCBQvwzTffIDc3F1FRURg6dChmz56t99fs6dOnMXHiRBw7dgxhYWGYNGkSpk+f7sSSk7u4cgU4fx5o2RKIjnZ2acxnbTCx56zPRESuzC2Cz6+//gqNRoO3334bLVq0wNmzZ/Hcc8/hzp07ePPNNwEARUVF6NmzJ3r06IH169fjzJkzGDVqFIKCgjB27FgnXwG5Im2Nx9atvpg+XQWNRgEPD4HXX1fjmWf+rNc1HubO5mzprM9ERK7OLYJPr1690KtXL93z5s2bIzMzE+vWrdMFnw8//BBlZWV47733oFQq0bZtW5w6dQrLly9n8KFatDUeanUAVq5MgxAKAIBGo8C0aYG4evU9qFTFsqnxMLbcBRFRfWN28CkqKjL7pIGBgVYVxhJqtRrBwX/NJpuRkYGuXbvqNX0lJydj6dKluHXrFho1amTwPKWlpSgtLdU9t+Q6yb7s2fykrcmoa/I/OdR4mFruIi8vT+/Y+lwLRkTyYHbwCQoKgkKhMHmMEAIKhQKVlZU2F8yUCxcuYM2aNbraHgDIzc1FXFyc3nHh4eG6fcaCz+LFizF//nz7FZassmEDMHYsoNEAHh7AO+8Ao0dL/z5yn6m4rrW50tPTa71GLrVgRFQ/mR18Dh48KPmbz5w5E0uXLjV5zLlz59C6dWvd86tXr6JXr1546qmn8Nxzz9lchlmzZmHKlCm650VFRWjatKnN5yXr5Ofn4+LFCowd2xgajbb5CRg3TuD++28gNtZL0i9dKSb/c2fWLHchh1owIqq/zA4+3bp1k/zNp06dihEjRpg8pnnz5rqfr127hscffxxdunTBO++8o3dcREQErl+/rrdN+zwiIsLo+X18fODj42NhycketP1usrNjodEM19tXWanAmjV7EBf3h+Q1DrZO/mcpVxpGbk2NV15eHpu8iMhtWd25ubCwEBs2bMC5c+cAAG3btsWoUaOgUqnMPkdYWBjCwsLMOvbq1at4/PHHkZiYiI0bN8LDQ/+v1KSkJMyePRvl5eXw9vYGAOzbtw+tWrUy2sxFrkUbBur6MrZHjYOxyf+k5mrDyE3VeBnr8Kxt/mKTFxG5I6uCz08//YTk5GT4+vriwQcfBAAsX74cCxcuxN69e5GQkCBpIa9evYrHHnsMMTExePPNN3Hz5k3dPm1tzjPPPIP58+dj9OjRmDFjBs6ePYtVq1ZhxYoVkpaF7K8+Nz+5yjDyutbmMtXh2VFlJCKyB6uCz4svvoj+/fvj3XffhZdX1SkqKiowZswYpKWl4bvvvpO0kPv27cOFCxdw4cIFRNcY3iOEAACoVCrs3bsXEydORGJiIkJDQzFnzhwOZXdTjm5+chZnDSMPCQnBoEGD8PHHHwPQr/Gqq8MzEZE7s7rGp3roAQAvLy9Mnz4dnTp1kqxwWiNGjKizLxAAtG/fHt9//73k70/OYc/mJ6lWRreFJcPItWWRsmkpKChI77k2hN2542eww/Ply9FQqc5J9v5ERM5gVfAJDAzEpUuX9EZbAcDly5cREBAgScGI7MnZq5FbM4wcsF+/muohDNAAEAD0p6/YseMfKCur3eRFROROrAo+KSkpGD16NN5880106dIFAPDDDz9g2rRpGDx4sKQFJLIXZ3bMrWsYubEmsLr61VgzYqxmCAOqh5+/ysgmLyKqD6wKPm+++SYUCgWeffZZVFRUAAC8vb0xYcIELFmyRNICEtVHpkaumdOx2BBrR4wZCmGAB7p2PYjvvntcb2tdc/wQEbm6mv+3M4tSqcSqVatw69YtnDp1CqdOnUJBQQFWrFjBOXHIaq7Q78ZRtCPXFAoNAOgCDgCDTWBqdd1NyNVHO5pSs0ZIG8KqUyg0uOee8wa3y2VWayKqn2xapNTPzw/t2rWTqiwkc87ud+MIdQ0jz86OtXgmZaCqtmf79u1WlcXY9AHR0TkmpxWoDwGUiOTHquBz9+5drFmzBgcPHsSNGzeg0ej/VXjixAlJCkfy486hxhzGwl1eXh7S09OtXjvMUFisa6h8zbLMmXMTFy96ITa2Av7+96C8PA6DBnnpbY+KegDAA24fQIlIvqwKPqNHj8bevXvxj3/8Aw8++GCdi5cS0V9MBQapJm80t59Q9bJERgKJidpn4XrH/bWdiMi9WRV8du3ahS+//BIPP/yw1OUhkj1bJ29UqwPwxRd9oe3Cx9FYRER/sSr4NGnShPP1EEmoZn8ZY5M3mtOv5ujRzqg5boGjsYiIqlgVfJYtW4YZM2Zg/fr1iImJkbpMRLIjVcdutToAGRlJBvZwNBYREWBl8OnUqRPu3r2L5s2bw8/PT7caulZBAf8HS2QpKToLG56TB+jSJYOjsYiIYGXwGTx4MK5evYpFixYhPDycnZuJXISxUWGdOx8FAAwaNIijsYhI1qwKPkeOHEFGRgY6dOggdXmIyAp1zcmjre1p3LixM4tJROR0VgWf1q1b488//5S6LERkJVNz8nDuHSKiv1gVfJYsWYKpU6di4cKFaNeuXa0+PoGBgZIUjshRrFnc09UYn5OHiIi0FEIIYemLPDyq+g/U7NsjhIBCoUBlZaU0pXOCoqIiqFQqqNVqBjiZsHZxTyIich3mfn9bVeNz8OBBqwtG5Gpq1vQYW+rBVI0QERG5B6uCT7du3cw67vnnn8err76K0NBQa96GyOHMXeqBiIjcU+0JPyT0wQcfoKioyJ5vQSQZtTpAF3qAv5Z6UKs5SzkRUX1h1+BjRfchIqcxNPmfdqkHIiKqH+wafIjciXbyv+oUCi71QERUnzD4EP2PdvI/bfipOfkfERG5P6s6NxPVVwkJJxEffwEFBcEIDi5g6CEiqmcYfEj2ai7aqVIVGww8XNyTiMj9WRx8KioqsGjRIowaNQrR0dEmjx06dCgnASTJ2Gt25ZrLPUh5biIici1WzdwcEBCAM2fOIDY21g5Fci7O3OyaOLsyERGZYteZm7t3745Dhw7Vy+BDtbnCOlacXZmIiKRgVfDp3bs3Zs6ciTNnziAxMRH+/v56+/v37y9J4cjxaoacwsJCfPzxx3W+zpE1LZxdmYiIrGVV8Hn++ecBAMuXL6+1z90XKZUzc5uTDLl27ZpeYLJXLZCx2ZXj4y9wBBYREdXJquCj0WjqPojcji3NROnp6bW22aMWyNTsyvYIPq7QzEdERNKxKvi8//77SElJgY+Pj972srIybNu2Dc8++6wkhSPHyc/PR15enqTntEd/G+3sytXDj71mV2aHaiKi+seqmZtHjhwJtVpda3txcTFGjhxpc6HIsbRf8IZqbVyNI2dXNtShOjs7ttaipexQTUTkPqyq8RFCQKFQ1Np+5coVqFQqmwtFjuVuX9zOmF3ZVIdqYzVlbAYjInI9FgWfjh07QqFQQKFQ4IknnoCX118vr6ysRHZ2Nnr16iV5Icl1GBpGbmxouZTsPbuyob482kBTV4dqUzVlbAYjInItFgWfJ598EgBw6tQpJCcno2HDhrp9SqUSsbGxGDhwoKQFJOepGWgM1XoAcMjQcnvOrlxXXx5zOlRzXiEiIvdgUfCZO3cuACA2NhYpKSlo0KCBXQpFzlcz5PTosR/79/fQq/X44ou+UCjgsKHl9qo5qWtyxLo6VHNeISIi92FVH5/hw4cDqPrCuHHjRq3h7c2aNbO9ZOQ0hpp2qoeev3ig5oIn9hxa7gjGQky/frtqbVepijmvEBGRm7Eq+Jw/fx6jRo3CkSNH9LZrOz1zAkP3ZqxpR6EQEKJ6p3aNXo0PoF8T4m6rmZsKMcY6VDt6XiEiIrKNVcFnxIgR8PLywq5duxAZGWlwhBe5L2NNO7NnF2PxYhUqKwFPT4GlS4sAADNmqFBZqdBte+aZwW45oqmuEGOoQ7Uj5xUiIiLbWRV8Tp06hePHj6N169ZSl4ecwFDNTFJSBjIykvSadtLSHsa4ccCFC0CLFgpERwcBAFJSam4LcmTxJVNXiBkwYABCQ0MBVI34Sk9P180rZKgZjIiIXI9VwadNmzaSz/JLzlN9xNTWrb549VUVNBoFFAqB8eNvY8yYO4iNfVhXgxMdrf/66Oja29xRXSEmNDQUkZGRtV7njHmFiIjIOlYFn6VLl2L69OlYtGgR2rVrB29vb739gYGBkhSOHCckJARXrgDTpwPavupCKPDuuw0xe3ZDuFmrldXMDTH2nleIiIjsw6rg06NHDwBA9+7d9fr3sHOzezt//q/Qo1VZWdWMVR9qdIyxJsTYc14hIiKyH6uCz8GDB6UuB7mAli0BDw/98OPpCbRo4bwyOYK1IYahhojI/VgVfLp164bvv/8eb7/9NrKysvDpp5+iSZMm2LJlC+Li4qQuIzlIdDTwzjvAuHH438gt4O2363dtjxZDDBGRPFi1OvuOHTuQnJwMX19fnDx5EqWlpQAAtVqNRYsWSVpAcqzRo4GLF4GDB6v+HT3a2SUiIiKSjlXB57XXXsP69evx7rvv6nVsfvjhh3HixAnJCkfOER0NPPaYPGp6iIhIXqwKPpmZmejatWut7SqVCoWFhbaWiYiIiMgurAo+ERERuHDhQq3thw8fRvPmzW0uFBEREZE9WBV8nnvuOUyePBlHjx6FQqHAtWvX8OGHH+Kll17ChAkTpC4jERERkSSsGtU1c+ZMaDQaPPHEEygpKUHXrl3h4+ODl156CZMmTZK6jCRj+fn5nCuHiIgkoxBCCGtfXFZWhgsXLuD27dto06YNGjZsKGXZnKKoqAgqlQpqtZozUDtZfn4+1q5dq3uuVgegoCAEwcH5ehMMpqamMvwQEcmcud/fVtX4aCmVSrRp08aWUxAZVb2m58SJjrXW0EpIOFnrOFOq1x5du+aB7GwvxMVVICqqasZG1h4REdV/NgUfIkdQqwN0oQcAhPDAzp19ER9/ASpVscEFc2uGmOq1R6ZCFGuPiIjqNwYfcnkFBSG60KMlhAcKCoKhUhVj48Z9dTaBaWt66gpR5tYeERGRe2LwIZcXHJwPhUKjF34UCg2CgwssbgKrK0QREVH9ZtVwdiJHUqmK0a/fLigUVX1xtAEHgMHaG7U6wOi5tCGqOm2IIiKi+o81PuSS8vPz9fruJCScRHz8BRQUBCM4uAAqVTGys2Mtrr3RhqiatUSs7SEikgcGH3I5poaxx8X9odtuqgnMFEMhioiI5IHBh1yOucPYbam9UamKGXiIiGSIwYdcVl0jsADW3hARkWUYfMhlmTsCy5zaG6VSadZ7mnscERG5JwYfclnW9uHRqh5iQkJCkJqaynW/iIhkjsGHHK6uhUfVajWAuvvwDBgwAKGhoQbPYSjEMNQQERGDDzmUuQuPapnqwxMaGorIyEiHlJuIiOoHBh9yKHNHbFXHEVhERCQVztxMTmFsxJapWZdrYkdkIiKylNvV+JSWlqJz5874+eefcfLkSdx///26fadPn8bEiRNx7NgxhIWFYdKkSZg+fbrzCktG1TVia9CgQQgKCjL6enZEJiIia7hd8Jk+fTqioqLw888/620vKipCz5490aNHD6xfvx5nzpzBqFGjEBQUhLFjxzqptGRMXSO2goKC2H+HiIgk51bBZ8+ePdi7dy927NiBPXv26O378MMPUVZWhvfeew9KpRJt27bFqVOnsHz5cgYfB6prxFZhYSEArplFRETO4TbB5/r163juuefw+eefw8/Pr9b+jIwMdO3aVa/fR3JyMpYuXYpbt26hUaNGjiyuLEk5YouIiMge3CL4CCEwYsQIjB8/Hp06dcLFixdrHZObm4u4uDi9beHh4bp9xoJPaWkpSktLdc+LioqkK7jMcMQWERG5OqeO6po5cyYUCoXJx6+//oo1a9aguLgYs2bNkrwMixcvhkql0j2aNm0q+XvIDUdsERGRq3Jqjc/UqVMxYsQIk8c0b94c33zzDTIyMuDj46O3r1OnThgyZAg2b96MiIgIXL9+XW+/9nlERITR88+aNQtTpkzRPS8qKqpX4aeuPjf2GB1V14itlJQUqFQqh5aJiIgIcHLwCQsLQ1hYWJ3HrV69Gq+99pru+bVr15CcnIzt27ejc+fOAICkpCTMnj0b5eXl8Pb2BgDs27cPrVq1Mtm/x8fHp1agqi9q9rkxJjU1VdKgYWrEllodgB9+UOL++z0QFaUBwKBDRESO4xZ9fJo1a6b3vGHDhgCA+Ph4REdHAwCeeeYZzJ8/H6NHj8aMGTNw9uxZrFq1CitWrHB4eV2FqZoea44zl7ERW1lZLYz2+5E6fBERERniFsHHHCqVCnv37sXEiRORmJiI0NBQzJkzh0PZnaTmiC0AWLkyrVa/n/j4C1CpiiUPX0RERIa4ZfCJjY2FEKLW9vbt2+P77793QonIkOojtrKzY032+yEiInIErtVFkjE1Ekvb76e66jM1ExEROYJb1viQawoJCUFqaqpes1VeXh7S09M5UzMREbkEBh+SlKkOypypmYiInI3BR0bqWkLCEThTMxERORODTz1Wvc+NqSUkOEsyERHJBYNPPabtc3PxYgVefbUxhFAAqBpNtXt3P8yZ0xmxsV52nT/H3FDF8EVERI7A4FPPhYSE4PRpQKM/oAqVlQoUF4fD3nMGGurwXBNnbiYiIkdh8JGBli0BDw/98OPpCbRo4Zj3Z6ghIiJXwXl8ZCA6GnjnnaqwA1T9+/bbVduJiIjkhDU+MjF6NJCcDFy4UFXTw9BDRERyxOAjI9HRDDxERCRvbOoiIiIi2WDwISIiItlg8CEiIiLZYPAhIiIi2WDwISIiItlg8CEiIiLZYPAhIiIi2WDwIatduQIcPFj1LxERkTtg8CGL5OfnIycnB8uWFSImRqB7dyAmRmDZskLk5OQgPz/f2UUkIiIyijM3y0x+fr7VK6Xn5+dj7dq1UKsDsHJlGoRQAAA0GgWmTQvE1avvQaUqRmpqKhcmJSIil8TgIyPa4KKlVgegoCAEwcH5UKmKdduNBRdtYCooCIEQ+pWFQnigoCAYKlWxyWBFRETkTAw+MlI9kJw40RE7d/aFEB5QKDTo128XEhJO1jrOkODgfCgUGr3wo1BoEBxcYJ+CExERSYR9fGRIrQ7QhR6gqrZm586+UKsDzHq9SlWMfv12QaHQAIAuOFWvNSIiInJFrPGRobqaqsyRkHAS8fEXUFAQjODgAoYeIiJyCww+MiRVU5VKVczAQ0REboVNXTLEpioiIpIr1vjIFJuqiIhIjhh8ZMzSpiqlUinpcURERI7G4CMjtgaXkJAQpKamWj0BIhERkbMphBDC2YVwJUVFRVCpVFCr1QgMDHR2cSRny8zNRERErsrc72/W+MiMJaHmyhXg/HmgZUsgOtqOhSIiInIQjuoigzZsAGJi8L9FSKueExERuTsGH9KTn5+P48evY+xYAU3VaHdoNMC4cQLHj1/n6utEROTW2NRFOtpFTLOzY6HRDNfbV1mpwJo1exAX9wdXXyciIrfFGh/S0XZ61s7sXF31mZ25+joREbkrBh+qhTM7ExFRfcWmLjKIMzsTEVF9xOBDRnERUiIiqm/Y1EVERESyweBDREREssHgQ0RERLLB4EM6XH2diIjqO3ZuJh2uvk5ERPUdgw/pYaghIqL6jE1dREREJBsMPkRERCQbDD5EREQkGww+REREJBsMPi7qyhXg4MGqf4mIiEgaDD4uaMMGICYG6N696t8NG2ofw2BERERkOQYfF3PlCjB2LKDRVD3XaIBx46q25+fnIycnB8uWFSImRvwvGAksW1aInJwc5OfnO7fwRERELo7z+LiY8+f/Cj1alZXA8eNqnDq1Fmp1AFauTIMQCgCARqPAtGmBuHr1PahUxUhNTeVcPEREREawxseF5OfnIzDwOjw8hN52T08BP79rAICCghAIof9rE8IDBQXBAGBy1mUiIiK5Y42Pi8jPz8fatWsBAH37dsTOnX0hhAcUCg369NmFI0dOAgCCg/OhUGj0wo9CoUFwcIFTyk1EROROGHxcRPWamoSEk4iPv4CCgmAEBxdApSrW7VOpitGv3y69YNSv3y69Y4iIiMgwBh8XpVIVGw0z8fEXMHDgDgACTZteYeghIiIyE4OPmzlxomOt2p6EhJPOLhYREZFbYOdmN6JWB+hCD1DVqXnnzr5QqwOcXDIiIiL3wODjRuoa0UVERESmMfi4Ee2IrupqjuhSKpWOLhYREZHbYPBxI9oRXdrwU3NEV0pKCicvJCIiMoGdm12EuTU1poe6q+xVPCIionqBwcdFhISEIDU11eDMy3l5eUhPT9c9NzXUnYiIiIxj8HEhbKYiIiKyL/bxcQPmNoOxYzMREZFprPFxA6aawbSUSiVrjIiIiOrgVjU+u3fvRufOneHr64tGjRrhySef1Nt/6dIl9OnTB35+fmjcuDGmTZuGiooK5xRWYiEhIYiMjDT6YOghIiKqm9vU+OzYsQPPPfccFi1ahO7du6OiogJnz57V7a+srESfPn0QERGBI0eOICcnB88++yy8vb2xaNEiJ5aciIiIXIVCCCGcXYi6VFRUIDY2FvPnz8fo0aMNHrNnzx707dsX165dQ3h4OABg/fr1mDFjBm7evGl2/5eioiKoVCqo1WoEBgZKdg1ERERkP+Z+f7tFU9eJEydw9epVeHh4oGPHjoiMjETv3r31anwyMjLQrl07XegBgOTkZBQVFeG///2vM4pNRERELsYtgs/vv/8OAJg3bx7++c9/YteuXWjUqBEee+wxFBRULdeQm5urF3oA6J7n5uYaPXdpaSmKior0HkRERFQ/OTX4zJw5EwqFwuTj119/hUZTtUTD7NmzMXDgQCQmJmLjxo1QKBT45JNPbCrD4sWLoVKpdI+mTZtKcWlERETkgpzauXnq1KkYMWKEyWOaN2+OnJwcAECbNm102318fNC8eXNcunQJABAREYEff/xR77XXr1/X7TNm1qxZmDJliu55UVERww8REVE95dTgExYWhrCwsDqPS0xMhI+PDzIzM/HII48AAMrLy3Hx4kXExMQAAJKSkrBw4ULcuHEDjRs3BgDs27cPgYGBeoGpJh8fH/j4+EhwNUREROTq3GI4e2BgIMaPH4+5c+eiadOmiImJwRtvvAEAeOqppwAAPXv2RJs2bTBs2DC8/vrryM3NxT//+U9MnDiRwYaIiIgAuEnwAYA33ngDXl5eGDZsGP7880907twZ33zzDRo1agQA8PT0xK5duzBhwgQkJSXB398fw4cPx6uvvurkkhMREZGrcIt5fByJ8/gQERG5n3o1jw8RERGRFBh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDa8nF2A+iw/Px9lZWVG9yuVSoSEhDiwRERERPLG4GMn+fn5WLt2bZ3HpaamMvwQERE5CJu67MRUTY81xxEREZHtGHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHzsRKlUSnocERER2Y4TGNpJSEgIUlNTOXMzERGRC2HwsSOGGiIiItfCpi4HuXIFOHiw6l8iIiJyDgYfB9iwAYiJAbp3r/p3wwZnl4iIiEieGHzs7MoVYOxYQKOpeq7RAOPGseaHiIjIGRh87Oz8+b9Cj1ZlJXDhgnPKQ0REJGcMPnbWsiXgUeMue3oCLVo4pzxERERyxuBjZ9HRwDvvVIUdoOrft9+u2k5ERESOxeHsDjB6NJCcXNW81aIFQw8REZGzMPg4SHQ0Aw8REZGzsamLiIiIZIPBh4iIiGSDwYeIiIhkg8GHiIiIZIPBh4iIiGSDwYeIiIhkg8GHiIiIZIPBh4iIiGSDwYeIiIhkg8GHiIiIZIPBh4iIiGSDa3XVIIQAABQVFTm5JERERGQu7fe29nvcGAafGoqLiwEATZs2dXJJiIiIyFLFxcVQqVRG9ytEXdFIZjQaDa5du4aAgAAoFAqzXlNUVISmTZvi8uXLCAwMtHMJXRfvQxXeB94DLd6HKrwPvAda9rwPQggUFxcjKioKHh7Ge/KwxqcGDw8PREdHW/XawMBAWX+gtXgfqvA+8B5o8T5U4X3gPdCy130wVdOjxc7NREREJBsMPkRERCQbDD4S8PHxwdy5c+Hj4+PsojgV70MV3gfeAy3ehyq8D7wHWq5wH9i5mYiIiGSDNT5EREQkGww+REREJBsMPkRERCQbDD5EREQkGww+Rqxbtw7t27fXTbKUlJSEPXv26PbfvXsXEydOREhICBo2bIiBAwfi+vXreue4dOkS+vTpAz8/PzRu3BjTpk1DRUWFoy9FMkuWLIFCoUBaWppum1zuw7x586BQKPQerVu31u2Xy324evUqhg4dipCQEPj6+qJdu3b46aefdPuFEJgzZw4iIyPh6+uLHj164Pz583rnKCgowJAhQxAYGIigoCCMHj0at2/fdvSlWC02NrbWZ0GhUGDixIkA5PNZqKysxCuvvIK4uDj4+voiPj4eCxYs0FsnSQ6fh+LiYqSlpSEmJga+vr7o0qULjh07pttfH+/Bd999h379+iEqKgoKhQKff/653n6prvn06dN49NFH0aBBAzRt2hSvv/66NBcgyKAvvvhC7N69W/z2228iMzNTvPzyy8Lb21ucPXtWCCHE+PHjRdOmTcWBAwfETz/9JB566CHRpUsX3esrKirEfffdJ3r06CFOnjwpvvzySxEaGipmzZrlrEuyyY8//ihiY2NF+/btxeTJk3Xb5XIf5s6dK9q2bStycnJ0j5s3b+r2y+E+FBQUiJiYGDFixAhx9OhR8fvvv4uvv/5aXLhwQXfMkiVLhEqlEp9//rn4+eefRf/+/UVcXJz4888/dcf06tVLdOjQQfznP/8R33//vWjRooUYPHiwMy7JKjdu3ND7HOzbt08AEAcPHhRCyOOzIIQQCxcuFCEhIWLXrl0iOztbfPLJJ6Jhw4Zi1apVumPk8HkYNGiQaNOmjTh06JA4f/68mDt3rggMDBRXrlwRQtTPe/Dll1+K2bNni/T0dAFAfPbZZ3r7pbhmtVotwsPDxZAhQ8TZs2fFRx99JHx9fcXbb79tc/kZfCzQqFEj8f/+3/8ThYWFwtvbW3zyySe6fefOnRMAREZGhhCi6oPh4eEhcnNzdcesW7dOBAYGitLSUoeX3RbFxcWiZcuWYt++faJbt2664COn+zB37lzRoUMHg/vkch9mzJghHnnkEaP7NRqNiIiIEG+88YZuW2FhofDx8REfffSREEKIX375RQAQx44d0x2zZ88eoVAoxNWrV+1XeDuaPHmyiI+PFxqNRjafBSGE6NOnjxg1apTetgEDBoghQ4YIIeTxeSgpKRGenp5i165detsTEhLE7NmzZXEPagYfqa75rbfeEo0aNdL7b2LGjBmiVatWNpeZTV1mqKysxLZt23Dnzh0kJSXh+PHjKC8vR48ePXTHtG7dGs2aNUNGRgYAICMjA+3atUN4eLjumOTkZBQVFeG///2vw6/BFhMnTkSfPn30rheA7O7D+fPnERUVhebNm2PIkCG4dOkSAPnchy+++AKdOnXCU089hcaNG6Njx4549913dfuzs7ORm5urdx9UKhU6d+6sdx+CgoLQqVMn3TE9evSAh4cHjh496riLkUhZWRk++OADjBo1CgqFQjafBQDo0qULDhw4gN9++w0A8PPPP+Pw4cPo3bs3AHl8HioqKlBZWYkGDRrobff19cXhw4dlcQ9qkuqaMzIy0LVrVyiVSt0xycnJyMzMxK1bt2wqIxcpNeHMmTNISkrC3bt30bBhQ3z22Wdo06YNTp06BaVSiaCgIL3jw8PDkZubCwDIzc3V+x+bdr92n7vYtm0bTpw4oddmrZWbmyub+9C5c2ds2rQJrVq1Qk5ODubPn49HH30UZ8+elc19+P3337Fu3TpMmTIFL7/8Mo4dO4YXXngBSqUSw4cP112Hoeusfh8aN26st9/LywvBwcFucx+q+/zzz1FYWIgRI0YAkNd/EzNnzkRRURFat24NT09PVFZWYuHChRgyZAgAyOLzEBAQgKSkJCxYsAD33nsvwsPD8dFHHyEjIwMtWrSQxT2oSaprzs3NRVxcXK1zaPc1atTI6jIy+JjQqlUrnDp1Cmq1Gp9++imGDx+OQ4cOObtYDnP58mVMnjwZ+/btq/UXjdxo/4oFgPbt26Nz586IiYnBxx9/DF9fXyeWzHE0Gg06deqERYsWAQA6duyIs2fPYv369Rg+fLiTS+ccGzZsQO/evREVFeXsojjcxx9/jA8//BBbt25F27ZtcerUKaSlpSEqKkpWn4ctW7Zg1KhRaNKkCTw9PZGQkIDBgwfj+PHjzi4aGcGmLhOUSiVatGiBxMRELF68GB06dMCqVasQERGBsrIyFBYW6h1//fp1REREAAAiIiJqjeTQPtce4+qOHz+OGzduICEhAV5eXvDy8sKhQ4ewevVqeHl5ITw8XBb3wZCgoCDcc889uHDhgmw+D5GRkWjTpo3etnvvvVfX5Ke9DkPXWf0+3LhxQ29/RUUFCgoK3OY+aP3xxx/Yv38/xowZo9sml88CAEybNg0zZ87E008/jXbt2mHYsGF48cUXsXjxYgDy+TzEx8fj0KFDuH37Ni5fvowff/wR5eXlaN68uWzuQXVSXbM9/zth8LGARqNBaWkpEhMT4e3tjQMHDuj2ZWZm4tKlS0hKSgIAJCUl4cyZM3q/3H379iEwMLDWl4ereuKJJ3DmzBmcOnVK9+jUqROGDBmi+1kO98GQ27dvIysrC5GRkbL5PDz88MPIzMzU2/bbb78hJiYGABAXF4eIiAi9+1BUVISjR4/q3YfCwkK9v4a/+eYbaDQadO7c2QFXIZ2NGzeicePG6NOnj26bXD4LAFBSUgIPD/2vEE9PT2g0GgDy+zz4+/sjMjISt27dwtdff42///3vsrsHgHS/96SkJHz33XcoLy/XHbNv3z60atXKpmYuABzObszMmTPFoUOHRHZ2tjh9+rSYOXOmUCgUYu/evUKIqiGrzZo1E99884346aefRFJSkkhKStK9XjtktWfPnuLUqVPiq6++EmFhYW43ZLWm6qO6hJDPfZg6dar49ttvRXZ2tvjhhx9Ejx49RGhoqLhx44YQQh734ccffxReXl5i4cKF4vz58+LDDz8Ufn5+4oMPPtAds2TJEhEUFCT+/e9/i9OnT4u///3vBoexduzYURw9elQcPnxYtGzZ0qWH7hpSWVkpmjVrJmbMmFFrnxw+C0IIMXz4cNGkSRPdcPb09HQRGhoqpk+frjtGDp+Hr776SuzZs0f8/vvvYu/evaJDhw6ic+fOoqysTAhRP+9BcXGxOHnypDh58qQAIJYvXy5Onjwp/vjjDyGENNdcWFgowsPDxbBhw8TZs2fFtm3bhJ+fH4ez29OoUaNETEyMUCqVIiwsTDzxxBO60COEEH/++ad4/vnnRaNGjYSfn5/4v//7P5GTk6N3josXL4revXsLX19fERoaKqZOnSrKy8sdfSmSqhl85HIfUlJSRGRkpFAqlaJJkyYiJSVFb/4audyHnTt3ivvuu0/4+PiI1q1bi3feeUdvv0ajEa+88ooIDw8XPj4+4oknnhCZmZl6x+Tn54vBgweLhg0bisDAQDFy5EhRXFzsyMuw2ddffy0A1Lo2IeTzWSgqKhKTJ08WzZo1Ew0aNBDNmzcXs2fP1ht+LIfPw/bt20Xz5s2FUqkUERERYuLEiaKwsFC3vz7eg4MHDwoAtR7Dhw8XQkh3zT///LN45JFHhI+Pj2jSpIlYsmSJJOVXCFFtmk0iIiKieox9fIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iIiISDYYfIiIiEg2GHyIiIhINhh8iMhmjz32GNLS0pxdDLubN28e7r//fmcXg4hswOBDRLJXVlbm0PcTQqCiosKh70lEVRh8iMgmI0aMwKFDh7Bq1SooFAooFApcvHgRZ8+eRe/evdGwYUOEh4dj2LBhyMvL073usccew6RJk5CWloZGjRohPDwc7777Lu7cuYORI0ciICAALVq0wJ49e3Sv+fbbb6FQKLB79260b98eDRo0wEMPPYSzZ8/qlenw4cN49NFH4evri6ZNm+KFF17AnTt3dPtjY2OxYMECPPvsswgMDMTYsWMBADNmzMA999wDPz8/NG/eHK+88opudehNmzZh/vz5+Pnnn3XXuWnTJly8eBEKhQKnTp3Snb+wsBAKhQLffvutXrn37NmDxMRE+Pj44PDhw9BoNFi8eDHi4uLg6+uLDh064NNPP5X6V0RE1TD4EJFNVq1ahaSkJDz33HPIyclBTk4OAgIC0L17d3Ts2BE//fQTvvrqK1y/fh2DBg3Se+3mzZsRGhqKH3/8EZMmTcKECRPw1FNPoUuXLjhx4gR69uyJYcOGoaSkRO9106ZNw7Jly3Ds2DGEhYWhX79+uoCSlZWFXr16YeDAgTh9+jS2b9+Ow4cPIzU1Ve8cb775Jjp06ICTJ0/ilVdeAQAEBARg06ZN+OWXX7Bq1Sq8++67WLFiBQAgJSUFU6dORdu2bXXXmZKSYtG9mjlzJpYsWYJz586hffv2WLx4Md5//32sX78e//3vf/Hiiy9i6NChOHTokEXnJSILSLLUKRHJWrdu3cTkyZN1zxcsWCB69uypd8zly5f1VjTv1q2beOSRR3T7KyoqhL+/vxg2bJhuW05OjgAgMjIyhBB/rQq9bds23TH5+fnC19dXbN++XQghxOjRo8XYsWP13vv7778XHh4e4s8//xRCCBETEyOefPLJOq/rjTfeEImJibrnc+fOFR06dNA7Jjs7WwAQJ0+e1G27deuWACAOHjyoV+7PP/9cd8zdu3eFn5+fOHLkiN75Ro8eLQYPHlxn2YjIOl7ODF1EVD/9/PPPOHjwIBo2bFhrX1ZWFu655x4AQPv27XXbPT09ERISgnbt2um2hYeHAwBu3Lihd46kpCTdz8HBwWjVqhXOnTune+/Tp0/jww8/1B0jhIBGo0F2djbuvfdeAECnTp1qlW379u1YvXo1srKycPv2bVRUVCAwMNDi6zem+nteuHABJSUl+Nvf/qZ3TFlZGTp27CjZexKRPgYfIpLc7du30a9fPyxdurTWvsjISN3P3t7eevsUCoXeNoVCAQDQaDQWvfe4cePwwgsv1NrXrFkz3c/+/v56+zIyMjBkyBDMnz8fycnJUKlU2LZtG5YtW2by/Tw8qnoMCCF027TNbjVVf8/bt28DAHbv3o0mTZroHefj42PyPYnIegw+RGQzpVKJyspK3fOEhATs2LEDsbGx8PKS/n8z//nPf3Qh5tatW/jtt990NTkJCQn45Zdf0KJFC4vOeeTIEcTExGD27Nm6bX/88YfeMTWvEwDCwsIAADk5ObqamuodnY1p06YNfHx8cOnSJXTr1s2ishKR9di5mYhsFhsbi6NHj+LixYvIy8vDxIkTUVBQgMGDB+PYsWPIysrC119/jZEjR9YKDtZ49dVXceDAAZw9exYjRoxAaGgonnzySQBVI7OOHDmC1NRUnDp1CufPn8e///3vWp2ba2rZsiUuXbqEbdu2ISsrC6tXr8Znn31W6zqzs7Nx6tQp5OXlobS0FL6+vnjooYd0nZYPHTqEf/7zn3VeQ0BAAF566SW8+OKL2Lx5M7KysnDixAmsWbMGmzdvtvreEJFpDD5EZLOXXnoJnp6eaNOmDcLCwlBWVoYffvgBlZWV6NmzJ9q1a4e0tDQEBQXpmoZssWTJEkyePBmJiYnIzc3Fzp07oVQqAVT1Gzp06BB+++03PProo+jYsSPmzJmDqKgok+fs378/XnzxRaSmpuL+++/HkSNHdKO9tAYOHIhevXrh8ccfR1hYGD766CMAwHvvvYeKigokJiYiLS0Nr732mlnXsWDBArzyyitYvHgx7r33XvTq1Qu7d+9GXFycFXeFiMyhENUbpomIXNi3336Lxx9/HLdu3UJQUJCzi0NEbog1PkRERCQbDD5EREQkG2zqIiIiItlgjQ8RERHJBoMPERERyQaDDxEREckGgw8RERHJBoMPERERyQaDDxEREckGgw8RERHJBoMPERERyQaDDxEREcnG/wdmed262Vc8gQAAAABJRU5ErkJggg==", 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", 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", 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", 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Nmvv371d5ebkuu+wytWnTxvV46aWXlJ+f75qvf//+rn/X3Kag5rYFDRk8eLDHNZ35WQkJCYqMjHQFm5ppNZ+dn5+vU6dOucKQdPpmmEOHDtUXX3zh8WcDgLeSYiKUOz5Nof/XnzPU4dCC8f2UFBNhcmVwlyVabmbNmqV33nlHmzdvrrP1pSEtW7bUwIEDtX///jqfDw8PV3h4uC/KdFtDTZr++uM4fvy4JOndd99Vp06daj0XHh7uCjhndgSu6YhdXV3d6Ps31MG7Pmd/1tmdkB0Oh1ufDQCBNmFIV43oFaeDJeVKjo0k2AQZU1tuDMPQrFmztHr1aq1fv14pKSkev0dVVZV2795tqZsl1jRpnsnfTZp9+/ZVeHi4CgoK1KNHj1qPLl26uPUeYWFhqqqq8luNDUlNTVVYWJg+/PBD17RTp05px44d6tu3ryk1AWjekmIilJHagWAThExtuZk5c6aWL1+ut956S1FRUSouLpYkxcTEuG6EOGnSJHXq1Em5ubmSpPnz5+uiiy5Sjx49dOzYMf3+97/XN998o+nTp5v2Pc5W06R5/6o9qjKMgDRpRkVF6Z577tHdd9+t6upqXXzxxXI6nfrwww8VHR2tbt26NfoeycnJOnDggPLy8tS5c2dFRUUFrNWrdevWuu2223Tvvfeqffv26tq1qx577DGVl5dr2rRpAakBAGAPpoabxYsXS5JGjhxZa/qLL76oKVOmSJIKCgoUEvLfBqb//Oc/mjFjhoqLi9WuXTsNGjRIW7dutdyvezOaNB9++GHFxcUpNzdXX3/9tdq2basLLrhA999/v1unf6699lqtWrVKo0aN0rFjx2qth0BYuHChqqurdeONN+rHH3/U4MGD9f7776tdu3YBqwEAEPwchmEYjc9mH6WlpYqJiZHT6VR0dHSt506cOKEDBw4oJSWFUY+DEOsPAOyroeP32SxztRQAAIAvEG7gsVtvvbXW5eZnPm699VazywMANHOWuBQcwWX+/Pm655576nyusaZCAAD8jXADj8XHxys+Pt7sMgAAqBOnpQAAgK0QburAqLnBifUGAJA4LVVLWFiYQkJCdOjQIcXFxSksLMx1iwJYl2EYOnnypI4ePaqQkBCFhYWZXRIAwESEmzOEhIQoJSVFRUVFOnTokNnlwEORkZHq2rVrrUEfAQDND+HmLGFhYeratat++ukn0+6zBM+FhoaqRYsWtLQBAAg3dam5g/XZd7EGAADWR/s9AACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFVPDTW5uroYMGaKoqCjFx8crOztb+/bta/R1r7/+uvr06aNWrVopLS1Na9asCUC1AAAgGJgabjZt2qSZM2fqo48+0tq1a3Xq1CldfvnlKisrq/c1W7du1Q033KBp06bpk08+UXZ2trKzs7Vnz54AVg4AAKzKYRiGYXYRNY4ePar4+Hht2rRJI0aMqHOeCRMmqKysTO+8845r2kUXXaQBAwZoyZIljX5GaWmpYmJi5HQ6FR0d7bPaAQCA/3hy/LZUnxun0ylJat++fb3zbNu2TWPGjKk1LSsrS9u2batz/srKSpWWltZ6AAAA+7JMuKmurtZdd92l4cOHq1+/fvXOV1xcrISEhFrTEhISVFxcXOf8ubm5iomJcT26dOni07oBAIC1WCbczJw5U3v27NFrr73m0/fNycmR0+l0PQoLC336/gAAwFpamF2AJM2aNUvvvPOONm/erM6dOzc4b2Jiog4fPlxr2uHDh5WYmFjn/OHh4QoPD/dZrQAAwNpMbbkxDEOzZs3S6tWrtX79eqWkpDT6moyMDK1bt67WtLVr1yojI8NfZQIAgCBiasvNzJkztXz5cr311luKiopy9ZuJiYlRRESEJGnSpEnq1KmTcnNzJUl33nmnMjMz9cQTT+iKK67Qa6+9pp07d+rPf/6zad8DAABYh6ktN4sXL5bT6dTIkSOVlJTkeqxYscI1T0FBgYqKilz/HzZsmJYvX64///nPSk9P18qVK/Xmm2822AkZAAA0H5Ya5yYQGOcGAIDgE7Tj3AAAADQV4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4QYAANgK4cYmipwV2ppfoiJnhdmlAABgqhZmF4CmW7GjQDmrdqvakEIcUu74NE0Y0tXssgAAMAUtN0GuyFnhCjaSVG1I96/aQwsOAKDZItwEuQMlZa5gU6PKMHSwpNycggAAMBnhJsilxLZWiKP2tFCHQ8mxkeYUBACAyQg3QS4pJkK549MU6jidcEIdDi0Y309JMREmVwYAgDnoUGwDE4Z01YhecTpYUq7k2EiCDQCgWSPc2ERSTAShBgAAcVoKAADYDOEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEGAADYCuEG+D9FzgptzS/hjuoAEOQYoRiQtGJHgXJW7Va1IYU4pNzxaZowpKvZZQEAvEDLDZq9ImeFK9hIUrUh3b9qDy04ABCkCDdo9g6UlLmCTY0qw9DBknJzCgIANAnhBs1eSmxrhThqTwt1OJQcG2lOQQCAJjE13GzevFlXXnmlOnbsKIfDoTfffLPB+Tdu3CiHw3HOo7i4ODAFw5aSYiKUOz5NoY7TCSfU4dCC8f24yzoABClTOxSXlZUpPT1dN910k8aPH+/26/bt26fo6GjX/+Pj4/1RHpqRCUO6akSvOB0sKVdybCTBBgCCmKnhZty4cRo3bpzHr4uPj1fbtm19XxCataSYCEINANhAUPa5GTBggJKSknTZZZfpww8/NLucgGEcFgAAGhdU49wkJSVpyZIlGjx4sCorK/X8889r5MiR2r59uy644II6X1NZWanKykrX/0tLSwNVrk8xDgsAAO4JqnDTu3dv9e7d2/X/YcOGKT8/X0899ZRefvnlOl+Tm5urefPmBapEv6hvHJYRveI4jQIAwFmC8rTUmYYOHar9+/fX+3xOTo6cTqfrUVhYGMDqfINxWAAAcF9QtdzUJS8vT0lJSfU+Hx4ervDw8ABW5Hs147CcGXAYhwUAgLqZGm6OHz9eq9XlwIEDysvLU/v27dW1a1fl5OTou+++00svvSRJWrRokVJSUnT++efrxIkTev7557V+/Xp98MEHZn2FgKgZh+X+VXtUZRiMwwIAQAPcDjeedMQ9cwyahuzcuVOjRo1y/X/27NmSpMmTJ2vZsmUqKipSQUGB6/mTJ0/qV7/6lb777jtFRkaqf//++te//lXrPeyKcVgAAHCPwzAMo/HZpJCQEDkcjgbnMQxDDodDVVVVPinOH0pLSxUTEyOn0+l2CAP8pchZoQMlZUqJbU1gBYAGeHL8drvlZsOGDU0uDMB/cXk/APiH2+EmMzPTn3UAzQqX9wOA/3jdofjYsWP6y1/+oi+++EKSdP755+umm25STEyMz4oD7Kqhy/sJNwDQNF6Nc7Nz506lpqbqqaee0g8//KAffvhBTz75pFJTU7Vr1y5f1wjYTs3l/Wfi8n4A8A23OxSf6ZJLLlGPHj20dOlStWhxuvHnp59+0vTp0/X1119r8+bNPi/UV+hQDKtYsaPgnMv76XMDAHXz5PjtVbiJiIjQJ598oj59+tSa/vnnn2vw4MEqL7fuyLmEG1hJkbOCy/sBwA2eHL+9Oi0VHR1da/yZGoWFhYqKivLmLYFmKSkmQhmpHQg2AOBDXoWbCRMmaNq0aVqxYoUKCwtVWFio1157TdOnT9cNN9zg6xoBAADc5tXVUo8//rgcDocmTZqkn376SZLUsmVL3XbbbVq4cKFPCwQAAPCEV31uapSXlys/P1+SlJqaqshI61/pQZ8bAACCj19GKK5LZGSk0tLSmvIWAAAAPuVVuDlx4oSefvppbdiwQUeOHFF1dXWt5xnrBgAAmMWrcDNt2jR98MEHuu666zR06NBGb6gJAAAQKF6Fm3feeUdr1qzR8OHDfV0PAABAk3h1KXinTp0YzwYAAFiSV+HmiSee0H333advvvnG1/UAAAA0iVenpQYPHqwTJ06oe/fuioyMVMuWLWs9/8MPP/ikOAAAAE95FW5uuOEGfffdd1qwYIESEhLoUAwAACzDq3CzdetWbdu2Tenp6b6uBwAAoEm86nPTp08fVVRU+LoWAACAJvMq3CxcuFC/+tWvtHHjRn3//fcqLS2t9QAAADCLV/eWCgk5nYnO7mtjGIYcDoeqqqp8U50fcG8pAACCj9/vLbVhwwavCgMAAPA3r8JNZmamW/Pdfvvtmj9/vmJjY735GAAAAI951efGXa+88gp9cAAAQED5Ndx40Z0HAACgSfwabgAAAAKNcAMAAGyFcAMAAGyFcAMAAGzF43Dz008/af78+fr2228bnfcXv/gFA+UBAICA8mqE4qioKO3evVvJycl+KMm/GKEYAIDg48nx26vTUpdeeqk2bdrkVXEAAAD+5NUIxePGjdOcOXO0e/duDRo0SK1bt671/FVXXeWT4gAAADzVpBtn1vmG3DgTAAD4mN9vnFldXe1VYQAAAP7mVZ+bl156SZWVledMP3nypF566aUmFwUAAOAtr05LhYaGqqioSPHx8bWmf//994qPj+e0FAAA8Cm/Xy1lGIYcDsc507/99lvFxMR485YAAMAGipwV2ppfoiJnhWk1eNTnZuDAgXI4HHI4HBo9erRatPjvy6uqqnTgwAGNHTvW50UCAADrW7GjQDmrdqvakEIcUu74NE0Y0jXgdXgUbrKzsyVJeXl5ysrKUps2bVzPhYWFKTk5Wddee61PCwQAANZX5KxwBRtJqjak+1ft0YhecUqKiQhoLR6Fm4ceekiSlJycrAkTJqhVq1Z+KSpYFTkrdKCkTCmxrQO+IgEAMNOBkjJXsKlRZRg6WFJu7XBTY/LkyZJOXx115MiRcy4N79o18E1QZrNKUxwAAGZIiW2tEIdqBZxQh0PJsZEBr8WrDsVfffWVLrnkEkVERKhbt25KSUlRSkqKkpOTlZKS4usaLa++pjgzO1MBABBISTERyh2fptD/u+Ao1OHQgvH9TDmT4VXLzZQpU9SiRQu98847SkpKqvPKqebESk1xAACYZcKQrhrRK04HS8qVHBtp2jHQq3CTl5enjz/+WH369PF1PUHJSk1xAACYKSkmwvQf9l6dlurbt69KSkp8XUvQslJTHAAAzZ1XIxSvX79ev/3tb7VgwQKlpaWpZcuWtZ638si//hyhuMhZYXpT3Nm4ggsAYAeeHL+bfFfwM/vb1IxczO0XrIEruAAAduH3u4Jv2LDBq8IQOFYaTAkAgEDyqs9NZmamQkJCtHTpUs2ZM0c9evRQZmamCgoKFBoa6usag5aZ99do6AouAADszKtw88YbbygrK0sRERH65JNPVFlZKUlyOp1asGCBTwsMVit2FGj4wvWauHS7hi9crxU7CgL6+TVXcJ2JK7gAAM2BV+HmkUce0ZIlS7R06dJanYmHDx+uXbt2+ay4YGWFQf24ggsA0Fx51edm3759GjFixDnTY2JidOzYsabWFPSsMqifVQZTAgAgkLwKN4mJidq/f7+Sk5NrTd+yZYu6d+/ui7qCmpUG9bPCYEoAAASSV6elZsyYoTvvvFPbt2+Xw+HQoUOH9Le//U333HOPbrvtNl/XGHQ4JQQAgHm8Cjdz5szRxIkTNXr0aB0/flwjRozQ9OnTdcstt+iXv/yl2++zefNmXXnllerYsaMcDofefPPNRl+zceNGXXDBBQoPD1ePHj20bNkyb76C300Y0lVb5ozSqzMu0pY5oxhfBgCAAPEq3DgcDv3mN7/RDz/8oD179uijjz7S0aNH9fDDD3v0PmVlZUpPT9czzzzj1vwHDhzQFVdcoVGjRikvL0933XWXpk+frvfff9+br+F3STERykjtQIsNAAAB5NUIxf7gcDi0evVqZWdn1zvPfffdp3fffVd79uxxTbv++ut17Ngxvffee259TnMaoRgAALvw5PjtVcuNWbZt26YxY8bUmpaVlaVt27aZVBEAALAar66WMktxcbESEhJqTUtISFBpaakqKioUEXHu6Z/KykrXIIPS6eQHAADsK6habryRm5urmJgY16NLly5mlwQAAPwoqMJNYmKiDh8+XGva4cOHFR0dXWerjSTl5OTI6XS6HoWFhYEoFQAAmCSoTktlZGRozZo1taatXbtWGRkZ9b4mPDxc4eHh/i4NAABYhKktN8ePH1deXp7y8vIknb7UOy8vTwUFp28ymZOTo0mTJrnmv/XWW/X111/r17/+tb788ks9++yz+vvf/667777bjPIBAIAFmRpudu7cqYEDB2rgwIGSpNmzZ2vgwIF68MEHJUlFRUWuoCNJKSkpevfdd7V27Vqlp6friSee0PPPP6+srCxT6gcAANZjmXFuAoVxbpqmyFmhAyVlSoltzeCEAICA8eT4HVR9bmCuFTsKlLNqt6oNKcQh5Y5P47YSAADLCaqrpWCeImeFK9hIp+94fv+qPSpyVphbGAAAZyHcwC0HSspcwaZGlWHoYEm5OQUBAFAPwg3ckhLbWiGO2tNCHQ4lx0aaUxAAAPUg3MAtSTERyh2fplDH6YQT6nBowfh+dCoGAFgOHYrhtglDumpErzgdLClXcmwkwQYAYEmEG3gkKSaCUAMAsDROSwEAAFsh3AAAAFsh3AAAAFsh3MBjRc4Kbc0vYQA/AIAl0aEYHuEWDAAAq6PlBm7jFgwAgGBAuIHbuAUDACAYEG7gNm7BAAAIBoQbuI1bMAAAggEdiuERbsEAALA6wg08xi0YAABWxmkpAABgK4QbAABgK4QbAABgK4QbAABgK4QbAABgK4QbAABMwo2I/YNLwQEAMAE3IvYfWm4AAAgwbkTsX4QbAAACjBsR+xfhBgCAAONGxP5FuAEAIMC4EbF/0aEYAAATcCNi/yHcAABgEm5E7B+clgLQrDHOCGA/tNwAaLYYZwSwJ1puADRLjDMC2BfhBkCzxDgjgH0RbgA0S4wzAtgX4QZAs8Q4I4B90aEYQLPFOCOAPRFuADRrjDMC2A+npQAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbuB3Rc4Kbc0v4YaEAICAYBA/+NWKHQWuOy+HOKTc8WmaMKSr2WUBAGyMlhv4TZGzwhVsJKnakO5ftYcWHACAXxFu4DcHSspcwaZGlWHoYEm5OQUBAJoFwg38JiW2tUIctaeFOhxKjo00pyAgAOhjBpiPcAO/SYqJUO74NIU6TiecUIdDC8b34yaFsK0VOwo0fOF6TVy6XcMXrteKHQVmlwQ0Sw7DMIzGZ7OP0tJSxcTEyOl0Kjo62uxymoUiZ4UOlpQrOTaSYAPbKnJWaPjC9bVOxYY6HNoyZxTbPeADnhy/uVoKfpcUE8HOHbbXUB8ztn8EqyJnhQ6UlCkltnVQbceEGwDwgZo+Zme33NDHDMHK3aE8rBiA6HMDAD5AHzPfoVO2+dwdysOq/cwsEW6eeeYZJScnq1WrVrrwwgv173//u955ly1bJofDUevRqlWrAFYLAHWbMKSrtswZpVdnXKQtc0YxYKUXrHqwtKv6gqQ7Q3lYeSwz009LrVixQrNnz9aSJUt04YUXatGiRcrKytK+ffsUHx9f52uio6O1b98+1/8dDked8wFAoNHHzHv1HSxH9IpjmfpBQ6ed3DnNauV+Zqa33Dz55JOaMWOGpk6dqr59+2rJkiWKjIzUCy+8UO9rHA6HEhMTXY+EhIQAVgwA8AerDvxZX+tGMJ8+a6zVxZ3TrFYey8zUlpuTJ0/q448/Vk5OjmtaSEiIxowZo23bttX7uuPHj6tbt26qrq7WBRdcoAULFuj8888PRMkAAD+xYqfs+lo3gv2+ee60ukwY0lUjesXVO5RHTQC6f9UeVRmGpfqZmRpuSkpKVFVVdU7LS0JCgr788ss6X9O7d2+98MIL6t+/v5xOpx5//HENGzZMe/fuVefOnc+Zv7KyUpWVla7/l5aW+vZLAAB8wmoHy/paN/okRgX96TN3g2Rjp1kbC0BmMb3PjacyMjKUkZHh+v+wYcN03nnn6bnnntPDDz98zvy5ubmaN29eIEsEAHjJSgfL+lo3dhz8j2X7mrjLl0HSiv3MTA03sbGxCg0N1eHDh2tNP3z4sBITE916j5YtW2rgwIHav39/nc/n5ORo9uzZrv+XlpaqS5cu3hcNAPArqxws62vdGJLcznKnz7xhpSDpa6Z2KA4LC9OgQYO0bt0617Tq6mqtW7euVutMQ6qqqrR7924lJSXV+Xx4eLiio6NrPQAA1mDlTrn1dapN79LONmMaJcVEKCO1Q1DW3hDTT0vNnj1bkydP1uDBgzV06FAtWrRIZWVlmjp1qiRp0qRJ6tSpk3JzcyVJ8+fP10UXXaQePXro2LFj+v3vf69vvvlG06dPN/NrAAA8FAydcutr3bBzq4cdmB5uJkyYoKNHj+rBBx9UcXGxBgwYoPfee8/VybigoEAhIf9tYPrPf/6jGTNmqLi4WO3atdOgQYO0detW9e3b16yvgACz4lDfADwTTGPa1HeazCqnz3Au7gqOoBIMv/QANG5rfokmLt1+zvRXZ1ykjNQOJlQEq/Pk+G36IH6Au6w81DcAz1h5ADhPWbnfUHNFuEHQsOropQA8Z5cbjXIvLGsyvc8N4C4rjl4KwHvB3ik3mPoNNTe03CBo2OWXHoD/CuZLkWlNti5abhBUgv2XHgD7oDXZumi5QdAJ5l96AOyD1mTrouUGANAgu40t5cvvQ2uyNRFuAAD1stvYUv74PgzmZz2clgJga4xB4r1Ajy3l73XFWFnNBy03AGzLbq0OgdbQ1UC+bqkIxLry5/ex26m7YEfLDQBbasqvdFp7TgvUKMKBalHx1/dhID/rIdwAsCVvxyDhQPVfgboaKFDjxfjj+3Cqy5o4LQXAluoag0SSPvvuWL03ZmTE2XOdeTVQZFiIyk5WqchZ4dPlEcjxYnx9dVMgT91x6st9tNwAsKWkmAjdN7bPOdMfXfOlPi38T52vYcTZuiXFRKjghzJd8+xWv7RoBXq8GF+OlRWoU3e0KHqGcAPAttI6x5wzrVpS9rNb6zw42OFO1f7oLxSIUy8ThnTVljmj9OqMi7Rlzqig6fjtTTDzdB1x6stznJYCYFv1nZoy6jndVHOgun/VHlUZRtCNOOuvK44CderF3fFirHZ6xpNTXd6so0Ce+rILwg1gI1bb6ZutJqzkvLFb1Wc9V9/BIVhHnPVnfyEr3UPJqpf3uxPMvF1HVlr+wYLTUrAtO1zO68l3COQ5+WBathOGdNXqmcPk8OB0UzDev8yf/YU2/39HZZzx3g6HTGnRCvbTM96uI+5h5TlabkzAr2v/a8qvO6usH0++QyCv8rHqL+eGpHdpp4VBfLrJHf76dV+zbZ15THYY0ohecU16X28E++mZpqwjd1oUrbLvsgLCTYAF44Eh2DTlQG+V9ePpdwjUTt/duqy4kw3W003u8ld/obq2rWrJlEBRVzgIcUglx0/4/PJ0f2jqOmro1JdV9l1WQbgJIMbQCAxvD/RWWj+efgdf/Gp3J5C4U5eVd7J2v8GhPwKclfp7nB0OHI7TncN/+Wqe5ba1+vhjHVlp32UV9LkJIMbQCAxvL+e10vrx9Ds09Zy8u/11GqvLrD4RwdQH6Ez+qNvX/YWs1t+j5pLxZyYOlAy5TpdVG1LOG7vrHcPISny9jqy077IKWm4CyEq/gOzM26ZfK60fb76Dt78IPfnV11hd3rSaNfUUlpVbihoSTHVb7ZReUkyE2rUu01mbmmsMo4UWXpb+YKV9l1UQbgIo2MfQCCbe7Iyttn68/Q6e1ttYIDk7fDRUl6c72aYe4IO1Od6MupsaIq12Ss/TMYzszGr7Lisg3ASYL34BWbGzphV5szO24i9Uf9fQUCCpL3zUV5cnO1lfHOCD9eqZQNcdTK1E7vJmDCM7s9q+y2yEGxM05YBlx51UQ8wIclb7hepv9QUSSV6FD3d3sp4e4OvaFoK1OT6QdQdr65Y7Jgzpqj6JUcp+dmutcXiCYRvwh+a272oI4SaI2HknVZfmFuTMVFcg2Zpf4nXrgjs7WU8O8A21IAVjc3wg6/ZnK5EVWpGbwxhGZ7PCcrc6wk0QCdYmeG80tyBnpjN3lBmpHVzT/d264O4BvrFtIVib492tu6kHMn+tRyv9+AjWbcAbVlruVka4CSLB2gTvjeYU5MzU0I4yEK0L7hyU3NkWgrU5vrG6fXEg88d6tOJgjmcvSzu2bvCjz32EmyASrE3w3mhOQc4s7uwoA/GLuLEDfHPdFnx5IPP1evR0MEeHQ5ozro9uGZHapM91l11bN/jR5z7CTZBpLs2vzSnImcXdHaXZrSLNdVvw9YHMl+uxscB5djAzDCl3zZeSId2S6d+AY+fWjeYa9L1BuAlCZh9sAuXsICdJW/NLbNXMbKZg2lE2l1B/JiuvH28Gc5SkR//5pa4a0NGv68/OrRvNNeh7g3ADS6sJcnZtZjZTsO0ogyHU+7Kfh9XXT2ODOdbc9+lMgbjhpq9DodX67jTHoO8Nh2GcvfnZW2lpqWJiYuR0OhUdHW12OXBDkbNCwxeuP2dntWXOKP6wfaDIWcGO0gf8FcDrWj9WO+DW5bnN+adPRZ0hUH+3K3YUnBMKvVkXdvhRFQzbirs8OX7TcgPLs3MzsxUEQ4uI1fmzn8fZ6ydYDri3jEiVjNOnoqoV2Btu+mok+GDvuxMs24o/EG5geVbuewBIgQvgwXbAvSUzVVcN6GhKy2BTQ3uw/6gKtm3F10LMLgBoTE3fg1CHQ1JgfwHaTZGzQlvzS1TkrDC7FFupCeBn8kcAb+iAa1VJMRHKSO0QdH+vgVqn/hKM24ov0XKDoEAnuqZrzk3U/haozr+0YgaO1Tt0N6a5byt0KEaj7NQhzar8vYzplB0Ygeic7avOsr5k531EMHe4t+K20hR0KIbP8Gvf/wKxjIO9/0CwCETnbKu1Ytp9HxHMHe6ttq0EEn1uUK/6OqTRX8N3ArWMfdl/gH475rNKPxb2EdZnlW0l0Ag3qFdz75AWCIFaxr7qlL1iR4GGL1yviUu3a/jC9Vqxo8CndSK4sI+AVXFaCvVq7h3SAiGQy7ipTdTN/dJSnKup26+d++rAXLTcoF5cgu1/gV7GTWmi5lc6ztaU7ZdWQPgTV0uhUcF8tUCwCIZlzBVXqI+n2y/bErzB1VLwqWC+WiBYBMMyDvZxP+A/nm6/XL0HfyPcmIRzzQhGVr60lL+p4EF/PvcEepu2098Q4cYEdh8XAr5npZ2OFVuZ+JsKLrQCNi7Q27Td/obocxNgnGuGp+y20/E1X/1NWSlANhfB0NfMDIE+TgTLcYk+NxbGuebgZNaBj8uvG+eLvykCpDms2ApoBYE+TtjxuES4CbBgOdfMr9j/MvPAZ8edjq/5YqwVAqS1NPf9T6CPE8FyXPIE49wEWDCMHdPU8SfsNDy/2cPL+/K2Cf5m1npv6t8U4/dYC+PfmDP+ldWPS56i5cYEVr/ipCm/Yu3WvG92y0mwdLw0e7035W/Kjr9agxWtaP/lq+OEu61gVj4ueYNwYxKrnmtuysHcjjsmKwwvb/WdjlXWu7d/U8ESIH3Fyqd8zP4xYTVNPU54+qPDqsclbxBuUEt9B/PIsBBtzS9pcIfoyY7J7B2su5/flAOfL1szrLzTscMByeoB0ht1beNmt7A1hlY07529vq3yo8MshBvUUtfBPHtgR13z7NZGd4ju7pgCuYP1xQ7emwNfc9qx2OWAZOUA6am6tvERveIsv002t1Y0X6lrfXdpHxn0PzqagnCDc5x5MI8MC3EFG6nuHeKZAaKxHVMgD/q+3MEzvHzDpl+couf/3wFVKzCdEc1u+bOy+v7GFl2fHhTbpB1b0fypvvW96vYMW/zo8JYlrpZ65plnlJycrFatWunCCy/Uv//97wbnf/3119WnTx+1atVKaWlpWrNmTYAqbT5q7h5ddrKqwStJzr6yQZK2zBmlV2dcpC1zRp3TIuKvK1POvlKnvj/4nQd/CMiVMcF0lVNT1Kz/P/+/A5JDuvmS7nWud398ZnO+mqYh9f2NhTgcQbNNNuXu9f5i1atA61vf5SerbXcFlCdMDzcrVqzQ7Nmz9dBDD2nXrl1KT09XVlaWjhw5Uuf8W7du1Q033KBp06bpk08+UXZ2trKzs7Vnz54AV948NHSQri9ASKp3x+SPg35dBzuzd/B2vLTybHWt/79sORDwzwzkpfnBoL6/sQu6tbP9NukvVg7UDe1TJwzp2uCPTTszPdw8+eSTmjFjhqZOnaq+fftqyZIlioyM1AsvvFDn/H/4wx80duxY3XvvvTrvvPP08MMP64ILLtCf/vSnAFfePDR0kPamFcbXB/36Dnatw0JN38HbfcdixvgwjEnTuIb+xuy+TfqD1QN1Y/tUK7aCBYKpfW5Onjypjz/+WDk5Oa5pISEhGjNmjLZt21bna7Zt26bZs2fXmpaVlaU333yzzvkrKytVWVnp+n9paWnTC29m6jsH7m1HUl+eU2+sSbau/j+BPKdvp06qZzOjI7FdOi/7W0PbuJ23SX8Ihv5z9FM6l6nhpqSkRFVVVUpISKg1PSEhQV9++WWdrykuLq5z/uLi4jrnz83N1bx583xTcDNW1w6xKVc2+GoH29DBLiO1Azt4PzLjyhaupnEf27hvBEugZn3XZvurpXJycmq19JSWlqpLly4mVmQvZv9iaOxgxx+8f5mx/s3e5tC8EKiDk6nhJjY2VqGhoTp8+HCt6YcPH1ZiYmKdr0lMTPRo/vDwcIWHh/umYNTJ7ADBwc5cZqx/s7c5NC/sY4KPqR2Kw8LCNGjQIK1bt841rbq6WuvWrVNGRkadr8nIyKg1vyStXbu23vnRPDTXTnMAAoN9THAx/bTU7NmzNXnyZA0ePFhDhw7VokWLVFZWpqlTp0qSJk2apE6dOik3N1eSdOeddyozM1NPPPGErrjiCr322mvauXOn/vznP5v5NQAAgEWYHm4mTJigo0eP6sEHH1RxcbEGDBig9957z9VpuKCgQCEh/21gGjZsmJYvX67f/va3uv/++9WzZ0+9+eab6tevn1lfAQAAWIjDMAyj8dnso7S0VDExMXI6nYqOjja7HAAA4AZPjt+mD+IHAADgS4QbAABgK4QbAABgK4QbAABgK4QbAABgK4QbAABgK4QbAABgK4QbAABgK6aPUBxoNWMWlpaWmlwJAABwV81x252xh5tduPnxxx8lSV26dDG5EgAA4Kkff/xRMTExDc7T7G6/UF1drUOHDikqKkoOh8PscgKqtLRUXbp0UWFhIbeeaCKWpW+wHH2HZekbLEff8fWyNAxDP/74ozp27FjrnpN1aXYtNyEhIercubPZZZgqOjqaP1ofYVn6BsvRd1iWvsFy9B1fLsvGWmxq0KEYAADYCuEGAADYCuGmGQkPD9dDDz2k8PBws0sJeixL32A5+g7L0jdYjr5j5rJsdh2KAQCAvdFyAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwY0ObN2/WlVdeqY4dO8rhcOjNN9+s9bxhGHrwwQeVlJSkiIgIjRkzRl999ZU5xVpcY8tyypQpcjgctR5jx441p1gLy83N1ZAhQxQVFaX4+HhlZ2dr3759teY5ceKEZs6cqQ4dOqhNmza69tprdfjwYZMqtiZ3luPIkSPP2SZvvfVWkyq2rsWLF6t///6uAeYyMjL0z3/+0/U826N7GluOZm2PhBsbKisrU3p6up555pk6n3/sscf0xz/+UUuWLNH27dvVunVrZWVl6cSJEwGu1PoaW5aSNHbsWBUVFbker776agArDA6bNm3SzJkz9dFHH2nt2rU6deqULr/8cpWVlbnmufvuu/WPf/xDr7/+ujZt2qRDhw5p/PjxJlZtPe4sR0maMWNGrW3yscceM6li6+rcubMWLlyojz/+WDt37tSll16qq6++Wnv37pXE9uiuxpajZNL2aMDWJBmrV692/b+6utpITEw0fv/737umHTt2zAgPDzdeffVVEyoMHmcvS8MwjMmTJxtXX321KfUEsyNHjhiSjE2bNhmGcXobbNmypfH666+75vniiy8MSca2bdvMKtPyzl6OhmEYmZmZxp133mleUUGsXbt2xvPPP8/22EQ1y9EwzNseablpZg4cOKDi4mKNGTPGNS0mJkYXXnihtm3bZmJlwWvjxo2Kj49X7969ddttt+n77783uyTLczqdkqT27dtLkj7++GOdOnWq1nbZp08fde3ale2yAWcvxxp/+9vfFBsbq379+iknJ0fl5eVmlBc0qqqq9Nprr6msrEwZGRlsj146eznWMGN7bHY3zmzuiouLJUkJCQm1pickJLieg/vGjh2r8ePHKyUlRfn5+br//vs1btw4bdu2TaGhoWaXZ0nV1dW66667NHz4cPXr10/S6e0yLCxMbdu2rTUv22X96lqOkjRx4kR169ZNHTt21Geffab77rtP+/bt06pVq0ys1pp2796tjIwMnThxQm3atNHq1avVt29f5eXlsT16oL7lKJm3PRJugCa4/vrrXf9OS0tT//79lZqaqo0bN2r06NEmVmZdM2fO1J49e7RlyxazSwlq9S3Hm2++2fXvtLQ0JSUlafTo0crPz1dqamqgy7S03r17Ky8vT06nUytXrtTkyZO1adMms8sKOvUtx759+5q2PXJaqplJTEyUpHN6/R8+fNj1HLzXvXt3xcbGav/+/WaXYkmzZs3SO++8ow0bNqhz586u6YmJiTp58qSOHTtWa362y7rVtxzrcuGFF0oS22QdwsLC1KNHDw0aNEi5ublKT0/XH/7wB7ZHD9W3HOsSqO2RcNPMpKSkKDExUevWrXNNKy0t1fbt22udI4V3vv32W33//fdKSkoyuxRLMQxDs2bN0urVq7V+/XqlpKTUen7QoEFq2bJlre1y3759KigoYLs8Q2PLsS55eXmSxDbphurqalVWVrI9NlHNcqxLoLZHTkvZ0PHjx2ul4gMHDigvL0/t27dX165dddddd+mRRx5Rz549lZKSogceeEAdO3ZUdna2eUVbVEPLsn379po3b56uvfZaJSYmKj8/X7/+9a/Vo0cPZWVlmVi19cycOVPLly/XW2+9paioKFe/hZiYGEVERCgmJkbTpk3T7Nmz1b59e0VHR+uXv/ylMjIydNFFF5lcvXU0thzz8/O1fPly/exnP1OHDh302Wef6e6779aIESPUv39/k6u3lpycHI0bN05du3bVjz/+qOXLl2vjxo16//332R490NByNHV7DPj1WfC7DRs2GJLOeUyePNkwjNOXgz/wwANGQkKCER4ebowePdrYt2+fuUVbVEPLsry83Lj88suNuLg4o2XLlka3bt2MGTNmGMXFxWaXbTl1LUNJxosvvuiap6Kiwrj99tuNdu3aGZGRkcY111xjFBUVmVe0BTW2HAsKCowRI0YY7du3N8LDw40ePXoY9957r+F0Os0t3IJuuukmo1u3bkZYWJgRFxdnjB492vjggw9cz7M9uqeh5Wjm9ugwDMPwb3wCAAAIHPrcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcALCUkydPml3COaxYE4D6EW4A+NXIkSM1a9YszZo1SzExMYqNjdUDDzygmju/JCcn6+GHH9akSZMUHR2tm2++WZK0ZcsWXXLJJYqIiFCXLl10xx13qKyszPW+zz77rHr27KlWrVopISFB1113neu5lStXKi0tTREREerQoYPGjBnjeu3IkSN111131aoxOztbU6ZMcf3f25oAWAPhBoDf/fWvf1WLFi3073//W3/4wx/05JNP6vnnn3c9//jjjys9PV2ffPKJHnjgAeXn52vs2LG69tpr9dlnn2nFihXasmWLZs2aJUnauXOn7rjjDs2fP1/79u3Te++9pxEjRkiSioqKdMMNN+imm27SF198oY0bN2r8+PHy9DZ6ntYEwDq4cSYAvxo5cqSOHDmivXv3yuFwSJLmzJmjt99+W59//rmSk5M1cOBArV692vWa6dOnKzQ0VM8995xr2pYtW5SZmamysjKtWbNGU6dO1bfffquoqKhan7dr1y4NGjRIBw8eVLdu3eqsZ8CAAVq0aJFrWnZ2ttq2batly5ZJklc1tWrVqknLCYDv0HIDwO8uuugiV7CRpIyMDH311VeqqqqSJA0ePLjW/J9++qmWLVumNm3auB5ZWVmqrq7WgQMHdNlll6lbt27q3r27brzxRv3tb39TeXm5JCk9PV2jR49WWlqa/vd//1dLly7Vf/7zH49r9rQmANZBuAFgutatW9f6//Hjx3XLLbcoLy/P9fj000/11VdfKTU1VVFRUdq1a5deffVVJSUl6cEHH1R6erqOHTum0NBQrV27Vv/85z/Vt29fPf300+rdu7crgISEhJxziurUqVNNrgmAdRBuAPjd9u3ba/3/o48+Us+ePRUaGlrn/BdccIE+//xz9ejR45xHWFiYJKlFixYaM2aMHnvsMX322Wc6ePCg1q9fL0lyOBwaPny45s2bp08++URhYWGuU0xxcXEqKipyfVZVVZX27NnT6HdwpyYA1kC4AeB3BQUFmj17tvbt26dXX31VTz/9tO68885657/vvvu0detWzZo1S3l5efrqq6/01ltvuTrvvvPOO/rjH/+ovLw8ffPNN3rppZdUXV2t3r17a/v27VqwYIF27typgoICrVq1SkePHtV5550nSbr00kv17rvv6t1339WXX36p2267TceOHWv0OzRWEwDraGF2AQDsb9KkSaqoqNDQoUMVGhqqO++803V5dV369++vTZs26Te/+Y0uueQSGYah1NRUTZgwQZLUtm1brVq1SnPnztWJEyfUs2dPvfrqqzr//PP1xRdfaPPmzVq0aJFKS0vVrVs3PfHEExo3bpwk6aabbtKnn36qSZMmqUWLFrr77rs1atSoRr9DYzUBsA6ulgLgV3VdnQQA/sRpKQAAYCuEGwAAYCuclgIAALZCyw0AALAVwg0AALAVwg0AALAVwg0AALAVwg0AALAVwg0AALAVwg0AALAVwg0AALAVwg0AALCV/x/YBjhC2T2t0QAAAABJRU5ErkJggg==", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize with IDAES surrogate plotting tools\n", + "surrogate_scatter2D(poly_surr, data_validation, filename=\"pysmo_poly_val_scatter2D.pdf\")\n", + "surrogate_parity(poly_surr, data_validation, filename=\"pysmo_poly_val_parity.pdf\")\n", + "surrogate_residual(poly_surr, data_validation, filename=\"pysmo_poly_val_residual.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_pysmo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb) file." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 3 +} \ No newline at end of file From eb9a006d61138b780cca5cb5ee5b5fd2d6907503 Mon Sep 17 00:00:00 2001 From: Brandon Paul Date: Tue, 10 Oct 2023 16:26:37 -0700 Subject: [PATCH 21/75] Fix test failures from misnamed references to StateBlock --- .../ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb | 2 +- .../ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb | 2 +- .../ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb | 2 +- .../ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb | 2 +- .../OMLT/SCO2_properties_keras_surrogate_embedding.ipynb | 2 +- .../OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb | 2 +- .../OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb | 2 +- .../OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb | 2 +- .../PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb | 2 +- .../PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb | 2 +- .../PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb | 2 +- .../PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb | 2 +- 12 files changed, 12 insertions(+), 12 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb index cb7f4fe1..5229a029 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb @@ -198,7 +198,7 @@ "outputs": [], "source": [ "@declare_process_block_class(\"SCO2StateBlock\",\n", - " block_class=_StateBlock)\n", + " block_class=StateBlock)\n", "class SCO2StateBlockData(StateBlockData):\n", " \"\"\"\n", " An example property package for ideal gas properties with Gibbs energy\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb index 3ec37222..e9b249ea 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb @@ -198,7 +198,7 @@ "outputs": [], "source": [ "@declare_process_block_class(\"SCO2StateBlock\",\n", - " block_class=_StateBlock)\n", + " block_class=StateBlock)\n", "class SCO2StateBlockData(StateBlockData):\n", " \"\"\"\n", " An example property package for ideal gas properties with Gibbs energy\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb index 413e0aa2..f8c52867 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb @@ -198,7 +198,7 @@ "outputs": [], "source": [ "@declare_process_block_class(\"SCO2StateBlock\",\n", - " block_class=_StateBlock)\n", + " block_class=StateBlock)\n", "class SCO2StateBlockData(StateBlockData):\n", " \"\"\"\n", " An example property package for ideal gas properties with Gibbs energy\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb index 9bc2c853..79804bfc 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb @@ -198,7 +198,7 @@ "outputs": [], "source": [ "@declare_process_block_class(\"SCO2StateBlock\",\n", - " block_class=_StateBlock)\n", + " block_class=StateBlock)\n", "class SCO2StateBlockData(StateBlockData):\n", " \"\"\"\n", " An example property package for ideal gas properties with Gibbs energy\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb index bf9ece23..dedcb078 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb @@ -198,7 +198,7 @@ "outputs": [], "source": [ "@declare_process_block_class(\"SCO2StateBlock\",\n", - " block_class=_StateBlock)\n", + " block_class=StateBlock)\n", "class SCO2StateBlockData(StateBlockData):\n", " \"\"\"\n", " An example property package for ideal gas properties with Gibbs energy\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb index 29b68cbd..c605267f 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb @@ -198,7 +198,7 @@ "outputs": [], "source": [ "@declare_process_block_class(\"SCO2StateBlock\",\n", - " block_class=_StateBlock)\n", + " block_class=StateBlock)\n", "class SCO2StateBlockData(StateBlockData):\n", " \"\"\"\n", " An example property package for ideal gas properties with Gibbs energy\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb index 6e566a20..5eeed1fe 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb @@ -198,7 +198,7 @@ "outputs": [], "source": [ "@declare_process_block_class(\"SCO2StateBlock\",\n", - " block_class=_StateBlock)\n", + " block_class=StateBlock)\n", "class SCO2StateBlockData(StateBlockData):\n", " \"\"\"\n", " An example property package for ideal gas properties with Gibbs energy\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb index 8f30874e..d9bbb7fe 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb @@ -198,7 +198,7 @@ "outputs": [], "source": [ "@declare_process_block_class(\"SCO2StateBlock\",\n", - " block_class=_StateBlock)\n", + " block_class=StateBlock)\n", "class SCO2StateBlockData(StateBlockData):\n", " \"\"\"\n", " An example property package for ideal gas properties with Gibbs energy\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb index 79e6a24e..99e68f3c 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb @@ -199,7 +199,7 @@ "outputs": [], "source": [ "@declare_process_block_class(\"SCO2StateBlock\",\n", - " block_class=_StateBlock)\n", + " block_class=StateBlock)\n", "class SCO2StateBlockData(StateBlockData):\n", " \"\"\"\n", " An example property package for ideal gas properties with Gibbs energy\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb index eec9bfe0..63b39374 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb @@ -199,7 +199,7 @@ "outputs": [], "source": [ "@declare_process_block_class(\"SCO2StateBlock\",\n", - " block_class=_StateBlock)\n", + " block_class=StateBlock)\n", "class SCO2StateBlockData(StateBlockData):\n", " \"\"\"\n", " An example property package for ideal gas properties with Gibbs energy\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb index f454ab75..6705629b 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb @@ -199,7 +199,7 @@ "outputs": [], "source": [ "@declare_process_block_class(\"SCO2StateBlock\",\n", - " block_class=_StateBlock)\n", + " block_class=StateBlock)\n", "class SCO2StateBlockData(StateBlockData):\n", " \"\"\"\n", " An example property package for ideal gas properties with Gibbs energy\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb index 81f5dd2e..d016d3ae 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb @@ -199,7 +199,7 @@ "outputs": [], "source": [ "@declare_process_block_class(\"SCO2StateBlock\",\n", - " block_class=_StateBlock)\n", + " block_class=StateBlock)\n", "class SCO2StateBlockData(StateBlockData):\n", " \"\"\"\n", " An example property package for ideal gas properties with Gibbs energy\n", From 6f2fc305595a1661dbdf65e4cf5d9ab43b2c336e Mon Sep 17 00:00:00 2001 From: Brandon Paul Date: Tue, 10 Oct 2023 17:35:24 -0700 Subject: [PATCH 22/75] Fix test failure by improving heater initializations --- .../OMLT/SCO2_flowsheet_keras_surrogate.ipynb | 36 +++++++++++-------- .../SCO2_flowsheet_keras_surrogate_doc.ipynb | 34 ++++++++++-------- .../SCO2_flowsheet_keras_surrogate_test.ipynb | 34 ++++++++++-------- .../SCO2_flowsheet_keras_surrogate_usr.ipynb | 34 ++++++++++-------- 4 files changed, 81 insertions(+), 57 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb index 27c44415..bd8af0f1 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb @@ -521,29 +521,36 @@ " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", " destination=m.fs.mixer.FG_out)\n", " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", - " destination=m.fs.HTR_pseudo_tube.inlet)\n", - "\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # initialize twice if needed\n", + " def init_once_or_twice(blk, outlvl=0):\n", + " try:\n", + " blk.initialize(outlvl=outlvl)\n", + " except:\n", + " blk.initialize(outlvl=outlvl)\n", + " \n", " # NETL Baseline \n", " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", " m.fs.boiler.inlet.temperature.fix(685.15)\n", " m.fs.boiler.inlet.pressure.fix(34.51)\n", - "\n", + " \n", " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", " m.fs.boiler.deltaP.fix(-0.21)\n", - "\n", - " m.fs.boiler.initialize(outlvl=outlvl)\n", - "\n", + " \n", + " init_once_or_twice(m.fs.boiler)\n", + " \n", " propagate_state(m.fs.s01)\n", - "\n", + " \n", " m.fs.turbine.ratioP.fix(1/3.68)\n", " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", " m.fs.turbine.initialize(outlvl=outlvl)\n", - "\n", + " \n", " propagate_state(m.fs.s02)\n", " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", - "\n", - " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + " \n", + " init_once_or_twice(m.fs.HTR_pseudo_shell)\n", "\n", "\n", " propagate_state(m.fs.s03)\n", @@ -642,7 +649,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -656,10 +663,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4 + "version": "3.10.12" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb index c4a1dbf3..2d12f8fe 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb @@ -521,29 +521,36 @@ " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", " destination=m.fs.mixer.FG_out)\n", " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", - " destination=m.fs.HTR_pseudo_tube.inlet)\n", - "\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # initialize twice if needed\n", + " def init_once_or_twice(blk, outlvl=0):\n", + " try:\n", + " blk.initialize(outlvl=outlvl)\n", + " except:\n", + " blk.initialize(outlvl=outlvl)\n", + " \n", " # NETL Baseline \n", " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", " m.fs.boiler.inlet.temperature.fix(685.15)\n", " m.fs.boiler.inlet.pressure.fix(34.51)\n", - "\n", + " \n", " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", " m.fs.boiler.deltaP.fix(-0.21)\n", - "\n", - " m.fs.boiler.initialize(outlvl=outlvl)\n", - "\n", + " \n", + " init_once_or_twice(m.fs.boiler)\n", + " \n", " propagate_state(m.fs.s01)\n", - "\n", + " \n", " m.fs.turbine.ratioP.fix(1/3.68)\n", " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", " m.fs.turbine.initialize(outlvl=outlvl)\n", - "\n", + " \n", " propagate_state(m.fs.s02)\n", " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", - "\n", - " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + " \n", + " init_once_or_twice(m.fs.HTR_pseudo_shell)\n", "\n", "\n", " propagate_state(m.fs.s03)\n", @@ -642,7 +649,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -656,9 +663,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4 + "version": "3.10.12" + } }, "nbformat": 4, "nbformat_minor": 3 diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb index c4a1dbf3..2d12f8fe 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb @@ -521,29 +521,36 @@ " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", " destination=m.fs.mixer.FG_out)\n", " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", - " destination=m.fs.HTR_pseudo_tube.inlet)\n", - "\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # initialize twice if needed\n", + " def init_once_or_twice(blk, outlvl=0):\n", + " try:\n", + " blk.initialize(outlvl=outlvl)\n", + " except:\n", + " blk.initialize(outlvl=outlvl)\n", + " \n", " # NETL Baseline \n", " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", " m.fs.boiler.inlet.temperature.fix(685.15)\n", " m.fs.boiler.inlet.pressure.fix(34.51)\n", - "\n", + " \n", " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", " m.fs.boiler.deltaP.fix(-0.21)\n", - "\n", - " m.fs.boiler.initialize(outlvl=outlvl)\n", - "\n", + " \n", + " init_once_or_twice(m.fs.boiler)\n", + " \n", " propagate_state(m.fs.s01)\n", - "\n", + " \n", " m.fs.turbine.ratioP.fix(1/3.68)\n", " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", " m.fs.turbine.initialize(outlvl=outlvl)\n", - "\n", + " \n", " propagate_state(m.fs.s02)\n", " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", - "\n", - " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + " \n", + " init_once_or_twice(m.fs.HTR_pseudo_shell)\n", "\n", "\n", " propagate_state(m.fs.s03)\n", @@ -642,7 +649,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -656,9 +663,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4 + "version": "3.10.12" + } }, "nbformat": 4, "nbformat_minor": 3 diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb index c4a1dbf3..2d12f8fe 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb @@ -521,29 +521,36 @@ " m.fs.s13 = Arc(source=m.fs.FG_cooler.outlet,\n", " destination=m.fs.mixer.FG_out)\n", " m.fs.s14 = Arc(source=m.fs.mixer.outlet,\n", - " destination=m.fs.HTR_pseudo_tube.inlet)\n", - "\n", + " destination=m.fs.HTR_pseudo_tube.inlet)\n", + "\n", + " # initialize twice if needed\n", + " def init_once_or_twice(blk, outlvl=0):\n", + " try:\n", + " blk.initialize(outlvl=outlvl)\n", + " except:\n", + " blk.initialize(outlvl=outlvl)\n", + " \n", " # NETL Baseline \n", " m.fs.boiler.inlet.flow_mol.fix(121.1)\n", " m.fs.boiler.inlet.temperature.fix(685.15)\n", " m.fs.boiler.inlet.pressure.fix(34.51)\n", - "\n", + " \n", " m.fs.boiler.outlet.temperature.fix(893.15) # Turbine inlet T = 620 C\n", " m.fs.boiler.deltaP.fix(-0.21)\n", - "\n", - " m.fs.boiler.initialize(outlvl=outlvl)\n", - "\n", + " \n", + " init_once_or_twice(m.fs.boiler)\n", + " \n", " propagate_state(m.fs.s01)\n", - "\n", + " \n", " m.fs.turbine.ratioP.fix(1/3.68)\n", " m.fs.turbine.efficiency_isentropic.fix(0.927)\n", " m.fs.turbine.initialize(outlvl=outlvl)\n", - "\n", + " \n", " propagate_state(m.fs.s02)\n", " m.fs.HTR_pseudo_shell.outlet.temperature.fix(489.15)\n", " m.fs.HTR_pseudo_shell.deltaP.fix(-0.07)\n", - "\n", - " m.fs.HTR_pseudo_shell.initialize(outlvl=outlvl)\n", + " \n", + " init_once_or_twice(m.fs.HTR_pseudo_shell)\n", "\n", "\n", " propagate_state(m.fs.s03)\n", @@ -642,7 +649,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -656,9 +663,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4 + "version": "3.10.12" + } }, "nbformat": 4, "nbformat_minor": 3 From f4431890546e9a55e8cff345e99c29d85ff6186c Mon Sep 17 00:00:00 2001 From: Brandon Paul Date: Wed, 11 Oct 2023 06:31:28 -0700 Subject: [PATCH 23/75] Force check-in missing saved model files --- .../OMLT/keras_surrogate/fingerprint.pb | 1 + .../OMLT/keras_surrogate/idaes_info.json | 1 + .../OMLT/keras_surrogate/keras_metadata.pb | 10 ++++++++++ .../OMLT/keras_surrogate/saved_model.pb | Bin 0 -> 136064 bytes .../variables/variables.data-00000-of-00001 | Bin 0 -> 24592 bytes .../keras_surrogate/variables/variables.index | Bin 0 -> 2259 bytes .../PySMO/pysmo_poly_surrogate.json | 1 + 7 files changed, 13 insertions(+) create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/fingerprint.pb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/idaes_info.json create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/keras_metadata.pb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/saved_model.pb create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/variables/variables.data-00000-of-00001 create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/variables/variables.index create mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/pysmo_poly_surrogate.json diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/fingerprint.pb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/fingerprint.pb new file mode 100644 index 00000000..c94ddb92 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/fingerprint.pb @@ -0,0 +1 @@ +™Ù²¡™—ñû®¾¶âŽéدŠ-êô¢ËªºÕû[ ¿˜âãÒû‘Óç(÷úÝ칉¨¢W2 \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/idaes_info.json b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/idaes_info.json new file mode 100644 index 00000000..f582d087 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/idaes_info.json @@ -0,0 +1 @@ +{"input_scaler": {"expected_columns": ["pressure", "temperature"], "offset": {"pressure": 7.460891, "temperature": 306.215965}, "factor": {"pressure": 27.532923, "temperature": 693.756024}}, "output_scaler": {"expected_columns": ["enth_mol", "entr_mol"], "offset": {"enth_mol": -403924.714779, "entr_mol": -67.269005}, "factor": {"enth_mol": 43668.96112499997, "entr_mol": 88.088999}}, "input_labels": ["pressure", "temperature"], "output_labels": ["enth_mol", "entr_mol"], "input_bounds": {"pressure": [7, 40], "temperature": [306, 1000]}} \ No newline at end of file diff --git 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["IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]", "IndexedParam[pressure]/IndexedParam[temperature]", "IndexedParam[temperature]/IndexedParam[pressure]"], "optimal_weights_array": [[-539145.2641931743], [-1572.9941129612596], [1028.1303702529963], [-41.89265612633253], [-2.854098382160082], [3.1084792045014056], [0.0040249321969904606], [-0.07298691795031877], [-2.7827021177926484e-06], [0.0006559340352560386], [7.62454692622566e-10], [4.50540106476475], [-0.0025967218940188964], [3.27147430041989e-05], [-0.05205092851352775], [149943.17003170087], [-3.5662256522946807]], "final_polynomial_order": 5, "errors": {"MAE": 116.22937611304296, "MSE": 39254.96789837278, "R2": 0.9997117200542968}, "extra_terms_feature_vector": ["IndexedParam[pressure]", "IndexedParam[temperature]"]}, "map": {"regression_data_columns": "list", "multinomials": "str", "additional_term_expressions": "other", "optimal_weights_array": "numpy", "final_polynomial_order": "str", "errors": "str", "extra_terms_feature_vector": "other"}}, "entr_mol": {"attr": {"regression_data_columns": ["pressure", "temperature"], "multinomials": 1, "additional_term_expressions": ["IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]", "IndexedParam[pressure]/IndexedParam[temperature]", "IndexedParam[temperature]/IndexedParam[pressure]"], "optimal_weights_array": [[-529.9581296941684], [-5.674476891947422], [3.6251620831469844], [-0.012206052330165947], [-0.010121999171951317], [0.0044164987227566545], [1.4212146246171698e-05], [-0.00012049491972756627], [-9.875650167428602e-09], [1.1673348430972035e-06], [2.72031843813476e-12], [0.010605178085763924], [-6.047902870413699e-06], [6.872924493404928e-08], [-0.00011146830780061758], [437.25207041949056], [0.0015391876304710196]], "final_polynomial_order": 5, "errors": {"MAE": 0.34548912239751245, "MSE": 0.3560561890323906, "R2": 0.9991570382929269}, "extra_terms_feature_vector": ["IndexedParam[pressure]", "IndexedParam[temperature]"]}, "map": {"regression_data_columns": "list", "multinomials": "str", "additional_term_expressions": "other", "optimal_weights_array": "numpy", "final_polynomial_order": "str", "errors": "str", "extra_terms_feature_vector": "other"}}}, "input_labels": ["pressure", "temperature"], "output_labels": ["enth_mol", "entr_mol"], "input_bounds": {"pressure": [7, 40], "temperature": [306, 1000]}, "surrogate_type": "poly"} \ No newline at end of file From d0d1140aa13c79105133c5507b4c08fb427881c0 Mon Sep 17 00:00:00 2001 From: Brandon Paul Date: Wed, 11 Oct 2023 06:47:36 -0700 Subject: [PATCH 24/75] Skip notebooks requiring ALAMO --- .github/workflows/core.yml | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index e3b8661f..effb3a44 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -74,6 +74,12 @@ jobs: uses: ./.github/actions/install with: install-target: -r requirements-dev.txt + - name: Skip notebooks that require Alamo + run: | + rm idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_test.ipynb + rm idaes_examples/notebooks/docs/surrogate/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb + rm idaes_examples/notebooks/docs/surrogate/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb + rm idaes_examples/notebooks/docs/surrogate/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb - name: Run pytest run: | pwd From 040c9b66062af4a6006d95fcecff984dd29b4e94 Mon Sep 17 00:00:00 2001 From: Brandon Paul Date: Wed, 11 Oct 2023 06:50:03 -0700 Subject: [PATCH 25/75] Fix indent --- .github/workflows/core.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index effb3a44..73217eaf 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -75,11 +75,11 @@ jobs: with: install-target: -r requirements-dev.txt - name: Skip notebooks that require Alamo - run: | - rm idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_test.ipynb - rm idaes_examples/notebooks/docs/surrogate/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb - rm idaes_examples/notebooks/docs/surrogate/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb - rm idaes_examples/notebooks/docs/surrogate/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb + run: | + rm idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_test.ipynb + rm idaes_examples/notebooks/docs/surrogate/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb + rm idaes_examples/notebooks/docs/surrogate/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb + rm idaes_examples/notebooks/docs/surrogate/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb - name: Run pytest run: | pwd From b5d891aa176285a013b899aa652e0e8113977c75 Mon Sep 17 00:00:00 2001 From: Brandon Paul Date: Wed, 11 Oct 2023 07:00:20 -0700 Subject: [PATCH 26/75] Fix typos --- .github/workflows/core.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 73217eaf..24cbcaff 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -77,9 +77,9 @@ jobs: - name: Skip notebooks that require Alamo run: | rm idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_test.ipynb - rm idaes_examples/notebooks/docs/surrogate/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb - rm idaes_examples/notebooks/docs/surrogate/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb - rm idaes_examples/notebooks/docs/surrogate/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb + rm idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb + rm idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb + rm idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb - name: Run pytest run: | pwd From 652e20ee1f229c392084fa9f23db7189a97185ef Mon Sep 17 00:00:00 2001 From: Brandon Paul Date: Wed, 11 Oct 2023 07:11:40 -0700 Subject: [PATCH 27/75] Try using actual notebooks from only ones that call training --- .github/workflows/core.yml | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 24cbcaff..32b850c4 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -76,10 +76,8 @@ jobs: install-target: -r requirements-dev.txt - name: Skip notebooks that require Alamo run: | - rm idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization_test.ipynb - rm idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb - rm idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb - rm idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb + rm idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization.ipynb + rm idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb - name: Run pytest run: | pwd From 01f917c13c18500bad509571527721fe5ee9463d Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 13:59:28 -0500 Subject: [PATCH 28/75] updated _toc.yml --- idaes_examples/notebooks/_toc.yml | 6 ++++++ .../docs/surrogates/alamo/SCO2_alamo_surrogate_doc.ipynb | 6 ------ .../surrogates/alamo/SCO2_alamo_surrogate_exercise.ipynb | 6 ------ .../surrogates/alamo/SCO2_alamo_surrogate_solution.ipynb | 6 ------ .../docs/surrogates/alamo/SCO2_alamo_surrogate_src.ipynb | 6 ------ .../docs/surrogates/alamo/SCO2_alamo_surrogate_test.ipynb | 6 ------ .../docs/surrogates/alamo/SCO2_alamo_surrogate_usr.ipynb | 6 ------ .../surrogates/alamo/supercritical_CO2_flowsheet_doc.ipynb | 6 ------ .../surrogates/alamo/supercritical_CO2_flowsheet_src.ipynb | 6 ------ .../surrogates/alamo/supercritical_CO2_flowsheet_test.ipynb | 6 ------ .../surrogates/alamo/supercritical_CO2_flowsheet_usr.ipynb | 6 ------ .../surrogates/alamo/supercritical_CO2_surrogate_doc.ipynb | 6 ------ .../surrogates/alamo/supercritical_CO2_surrogate_src.ipynb | 6 ------ .../surrogates/alamo/supercritical_CO2_surrogate_test.ipynb | 6 ------ .../surrogates/alamo/supercritical_CO2_surrogate_usr.ipynb | 6 ------ .../surrogates/omlt/supercritical_CO2_surrogate_doc.ipynb | 6 ------ .../surrogates/omlt/supercritical_CO2_surrogate_src.ipynb | 6 ------ .../surrogates/omlt/supercritical_CO2_surrogate_test.ipynb | 6 ------ .../surrogates/omlt/supercritical_CO2_surrogate_usr.ipynb | 6 ------ .../surrogates/pysmo/supercritical_CO2_surrogate_doc.ipynb | 6 ------ .../surrogates/pysmo/supercritical_CO2_surrogate_src.ipynb | 6 ------ .../surrogates/pysmo/supercritical_CO2_surrogate_test.ipynb | 6 ------ .../surrogates/pysmo/supercritical_CO2_surrogate_usr.ipynb | 6 ------ 23 files changed, 6 insertions(+), 132 deletions(-) delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_doc.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_exercise.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_solution.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_src.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_test.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_usr.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_doc.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_src.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_test.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_usr.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_doc.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_src.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_test.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_usr.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_doc.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_src.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_test.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_usr.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_doc.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_src.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_test.ipynb delete mode 100644 idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_usr.ipynb diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index bcbace89..e0cb6118 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -95,6 +95,12 @@ parts: - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc + - caption: Power generation + chapters: + - file: docs/power_gen/supercritical/index + sections: + - file: docs/power_gen/supercritical/supercritical_power_plant_doc + - file: docs/power_gen/supercritical/supercritical_steam_cycle_doc # ----------------------------- # active (not documented) # ----------------------------- diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_doc.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_doc.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_exercise.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_exercise.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_exercise.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_solution.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_solution.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_solution.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_src.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_src.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_src.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_test.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_test.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_usr.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/SCO2_alamo_surrogate_usr.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_doc.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_doc.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_src.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_src.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_src.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_test.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_test.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_test.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_usr.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_flowsheet_usr.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_doc.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_doc.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_src.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_src.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_src.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_test.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_test.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_usr.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/alamo/supercritical_CO2_surrogate_usr.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_doc.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_doc.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_src.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_src.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_src.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_test.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_test.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_usr.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/omlt/supercritical_CO2_surrogate_usr.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_doc.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_doc.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_src.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_src.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_src.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_test.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_test.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_usr.ipynb deleted file mode 100644 index 363fcab7..00000000 --- a/idaes_examples/notebooks/docs/surrogates/pysmo/supercritical_CO2_surrogate_usr.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} From d979264e0d91c8e6c7fd1b2405437a883f8aa7bc Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 15:04:39 -0500 Subject: [PATCH 29/75] Skipping ALAMO files for testing --- .github/workflows/core.yml | 6 +----- idaes_examples/notebooks/_toc.yml | 10 +++++----- 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 32b850c4..78006733 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -74,15 +74,11 @@ jobs: uses: ./.github/actions/install with: install-target: -r requirements-dev.txt - - name: Skip notebooks that require Alamo - run: | - rm idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization.ipynb - rm idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb - name: Run pytest run: | pwd ls idaes_examples - pytest -v idaes_examples + pytest -v idaes_examples -k "not idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization and not idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate" - name: Upload pytest-xdist worker logs if: success() || failure() uses: actions/upload-artifact@v3 diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index e0cb6118..ed41c8d2 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -96,11 +96,11 @@ parts: - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc - caption: Power generation - chapters: - - file: docs/power_gen/supercritical/index - sections: - - file: docs/power_gen/supercritical/supercritical_power_plant_doc - - file: docs/power_gen/supercritical/supercritical_steam_cycle_doc + chapters: + - file: docs/power_gen/supercritical/index + sections: + - file: docs/power_gen/supercritical/supercritical_power_plant_doc + - file: docs/power_gen/supercritical/supercritical_steam_cycle_doc # ----------------------------- # active (not documented) # ----------------------------- From ad404916a94bad43354e4ac0be0aea96d10af516 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 15:06:45 -0500 Subject: [PATCH 30/75] Resolving conflict in _toc.yml --- idaes_examples/notebooks/_toc.yml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index ed41c8d2..dd9e5f91 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -95,12 +95,12 @@ parts: - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc - - caption: Power generation - chapters: - - file: docs/power_gen/supercritical/index - sections: - - file: docs/power_gen/supercritical/supercritical_power_plant_doc - - file: docs/power_gen/supercritical/supercritical_steam_cycle_doc +- caption: Power generation + chapters: + - file: docs/power_gen/supercritical/index + sections: + - file: docs/power_gen/supercritical/supercritical_power_plant_doc + - file: docs/power_gen/supercritical/supercritical_steam_cycle_doc # ----------------------------- # active (not documented) # ----------------------------- From e2c6bd3748fe58a445dbb2dc84c1a6eed01b5fa5 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 15:25:29 -0500 Subject: [PATCH 31/75] Skipping using paths-ignore --- .github/workflows/core.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 78006733..863c27a1 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -4,6 +4,9 @@ on: push: branches: - main + paths-ignore: + - 'idaes_examples/notebooks/docs/surrogates/alamo/**' + - 'idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/**' repository_dispatch: # to run this, send a POST API call at repos/IDAES/idaes-pse/dispatches with the specified event_type # e.g. `gh repos/IDAES/idaes-pse/dispatches -F event_type=ci_run_tests` @@ -78,7 +81,7 @@ jobs: run: | pwd ls idaes_examples - pytest -v idaes_examples -k "not idaes_examples/notebooks/docs/surrogates/alamo/alamo_flowsheet_optimization and not idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate" + pytest -v idaes_examples - name: Upload pytest-xdist worker logs if: success() || failure() uses: actions/upload-artifact@v3 From 395705975043aba3c914134dc3ef7e05def7fd0d Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 15:52:26 -0500 Subject: [PATCH 32/75] Resolving _toc conflict --- idaes_examples/notebooks/_toc.yml | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index dd9e5f91..5f3de632 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -82,19 +82,19 @@ parts: - file: docs/surrogates/omlt/keras_flowsheet_optimization_doc - file: docs/surrogates/SCO2_example/ALAMO/index sections: - - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc - - file: docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc - - file: docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc - - file: docs/surrogates/SCO2_example/OMLT/index - sections: - - file: docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc - - file: docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc - - file: docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc - - file: docs/surrogates/SCO2_example/PySMO/index - sections: - - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc - - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc - - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc + - file: docs/surrogates/SCO2_example/alamo/SCO2_alamo_surrogate_doc + - file: docs/surrogates/SCO2_example/alamo/SCO2_properties_alamo_surrogate_embedding_doc + - file: docs/surrogates/SCO2_example/alamo/SCO2_flowsheet_alamo_surrogate_doc + - file: docs/surrogates/SCO2_example/omlt/index + sections: + - file: docs/surrogates/SCO2_example/omlt/SCO2_keras_surrogate_doc + - file: docs/surrogates/SCO2_example/omlt/SCO2_properties_keras_surrogate_embedding_doc + - file: docs/surrogates/SCO2_example/omlt/SCO2_flowsheet_keras_surrogate_doc + - file: docs/surrogates/SCO2_example/pysmo/index + sections: + - file: docs/surrogates/SCO2_example/pysmo/SCO2_pysmo_surrogate_doc + - file: docs/surrogates/SCO2_example/pysmo/SCO2_properties_pysmo_surrogate_embedding_doc + - file: docs/surrogates/SCO2_example/pysmo/SCO2_flowsheet_pysmo_surrogate_doc - caption: Power generation chapters: - file: docs/power_gen/supercritical/index From 4b27e5fa26b059936d158956f102f005243bac18 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 15:55:16 -0500 Subject: [PATCH 33/75] Fixed typo in _toc --- idaes_examples/notebooks/_toc.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index 5f3de632..79a5cfa9 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -80,7 +80,7 @@ parts: - file: docs/surrogates/omlt/index sections: - file: docs/surrogates/omlt/keras_flowsheet_optimization_doc - - file: docs/surrogates/SCO2_example/ALAMO/index + - file: docs/surrogates/SCO2_example/alamo/index sections: - file: docs/surrogates/SCO2_example/alamo/SCO2_alamo_surrogate_doc - file: docs/surrogates/SCO2_example/alamo/SCO2_properties_alamo_surrogate_embedding_doc From 28f444b86da41ab1eb48dcc7d9fd1aae70c58225 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 15:56:57 -0500 Subject: [PATCH 34/75] fixed paths in _toc --- idaes_examples/notebooks/_toc.yml | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index 79a5cfa9..b30f6662 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -80,21 +80,21 @@ parts: - file: docs/surrogates/omlt/index sections: - file: docs/surrogates/omlt/keras_flowsheet_optimization_doc - - file: docs/surrogates/SCO2_example/alamo/index + - file: docs/surrogates/sco2_example/alamo/index sections: - - file: docs/surrogates/SCO2_example/alamo/SCO2_alamo_surrogate_doc - - file: docs/surrogates/SCO2_example/alamo/SCO2_properties_alamo_surrogate_embedding_doc - - file: docs/surrogates/SCO2_example/alamo/SCO2_flowsheet_alamo_surrogate_doc - - file: docs/surrogates/SCO2_example/omlt/index + - file: docs/surrogates/sco2_example/alamo/SCO2_alamo_surrogate_doc + - file: docs/surrogates/sco2_example/alamo/SCO2_properties_alamo_surrogate_embedding_doc + - file: docs/surrogates/sco2_example/alamo/SCO2_flowsheet_alamo_surrogate_doc + - file: docs/surrogates/sco2_example/omlt/index sections: - - file: docs/surrogates/SCO2_example/omlt/SCO2_keras_surrogate_doc - - file: docs/surrogates/SCO2_example/omlt/SCO2_properties_keras_surrogate_embedding_doc - - file: docs/surrogates/SCO2_example/omlt/SCO2_flowsheet_keras_surrogate_doc - - file: docs/surrogates/SCO2_example/pysmo/index + - file: docs/surrogates/sco2_example/omlt/SCO2_keras_surrogate_doc + - file: docs/surrogates/sco2_example/omlt/SCO2_properties_keras_surrogate_embedding_doc + - file: docs/surrogates/sco2_example/omlt/SCO2_flowsheet_keras_surrogate_doc + - file: docs/surrogates/sco2_example/pysmo/index sections: - - file: docs/surrogates/SCO2_example/pysmo/SCO2_pysmo_surrogate_doc - - file: docs/surrogates/SCO2_example/pysmo/SCO2_properties_pysmo_surrogate_embedding_doc - - file: docs/surrogates/SCO2_example/pysmo/SCO2_flowsheet_pysmo_surrogate_doc + - file: docs/surrogates/sco2_example/pysmo/SCO2_pysmo_surrogate_doc + - file: docs/surrogates/sco2_example/pysmo/SCO2_properties_pysmo_surrogate_embedding_doc + - file: docs/surrogates/sco2_example/pysmo/SCO2_flowsheet_pysmo_surrogate_doc - caption: Power generation chapters: - file: docs/power_gen/supercritical/index From 60668fdf3c4910ce66c2a565fef29a72623ff348 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 16:05:10 -0500 Subject: [PATCH 35/75] Reverting to previous version of _ toc --- idaes_examples/notebooks/_toc.yml | 48 ++++++++++++++++--------------- 1 file changed, 25 insertions(+), 23 deletions(-) diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index b30f6662..0524c4a8 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -80,27 +80,30 @@ parts: - file: docs/surrogates/omlt/index sections: - file: docs/surrogates/omlt/keras_flowsheet_optimization_doc - - file: docs/surrogates/sco2_example/alamo/index - sections: - - file: docs/surrogates/sco2_example/alamo/SCO2_alamo_surrogate_doc - - file: docs/surrogates/sco2_example/alamo/SCO2_properties_alamo_surrogate_embedding_doc - - file: docs/surrogates/sco2_example/alamo/SCO2_flowsheet_alamo_surrogate_doc - - file: docs/surrogates/sco2_example/omlt/index - sections: - - file: docs/surrogates/sco2_example/omlt/SCO2_keras_surrogate_doc - - file: docs/surrogates/sco2_example/omlt/SCO2_properties_keras_surrogate_embedding_doc - - file: docs/surrogates/sco2_example/omlt/SCO2_flowsheet_keras_surrogate_doc - - file: docs/surrogates/sco2_example/pysmo/index - sections: - - file: docs/surrogates/sco2_example/pysmo/SCO2_pysmo_surrogate_doc - - file: docs/surrogates/sco2_example/pysmo/SCO2_properties_pysmo_surrogate_embedding_doc - - file: docs/surrogates/sco2_example/pysmo/SCO2_flowsheet_pysmo_surrogate_doc -- caption: Power generation - chapters: - - file: docs/power_gen/supercritical/index - sections: - - file: docs/power_gen/supercritical/supercritical_power_plant_doc - - file: docs/power_gen/supercritical/supercritical_steam_cycle_doc + - file: docs/surrogates/SCO2_example/ALAMO/index + sections: + - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate + - file: docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding + - file: docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate + - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc + - file: docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc + - file: docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc + - file: docs/surrogates/SCO2_example/OMLT/index + sections: + - file: docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate + - file: docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding + - file: docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate + - file: docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc + - file: docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc + - file: docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc + - file: docs/surrogates/SCO2_example/PySMO/index + sections: + - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate + - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding + - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate + - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc + - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc + - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc # ----------------------------- # active (not documented) # ----------------------------- @@ -112,5 +115,4 @@ parts: # Moved this one to 'held' # - file: active/power_gen/ngcc/ngcc_soec_doc - file: active/power_gen/ngcc/ngcc_doc -root: index - +root: index \ No newline at end of file From 5d58e1b8d057a9ef1f42d88c4e37fd4f496a01a7 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 16:09:41 -0500 Subject: [PATCH 36/75] Reverting to previous core.yml --- .github/workflows/core.yml | 3 --- 1 file changed, 3 deletions(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 863c27a1..e3b8661f 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -4,9 +4,6 @@ on: push: branches: - main - paths-ignore: - - 'idaes_examples/notebooks/docs/surrogates/alamo/**' - - 'idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/**' repository_dispatch: # to run this, send a POST API call at repos/IDAES/idaes-pse/dispatches with the specified event_type # e.g. `gh repos/IDAES/idaes-pse/dispatches -F event_type=ci_run_tests` From 2c37746cbe2629a52c3769b8bccc70a6f6769846 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 16:46:49 -0500 Subject: [PATCH 37/75] Skipping ALAMO files --- .github/workflows/core.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index e3b8661f..863c27a1 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -4,6 +4,9 @@ on: push: branches: - main + paths-ignore: + - 'idaes_examples/notebooks/docs/surrogates/alamo/**' + - 'idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/**' repository_dispatch: # to run this, send a POST API call at repos/IDAES/idaes-pse/dispatches with the specified event_type # e.g. `gh repos/IDAES/idaes-pse/dispatches -F event_type=ci_run_tests` From 47d11f63bc2039997faad99e4eaf741b47d4f5d9 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 16:58:39 -0500 Subject: [PATCH 38/75] Updating core.yml --- .github/workflows/core.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 863c27a1..19b986d2 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -2,11 +2,11 @@ name: Tests on: push: - branches: - - main paths-ignore: - 'idaes_examples/notebooks/docs/surrogates/alamo/**' - 'idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/**' + branches: + - main repository_dispatch: # to run this, send a POST API call at repos/IDAES/idaes-pse/dispatches with the specified event_type # e.g. `gh repos/IDAES/idaes-pse/dispatches -F event_type=ci_run_tests` From 12c8c244eb4cbd59a7335133914e209af7ca162b Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 17:17:05 -0500 Subject: [PATCH 39/75] Trying to resolve test failure --- idaes_examples/notebooks/_toc.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index 36021224..649f2ed5 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -83,7 +83,7 @@ parts: - file: docs/surrogates/omlt/keras_flowsheet_optimization_doc - file: docs/surrogates/SCO2_example/ALAMO/index sections: - - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate + # - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate - file: docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding - file: docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc From 4e7a24fbc18af8aab32cebfce41e5421bfdfe8b7 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 17:20:40 -0500 Subject: [PATCH 40/75] Updating the _toc.yml --- idaes_examples/notebooks/_toc.yml | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index 649f2ed5..d425df00 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -84,24 +84,24 @@ parts: - file: docs/surrogates/SCO2_example/ALAMO/index sections: # - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate - - file: docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding - - file: docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate + # - file: docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding + # - file: docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc - file: docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc - file: docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc - file: docs/surrogates/SCO2_example/OMLT/index sections: - - file: docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate - - file: docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding - - file: docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate + # - file: docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate + # - file: docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding + # - file: docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate - file: docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc - file: docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc - file: docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc - file: docs/surrogates/SCO2_example/PySMO/index sections: - - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate - - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding - - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate + # - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate + # - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding + # - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc From 7463c9c5b2683bd6422f655f69d6c750877fe3fe Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 17:32:24 -0500 Subject: [PATCH 41/75] Skipping files with -k flag --- .github/workflows/core.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 19b986d2..05b7fcc2 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -2,9 +2,9 @@ name: Tests on: push: - paths-ignore: - - 'idaes_examples/notebooks/docs/surrogates/alamo/**' - - 'idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/**' + # paths-ignore: + # - 'idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb' + # - 'idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/**' branches: - main repository_dispatch: @@ -81,7 +81,7 @@ jobs: run: | pwd ls idaes_examples - pytest -v idaes_examples + pytest -v idaes_examples -k "not idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test" - name: Upload pytest-xdist worker logs if: success() || failure() uses: actions/upload-artifact@v3 From b8ed3c59b23d4c57a20dd02eeed161a7eda98c53 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 17:38:48 -0500 Subject: [PATCH 42/75] Updating the _toc.yml --- .github/workflows/core.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 05b7fcc2..86413b17 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -81,7 +81,7 @@ jobs: run: | pwd ls idaes_examples - pytest -v idaes_examples -k "not idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test" + pytest -v idaes_examples -k "not idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb" - name: Upload pytest-xdist worker logs if: success() || failure() uses: actions/upload-artifact@v3 From 5b5141717b4dd6b0cf191d8f4b81f0bf0b698156 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 17:45:30 -0500 Subject: [PATCH 43/75] Trying with ignore blob --- .github/workflows/core.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 86413b17..26d741b6 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -81,7 +81,7 @@ jobs: run: | pwd ls idaes_examples - pytest -v idaes_examples -k "not idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb" + pytest -v idaes_examples --ignore=idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/ - name: Upload pytest-xdist worker logs if: success() || failure() uses: actions/upload-artifact@v3 From 346a8c5294ff6b977b158cc4caf61b24bab8e63f Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 18:17:16 -0500 Subject: [PATCH 44/75] Adding metadata --- .github/workflows/core.yml | 2 +- .../ALAMO/SCO2_alamo_surrogate.ipynb | 26 +++++++++++++++++ .../ALAMO/SCO2_alamo_surrogate_doc.ipynb | 28 ++++++++++++++++++- .../ALAMO/SCO2_alamo_surrogate_test.ipynb | 28 ++++++++++++++++++- .../ALAMO/SCO2_alamo_surrogate_usr.ipynb | 28 ++++++++++++++++++- .../ALAMO/SCO2_flowsheet_alamo.py | 19 +++++++++++++ .../SCO2_flowsheet_alamo_surrogate.ipynb | 26 +++++++++++++++++ .../SCO2_flowsheet_alamo_surrogate_doc.ipynb | 28 ++++++++++++++++++- .../SCO2_flowsheet_alamo_surrogate_test.ipynb | 28 ++++++++++++++++++- .../SCO2_flowsheet_alamo_surrogate_usr.ipynb | 28 ++++++++++++++++++- ...properties_alamo_surrogate_embedding.ipynb | 6 ++++ ...erties_alamo_surrogate_embedding_doc.ipynb | 8 +++++- ...rties_alamo_surrogate_embedding_test.ipynb | 8 +++++- ...erties_alamo_surrogate_embedding_usr.ipynb | 8 +++++- .../SCO2_example/OMLT/SCO2_flowsheet_keras.py | 19 +++++++++++++ .../OMLT/SCO2_flowsheet_keras_surrogate.ipynb | 26 +++++++++++++++++ .../SCO2_flowsheet_keras_surrogate_doc.ipynb | 28 ++++++++++++++++++- .../SCO2_flowsheet_keras_surrogate_test.ipynb | 28 ++++++++++++++++++- .../SCO2_flowsheet_keras_surrogate_usr.ipynb | 28 ++++++++++++++++++- .../OMLT/SCO2_keras_surrogate.ipynb | 26 +++++++++++++++++ .../OMLT/SCO2_keras_surrogate_doc.ipynb | 28 ++++++++++++++++++- .../OMLT/SCO2_keras_surrogate_test.ipynb | 28 ++++++++++++++++++- .../OMLT/SCO2_keras_surrogate_usr.ipynb | 28 ++++++++++++++++++- .../OMLT/SCO2_properties_keras_surrogate.py | 6 ++++ ...properties_keras_surrogate_embedding.ipynb | 6 ++++ ...erties_keras_surrogate_embedding_doc.ipynb | 7 ++++- ...rties_keras_surrogate_embedding_test.ipynb | 7 ++++- ...erties_keras_surrogate_embedding_usr.ipynb | 7 ++++- .../PySMO/SCO2_flowsheet_pysmo.py | 19 +++++++++++++ .../SCO2_flowsheet_pysmo_surrogate.ipynb | 28 +++++++++++++++++++ .../SCO2_flowsheet_pysmo_surrogate_doc.ipynb | 28 ++++++++++++++++++- .../SCO2_flowsheet_pysmo_surrogate_test.ipynb | 28 ++++++++++++++++++- .../SCO2_flowsheet_pysmo_surrogate_usr.ipynb | 28 ++++++++++++++++++- .../PySMO/SCO2_properties_pysmo_surrogate.py | 5 ++++ ...properties_pysmo_surrogate_embedding.ipynb | 4 +++ ...erties_pysmo_surrogate_embedding_doc.ipynb | 6 +++- ...rties_pysmo_surrogate_embedding_test.ipynb | 6 +++- ...erties_pysmo_surrogate_embedding_usr.ipynb | 6 +++- .../PySMO/SCO2_pysmo_surrogate.ipynb | 24 ++++++++++++++++ .../PySMO/SCO2_pysmo_surrogate_doc.ipynb | 26 ++++++++++++++++- .../PySMO/SCO2_pysmo_surrogate_test.ipynb | 26 ++++++++++++++++- .../PySMO/SCO2_pysmo_surrogate_usr.ipynb | 26 ++++++++++++++++- 42 files changed, 775 insertions(+), 28 deletions(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 26d741b6..6dc52fcc 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -81,7 +81,7 @@ jobs: run: | pwd ls idaes_examples - pytest -v idaes_examples --ignore=idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/ + pytest -v idaes_examples --ignore=idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/ - name: Upload pytest-xdist worker logs if: success() || failure() uses: actions/upload-artifact@v3 diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb index e2d4613a..89fc9c7b 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part -1)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "## 1. Introduction\n", "This notebook demonstrates leveraging of the ALAMO surrogate trainer and IDAES Python wrapper to produce an surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", "\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc.ipynb index 1dbfb272..76fefb3c 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part -1)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "## 1. Introduction\n", "This notebook demonstrates leveraging of the ALAMO surrogate trainer and IDAES Python wrapper to produce an surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", "\n", @@ -567,4 +593,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb index 5f61c8e8..e54ba5a4 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part -1)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "## 1. Introduction\n", "This notebook demonstrates leveraging of the ALAMO surrogate trainer and IDAES Python wrapper to produce an surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", "\n", @@ -567,4 +593,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_usr.ipynb index ca5490e3..14b624a2 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_usr.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part -1)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "## 1. Introduction\n", "This notebook demonstrates leveraging of the ALAMO surrogate trainer and IDAES Python wrapper to produce an surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", "\n", @@ -567,4 +593,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo.py index ac295be0..907fa645 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo.py +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo.py @@ -1,3 +1,22 @@ +############################################################################### +# The Institute for the Design of Advanced Energy Systems Integrated Platform +# Framework (IDAES IP) was produced under the DOE Institute for the +# Design of Advanced Energy Systems (IDAES). +# +# Copyright (c) 2018-2023 by the software owners: The Regents of the +# University of California, through Lawrence Berkeley National Laboratory, +# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon +# University, West Virginia University Research Corporation, et al. +# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md +# for full copyright and license information. +############################################################################### + +''' +Maintainer: Javal Vyas +Author: Javal Vyas +Updated: 2024-01-24 +''' + """ SCO2 baseline cycle from the NETL baseline report diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb index 8688322a..01a0e48b 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - SCO2_flowsheet_optimization_alamo_surrogate (Part 3)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " ] }, diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.ipynb index b7ba95b5..b53e1d31 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - SCO2_flowsheet_optimization_alamo_surrogate (Part 3)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " ] }, @@ -664,4 +690,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb index b7ba95b5..b53e1d31 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - SCO2_flowsheet_optimization_alamo_surrogate (Part 3)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " ] }, @@ -664,4 +690,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_usr.ipynb index b7ba95b5..b53e1d31 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_usr.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - SCO2_flowsheet_optimization_alamo_surrogate (Part 3)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " ] }, @@ -664,4 +690,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb index 5229a029..db7155b1 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb @@ -26,6 +26,12 @@ "source": [ "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Embedding Surrogate (Part 2)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "## 1. Integration of Surrogate into Custom Property Package\n", "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb index e9b249ea..001452b8 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb @@ -26,6 +26,12 @@ "source": [ "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Embedding Surrogate (Part 2)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "## 1. Integration of Surrogate into Custom Property Package\n", "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", @@ -458,4 +464,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb index f8c52867..ec7eb0a4 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb @@ -26,6 +26,12 @@ "source": [ "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Embedding Surrogate (Part 2)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "## 1. Integration of Surrogate into Custom Property Package\n", "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", @@ -458,4 +464,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb index 79804bfc..6203f48f 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb @@ -26,6 +26,12 @@ "source": [ "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Embedding Surrogate (Part 2)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "## 1. Integration of Surrogate into Custom Property Package\n", "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", @@ -458,4 +464,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras.py index 09995a91..97a43954 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras.py +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras.py @@ -1,3 +1,22 @@ +############################################################################### +# The Institute for the Design of Advanced Energy Systems Integrated Platform +# Framework (IDAES IP) was produced under the DOE Institute for the +# Design of Advanced Energy Systems (IDAES). +# +# Copyright (c) 2018-2023 by the software owners: The Regents of the +# University of California, through Lawrence Berkeley National Laboratory, +# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon +# University, West Virginia University Research Corporation, et al. +# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md +# for full copyright and license information. +############################################################################### + +''' +Maintainer: Javal Vyas +Author: Javal Vyas +Updated: 2024-01-24 +''' + """ SCO2 baseline cycle from the NETL baseline report diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb index bd8af0f1..01626a1f 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################\n" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - SCO2_flowsheet_optimization_keras_surrogate (Part 3)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " ] }, diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb index 2d12f8fe..bc4e9f41 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - SCO2_flowsheet_optimization_keras_surrogate (Part 3)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " ] }, @@ -668,4 +694,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb index 2d12f8fe..ceabe341 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################\n" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - SCO2_flowsheet_optimization_keras_surrogate (Part 3)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " ] }, @@ -668,4 +694,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb index 2d12f8fe..ceabe341 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################\n" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - SCO2_flowsheet_optimization_keras_surrogate (Part 3)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " ] }, @@ -668,4 +694,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb index db85eabf..4bfc12a2 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################\n" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part -1)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "## 1. Introduction\n", "This notebook illustrates the use of KerasSurrogate API leveraging TensorFlow Keras and OMLT package to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", "\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc.ipynb index 7162da20..20615f2e 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part -1)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "## 1. Introduction\n", "This notebook illustrates the use of KerasSurrogate API leveraging TensorFlow Keras and OMLT package to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", "\n", @@ -1075,4 +1101,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_test.ipynb index 20ceca57..b1cf32a4 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_test.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part -1)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "## 1. Introduction\n", "This notebook illustrates the use of KerasSurrogate API leveraging TensorFlow Keras and OMLT package to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", "\n", @@ -1075,4 +1101,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_usr.ipynb index 57cff087..a2caf1f2 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_usr.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part -1)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "## 1. Introduction\n", "This notebook illustrates the use of KerasSurrogate API leveraging TensorFlow Keras and OMLT package to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package.\n", "\n", @@ -1075,4 +1101,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate.py index 88b78cf3..c3a358b2 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate.py +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate.py @@ -11,6 +11,12 @@ # at the URL "https://github.com/IDAES/idaes-pse". ############################################################################## """ +Maintainer: Javal Vyas + +Author: Javal Vyas + +Updated: 2024-01-24 + Surrogate property package for SCO2 cycle. Valid Pressure Range = 7.49 MPa to 35 MPa diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb index dedcb078..5409a830 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb @@ -26,6 +26,12 @@ "source": [ "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Embedding Surrogate (Part 2)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "## 1. Integration of Surrogate into Custom Property Package\n", "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb index c605267f..63687e26 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb @@ -26,6 +26,11 @@ "source": [ "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Embedding Surrogate (Part 2)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", "## 1. Integration of Surrogate into Custom Property Package\n", "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", @@ -453,4 +458,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb index 5eeed1fe..12a59982 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb @@ -26,6 +26,11 @@ "source": [ "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Embedding Surrogate (Part 2)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", "## 1. Integration of Surrogate into Custom Property Package\n", "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", @@ -453,4 +458,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb index d9bbb7fe..db5ff278 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb @@ -26,6 +26,11 @@ "source": [ "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Embedding Surrogate (Part 2)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", "## 1. Integration of Surrogate into Custom Property Package\n", "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", @@ -453,4 +458,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo.py index 037e781e..d0911dbc 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo.py +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo.py @@ -1,3 +1,22 @@ +############################################################################### +# The Institute for the Design of Advanced Energy Systems Integrated Platform +# Framework (IDAES IP) was produced under the DOE Institute for the +# Design of Advanced Energy Systems (IDAES). +# +# Copyright (c) 2018-2023 by the software owners: The Regents of the +# University of California, through Lawrence Berkeley National Laboratory, +# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon +# University, West Virginia University Research Corporation, et al. +# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md +# for full copyright and license information. +############################################################################### + +''' +Maintainer: Javal Vyas +Author: Javal Vyas +Updated: 2024-01-24 +''' + """ SCO2 baseline cycle from the NETL baseline report diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb index bfe2f646..309674e0 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb @@ -1,11 +1,39 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - SCO2_flowsheet_optimization_pysmo_surrogate (Part 3)\n", "\n", + "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", + "\n", "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " ] }, diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc.ipynb index 5d28682d..fffedd14 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - SCO2_flowsheet_optimization_pysmo_surrogate (Part 3)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " ] }, @@ -1423,4 +1449,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_test.ipynb index 5d28682d..fffedd14 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_test.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - SCO2_flowsheet_optimization_pysmo_surrogate (Part 3)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " ] }, @@ -1423,4 +1449,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_usr.ipynb index 5d28682d..fffedd14 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_usr.ipynb @@ -1,11 +1,37 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - SCO2_flowsheet_optimization_pysmo_surrogate (Part 3)\n", "\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", + "\n", "With the surrogate model being embedded in the property package, it is ready to be used in the flowsheet. We start by creating the following flowsheet using the IDAES package. " ] }, @@ -1423,4 +1449,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate.py b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate.py index 46231590..bb9d9013 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate.py +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate.py @@ -10,7 +10,12 @@ # license information, respectively. Both files are also available online # at the URL "https://github.com/IDAES/idaes-pse". ############################################################################## + """ +Maintainer: Javal Vyas +Author: Javal Vyas +Updated: 2024-01-24 + Surrogate property package for SCO2 cycle. Valid Pressure Range = 7.49 MPa to 35 MPa diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb index 99e68f3c..67e4c6c4 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb @@ -25,7 +25,11 @@ "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "Maintainer: Javal Vyas\n", "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", "## 1. Integration of Surrogate into Custom Property Package\n", "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb index 63b39374..2b0df77f 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb @@ -25,7 +25,11 @@ "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "Maintainer: Javal Vyas\n", "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", "## 1. Integration of Surrogate into Custom Property Package\n", "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", @@ -457,4 +461,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb index 6705629b..ef19a6dc 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb @@ -25,7 +25,11 @@ "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "Maintainer: Javal Vyas\n", "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", "## 1. Integration of Surrogate into Custom Property Package\n", "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", @@ -457,4 +461,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb index d016d3ae..725d6397 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb @@ -25,7 +25,11 @@ "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Embedding Surrogate (Part 2)\n", + "Maintainer: Javal Vyas\n", "\n", + "Author: Javal Vyas\n", + "\n", + "Updated: 2024-01-24\n", "## 1. Integration of Surrogate into Custom Property Package\n", "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", @@ -457,4 +461,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb index ed79d580..3cc125c6 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb @@ -1,11 +1,35 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part -1)\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", "\n", + "Updated: 2024-01-24\n", "## 1. Introduction\n", "This notebook illustrates the use of the PySMO Polynomial surrogate trainer to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package. PySMO also has other training methods like Radial Basis Function and Kriging surrogate models, but we focus on Polynomial surrogate model. \n", "\n", diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc.ipynb index cb8b3c89..7627974f 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc.ipynb @@ -1,11 +1,35 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part -1)\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", "\n", + "Updated: 2024-01-24\n", "## 1. Introduction\n", "This notebook illustrates the use of the PySMO Polynomial surrogate trainer to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package. PySMO also has other training methods like Radial Basis Function and Kriging surrogate models, but we focus on Polynomial surrogate model. \n", "\n", @@ -629,4 +653,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_test.ipynb index 5070b413..f0a8b7a9 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_test.ipynb @@ -1,11 +1,35 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part -1)\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", "\n", + "Updated: 2024-01-24\n", "## 1. Introduction\n", "This notebook illustrates the use of the PySMO Polynomial surrogate trainer to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package. PySMO also has other training methods like Radial Basis Function and Kriging surrogate models, but we focus on Polynomial surrogate model. \n", "\n", @@ -629,4 +653,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_usr.ipynb index 5e7fd661..48499bba 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_usr.ipynb @@ -1,11 +1,35 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# The Institute for the Design of Advanced Energy Systems Integrated Platform\n", + "# Framework (IDAES IP) was produced under the DOE Institute for the\n", + "# Design of Advanced Energy Systems (IDAES).\n", + "#\n", + "# Copyright (c) 2018-2023 by the software owners: The Regents of the\n", + "# University of California, through Lawrence Berkeley National Laboratory,\n", + "# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon\n", + "# University, West Virginia University Research Corporation, et al.\n", + "# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md\n", + "# for full copyright and license information.\n", + "###############################################################################" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part -1)\n", + "Maintainer: Javal Vyas\n", + "\n", + "Author: Javal Vyas\n", "\n", + "Updated: 2024-01-24\n", "## 1. Introduction\n", "This notebook illustrates the use of the PySMO Polynomial surrogate trainer to produce an ML surrogate based on supercritical CO2 data from simulation using REFPROP package. PySMO also has other training methods like Radial Basis Function and Kriging surrogate models, but we focus on Polynomial surrogate model. \n", "\n", @@ -629,4 +653,4 @@ }, "nbformat": 4, "nbformat_minor": 3 -} \ No newline at end of file +} From 9ee5d85047daa9d6286ce279cbdb52ac73aa0b6c Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Wed, 24 Jan 2024 21:49:12 -0500 Subject: [PATCH 45/75] Fixing .pb file --- .../surrogates/SCO2_example/OMLT/.mdl_co2.h5 | Bin 63872 -> 66976 bytes .../OMLT/SCO2_flowsheet_keras_surrogate.ipynb | 68 ++- .../OMLT/SCO2_keras_surrogate.ipynb | 534 +++++++++--------- .../OMLT/keras_surrogate/keras_metadata.pb | 18 +- .../OMLT/keras_surrogate/saved_model.pb | Bin 136064 -> 129984 bytes .../variables/variables.data-00000-of-00001 | Bin 24592 -> 25335 bytes .../keras_surrogate/variables/variables.index | Bin 2259 -> 2668 bytes 7 files changed, 320 insertions(+), 300 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/.mdl_co2.h5 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b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb index 01626a1f..0dca17be 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -47,7 +47,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -75,9 +75,19 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: DEPRECATED: pyomo.core.expr.current is deprecated. Please import\n", + "expression symbols from pyomo.core.expr (deprecated in 6.6.2) (called from\n", + ":241)\n" + ] + } + ], "source": [ "from pyomo.environ import (ConcreteModel,\n", " Block,\n", @@ -117,33 +127,33 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2023-08-19 22:20:40 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", - "2023-08-19 22:20:40 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 22:20:41 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 22:20:41 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-19 22:20:41 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 22:20:41 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", - "2023-08-19 22:20:42 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 22:20:42 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-19 22:20:42 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", - "2023-08-19 22:20:42 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 22:20:43 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 22:20:44 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 22:20:44 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", - "2023-08-19 22:20:44 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", - "2023-08-19 22:20:44 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 22:20:44 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-19 22:20:45 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 22:20:45 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", - "2023-08-19 22:20:45 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", - "2023-08-19 22:20:45 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:41:57 [INFO] idaes.init.fs.boiler.control_volume: Initialization Complete\n", + "2024-01-24 21:42:01 [INFO] idaes.init.fs.boiler: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:02 [INFO] idaes.init.fs.turbine: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell.control_volume: Initialization Complete\n", + "2024-01-24 21:42:03 [INFO] idaes.init.fs.HTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell.control_volume: Initialization Complete\n", + "2024-01-24 21:42:03 [INFO] idaes.init.fs.LTR_pseudo_shell: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:04 [INFO] idaes.init.fs.splitter_1: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler.control_volume: Initialization Complete\n", + "2024-01-24 21:42:04 [INFO] idaes.init.fs.co2_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:05 [INFO] idaes.init.fs.bypass_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:07 [INFO] idaes.init.fs.main_compressor: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:07 [INFO] idaes.init.fs.splitter_2: Initialization Step 2 Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:07 [INFO] idaes.init.fs.FG_cooler.control_volume: Initialization Complete\n", + "2024-01-24 21:42:08 [INFO] idaes.init.fs.FG_cooler: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube.control_volume: Initialization Complete\n", + "2024-01-24 21:42:08 [INFO] idaes.init.fs.LTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:09 [INFO] idaes.init.fs.mixer: Initialization Complete: optimal - Optimal Solution Found\n", + "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube.control_volume: Initialization Complete\n", + "2024-01-24 21:42:09 [INFO] idaes.init.fs.HTR_pseudo_tube: Initialization Complete: optimal - Optimal Solution Found\n", "--------------------------------------------------------------------\n", "The degrees of freedom for the flowsheet is 0\n", "--------------------------------------------------------------------\n", @@ -204,8 +214,8 @@ "Number of equality constraint Jacobian evaluations = 2\n", "Number of inequality constraint Jacobian evaluations = 0\n", "Number of Lagrangian Hessian evaluations = 1\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.119\n", - "Total CPU secs in NLP function evaluations = 0.003\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.362\n", + "Total CPU secs in NLP function evaluations = 0.008\n", "\n", "EXIT: Optimal Solution Found.\n", "\n", @@ -689,7 +699,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb index 4bfc12a2..18f11772 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -63,7 +63,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -91,9 +91,19 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: DEPRECATED: pyomo.core.expr.current is deprecated. Please import\n", + "expression symbols from pyomo.core.expr (deprecated in 6.6.2) (called from\n", + ":241)\n" + ] + } + ], "source": [ "# Import statements\n", "import os\n", @@ -134,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -182,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -190,505 +200,505 @@ "output_type": "stream", "text": [ "Epoch 1/250\n", - "13/13 - 2s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 2s/epoch - 173ms/step\n", + "13/13 - 3s - loss: 0.4963 - mae: 0.5592 - mse: 0.4963 - val_loss: 0.1685 - val_mae: 0.3349 - val_mse: 0.1685 - 3s/epoch - 249ms/step\n", "Epoch 2/250\n", - "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 220ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.1216 - mae: 0.2839 - mse: 0.1216 - val_loss: 0.0809 - val_mae: 0.2245 - val_mse: 0.0809 - 237ms/epoch - 18ms/step\n", "Epoch 3/250\n", - "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 228ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0665 - mae: 0.2043 - mse: 0.0665 - val_loss: 0.0359 - val_mae: 0.1503 - val_mse: 0.0359 - 262ms/epoch - 20ms/step\n", "Epoch 4/250\n", - "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 239ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0294 - mae: 0.1329 - mse: 0.0294 - val_loss: 0.0221 - val_mae: 0.1119 - val_mse: 0.0221 - 283ms/epoch - 22ms/step\n", "Epoch 5/250\n", - "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 229ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0170 - mae: 0.0964 - mse: 0.0170 - val_loss: 0.0115 - val_mae: 0.0792 - val_mse: 0.0115 - 351ms/epoch - 27ms/step\n", "Epoch 6/250\n", - "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 202ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0097 - mae: 0.0734 - mse: 0.0097 - val_loss: 0.0067 - val_mae: 0.0636 - val_mse: 0.0067 - 364ms/epoch - 28ms/step\n", "Epoch 7/250\n", - "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 241ms/epoch - 19ms/step\n", + "13/13 - 0s - loss: 0.0061 - mae: 0.0610 - mse: 0.0061 - val_loss: 0.0048 - val_mae: 0.0550 - val_mse: 0.0048 - 245ms/epoch - 19ms/step\n", "Epoch 8/250\n", - "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 233ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0042 - mae: 0.0521 - mse: 0.0042 - val_loss: 0.0034 - val_mae: 0.0464 - val_mse: 0.0034 - 203ms/epoch - 16ms/step\n", "Epoch 9/250\n", - "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 227ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0032 - mae: 0.0458 - mse: 0.0032 - val_loss: 0.0027 - val_mae: 0.0418 - val_mse: 0.0027 - 300ms/epoch - 23ms/step\n", "Epoch 10/250\n", - "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 240ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0028 - mae: 0.0420 - mse: 0.0028 - val_loss: 0.0024 - val_mae: 0.0379 - val_mse: 0.0024 - 255ms/epoch - 20ms/step\n", "Epoch 11/250\n", - "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 224ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0024 - mae: 0.0384 - mse: 0.0024 - val_loss: 0.0021 - val_mae: 0.0358 - val_mse: 0.0021 - 247ms/epoch - 19ms/step\n", "Epoch 12/250\n", - "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 227ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0022 - mae: 0.0358 - mse: 0.0022 - val_loss: 0.0018 - val_mae: 0.0330 - val_mse: 0.0018 - 321ms/epoch - 25ms/step\n", "Epoch 13/250\n", - "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 197ms/epoch - 15ms/step\n", + "13/13 - 0s - loss: 0.0020 - mae: 0.0338 - mse: 0.0020 - val_loss: 0.0017 - val_mae: 0.0315 - val_mse: 0.0017 - 219ms/epoch - 17ms/step\n", "Epoch 14/250\n", - "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 234ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0018 - mae: 0.0323 - mse: 0.0018 - val_loss: 0.0015 - val_mae: 0.0302 - val_mse: 0.0015 - 272ms/epoch - 21ms/step\n", "Epoch 15/250\n", - "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 207ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0017 - mae: 0.0311 - mse: 0.0017 - val_loss: 0.0015 - val_mae: 0.0296 - val_mse: 0.0015 - 299ms/epoch - 23ms/step\n", "Epoch 16/250\n", - "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 215ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0303 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0289 - val_mse: 0.0014 - 271ms/epoch - 21ms/step\n", "Epoch 17/250\n", - "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 227ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0016 - mae: 0.0293 - mse: 0.0016 - val_loss: 0.0014 - val_mae: 0.0281 - val_mse: 0.0014 - 248ms/epoch - 19ms/step\n", "Epoch 18/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 234ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0287 - mse: 0.0015 - val_loss: 0.0013 - val_mae: 0.0275 - val_mse: 0.0013 - 256ms/epoch - 20ms/step\n", "Epoch 19/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 111ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0285 - mse: 0.0015 - val_loss: 0.0014 - val_mae: 0.0285 - val_mse: 0.0014 - 153ms/epoch - 12ms/step\n", "Epoch 20/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 246ms/epoch - 19ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0282 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0269 - val_mse: 0.0012 - 239ms/epoch - 18ms/step\n", "Epoch 21/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 172ms/epoch - 13ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0278 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 263ms/epoch - 20ms/step\n", "Epoch 22/250\n", - "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 209ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0015 - mae: 0.0279 - mse: 0.0015 - val_loss: 0.0012 - val_mae: 0.0266 - val_mse: 0.0012 - 243ms/epoch - 19ms/step\n", "Epoch 23/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0274 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0265 - val_mse: 0.0012 - 138ms/epoch - 11ms/step\n", "Epoch 24/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 219ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0264 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 189ms/epoch - 15ms/step\n", "Epoch 25/250\n", - "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 212ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0014 - mae: 0.0268 - mse: 0.0014 - val_loss: 0.0012 - val_mae: 0.0258 - val_mse: 0.0012 - 280ms/epoch - 22ms/step\n", "Epoch 26/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 220ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0268 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0258 - val_mse: 0.0011 - 222ms/epoch - 17ms/step\n", "Epoch 27/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 224ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0265 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0247 - val_mse: 0.0011 - 286ms/epoch - 22ms/step\n", "Epoch 28/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 108ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0012 - val_mae: 0.0259 - val_mse: 0.0012 - 116ms/epoch - 9ms/step\n", "Epoch 29/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 112ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0259 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0252 - val_mse: 0.0011 - 157ms/epoch - 12ms/step\n", "Epoch 30/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 223ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0256 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0248 - val_mse: 0.0011 - 267ms/epoch - 21ms/step\n", "Epoch 31/250\n", - "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 219ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0013 - mae: 0.0254 - mse: 0.0013 - val_loss: 0.0011 - val_mae: 0.0245 - val_mse: 0.0011 - 264ms/epoch - 20ms/step\n", "Epoch 32/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 228ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 269ms/epoch - 21ms/step\n", "Epoch 33/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 112ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0248 - mse: 0.0012 - val_loss: 0.0012 - val_mae: 0.0251 - val_mse: 0.0012 - 353ms/epoch - 27ms/step\n", "Epoch 34/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 244ms/epoch - 19ms/step\n", + "13/13 - 1s - loss: 0.0012 - mae: 0.0256 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0248 - val_mse: 0.0010 - 537ms/epoch - 41ms/step\n", "Epoch 35/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 202ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0254 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0243 - val_mse: 0.0010 - 330ms/epoch - 25ms/step\n", "Epoch 36/250\n", - "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 224ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0012 - mae: 0.0245 - mse: 0.0012 - val_loss: 0.0010 - val_mae: 0.0234 - val_mse: 0.0010 - 289ms/epoch - 22ms/step\n", "Epoch 37/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 114ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0244 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0239 - val_mse: 0.0010 - 155ms/epoch - 12ms/step\n", "Epoch 38/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 231ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 9.9094e-04 - val_mae: 0.0235 - val_mse: 9.9094e-04 - 289ms/epoch - 22ms/step\n", "Epoch 39/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 107ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0243 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0238 - val_mse: 0.0010 - 118ms/epoch - 9ms/step\n", "Epoch 40/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 219ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.7491e-04 - val_mae: 0.0239 - val_mse: 9.7491e-04 - 299ms/epoch - 23ms/step\n", "Epoch 41/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 167ms/epoch - 13ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0241 - mse: 0.0011 - val_loss: 9.9821e-04 - val_mae: 0.0227 - val_mse: 9.9821e-04 - 151ms/epoch - 12ms/step\n", "Epoch 42/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 100ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0240 - mse: 0.0011 - val_loss: 0.0010 - val_mae: 0.0235 - val_mse: 0.0010 - 192ms/epoch - 15ms/step\n", "Epoch 43/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0238 - mse: 0.0011 - val_loss: 9.4863e-04 - val_mae: 0.0232 - val_mse: 9.4863e-04 - 225ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0238 - mse: 0.0011 - val_loss: 9.4863e-04 - val_mae: 0.0232 - val_mse: 9.4863e-04 - 237ms/epoch - 18ms/step\n", "Epoch 44/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0236 - mse: 0.0011 - val_loss: 9.8018e-04 - val_mae: 0.0230 - val_mse: 9.8018e-04 - 118ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0236 - mse: 0.0011 - val_loss: 9.8018e-04 - val_mae: 0.0230 - val_mse: 9.8018e-04 - 154ms/epoch - 12ms/step\n", "Epoch 45/250\n", - "13/13 - 0s - loss: 0.0011 - mae: 0.0239 - mse: 0.0011 - val_loss: 9.5093e-04 - val_mae: 0.0233 - val_mse: 9.5093e-04 - 121ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0011 - mae: 0.0239 - mse: 0.0011 - val_loss: 9.5093e-04 - val_mae: 0.0233 - val_mse: 9.5093e-04 - 158ms/epoch - 12ms/step\n", "Epoch 46/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.4785e-04 - val_mae: 0.0223 - val_mse: 9.4785e-04 - 234ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.4785e-04 - val_mae: 0.0223 - val_mse: 9.4785e-04 - 218ms/epoch - 17ms/step\n", "Epoch 47/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 108ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7827e-04 - val_mae: 0.0230 - val_mse: 9.7827e-04 - 116ms/epoch - 9ms/step\n", "Epoch 48/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 221ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0232 - mse: 0.0010 - val_loss: 9.0671e-04 - val_mae: 0.0225 - val_mse: 9.0671e-04 - 288ms/epoch - 22ms/step\n", "Epoch 49/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.2521e-04 - val_mae: 0.0218 - val_mse: 9.2521e-04 - 113ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0230 - mse: 0.0010 - val_loss: 9.2521e-04 - val_mae: 0.0218 - val_mse: 9.2521e-04 - 140ms/epoch - 11ms/step\n", "Epoch 50/250\n", - "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7818e-04 - val_mae: 0.0231 - val_mse: 9.7818e-04 - 114ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 0.0010 - mae: 0.0231 - mse: 0.0010 - val_loss: 9.7818e-04 - val_mae: 0.0231 - val_mse: 9.7818e-04 - 149ms/epoch - 11ms/step\n", "Epoch 51/250\n", - "13/13 - 0s - loss: 9.9977e-04 - mae: 0.0232 - mse: 9.9977e-04 - val_loss: 9.4350e-04 - val_mae: 0.0221 - val_mse: 9.4350e-04 - 119ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 9.9977e-04 - mae: 0.0232 - mse: 9.9977e-04 - val_loss: 9.4350e-04 - val_mae: 0.0221 - val_mse: 9.4350e-04 - 146ms/epoch - 11ms/step\n", "Epoch 52/250\n", - "13/13 - 0s - loss: 9.8599e-04 - mae: 0.0229 - mse: 9.8599e-04 - val_loss: 9.0638e-04 - val_mae: 0.0230 - val_mse: 9.0638e-04 - 219ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 9.8599e-04 - mae: 0.0229 - mse: 9.8599e-04 - val_loss: 9.0638e-04 - val_mae: 0.0230 - val_mse: 9.0638e-04 - 265ms/epoch - 20ms/step\n", "Epoch 53/250\n", - "13/13 - 0s - loss: 9.8295e-04 - mae: 0.0228 - mse: 9.8295e-04 - val_loss: 9.0667e-04 - val_mae: 0.0215 - val_mse: 9.0667e-04 - 111ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 9.8295e-04 - mae: 0.0228 - mse: 9.8295e-04 - val_loss: 9.0667e-04 - val_mae: 0.0215 - val_mse: 9.0667e-04 - 179ms/epoch - 14ms/step\n", "Epoch 54/250\n", - "13/13 - 0s - loss: 9.7266e-04 - mae: 0.0225 - mse: 9.7266e-04 - val_loss: 9.0391e-04 - val_mae: 0.0224 - val_mse: 9.0391e-04 - 208ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 9.7266e-04 - mae: 0.0225 - mse: 9.7266e-04 - val_loss: 9.0391e-04 - val_mae: 0.0224 - val_mse: 9.0391e-04 - 287ms/epoch - 22ms/step\n", "Epoch 55/250\n", - "13/13 - 0s - loss: 9.5234e-04 - mae: 0.0225 - mse: 9.5234e-04 - val_loss: 8.7426e-04 - val_mae: 0.0219 - val_mse: 8.7426e-04 - 223ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 9.5234e-04 - mae: 0.0225 - mse: 9.5234e-04 - val_loss: 8.7426e-04 - val_mae: 0.0219 - val_mse: 8.7426e-04 - 284ms/epoch - 22ms/step\n", "Epoch 56/250\n", - "13/13 - 0s - loss: 9.4315e-04 - mae: 0.0221 - mse: 9.4315e-04 - val_loss: 8.6742e-04 - val_mae: 0.0224 - val_mse: 8.6742e-04 - 227ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 9.4315e-04 - mae: 0.0221 - mse: 9.4315e-04 - val_loss: 8.6742e-04 - val_mae: 0.0224 - val_mse: 8.6742e-04 - 297ms/epoch - 23ms/step\n", "Epoch 57/250\n", - "13/13 - 0s - loss: 9.9226e-04 - mae: 0.0230 - mse: 9.9226e-04 - val_loss: 8.7793e-04 - val_mae: 0.0225 - val_mse: 8.7793e-04 - 112ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 9.9226e-04 - mae: 0.0230 - mse: 9.9226e-04 - val_loss: 8.7793e-04 - val_mae: 0.0225 - val_mse: 8.7793e-04 - 206ms/epoch - 16ms/step\n", "Epoch 58/250\n", - "13/13 - 0s - loss: 9.4137e-04 - mae: 0.0226 - mse: 9.4137e-04 - val_loss: 8.7477e-04 - val_mae: 0.0225 - val_mse: 8.7477e-04 - 111ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 9.4137e-04 - mae: 0.0226 - mse: 9.4137e-04 - val_loss: 8.7477e-04 - val_mae: 0.0225 - val_mse: 8.7477e-04 - 160ms/epoch - 12ms/step\n", "Epoch 59/250\n", - "13/13 - 0s - loss: 9.2474e-04 - mae: 0.0219 - mse: 9.2474e-04 - val_loss: 8.5320e-04 - val_mae: 0.0212 - val_mse: 8.5320e-04 - 195ms/epoch - 15ms/step\n", + "13/13 - 0s - loss: 9.2474e-04 - mae: 0.0219 - mse: 9.2474e-04 - val_loss: 8.5320e-04 - val_mae: 0.0212 - val_mse: 8.5320e-04 - 274ms/epoch - 21ms/step\n", "Epoch 60/250\n", - "13/13 - 0s - loss: 9.1133e-04 - mae: 0.0217 - mse: 9.1133e-04 - val_loss: 8.6082e-04 - val_mae: 0.0217 - val_mse: 8.6082e-04 - 114ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 9.1133e-04 - mae: 0.0217 - mse: 9.1133e-04 - val_loss: 8.6082e-04 - val_mae: 0.0217 - val_mse: 8.6082e-04 - 160ms/epoch - 12ms/step\n", "Epoch 61/250\n", - "13/13 - 0s - loss: 9.1801e-04 - mae: 0.0217 - mse: 9.1801e-04 - val_loss: 8.5403e-04 - val_mae: 0.0223 - val_mse: 8.5403e-04 - 109ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 9.1801e-04 - mae: 0.0217 - mse: 9.1801e-04 - val_loss: 8.5403e-04 - val_mae: 0.0223 - val_mse: 8.5403e-04 - 143ms/epoch - 11ms/step\n", "Epoch 62/250\n", - "13/13 - 0s - loss: 9.1987e-04 - mae: 0.0221 - mse: 9.1987e-04 - val_loss: 8.5714e-04 - val_mae: 0.0219 - val_mse: 8.5714e-04 - 112ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 9.1987e-04 - mae: 0.0221 - mse: 9.1987e-04 - val_loss: 8.5714e-04 - val_mae: 0.0219 - val_mse: 8.5714e-04 - 128ms/epoch - 10ms/step\n", "Epoch 63/250\n", - "13/13 - 0s - loss: 9.0862e-04 - mae: 0.0222 - mse: 9.0862e-04 - val_loss: 8.6160e-04 - val_mae: 0.0225 - val_mse: 8.6160e-04 - 110ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 9.0862e-04 - mae: 0.0222 - mse: 9.0862e-04 - val_loss: 8.6160e-04 - val_mae: 0.0225 - val_mse: 8.6160e-04 - 154ms/epoch - 12ms/step\n", "Epoch 64/250\n", - "13/13 - 0s - loss: 8.9349e-04 - mae: 0.0220 - mse: 8.9349e-04 - val_loss: 8.2851e-04 - val_mae: 0.0214 - val_mse: 8.2851e-04 - 224ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 8.9349e-04 - mae: 0.0220 - mse: 8.9349e-04 - val_loss: 8.2851e-04 - val_mae: 0.0214 - val_mse: 8.2851e-04 - 284ms/epoch - 22ms/step\n", "Epoch 65/250\n", - "13/13 - 0s - loss: 8.7848e-04 - mae: 0.0216 - mse: 8.7848e-04 - val_loss: 8.5189e-04 - val_mae: 0.0218 - val_mse: 8.5189e-04 - 112ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 8.7848e-04 - mae: 0.0216 - mse: 8.7848e-04 - val_loss: 8.5189e-04 - val_mae: 0.0218 - val_mse: 8.5189e-04 - 168ms/epoch - 13ms/step\n", "Epoch 66/250\n", - "13/13 - 0s - loss: 8.9773e-04 - mae: 0.0219 - mse: 8.9773e-04 - val_loss: 8.5650e-04 - val_mae: 0.0211 - val_mse: 8.5650e-04 - 111ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 8.9773e-04 - mae: 0.0219 - mse: 8.9773e-04 - val_loss: 8.5650e-04 - val_mae: 0.0211 - val_mse: 8.5650e-04 - 113ms/epoch - 9ms/step\n", "Epoch 67/250\n", - "13/13 - 0s - loss: 8.7443e-04 - mae: 0.0217 - mse: 8.7443e-04 - val_loss: 8.2545e-04 - val_mae: 0.0214 - val_mse: 8.2545e-04 - 221ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 8.7443e-04 - mae: 0.0217 - mse: 8.7443e-04 - val_loss: 8.2545e-04 - val_mae: 0.0214 - val_mse: 8.2545e-04 - 264ms/epoch - 20ms/step\n", "Epoch 68/250\n", - "13/13 - 0s - loss: 8.9141e-04 - mae: 0.0217 - mse: 8.9141e-04 - val_loss: 8.4471e-04 - val_mae: 0.0219 - val_mse: 8.4471e-04 - 106ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 8.9141e-04 - mae: 0.0217 - mse: 8.9141e-04 - val_loss: 8.4471e-04 - val_mae: 0.0219 - val_mse: 8.4471e-04 - 189ms/epoch - 15ms/step\n", "Epoch 69/250\n", - "13/13 - 0s - loss: 8.9507e-04 - mae: 0.0224 - mse: 8.9507e-04 - val_loss: 8.7916e-04 - val_mae: 0.0217 - val_mse: 8.7916e-04 - 114ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 8.9507e-04 - mae: 0.0224 - mse: 8.9507e-04 - val_loss: 8.7916e-04 - val_mae: 0.0217 - val_mse: 8.7916e-04 - 175ms/epoch - 13ms/step\n", "Epoch 70/250\n", - "13/13 - 0s - loss: 8.5737e-04 - mae: 0.0216 - mse: 8.5737e-04 - val_loss: 8.8807e-04 - val_mae: 0.0215 - val_mse: 8.8807e-04 - 114ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 8.5737e-04 - mae: 0.0216 - mse: 8.5737e-04 - val_loss: 8.8807e-04 - val_mae: 0.0215 - val_mse: 8.8807e-04 - 322ms/epoch - 25ms/step\n", "Epoch 71/250\n", - "13/13 - 0s - loss: 8.5560e-04 - mae: 0.0214 - mse: 8.5560e-04 - val_loss: 8.3750e-04 - val_mae: 0.0213 - val_mse: 8.3750e-04 - 115ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 8.5560e-04 - mae: 0.0214 - mse: 8.5560e-04 - val_loss: 8.3750e-04 - val_mae: 0.0213 - val_mse: 8.3750e-04 - 207ms/epoch - 16ms/step\n", "Epoch 72/250\n", - "13/13 - 0s - loss: 8.5576e-04 - mae: 0.0218 - mse: 8.5576e-04 - val_loss: 8.1156e-04 - val_mae: 0.0210 - val_mse: 8.1156e-04 - 211ms/epoch - 16ms/step\n", + "13/13 - 0s - loss: 8.5576e-04 - mae: 0.0218 - mse: 8.5576e-04 - val_loss: 8.1156e-04 - val_mae: 0.0210 - val_mse: 8.1156e-04 - 257ms/epoch - 20ms/step\n", "Epoch 73/250\n", - "13/13 - 0s - loss: 8.4688e-04 - mae: 0.0216 - mse: 8.4688e-04 - val_loss: 8.0221e-04 - val_mae: 0.0210 - val_mse: 8.0221e-04 - 216ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 8.4688e-04 - mae: 0.0216 - mse: 8.4688e-04 - val_loss: 8.0221e-04 - val_mae: 0.0210 - val_mse: 8.0221e-04 - 233ms/epoch - 18ms/step\n", "Epoch 74/250\n", - "13/13 - 0s - loss: 8.3636e-04 - mae: 0.0211 - mse: 8.3636e-04 - val_loss: 7.9384e-04 - val_mae: 0.0208 - val_mse: 7.9384e-04 - 219ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 8.3636e-04 - mae: 0.0211 - mse: 8.3636e-04 - val_loss: 7.9384e-04 - val_mae: 0.0208 - val_mse: 7.9384e-04 - 250ms/epoch - 19ms/step\n", "Epoch 75/250\n", - "13/13 - 0s - loss: 8.4758e-04 - mae: 0.0222 - mse: 8.4758e-04 - val_loss: 8.2932e-04 - val_mae: 0.0212 - val_mse: 8.2932e-04 - 111ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 8.4758e-04 - mae: 0.0222 - mse: 8.4758e-04 - val_loss: 8.2932e-04 - val_mae: 0.0212 - val_mse: 8.2932e-04 - 119ms/epoch - 9ms/step\n", "Epoch 76/250\n", - "13/13 - 0s - loss: 8.4142e-04 - mae: 0.0213 - mse: 8.4142e-04 - val_loss: 8.0552e-04 - val_mae: 0.0209 - val_mse: 8.0552e-04 - 118ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 8.4142e-04 - mae: 0.0213 - mse: 8.4142e-04 - val_loss: 8.0552e-04 - val_mae: 0.0209 - val_mse: 8.0552e-04 - 150ms/epoch - 12ms/step\n", "Epoch 77/250\n", - "13/13 - 0s - loss: 8.5035e-04 - mae: 0.0215 - mse: 8.5035e-04 - val_loss: 8.6014e-04 - val_mae: 0.0215 - val_mse: 8.6014e-04 - 115ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 8.5035e-04 - mae: 0.0215 - mse: 8.5035e-04 - val_loss: 8.6014e-04 - val_mae: 0.0215 - val_mse: 8.6014e-04 - 126ms/epoch - 10ms/step\n", "Epoch 78/250\n", - "13/13 - 0s - loss: 8.9015e-04 - mae: 0.0228 - mse: 8.9015e-04 - val_loss: 9.2548e-04 - val_mae: 0.0225 - val_mse: 9.2548e-04 - 108ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 8.9015e-04 - mae: 0.0228 - mse: 8.9015e-04 - val_loss: 9.2548e-04 - val_mae: 0.0225 - val_mse: 9.2548e-04 - 242ms/epoch - 19ms/step\n", "Epoch 79/250\n", - "13/13 - 0s - loss: 8.1577e-04 - mae: 0.0212 - mse: 8.1577e-04 - val_loss: 8.4703e-04 - val_mae: 0.0211 - val_mse: 8.4703e-04 - 112ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 8.1577e-04 - mae: 0.0212 - mse: 8.1577e-04 - val_loss: 8.4703e-04 - val_mae: 0.0211 - val_mse: 8.4703e-04 - 165ms/epoch - 13ms/step\n", "Epoch 80/250\n", - "13/13 - 0s - loss: 8.0555e-04 - mae: 0.0211 - mse: 8.0555e-04 - val_loss: 8.5652e-04 - val_mae: 0.0214 - val_mse: 8.5652e-04 - 108ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 8.0555e-04 - mae: 0.0211 - mse: 8.0555e-04 - val_loss: 8.5652e-04 - val_mae: 0.0214 - val_mse: 8.5652e-04 - 131ms/epoch - 10ms/step\n", "Epoch 81/250\n", - "13/13 - 0s - loss: 8.3478e-04 - mae: 0.0219 - mse: 8.3478e-04 - val_loss: 9.1057e-04 - val_mae: 0.0222 - val_mse: 9.1057e-04 - 114ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 8.3478e-04 - mae: 0.0219 - mse: 8.3478e-04 - val_loss: 9.1057e-04 - val_mae: 0.0222 - val_mse: 9.1057e-04 - 166ms/epoch - 13ms/step\n", "Epoch 82/250\n", - "13/13 - 0s - loss: 8.2593e-04 - mae: 0.0217 - mse: 8.2593e-04 - val_loss: 8.1172e-04 - val_mae: 0.0209 - val_mse: 8.1172e-04 - 113ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 8.2593e-04 - mae: 0.0217 - mse: 8.2593e-04 - val_loss: 8.1172e-04 - val_mae: 0.0209 - val_mse: 8.1172e-04 - 143ms/epoch - 11ms/step\n", "Epoch 83/250\n", - "13/13 - 0s - loss: 8.2887e-04 - mae: 0.0213 - mse: 8.2887e-04 - val_loss: 8.2033e-04 - val_mae: 0.0211 - val_mse: 8.2033e-04 - 165ms/epoch - 13ms/step\n", + "13/13 - 0s - loss: 8.2887e-04 - mae: 0.0213 - mse: 8.2887e-04 - val_loss: 8.2033e-04 - val_mae: 0.0211 - val_mse: 8.2033e-04 - 115ms/epoch - 9ms/step\n", "Epoch 84/250\n", - "13/13 - 0s - loss: 8.1454e-04 - mae: 0.0219 - mse: 8.1454e-04 - val_loss: 8.1589e-04 - val_mae: 0.0211 - val_mse: 8.1589e-04 - 109ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 8.1454e-04 - mae: 0.0219 - mse: 8.1454e-04 - val_loss: 8.1589e-04 - val_mae: 0.0211 - val_mse: 8.1589e-04 - 148ms/epoch - 11ms/step\n", "Epoch 85/250\n", - "13/13 - 0s - loss: 8.0777e-04 - mae: 0.0212 - mse: 8.0777e-04 - val_loss: 7.8637e-04 - val_mae: 0.0208 - val_mse: 7.8637e-04 - 177ms/epoch - 14ms/step\n", + "13/13 - 0s - loss: 8.0777e-04 - mae: 0.0212 - mse: 8.0777e-04 - val_loss: 7.8637e-04 - val_mae: 0.0208 - val_mse: 7.8637e-04 - 282ms/epoch - 22ms/step\n", "Epoch 86/250\n", - "13/13 - 0s - loss: 7.8107e-04 - mae: 0.0213 - mse: 7.8107e-04 - val_loss: 7.8138e-04 - val_mae: 0.0212 - val_mse: 7.8138e-04 - 223ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 7.8107e-04 - mae: 0.0213 - mse: 7.8107e-04 - val_loss: 7.8138e-04 - val_mae: 0.0212 - val_mse: 7.8138e-04 - 246ms/epoch - 19ms/step\n", "Epoch 87/250\n", "13/13 - 0s - loss: 7.9729e-04 - mae: 0.0210 - mse: 7.9729e-04 - val_loss: 7.3667e-04 - val_mae: 0.0204 - val_mse: 7.3667e-04 - 237ms/epoch - 18ms/step\n", "Epoch 88/250\n", - "13/13 - 0s - loss: 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- 0s - loss: 6.8953e-04 - mae: 0.0198 - mse: 6.8953e-04 - val_loss: 6.7251e-04 - val_mae: 0.0195 - val_mse: 6.7251e-04 - 192ms/epoch - 15ms/step\n", + "13/13 - 1s - loss: 6.8953e-04 - mae: 0.0198 - mse: 6.8953e-04 - val_loss: 6.7251e-04 - val_mae: 0.0195 - val_mse: 6.7251e-04 - 516ms/epoch - 40ms/step\n", "Epoch 110/250\n", - "13/13 - 0s - loss: 6.6819e-04 - mae: 0.0197 - mse: 6.6819e-04 - val_loss: 6.8310e-04 - val_mae: 0.0197 - val_mse: 6.8310e-04 - 102ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 6.6819e-04 - mae: 0.0197 - mse: 6.6819e-04 - val_loss: 6.8310e-04 - val_mae: 0.0197 - val_mse: 6.8310e-04 - 146ms/epoch - 11ms/step\n", "Epoch 111/250\n", - "13/13 - 0s - loss: 6.7136e-04 - mae: 0.0197 - mse: 6.7136e-04 - val_loss: 6.5858e-04 - val_mae: 0.0199 - val_mse: 6.5858e-04 - 224ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 6.7136e-04 - mae: 0.0197 - mse: 6.7136e-04 - val_loss: 6.5858e-04 - val_mae: 0.0199 - val_mse: 6.5858e-04 - 208ms/epoch - 16ms/step\n", "Epoch 112/250\n", - 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- 218ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 5.7425e-04 - mae: 0.0184 - mse: 5.7425e-04 - val_loss: 5.7895e-04 - val_mae: 0.0183 - val_mse: 5.7895e-04 - 239ms/epoch - 18ms/step\n", "Epoch 134/250\n", - "13/13 - 0s - loss: 5.8783e-04 - mae: 0.0186 - mse: 5.8783e-04 - val_loss: 5.7846e-04 - val_mae: 0.0188 - val_mse: 5.7846e-04 - 230ms/epoch - 18ms/step\n", + "13/13 - 0s - loss: 5.8783e-04 - mae: 0.0186 - mse: 5.8783e-04 - val_loss: 5.7846e-04 - val_mae: 0.0188 - val_mse: 5.7846e-04 - 285ms/epoch - 22ms/step\n", "Epoch 135/250\n", - "13/13 - 0s - loss: 5.8541e-04 - mae: 0.0188 - mse: 5.8541e-04 - val_loss: 6.7887e-04 - val_mae: 0.0191 - val_mse: 6.7887e-04 - 108ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 5.8541e-04 - mae: 0.0188 - mse: 5.8541e-04 - val_loss: 6.7887e-04 - val_mae: 0.0191 - val_mse: 6.7887e-04 - 178ms/epoch - 14ms/step\n", "Epoch 136/250\n", - "13/13 - 0s - loss: 5.9158e-04 - mae: 0.0185 - mse: 5.9158e-04 - val_loss: 5.9231e-04 - val_mae: 0.0188 - val_mse: 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- val_loss: 4.9877e-04 - val_mae: 0.0171 - val_mse: 4.9877e-04 - 227ms/epoch - 17ms/step\n", + "13/13 - 0s - loss: 4.9657e-04 - mae: 0.0171 - mse: 4.9657e-04 - val_loss: 4.9877e-04 - val_mae: 0.0171 - val_mse: 4.9877e-04 - 258ms/epoch - 20ms/step\n", "Epoch 164/250\n", - "13/13 - 0s - loss: 4.8858e-04 - mae: 0.0170 - mse: 4.8858e-04 - val_loss: 5.0099e-04 - val_mae: 0.0169 - val_mse: 5.0099e-04 - 99ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 4.8858e-04 - mae: 0.0170 - mse: 4.8858e-04 - val_loss: 5.0099e-04 - val_mae: 0.0169 - val_mse: 5.0099e-04 - 150ms/epoch - 12ms/step\n", "Epoch 165/250\n", - "13/13 - 0s - loss: 4.7747e-04 - mae: 0.0170 - mse: 4.7747e-04 - val_loss: 5.8449e-04 - val_mae: 0.0174 - val_mse: 5.8449e-04 - 97ms/epoch - 7ms/step\n", + "13/13 - 0s - loss: 4.7747e-04 - mae: 0.0170 - mse: 4.7747e-04 - val_loss: 5.8449e-04 - val_mae: 0.0174 - val_mse: 5.8449e-04 - 158ms/epoch - 12ms/step\n", "Epoch 166/250\n", - "13/13 - 0s - loss: 4.9897e-04 - mae: 0.0171 - mse: 4.9897e-04 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- 276ms/epoch - 21ms/step\n", "Epoch 232/250\n", - "13/13 - 0s - loss: 3.3844e-04 - mae: 0.0141 - mse: 3.3844e-04 - val_loss: 3.8716e-04 - val_mae: 0.0135 - val_mse: 3.8716e-04 - 109ms/epoch - 8ms/step\n", + "13/13 - 0s - loss: 3.3844e-04 - mae: 0.0141 - mse: 3.3844e-04 - val_loss: 3.8716e-04 - val_mae: 0.0135 - val_mse: 3.8716e-04 - 134ms/epoch - 10ms/step\n", "Epoch 233/250\n", - "13/13 - 0s - loss: 3.6741e-04 - mae: 0.0145 - mse: 3.6741e-04 - val_loss: 3.8668e-04 - val_mae: 0.0136 - val_mse: 3.8668e-04 - 117ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 3.6741e-04 - mae: 0.0145 - mse: 3.6741e-04 - val_loss: 3.8668e-04 - val_mae: 0.0136 - val_mse: 3.8668e-04 - 146ms/epoch - 11ms/step\n", "Epoch 234/250\n", - "13/13 - 0s - loss: 3.4129e-04 - mae: 0.0139 - mse: 3.4129e-04 - val_loss: 3.4933e-04 - val_mae: 0.0133 - val_mse: 3.4933e-04 - 118ms/epoch - 9ms/step\n", + "13/13 - 0s - loss: 3.4129e-04 - mae: 0.0139 - mse: 3.4129e-04 - val_loss: 3.4933e-04 - val_mae: 0.0133 - val_mse: 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[==============================] - 0s 4ms/step\n" ] }, { @@ -939,7 +949,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -993,7 +1003,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "4/4 [==============================] - 0s 3ms/step\n" + "4/4 [==============================] - 0s 4ms/step\n" ] }, { @@ -1020,7 +1030,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "4/4 [==============================] - 0s 4ms/step\n" + "4/4 [==============================] - 0s 5ms/step\n" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/keras_metadata.pb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/keras_metadata.pb index cb8716e6..0429ce74 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/keras_metadata.pb +++ 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-merge +*.npy -text -merge +*.png -text -merge \ No newline at end of file From 33cd527ee519c5c217eaa053228cd8593ca1548c Mon Sep 17 00:00:00 2001 From: JavalVyas2000 <73403218+JavalVyas2000@users.noreply.github.com> Date: Fri, 26 Jan 2024 01:24:22 +0530 Subject: [PATCH 47/75] Create .gitattributes --- .gitattributes | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 .gitattributes diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 00000000..469a1d7b --- /dev/null +++ b/.gitattributes @@ -0,0 +1,10 @@ +# -text: do not apply line ending normalization +# -merge: do not try to merge +# `binary` can also be used corresponding to `-text -merge -diff` +variables.data-* -text -merge +variables.index -text -merge +*.pb -text -merge +*.pkl -text -merge +*.srw -text -merge +*.npy -text -merge +*.png -text -merge From dbe0e3ed4e592c41956407e24d6c54c5215e60e7 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Thu, 25 Jan 2024 15:11:21 -0500 Subject: [PATCH 48/75] Removing .txt file --- .gitattributes.txt | 10 ---------- 1 file changed, 10 deletions(-) delete mode 100644 .gitattributes.txt diff --git a/.gitattributes.txt b/.gitattributes.txt deleted file mode 100644 index 3d0e57a4..00000000 --- a/.gitattributes.txt +++ /dev/null @@ -1,10 +0,0 @@ -# -text: do not apply line ending normalization -# -merge: do not try to merge -# `binary` can also be used corresponding to `-text -merge -diff` -variables.data-* -text -merge -variables.index -text -merge -*.pb -text -merge -*.pkl -text -merge -*.srw -text -merge -*.npy -text -merge -*.png -text -merge \ No newline at end of file From 10c32004dac2dd0e3f94137b4ad248dd0dfdb946 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Thu, 25 Jan 2024 15:57:03 -0500 Subject: [PATCH 49/75] Cleaning the comments --- .github/workflows/core.yml | 3 --- idaes_examples/notebooks/_toc.yml | 9 --------- 2 files changed, 12 deletions(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 6dc52fcc..848820b6 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -2,9 +2,6 @@ name: Tests on: push: - # paths-ignore: - # - 'idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb' - # - 'idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/**' branches: - main repository_dispatch: diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index d425df00..1a2b68d4 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -83,25 +83,16 @@ parts: - file: docs/surrogates/omlt/keras_flowsheet_optimization_doc - file: docs/surrogates/SCO2_example/ALAMO/index sections: - # - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate - # - file: docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding - # - file: docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc - file: docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc - file: docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc - file: docs/surrogates/SCO2_example/OMLT/index sections: - # - file: docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate - # - file: docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding - # - file: docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate - file: docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc - file: docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc - file: docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc - file: docs/surrogates/SCO2_example/PySMO/index sections: - # - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate - # - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding - # - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc From 4cfd7ebb53b8ae770de9e25bea52b4382246ca2b Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Fri, 9 Feb 2024 16:29:02 -0500 Subject: [PATCH 50/75] Adding the changes --- .github/workflows/core.yml | 2 +- idaes_examples/notebooks/_toc.yml | 24 +++++++++--------- .../OMLT/keras_surrogate/fingerprint.pb | 1 - .../OMLT/keras_surrogate/idaes_info.json | 1 - .../OMLT/keras_surrogate/keras_metadata.pb | 10 -------- .../OMLT/keras_surrogate/saved_model.pb | Bin 129984 -> 0 bytes .../variables/variables.data-00000-of-00001 | Bin 25335 -> 0 bytes .../keras_surrogate/variables/variables.index | Bin 2668 -> 0 bytes .../PySMO/pysmo_poly_surrogate.json | 1 - .../500_Points_DataSet.csv | 0 .../{SCO2_example => sco2}/CO2_flowsheet.png | Bin .../{SCO2_example/ALAMO => sco2}/__init__.py | 0 .../OMLT => sco2/alamo}/__init__.py | 0 .../ALAMO => sco2/alamo}/alamo_run.trc | 0 .../ALAMO => sco2/alamo}/alamo_surrogate.json | 0 .../alamo/alamo_training.ipynb} | 2 +- .../alamo/alamo_training_doc.ipynb} | 2 +- .../alamo/alamo_training_test.ipynb} | 2 +- .../alamo/alamo_training_usr.ipynb} | 2 +- .../alamo/flowsheet_optimization.ipynb} | 6 ++--- .../alamo/flowsheet_optimization.py} | 2 +- .../alamo/flowsheet_optimization_doc.ipynb} | 6 ++--- .../alamo/flowsheet_optimization_test.ipynb} | 6 ++--- .../alamo/flowsheet_optimization_usr.ipynb} | 6 ++--- .../ALAMO => sco2/alamo}/index.md | 0 .../alamo/properties.py} | 3 --- .../alamo/surrogate_embedding.ipynb} | 9 +++---- .../alamo/surrogate_embedding_doc.ipynb} | 9 +++---- .../alamo/surrogate_embedding_test.ipynb} | 9 +++---- .../alamo/surrogate_embedding_usr.ipynb} | 6 ++--- .../OMLT => sco2/omlt}/.mdl_co2.h5 | Bin .../PySMO => sco2/omlt}/__init__.py | 0 .../omlt/flowsheet_optimization.ipynb} | 6 ++--- .../omlt/flowsheet_optimization.py} | 2 +- .../omlt/flowsheet_optimization_doc.ipynb} | 6 ++--- .../omlt/flowsheet_optimization_test.ipynb} | 6 ++--- .../omlt/flowsheet_optimization_usr.ipynb} | 6 ++--- .../{SCO2_example/OMLT => sco2/omlt}/index.md | 0 .../omlt/keras_training.ipynb} | 2 +- .../omlt/keras_training_doc.ipynb} | 2 +- .../omlt/keras_training_test.ipynb} | 2 +- .../omlt/keras_training_usr.ipynb} | 2 +- .../omlt/properties.py} | 3 --- .../omlt/surrogate_embedding.ipynb} | 9 +++---- .../omlt/surrogate_embedding_doc.ipynb} | 9 +++---- .../omlt/surrogate_embedding_test.ipynb} | 9 +++---- .../omlt/surrogate_embedding_usr.ipynb} | 9 +++---- .../{SCO2_example => sco2/pysmo}/__init__.py | 0 .../pysmo/flowsheet_optimization.ipynb} | 6 ++--- .../pysmo/flowsheet_optimization.py} | 2 +- .../pysmo/flowsheet_optimization_doc.ipynb} | 6 ++--- .../pysmo/flowsheet_optimization_test.ipynb} | 6 ++--- .../pysmo/flowsheet_optimization_usr.ipynb} | 6 ++--- .../PySMO => sco2/pysmo}/index.md | 0 .../pysmo/properties.py} | 3 --- .../pysmo/pysmo_training.ipynb} | 2 +- .../pysmo/pysmo_training_doc.ipynb} | 2 +- .../pysmo/pysmo_training_test.ipynb} | 2 +- .../pysmo/pysmo_training_usr.ipynb} | 2 +- .../pysmo/surrogate_embedding.ipynb} | 9 +++---- .../pysmo/surrogate_embedding_doc.ipynb} | 9 +++---- .../pysmo/surrogate_embedding_test.ipynb} | 9 +++---- .../pysmo/surrogate_embedding_usr.ipynb} | 11 +++----- 63 files changed, 101 insertions(+), 156 deletions(-) delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/fingerprint.pb delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/idaes_info.json delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/keras_metadata.pb delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/saved_model.pb delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/variables/variables.data-00000-of-00001 delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/variables/variables.index delete mode 100644 idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/pysmo_poly_surrogate.json rename idaes_examples/notebooks/docs/surrogates/{SCO2_example => sco2}/500_Points_DataSet.csv (100%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example => sco2}/CO2_flowsheet.png (100%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO => sco2}/__init__.py (100%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT => sco2/alamo}/__init__.py (100%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO => sco2/alamo}/alamo_run.trc (100%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO => sco2/alamo}/alamo_surrogate.json (100%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb => sco2/alamo/alamo_training.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_alamo_surrogate_doc.ipynb => sco2/alamo/alamo_training_doc.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb => sco2/alamo/alamo_training_test.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_alamo_surrogate_usr.ipynb => sco2/alamo/alamo_training_usr.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb => sco2/alamo/flowsheet_optimization.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_flowsheet_alamo.py => sco2/alamo/flowsheet_optimization.py} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.ipynb => sco2/alamo/flowsheet_optimization_doc.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb => sco2/alamo/flowsheet_optimization_test.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_usr.ipynb => sco2/alamo/flowsheet_optimization_usr.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO => sco2/alamo}/index.md (100%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_properties_alamo_surrogate.py => sco2/alamo/properties.py} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb => sco2/alamo/surrogate_embedding.ipynb} (96%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb => sco2/alamo/surrogate_embedding_doc.ipynb} (97%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb => sco2/alamo/surrogate_embedding_test.ipynb} (97%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb => sco2/alamo/surrogate_embedding_usr.ipynb} (97%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT => sco2/omlt}/.mdl_co2.h5 (100%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO => sco2/omlt}/__init__.py (100%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb => sco2/omlt/flowsheet_optimization.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_flowsheet_keras.py => sco2/omlt/flowsheet_optimization.py} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb => sco2/omlt/flowsheet_optimization_doc.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb => sco2/omlt/flowsheet_optimization_test.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb => sco2/omlt/flowsheet_optimization_usr.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT => sco2/omlt}/index.md (100%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_keras_surrogate.ipynb => sco2/omlt/keras_training.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_keras_surrogate_doc.ipynb => sco2/omlt/keras_training_doc.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_keras_surrogate_test.ipynb => sco2/omlt/keras_training_test.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_keras_surrogate_usr.ipynb => sco2/omlt/keras_training_usr.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_properties_keras_surrogate.py => sco2/omlt/properties.py} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb => sco2/omlt/surrogate_embedding.ipynb} (96%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb => sco2/omlt/surrogate_embedding_doc.ipynb} (97%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb => sco2/omlt/surrogate_embedding_test.ipynb} (97%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb => sco2/omlt/surrogate_embedding_usr.ipynb} (97%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example => sco2/pysmo}/__init__.py (100%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb => sco2/pysmo/flowsheet_optimization.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_flowsheet_pysmo.py => sco2/pysmo/flowsheet_optimization.py} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc.ipynb => sco2/pysmo/flowsheet_optimization_doc.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_test.ipynb => sco2/pysmo/flowsheet_optimization_test.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_usr.ipynb => sco2/pysmo/flowsheet_optimization_usr.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO => sco2/pysmo}/index.md (100%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_properties_pysmo_surrogate.py => sco2/pysmo/properties.py} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb => sco2/pysmo/pysmo_training.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_pysmo_surrogate_doc.ipynb => sco2/pysmo/pysmo_training_doc.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_pysmo_surrogate_test.ipynb => sco2/pysmo/pysmo_training_test.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_pysmo_surrogate_usr.ipynb => sco2/pysmo/pysmo_training_usr.ipynb} (99%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb => sco2/pysmo/surrogate_embedding.ipynb} (96%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb => sco2/pysmo/surrogate_embedding_doc.ipynb} (97%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb => sco2/pysmo/surrogate_embedding_test.ipynb} (97%) rename idaes_examples/notebooks/docs/surrogates/{SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb => sco2/pysmo/surrogate_embedding_usr.ipynb} (96%) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 848820b6..1c7213b5 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -78,7 +78,7 @@ jobs: run: | pwd ls idaes_examples - pytest -v idaes_examples --ignore=idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/ + pytest -v idaes_examples --ignore=idaes_examples/notebooks/docs/surrogates/sco2/alamo/ - name: Upload pytest-xdist worker logs if: success() || failure() uses: actions/upload-artifact@v3 diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index 1a2b68d4..33d21253 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -81,21 +81,21 @@ parts: - file: docs/surrogates/omlt/index sections: - file: docs/surrogates/omlt/keras_flowsheet_optimization_doc - - file: docs/surrogates/SCO2_example/ALAMO/index + - file: docs/surrogates/sco2/alamo/index sections: - - file: docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc - - file: docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc - - file: docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc - - file: docs/surrogates/SCO2_example/OMLT/index + - file: docs/surrogates/sco2/alamo/alamo_training_doc + - file: docs/surrogates/sco2/alamo/surrogate_embedding_doc + - file: docs/surrogates/sco2/alamo/flowsheet_optimization_doc + - file: docs/surrogates/sco2/omlt/index sections: - - file: docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc - - file: docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc - - file: docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc - - file: docs/surrogates/SCO2_example/PySMO/index + - file: docs/surrogates/sco2/omlt/omlt_training_doc + - file: docs/surrogates/sco2/omlt/surrogate_embedding_doc + - file: docs/surrogates/sco2/omlt/flowsheet_optimization_doc + - file: docs/surrogates/sco2/pysmo/index sections: - - file: docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc - - file: docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc - - file: docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc + - file: docs/surrogates/sco2/pysmo/pysmo_training_doc + - file: docs/surrogates/sco2/pysmo/surrogate_embedding_doc + - file: docs/surrogates/sco2/pysmo/flowsheet_optimization_doc - caption: Power generation chapters: - file: docs/power_gen/supercritical/index diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/fingerprint.pb b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/fingerprint.pb deleted file mode 100644 index c94ddb92..00000000 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/fingerprint.pb +++ /dev/null @@ -1 +0,0 @@ -™Ù²¡™—ñû®¾¶âŽéدŠ-êô¢ËªºÕû[ ¿˜âãÒû‘Óç(÷úÝ칉¨¢W2 \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/idaes_info.json b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/idaes_info.json deleted file mode 100644 index f582d087..00000000 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/idaes_info.json +++ /dev/null @@ -1 +0,0 @@ -{"input_scaler": {"expected_columns": ["pressure", "temperature"], "offset": {"pressure": 7.460891, "temperature": 306.215965}, "factor": {"pressure": 27.532923, "temperature": 693.756024}}, "output_scaler": {"expected_columns": ["enth_mol", "entr_mol"], "offset": {"enth_mol": 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--git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/variables/variables.index b/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/keras_surrogate/variables/variables.index deleted file mode 100644 index 0cd2376b6b7307bb475d5528e117427288e57cac..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2668 zcmZQzVB=tvV&Y(AkP(P?_HcFf4)FK%3vqPvagFzP@^W(A(YNw!k3*|lvo^}Sdgion_5zonOv-Ipr4#ynpdK)=NJ+ay4hL=^Z+@=FqP2x)i#($IKys)&#hpCNH}ydl+&7a$D< zkCsRS?Jy$FjxVIz@d4%!1{0y2#LCp7`0~t>jQH}@%=C|9K zoZ&;jr;x<)AsJ|i-UX{RQAKfLV@L#?tUfSm9N;l6$)5F`!bpM`_d*9`T-*DI{lrBQ zMAwFJkS^_)$+LiwWJGi%9gqh4T6=baImMB*!wqN>W29pQv00*_5U63#&c-N`1H6GB ztMydpOhdao)bFNwEND1%@g&zCLaNBt`<0!U9cD zF`T;jnK~6Bfk|OT2*^04px7SbBY{cbK^;g}iB)$ngEk+c(o9M%NsKqdUlB4WTmV(% ze_tQJ&o2XPED&h!TmdQG{r1;QE(N}n)a1lULK<#>G%TwYwBnNI%PaxbWFyQu diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/pysmo_poly_surrogate.json b/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/pysmo_poly_surrogate.json deleted file mode 100644 index 5258094b..00000000 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/pysmo_poly_surrogate.json +++ /dev/null @@ -1 +0,0 @@ -{"model_encoding": {"enth_mol": {"attr": {"regression_data_columns": ["pressure", "temperature"], "multinomials": 1, "additional_term_expressions": ["IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]", "IndexedParam[pressure]/IndexedParam[temperature]", "IndexedParam[temperature]/IndexedParam[pressure]"], "optimal_weights_array": [[-539145.2641931743], [-1572.9941129612596], [1028.1303702529963], [-41.89265612633253], [-2.854098382160082], [3.1084792045014056], [0.0040249321969904606], [-0.07298691795031877], [-2.7827021177926484e-06], [0.0006559340352560386], [7.62454692622566e-10], [4.50540106476475], [-0.0025967218940188964], [3.27147430041989e-05], [-0.05205092851352775], [149943.17003170087], [-3.5662256522946807]], "final_polynomial_order": 5, "errors": {"MAE": 116.22937611304296, "MSE": 39254.96789837278, "R2": 0.9997117200542968}, "extra_terms_feature_vector": ["IndexedParam[pressure]", "IndexedParam[temperature]"]}, "map": {"regression_data_columns": "list", "multinomials": "str", "additional_term_expressions": "other", "optimal_weights_array": "numpy", "final_polynomial_order": "str", "errors": "str", "extra_terms_feature_vector": "other"}}, "entr_mol": {"attr": {"regression_data_columns": ["pressure", "temperature"], "multinomials": 1, "additional_term_expressions": ["IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]", "IndexedParam[pressure]/IndexedParam[temperature]", "IndexedParam[temperature]/IndexedParam[pressure]"], "optimal_weights_array": [[-529.9581296941684], [-5.674476891947422], [3.6251620831469844], [-0.012206052330165947], [-0.010121999171951317], [0.0044164987227566545], [1.4212146246171698e-05], [-0.00012049491972756627], [-9.875650167428602e-09], [1.1673348430972035e-06], [2.72031843813476e-12], [0.010605178085763924], [-6.047902870413699e-06], [6.872924493404928e-08], [-0.00011146830780061758], [437.25207041949056], [0.0015391876304710196]], "final_polynomial_order": 5, "errors": {"MAE": 0.34548912239751245, "MSE": 0.3560561890323906, "R2": 0.9991570382929269}, "extra_terms_feature_vector": ["IndexedParam[pressure]", "IndexedParam[temperature]"]}, "map": {"regression_data_columns": "list", "multinomials": "str", "additional_term_expressions": "other", "optimal_weights_array": "numpy", "final_polynomial_order": "str", "errors": "str", "extra_terms_feature_vector": "other"}}}, "input_labels": ["pressure", "temperature"], "output_labels": ["enth_mol", "entr_mol"], "input_bounds": {"pressure": [7, 40], "temperature": [306, 1000]}, "surrogate_type": "poly"} \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/500_Points_DataSet.csv b/idaes_examples/notebooks/docs/surrogates/sco2/500_Points_DataSet.csv similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/500_Points_DataSet.csv rename to idaes_examples/notebooks/docs/surrogates/sco2/500_Points_DataSet.csv diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/CO2_flowsheet.png b/idaes_examples/notebooks/docs/surrogates/sco2/CO2_flowsheet.png similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/CO2_flowsheet.png rename to idaes_examples/notebooks/docs/surrogates/sco2/CO2_flowsheet.png diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/__init__.py b/idaes_examples/notebooks/docs/surrogates/sco2/__init__.py similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/__init__.py rename to idaes_examples/notebooks/docs/surrogates/sco2/__init__.py diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/__init__.py b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/__init__.py similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/__init__.py rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/__init__.py diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_run.trc similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_run.trc rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_run.trc diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_surrogate.json b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_surrogate.json similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/alamo_surrogate.json rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_surrogate.json diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb index 89fc9c7b..74963d2b 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb @@ -567,7 +567,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_alamo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb index 76fefb3c..6347febe 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb @@ -567,7 +567,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_alamo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.md) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_doc.md) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb index e54ba5a4..bf1b4b99 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb @@ -567,7 +567,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_alamo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_test.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_usr.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb index 14b624a2..b9bafbf0 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_alamo_surrogate_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb @@ -567,7 +567,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_alamo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_usr.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb index 01a0e48b..55b9d0c2 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - SCO2_flowsheet_optimization_alamo_surrogate (Part 3)\n", + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - flowsheet_optimization (Part 3)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -97,7 +97,7 @@ "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", "from idaes.core.util.model_statistics import degrees_of_freedom\n", "from idaes.core.util.initialization import propagate_state\n", - "from SCO2_properties_alamo_surrogate import SCO2ParameterBlock\n", + "from properties import SCO2ParameterBlock\n", "\n", "import idaes.logger as idaeslog\n", "\n", @@ -110,7 +110,7 @@ "source": [ "# 2. Constructing the flowsheet\n", "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo.py b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.py similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo.py rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.py index 907fa645..7190e06b 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.py @@ -43,7 +43,7 @@ from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption from idaes.core.util.model_statistics import degrees_of_freedom from idaes.core.util.initialization import propagate_state -from SCO2_properties_alamo_surrogate import SCO2ParameterBlock +from properties import SCO2ParameterBlock import idaes.logger as idaeslog def main(): diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_doc.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_doc.ipynb index b53e1d31..7603d3e1 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_doc.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - SCO2_flowsheet_optimization_alamo_surrogate (Part 3)\n", + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - flowsheet_optimization (Part 3)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -97,7 +97,7 @@ "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", "from idaes.core.util.model_statistics import degrees_of_freedom\n", "from idaes.core.util.initialization import propagate_state\n", - "from SCO2_properties_alamo_surrogate import SCO2ParameterBlock\n", + "from properties import SCO2ParameterBlock\n", "\n", "import idaes.logger as idaeslog\n", "\n", @@ -110,7 +110,7 @@ "source": [ "# 2. Constructing the flowsheet\n", "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_test.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_test.ipynb index b53e1d31..7603d3e1 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_test.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - SCO2_flowsheet_optimization_alamo_surrogate (Part 3)\n", + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - flowsheet_optimization (Part 3)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -97,7 +97,7 @@ "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", "from idaes.core.util.model_statistics import degrees_of_freedom\n", "from idaes.core.util.initialization import propagate_state\n", - "from SCO2_properties_alamo_surrogate import SCO2ParameterBlock\n", + "from properties import SCO2ParameterBlock\n", "\n", "import idaes.logger as idaeslog\n", "\n", @@ -110,7 +110,7 @@ "source": [ "# 2. Constructing the flowsheet\n", "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_usr.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb index b53e1d31..7603d3e1 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - SCO2_flowsheet_optimization_alamo_surrogate (Part 3)\n", + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - flowsheet_optimization (Part 3)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -97,7 +97,7 @@ "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", "from idaes.core.util.model_statistics import degrees_of_freedom\n", "from idaes.core.util.initialization import propagate_state\n", - "from SCO2_properties_alamo_surrogate import SCO2ParameterBlock\n", + "from properties import SCO2ParameterBlock\n", "\n", "import idaes.logger as idaeslog\n", "\n", @@ -110,7 +110,7 @@ "source": [ "# 2. Constructing the flowsheet\n", "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/index.md b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/index.md similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/index.md rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/index.md diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate.py b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/properties.py similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate.py rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/properties.py index 19d12d08..3b2f8bbb 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/properties.py @@ -18,9 +18,6 @@ """ -# Changes the divide behavior to not do integer division -from __future__ import division - # Import Python libraries import logging diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding.ipynb similarity index 96% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding.ipynb index db7155b1..430fdbe4 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding.ipynb @@ -36,7 +36,7 @@ "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", "\n", - "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_alamo_surrogate.py\" file which is how embedding file should look like. \n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", "\n", "### 1.1 Steps in Creating a Property Package\n", "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", @@ -66,9 +66,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Changes the divide behavior to not do integer division\n", - "from __future__ import division\n", - "\n", "# Import Python libraries\n", "import logging\n", "\n", @@ -192,7 +189,7 @@ "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", "\n", "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the SCO2_alamo_surrogate.ipynb file) using the Alamopy Surrogate API of IDAES package\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the alamo_training.ipynb file) using the Alamopy Surrogate API of IDAES package\n", "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" ] @@ -452,7 +449,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_alamo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_doc.ipynb similarity index 97% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_doc.ipynb index 6203f48f..31b5027b 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_doc.ipynb @@ -36,7 +36,7 @@ "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", "\n", - "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_alamo_surrogate.py\" file which is how embedding file should look like. \n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", "\n", "### 1.1 Steps in Creating a Property Package\n", "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", @@ -66,9 +66,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Changes the divide behavior to not do integer division\n", - "from __future__ import division\n", - "\n", "# Import Python libraries\n", "import logging\n", "\n", @@ -192,7 +189,7 @@ "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", "\n", "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the SCO2_alamo_surrogate_usr.ipynb file) using the Alamopy Surrogate API of IDAES package\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the alamo_training_doc.md file) using the Alamopy Surrogate API of IDAES package\n", "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" ] @@ -452,7 +449,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_alamo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.md). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_test.ipynb similarity index 97% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_test.ipynb index ec7eb0a4..9640004e 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_test.ipynb @@ -36,7 +36,7 @@ "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", "\n", - "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_alamo_surrogate.py\" file which is how embedding file should look like. \n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", "\n", "### 1.1 Steps in Creating a Property Package\n", "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", @@ -66,9 +66,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Changes the divide behavior to not do integer division\n", - "from __future__ import division\n", - "\n", "# Import Python libraries\n", "import logging\n", "\n", @@ -192,7 +189,7 @@ "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", "\n", "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the SCO2_alamo_surrogate_test.ipynb file) using the Alamopy Surrogate API of IDAES package\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the alamo_training_test.ipynb file) using the Alamopy Surrogate API of IDAES package\n", "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" ] @@ -452,7 +449,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_alamo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_usr.ipynb similarity index 97% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_usr.ipynb index 001452b8..c70b43e1 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_properties_alamo_surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_usr.ipynb @@ -36,7 +36,7 @@ "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", "\n", - "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_alamo_surrogate.py\" file which is how embedding file should look like. \n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", "\n", "### 1.1 Steps in Creating a Property Package\n", "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", @@ -192,7 +192,7 @@ "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", "\n", "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the SCO2_alamo_surrogate_doc.md file) using the Alamopy Surrogate API of IDAES package\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called alamo_surrogate (look at the alamo_training_usr.ipynb file) using the Alamopy Surrogate API of IDAES package\n", "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" ] @@ -452,7 +452,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_alamo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/ALAMO/SCO2_flowsheet_alamo_surrogate_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.md). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/.mdl_co2.h5 b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/.mdl_co2.h5 similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/.mdl_co2.h5 rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/.mdl_co2.h5 diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/__init__.py b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/__init__.py similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/__init__.py rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/__init__.py diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb index 0dca17be..827abb3d 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - SCO2_flowsheet_optimization_keras_surrogate (Part 3)\n", + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - flowsheet_optimization (Part 3)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -107,7 +107,7 @@ "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", "from idaes.core.util.model_statistics import degrees_of_freedom\n", "from idaes.core.util.initialization import propagate_state\n", - "from SCO2_properties_keras_surrogate import SCO2ParameterBlock\n", + "from properties import SCO2ParameterBlock\n", "\n", "import idaes.logger as idaeslog\n", "\n", @@ -120,7 +120,7 @@ "source": [ "# 2. Constructing the flowsheet\n", "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras.py b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.py similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras.py rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.py index 97a43954..f8658c3e 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.py @@ -43,7 +43,7 @@ from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption from idaes.core.util.model_statistics import degrees_of_freedom from idaes.core.util.initialization import propagate_state -from SCO2_properties_keras_surrogate import SCO2ParameterBlock +from properties import SCO2ParameterBlock import idaes.logger as idaeslog def main(): diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb index bc4e9f41..c20d883f 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - SCO2_flowsheet_optimization_keras_surrogate (Part 3)\n", + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - flowsheet_optimization (Part 3)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -97,7 +97,7 @@ "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", "from idaes.core.util.model_statistics import degrees_of_freedom\n", "from idaes.core.util.initialization import propagate_state\n", - "from SCO2_properties_keras_surrogate import SCO2ParameterBlock\n", + "from properties import SCO2ParameterBlock\n", "\n", "import idaes.logger as idaeslog\n", "\n", @@ -110,7 +110,7 @@ "source": [ "# 2. Constructing the flowsheet\n", "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb index ceabe341..316819c4 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - SCO2_flowsheet_optimization_keras_surrogate (Part 3)\n", + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - flowsheet_optimization (Part 3)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -97,7 +97,7 @@ "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", "from idaes.core.util.model_statistics import degrees_of_freedom\n", "from idaes.core.util.initialization import propagate_state\n", - "from SCO2_properties_keras_surrogate import SCO2ParameterBlock\n", + "from properties import SCO2ParameterBlock\n", "\n", "import idaes.logger as idaeslog\n", "\n", @@ -110,7 +110,7 @@ "source": [ "# 2. Constructing the flowsheet\n", "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb index ceabe341..316819c4 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - SCO2_flowsheet_optimization_keras_surrogate (Part 3)\n", + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - flowsheet_optimization (Part 3)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -97,7 +97,7 @@ "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", "from idaes.core.util.model_statistics import degrees_of_freedom\n", "from idaes.core.util.initialization import propagate_state\n", - "from SCO2_properties_keras_surrogate import SCO2ParameterBlock\n", + "from properties import SCO2ParameterBlock\n", "\n", "import idaes.logger as idaeslog\n", "\n", @@ -110,7 +110,7 @@ "source": [ "# 2. Constructing the flowsheet\n", "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/index.md b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/index.md similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/index.md rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/index.md diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb index 18f11772..ecb167ba 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb @@ -1085,7 +1085,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_keras_surrogate_embedding.ipynb](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding.ipynb](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb index 20615f2e..565b6514 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb @@ -1075,7 +1075,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_keras_surrogate_embedding_doc.md](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb_doc.md) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding_doc.md](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb_doc.md) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_test.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb index b1cf32a4..bff0eb63 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb @@ -1075,7 +1075,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_keras_surrogate_embedding_test.ipynb](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb_test.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding_test.ipynb](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb_test.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_usr.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb index a2caf1f2..2f3ab63c 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_keras_surrogate_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb @@ -1075,7 +1075,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_keras_surrogate_embedding_usr.ipynb](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb_usr.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding_usr.ipynb](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb_usr.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate.py b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate.py rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py index c3a358b2..7ebce138 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py @@ -23,9 +23,6 @@ Valid Temperature Range = 306.25 K to 1000 K """ -# Changes the divide behavior to not do integer division -from __future__ import division - # Import Python libraries import logging diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb similarity index 96% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb index 5409a830..58726e40 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb @@ -36,7 +36,7 @@ "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", "\n", - "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_keras_surrogate.py\" file which is how embedding file should look like. \n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", "\n", "### 1.1 Steps in Creating a Property Package\n", "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", @@ -66,9 +66,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Changes the divide behavior to not do integer division\n", - "from __future__ import division\n", - "\n", "# Import Python libraries\n", "import logging\n", "\n", @@ -192,7 +189,7 @@ "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", "\n", "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the SCO2_keras_surrogate.ipynb file) using the keras Surrogate API of IDAES package\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the keras_training.ipynb file) using the keras Surrogate API of IDAES package\n", "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" ] @@ -447,7 +444,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_keras_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb similarity index 97% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb index 63687e26..82ef41ba 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb @@ -35,7 +35,7 @@ "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", "\n", - "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_keras_surrogate.py\" file which is how embedding file should look like. \n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", "\n", "### 1.1 Steps in Creating a Property Package\n", "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", @@ -65,9 +65,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Changes the divide behavior to not do integer division\n", - "from __future__ import division\n", - "\n", "# Import Python libraries\n", "import logging\n", "\n", @@ -191,7 +188,7 @@ "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", "\n", "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the SCO2_keras_surrogate_doc.md file) using the keras Surrogate API of IDAES package\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the keras_training_doc.md file) using the keras Surrogate API of IDAES package\n", "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" ] @@ -446,7 +443,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_keras_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.md). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.md). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb similarity index 97% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb index db5ff278..12e2a7ac 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb @@ -35,7 +35,7 @@ "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", "\n", - "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_keras_surrogate.py\" file which is how embedding file should look like. \n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", "\n", "### 1.1 Steps in Creating a Property Package\n", "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", @@ -65,9 +65,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Changes the divide behavior to not do integer division\n", - "from __future__ import division\n", - "\n", "# Import Python libraries\n", "import logging\n", "\n", @@ -191,7 +188,7 @@ "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", "\n", "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the SCO2_keras_surrogate_usr.ipynb file) using the keras Surrogate API of IDAES package\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the keras_training_test.ipynb file) using the keras Surrogate API of IDAES package\n", "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" ] @@ -446,7 +443,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_keras_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb similarity index 97% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb index 12a59982..67a78b77 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_properties_keras_surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb @@ -35,7 +35,7 @@ "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", "\n", - "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_keras_surrogate.py\" file which is how embedding file should look like. \n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", "\n", "### 1.1 Steps in Creating a Property Package\n", "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", @@ -65,9 +65,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Changes the divide behavior to not do integer division\n", - "from __future__ import division\n", - "\n", "# Import Python libraries\n", "import logging\n", "\n", @@ -191,7 +188,7 @@ "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", "\n", "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the SCO2_keras_surrogate_test.ipynb file) using the keras Surrogate API of IDAES package\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called keras_surrogate (look at the keras_training_usr.ipynb file) using the keras Surrogate API of IDAES package\n", "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" ] @@ -446,7 +443,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_keras_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/OMLT/SCO2_flowsheet_keras_surrogate_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/__init__.py b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/__init__.py similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/__init__.py rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/__init__.py diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb index 309674e0..a535274c 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - SCO2_flowsheet_optimization_pysmo_surrogate (Part 3)\n", + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - flowsheet_optimization (Part 3)\n", "\n", "\n", "Maintainer: Javal Vyas\n", @@ -99,7 +99,7 @@ "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", "from idaes.core.util.model_statistics import degrees_of_freedom\n", "from idaes.core.util.initialization import propagate_state\n", - "from SCO2_properties_pysmo_surrogate import SCO2ParameterBlock\n", + "from properties import SCO2ParameterBlock\n", "\n", "import idaes.logger as idaeslog\n", "\n", @@ -112,7 +112,7 @@ "source": [ "# 2. Constructing the flowsheet\n", "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo.py b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.py similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo.py rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.py index d0911dbc..80f14165 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.py @@ -43,7 +43,7 @@ from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption from idaes.core.util.model_statistics import degrees_of_freedom from idaes.core.util.initialization import propagate_state -from SCO2_properties_pysmo_surrogate import SCO2ParameterBlock +from properties import SCO2ParameterBlock import idaes.logger as idaeslog def main(): diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.ipynb index fffedd14..5ab2578c 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - SCO2_flowsheet_optimization_pysmo_surrogate (Part 3)\n", + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - flowsheet_optimization (Part 3)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -97,7 +97,7 @@ "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", "from idaes.core.util.model_statistics import degrees_of_freedom\n", "from idaes.core.util.initialization import propagate_state\n", - "from SCO2_properties_pysmo_surrogate import SCO2ParameterBlock\n", + "from properties import SCO2ParameterBlock\n", "\n", "import idaes.logger as idaeslog\n", "\n", @@ -110,7 +110,7 @@ "source": [ "# 2. Constructing the flowsheet\n", "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_test.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb index fffedd14..5ab2578c 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - SCO2_flowsheet_optimization_pysmo_surrogate (Part 3)\n", + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - flowsheet_optimization (Part 3)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -97,7 +97,7 @@ "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", "from idaes.core.util.model_statistics import degrees_of_freedom\n", "from idaes.core.util.initialization import propagate_state\n", - "from SCO2_properties_pysmo_surrogate import SCO2ParameterBlock\n", + "from properties import SCO2ParameterBlock\n", "\n", "import idaes.logger as idaeslog\n", "\n", @@ -110,7 +110,7 @@ "source": [ "# 2. Constructing the flowsheet\n", "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_usr.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb index fffedd14..5ab2578c 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - SCO2_flowsheet_optimization_pysmo_surrogate (Part 3)\n", + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - flowsheet_optimization (Part 3)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -97,7 +97,7 @@ "from idaes.models.unit_models.pressure_changer import ThermodynamicAssumption\n", "from idaes.core.util.model_statistics import degrees_of_freedom\n", "from idaes.core.util.initialization import propagate_state\n", - "from SCO2_properties_pysmo_surrogate import SCO2ParameterBlock\n", + "from properties import SCO2ParameterBlock\n", "\n", "import idaes.logger as idaeslog\n", "\n", @@ -110,7 +110,7 @@ "source": [ "# 2. Constructing the flowsheet\n", "\n", - "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the SCO2_properties_keras_surrogate.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", + "To construct the flowsheet we need to define a ConcreteModel using pyomo and then add a FlowsheetBlock to the ConcreteModel. Here since we are focusing on the steady state process, we shall have the dynamic flag as False in the FlowsheetBlock. Next, we define the properties in the FlowsheetBlock that we imported from the properties.py file. Then start adding the unit models to the FlowsheetBlock with the suitable arguements, after which we connect them using Arcs as in the flowsheet above. \n", "\n", "Once we have the connected flowsheet, we initialize individual unit models. Before initializing, we fix desired variables for the desired behavior of the unit model and then use `propagate_state` to pass on the state variables to next unit model in the flowsheet. After completely initializing the flowsheet, we convert the network to a mathematical form by using `network.expand_arcs` from the TransformationFactory and apply it on the flowsheet block. Then we call the solver and solve the flowsheet to calculate the total work in the process. " ] diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/index.md b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/index.md similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/index.md rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/index.md diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate.py b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate.py rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py index bb9d9013..accad220 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py @@ -23,9 +23,6 @@ """ -# Changes the divide behavior to not do integer division -from __future__ import division - # Import Python libraries import logging diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb index 3cc125c6..02898325 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb @@ -627,7 +627,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_pysmo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb index 7627974f..6fbb5b53 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb @@ -627,7 +627,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_pysmo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.md) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.md) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_test.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb index f0a8b7a9..96cce342 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb @@ -627,7 +627,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_pysmo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb similarity index 99% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_usr.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb index 48499bba..b489f502 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_pysmo_surrogate_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb @@ -627,7 +627,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [SCO2_properties_pysmo_surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_usr.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb similarity index 96% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb index 67e4c6c4..56df32cb 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb @@ -34,7 +34,7 @@ "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", "\n", - "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_pysmo_surrogate.py\" file which is how embedding file should look like. \n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", "\n", "### 1.1 Steps in Creating a Property Package\n", "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", @@ -64,9 +64,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Changes the divide behavior to not do integer division\n", - "from __future__ import division\n", - "\n", "# Import Python libraries\n", "import logging\n", "\n", @@ -191,7 +188,7 @@ "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", "\n", "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the SCO2_pysmo_surrogate.ipynb file) using the PySMO Surrogate API of IDAES package\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the pysmo_training.ipynb file) using the PySMO Surrogate API of IDAES package\n", "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" ] @@ -449,7 +446,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_pysmo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb similarity index 97% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb index 2b0df77f..3c647096 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb @@ -34,7 +34,7 @@ "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", "\n", - "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_pysmo_surrogate.py\" file which is how embedding file should look like. \n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", "\n", "### 1.1 Steps in Creating a Property Package\n", "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", @@ -64,9 +64,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Changes the divide behavior to not do integer division\n", - "from __future__ import division\n", - "\n", "# Import Python libraries\n", "import logging\n", "\n", @@ -191,7 +188,7 @@ "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", "\n", "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the SCO2_pysmo_surrogate_doc.md file) using the PySMO Surrogate API of IDAES package\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the pysmo_training_doc.md file) using the PySMO Surrogate API of IDAES package\n", "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" ] @@ -449,7 +446,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_pysmo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.md). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.md). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb similarity index 97% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb index 725d6397..df1dfb7d 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb @@ -34,7 +34,7 @@ "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", "\n", - "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_pysmo_surrogate.py\" file which is how embedding file should look like. \n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", "\n", "### 1.1 Steps in Creating a Property Package\n", "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", @@ -64,9 +64,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Changes the divide behavior to not do integer division\n", - "from __future__ import division\n", - "\n", "# Import Python libraries\n", "import logging\n", "\n", @@ -191,7 +188,7 @@ "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", "\n", "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the SCO2_pysmo_surrogate_usr.ipynb file) using the PySMO Surrogate API of IDAES package\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the pysmo_training_test.ipynb file) using the PySMO Surrogate API of IDAES package\n", "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" ] @@ -449,7 +446,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_pysmo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_usr.ipynb similarity index 96% rename from idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb rename to idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_usr.ipynb index ef19a6dc..9e9b06b6 100644 --- a/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_properties_pysmo_surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_usr.ipynb @@ -34,7 +34,7 @@ "\n", "Here we shall see how to integrate the trained surrogate in the custom property package. One can read more about making a properties package from read the docs. To integrate the surrogate we first define the physical paramter block which will return the properties based on the state variables. State variables would be called from the State Block as Pyomo variables. We will define the surrogate input and output as pyomo variables as well. Once we have defined the variables in the state block then we define our surrogate block.\n", "\n", - "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"SCO2_properties_pysmo_surrogate.py\" file which is how embedding file should look like. \n", + "*NOTE:* For ease of explaination the property package is written in \".ipynb\" format, ideally it should be in a python script. Each class of this package is separated in different cell for the same reason, in practive all the classes in this notebook should be part of the same python script. This folder includes \"properties.py\" file which is how embedding file should look like. \n", "\n", "### 1.1 Steps in Creating a Property Package\n", "Creating a new property package can be broken down into the following steps, which will be demonstrated in the next part of this tutorial.\n", @@ -64,9 +64,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Changes the divide behavior to not do integer division\n", - "from __future__ import division\n", - "\n", "# Import Python libraries\n", "import logging\n", "\n", @@ -175,7 +172,7 @@ " 'length': units.m,\n", " 'mass': units.kg,\n", " 'amount': units.mol,\n", - " 'temperature': units.K})" + " 'temperatureo': units.K})" ] }, { @@ -191,7 +188,7 @@ "We start by defining the 5 state variables: flow_mol, pressure, temperature, enth_mol and entr_mol as the Pyomo Var, each of this variable has a unit for unit consistency. This is done in _make_state_vars function. We get the enth_mol and entr_mol variables from trained surrogate which we define in this function as well. To get the output variables from the surrogate:\n", "\n", "1. Define the input and output variables to the trained surrogate\n", - "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the SCO2_pysmo_surrogate_test.ipynb file) using the PySMO Surrogate API of IDAES package\n", + "2. Load the surrogate from the folder it is saved in, here it is saved in the folder called pysmo_surrogate (look at the pysmo_training_usr.ipynb file) using the PySMO Surrogate API of IDAES package\n", "3. Define a `SurrogateBlock` and call the build_model method on the block with the input variables, output variables, model formulation and the loaded surrogate as the arguements. \n", "4. Define the constraints necessary for ensuring physical feasibility of the system like the mass balance and energy balance. Check for the state variables to be within the bounds. \n" ] @@ -449,7 +446,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [SCO2_flowsheet_pysmo_surrogate](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/SCO2_example/PySMO/SCO2_flowsheet_pysmo_surrogate_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb). " ] } ], From 63468d0c674c02e77d6723e7c0ed9ecc3d8136ce Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Fri, 9 Feb 2024 16:35:12 -0500 Subject: [PATCH 51/75] Fixing notebook name --- idaes_examples/notebooks/_toc.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/idaes_examples/notebooks/_toc.yml b/idaes_examples/notebooks/_toc.yml index 33d21253..c6c6cdd0 100644 --- a/idaes_examples/notebooks/_toc.yml +++ b/idaes_examples/notebooks/_toc.yml @@ -88,7 +88,7 @@ parts: - file: docs/surrogates/sco2/alamo/flowsheet_optimization_doc - file: docs/surrogates/sco2/omlt/index sections: - - file: docs/surrogates/sco2/omlt/omlt_training_doc + - file: docs/surrogates/sco2/omlt/keras_training_doc - file: docs/surrogates/sco2/omlt/surrogate_embedding_doc - file: docs/surrogates/sco2/omlt/flowsheet_optimization_doc - file: docs/surrogates/sco2/pysmo/index From 7c121a199f36f140647b4179b997a829b91933ea Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Fri, 9 Feb 2024 19:11:27 -0500 Subject: [PATCH 52/75] fixing image path --- .../docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb | 2 +- .../notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb index 827abb3d..ed50186a 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb @@ -58,7 +58,7 @@ "\n", "\n", "def datafile_path(name):\n", - " return Path(\"..\") / name\n", + " return Path(\".\") / name\n", "\n", "\n", "Image(datafile_path(\"CO2_Flowsheet.png\"))" diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb index ecb167ba..b685f5b9 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb @@ -74,7 +74,7 @@ "\n", "\n", "def datafile_path(name):\n", - " return Path(\"..\") / name\n", + " return Path(\"...\") / name\n", "\n", "\n", "Image(datafile_path(\"CO2_Flowsheet.png\"))" From a8bc96b1bc679e9e3e10a2ad41fc1f6bc73b0e5e Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Mon, 12 Feb 2024 18:10:36 -0500 Subject: [PATCH 53/75] Fixing path for image --- .../docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb | 2 +- .../notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb index ed50186a..827abb3d 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb @@ -58,7 +58,7 @@ "\n", "\n", "def datafile_path(name):\n", - " return Path(\".\") / name\n", + " return Path(\"..\") / name\n", "\n", "\n", "Image(datafile_path(\"CO2_Flowsheet.png\"))" diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb index b685f5b9..ecb167ba 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb @@ -74,7 +74,7 @@ "\n", "\n", "def datafile_path(name):\n", - " return Path(\"...\") / name\n", + " return Path(\"..\") / name\n", "\n", "\n", "Image(datafile_path(\"CO2_Flowsheet.png\"))" From 3305035e29acce320c296a04bc64350cce9a54c9 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Mon, 12 Feb 2024 18:24:09 -0500 Subject: [PATCH 54/75] Fixing path in all notebooks --- .../notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb | 2 +- .../docs/surrogates/sco2/alamo/alamo_training_doc.ipynb | 2 +- .../docs/surrogates/sco2/alamo/alamo_training_test.ipynb | 2 +- .../docs/surrogates/sco2/alamo/alamo_training_usr.ipynb | 2 +- .../docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb | 2 +- .../docs/surrogates/sco2/alamo/flowsheet_optimization_doc.ipynb | 2 +- .../surrogates/sco2/alamo/flowsheet_optimization_test.ipynb | 2 +- .../docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb | 2 +- .../docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb | 2 +- .../docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb | 2 +- .../docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb | 2 +- .../docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb | 2 +- .../notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb | 2 +- .../docs/surrogates/sco2/omlt/keras_training_doc.ipynb | 2 +- .../docs/surrogates/sco2/omlt/keras_training_test.ipynb | 2 +- .../docs/surrogates/sco2/omlt/keras_training_usr.ipynb | 2 +- .../docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb | 2 +- .../docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.ipynb | 2 +- .../surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb | 2 +- .../docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb | 2 +- .../notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb | 2 +- .../docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb | 2 +- .../docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb | 2 +- .../docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb | 2 +- 24 files changed, 24 insertions(+), 24 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb index 74963d2b..19b8a969 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb @@ -77,7 +77,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb index 6347febe..80943c3f 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb @@ -77,7 +77,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb index bf1b4b99..e65fc5ae 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb @@ -77,7 +77,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb index b9bafbf0..d22b981a 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb @@ -77,7 +77,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb index 55b9d0c2..000282cd 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb @@ -61,7 +61,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_doc.ipynb index 7603d3e1..4be77c7a 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_doc.ipynb @@ -61,7 +61,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_test.ipynb index 7603d3e1..4be77c7a 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_test.ipynb @@ -61,7 +61,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb index 7603d3e1..4be77c7a 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb @@ -61,7 +61,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb index 827abb3d..2e240888 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb @@ -61,7 +61,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb index c20d883f..44f8241f 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.ipynb @@ -61,7 +61,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb index 316819c4..b8ed26f3 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb @@ -61,7 +61,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb index 316819c4..b8ed26f3 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb @@ -61,7 +61,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb index ecb167ba..253b55a8 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb @@ -77,7 +77,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb index 565b6514..36b6d059 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb @@ -77,7 +77,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb index bff0eb63..db1ffc12 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb @@ -77,7 +77,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb index 2f3ab63c..d91bcada 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb @@ -77,7 +77,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb index a535274c..9e79daba 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb @@ -63,7 +63,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.ipynb index 5ab2578c..1be8ad23 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.ipynb @@ -61,7 +61,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb index 5ab2578c..1be8ad23 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb @@ -61,7 +61,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb index 5ab2578c..1be8ad23 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb @@ -61,7 +61,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb index 02898325..9cd4f4a9 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb @@ -76,7 +76,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb index 6fbb5b53..394b5e74 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb @@ -76,7 +76,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb index 96cce342..89edba50 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb @@ -76,7 +76,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb index b489f502..ff875cf1 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb @@ -76,7 +76,7 @@ " return Path(\"..\") / name\n", "\n", "\n", - "Image(datafile_path(\"CO2_Flowsheet.png\"))" + "Image(datafile_path(\"CO2_flowsheet.png\"))" ] }, { From 831e1bd21fa1a57152392baa6327e3bc5fd95dc4 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Mon, 12 Feb 2024 19:01:41 -0500 Subject: [PATCH 55/75] Fixing surrogate paths for pysmo --- .../docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb | 2 +- .../docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb index 56df32cb..44ee59fe 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb @@ -243,7 +243,7 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " self.pysmo_surrogate = PysmoSurrogate.load_from_file(\"pysmo_poly_surrogate.json\")\n", + " self.pysmo_surrogate = PysmoSurrogate.load_from_file('pysmo_poly_surrogate.json')\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.pysmo_surrogate,\n", diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb index 3c647096..9e51965f 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb @@ -243,7 +243,7 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " self.pysmo_surrogate = PysmoSurrogate.load_from_file(\"pysmo_poly_surrogate.json\")\n", + " self.pysmo_surrogate = PysmoSurrogate.load_from_file('pysmo_poly_surrogate.json')\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.pysmo_surrogate,\n", From e6a65e55b333221baefc0900193220324ef372c6 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Mon, 12 Feb 2024 19:20:14 -0500 Subject: [PATCH 56/75] Trying to fix path --- .../docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb | 2 +- .../docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb index 12e2a7ac..8e26769e 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb @@ -239,7 +239,7 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " self.keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder('keras_surrogate')\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.keras_surrogate,\n", diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb index df1dfb7d..af9c33cc 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb @@ -243,7 +243,7 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " self.pysmo_surrogate = PysmoSurrogate.load_from_file(\"pysmo_poly_surrogate.json\")\n", + " self.pysmo_surrogate = PysmoSurrogate.load_from_file('pysmo_poly_surrogate.json')\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.pysmo_surrogate,\n", From e2e34f667348926357522e16d04f73f489c1c9f2 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Mon, 12 Feb 2024 19:30:31 -0500 Subject: [PATCH 57/75] fixing paths --- .../surrogates/sco2/omlt/surrogate_embedding_test.ipynb | 6 +++++- .../surrogates/sco2/pysmo/surrogate_embedding_test.ipynb | 6 +++++- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb index 8e26769e..c207a550 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb @@ -90,6 +90,8 @@ "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", "from idaes.core.surrogate.keras_surrogate import KerasSurrogate\n", "\n", + "import os \n", + "\n", "from pyomo.util.model_size import build_model_size_report\n", "\n", "# Some more information about this module\n", @@ -239,7 +241,9 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " self.keras_surrogate = KerasSurrogate.load_from_folder('keras_surrogate')\n", + " current_file_directory = os.path.dirname(os.path.abspath(__file__))\n", + " relative_path = os.path.join(current_file_directory, 'keras_surrogate')\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(relative_path)\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.keras_surrogate,\n", diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb index af9c33cc..b20c3e16 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb @@ -89,6 +89,8 @@ "from idaes.core.surrogate.surrogate_block import SurrogateBlock\n", "from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate\n", "\n", + "import os \n", + "\n", "from pyomo.util.model_size import build_model_size_report\n", "\n", "# Some more information about this module\n", @@ -243,7 +245,9 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " self.pysmo_surrogate = PysmoSurrogate.load_from_file('pysmo_poly_surrogate.json')\n", + " current_file_directory = os.path.dirname(os.path.abspath(__file__))\n", + " relative_path = os.path.join(current_file_directory, 'pysmo_poly_surrogate.json')\n", + " self.pysmo_surrogate = PysmoSurrogate.load_from_file(relative_path)\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.pysmo_surrogate,\n", From d8d8f6a9455b178566b1d5bbab6cdc42e7a0a0eb Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Mon, 12 Feb 2024 19:44:58 -0500 Subject: [PATCH 58/75] fixing paths --- .../docs/surrogates/sco2/omlt/properties.py | 2 +- .../sco2/omlt/surrogate_embedding.ipynb | 2 +- .../sco2/omlt/surrogate_embedding_doc.ipynb | 2 +- .../sco2/omlt/surrogate_embedding_test.ipynb | 20 +++++++++++++++---- .../sco2/omlt/surrogate_embedding_usr.ipynb | 2 +- 5 files changed, 20 insertions(+), 8 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py index 7ebce138..64101604 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py @@ -269,7 +269,7 @@ def _make_state_vars(self): inputs=[self.pressure,self.temperature] outputs=[self.enth_mol,self.entr_mol] - self.keras_surrogate = KerasSurrogate.load_from_folder("keras_surrogate") + self.keras_surrogate = KerasSurrogate.load_from_folder("sco2_keras_surr") self.surrogate_enth = SurrogateBlock() self.surrogate_enth.build_model( self.keras_surrogate, diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb index 58726e40..e00f5fc9 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb @@ -240,7 +240,7 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " self.keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(\"sco2_keras_surr\")\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.keras_surrogate,\n", diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb index 82ef41ba..71d171a6 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb @@ -239,7 +239,7 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " self.keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(\"sco2_keras_surr\")\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.keras_surrogate,\n", diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb index c207a550..1d1257b1 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb @@ -241,9 +241,7 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " current_file_directory = os.path.dirname(os.path.abspath(__file__))\n", - " relative_path = os.path.join(current_file_directory, 'keras_surrogate')\n", - " self.keras_surrogate = KerasSurrogate.load_from_folder(relative_path)\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder('sco2_keras_surr')\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.keras_surrogate,\n", @@ -452,8 +450,22 @@ } ], "metadata": { + "kernelspec": { + "display_name": "idaes-pse", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.16" }, "orig_nbformat": 4 }, diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb index 67a78b77..463a9937 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb @@ -239,7 +239,7 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " self.keras_surrogate = KerasSurrogate.load_from_folder(\"keras_surrogate\")\n", + " self.keras_surrogate = KerasSurrogate.load_from_folder(\"sco2_keras_surr\")\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.keras_surrogate,\n", From 61fe8ade05382482e2f71fef14733a37b72b0533 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Mon, 12 Feb 2024 19:56:18 -0500 Subject: [PATCH 59/75] Reorganizing the files --- .../notebooks/docs/surrogates/sco2/{alamo => }/alamo_run.trc | 0 .../docs/surrogates/sco2/{alamo => }/alamo_surrogate.json | 0 .../notebooks/docs/surrogates/sco2/pysmo_poly_surrogate.json | 1 + 3 files changed, 1 insertion(+) rename idaes_examples/notebooks/docs/surrogates/sco2/{alamo => }/alamo_run.trc (100%) rename idaes_examples/notebooks/docs/surrogates/sco2/{alamo => }/alamo_surrogate.json (100%) create mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/pysmo_poly_surrogate.json diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_run.trc b/idaes_examples/notebooks/docs/surrogates/sco2/alamo_run.trc similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_run.trc rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo_run.trc diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_surrogate.json b/idaes_examples/notebooks/docs/surrogates/sco2/alamo_surrogate.json similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_surrogate.json rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo_surrogate.json diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo_poly_surrogate.json b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo_poly_surrogate.json new file mode 100644 index 00000000..5258094b --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo_poly_surrogate.json @@ -0,0 +1 @@ +{"model_encoding": {"enth_mol": {"attr": {"regression_data_columns": ["pressure", "temperature"], "multinomials": 1, "additional_term_expressions": ["IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]", "IndexedParam[pressure]/IndexedParam[temperature]", "IndexedParam[temperature]/IndexedParam[pressure]"], "optimal_weights_array": [[-539145.2641931743], [-1572.9941129612596], [1028.1303702529963], [-41.89265612633253], [-2.854098382160082], [3.1084792045014056], [0.0040249321969904606], [-0.07298691795031877], [-2.7827021177926484e-06], [0.0006559340352560386], [7.62454692622566e-10], [4.50540106476475], [-0.0025967218940188964], [3.27147430041989e-05], [-0.05205092851352775], [149943.17003170087], [-3.5662256522946807]], "final_polynomial_order": 5, "errors": {"MAE": 116.22937611304296, "MSE": 39254.96789837278, "R2": 0.9997117200542968}, "extra_terms_feature_vector": ["IndexedParam[pressure]", "IndexedParam[temperature]"]}, "map": {"regression_data_columns": "list", "multinomials": "str", "additional_term_expressions": "other", "optimal_weights_array": "numpy", "final_polynomial_order": "str", "errors": "str", "extra_terms_feature_vector": "other"}}, "entr_mol": {"attr": {"regression_data_columns": ["pressure", "temperature"], "multinomials": 1, "additional_term_expressions": ["IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", "IndexedParam[pressure]*IndexedParam[pressure]*IndexedParam[temperature]", "IndexedParam[pressure]/IndexedParam[temperature]", "IndexedParam[temperature]/IndexedParam[pressure]"], "optimal_weights_array": [[-529.9581296941684], [-5.674476891947422], [3.6251620831469844], [-0.012206052330165947], [-0.010121999171951317], [0.0044164987227566545], [1.4212146246171698e-05], [-0.00012049491972756627], [-9.875650167428602e-09], [1.1673348430972035e-06], [2.72031843813476e-12], [0.010605178085763924], [-6.047902870413699e-06], [6.872924493404928e-08], [-0.00011146830780061758], [437.25207041949056], [0.0015391876304710196]], "final_polynomial_order": 5, "errors": {"MAE": 0.34548912239751245, "MSE": 0.3560561890323906, "R2": 0.9991570382929269}, "extra_terms_feature_vector": ["IndexedParam[pressure]", "IndexedParam[temperature]"]}, "map": {"regression_data_columns": "list", "multinomials": "str", "additional_term_expressions": "other", "optimal_weights_array": "numpy", "final_polynomial_order": "str", "errors": "str", "extra_terms_feature_vector": "other"}}}, "input_labels": ["pressure", "temperature"], "output_labels": ["enth_mol", "entr_mol"], "input_bounds": {"pressure": [7, 40], "temperature": [306, 1000]}, "surrogate_type": "poly"} \ No newline at end of file From a1ab28c72da22cfc00451457551e9324b55177ff Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Mon, 12 Feb 2024 21:47:46 -0500 Subject: [PATCH 60/75] fixing paths --- .../docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb index b20c3e16..98874e5b 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb @@ -245,9 +245,7 @@ " \n", " inputs=[self.pressure,self.temperature]\n", " outputs=[self.enth_mol,self.entr_mol]\n", - " current_file_directory = os.path.dirname(os.path.abspath(__file__))\n", - " relative_path = os.path.join(current_file_directory, 'pysmo_poly_surrogate.json')\n", - " self.pysmo_surrogate = PysmoSurrogate.load_from_file(relative_path)\n", + " self.pysmo_surrogate = PysmoSurrogate.load_from_file('pysmo_poly_surrogate.json')\n", " self.surrogate_enth = SurrogateBlock()\n", " self.surrogate_enth.build_model(\n", " self.pysmo_surrogate,\n", From 122a59a83679c1869d48039edd78babfbcf4981b Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Mon, 12 Feb 2024 21:57:31 -0500 Subject: [PATCH 61/75] Attempting to fix paths --- .../notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb | 2 +- .../docs/surrogates/sco2/omlt/keras_training_doc.ipynb | 2 +- .../docs/surrogates/sco2/omlt/keras_training_test.ipynb | 2 +- .../docs/surrogates/sco2/omlt/keras_training_usr.ipynb | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb index 253b55a8..a9960d25 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb @@ -793,7 +793,7 @@ " input_scaler=input_scaler,\n", " output_scaler=output_scaler,\n", ")\n", - "keras_surrogate.save_to_folder(\"keras_surrogate\")" + "keras_surrogate.save_to_folder(\"sco2_keras_surr\")" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb index 36b6d059..fbf2188b 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb @@ -783,7 +783,7 @@ " input_scaler=input_scaler,\n", " output_scaler=output_scaler,\n", ")\n", - "keras_surrogate.save_to_folder(\"keras_surrogate\")" + "keras_surrogate.save_to_folder(\"sco2_keras_surr\")" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb index db1ffc12..3a1cba41 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb @@ -783,7 +783,7 @@ " input_scaler=input_scaler,\n", " output_scaler=output_scaler,\n", ")\n", - "keras_surrogate.save_to_folder(\"keras_surrogate\")" + "keras_surrogate.save_to_folder(\"sco2_keras_surr\")" ] }, { diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb index d91bcada..b0bf836a 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb @@ -783,7 +783,7 @@ " input_scaler=input_scaler,\n", " output_scaler=output_scaler,\n", ")\n", - "keras_surrogate.save_to_folder(\"keras_surrogate\")" + "keras_surrogate.save_to_folder(\"sco2_keras_surr\")" ] }, { From 11b50af640b7ad2693133a566a8c299262ae9850 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Thu, 15 Feb 2024 14:11:47 -0500 Subject: [PATCH 62/75] fixing pysmo path --- .../notebooks/docs/surrogates/sco2/pysmo/properties.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py index accad220..e7bd7b73 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py @@ -48,6 +48,8 @@ from idaes.core.surrogate.surrogate_block import SurrogateBlock from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate +import os + from pyomo.util.model_size import build_model_size_report # Some more information about this module @@ -269,9 +271,11 @@ def _make_state_vars(self): units=units.kJ/units.kmol, doc='Enthalpy [kJ/ kmol]') - inputs=[self.pressure,self.temperature] - outputs=[self.enth_mol,self.entr_mol] - self.pysmo_surrogate = PysmoSurrogate.load_from_file("pysmo_poly_surrogate.json") + inputs = [self.pressure,self.temperature] + outputs = [self.enth_mol,self.entr_mol] + curr_dir = os.path.dirname(__file__) + rel_path = os.path.join(curr_dir,"pysmo_poly_surrogate.json") + self.pysmo_surrogate = PysmoSurrogate.load_from_file(rel_path) self.surrogate_enth = SurrogateBlock() self.surrogate_enth.build_model( self.pysmo_surrogate, From 04e514e502aa83b9da1957e19f62bad6811f8d42 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Thu, 15 Feb 2024 14:22:44 -0500 Subject: [PATCH 63/75] fixing path --- .../notebooks/docs/surrogates/sco2/omlt/properties.py | 2 ++ .../notebooks/docs/surrogates/sco2/pysmo/properties.py | 5 ++--- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py index 64101604..3a17132b 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py @@ -48,6 +48,8 @@ from idaes.core.surrogate.surrogate_block import SurrogateBlock from idaes.core.surrogate.keras_surrogate import KerasSurrogate +import os + from pyomo.util.model_size import build_model_size_report # Some more information about this module diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py index e7bd7b73..93ee87f3 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py @@ -273,9 +273,8 @@ def _make_state_vars(self): inputs = [self.pressure,self.temperature] outputs = [self.enth_mol,self.entr_mol] - curr_dir = os.path.dirname(__file__) - rel_path = os.path.join(curr_dir,"pysmo_poly_surrogate.json") - self.pysmo_surrogate = PysmoSurrogate.load_from_file(rel_path) + print(os.path.dirname(__file__)) + self.pysmo_surrogate = PysmoSurrogate.load_from_file("pysmo_poly_surrogate.json") self.surrogate_enth = SurrogateBlock() self.surrogate_enth.build_model( self.pysmo_surrogate, From 234a50380679b0f23ec7f6f85e3f97bed7b6a68e Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Thu, 15 Feb 2024 14:35:12 -0500 Subject: [PATCH 64/75] adding dummy file to add folder --- .../docs/surrogates/sco2/sco2_keras_surr/dummy_file.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/dummy_file.txt diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/dummy_file.txt b/idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/dummy_file.txt new file mode 100644 index 00000000..100b2ef9 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/dummy_file.txt @@ -0,0 +1 @@ +This is a dummy file to add the folder. \ No newline at end of file From 563707c649fd3acd506a419836df7ee041754915 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 <73403218+JavalVyas2000@users.noreply.github.com> Date: Thu, 15 Feb 2024 14:49:21 -0500 Subject: [PATCH 65/75] Adding sco2_keras_surr folder --- .../sco2/sco2_keras_surr/fingerprint.pb | 1 + .../sco2/sco2_keras_surr/idaes_info.json | 1 + .../sco2/sco2_keras_surr/keras_metadata.pb | 10 ++++++++++ .../sco2/sco2_keras_surr/saved_model.pb | Bin 0 -> 129984 bytes 4 files changed, 12 insertions(+) create mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/fingerprint.pb create mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/idaes_info.json create mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/keras_metadata.pb create mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/saved_model.pb diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/fingerprint.pb b/idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/fingerprint.pb new file mode 100644 index 00000000..c94ddb92 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/fingerprint.pb @@ -0,0 +1 @@ +™Ù²¡™—ñû®¾¶âŽéدŠ-êô¢ËªºÕû[ 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00:00:00 2001 From: JavalVyas2000 <73403218+JavalVyas2000@users.noreply.github.com> Date: Thu, 15 Feb 2024 15:30:21 -0500 Subject: [PATCH 66/75] Adding pysmo_poly_surrogate.json --- .../docs/surrogates/sco2/pysmo/pysmo_poly_surrogate.json | 1 + 1 file changed, 1 insertion(+) create mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_poly_surrogate.json diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_poly_surrogate.json b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_poly_surrogate.json new file mode 100644 index 00000000..f7f6e287 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_poly_surrogate.json @@ -0,0 +1 @@ +{"model_encoding": {"enth_mol": {"attr": {"regression_data_columns": ["pressure", "temperature"], "multinomials": 1, "additional_term_expressions": ["IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", 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.../sco2/omlt/sco2_keras_surr/dummy_file.txt | 1 + .../surrogates/sco2/pysmo_poly_surrogate.json | 1 - .../sco2/sco2_keras_surr/dummy_file.txt | 1 - .../sco2/sco2_keras_surr/fingerprint.pb | 1 - .../sco2/sco2_keras_surr/idaes_info.json | 1 - .../sco2/sco2_keras_surr/keras_metadata.pb | 10 ---------- .../sco2/sco2_keras_surr/saved_model.pb | Bin 129984 -> 0 bytes 9 files changed, 1 insertion(+), 14 deletions(-) rename idaes_examples/notebooks/docs/surrogates/sco2/{ => alamo}/alamo_run.trc (100%) rename idaes_examples/notebooks/docs/surrogates/sco2/{ => alamo}/alamo_surrogate.json (100%) create mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/dummy_file.txt delete mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/pysmo_poly_surrogate.json delete mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/dummy_file.txt delete mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/fingerprint.pb delete mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/idaes_info.json delete mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/keras_metadata.pb delete mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/sco2_keras_surr/saved_model.pb diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo_run.trc b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_run.trc similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/sco2/alamo_run.trc rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_run.trc diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo_surrogate.json b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_surrogate.json similarity index 100% rename from idaes_examples/notebooks/docs/surrogates/sco2/alamo_surrogate.json rename to idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_surrogate.json diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/dummy_file.txt b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/dummy_file.txt new file mode 100644 index 00000000..7c5de4bc --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/dummy_file.txt @@ -0,0 +1 @@ +This is a dummy file \ No newline at end of file diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo_poly_surrogate.json b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo_poly_surrogate.json deleted file mode 100644 index 5258094b..00000000 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo_poly_surrogate.json +++ /dev/null @@ -1 +0,0 @@ -{"model_encoding": {"enth_mol": {"attr": {"regression_data_columns": ["pressure", "temperature"], "multinomials": 1, "additional_term_expressions": ["IndexedParam[pressure]*IndexedParam[temperature]*IndexedParam[temperature]", 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.../sco2/omlt/sco2_keras_surr/fingerprint.pb | 1 + .../sco2/omlt/sco2_keras_surr/idaes_info.json | 1 + .../omlt/sco2_keras_surr/keras_metadata.pb | 10 ++++++++++ .../sco2/omlt/sco2_keras_surr/saved_model.pb | Bin 0 -> 129984 bytes 4 files changed, 12 insertions(+) create mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/fingerprint.pb create mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/idaes_info.json create mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/keras_metadata.pb create mode 100644 idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/saved_model.pb diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/fingerprint.pb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/fingerprint.pb new file mode 100644 index 00000000..c94ddb92 --- /dev/null +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/fingerprint.pb 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b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/sco2_keras_surr/variables/variables.index new file mode 100644 index 0000000000000000000000000000000000000000..0cd2376b6b7307bb475d5528e117427288e57cac GIT binary patch literal 2668 zcmZQzVB=tvV&Y(AkP(P?_HcFf4)FK%3vqPvagFzP@^W(A(YNw!k3*|lvo^}Sdgion_5zonOv-Ipr4#ynpdK)=NJ+ay4hL=^Z+@=FqP2x)i#($IKys)&#hpCNH}ydl+&7a$D< zkCsRS?Jy$FjxVIz@d4%!1{0y2#LCp7`0~t>jQH}@%=C|9K zoZ&;jr;x<)AsJ|i-UX{RQAKfLV@L#?tUfSm9N;l6$)5F`!bpM`_d*9`T-*DI{lrBQ zMAwFJkS^_)$+LiwWJGi%9gqh4T6=baImMB*!wqN>W29pQv00*_5U63#&c-N`1H6GB ztMydpOhdao)bFNwEND1%@g&zCLaNBt`<0!U9cD zF`T;jnK~6Bfk|OT2*^04px7SbBY{cbK^;g}iB)$ngEk+c(o9M%NsKqdUlB4WTmV(% ze_tQJ&o2XPED&h!TmdQG{r1;QE(N}n)a1lULK<#>G%TwYwBnNI%PaxbWFyQu literal 0 HcmV?d00001 From 5e594cf0264ad81d21720b56667595e4f10280a5 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Thu, 15 Feb 2024 18:42:39 -0500 Subject: [PATCH 72/75] fixing reference paths --- .../surrogates/sco2/alamo/alamo_training.ipynb | 4 ++-- .../sco2/alamo/alamo_training_doc.ipynb | 4 ++-- .../sco2/alamo/alamo_training_test.ipynb | 4 ++-- .../sco2/alamo/alamo_training_usr.ipynb | 4 ++-- .../sco2/alamo/surrogate_embedding.ipynb | 2 +- .../sco2/alamo/surrogate_embedding_doc.ipynb | 2 +- .../sco2/alamo/surrogate_embedding_test.ipynb | 2 +- .../sco2/alamo/surrogate_embedding_usr.ipynb | 2 +- .../surrogates/sco2/omlt/keras_training.ipynb | 4 ++-- .../sco2/omlt/keras_training_doc.ipynb | 4 ++-- .../sco2/omlt/keras_training_test.ipynb | 4 ++-- .../sco2/omlt/keras_training_usr.ipynb | 4 ++-- .../sco2/omlt/surrogate_embedding.ipynb | 18 ++++++++++++++++-- .../sco2/omlt/surrogate_embedding_doc.ipynb | 2 +- .../sco2/omlt/surrogate_embedding_test.ipynb | 2 +- .../sco2/omlt/surrogate_embedding_usr.ipynb | 2 +- .../surrogates/sco2/pysmo/pysmo_training.ipynb | 4 ++-- .../sco2/pysmo/pysmo_training_doc.ipynb | 4 ++-- .../sco2/pysmo/pysmo_training_test.ipynb | 4 ++-- .../sco2/pysmo/pysmo_training_usr.ipynb | 4 ++-- .../sco2/pysmo/surrogate_embedding.ipynb | 2 +- .../sco2/pysmo/surrogate_embedding_doc.ipynb | 2 +- .../sco2/pysmo/surrogate_embedding_test.ipynb | 2 +- .../sco2/pysmo/surrogate_embedding_usr.ipynb | 2 +- 24 files changed, 51 insertions(+), 37 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb index 19b8a969..fbdf0fd8 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part -1)\n", + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part 1)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -567,7 +567,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb index 80943c3f..8f73584e 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_doc.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part -1)\n", + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part 1)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -567,7 +567,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_doc.md) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_doc.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb index e65fc5ae..554a9781 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_test.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part -1)\n", + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part 1)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -567,7 +567,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_test.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_test.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb index d22b981a..06bef3c4 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/alamo_training_usr.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part -1)\n", + "# Supercritical CO2 Property Surrogate with ALAMO Surrogate Object - Training Surrogate (Part 1)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -567,7 +567,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_usr.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_usr.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding.ipynb index 430fdbe4..9fa9eb01 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding.ipynb @@ -449,7 +449,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_doc.ipynb index 31b5027b..73bf7797 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_doc.ipynb @@ -449,7 +449,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.md). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_doc.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_doc.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_test.ipynb index 9640004e..bf32b875 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_test.ipynb @@ -449,7 +449,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_test.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_usr.ipynb index c70b43e1..184fc468 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/alamo/surrogate_embedding_usr.ipynb @@ -452,7 +452,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/alamo/flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_usr.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb index a9960d25..9acc449d 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part -1)\n", + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part 1)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -1085,7 +1085,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding.ipynb](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding.ipynb](./surrogate_embedding.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb index fbf2188b..4b4d5aab 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_doc.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part -1)\n", + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part 1)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -1075,7 +1075,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding_doc.md](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb_doc.md) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding_doc.ipynb](./surrogate_embedding_doc.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb index 3a1cba41..cacf5254 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_test.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part -1)\n", + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part 1)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -1075,7 +1075,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding_test.ipynb](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb_test.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding_test.ipynb](./surrogate_embedding_test.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb index b0bf836a..0b0c7558 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/keras_training_usr.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part -1)\n", + "# Supercritical CO2 Property Surrogate with OMLT Surrogate Object - Training Surrogate (Part 1)\n", "\n", "Maintainer: Javal Vyas\n", "\n", @@ -1075,7 +1075,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding_usr.ipynb](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb_usr.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding_usr.ipynb](./surrogate_embedding_usr.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb index e00f5fc9..3af20892 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding.ipynb @@ -444,13 +444,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](..\\..\\..\\properties\\custom\\custom_physical_property_packages.ipynb). " ] } ], "metadata": { + "kernelspec": { + "display_name": "idaes-pse", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.16" }, "orig_nbformat": 4 }, diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb index 71d171a6..dba5cb3d 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_doc.ipynb @@ -443,7 +443,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.md). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_doc.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](..\\..\\..\\properties\\custom\\custom_physical_property_packages_doc.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb index 1d1257b1..cdd26cd6 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_test.ipynb @@ -445,7 +445,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_test.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb index 463a9937..40bbecf5 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/surrogate_embedding_usr.ipynb @@ -443,7 +443,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/omlt/flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_usr.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb index 9cd4f4a9..d48df4a0 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part -1)\n", + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part 1)\n", "Maintainer: Javal Vyas\n", "\n", "Author: Javal Vyas\n", @@ -627,7 +627,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb index 394b5e74..d85593b8 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_doc.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part -1)\n", + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part 1)\n", "Maintainer: Javal Vyas\n", "\n", "Author: Javal Vyas\n", @@ -627,7 +627,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.md) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_doc.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb index 89edba50..13044384 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_test.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part -1)\n", + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part 1)\n", "Maintainer: Javal Vyas\n", "\n", "Author: Javal Vyas\n", @@ -627,7 +627,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_test.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb index ff875cf1..ed8d224c 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/pysmo_training_usr.ipynb @@ -24,7 +24,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part -1)\n", + "# Supercritical CO2 Property Surrogate with PySMO Surrogate Object - Training Surrogate (Part 1)\n", "Maintainer: Javal Vyas\n", "\n", "Author: Javal Vyas\n", @@ -627,7 +627,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_usr.ipynb) file." + "Now, the surrogate is trained and validated, we shall embed it in the property package, which is demonstrated in the [surrogate_embedding](./surrogate_embedding_usr.ipynb) file." ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb index 44ee59fe..a2b04ccc 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding.ipynb @@ -446,7 +446,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb index 9e51965f..ec1968c1 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_doc.ipynb @@ -446,7 +446,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_doc.md). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_doc.md). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_doc.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_doc.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb index 98874e5b..054e2f66 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_test.ipynb @@ -448,7 +448,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_test.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_test.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_test.ipynb). " ] } ], diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_usr.ipynb b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_usr.ipynb index 9e9b06b6..d67ce305 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_usr.ipynb +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/surrogate_embedding_usr.ipynb @@ -446,7 +446,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](https://github.com/IDAES/examples/blob/supercritcial_CO2_example/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](https://github.com/IDAES/examples/blob/main/idaes_examples/notebooks/docs/properties/custom/custom_physical_property_packages_usr.ipynb). " + "Now we have our property package ready for being used in the flowsheet for optimization. We shall see that in the next part of this tutorial, [flowsheet_optimization](./flowsheet_optimization_usr.ipynb). To learn in detail about making a custom property package, one should go through [Property Package Example](../../../properties/custom/custom_physical_property_packages_usr.ipynb). " ] } ], From fae5bacf3546cb87702369a2b932c89931fa75f1 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Thu, 15 Feb 2024 18:53:19 -0500 Subject: [PATCH 73/75] fixing reference paths --- .../notebooks/docs/surrogates/sco2/omlt/properties.py | 2 -- .../notebooks/docs/surrogates/sco2/pysmo/properties.py | 2 -- 2 files changed, 4 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py index 3a17132b..64101604 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/omlt/properties.py @@ -48,8 +48,6 @@ from idaes.core.surrogate.surrogate_block import SurrogateBlock from idaes.core.surrogate.keras_surrogate import KerasSurrogate -import os - from pyomo.util.model_size import build_model_size_report # Some more information about this module diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py index 93ee87f3..26957105 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py @@ -48,8 +48,6 @@ from idaes.core.surrogate.surrogate_block import SurrogateBlock from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate -import os - from pyomo.util.model_size import build_model_size_report # Some more information about this module From 2c29d6b8decc43796016fc203ba4d1dc5ea99f3f Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Thu, 15 Feb 2024 19:08:05 -0500 Subject: [PATCH 74/75] adding changes --- .../notebooks/docs/surrogates/sco2/pysmo/properties.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py index 26957105..93ee87f3 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py @@ -48,6 +48,8 @@ from idaes.core.surrogate.surrogate_block import SurrogateBlock from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate +import os + from pyomo.util.model_size import build_model_size_report # Some more information about this module From 4b0cb3052bf27066fdf75ef2a7fc626867388e53 Mon Sep 17 00:00:00 2001 From: JavalVyas2000 Date: Thu, 15 Feb 2024 19:08:51 -0500 Subject: [PATCH 75/75] clean up --- .../notebooks/docs/surrogates/sco2/pysmo/properties.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py index 93ee87f3..e8c8528f 100644 --- a/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py +++ b/idaes_examples/notebooks/docs/surrogates/sco2/pysmo/properties.py @@ -48,8 +48,6 @@ from idaes.core.surrogate.surrogate_block import SurrogateBlock from idaes.core.surrogate.pysmo_surrogate import PysmoSurrogate -import os - from pyomo.util.model_size import build_model_size_report # Some more information about this module @@ -273,7 +271,6 @@ def _make_state_vars(self): inputs = [self.pressure,self.temperature] outputs = [self.enth_mol,self.entr_mol] - print(os.path.dirname(__file__)) self.pysmo_surrogate = PysmoSurrogate.load_from_file("pysmo_poly_surrogate.json") self.surrogate_enth = SurrogateBlock() self.surrogate_enth.build_model(